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Uniting innovators, HackerNoon elevates the 2.0 Conferences, spotlighting insights from Dubai’s major industry events for a worldwide audience. Dubai, UAE — March 26, 2024- The 2.0 Conferences series featuring five global events — the Health, Marketing, CXO, Internet, and Education 2.0 Conferences — were successfully hosted at the InterContinental, Dubai Festival City, Dubai, UAE, from February 20–22, 2024. Attendees were treated to an unparalleled experience, gaining incredible insights from industry leaders, discussing trending topics, and embracing opportunities for networking and collaboration. HackerNoon, serving as the event’s media partner, played a crucial role in amplifying the reach and engagement of the conference, leveraging its extensive tech and business journalism expertise to highlight the event’s key moments and insights. The essence of The 2.0 Conferences lies in its gathering of forward-thinking professionals across various industries. It promotes innovation and strategic thinking by connecting attendees with experts and thought leaders. This initiative is rooted in a mission to encourage creative problem-solving, promote new perspectives, and facilitate the sharing of groundbreaking ideas. The conference offers more than just presentations; it’s a platform for building lasting professional connections and sharing valuable experiences. HackerNoon, known for its commitment to providing a space for technologists to share knowledge and ideas, significantly contributed to the success of The 2.0 Conferences. With a community of over 45,000 writers and 4 million monthly readers, HackerNoon ensured that the insights and innovations shared at the conference reached a broader audience. This collaboration allowed for dissemination of cutting-edge ideas and strategies discussed during the event, making the content accessible to a global community interested in technological advancements and industry trends. “The collaboration with HackerNoon brought a new dimension to our conference. Their coverage extended our reach and helped highlight the significant contributions and insights shared by our speakers and participants,” commented Yashasvi Kapoor, Manager of Delegate Relations at The 2.0 Conferences. This strategic collaboration between HackerNoon and The 2.0 Conferences brought tangible benefits to the table, enhancing the attendee experience and broadening the event’s influence. The partnership ensured the conferences’ innovative ideas reached a global audience, harnessing a deeper understanding and appreciation of the potential to shape the future. About The 2.0 Conferences The 2.0 Conferences is dedicated to bringing together thought leaders and innovators from various sectors to share ideas, strategies, and insights. Focusing on promoting innovation and strategic thinking, the conference series aims to inspire change and drive progress across multiple industries. Our commitment to creating meaningful connections and learning opportunities ensures a valuable and impactful experience for all participants. For more information, check out their website-www.the2conf.com. Uniting innovators, HackerNoon elevates the 2.0 Conferences, spotlighting insights from Dubai’s major industry events for a worldwide audience. Uniting innovators, HackerNoon elevates the 2.0 Conferences, spotlighting insights from Dubai’s major industry events for a worldwide audience. Dubai, UAE — March 26, 2024- The 2.0 Conferences series featuring five global events — the Health, Marketing, CXO, Internet, and Education 2.0 Conferences — were successfully hosted at the InterContinental, Dubai Festival City, Dubai, UAE, from February 20–22, 2024. Attendees were treated to an unparalleled experience, gaining incredible insights from industry leaders, discussing trending topics, and embracing opportunities for networking and collaboration. HackerNoon , serving as the event’s media partner, played a crucial role in amplifying the reach and engagement of the conference, leveraging its extensive tech and business journalism expertise to highlight the event’s key moments and insights. Dubai, UAE — March 26, 2024- The 2.0 Conferences HackerNoon The essence of The 2.0 Conferences lies in its gathering of forward-thinking professionals across various industries. It promotes innovation and strategic thinking by connecting attendees with experts and thought leaders. This initiative is rooted in a mission to encourage creative problem-solving, promote new perspectives, and facilitate the sharing of groundbreaking ideas. The conference offers more than just presentations; it’s a platform for building lasting professional connections and sharing valuable experiences. HackerNoon, known for its commitment to providing a space for technologists to share knowledge and ideas, significantly contributed to the success of The 2.0 Conferences. With a community of over 45,000 writers and 4 million monthly readers, HackerNoon ensured that the insights and innovations shared at the conference reached a broader audience. This collaboration allowed for dissemination of cutting-edge ideas and strategies discussed during the event, making the content accessible to a global community interested in technological advancements and industry trends. “The collaboration with HackerNoon brought a new dimension to our conference. Their coverage extended our reach and helped highlight the significant contributions and insights shared by our speakers and participants,” commented Yashasvi Kapoor, Manager of Delegate Relations at The 2.0 Conferences. “The collaboration with HackerNoon brought a new dimension to our conference. Their coverage extended our reach and helped highlight the significant contributions and insights shared by our speakers and participants,” commented Yashasvi Kapoor, Manager of Delegate Relations at The 2.0 Conferences. This strategic collaboration between HackerNoon and The 2.0 Conferences brought tangible benefits to the table, enhancing the attendee experience and broadening the event’s influence. The partnership ensured the conferences’ innovative ideas reached a global audience, harnessing a deeper understanding and appreciation of the potential to shape the future. About The 2.0 Conferences About The 2.0 Conferences The 2.0 Conferences is dedicated to bringing together thought leaders and innovators from various sectors to share ideas, strategies, and insights. Focusing on promoting innovation and strategic thinking, the conference series aims to inspire change and drive progress across multiple industries. Our commitment to creating meaningful connections and learning opportunities ensures a valuable and impactful experience for all participants. For more information, check out their website- www.the2conf.com . www.the2conf.com

The 2.0 Conferences Spark Global Conversations With HackerNoon As Esteemed Media Partner

2.0 সম্মেলনগুলি সম্মানিত মিডিয়া পার্টনার হিসাবে হ্যাকারনুন-এর সাথে বিশ্বব্যাপী কথোপকথন শুরু করে

Die 2.0-Konferenzen lösen weltweite Gespräche aus, mit HackerNoon als geschätztem Medienpartner

Las conferencias 2.0 provocan conversaciones globales con HackerNoon como estimado socio de medios

Les conférences 2.0 suscitent des conversations mondiales avec HackerNoon comme partenaire média estimé

2.0 सम्मेलन ने हैकरनून के साथ वैश्विक स्तर पर चर्चा को बढ़ावा दिया, जो एक प्रतिष्ठित मीडिया पार्टनर है

2.0カンファレンスはHackerNoonを信頼のメディアパートナーとして迎え、世界的な会話を巻き起こす

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As conferências 2.0 geram conversas globais com HackerNoon como estimado parceiro de mídia

Конференции 2.0 вызывают глобальные дискуссии с HackerNoon в качестве уважаемого медиа-партнера

2.0 Konferansları, Değerli Medya Ortağı HackerNoon ile Küresel Konuşmaları Ateşliyor

Hội nghị 2.0 khơi dậy các cuộc trò chuyện toàn cầu với HackerNoon với tư cách là đối tác truyền thông đáng kính

2.0 会议引发全球对话，HackerNoon 成为受人尊敬的媒体合作伙伴

A Sparse Bayesian Learning for Diagnosis of Nonstationary and Spatially Correlated Faults:References

This paper is available on arxiv under CC 4.0 license.
Authors:
(1) Jihoon Chung;
(2)  Zhenyu (James) Kong. Table of Links Abstract & Introduction
Review of Related Work
Proposed Research Methodology
Numerical Case Studies
Real-World Simulation Case Studies
Conclusion
References VI. CONCLUSIONS This paper proposes a novel sparse hierarchical Bayesian method, CSSBL, to effectively identify the sparse process faults in multistation assembly systems. The method identifies process faults by considering the spatial correlation of KCCs and nonstationary process faults among the multiple KPCs. Since posterior distributions of KCCs in the proposed method are computationally intractable, this paper derives approximate posterior distributions of KCCs via Variational Bayes inference. The proposed method’s effectiveness is validated by numerical cases and real-world simulation application using an actual auto-body assembly system. In this work, the temporal correlations of the KCCs are not considered. However, a temporal correlation exists among the KCCs because of the degradation of wear of production tooling over time [23] or the machine-tool thermal distortion [22]. Therefore, extending the proposed CSSBL to consider the temporal correlation of KCCs makes the method more realistic in the actual multistation assembly system. Since the proposed method is not designed for specific processes, it can be effectively applied to other domains, including healthcare and communication systems, for their process monitoring. APPENDIX A APPENDIX B APPENDIX C APPENDIX D APPENDIX E APPENDIX F This paper is available on arxiv under CC 4.0 license. Authors: (1) Jihoon Chung; (2)  Zhenyu (James) Kong. This paper is available on arxiv under CC 4.0 license. Authors: (1) Jihoon Chung; (2)  Zhenyu (James) Kong. Table of Links Abstract & Introduction Review of Related Work Proposed Research Methodology Numerical Case Studies Real-World Simulation Case Studies Conclusion References Abstract & Introduction Abstract & Introduction Review of Related Work Review of Related Work Proposed Research Methodology Proposed Research Methodology Numerical Case Studies Numerical Case Studies Real-World Simulation Case Studies Real-World Simulation Case Studies Conclusion Conclusion References References VI. CONCLUSIONS This paper proposes a novel sparse hierarchical Bayesian method, CSSBL, to effectively identify the sparse process faults in multistation assembly systems. The method identifies process faults by considering the spatial correlation of KCCs and nonstationary process faults among the multiple KPCs. Since posterior distributions of KCCs in the proposed method are computationally intractable, this paper derives approximate posterior distributions of KCCs via Variational Bayes inference. The proposed method’s effectiveness is validated by numerical cases and real-world simulation application using an actual auto-body assembly system. In this work, the temporal correlations of the KCCs are not considered. However, a temporal correlation exists among the KCCs because of the degradation of wear of production tooling over time [23] or the machine-tool thermal distortion [22]. Therefore, extending the proposed CSSBL to consider the temporal correlation of KCCs makes the method more realistic in the actual multistation assembly system. Since the proposed method is not designed for specific processes, it can be effectively applied to other domains, including healthcare and communication systems, for their process monitoring. APPENDIX A APPENDIX B APPENDIX C APPENDIX D APPENDIX E APPENDIX F

A Sparse Bayesian Learning for Diagnosis of Nonstationary and Spatially Correlated Faults:Conclusion

This paper is available on arxiv under CC 4.0 license.
Authors:
(1) Jihoon Chung;
(2)  Zhenyu (James) Kong. Table of Links Abstract & Introduction
Review of Related Work
Proposed Research Methodology
Numerical Case Studies
Real-World Simulation Case Studies
Conclusion
References V. REAL-WORLD SIMULATION CASE STUDIES An assembly operation from an actual auto-body assembly process is used as a real-world case study. Fig. 5 describes a car’s floor pan, which is the assembled product from this process. The assembled product consists of four parts, including the right bracket, left bracket, right floor pan, and left floor pan. Fig. 6 shows the process assembly procedure consisting of three stations. During the assembly process, the parts are held by fixtures, which are the KCCs in this process [7]. KPCs are measured from four points, namely, M1, M2, M3, and M4, respectively, as shown in Fig. 5. Every part has a designated location for measuring the KPCs. For instance, part 1 has M1, part 2 has M2, part 3 has M3, and part 4 has M4. These measurements can be taken at each station once the relevant part has been assembled in the preceding stations. For example, M3 on part 3 cannot be measured in station 1 because part 3 has not yet been assembled at station 1 [7]. In the designated location, KPCs are measured in three directions (X, Y, and Z). In this assembly process, there exists a total of 33 KCCs, which are dimensional errors of fixture locators [8]. The fault pattern matrix Φ is established based on the literature [7], [11], [47] and provided in Appendix F. Since the number of measurements (12) is less than that of KCCs (33), it causes an underdetermined system in the fault quality linear model. Therefore, sparse estimation is required to identify process faults. In this assembly process, two lists of correlated KCCs exist; the rest are assumed to be independent [8]. The first list comprises six KCCs, including KCC8, KCC9, KCC10, KCC11, KCC12, and KCC13. The second list is composed of KCC31, KCC32, and KCC33. The correlation structures of the two groups are described (for simplification, the lower triangular terms are removed) as follows [8]: To provide nonstationary process faults along the KPCs samples collected over time, two groups are provided. In the first group, the first correlated list is provided as process faults. For the process faults of the second group, all the correlated lists and one independent KCCs are included. Therefore, the case study represents the situation when the process faults evolve over time since the variation of certain KCCs will be propagated to other KCCs if the process faults are not mitigated immediately during the process. 50 KPCs samples are generated from each group. The variances of process faults, non-process faults, and noise of KPCs are determined to be the same value as in Section IV. In addition, performance evaluation measures and benchmark methods used in Section IV are still utilized in Section V. Table III shows the performance comparison in AUC and NMSE by varying the correlation coefficients. For convenience, the correlation coefficients k1 and k2 are equal and are selected from {0.1, 0.3, 0.6, 0.9, 0.95} in the case studies. Specifically, the AUC of the proposed method is close to 1.0 at every correlations levels, representing the perfect classification between the process faults and non-process faults in terms of estimated variance. The effectiveness of the proposed method is significant in NMSE when a high correlation exists. Specifically, the NMSE of the proposed method is less than 10% of that of all the benchmark methods when the correlation coefficient is 0.9 and 0.95. To demonstrate the generalizability of the proposed method, the correlated KCCs are randomly selected in further case studies. Assuming there exist two correlated lists with the size of three, respectively. Therefore, six KCCs are randomly selected from 33 KCCs for two correlated lists in each trial. As in the previous study, one correlated list is provided as process faults in the first group, while two correlated lists and two independent KCCs are used as process faults in the remaining second group. Fig. 7 shows the performances of all the methods in AUC and NMSE results in ten different spatial correlations (i.e., k1 = k2). From Fig. 7 (a), it is prominent that the proposed method successfully differentiates between process faults and non-process faults. Specifically, the AUC of the proposed method is nearly 1.0, while those of most benchmark methods are below 0.9 in the highly correlated case studies. In addition, Fig. 7 (b) represents that the proposed method provides valuable information to the practitioners to determine when to stop the operations to maintain the process when a high correlation exists between KCCs. All the benchmark methods illustrate similar trends compared to Section IV since the methods cannot consider both the spatial correlation of KCCs and the nonstationary process faults. This paper is available on arxiv under CC 4.0 license. Authors: (1) Jihoon Chung; (2)  Zhenyu (James) Kong. This paper is available on arxiv under CC 4.0 license. Authors: (1) Jihoon Chung; (2)  Zhenyu (James) Kong. Table of Links Abstract & Introduction Review of Related Work Proposed Research Methodology Numerical Case Studies Real-World Simulation Case Studies Conclusion References Abstract & Introduction Abstract & Introduction Review of Related Work Review of Related Work Proposed Research Methodology Proposed Research Methodology Numerical Case Studies Numerical Case Studies Real-World Simulation Case Studies Real-World Simulation Case Studies Conclusion Conclusion References References V. REAL-WORLD SIMULATION CASE STUDIES An assembly operation from an actual auto-body assembly process is used as a real-world case study. Fig. 5 describes a car’s floor pan, which is the assembled product from this process. The assembled product consists of four parts, including the right bracket, left bracket, right floor pan, and left floor pan. Fig. 6 shows the process assembly procedure consisting of three stations. During the assembly process, the parts are held by fixtures, which are the KCCs in this process [7]. KPCs are measured from four points, namely, M1, M2, M3, and M4, respectively, as shown in Fig. 5. Every part has a designated location for measuring the KPCs. For instance, part 1 has M1, part 2 has M2, part 3 has M3, and part 4 has M4. These measurements can be taken at each station once the relevant part has been assembled in the preceding stations. For example, M3 on part 3 cannot be measured in station 1 because part 3 has not yet been assembled at station 1 [7]. In the designated location, KPCs are measured in three directions (X, Y, and Z). In this assembly process, there exists a total of 33 KCCs, which are dimensional errors of fixture locators [8]. The fault pattern matrix Φ is established based on the literature [7], [11], [47] and provided in Appendix F. Since the number of measurements (12) is less than that of KCCs (33), it causes an underdetermined system in the fault quality linear model. Therefore, sparse estimation is required to identify process faults. In this assembly process, two lists of correlated KCCs exist; the rest are assumed to be independent [8]. The first list comprises six KCCs, including KCC8, KCC9, KCC10, KCC11, KCC12, and KCC13. The second list is composed of KCC31, KCC32, and KCC33. The correlation structures of the two groups are described (for simplification, the lower triangular terms are removed) as follows [8]: To provide nonstationary process faults along the KPCs samples collected over time, two groups are provided. In the first group, the first correlated list is provided as process faults. For the process faults of the second group, all the correlated lists and one independent KCCs are included. Therefore, the case study represents the situation when the process faults evolve over time since the variation of certain KCCs will be propagated to other KCCs if the process faults are not mitigated immediately during the process. 50 KPCs samples are generated from each group. The variances of process faults, non-process faults, and noise of KPCs are determined to be the same value as in Section IV. In addition, performance evaluation measures and benchmark methods used in Section IV are still utilized in Section V. Table III shows the performance comparison in AUC and NMSE by varying the correlation coefficients. For convenience, the correlation coefficients k1 and k2 are equal and are selected from {0.1, 0.3, 0.6, 0.9, 0.95} in the case studies. Specifically, the AUC of the proposed method is close to 1.0 at every correlations levels, representing the perfect classification between the process faults and non-process faults in terms of estimated variance. The effectiveness of the proposed method is significant in NMSE when a high correlation exists. Specifically, the NMSE of the proposed method is less than 10% of that of all the benchmark methods when the correlation coefficient is 0.9 and 0.95. To demonstrate the generalizability of the proposed method, the correlated KCCs are randomly selected in further case studies. Assuming there exist two correlated lists with the size of three, respectively. Therefore, six KCCs are randomly selected from 33 KCCs for two correlated lists in each trial. As in the previous study, one correlated list is provided as process faults in the first group, while two correlated lists and two independent KCCs are used as process faults in the remaining second group. Fig. 7 shows the performances of all the methods in AUC and NMSE results in ten different spatial correlations (i.e., k1 = k2). From Fig. 7 (a), it is prominent that the proposed method successfully differentiates between process faults and non-process faults. Specifically, the AUC of the proposed method is nearly 1.0, while those of most benchmark methods are below 0.9 in the highly correlated case studies. In addition, Fig. 7 (b) represents that the proposed method provides valuable information to the practitioners to determine when to stop the operations to maintain the process when a high correlation exists between KCCs. All the benchmark methods illustrate similar trends compared to Section IV since the methods cannot consider both the spatial correlation of KCCs and the nonstationary process faults.

