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In the exciting journey of developing a RISC-V assembler using C#, we delve into the intricate processes that bring assembly code to life. In this article, we focus on the Label Processing phase and discuss the essential aspects of handling labels, calculating addresses, and dealing with offsets. Phase 1: Label Processing The Label Processing phase is crucial for creating a robust assembler. Labels provide a symbolic representation of memory addresses and play a pivotal role in facilitating jumps and branches in the code. Let's break down the steps involved in processing labels. Identifying Labels The first step is to iterate through the assembly code and identify labels. Labels in assembly code typically end with a colon “:”. We use a simple algorithm to extract and store these labels along with their corresponding addresses. public static void ProcessLabels(string[] code)
{
    foreach (var assemblyLine in code)
    {
        var line = assemblyLine.Trim();
        if (string.IsNullOrEmpty(line)) continue;
        if (line.StartsWith(".")) continue;
        if (line.EndsWith(":"))
        {
            string label = line.Substring(0, line.Length - 1);
            AddressLookupTable.Add(label, Address.CurrentAddress);
            continue;
        }
        Address.CurrentAddress += 4;
    }
} Calculating Addresses To maintain the coherence of memory addresses, we increment the address by 4 (decimal) for each instruction, excluding empty lines, labels, and directives. In RISC (Reduced Instruction Set Computing) architectures, all instructions are of the same fixed size. Each instruction is 32 bits (4 bytes) long. By incrementing the address by 4 for each instruction, the assembler ensures that subsequent instructions are stored in consecutive memory locations. This sequential arrangement facilitates easier memory access during the execution phase, as the processor can fetch instructions efficiently without having to perform complex calculations to determine the next instruction's location. Addressing Offsets The concept of offsets is crucial for resolving the actual memory addresses of labels during execution. Since the assembler does not know the final memory layout, we use offsets to bridge the gap. The offset is calculated by subtracting the current address ( ) from the label's address. This difference represents the distance between the current instruction and the target label in terms of memory locations. Address.CurrentAddress The calculated offset is then utilized in the machine code generation process. It allows the assembler to generate machine code instructions that incorporate the correct memory addresses, accounting for the relative positions of labels within the code. For example, if the current address is 28 and a label is at 10, the offset is -18. During execution at address , the location of the label is ( ). 0x00FF28 0x00FF06 0x00FF28 - 0x12 Machine Code Generation During the generation of machine code, we encounter the challenge of handling labels, especially when they represent memory addresses. If an immediate value is not an integer, it implies a label reference. In such cases, we retrieve the address from the lookup table. int labelAddress = AddressLookupTable[assemblyLine];
int offset = labelAddress - Address.CurrentAddress; As we progress in developing the assembler, understanding labels, addresses, and offsets lays a solid foundation for the subsequent phases. SharpRISCV The repository serves as the starting point for enthusiasts eager to explore and contribute to the project. SharpRISCV If you find the project helpful and informative, don’t forget to give it a star on GitHub to show your support. In the previous parts, we delved into i , and now, with a grasp of addressing modes, labels, and offsets, we eagerly anticipate the exploration of directives in the upcoming part. Stay tuned for the next installment in our journey through the world of RISC-V assembly and C# magic. nstruction types, parsing instructions, and converting to machine code