Sparse Bayesian Learning: Real-World Simulation Case Studies

This paper is available on arxiv under CC 4.0 license.
Authors:
(1) Jihoon Chung;
(2)  Zhenyu (James) Kong. Table of Links Abstract & Introduction
Review of Related Work
Proposed Research Methodology
Numerical Case Studies
Real-World Simulation Case Studies
Conclusion
References III. PROPOSED RESEARCH METHODOLOGY This section proposes a novel sparse Bayesian hierarchical method: clustering and spatially correlated sparse Bayesian learning (CSSBL). The proposed CSSBL is described in Section III-A, followed by Bayesian inference in Section III-B. A. Proposed Methodology The proposed methodology is a sparse Bayesian hierarchical model considering the spatial correlations of KCCs and nonstationarity of process faults along the KPCs samples. From Eq. (2), fault quality linear model of k th KPCs sample (yk ) can be written as follows: The prior distributions in the proposed method consist of the following two hierarchical layers. The first layer provides several prior distributions. First, the prior distribution of precision variable α in Eq. (4) is provided. Second, the prior distribution exploiting the spatial correlation of KCCs is offered. Finally, the prior distribution in the first layer clusters the KPCS samples into groups sharing the same process faults. The second layer consists of prior distribution encouraging the sparsity of process faults. Fig. 2 describes a graphical representation of the hierarchical layers in the proposed method. In the first layer of the hierarchical model in Fig. 2, the Gamma distribution is provided as the prior distribution of α in Eq. (4) as follows: To handle the spatial correlation of KCCs, the proposed approach considers two types of correlation structures of fixture locators in multistation assembly systems. One is an independent locator whose deviation is uncorrelated to the deviation of other locators (i.e., independent tolerance mode in [21]). The other is correlated locators (i.e., composite tolerance mode in [21]). Specifically, the locators in the correlated list vary with a certain correlation with other locators in the list [21]. To consider this structure, the proposed method models the covariance matrix (Ck) of the prior distribution of xk in Eq. (5) as follows: B. Bayesian Inference of the Proposed Methodology To address this challenge, this paper employs Variational Bayes inference (VBI) to derive approximate posterior distributions of hidden variables. Specifically, Variational Bayes Expectation Maximization (VBEM) [41] estimates hidden variables and hyperparameters to identify KCCs with excessive variance in the proposed method. VBEM consists of Estep: Variational Bayesian expectation step to estimate hidden variables X, Z, Γ, α by approximating the posterior distribution of hidden variables; and M-step: Variational Bayesian maximization step to update hyperparameters B by maximizing the expected value of the logarithm of the complete likelihood  [37]. This paper is available on arxiv under CC 4.0 license. Authors: (1) Jihoon Chung; (2)  Zhenyu (James) Kong. This paper is available on arxiv under CC 4.0 license. Authors: (1) Jihoon Chung; (2)  Zhenyu (James) Kong. Table of Links Abstract & Introduction Review of Related Work Proposed Research Methodology Numerical Case Studies Real-World Simulation Case Studies Conclusion References Abstract & Introduction Abstract & Introduction Review of Related Work Review of Related Work Proposed Research Methodology Proposed Research Methodology Numerical Case Studies Numerical Case Studies Real-World Simulation Case Studies Real-World Simulation Case Studies Conclusion Conclusion References References III. PROPOSED RESEARCH METHODOLOGY This section proposes a novel sparse Bayesian hierarchical method: clustering and spatially correlated sparse Bayesian learning (CSSBL). The proposed CSSBL is described in Section III-A, followed by Bayesian inference in Section III-B. A. Proposed Methodology A. Proposed Methodology The proposed methodology is a sparse Bayesian hierarchical model considering the spatial correlations of KCCs and nonstationarity of process faults along the KPCs samples. From Eq. (2), fault quality linear model of k th KPCs sample (yk ) can be written as follows: The prior distributions in the proposed method consist of the following two hierarchical layers. The first layer provides several prior distributions. First, the prior distribution of precision variable α in Eq. (4) is provided. Second, the prior distribution exploiting the spatial correlation of KCCs is offered. Finally, the prior distribution in the first layer clusters the KPCS samples into groups sharing the same process faults. The second layer consists of prior distribution encouraging the sparsity of process faults. Fig. 2 describes a graphical representation of the hierarchical layers in the proposed method. In the first layer of the hierarchical model in Fig. 2, the Gamma distribution is provided as the prior distribution of α in Eq. (4) as follows: To handle the spatial correlation of KCCs, the proposed approach considers two types of correlation structures of fixture locators in multistation assembly systems. One is an independent locator whose deviation is uncorrelated to the deviation of other locators (i.e., independent tolerance mode in [21]). The other is correlated locators (i.e., composite tolerance mode in [21]). Specifically, the locators in the correlated list vary with a certain correlation with other locators in the list [21]. To consider this structure, the proposed method models the covariance matrix (Ck) of the prior distribution of xk in Eq. (5) as follows: B. Bayesian Inference of the Proposed Methodology B. Bayesian Inference of the Proposed Methodology To address this challenge, this paper employs Variational Bayes inference (VBI) to derive approximate posterior distributions of hidden variables. Specifically, Variational Bayes Expectation Maximization (VBEM) [41] estimates hidden variables and hyperparameters to identify KCCs with excessive variance in the proposed method. VBEM consists of Estep: Variational Bayesian expectation step to estimate hidden variables X, Z, Γ, α by approximating the posterior distribution of hidden variables; and M-step: Variational Bayesian maximization step to update hyperparameters B by maximizing the expected value of the logarithm of the complete likelihood  [37].

Sparse Bayesian Learning: Proposed Research Methodology

This paper is available on arxiv under CC 4.0 license.
Authors:
(1) Jihoon Chung;
(2)  Zhenyu (James) Kong. Table of Links Abstract & Introduction
Review of Related Work
Proposed Research Methodology
Numerical Case Studies
Real-World Simulation Case Studies
Conclusion
References IV. NUMERICAL CASE STUDIES This section provides numerical studies to compare the performance between the proposed method and benchmark methods. The studies evaluate the performance by varying the correlations (ki in Eq. (7)) between the KCCs in nonstationary process faults. All the numerical case studies consist of 20 independent trials. The code of the proposed algorithm is implemented in Matlab 2021. The CPU used in case studies is an Intel® Core™ Processor i7-8750H. This section compares the proposed method with the following benchmark methods. • User Grouping Sparse Bayesian Learning (i.e., UGSBL) proposed in [2] is an SBL method that clusters the KPCs samples into groups sharing the same process faults in sparse estimation. • Spatially Correlated Bayesian Learning (i.e., SCBL) proposed in [8] is an SBL method to diagnose process faults by exploiting the spatial correlation of KCCs. • [10] proposed the SBL method that considers prior knowledge of the process faults in sparse estimation. • MSBL proposed in [32] is a basic SBL method that assumes the independence of KCCs and stationarity of process faults. Performance Evaluation: To evaluate the effectiveness of the proposed method, two performance measures are used in this paper. The first measure evaluates the process fault detection capability, and the second measure is related to estimation accuracy. Each measure is evaluated by varying the correlation coefficients of spatially correlated KCCs. Fault Detection Capability: First, the proposed method requires correctly identifying process faults whose KCCs have excessive variance. In order to assess the capability of detecting process faults under sparse conditions, the area under the receiver operating characteristics (ROCs) curve (AUC) is utilized [46]. AUC is widely used for evaluating the accuracy of binary classification. Specifically, AUC measures the quality of the classification accuracy between process faults and non process faults irrespective of the threshold of variance. In other words, AUC accounts for the trade-off between the type 1 and type 2 errors. When the method is capable of achieving perfect classification results, the resulting AUC value will be 1. However, the AUC with a value close to 0.5 denotes that the estimated variances of KCCs from the method are similar between the process faults and non-process faults. Table I represents that the proposed method always achieves the best performance of AUC in the various spatial correlation between KCCs. Specifically, the AUC of the proposed method is close to 1.0 at every correlations levels, representing the perfect classification between the process faults and nonprocess faults in terms of estimated variance. Since UGSBL clusters the KPCs samples that share the same process faults, the method shows a comparable result to the proposed method  when the spatial correlation of KCCs is relatively low and moderate. However, the performance degrades in the high spatial correlation since the method assumes independence between all KCCs. The performance of SSBL is robust to correlations. This is because SSBL utilizes the correlation of correlated KCCs in its estimation. However, SSBL performs worse than the proposed method since this method assumes the same process faults between all KPCs samples, which are unsuitable for dealing with nonstationary process faults. MSBL and [10] show poor performances since these methods assume the independence of KCCs and stationarity of process faults. 2) Estimation Accuracy: In addition to correctly identifying the process fault, the method needs to accurately estimate the variance of process faults. After the process faults are identified, the practitioners are interested in how large the variances are to make decisions for the maintenance of the process. To achieve this objective, this paper provides the normalized mean squared error (NMSE) between true and estimated variance. Table II represents that the proposed method achieves the best performance in the estimation accuracy of the variance in most of the correlations. It implies that the proposed method is useful not only for the process faults identification but also for measuring how severe the process faults are. Compared to benchmark methods, the effectiveness of the proposed method in the NMSE results is noticeable in the presence of high spatial correlations between KCCs. Unlike AUC performance, the NMSE of SSBL increases as the correlation increases. It implies that SSBL successfully identifies the process faults utilizing the correlation information but cannot accurately estimate the variance since SSBL assumes the stationary process faults over KPC samples. The performance of UGSBL, MSBL, and [10] represent similar trends to the performance of AUC. This paper is available on arxiv under CC 4.0 license. Authors: (1) Jihoon Chung; (2)  Zhenyu (James) Kong. This paper is available on arxiv under CC 4.0 license. Authors: (1) Jihoon Chung; (2)  Zhenyu (James) Kong. Table of Links Abstract & Introduction Review of Related Work Proposed Research Methodology Numerical Case Studies Real-World Simulation Case Studies Conclusion References Abstract & Introduction Abstract & Introduction Review of Related Work Review of Related Work Proposed Research Methodology Proposed Research Methodology Numerical Case Studies Numerical Case Studies Real-World Simulation Case Studies Real-World Simulation Case Studies Conclusion Conclusion References References IV. NUMERICAL CASE STUDIES This section provides numerical studies to compare the performance between the proposed method and benchmark methods. The studies evaluate the performance by varying the correlations (ki in Eq. (7)) between the KCCs in nonstationary process faults. All the numerical case studies consist of 20 independent trials. The code of the proposed algorithm is implemented in Matlab 2021. The CPU used in case studies is an Intel® Core™ Processor i7-8750H. This section compares the proposed method with the following benchmark methods. • User Grouping Sparse Bayesian Learning (i.e., UGSBL) proposed in [2] is an SBL method that clusters the KPCs samples into groups sharing the same process faults in sparse estimation. • Spatially Correlated Bayesian Learning (i.e., SCBL) proposed in [8] is an SBL method to diagnose process faults by exploiting the spatial correlation of KCCs. • [10] proposed the SBL method that considers prior knowledge of the process faults in sparse estimation. • MSBL proposed in [32] is a basic SBL method that assumes the independence of KCCs and stationarity of process faults. Performance Evaluation: To evaluate the effectiveness of the proposed method, two performance measures are used in this paper. The first measure evaluates the process fault detection capability, and the second measure is related to estimation accuracy. Each measure is evaluated by varying the correlation coefficients of spatially correlated KCCs. Performance Evaluation: Fault Detection Capability: First, the proposed method requires correctly identifying process faults whose KCCs have excessive variance. In order to assess the capability of detecting process faults under sparse conditions, the area under the receiver operating characteristics (ROCs) curve (AUC) is utilized [46]. AUC is widely used for evaluating the accuracy of binary classification. Specifically, AUC measures the quality of the classification accuracy between process faults and non process faults irrespective of the threshold of variance. In other words, AUC accounts for the trade-off between the type 1 and type 2 errors. When the method is capable of achieving perfect classification results, the resulting AUC value will be 1. However, the AUC with a value close to 0.5 denotes that the estimated variances of KCCs from the method are similar between the process faults and non-process faults. Fault Detection Capability: First, the proposed method requires correctly identifying process faults whose KCCs have excessive variance. In order to assess the capability of detecting process faults under sparse conditions, the area under the receiver operating characteristics (ROCs) curve (AUC) is utilized [46]. AUC is widely used for evaluating the accuracy of binary classification. Specifically, AUC measures the quality of the classification accuracy between process faults and non process faults irrespective of the threshold of variance. In other words, AUC accounts for the trade-off between the type 1 and type 2 errors. When the method is capable of achieving perfect classification results, the resulting AUC value will be 1. However, the AUC with a value close to 0.5 denotes that the estimated variances of KCCs from the method are similar between the process faults and non-process faults. Table I represents that the proposed method always achieves the best performance of AUC in the various spatial correlation between KCCs. Specifically, the AUC of the proposed method is close to 1.0 at every correlations levels, representing the perfect classification between the process faults and nonprocess faults in terms of estimated variance. Since UGSBL clusters the KPCs samples that share the same process faults, the method shows a comparable result to the proposed method  when the spatial correlation of KCCs is relatively low and moderate. However, the performance degrades in the high spatial correlation since the method assumes independence between all KCCs. The performance of SSBL is robust to correlations. This is because SSBL utilizes the correlation of correlated KCCs in its estimation. However, SSBL performs worse than the proposed method since this method assumes the same process faults between all KPCs samples, which are unsuitable for dealing with nonstationary process faults. MSBL and [10] show poor performances since these methods assume the independence of KCCs and stationarity of process faults. 2) Estimation Accuracy: In addition to correctly identifying the process fault, the method needs to accurately estimate the variance of process faults. After the process faults are identified, the practitioners are interested in how large the variances are to make decisions for the maintenance of the process. To achieve this objective, this paper provides the normalized mean squared error (NMSE) between true and estimated variance. Table II represents that the proposed method achieves the best performance in the estimation accuracy of the variance in most of the correlations. It implies that the proposed method is useful not only for the process faults identification but also for measuring how severe the process faults are. Compared to benchmark methods, the effectiveness of the proposed method in the NMSE results is noticeable in the presence of high spatial correlations between KCCs. Unlike AUC performance, the NMSE of SSBL increases as the correlation increases. It implies that SSBL successfully identifies the process faults utilizing the correlation information but cannot accurately estimate the variance since SSBL assumes the stationary process faults over KPC samples. The performance of UGSBL, MSBL, and [10] represent similar trends to the performance of AUC.

Sparse Bayesian Learning: Numerical Case Studies

This paper is available on arxiv under CC 4.0 license.
Authors:
(1) Jihoon Chung;
(2)  Zhenyu (James) Kong. Table of Links Abstract & Introduction
Review of Related Work
Proposed Research Methodology
Numerical Case Studies
Real-World Simulation Case Studies
Conclusion
References II. REVIEW OF RELATED WORK The related existing studies of fault diagnosis in manufacturing systems are reviewed in Section II A. Then, the literature related to sparse Bayesian learning is provided in Section II-B. Afterward, the research gaps in the current work are identified in Section II-C. A. Fault Diagnosis Methodologies in Manufacturing Systems Numerous studies have focused on fault diagnosis methodologies for manufacturing systems, utilizing the fault-quality model outlined in Eq. (1). [14] developed a PCA-based orthogonal diagonalization strategy to transform the measurement data. It enabled the estimation of the variance of KCCs in a multistation assembly system. [13] presented a fault diagnosis method in the multistation assembly systems integrating the state space model of the process and matrix perturbation theory. [25], [26] proposed a fault diagnosis method in the machining process considering the process physics regarding how fixtures generate the patterns. Using this method, root cause identification was conducted sequentially. The approaches mentioned above assume that the number of measurements is greater than the number of KCCs (i.e., M > N in Eq. (1)). However, this assumption may not always be consistent with industrial practice. These approaches become ineffective when this assumption is violated because the fault-quality linear model leads to an underdetermined system, resulting in a nonunique solution. To overcome an underdetermined system in the fault-quality linear model, sparse learning can be utilized, which has gained considerable attention in fault diagnosis and detection within manufacturing systems. For fault diagnosis in the manufacturing system, [27] developed a fault diagnosis method using dictionary learning and sparse representationbased classification. [28] proposed a novel root cause diagnostic framework satisfying the assumption that sparse inputs affect the process output. [29] proposed a group-sparsity learning approach for bearing fault diagnosis. In addition to fault diagnosis and detection in the general manufacturing system, sparse learning has been widely utilized to address the issue of an underdetermined system in the multistation assembly system. In particular, sparse Bayesian learning has been widely utilized to incorporate the sparsity of process faults as the prior distribution. [5] proposed a fault diagnosis approach by enhancing the relevant vector machine to detect process faults using the sparse estimate of the variance change of KCCs. [8] developed a spatially correlated sparse Bayesian learning to consider the spatial correlation of KCCs in sparse estimation. The work identifies the KCCs with mean shifts. [10] proposed a Bayesian model to identify the KCCs that have variance increases in a multistation manufacturing system using the sparse variance component prior. [9] developed a novel sparse Bayesian learning to figure out the KCCs with mean shifts by considering the temporal correlation of KCCs and the prior knowledge of process faults. B. Sparse Bayesian Learning After the introduction of sparse Bayesian learning (SBL) by [30], many researchers have extended this approach significantly. For instance, [31] was the first to apply SBL to sparse estimation for the single measurement vector model given in Eq. (1). Subsequently, [32] further extended it to the MMV model (Eq. (2)) by developing the MSBL algorithm under the common support assumption. The notable advantage of SBL and MSBL is that their global minimum is always the sparsest solution, while that of the minimization-based sparse algorithms [17] [33] is usually not the sparsest solution [16], [34]. Based on the MMV model, many previous studies exploit the spatial correlation in solution vectors (i.e., rows in X in Eq. (2)). [35] proposed a block structure to exploit the intra-block correlation for sparse estimation. [36] developed a Bayesian method for recovery of block-sparse solution whose block-sparse structures are entirely unknown. [37] modeled the spatial structure of the solution as Markov dependency by the Beta process. Besides considering the spatial correlation of sparse solutions, work that considers nonstationary sparse solutions has been studied recently under the MMV model in the SBL framework. [38] developed a method using the Dirichlet process to cluster the measurements into groups with common sparsity patterns. Compared to [38], [2] proposed a more general method having two sparsity components: a commonly shared sparsity and an individual sparsity to deal with outliers that deviated from the uniform sparsity pattern in each group. C. Research Gap Analysis The research presented in Section II-A focuses on utilizing the sparsity of process faults for accurate fault diagnosis when low dimensional measurements exist in actual manufacturing systems. However, there is a lack of efforts to identify process faults by considering the spatial correlation and the nonstationary process faults, which is common in industrial practice. Section II-B introduces methods in SBL. It introduces the work concerning the spatial correlation in the solution vector and dealing with the nonstationary sparse solution individually. However, it still lacks the work that uses both properties simultaneously. Therefore, this paper proposes a novel SBL method considering both properties for accurate fault diagnosis. This paper is available on arxiv under CC 4.0 license. Authors: (1) Jihoon Chung; (2)  Zhenyu (James) Kong. This paper is available on arxiv under CC 4.0 license. Authors: (1) Jihoon Chung; (2)  Zhenyu (James) Kong. Table of Links Abstract & Introduction Review of Related Work Proposed Research Methodology Numerical Case Studies Real-World Simulation Case Studies Conclusion References Abstract & Introduction Abstract & Introduction Review of Related Work Review of Related Work Proposed Research Methodology Proposed Research Methodology Numerical Case Studies Numerical Case Studies Real-World Simulation Case Studies Real-World Simulation Case Studies Conclusion Conclusion References References II. REVIEW OF RELATED WORK The related existing studies of fault diagnosis in manufacturing systems are reviewed in Section II A. Then, the literature related to sparse Bayesian learning is provided in Section II-B. Afterward, the research gaps in the current work are identified in Section II-C. A. Fault Diagnosis Methodologies in Manufacturing Systems Numerous studies have focused on fault diagnosis methodologies for manufacturing systems, utilizing the fault-quality model outlined in Eq. (1). [14] developed a PCA-based orthogonal diagonalization strategy to transform the measurement data. It enabled the estimation of the variance of KCCs in a multistation assembly system. [13] presented a fault diagnosis method in the multistation assembly systems integrating the state space model of the process and matrix perturbation theory. [25], [26] proposed a fault diagnosis method in the machining process considering the process physics regarding how fixtures generate the patterns. Using this method, root cause identification was conducted sequentially. The approaches mentioned above assume that the number of measurements is greater than the number of KCCs (i.e., M > N in Eq. (1)). However, this assumption may not always be consistent with industrial practice. These approaches become ineffective when this assumption is violated because the fault-quality linear model leads to an underdetermined system, resulting in a nonunique solution. A. To overcome an underdetermined system in the fault-quality linear model, sparse learning can be utilized, which has gained considerable attention in fault diagnosis and detection within manufacturing systems. For fault diagnosis in the manufacturing system, [27] developed a fault diagnosis method using dictionary learning and sparse representationbased classification. [28] proposed a novel root cause diagnostic framework satisfying the assumption that sparse inputs affect the process output. [29] proposed a group-sparsity learning approach for bearing fault diagnosis. In addition to fault diagnosis and detection in the general manufacturing system, sparse learning has been widely utilized to address the issue of an underdetermined system in the multistation assembly system. In particular, sparse Bayesian learning has been widely utilized to incorporate the sparsity of process faults as the prior distribution. [5] proposed a fault diagnosis approach by enhancing the relevant vector machine to detect process faults using the sparse estimate of the variance change of KCCs. [8] developed a spatially correlated sparse Bayesian learning to consider the spatial correlation of KCCs in sparse estimation. The work identifies the KCCs with mean shifts. [10] proposed a Bayesian model to identify the KCCs that have variance increases in a multistation manufacturing system using the sparse variance component prior. [9] developed a novel sparse Bayesian learning to figure out the KCCs with mean shifts by considering the temporal correlation of KCCs and the prior knowledge of process faults. B. Sparse Bayesian Learning B. Sparse Bayesian Learning After the introduction of sparse Bayesian learning (SBL) by [30], many researchers have extended this approach significantly. For instance, [31] was the first to apply SBL to sparse estimation for the single measurement vector model given in Eq. (1). Subsequently, [32] further extended it to the MMV model (Eq. (2)) by developing the MSBL algorithm under the common support assumption. The notable advantage of SBL and MSBL is that their global minimum is always the sparsest solution, while that of the minimization-based sparse algorithms [17] [33] is usually not the sparsest solution [16], [34]. Based on the MMV model, many previous studies exploit the spatial correlation in solution vectors (i.e., rows in X in Eq. (2)). [35] proposed a block structure to exploit the intra-block correlation for sparse estimation. [36] developed a Bayesian method for recovery of block-sparse solution whose block-sparse structures are entirely unknown. [37] modeled the spatial structure of the solution as Markov dependency by the Beta process. Besides considering the spatial correlation of sparse solutions, work that considers nonstationary sparse solutions has been studied recently under the MMV model in the SBL framework. [38] developed a method using the Dirichlet process to cluster the measurements into groups with common sparsity patterns. Compared to [38], [2] proposed a more general method having two sparsity components: a commonly shared sparsity and an individual sparsity to deal with outliers that deviated from the uniform sparsity pattern in each group. C. Research Gap Analysis C. Research Gap Analysis The research presented in Section II-A focuses on utilizing the sparsity of process faults for accurate fault diagnosis when low dimensional measurements exist in actual manufacturing systems. However, there is a lack of efforts to identify process faults by considering the spatial correlation and the nonstationary process faults, which is common in industrial practice. Section II-B introduces methods in SBL. It introduces the work concerning the spatial correlation in the solution vector and dealing with the nonstationary sparse solution individually. However, it still lacks the work that uses both properties simultaneously. Therefore, this paper proposes a novel SBL method considering both properties for accurate fault diagnosis.