Understanding RISC-V Assembly Language by Building an Assembler in C# - Part 2

Introduction: In the ever-evolving landscape of computer architecture, RISC-V stands out as an open-source instruction set architecture that offers flexibility and adaptability. To facilitate learning and exploration of RISC-V assembly language, a powerful tool has emerged—SharpRISCV. This browser-based RISC-V assembler supports building Linux ELF, Windows PE, Intel Hex, and RAW output, providing a comprehensive platform for enthusiasts and learners. Features: SharpRISCV supports the assembly of code for both Linux ELF and Windows PE formats. This versatility enables users to explore RISC-V on different operating systems, broadening the scope of learning and experimentation. Cross-Platform Support: The assembler generates output in multiple formats, including Linux ELF, Windows PE, Intel Hex, and RAW. This diversity caters to users with varied preferences and requirements, ensuring compatibility with different tools and environments. Output Formats: For Linux ELF and Windows PE outputs, SharpRISCV goes beyond basic assembly. It provides a hex dump visualization of the generated code, allowing users to inspect the machine code at a granular level. This feature aids in understanding the correspondence between assembly instructions and machine code. Hex Dump Visualization: SharpRISCV enhances the learning experience by offering interactive byte descriptions. Users can hover over specific bytes in the hex dump, and the tool provides detailed descriptions of the corresponding instructions. This interactive feature facilitates a deeper understanding of the RISC-V assembly language. Interactive Byte Descriptions: Supported Instruction Types: R Type U Type I Type B Type S Type J Type Assembler Directives: : Specifies the beginning of the text section. .text : Marks the beginning of the data section. .data and : Define string literals by adding a null terminator. .string .asciz .asciz : Reserves space for a word (32 bits). .word and : Handle high and low parts of a value, respectively. .%hi .%lo Accessing SharpRISCV: To start your RISC-V assembly journey, visit the RISC-V web interface at . The user-friendly interface makes it easy to write, assemble, and explore RISC-V code directly from your web browser. SharpRISCV Contribute to the Project: SharpRISCV is an open-source project available on GitHub at . If you find this tool valuable and helpful, consider giving it a star on GitHub. Your support encourages the continuous improvement of SharpRISCV and contributes to the growth of the RISC-V community. github.com/rizwan3d/SharpRISCV Embark on your RISC-V assembly journey today with SharpRISCV, where learning meets exploration in the world of open architecture!

SharpRISCV Overview: A Browser-Based RISC-V Assembler for Seamless Learning and Exploration

RISC-V is an open-source instruction set architecture (ISA) that has gained popularity due to its simplicity and flexibility. In this article, we’ll explore the fundamentals of RISC-V assembly language by building an assembler in C#. Our goal is to read RISC-V assembly code, identify the instruction type, and convert it into machine code. We’ll use Visual Studio as our development environment for this project. Setting Up the Project Let’s begin by creating a new project in Visual Studio. We’ll build our RISC-V assembler step by step. We mention that we will be building the assembler incrementally, and the first task is to set up a loop to read a file containing RISC-V assembly code and iterate over each line. This loop will serve as the foundation for processing the assembly code. string[] lines = File.ReadAllLines(filePath);

foreach (string line in lines)
{
  // Process the 'line' here, e.g., identify instruction type, parse, and convert to machine code
} Identifying Instruction Types RISC-V instructions are categorized into different types: R, U, I, B, S, and J. To determine the type of instruction, we’ll use a lookup table for opcodes, func2, and func7. You can find the lookup table in this . file Here’s an example of how to identify the instruction type: switch (opCode)
{
    case (OpCode)0b0110011:
        return InstructionType.R;
    case (OpCode)0b0010111:
        return InstructionType.U;
    case (OpCode)0b0110111:
        return InstructionType.U;
    case (OpCode)0b0010011:
        return InstructionType.I;
    case (OpCode)0b1100011:
        return InstructionType.B;
    case (OpCode)0b0000011:
        return InstructionType.I;
    case (OpCode)0b0100011:
        return InstructionType.S;
    case (OpCode)0b1101111:
        return InstructionType.J;
    default:
        return InstructionType.Unknown;
} You can find the implementation of this function in the file. RiscVAssembler.cs Parsing Instructions Now that we can identify the instruction type, let’s parse each instruction based on its type. We’ll start with the R-type instructions, which have the syntax: . op rd, rs1, rs2 For example, the instruction can be parsed as follows: add x10, x1, x2 Regex rTypeRegex = new Regex(@"^(\w+)\s+(\w+),\s+(\w+),\s+(\w+)$");
Match rTypeMatch = rTypeRegex.Match(instruction);
if (rTypeMatch.Success)
{
    return new RiscVInstruction
    {
        Instruction = instruction,
        Opcode = rTypeMatch.Groups[1].Value,
        Rd = rTypeMatch.Groups[2].Value,
        Rs1 = rTypeMatch.Groups[3].Value,
        Rs2 = rTypeMatch.Groups[4].Value,
        Immediate = null,
        InstructionType = InstructionType.R
    };
} You can find the complete implementation of the R-type instruction parser in the file. R_Parser.cs Converting to Machine Code Once we’ve parsed an instruction, we can convert it into machine code. Each instruction type has its own format. For R-type instructions, the format is as follows: R type: .insn r opcode6, func3, func7, rd, rs1, rs2
+-------+-----+-----+-------+----+---------+
| func7 | rs2 | rs1 | func3 | rd | opcode6 |
+-------+-----+-----+-------+----+---------+
31      25    20    15      12   7        0 For example, the instruction is translated into , where: add x10, x1, x2 00000000001000001000010100110011 Opcode 6: 0110011 Rd = 01010 Func 3 = 000 Rs1 = 00001 Rs2 = 00010 Func7 = 0000000 Here’s an example of how to convert the parsed instruction into machine code: string opcode = ((int)instruction.OpcodeBin).ToBinary(7);
string rdBinary = Convert.ToString(int.Parse(instruction.Rd.Substring(1)), 2).PadLeft(5, '0');
string func3 = ((int)instruction.Funct3).ToBinary(3);
string rs1Binary = Convert.ToString(int.Parse(instruction.Rs1.Substring(1)), 2).PadLeft(5, '0');
string rs2Binary = Convert.ToString(int.Parse(instruction.Rs2.Substring(1)), 2).PadLeft(5, '0');
string func7 = ((int)instruction.Funct7).ToBinary(7);