Sparse Bayesian Learning: Review of Related Work

This paper is available on arxiv under CC 4.0 license.
Authors:
(1) Jihoon Chung;
(2)  Zhenyu (James) Kong. Table of Links Abstract & Introduction
Review of Related Work
Proposed Research Methodology
Numerical Case Studies
Real-World Simulation Case Studies
Conclusion
References Abstract Sensor technology developments provide a basis for effective fault diagnosis in manufacturing systems. However, the limited number of sensors due to physical constraints or undue costs hinders the accurate diagnosis in the actual process. In addition, time-varying operational conditions that generate nonstationary process faults and the correlation information in the process require to consider for accurate fault diagnosis in the manufacturing systems. This article proposes a novel fault diagnosis method: clustering spatially correlated sparse Bayesian learning (CSSBL), and explicitly demonstrates its applicability in a multistation assembly system that is vulnerable to the above challenges. Specifically, the method is based on a practical  assumption that it will likely have a few process faults (sparse). In addition, the hierarchical structure of CSSBL has several parameterized prior distributions to address the above challenges. As posterior distributions of process faults do not have closed form, this paper derives approximate posterior distributions through Variational Bayes inference. The proposed method’s efficacy is provided through numerical and real-world case studies utilizing an actual autobody assembly system. The generalizability of the proposed method allows the technique to be applied in fault diagnosis in other domains, including communication and healthcare systems. Note to Practitioners—This article proposes a new process fault diagnosis method: clustering spatially correlated sparse Bayesian learning. This method effectively diagnoses time-varying defects by leveraging the correlation structures in the process when sensor measurements are insufficient. The actual autobody assembly process is utilized to show the proposed method’s effectiveness. The proposed method performs superior to the benchmark methods in fault detection capability. In addition, the proposed method accurately estimates the severity of the process faults, providing significant information to the practitioners for their decision-making in the maintenance schedule. Specifically, the error between the estimation from the proposed method and the actual severity of the process faults achieves less than 10% of error of all the benchmark methods when there exists a high correlation between the variations of the fixture locators in the autobody assembly system. Index Terms—Sparse Bayesian Learning, Spatially Correlated Faults, Nonstationary Faults, Variational Bayes Inference, MultiStage Assembly Systems. I. INTRODUCTION The sparse estimation has received considerable attention in signal processing due to its ability to reconstruct a high-dimensional sparse source signal from a low-dimensional measurement [1]. Specifically, sparse estimation has broad applications in a wide range of industries in identifying the sources of sensor measurements. These applications include channel estimation in wireless systems [2], Electroencephalography (EEG) source localization in neuroimaging [3], radar detection [4], and fault diagnosis in manufacturing systems [5]. However, time-varying operational conditions in the manufacturing systems cause nonstationary process faults, hindering the accurate sparse estimation. For example, the component degradation (e.g., fixture wearing in assembly systems) will vary over time, violating the stationary process faults assumptions of most existing sparse estimation methods [6], [7]. In addition, the correlation information that occurred due to the structure of the manufacturing system should consider for effective fault diagnosis [8], but it is often neglected in sparse estimation [9], [10]. One motivating example to address the above challenges is the process fault diagnosis in the multistation assembly systems. The systems perform assembly operations from multiple stations to assemble a final product. The final product’s quality relies on several factors known as key control characteristics (KCCs) [8]. The positioning accuracy of fixture locators is KCCs in the multistation assembly [5]. Fixture locators carry out the clamping of parts during the assembly process. Therefore, any deviations from their nominal positions can lead to dimensional quality issues in the final product. Hence, fault diagnosis in multistation assemblies estimates the mean and variance of KCCs, namely, the variations of fixture locators [9]. This article focuses on process faults due to excessive variance, which is a more challenging task to diagnose than mean shifts [7]. Since monitoring the dimensional variation of KCCs is not feasible due to physical constraints in the process [10], the key product characteristics (KPCs), which are essentially measurements obtained from the final product, can be utilized to estimate the variance of KCCs. Specifically, a fault-quality linear model represents the relationship between KPCs and KCCs as follows [11], [12]: Numerous studies have employed Eq. (1) on fault diagnosis in multistation assembly systems [13], [14]. However, they assume that the number of measurements (M) is greater than the number of KCCs (N) which may not hold in actual manufacturing applications. This is because using an excessive number of sensors (measurements) can result in undue costs [8]. If this assumption is violated, Eq. (1) becomes an underdetermined system that has a nonunique solution. To address this challenge, the sparse solution assumption [15] that x in Eq. (1) has a minimal number of non zero elements is required. In the fault diagnosis problem, sparsity refers to the small number of process faults in the fault-quality linear model, which is a reasonable assumption since there are typically only a few process faults in practice [5]. Among the several sparse estimation methods, the Bayesian approach called sparse Bayesian learning has received much attention recently because of its superior estimation performance guaranteed from the several theoretical properties [16]–[18]. Sparse Bayesian learning has been applied for fault diagnosis in multistation assembly systems [10], [19]. These studies successfully identified process faults by providing prior distribution of KCCs (i.e., x in Eq. (1)) to encourage the sparsity of process faults. Especially the work in [10] applied Bayesian learning to diagnose the process faults based on the following multiple measurements vectors (MMV) model in sparse Bayesian learning [20]: Beyond the spatial correlation between KCCs, the non stationarity of process faults also needs to be considered for accurate fault diagnosis. In practice, the external time-varying operational condition, including temperature and noise, may cause nonstationary process faults over time in the manufacturing systems [6]. For example, the fixture locators in the assembly process can be deviated because of the thermal expansions of pins caused by the ambient temperature of the process that varies over time [22]. In addition, the variation of some locators will be propagated to other locators over time if the process faults are not mitigated immediately [23]. It results in nonstationary process faults along the KPCs samples collected over time. In other words, the KCCs with excessive variance differ depending on the columns of X in Eq. (2). Therefore, the stationary process faults assumption applied in the previous studies [8], [10] needs to be addressed. To consider both the spatial correlation of KCCs and the nonstationary process faults along the KPCs samples, this paper proposes a novel sparse Bayesian learning method, namely, clustering and spatially correlated sparse Bayesian learning (CSSBL). Given KPCs samples, the proposed method clusters the samples into groups sharing the same process faults. At the same time, our method estimates the variance of KCCs of each group to identify the process faults accurately. The following summarizes the contributions of this study: • From the methodological point of view, this paper proposes a novel sparse Bayesian learning that can consider both the spatial correlation of KCCs and the nonstationary process faults. Since the posterior distribution of the sparse solution in the proposed method is computationally intractable, this article derives the approximate posterior distribution of the sparse solution via Variational Bayes inference [24]. • From the application perspective, the proposed method is applied to fault diagnosis in the actual auto-body assembly process. The method performs better for process fault detection capability than the benchmark methods. Furthermore, the proposed method accurately estimates locators’ variance representing the severity of the process faults to assist practitioners’ decision-making in their maintenance policy. The subsequent sections of this paper are structured in the following manner. Section II presents an overview of relevant literature, while Section III introduces the proposed methodology. The methodology’s effectiveness is evaluated through numerical case studies in Section IV. Section V provides realworld case studies which are fault diagnosis problems in the multistation assembly system. Finally, Section VI discusses conclusions and future work. This paper is available on arxiv under CC 4.0 license. Authors: (1) Jihoon Chung; (2)  Zhenyu (James) Kong. This paper is available on arxiv under CC 4.0 license. Authors: (1) Jihoon Chung; (2)  Zhenyu (James) Kong. Table of Links Abstract & Introduction Review of Related Work Proposed Research Methodology Numerical Case Studies Real-World Simulation Case Studies Conclusion References Abstract & Introduction Abstract & Introduction Review of Related Work Review of Related Work Proposed Research Methodology Proposed Research Methodology Numerical Case Studies Numerical Case Studies Real-World Simulation Case Studies Real-World Simulation Case Studies Conclusion Conclusion References References Abstract Sensor technology developments provide a basis for effective fault diagnosis in manufacturing systems. However, the limited number of sensors due to physical constraints or undue costs hinders the accurate diagnosis in the actual process. In addition, time-varying operational conditions that generate nonstationary process faults and the correlation information in the process require to consider for accurate fault diagnosis in the manufacturing systems. This article proposes a novel fault diagnosis method: clustering spatially correlated sparse Bayesian learning (CSSBL), and explicitly demonstrates its applicability in a multistation assembly system that is vulnerable to the above challenges. Specifically, the method is based on a practical  assumption that it will likely have a few process faults (sparse). In addition, the hierarchical structure of CSSBL has several parameterized prior distributions to address the above challenges. As posterior distributions of process faults do not have closed form, this paper derives approximate posterior distributions through Variational Bayes inference. The proposed method’s efficacy is provided through numerical and real-world case studies utilizing an actual autobody assembly system. The generalizability of the proposed method allows the technique to be applied in fault diagnosis in other domains, including communication and healthcare systems. Note to Practitioners —This article proposes a new process fault diagnosis method: clustering spatially correlated sparse Bayesian learning. This method effectively diagnoses time-varying defects by leveraging the correlation structures in the process when sensor measurements are insufficient. The actual autobody assembly process is utilized to show the proposed method’s effectiveness. The proposed method performs superior to the benchmark methods in fault detection capability. In addition, the proposed method accurately estimates the severity of the process faults, providing significant information to the practitioners for their decision-making in the maintenance schedule. Specifically, the error between the estimation from the proposed method and the actual severity of the process faults achieves less than 10% of error of all the benchmark methods when there exists a high correlation between the variations of the fixture locators in the autobody assembly system. Note to Practitioners Index Terms —Sparse Bayesian Learning, Spatially Correlated Faults, Nonstationary Faults, Variational Bayes Inference, MultiStage Assembly Systems. Index Terms I. INTRODUCTION The sparse estimation has received considerable attention in signal processing due to its ability to reconstruct a high-dimensional sparse source signal from a low-dimensional measurement [1]. Specifically, sparse estimation has broad applications in a wide range of industries in identifying the sources of sensor measurements. These applications include channel estimation in wireless systems [2], Electroencephalography (EEG) source localization in neuroimaging [3], radar detection [4], and fault diagnosis in manufacturing systems [5]. However, time-varying operational conditions in the manufacturing systems cause nonstationary process faults, hindering the accurate sparse estimation. For example, the component degradation (e.g., fixture wearing in assembly systems) will vary over time, violating the stationary process faults assumptions of most existing sparse estimation methods [6], [7]. In addition, the correlation information that occurred due to the structure of the manufacturing system should consider for effective fault diagnosis [8], but it is often neglected in sparse estimation [9], [10]. One motivating example to address the above challenges is the process fault diagnosis in the multistation assembly systems. The systems perform assembly operations from multiple stations to assemble a final product. The final product’s quality relies on several factors known as key control characteristics (KCCs) [8]. The positioning accuracy of fixture locators is KCCs in the multistation assembly [5]. Fixture locators carry out the clamping of parts during the assembly process. Therefore, any deviations from their nominal positions can lead to dimensional quality issues in the final product. Hence, fault diagnosis in multistation assemblies estimates the mean and variance of KCCs, namely, the variations of fixture locators [9]. This article focuses on process faults due to excessive variance, which is a more challenging task to diagnose than mean shifts [7]. Since monitoring the dimensional variation of KCCs is not feasible due to physical constraints in the process [10], the key product characteristics (KPCs), which are essentially measurements obtained from the final product, can be utilized to estimate the variance of KCCs. Specifically, a fault-quality linear model represents the relationship between KPCs and KCCs as follows [11], [12]: Numerous studies have employed Eq. (1) on fault diagnosis in multistation assembly systems [13], [14]. However, they assume that the number of measurements (M) is greater than the number of KCCs (N) which may not hold in actual manufacturing applications. This is because using an excessive number of sensors (measurements) can result in undue costs [8]. If this assumption is violated, Eq. (1) becomes an underdetermined system that has a nonunique solution. To address this challenge, the sparse solution assumption [15] that x in Eq. (1) has a minimal number of non zero elements is required. In the fault diagnosis problem, sparsity refers to the small number of process faults in the fault-quality linear model, which is a reasonable assumption since there are typically only a few process faults in practice [5]. Among the several sparse estimation methods, the Bayesian approach called sparse Bayesian learning has received much attention recently because of its superior estimation performance guaranteed from the several theoretical properties [16]–[18]. Sparse Bayesian learning has been applied for fault diagnosis in multistation assembly systems [10], [19]. These studies successfully identified process faults by providing prior distribution of KCCs (i.e., x in Eq. (1)) to encourage the sparsity of process faults. Especially the work in [10] applied Bayesian learning to diagnose the process faults based on the following multiple measurements vectors (MMV) model in sparse Bayesian learning [20]: Beyond the spatial correlation between KCCs, the non stationarity of process faults also needs to be considered for accurate fault diagnosis. In practice, the external time-varying operational condition, including temperature and noise, may cause nonstationary process faults over time in the manufacturing systems [6]. For example, the fixture locators in the assembly process can be deviated because of the thermal expansions of pins caused by the ambient temperature of the process that varies over time [22]. In addition, the variation of some locators will be propagated to other locators over time if the process faults are not mitigated immediately [23]. It results in nonstationary process faults along the KPCs samples collected over time. In other words, the KCCs with excessive variance differ depending on the columns of X in Eq. (2). Therefore, the stationary process faults assumption applied in the previous studies [8], [10] needs to be addressed. To consider both the spatial correlation of KCCs and the nonstationary process faults along the KPCs samples, this paper proposes a novel sparse Bayesian learning method, namely, clustering and spatially correlated sparse Bayesian learning (CSSBL). Given KPCs samples, the proposed method clusters the samples into groups sharing the same process faults. At the same time, our method estimates the variance of KCCs of each group to identify the process faults accurately. The following summarizes the contributions of this study: • From the methodological point of view, this paper proposes a novel sparse Bayesian learning that can consider both the spatial correlation of KCCs and the nonstationary process faults. Since the posterior distribution of the sparse solution in the proposed method is computationally intractable, this article derives the approximate posterior distribution of the sparse solution via Variational Bayes inference [24]. • From the application perspective, the proposed method is applied to fault diagnosis in the actual auto-body assembly process. The method performs better for process fault detection capability than the benchmark methods. Furthermore, the proposed method accurately estimates locators’ variance representing the severity of the process faults to assist practitioners’ decision-making in their maintenance policy. The subsequent sections of this paper are structured in the following manner. Section II presents an overview of relevant literature, while Section III introduces the proposed methodology. The methodology’s effectiveness is evaluated through numerical case studies in Section IV. Section V provides realworld case studies which are fault diagnosis problems in the multistation assembly system. Finally, Section VI discusses conclusions and future work.

Sparse Bayesian Learning: Abstract & Introduction

2 weeks, 4-5 hours of Sleep each day, and the Birth of Miraki: A Savage Hackathon Tale Let's be real, most hackathons are a chaotic circus. You've got sleep-deprived devs chugging energy drinks like it's water, half-baked ideas flying around, and enough tech jargon to make your head spin. But hey, that's where the magic (or madness) happens. My team decided to dive headfirst into the Web3 online hackathon with a mission to make life less miserable for DAO contributors. You know the drill – tools scattered across the internet like lost socks, communication breakdowns that would give couples therapy a run for its money, and a general sense of "Where the heck is anything? Introducing Miraki: VS Code for DAO tools This is just a small explainer. More details about Miraki and the team behind will be published in later blogs. Miraki is our brainchild, a browser-based devtool that aims to bring order to the wild world of DAOs. Think of it as VSCode, but with a Web3 twist. Here's the breakdown: Workspaces: Your customizable command center for a specific DAO or project. Imagine all those DAO tools finally living in harmony, under one glorious roof. Public or private spaces – you choose the level of chaos you're comfortable with. Anyone who wants to participate in your DAO or project can join your workspace (if public) and they’ll see all the apps the workspace uses in the dashboard along with all the activities within.



Apps: Like VS Code extensions, but for all your DAO needs. Plug in Trello for project management, Snapshot for voting, or even Confluence for collectively losing your minds over documentation. All these apps can be found on miraki app store, and in case you have a specific app that hasn’t been made yet, you can make it yourself.


Universal Search: Because let's face it, nobody has time to play hide-and-seek with vital info across a million different tools. One single search box for entire workspace. Your contributors will be able to search across different apps within the workspace. The Hackathon Hustle: Caffeine, Code, and Controlled Chaos The frontend was a React party, with TypeScript and Vite adding that extra bit of class. Our MVP sported three must-have apps: Trello, Snapshot, and DappSuit (swagger UI for Solana programs) to show that integration of such diverse app into one platform is possible. Shoutout to Turbo monorepo for keeping our sprawling codebase from turning into a spaghetti monster. We ideated fancy isolation techniques for our apps: iframes, web workers, we read blogs from figma to understand how they implemented plugins, we digged deep into vs-codes original source code, the whole nine yards. But pragmatism won the day – in the true spirit of hackathons, we embraced the "it works (for now)" mantra to avoid disappearing down that rabbit hole. The backend? Django and Postgres, a classic combo for building the foundation of our little empire. React Pluggable gave us that sweet extensibility we craved, without the crippling overengineering headaches. And that’s how we managed to wrap up the project just a day before last submission date. The Savage Truth Was it smooth sailing? Pfft. More like navigating a storm in a leaky boat. But amidst the sleep deprivation, the caffeine-induced jitters, and enough bugs to rival an Amazon rainforest, a truly useful tool emerged. Miraki ain't perfect (yet), but it's got the potential to make DAOs less of a soul-sucking experience for everyone involved. And that, my friends, is what hackathons are all about – taking a problem, caffeinating yourself into oblivion, and emerging with something that might just make the tech world a slightly better place. So, what's next for Miraki? We're aiming to unleash this beast into the wild, gather feedback, and iterate like there's no tomorrow. If you're a fellow DAO dweller tired of feeling like a lost explorer in a digital jungle, stay tuned. Miraki might just be your salvation. PS: The blog is rephrased using Gemini AI. Because the writer was on Leet-code when he should be taking English writing classes. Follow Miraki on https://twitter.com/Miraki_io. All feedback is most-welcomed. You can contact us through email at iampiyushjaiswal103@gmail.com. 2 weeks, 4-5 hours of Sleep each day, and the Birth of Miraki: A Savage Hackathon Tale 2 weeks, 4-5 hours of Sleep each day, and the Birth of Miraki: A Savage Hackathon Tale Let's be real, most hackathons are a chaotic circus. You've got sleep-deprived devs chugging energy drinks like it's water, half-baked ideas flying around, and enough tech jargon to make your head spin. But hey, that's where the magic (or madness) happens. My team decided to dive headfirst into the Web3 online hackathon with a mission to make life less miserable for DAO contributors. You know the drill – tools scattered across the internet like lost socks, communication breakdowns that would give couples therapy a run for its money, and a general sense of "Where the heck is anything? Introducing Miraki: VS Code for DAO tools Introducing Miraki: VS Code for DAO tools This is just a small explainer. More details about Miraki and the team behind will be published in later blogs. This is just a small explainer. More details about Miraki and the team behind will be published in later blogs. Miraki is our brainchild, a browser-based devtool that aims to bring order to the wild world of DAOs. Think of it as VSCode, but with a Web3 twist. Here's the breakdown: Workspaces: Your customizable command center for a specific DAO or project. Imagine all those DAO tools finally living in harmony, under one glorious roof. Public or private spaces – you choose the level of chaos you're comfortable with. Anyone who wants to participate in your DAO or project can join your workspace (if public) and they’ll see all the apps the workspace uses in the dashboard along with all the activities within. Apps: Like VS Code extensions, but for all your DAO needs. Plug in Trello for project management, Snapshot for voting, or even Confluence for collectively losing your minds over documentation. All these apps can be found on miraki app store, and in case you have a specific app that hasn’t been made yet, you can make it yourself. Universal Search: Because let's face it, nobody has time to play hide-and-seek with vital info across a million different tools. One single search box for entire workspace. Your contributors will be able to search across different apps within the workspace. Workspaces: Your customizable command center for a specific DAO or project. Imagine all those DAO tools finally living in harmony, under one glorious roof. Public or private spaces – you choose the level of chaos you're comfortable with. Anyone who wants to participate in your DAO or project can join your workspace (if public) and they’ll see all the apps the workspace uses in the dashboard along with all the activities within. Workspaces: Your customizable command center for a specific DAO or project. Imagine all those DAO tools finally living in harmony, under one glorious roof. Public or private spaces – you choose the level of chaos you're comfortable with. Anyone who wants to participate in your DAO or project can join your workspace (if public) and they’ll see all the apps the workspace uses in the dashboard along with all the activities within. Workspaces: Apps: Like VS Code extensions, but for all your DAO needs. Plug in Trello for project management, Snapshot for voting, or even Confluence for collectively losing your minds over documentation. All these apps can be found on miraki app store, and in case you have a specific app that hasn’t been made yet, you can make it yourself. Apps : Like VS Code extensions, but for all your DAO needs. Plug in Trello for project management, Snapshot for voting, or even Confluence for collectively losing your minds over documentation. All these apps can be found on miraki app store, and in case you have a specific app that hasn’t been made yet, you can make it yourself. Apps Universal Search: Because let's face it, nobody has time to play hide-and-seek with vital info across a million different tools. One single search box for entire workspace. Your contributors will be able to search across different apps within the workspace. Universal Search: Because let's face it, nobody has time to play hide-and-seek with vital info across a million different tools. One single search box for entire workspace. Your contributors will be able to search across different apps within the workspace. Universal Search: The Hackathon Hustle: Caffeine, Code, and Controlled Chaos The Hackathon Hustle: Caffeine, Code, and Controlled Chaos The frontend was a React party, with TypeScript and Vite adding that extra bit of class. Our MVP sported three must-have apps: Trello, Snapshot, and DappSuit (swagger UI for Solana programs) to show that integration of such diverse app into one platform is possible. Shoutout to Turbo monorepo for keeping our sprawling codebase from turning into a spaghetti monster. We ideated fancy isolation techniques for our apps: iframes, web workers, we read blogs from figma to understand how they implemented plugins, we digged deep into vs-codes original source code, the whole nine yards. But pragmatism won the day – in the true spirit of hackathons, we embraced the "it works (for now)" mantra to avoid disappearing down that rabbit hole. figma The backend? Django and Postgres, a classic combo for building the foundation of our little empire. React Pluggable gave us that sweet extensibility we craved, without the crippling overengineering headaches. And that’s how we managed to wrap up the project just a day before last submission date. React Pluggable The Savage Truth The Savage Truth Was it smooth sailing? Pfft . Pfft More like navigating a storm in a leaky boat. But amidst the sleep deprivation, the caffeine-induced jitters, and enough bugs to rival an Amazon rainforest, a truly useful tool emerged. Miraki ain't perfect (yet), but it's got the potential to make DAOs less of a soul-sucking experience for everyone involved. And that, my friends, is what hackathons are all about – taking a problem, caffeinating yourself into oblivion, and emerging with something that might just make the tech world a slightly better place. So, what's next for Miraki? So, what's next for Miraki? We're aiming to unleash this beast into the wild, gather feedback, and iterate like there's no tomorrow. If you're a fellow DAO dweller tired of feeling like a lost explorer in a digital jungle, stay tuned. Miraki might just be your salvation. PS: The blog is rephrased using Gemini AI. Because the writer was on Leet-code when he should be taking English writing classes. PS: The blog is rephrased using Gemini AI. Because the writer was on Leet-code when he should be taking English writing classes. Follow Miraki on https://twitter.com/Miraki_io . https://twitter.com/Miraki_io All feedback is most-welcomed. You can contact us through email at iampiyushjaiswal103@gmail.com. iampiyushjaiswal103@gmail.com.

From a Spark of Inspiration to a Product That Makes a Difference – Our Hackathon Journey

In this article, I aim to share my experience standardizing the ETL process within frameworks like Spark and Flink, particularly for those not using data processing frameworks like dbt. Context Data processing is a comprehensive field that encompasses the regular processing of substantial information volumes in both batch and stream modes. These procedures, often referred to as ETL processes, are developed using a variety of programming languages and frameworks. Typically, the main stages of data processing are as follows: Data is collected from various sources for subsequent processing. Reading data: The data undergoes various transformations and manipulations to attain the required structure and format. Transformation: The transformed data is then stored in the target storage. Writing: Schematically, it is represented as shown in the diagram: Challenges After the main stages are implemented, several other stages can be added to enhance and refine the process: This stage involves verifying data compliance with requirements and expectations, including data cleaning to ensure accuracy and completeness. Data quality check: The registration process tracks and documents available data sets. It may also involve data and metadata management for effective data organization. Data registration in the data catalog: This allows visualization of relationships between data assets and ETL processes to understand dependencies. It can help identify potential data quality issues and improve overall data quality. Building data lineage: Additional processing may define the level of data confidentiality and criticality, ensuring proper data protection and security. Data classification: Each of the stages mentioned can be implemented using various technologies and tools, allowing flexibility in choosing the right solution for each specific stage of data processing. Solution Reading data The first step in the ETL process involves reading data from the source. Various types of sources may be used at this stage, such as files, databases, message brokers, and more. The data may also come in different formats, such as CSV, JSON, Parquet, Delta, Hudi, etc. Here is some boilerplate code for reading data from these different sources (implemented as Strategy pattern): from abc import ABC, abstractmethod
from pyspark.sql import SparkSession, DataFrame

# Define the strategy interface
class ReadStrategy(ABC):
    @abstractmethod
    def read_data(self) -> DataFrame:
        pass

# File System Read Strategy - represents a file system reading    
class FileSystemReadStrategy(ReadStrategy):
    def __init__(self, spark: SparkSession, path: str):
        self.spark = spark
        self.path = path
    
    @abstractmethod
    def read_data(self):
        pass
    
# Implement the strategy for CSV
class CSVReadStrategy(FileSystemReadStrategy):
    def read_data(self) -> DataFrame:
        return self.spark.read.format("csv").option("header", "true").load(self.path)


# Implement the strategy for Delta
class DeltaReadStrategy(ReadStrategy):
    def read_data(self) -> DataFrame:
        return self.spark.read.format("delta").load(self.path)

# Implement the strategy for Parquet
class ParquetReadStrategy(ReadStrategy):
    def read_data(self) -> DataFrame:
        return self.spark.read.format("parquet").load(self.path)

# Context class
class DataReader:
    def __init__(self, read_strategy: ReadStrategy):
        self.read_strategy = read_strategy

    def read(self) -> DataFrame:
        return self.read_strategy.read_data() Usage example: spark = SparkSession.builder.appName("our-ETL-job").getOrCreate()
path = "path-to-our-data"
src_df = DataReader(DeltaReadStrategy(spark, path)).read() This approach offers several benefits: It enables the alteration of the reading strategy (source format). It facilitates the addition of new reading strategies without modifying existing code. Each strategy can have its unique parameters. For example, CSV may have a delimiter, while Delta may have a version, etc. Instead of providing a path, we can create a dedicated class, like , which can hold all necessary data reading parameters. ReadConfig It allows the incorporation of additional logic into strategies without affecting the main code. For instance: We can call by default for a file source. input_file_name() If we have a data catalog, we can check whether the table exists there and register it if not. Each strategy can be tested individually. Transformation After reading the data, it must be transformed. This can include various operations like filtering, aggregating, and joining. Since the transformation process can be complex and time-consuming, it's crucial to standardize it for ease of understanding. From my experience, using the method of the DataFrame API is an effective way to standardize this process. This method lets us chain multiple transformations, enhancing code readability. Each transformation can be declared as a separate pure function, performing a specific transformation, and can be tested independently. transform import pyspark.sql.functions as F
from pyspark.sql import DataFrame

desired_columns = ["name", "age", "country"]

def with_country_code(df: DataFrame) -> DataFrame:
    return df.withColumn("country_code", F.when(F.col("country") == "USA", "US").otherwise("Other"))

def with_age_category(df: DataFrame) -> DataFrame:
    return df.withColumn("age_category", F.when(F.col("age") < 18, "child").when((F.col("age") >= 18) & (F.col("age") < 65), "adult").otherwise("senior"))

def filter_adult(df: DataFrame) -> DataFrame:
    return df.filter(F.col("age_category") == "adult")

def select_columns(columns: list) -> callable[[DataFrame], DataFrame]:
    def inner(df: DataFrame) -> DataFrame:
        return df.select(columns)
    return inner

transformed_df = (src_df
                  .transform(with_country_code)
                  .transform(with_age_category)
                  .transform(filter_adult)
                  .transform(select_columns(desired_columns))
                  ) Writing The last stage in the ETL process is writing the transformed data into the target storage, which could be a file system, a database, or a message broker. The loading phase may also incorporate extra steps like data validation, data registration, and data lineage. For this stage, we can apply the same methods as reading data, but with writing strategies. from abc import ABC, abstractmethod
from pyspark.sql import DataFrame, SparkSession

class WriteStrategy(ABC):
    @abstractmethod
    def write_data(self, df: DataFrame):
        pass

class FileSystemWriteStrategy(WriteStrategy):
    def __init__(self, spark: SparkSession, path: str, format: str, mode: str = "overwrite"):
        self.spark = spark
        self.path = path
        self.format = format
        self.mode = mode

    def write_data(self, df: DataFrame):
        df.write.format(self.format).mode(self.mode).save(self.path)

class DataWriter:
    def __init__(self, write_strategy: WriteStrategy):
        self.write_strategy = write_strategy

    def write(self, df: DataFrame):
        self.write_strategy.write_data(df) Usage example: path = "path-to-our-data"
writer = DataWriter(FileSystemWriteStrategy(spark, path, "delta"))
writer.write(transformed_df) Conclusion This implementation standardizes the ETL process, making it more comprehensible and maintainable. It facilitates the addition of new reading and writing strategies without modifying existing code. Each strategy can possess unique parameters, and additional logic can be incorporated into strategies without disrupting the main code. The approach also enables separate testing of each strategy. Moreover, we can introduce additional stages to the ETL process, such as data quality checks, data registration in the data catalog, building data lineage, and data classification.

Dev Standards for Spark-jobs

স্পার্ক-জবসের জন্য ডেভ স্ট্যান্ডার্ড

Entwicklungsstandards für Spark-Jobs

Estándares de desarrollo para trabajos Spark

Normes de développement pour les tâches Spark

स्पार्क-नौकरियों के लिए विकास मानक

Spark ジョブの開発標準

Spark 작업을 위한 개발 표준

Padrões de desenvolvimento para trabalhos Spark

Стандарты разработки для Spark-заданий

Spark işleri için Geliştirme Standartları

Tiêu chuẩn dành cho nhà phát triển cho công việc Spark

Spark 作业的开发标准

Hello there, fellow coders! Today, I'm excited to share my first "Today I Learned" entry as I embark on my coding journey with the Sparta Coding Club. It's been a fantastic week, and I'm thrilled to tell you all about it. Joining the Sparta Coding Club was a fantastic decision. The world of coding has always fascinated me, and I felt it was time to take the plunge. What really convinced me to join this particular club was its reputation for providing an excellent learning environment and structured guidance. The prospect of collaborating with like-minded individuals who share my passion for coding was just too enticing to resist. My First Session In the very first session, we dived headfirst into the realm of HTML, the foundational language of web development. It was exhilarating to learn about HTML tags, how they structure web content, and, most importantly, how to create a basic webpage. Our instructor also emphasized the importance of semantic HTML and its impact on web accessibility and search engine optimization. Furthermore, it is important to Google any information you need related to HTML rather than spending hours trying to by heart the syntax. And in the second session, we jumped into Bootstrap, which is a collection of stylish CSS. It just makes your work look more attractive by not putting in much effort. We also got a taste of JavaScript using the console in the browser as it is the only programming language the browser natively understands. What went really well today was the instructor's teaching approach. He broke down complex concepts into bite-sized, digestible pieces, which made it much easier for newcomers like me to understand. The hands-on practice was another highlight, allowing me to put my newly acquired knowledge to the test. Admittedly, I did face a bit of a challenge with nesting HTML elements correctly, but I'm confident that practice will make perfect. Looking ahead, I'm eager to delve into CSS, a vital component of web design and beautification. Additionally, I can't wait to explore JavaScript, which brings interactivity to web applications. My ultimate goal is to complete a small project that combines HTML, CSS, and JavaScript to solidify my understanding and put my skills to the test. So, there you have it—my first week with Sparta Coding Club, and it's safe to say I'm already hooked. Stay tuned for more updates on my coding journey and the exciting world of web development. Until next time, happy coding!