return new MachineCode($"{func7}{rs2Binary}{rs1Binary}{func3}{rdBinary}{opcode}", instruction.Instruction); You can find the complete implementation of machine code generation in the file. R_MachineCode.cs Conclusion In this article, we’ve embarked on a journey to learn RISC-V assembly language by building an assembler in C#. We’ve covered the basics of reading RISC-V assembly code, identifying instruction types, parsing instructions, and converting them into machine code. This project serves as a valuable learning experience for understanding the inner workings of RISC-V assembly language and its translation into machine code. To delve deeper into the RISC-V architecture, refer to the . RISC-V Specification Here’s the GitHub repository link for the project where you can find the code for building a RISC-V assembler in C#: . SharpRISCV GitHub Repository If you find the project helpful and informative, don’t forget to give it a star on GitHub to show your support. In the of our RISC-V assembly language learning series, we will explore , which are essential concepts for understanding and writing more complex assembly programs. Stay tuned for the next installment! next part addressing modes, labels, and offsets Also published . here

Understanding RISC-V Assembly Language by Building an Assembler in C#

Andre’s tutor hastily assembled the chief Hungarian lords

Paris is known for its romance, fashion, and art, but for a week in March it was also the epicenter of the blockchain world. Paris Blockchain Week 2023 (PBW 2023) brought together some of the brightest minds and influential voices in the crypto industry to discuss and debate the current state and future prospects of the decentralized ecosystem. The Avengers of Crypto on Stage One of the highlights of PBW 2023 was the panel discussion titled “State of the Chain 2023” moderated by Cointelegraph’s editor-in-chief Kristina Lucrezia Cornèr. The panel featured five experts from different sectors of the blockchain space: Ryan Nitz, the head of solutions architecture at Coinbase; Matthew Savarese, the head of strategy at Nasdaq Digital Assets; Rich Widmann, the head of strategy for Web3 at Google; Dante Disparte, the chief strategy officer and head of global policy at Circle; and Denelle Dixon, the CEO of Stellar Development Foundation. Takeaways from the Panel Discussion The panelists shared their insights on the current state of the Web3 space and their predictions on how 2023 would evolve. Here are some of the key takeaways from the panel discussion: The state of blockchain is not strong, but innovation is happening. Disparte expressed his belief that the chain’s current state is “not strong” due to the recent challenges and failures in the industry, such as the FTX bankruptcy, the USDC collapse, and the bank crisis. However, people will continue building and developing amid these challenges. He compared the current situation to past tech and financial industry failures that led to innovation and reform. Web2 brands are getting involved in Web3 and crypto. Nitz shared that Coinbase is trying its best to help onboard Web2 companies and eliminate the complexity barrier. Web2 brands are evolving and starting getting involved in Web3 and crypto, such as Twitter, Reddit, Shopify, and Spotify. Web3 is the next evolution of the internet. Widmann emphasized that Google is supporting Web3 as a way to build value for users in the next evolution of the internet. Google is providing cloud infrastructure, data analytics, machine learning, and artificial intelligence for Web3 applications. Additionally, Google is exploring how to leverage blockchain technology for its own products and services. Nasdaq is using blockchain to enhance its core business. Savarese revealed that Nasdaq is using blockchain technology as a way to enhance its core business of providing market infrastructure, data, and analytics for capital markets. It is integrating blockchain technology into its existing platforms, such as Nasdaq Private Market and Nasdaq Linq. Nasdaq is collaborating with other industry players to create standards and best practices for blockchain adoption. In Conclusion The future of the blockchain is bright, despite the current challenges and setbacks. The panelists showed that there is a lot of