My First Week With the Sparta Coding Club - Today I Learned

Data Drama: Navigating the Spark-Flink Dilemma 

In the heart of Saudi Arabia’s vast desert landscape, where sand dunes meet the shores of the Red Sea, lies a futuristic vision that aims to redefine the way we live - . This mass-scale mega-city project isn’t just about constructing a it’s about breathing life into a vision that combines cutting-edge technology with sustainable living to create an oasis like no other. NEOM smart city ; This grand endeavor seeks to establish a modern utopia, a place where imagination meets technology and the limits of possibility are pushed beyond what we thought was possible. So, let’s explore the potential this city holds to be the next big thing and whether it can spark a wave of similar smart city projects worldwide. The Rise of Smart Cities I believe that it’s an undeniable fact that smart cities are revolutionizing urban development and bringing a paradigm shift in the way we live and interact with one another. Smart cities are the manifestation of our collective aspiration to harmonize urban living with technological innovation. These cities to optimize resource allocation, enhance efficiency, and improve the quality of life for their citizens. leverage data-driven solutions Let’s look at some preceding NEOM to grasp what may be in store for us. smart city projects Songdo, South Korea Often known as the , Songdo is an international business district near Incheon. Designed from the ground up, Songdo is characterized by its cutting-edge technology integration and sustainable infrastructure. world’s first smart city With data being at the heart of all Seoul city projects, Songdo is no exception. There are monitoring everything from energy consumption to traffic flow and waste management in real-time, ensuring efficiency and environmental consciousness. The city also features a , through which waste is whisked away with the help of underground tubes. , sensors embedded throughout the city central pneumatic waste collection system Songdo's presence is a reminder that the ideals set forth by NEOM and other forward-thinking projects are already being realized elsewhere to enrich the lives of residents while setting new standards for urban development. Singapore Being named the “ ,” Singapore is one of the frontrunners in the global race for going ‘smart.’ With the city-state having long been recognized for its strategic urban planning and efforts to promote sustainability, Singapore’s transition to a smart city began with the launch of its “ ” initiative. This initiative aimed to leverage data and technology to improve citizens' lives. smartest city in Asia Smart Nation They introduced a number of smart technologies in both the private and public sectors. Ranging from a contactless payment system, to aid the millions of passengers availing public transport every day to the creation of robotic patrol police, and a facilitating online consultations and an IoT device to monitor patients’ health. , SimplyGo digital health system Singapore sets a precedent for the relationship between technology and urban living, standing out as a shining example of how embracing innovation can lead to sustainable and harmonious urban environments. NEOM: A Glimpse into the Future NEOM isn't just a city; it’s aimed to be a manifesto for urban development. The vision is to create an oasis where humans and technology can coexist synergistically. Let’s explore what plans the Saudi government has for this colossal smart city project. About NEOM NEOM is a smart city project planned by the Kingdom of Saudi Arabia as part of its initiative. This plan aims to reduce Saudi Arabia’s dependence on oil, diversify its economy, and boost the country’s public and private sectors. Saudi Vision 2030 NEOM is to be built in the country’s northwestern Tabuk province, north of the Red Sea and in close proximity to the east of Egypt and the south of Jordan. The area for the project is 26,500km2 and currently encompasses four different planned regions so far: Sindalah Nestled along the Red Sea coast, is an island destination that embodies the spirit of leisure, luxury, and recreation. With pristine beaches, luxury resorts, and a thriving entertainment scene, this region is envisioned as a haven for relaxation and entertainment. It offers a plethora of underwater activities and will also be a yachting hub for some of the world’s most prominent yachting enthusiasts with its 86-berth marina and 75 off-shore buoys. Sindalah Trojena emerges as a dynamic ski resort with elevations ranging from 1,500 to 2,600 meters and a pristine expanse spanning nearly 60 square kilometers. Trojena offers an enchanting playground for winter sports enthusiasts. Blanketed in snow during sub-zero winters, this alpine haven promises exhilarating slopes, breathtaking panoramas, and an unforgettable skiing experience that embodies the spirit of adventure within NEOM's visionary landscape. Trojena Oxagon Previously known as Neom Industrial City, is an octagonal floating industrial complex. Situated around 25 kilometers north of Duba, Oxagon envelops a sprawling expanse of 200–250 square kilometers. This marvel is set to redefine modern manufacturing and industrial research, positioning itself as a dynamic hub for progress centered around the expansion of Duba port. Expected to be powered by 100% renewable energy, Oxagon's blueprint includes a desalination plant, a hydrogen facility, and an oceanographic research center, emphasizing NEOM's commitment to sustainable practices and scientific exploration. Oxagon The Line At the heart of NEOM's vision lies , a linear city stretching across 170 kilometers. The Line promises to be a blueprint for sustainability and efficiency. Free from the constraints of conventional urban design, it will be powered by 100% renewable energy and will feature cutting-edge transportation systems and AI-powered services. The Line's unique structure emphasizes proximity to nature, ensuring that no resident is more than a five-minute walk away from green spaces, offering a harmonious blend of urban and natural environments. The Line NEOM’s Impact Right now, the whole world is keeping a close eye on NEOM and waiting to see how it unfolds. If the project turns out to be a success, it could have implications spanning far and wide. Economic and Societal Impact The economic and societal impact of NEOM will be unprecedented. Job creation in emerging sectors such as data analytics, cybersecurity, and could reshape the employment landscape. NEOM is expected to create hundreds of thousands of job opportunities for locals and immigrants alike in the country. The Line alone is by 2030. AI programming expected to create 380,000 jobs Furthermore, the very essence of smart cities, which are convenience, efficiency, and sustainability, has the potential to elevate citizens' well-being. The findings of a by the McKinsey Global Institute show that cities that use smart technologies improve some key quality-of-life indicators by 10 to 30 percent. This contributes to shorter commutes, fewer crimes, a reduced health burden, and more lives saved. In addition to that, when citizens are freed from mundane tasks by automation and empowered by technology, they can focus on creative pursuits, personal growth, and contributing meaningfully to society. report Global Implications NEOM's success could also potentially send ripples across the globe, inspiring other nations to follow suit. As we witness various countries embarking on their smart city journeys, NEOM's innovative approach might serve as a benchmark for what's possible. From China's tech-driven city of to India's plans for sustainable urban development in , the world is closely watching and learning from NEOM. Xiong'an Lavasa Challenges facing NEOM The path to smart city utopia isn’t without thorns, and NEOM faces a spectrum of challenges and severe criticism that demand careful consideration. NEOM has not been without its share of controversies, with notable issues including the from their ancestral lands and allegations of human rights violations against migrant laborers. forced displacement of the Huwaitat tribe Critics have also voiced legitimate concerns about the feasibility and long-term sustainability of NEOM. The sheer magnitude of the project, coupled with its potential environmental repercussions, has prompted valid inquiries. Realizing the city’s grand ambitions, such as achieving full reliance on , building a floating trade hub, and implementing autonomous transportation, presents substantial challenges. renewable energy The geopolitical landscape surrounding NEOM introduces an additional layer of uncertainty. Given its proximity to Egypt and Jordan, the intricate dynamics of the region add complexity that could influence the project's outcomes. Then, there are also concerns about the amalgamation of technology in the design for NEOM. A prominent reason why Songdo didn’t achieve the success it was meant for was its . The city was designed with technology in mind, keeping humans in the peripheral rather than keeping the needs of humans in mind and using technology to fulfill those needs. NEOM must avoid making the same mistake. technology-centric design Will Smart Cities Be the Next Big Thing? As the sun sets over the desert, NEOM illuminates the horizon with countless possibilities. In the wake of this transformative vision, the question looms: Will smart cities become the next big thing? As we witness the convergence of , sustainability, and urban living, it's undeniable that smart cities hold immense promise. NEOM's grand endeavor underscores the potential for harmonizing innovation and nature. However, for this vision to thrive globally, challenges like environmental impact, inclusivity, and geopolitical complexities must be navigated with wisdom. technology The path forward demands a delicate balance between progress and responsibility, ensuring that the evolution of urban landscapes becomes a testament to human advancement that respects our planet and fosters shared prosperity.

Saudi NEOM Sparks a Brighter Future: Will Smart Cities Be the Next Big Thing?

সৌদি NEOM একটি উজ্জ্বল ভবিষ্যত তৈরি করে: স্মার্ট শহরগুলি কি পরবর্তী বড় জিনিস হবে?

Saudi-Arabiens NEOM eröffnet eine bessere Zukunft: Werden Smart Cities das nächste große Ding sein?

NEOM saudita genera un futuro más brillante: ¿Serán las ciudades inteligentes la próxima gran novedad?

NEOM saoudien ouvre un avenir meilleur : les villes intelligentes seront-elles la prochaine grande nouveauté ?

सऊदी NEOM एक उज्जवल भविष्य की आशा करता है: क्या स्मार्ट शहर अगली बड़ी चीज़ होंगे?

サウジのNEOMが明るい未来を拓く: スマートシティが次の目玉となるのか?

사우디 NEOM은 더 밝은 미래를 촉발합니다: 스마트 시티가 차세대 혁신이 될까요?

NEOM saudita desperta um futuro mais brilhante: as cidades inteligentes serão a próxima grande novidade?

Suudi NEOM Daha Parlak Bir Geleceğe Yol Açıyor: Akıllı Şehirler Bir Sonraki Büyük Şey mi Olacak?

Saudi NEOM khơi dậy một tương lai tươi sáng hơn: Liệu các thành phố thông minh có trở thành điều lớn lao tiếp theo không?

沙特 NEOM 开启更光明的未来：智慧城市会成为下一个重大事件吗？

In the digital age, the ability for processing vast amounts of data rapidly and efficiently has become paramount.. As we trace back to the early days of computing, data was primarily batch-processed, meaning it was collected over a period and processed all at once. However, With explosion of the internet, social media, and IoT devices, the need for real-time data processing was born. Stream processing allows businesses to analyze and act upon data almost instantly as it's generated, offering a competitive edge in industries where even milliseconds matter. Imagine financial transactions that need fraud detection in real-time or social media platforms that require instant content recommendations. Enter Apache Spark and Apache Flink – two of the most renowned big data processing instruments. While both have their merits, the choice between them isn't always clear-cut. Spark, initially designed for batch processing, later incorporated streaming capabilities. On the other hand, Flink was designed ground-up for stream processing, promising true real-time capabilities. It's essential for noting that this article won't delve deep into each framework's intricacies. Instead, our focus will be on comparing their key capabilities and architectural nuances that can influence the final tool selection for a project. The decision to choose one over the other can significantly impact the efficiency, scalability, and success of a data-driven project. Let's shed light on scenarios where Flink might just outshine Spark. Core Contrasts: The Architectural Divergence of Flink and Spark When we talked about big data, the tools we use mattered a lot. Think of these tools like the foundations of a house. A strong foundation means the house can stand tall and firm. In the world of data processing, Apache Flink and Apache Spark are like two different types of foundations. Apache Spark started with a focus on handling big chunks of data all at once, known as batch processing. When it added the ability to process data on-the-fly, it did so by breaking the data into small batches. This approach affects how fast it can process data and how it manages information over time. On the other side, Apache Flink was built from the start to handle data as it comes in, moment by moment. This means it's all about real-time processing. Because of this, Flink has some unique features, especially when it comes to handling data quickly and managing information over long periods. As we dig deeper into how these two tools are built, we'll see some key differences that might make one a better fit than the other for certain jobs. It's not just about the technical details; it's about picking the right tool for the task. Real-time vs. Micro-batching When we talk about processing data, speed is often key. Imagine getting news a day late or seeing a friend's message hours after it was sent. In the data world, how fast we process information can make a big difference. Apache Spark employs a micro-batching approach. Instead of processing data as it arrives, Spark groups it into small batches and processes each batch sequentially. This method is akin to collecting letters over an hour and then delivering them all at once. While efficient for certain tasks, this approach can introduce a slight delay, especially when rapid insights are required. On the other hand, Apache Flink is all about immediacy. It processes data in real-time, ensuring that as soon as data enters the system, it's processed without delay. It's like delivering a letter the moment it's handed over. This real-time capability of Flink makes it particularly suitable for applications where instant analytics and actions are crucial, such as fraud detection or live monitoring. The choice between real-time processing and micro-batching often hinges on the specific requirements of a project and the nature of the data being processed. State Management Handling data isn't just about speed; it's also about remembering information over time. This is what we call 'state management'. It's like remembering where you left off in a book, so you can pick up from the same spot later. In Apache Spark, state management can be a bit tricky. Because it processes data in small batches, it sometimes needs extra steps to remember information between these batches. It's like using bookmarks for every few pages in a book. Apache Flink, with its real-time processing, has a smoother approach. It's designed to easily remember and manage states as data flows through. Imagine reading a book and effortlessly recalling details from previous chapters without needing bookmarks. Effective state management is vital, especially when data patterns and histories are crucial for making decisions. Event Time Accuracy Imagine watching a movie where scenes appear out of order. It would be confusing, right? In the world of data processing, the order in which events or data points are processed can be just as crucial. Apache Spark, with its micro-batching approach, might sometimes struggle with the exact order of events. Because it's processing data in small groups, there's a chance that events might not always be handled in the precise order they arrived. It's like watching a movie where a few scenes are shuffled around. Apache Flink, on the other hand, places a strong emphasis on event time accuracy. Even if data arrives late or out of sequence, Flink can sort it and process it in the correct order. It ensures that the storyline (or data flow) remains consistent and accurate, just like watching a movie scene by scene as it's meant to be. Getting the order right is essential, especially when analyzing patterns or detecting specific sequences in data. API and Ecosystem Think of an API (Application Programming Interface) as a menu in a restaurant. It shows you what's available and how you can order it. In the world of data processing, a good API makes it easier for developers to use a tool and access its features. Apache Spark boasts a mature API, offering a wide range of functions and libraries. From machine learning to graph processing, it's akin to a menu with a diverse array of dishes and cuisines to choose from. Its extensive library ecosystem means developers have a plethora of tools at their disposal, catering to a variety of data processing needs. In contrast, Apache Flink, though newer, has an API laser-focused on stream processing. It's intuitive, designed with real-time operations in mind. While its offerings might seem more specialized, they are meticulously crafted for specific streaming challenges, ensuring developers have the right tools for real-time data tasks. Beyond the API, the surrounding ecosystem of a tool is equally crucial. Both Spark and Flink boast vibrant communities that offer support, plugins, and extensions. It's akin to having a team of chefs ready to customize your order or introduce innovative dishes. In the end, selecting the right API and ecosystem can be the determining factor between a seamless project execution and one riddled with obstacles. Scalability and performance Imagine you're at a party, and more guests keep arriving. If the venue can't accommodate everyone comfortably, the party might become less enjoyable. In the world of data processing, as data grows, the system must be able to handle it without slowing down. This ability to grow and manage increased loads is called scalability. Apache Spark, with its batch-oriented nature, can scale to handle large datasets. However, as data volume grows, there might be some performance hiccups, especially when dealing with real-time data streams. It's like a party venue that's great for a set number of guests but gets cramped if too many show up unexpectedly. Apache Flink, designed for streaming, shines in scalability. It can handle massive data flows without compromising performance. It's like a venue that can effortlessly expand its space to accommodate more guests, ensuring the party goes on without a hitch. Performance, the speed and efficiency of processing, is equally crucial. No one likes a laggy system, just as no one enjoys a party where the music keeps pausing. Both Spark and Flink are high-performers, but their efficiencies can vary based on the nature and volume of data. Choosing a system that scales well and maintains performance is key to ensuring smooth data operations. Ensuring Fault Tolerance In the delicate balance of data systems, stability and recovery are paramount. Much like a tower of cards, which requires precision and care, even a minor hiccup can lead to significant disruptions. In the world of data processing, when systems encounter issues, it's vital they bounce back swiftly without losing data. This resilience against potential pitfalls is what we term fault tolerance. Apache Spark, with its micro-batching approach, provides recovery mechanisms. If a batch fails, it can be reprocessed. However, this might lead to some delays, especially if there's a backlog of data batches to be processed. It's akin to rebuilding several layers of a card tower after a disturbance. On the other hand, Apache Flink, designed for streaming, has a robust fault tolerance system. It continuously saves the state of data processing at regular intervals. In case of a disruption, Flink can quickly resume from its last saved state, minimizing data loss and recovery time. It's like having a blueprint to swiftly rebuild a card tower, ensuring it stands tall once again. In a data-centric environment, the ability to recover swiftly and effectively is paramount. The Verdict: Flink, Spark, and the Road Ahead In the ever-evolving landscape of big data processing, the tools we choose can significantly shape the outcomes of our projects. Both Apache Flink and Apache Spark stand as powerful contenders, each with its unique strengths and challenges. Apache Spark, with its roots in batch processing, offers a mature ecosystem and a versatile API. Its micro-batching approach, while effective, might introduce latency in certain scenarios. On the other hand, Apache Flink, built for true stream processing, excels in real-time data handling, state management, and event time accuracy. Its design ensures swift data processing with minimal delays, making it a go-to for applications where real-time insights are paramount. However, no tool is a one-size-fits-all solution. The choice between Flink and Spark should be guided not only by the specific requirements of a project and the nature of the data but also by factors such as the expertise of the development team, budget constraints, project timelines, and long-term maintenance considerations. But our exploration doesn't end here. While we've delved deep into the technical intricacies of these frameworks, how do they fare in real-world scenarios? In our upcoming article, we'll dive into various business cases, examining how Flink and Spark perform in practical applications. From transaction processing to traffic analysis, we'll provide insights to help you make an informed decision for your next big data venture. Stay tuned!

Streaming Wars: Why Apache Flink Could Outshine Spark

The Tragedy of Pudd'nhead Wilson by Mark Twain, is part of the HackerNoon Books Series. You can jump to any chapter in this book . Driscoll Spares His Slaves here Driscoll Spares His Slaves Adam was but human—this explains it all. He did not want the apple for the apple’s sake, he wanted it only because it was forbidden. The mistake was in not forbidding the serpent; then he would have eaten the serpent. —Pudd’nhead Wilson’s Calendar. Pudd’nhead Wilson had a trifle of money when he arrived, and he bought a small house on the extreme western verge of the town. Between it and Judge Driscoll’s house there was only a grassy yard, with a paling fence dividing the properties in the middle. He hired a small office down in the town and hung out a tin sign with these words on it: DAVID WILSON. ATTORNEY AND COUNSELOR-AT-LAW. SURVEYING, CONVEYANCING, ETC. But his deadly remark had ruined his chance—at least in the law. No clients came. He took down his sign, after a while, and put it up on his own house with the law features knocked out of it. It offered his services now in the humble capacities of land-surveyor and expert accountant. Now and then he got a job of surveying to do, and now and then a merchant got him to straighten out his books. With Scotch patience and pluck he resolved to live down his reputation and work his way into the legal field yet. Poor fellow, he could not foresee that it was going to take him such a weary long time to do it. He had a rich abundance of idle time, but it never hung heavy on his hands, for he interested himself in every new thing that was born into the universe of ideas, and studied it and experimented upon it at his house. One of his pet fads was palmistry. To another one he gave no name, neither would he explain to anybody what its purpose was, but merely said it was an amusement. In fact he had found that his fads added to his reputation as a pudd’nhead; therefore he was growing chary of being too communicative about them. The fad without a name was one which dealt with people’s finger-marks. He carried in his coat pocket a shallow box with grooves in it, and in the grooves strips of glass five inches long and three inches wide. Along the lower edge of each strip was pasted a slip of white paper. He asked people to pass their hands through their hair (thus collecting upon them a thin coating of the natural oil) and then make a thumb-mark on a glass strip, following it with the mark of the ball of each finger in succession. Under this row of faint grease-prints he would write a record on the strip of white paper—thus: John Smith, — right hand and add the day of the month and the year, then take Smith’s left hand on another glass strip, and add name and date and the words “left hand.” The strips were now returned to the grooved box, and took their place among what Wilson called his “records.” He often studied his records, examining and poring over them with absorbing interest until far into the night; but what he found there—if he found anything—he revealed to no one. Sometimes he copied on paper the involved and delicate pattern left by the ball of a finger, and then vastly enlarged it with a pantograph so that he could examine its web of curving lines with ease and convenience. One sweltering afternoon—it was the first day of July, 1830—he was at work over a set of tangled account-books in his work-room, which looked westward over a stretch of vacant lots, when a conversation outside disturbed him. It was carried on in yells, which showed that the people engaged in it were not close together: “Say, Roxy, how does yo’ baby come on?” This from the distant voice. “Fust-rate; how does come on, Jasper?” This yell was from close by. you “Oh, I’s middlin’; hain’t got noth’n’ to complain of. I’s gwine to come a-court’n’ you bimeby, Roxy.” “ is, you black mud-cat! Yah—yah—yah! I got somep’n’ better to do den ’sociat’n’ wid niggers as black as you is. Is ole Miss Cooper’s Nancy done give you de mitten?” Roxy followed this sally with another discharge of care-free laughter. You “You’s jealous, Roxy, dat’s what’s de matter wid , you hussy—yah—yah—yah! Dat’s de time I got you!” you “Oh, yes, got me, hain’t you. ’Clah to goodness if dat conceit o’ yo’n strikes in, Jasper, it gwine to kill you sho’. If you b’longed to me I’d sell you down de river ’fo’ you git too fur gone. Fust time I runs acrost yo’ marster, I’s gwine to tell him so.” you This idle and aimless jabber went on and on, both parties enjoying the friendly duel and each well satisfied with his own share of the wit exchanged—for wit they considered it. Wilson stepped to the window to observe the combatants; he could not work while their chatter continued. Over in the vacant lots was Jasper, young, coal-black and of magnificent build, sitting on a wheelbarrow in the pelting sun—at work, supposably, whereas he was in fact only preparing for it by taking an hour’s rest before beginning. In front of Wilson’s porch stood Roxy, with a local hand-made baby-wagon, in which sat her two charges—one at each end and facing each other. From Roxy’s manner of speech, a stranger would have expected her to be black, but she was not. Only one sixteenth of her was black, and that sixteenth did not show. She was of majestic form and stature, her attitudes were imposing and statuesque, and her gestures and movements distinguished by a noble and stately grace. Her complexion was very fair, with the rosy glow of vigorous health in the cheeks, her face was full of character and expression, her eyes were brown and liquid, and she had a heavy suit of fine soft hair which was also brown, but the fact was not apparent because her head was bound about with a checkered handkerchief and the hair was concealed under it. Her face was shapely, intelligent and comely—even beautiful. She had an easy, independent carriage—when she was among her own caste—and a high and “sassy” way, withal; but of course she was meek and humble enough where white people were. To all intents and purposes Roxy was as white as anybody, but the one sixteenth of her which was black outvoted the other fifteen parts and made her a negro. She was a slave, and salable as such. Her child was thirty-one parts white, and he, too, was a slave, and by a fiction of law and custom a negro. He had blue eyes and flaxen curls like his white comrade, but even the father of the white child was able to tell the children apart—little as he had commerce with them—by their clothes: for the white babe wore ruffled soft muslin and a coral necklace, while the other wore merely a coarse tow-linen shirt which barely reached to its knees, and no jewelry. The white child’s name was Thomas à Becket Driscoll, the other’s name was Valet de Chambre: no surname—slaves hadn’t the privilege. Roxana had heard that phrase somewhere, the fine sound of it had pleased her ear, and as she had supposed it was a name, she loaded it on to her darling. It soon got shorted to “Chambers,” of course. Wilson knew Roxy by sight, and when the duel of wit began to play out, he stepped outside to gather in a record or two. Jasper went to work energetically, at once, perceiving that his leisure was observed. Wilson inspected the children and asked— “How old are they, Roxy?” “Bofe de same age, sir—five months. Bawn de fust o’ Feb’uary.” “They’re handsome little chaps. One’s just as handsome as the other, too.” A delighted smile exposed the girl’s white teeth, and she said: “Bless yo’ soul, Misto Wilson, it’s pow’ful nice o’ you to say dat, ’ca’se one of ’em ain’t on’y a nigger. Mighty prime little nigger, al’ays says, but dat’s ’ca’se it’s mine, o’ course.” I “How do you tell them apart, Roxy, when they haven’t any clothes on?” Roxy laughed a laugh proportioned to her size, and said: “Oh, kin tell ’em ’part, Misto Wilson, but I bet Marse Percy couldn’t, not to save his life.” I Wilson chatted along for awhile, and presently got Roxy’s finger-prints for his collection—right hand and left—on a couple of his glass strips; then labeled and dated them, and took the “records” of both children, and labeled and dated them also. Two months later, on the 3d of September, he took this trio of finger-marks again. He liked to have a “series,” two or three “takings” at intervals during the period of childhood, these to be followed by others at intervals of several years. The next day—that is to say, on the 4th of September—something occurred which profoundly impressed Roxana. Mr. Driscoll missed another small sum of money—which is a way of saying that this was not a new thing, but had happened before. In truth it had happened three times before. Driscoll’s patience was exhausted. He was a fairly humane man toward slaves and other animals; he was an exceedingly humane man toward the erring of his own race. Theft he could not abide, and plainly there was a thief in his house. Necessarily the thief must be one of his negroes. Sharp measures must be taken. He called his servants before him. There were three of these, besides Roxy: a man, a woman, and a boy twelve years old. They were not related. Mr. Driscoll said: “You have all been warned before. It has done no good. This time I will teach you a lesson. I will sell the thief. Which of you is the guilty one?” They all shuddered at the threat, for here they had a good home, and a new one was likely to be a change for the worse. The denial was general. None had stolen anything—not money, anyway—a little sugar, or cake, or honey, or something like that, that “Marse Percy wouldn’t mind or miss,” but not money—never a cent of money. They were eloquent in their protestations, but Mr. Driscoll was not moved by them. He answered each in turn with a stern “Name the thief!” The truth was, all were guilty but Roxana; she suspected that the others were guilty, but she did not know them to be so. She was horrified to think how near she had come to being guilty herself; she had been saved in the nick of time by a revival in the colored Methodist Church, a fortnight before, at which time and place she “got religion.” The very next day after that gracious experience, while her change of style was fresh upon her and she was vain of her purified condition, her master left a couple dollars lying unprotected on his desk, and she happened upon that temptation when she was polishing around with a dust-rag. She looked at the money awhile with a steady rising resentment, then she burst out with— “Dad blame dat revival, I wisht it had ’a’ be’n put off till to-morrow!” Then she covered the tempter with a book, and another member of the kitchen cabinet got it. She made this sacrifice as a matter of religious etiquette; as a thing necessary just now, but by no means to be wrested into a precedent; no, a week or two would limber up her piety, then she would be rational again, and the next two dollars that got left out in the cold would find a comforter—and she could name the comforter. Was she bad? Was she worse than the general run of her race? No. They had an unfair show in the battle of life, and they held it no sin to take military advantage of the enemy—in a small way; in a small way, but not in a large one. They would smouch provisions from the pantry whenever they got a chance; or a brass thimble, or a cake of wax, or an emery-bag, or a paper of needles, or a silver spoon, or a dollar bill, or small articles of clothing, or any other property of light value; and so far were they from considering such reprisals sinful, that they would go to church and shout and pray the loudest and sincerest with their plunder in their pockets. A farm smoke-house had to be kept heavily padlocked, for even the colored deacon himself could not resist a ham when Providence showed him in a dream, or otherwise, where such a thing hung lonesome and longed for some one to love. But with a hundred hanging before him the deacon would not take two—that is, on the same night. On frosty nights the humane negro prowler would warm the end of a plank and put it up under the cold claws of chickens roosting in a tree; a drowsy hen would step on to the comfortable board, softly clucking her gratitude, and the prowler would dump her into his bag, and later into his stomach, perfectly sure that in taking this trifle from the man who daily robbed him of an inestimable treasure—his liberty—he was not committing any sin that God would remember against him in the Last Great Day. “Name the thief!” For the fourth time Mr. Driscoll had said it, and always in the same hard tone. And now he added these words of awful import: “I give you one minute”—he took out his watch. “If at the end of that time you have not confessed, I will not only sell all four of you, —I will sell you down the river!” but It was equivalent to condemning them to hell! No Missouri negro doubted this. Roxy reeled in her tracks and the color vanished out of her face; the others dropped to their knees as if they had been shot; tears gushed from their eyes, their supplicating hands went up, and three answers came in the one instant: “I done it!” “I done it!” “I done it!—have mercy, marster—Lord have mercy on us po’ niggers!” “Very good,” said the master, putting up his watch, “I will sell you though you don’t 40deserve it. You ought to be sold down the river.” here The culprits flung themselves prone, in an ecstasy of gratitude, and kissed his feet, declaring that they would never forget his goodness and never cease to pray for him as long as they lived. They were sincere, for like a god he had stretched forth his mighty hand and closed the gates of hell against them. He knew, himself, that he had done a noble and gracious thing, and was privately well pleased with his magnanimity; and that night he set the incident down in his diary, so that his son might read it in after years, and be thereby moved to deeds of gentleness and humanity himself. About HackerNoon Book Series: We bring you the most important technical, scientific, and insightful public domain books. This book is part of the public domain. Mark Twain (2004). The Tragedy of Pudd'nhead Wilson. Urbana, Illinois: Project Gutenberg. Retrieved October 2022 https://www.gutenberg.org/cache/epub/102/pg102-images.html This eBook is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at , located at . www.gutenberg.org https://www.gutenberg.org/policy/license.html

Driscoll Spares His Slaves

Beginnings are hard, but they're also the spark that ignites the journey ahead. As we type our first words here on Hackernoon, we're thrilled to welcome you to the . Nifty Sparks community We're diving into the metaverse, uncovering immersive experiences, and sharing the latest insights and updates with you. Why Nifty Sparks, Why Now Why are we here? Because we believe in the power of the metaverse to transform the way we interact, connect, and innovate. The digital realm is evolving, and the time is ripe to spark conversations about all things metaverse. Our passion for this realm has fueled our drive to create a space where we can explore, learn, and grow together. Building a Vibrant Community This isn't just a newsletter; it's a community. We're creating a space where like-minded individuals can gather to discuss, share, and collaborate. Whether you're a creator, a tech enthusiast, a brand looking to make a mark, or simply curious about the metaverse, you've found your tribe here. What to Expect Clarity is key, and we're all about delivering that. Expect regular posts that dive deep into metaverse trends, share how-to guides, showcase success stories, and provide insights into the latest developments. There's no rulebook here. We encourage you to explore, engage, and experiment. If we're having fun, we know you will too. Feel free to share your thoughts, ideas, and suggestions—our community's growth is driven by your participation. Thank you for joining us on this exciting adventure. The metaverse holds endless possibilities, and together, we're ready to unlock its full potential. Stay tuned for immersive experiences, insightful guides, and a whole lot of metaverse magic! Here's to the beginning of something truly nifty! ✨ — The Nifty Sparks Team 🔗 🔗 Visit Niftysparks.com Also published . here

Traveling Through the Metaverse: Welcome to Nifty Sparks! 🥽✨

With each update that , the search algorithm becomes more and more sophisticated. It’s easy to forget that the fact a computer can understand what you type into that search bar and serve up a relevant piece of content is pretty impressive. Google rolls out Modern search engines are able to work today because of the pioneering work of Karen Spärck Jones. Without her, I wouldn’t be able to search for “pizza open now” and get an exhaustive list of takeaway restaurants in less than one second. Search engines need to know what a webpage is about Lots of factors affect how Google judges the quality of a piece of content and where it positions your site on the results page. But to begin with, the search engine needs to be able to understand That way, it can file it under the right terms and later match it to a search query. what your content is about. Think of it like a library. You have to know more or less what a book is about to put it in the right section so people can find it easily. Human readers can skim a text and get the main gist of what it’s about. But it’s not that simple for a computer. Rare words (usually) = meaningful words Search engines figure out the main topic of a text by looking at how often it uses words in comparison to the average text. The logic behind this system is that By looking at the rarest words a text uses, the search engine can identify the most important words in a text and thus understand what the text is about. the more common a word is, the less important it is. Let’s look at an example. Say I was writing a post about my most recent obsession, Cuban salsa. I’ll probably use some common words like “a” and “the” a lot. I'll also use some more specific, rarer terms like “dancing,” “salsa,” and “Cuban salsa.” The search engine will rank the words in my text in order of importance based on the rule that the rarer a word is, the more important it is. “a,” “the,” “and” – common, not important “Dancing,” “salsa” – fairly rare, pretty important “Cuban salsa” – even rarer, most important Karen Spärck Jones’ term frequency model But isn’t the only thing that makes a word important to the meaning of a text. (how often it’s used) also matters. rarity Frequency Karen Spärck Jones was the programmer who created the scientific method search engines use to figure out which words are important. Her model calculates and combines the rarity of a term with how often it appears in a text. It's got a pretty catchy name: “ ” – or for short. Term Frequency-Inverse Document Frequency tf-idf ‘Inverse document frequency' looks at how often a term appears on all other pages. You get an idf score by dividing the total number of documents in a set (in this case, that set is all the webpages on the internet) by the number of documents with the term you're investigating. Rare words get higher scores. The ‘Term frequency’ part identifies how often a term shows up in a piece of text. If a word is used a lot in a text, it’s probably important to the topic of that text. For instance, my article might mention “Havana” once or twice. While ‘Havana’ is a rare (therefore important) term, it’s not used anywhere near as much as ‘Cuban salsa’, so we know ‘Havana’ isn’t the main topic of the text. Based on Karen Spärck Jones’ formulas, search engines combine the tf and idf scores for a text to find the most important terms. Because ‘Cuban salsa’ is a term with a really high idf score and a pretty high tf in my article, my post will get a high tf-idf score for that term. The search engine will understand that the topic of my text is ‘Cuban salsa.’ The tf-idf scores allow Google to file my article in the right place in the Google index (the where Google stores The. Entire. Internet). Later, when someone searches for ‘Cuban salsa,’ it’ll retrieve my piece and compare it against all the other relevant articles. If Google considers my piece to be the ‘ ’ article for a specific searcher, I’ll rank top. massive database most helpful A pioneer of Natural Language Processing But Spärck Jones’ work didn’t start with her term frequency model. Her earlier research, in the 1960s, focused on the problem of how to program computers to understand human language. Given the fact that so many words have multiple meanings depending on the context, overcoming this problem was no easy feat. Spärck Jones suggested that a way to get computers to understand natural language is to group related words together. For instance, the word ‘pass’ can have several meanings. But if it’s surrounded by terms like ‘exam,’ ‘mark,’ and ‘grade,’ it means ‘succeed.’ Whereas if it’s in a paragraph with ‘players,’ ‘ball,’ and ‘goal,’ it means to change possession. Her work formed a lot of the foundation for (NLP) technology that we use today. It’s the technology that tools like , , and need to function. natural language processing Grammarly Google Translate Siri So, Spärck Jones didn't just enable our 1 am pizza orders. We should also thank the Yorkshire-born programmer for the fact that we can effortlessly fix spelling, order a coffee in another language, and get voice-activated weather updates. Also published . here

Why We Should Thank Karen Spärck Jones for Search Engines

It’s difficult to produce and sell quality items when you don’t know where your assets are located at a given time. Or when your inventory is constantly running out of essential materials. Or when equipment unexpectedly breaks down despite scheduled inspection rounds. And on top of this, your supplier doesn’t bother to inform you about shipment delays or delivers spoiled items because the cooling system broke down and no one noticed. Are you looking to take full control over your manufacturing process and monitor your supply chain? Then an might be exactly what you need. Industrial Internet of Things solution Keep reading this article to discover top Industrial IoT applications that will ensure the unfortunate situations above don’t compromise your operations. What is Industrial IoT, and how does it work? The Industrial Internet of Things (IIoT) is the utilization of sensors to gather and process data from connected devices with the goal of understanding and improving the industrial processes. IIoT can also use actuators and programmable controllers to communicate with devices and make them act in a certain way. The number of IoT devices is steadily growing worldwide. Statista . And this number is expected to surpass 29 billion by 2030 as the demand for IoT grows. Ericsson states that for a factory to be considered “smart,” it . reported 11.28 billion devices in 2021 needs to deploy 0.5 IoT devices per square meter So, how do IoT devices work together? And what do you need to know to use them at your organization? The anatomy of IoT IoT architecture consists of software and hardware components that interact together to aggregate, transmit, and analyze data coming from different devices. Below, you will find a brief description of the five main IoT layers. For a detailed explanation, check out our . detailed guide on IoT architecture . Covers various IoT devices equipped with sensors and actuators. Sensors gather data from the corresponding devices and transmit it as a signal to a processing unit. Actuators, on the contrary, receive commands — i.e., signals — from processing centers and make devices act upon them. Device layer . Spans communication technologies, such as Bluetooth, 4G/5G, and Wi-Fi, to mention a few examples. Network layer . Comprises intermediary devices between IoT units and cloud servers. These devices process critical data locally and send the remaining information to the cloud at scheduled intervals, thus reducing infrastructure costs. While edge computing devices aren’t part of the traditional IoT architecture, they’re getting increasingly popular with the Internet of Things adopters. Edge computing layer . Receives, processes, and stores data gathered from IoT sensors. Device and application support layer . Covers different applications that work on IoT data. This includes analytical algorithms, data visualization tools, software that issues commands to actuators, etc. Application layer While the “traditional” IoT is customer-centric and captures how people interact with their smart devices, Industrial IoT focuses on enterprise-wide applications and implies large-scale deployments. IIoT platforms for even greater benefits IoT is not a new concept, and many industrial organizations use IoT sensors to gather data from connected devices. However, IoT is often for each Industrial IoT application, which limits data sharing and restricts the benefits. deployed in isolated silos To mitigate this, organizations can benefit from the to build IIoT platforms that span the entire enterprise. This setup will break isolated IoT pipelines and establish a centralized point for managing IoT devices across different use cases. It will also allow you to integrate more data and have a unified view on all operations. Internet of Things development services Top 10 Industrial IoT applications Here are 10 key Industrial Internet of Things examples that will help you save time and money without compromising quality: Predictive maintenance Quality control Supply chain management Asset tracking Inventory monitoring Remote equipment and production site control Energy management Automation Process optimization and advanced analytics Safety control Predictive maintenance Manufacturers , which is equivalent to 15 hours a week. Another research shows that unplanned device failure can . go through 800 hours of equipment downtime annually cost a company as much as $260,000 per hour Fortunately, Industrial IoT can be applied to prevent equipment failure and cut down on associated expenses. IoT sensors placed on equipment and vehicles can gather data on its performance and condition, such as vibrations, voltage, and temperature. These sensors detect and report any suspicious changes and slight performance degradations, giving technicians enough time to interfere and fix the issue before it impacts the production process. Volvo, one of the leading car manufacturers, outfits its trucks with IoT sensors that capture vehicle data and send it for analysis. Their comprehensive IoT platform can process millions of data records instantly. This tactic enables Volvo to and diagnostic time by a whopping 70%. reduce track repair time by 25% Quality control their products at any stage, starting with raw materials, and all the way as the item moves along the production line. This even includes the amount of waste emitted during the manufacturing process. IoT sensors can continue gathering data even after the item is purchased. They can transmit information on customer satisfaction and keep track of any issues the client encounters with the product. IoT allows manufacturers to monitor Recently, a research team deployed IoT sensors together with convolutional neural networks to identify defects in Printed Circuit Boards (PCBs) manufacturing before the products reached the final production stages. As a result, they could . spot defected PCBs with 97% accuracy There are many benefits of combining IoT and AI, like in the IIoT example above. To learn more, check out this from our innovation analysts. Artificial Intelligence of Things (AIoT) blog post Supply chain management Industrial IoT systems allow managers to track different events across the supply chain. They can access real-time information on the location of materials in transit and get a notification if there is an unforeseen delay, accompanied with the supplier’s justification. Sensors also help monitor temperature, humidity, and other relevant conditions of transported goods. In May 2021, DHL to over 120 countries worldwide. The company used IoT sensors to monitor transportation conditions and ensure the temperature did not exceed the threshold, which would result in spoilage. transported around 200 million COVID-19 vaccines In another real-life IIoT application, California-based Golden State Foods uses IBM’s IoT trackers to monitor storage conditions while transporting meat. Furthermore, deploying IoT in supply chains can help in logistics optimization as companies can identify shorter, less crowded routes. Enhanced with AI, such systems can predict traffic congestions, take into account weather conditions and accidents on the transportation route and make the necessary changes. Consider for comprehensive . consulting artificial intelligence services vendors logistics optimization solutions An interesting example of combining AI and IIoT comes from a logistics innovation company. The client turned to ITRex to develop an that allows companies to monitor cargo in real time, calculate shipping costs based on 60 parameters, fill in paperless documents, and more. Currently, this IIoT platform has over 25,000 users, including large players in the transportation sector, such as DHL and Maersk. AI-powered IoT system Asset tracking Large facilities typically have too many assets that employees can’t keep track of. For instance, studies show that searching for equipment. This problem is not limited to healthcare. The same goes for large factories, stores, etc. nurses waste around 10% of their time One of the Industrial IoT applications is that it can automate asset finding and monitoring. Companies can outfit their assets, such as finished goods, raw materials, tools, and packages, with radio-frequency identification (RFID) tags. These tags are scanned with RFID readers to display information on the corresponding item. Another option is to use equipment sensors that transmit signals over Bluetooth. If power consumption is an issue and you need long-range connectivity, opt for narrowband IoT (NB-IoT), which relies on low-power wide area network (LPWAN) technology. In addition to locating items, IIoT-powered asset tracking can help you protect raw materials and avoid waste. For instance, if employees are working with large paint barrels, it’s tempting to grab the barrel that is the closest to the entrance point instead of walking through the barrel storage unit checking for expiration dates. With IoT tags, employees will be notified about the location of the barrels nearing their expiration date and receive a warning if they still grab the closest item. There are many vendors who have their own IoT tracking systems. For instance, Bosch built a cloud-based , which relies on Bluetooth to transmit signals. This system was designed in collaboration with construction firms and can be integrated with popular enterprise-wide software used in manufacturing. Bosch claims the tags can withstand water and dust and their batteries can last up to two years. This system captures detailed asset-related information. For instance, it can understand when a tool was dropped. And if an item goes missing, the system can show where it was last seen. Bluehound asset tracking solution In another example, Volvo in its large truck plants, where they assemble around 72 vehicles per day. uses IoT-powered trackers to track vehicles https://www.youtube.com/watch?v=M1TUEzcc5Q0&embedable=true Inventory monitoring Another Internet of Things industrial application is fitting the inventory with IoT sensors that will transmit its status in real time. For example, if materials or manufactured products are overheated in storage, smart sensors will ask an IoT-enabled thermostat to lower the temperature in the storage unit. Companies can combine Industrial IoT and analytics to rearrange inventory in an optimal way. For instance, the tools and raw materials that are used the most frequently will be moved closer to access points. IIoT can monitor inventory levels and automatically place an order if stock decreases below a predetermined threshold. This eliminates the need for manual checks. If you are a retailer owning a store with electronic shelves, you can deploy e-labels. Powered by Bluetooth beacons, these tags allow customers to place orders simply by pressing on them. Suppliers will receive order alerts and can either confirm or decline. The e-label can also display “on-order” tag to prevent customers from placing repetitive orders. Levi Strauss, a clothing company, deploys . This enables the company to know how many items they still have left of each model and when the new arrivals are expected. RFID tags to monitor stock levels Remote equipment and production site control Giving managers control over remote equipment is another important Industrial IoT application. Sensors can monitor machinery and allow human operators to activate equipment, shut it down, or change the settings. For instance, if water pump leakage is detected, the technician can deactivate the pump without having to access it physically. This use case is particularly convenient for machinery installed in hard-to-reach locations. Speaking of remote locations, IIoT can be deployed to control tower beacon lights. These lights have very specific requirements, and any organization that owns towers, like the telecom sector, has to conform to these standards. IoT sensors can make sure the lights are located at the right height, and have the required color and brightness, and notify the operator immediately in the case of violations. IIoT is also helpful in monitoring remote sites. For example, the British oil and gas company Shell, to install IoT sensors in their wells. The sensors are supposed to gather information on the wells’ pressure and temperature to minimize human intervention. This is not the only IoT industrial application that Shell deployed. The oil and gas giant also used the technology . The company claims to have in its oilfields. partnered with an IIoT startup Hiber to protect its pipelines saved $1 million deploying IoT sensors Energy management Companies can attach IoT sensors to its infrastructure to monitor the environment and cut on energy consumption when possible. For instance, sensors can turn off the lights when everyone leaves the building. They can also gather and analyze data on the heating and ventilation system to find the optimal performance. Another application of Industrial IoT is detecting when one piece of equipment starts to excessively consume electricity. This is often an indication that the machine is damaged and needs closer inspection. The Chinese company Chengdu Xihui Water Environmental Co., Ltd relied on MindSphere, the Industrial IoT as a service solution from Siemens, to save on energy consumption. The company operates wastewater treatment stations, and energy bills account for almost half of their operational costs. Chengdu Xihui deployed IoT sensors on its equipment to gather real-time data. After thorough analysis, the company understood when the equipment is at its highest energy consumption rate and managed to match this with off-peak electricity prices, . reducing the expenses by 10% Automation With the help of IIoT and robotics, it’s possible to that are typically performed by human employees. automate tedious tasks One example of this Industrial IoT application comes from Amazon. The retail giant uses several types of robots at its warehouses to automate tedious and even dangerous tasks. For instance, , read their label with the help of AI and , and place the right package into a GoCart to move packages within the facility. Cardinal can easily lift heavy packages that can cause injuries in human employees. Cardinal robot can lift packages computer vision https://www.youtube.com/watch?v=3dB-YHAP5RM&embedable=true Proteus is another robot that can move GoCarts through order fulfillment centers while navigating around people and robots. https://www.youtube.com/watch?v=AmmEbYkYfHY&embedable=true IoT-based automation is not limited to warehouse-related activities. Robots can also take over production tasks. For example, Lear Corporation, a car seat manufacturer, to assemble car seats. UR5 works alongside human employees and can perform tasks, such as screwdriving, lifting, and placing seats. relies on UR5 robots Robots can also replace humans in toxic environments, like painting facilities with poisonous emissions. Process optimization and advanced analytics Another Industrial IoT application example is spotting opportunities for process and product optimization with the help of analytics. IoT enables manufacturers to identify and predict bottlenecks in production processes and address the issue preemptively. The technology can also help update performance benchmarks. These production standards, such as the amount of time it takes to produce a product part, are set and rarely updated. Tools like allow managers to gather performance data and optimize job standards to set realistic deadlines and prices of new products. Machine Metrics Applying advanced analytics to data incoming from purchased products helps companies understand how customers actually utilize the items, and update the warranty policy accordingly. IoT sensors attached to packages enable companies to monitor the products during transportation and optimize the packaging to minimize item damage. Safety control IoT can contribute to workers’ safety by monitoring the environment, employee behavior, and compliance with safety protocols. Employees working at high altitudes or operating heavy machinery can wear sensors that will evaluate their body temperature, perspiration, and working technique, and alert managers if they detect a likelihood of a hazardous scenario. Given that musculoskeletal disorders are , these sensors can significantly enhance safety. responsible for 33% of workplace injuries For instance, a workplace wearables firm called Kinetic the size of a smartphone. It can detect posture and movements that are highly dangerous on the job and notify the employee immediately through light vibrations. built a smart device Another example of IIoT comes from Uruguay, where a biofuel plant . It contains sensors that track people’s movements and detect if someone falls or spends too much time in an area with noxious gasses. supplies all workers with a smart helmet Factories can also enhance safety by deploying environmental sensors. These devices can monitor the surroundings for dangers. For instance, they can detect extreme temperatures that can result in a fire and spot any hazardous gas leakage, and alert workers. On a final note Industrial IoT has many exciting applications. It can decrease time and costs associated with equipment failure, take over tedious tasks that pose health risks for human employees, make sure the stock is always replenished so that you don’t get stuck waiting for raw materials. And these are just a few of its benefits. However, there are challenges associated with deploying this technology. The main problem with IoT applications in industry is . As more information is aggregated, processed, and stored, the higher the risk of cyberattacks. Another challenge is the . Keep these aspects in mind when choosing your IIoT vendor. We can help you equip your devices with sensors, build a platform that enables you to manage the IoT devices, develop tailored to your business needs. maintaining data security costs associated with IoT solutions custom big data analytics solutions Whether you want to build IIoT infrastructure from scratch, improve your existing initiatives, or supplement IoT with AI and advanced analytics, ! We will work with you to produce a customized solution that meets your unique business needs. drop us a line Also published . here

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