innovation, collaboration, and value creation happening in the Web3 space, across different sectors and domains. Blockchain technology is not only transforming the way we exchange value, but also . the way we build and interact with the internet Blockchain technology is . Blockchain technology can offer more efficiency, transparency, security, and inclusion for both institutions and individuals. not a threat to the traditional financial system, but a complement and an enhancement We’ve got a glimpse of what to expect in 2023, such as more Web2 involvement, more Web3 applications, more blockchain integration, and more cross-border payments. Blockchain technology is still evolving and maturing, and . we need to be patient and resilient in the face of adversity We need to , to shape the future of finance and the Web3 economy. keep learning and building together, as a community and as an industry If you’re feeling inspired by PBW 2023, you can also check out some of the upcoming events in the crypto industry, such as Consensus 2023, EthCC 2023, or Devcon 7. 🔥If you've been reading this far, you are an absolute legend 😎 and I sincerely appreciate it 🫰. Consider leaving a like or a comment for more content like this. Follow me on twitter: @stephabauva 👨‍🚀 See ya 👋

PBW 2023: The Avengers of Crypto Assemble 

PBW 2023: Los Vengadores de Crypto Assemble

PBW 2023 : les vengeurs de la crypto se rassemblent

PBW 2023: द एवेंजर्स ऑफ क्रिप्टो असेंबल

PBW 2023: クリプト アセンブルのアベンジャーズ

PBW 2023: Os Vingadores do Crypto Assemble

PBW 2023: The Avengers of Crypto Assemble

PBW 2023：加密复仇者集结

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 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

Empathizing with your customers is the first step in creating a successful e-commerce user experience (UX). In our case, we consider the role of empathy in the context of e-commerce in a narrower niche – baby carrier for newborn children. Baby K'tan is a solution that will help parents be closer to their children during the first years of their lives. Baby wearing allows caregivers to keep the baby close and safe while keeping both hands free. Focusing on the target audience and adhering to the authenticity of the Baby K'tan brand, the updated regional online store helps parents focus on what is most important to them - the safety and benefits for the child, while reducing the time spent searching for goods and choosing it. A particular challenge for in this case is maximum responsibility, since we are talking about the health of a newborn child and his mother. We know that during the postpartum period, all the mother's attention is focused only on the child. And this fragile balance must not be disturbed. The goal was to make navigation as simple as possible. And at the same time, maintain consistency in presenting relevant information throughout the user journey. The complexity of the product lies in the need to accompany it with a large amount of information describing its properties and confirming its reliability. At the stage of initial communication and further testing with a focus group, it was possible to divide this information into three levels of importance and develop a system for its presentation. At the initial stages of developing the project concept, there was a low point due to the difficulty of combining two goals: important information should be transmitted to the user quickly and easily accessible, but without losing the quality of the information and important aspects. The key was empathy and immersion in the lifestyle and needs of modern parents after the birth of a child. After prototype testing when we saw that this puzzle finally came together, there were emotions that can be compared to an “aha! moment.” To make the most empathic UX design, you need to try to understand in what conditions, at what time and under what circumstances your user will interact with your design. The main users are mothers who are preparing for the birth of a child and mothers of newborn babies and are looking for devices to ease the postpartum period. The main problem she faces is the lack of time that she can spend studying the issue and the solution should be easy and obvious The main task is to emphasize expert opinion. The period after birth is extremely important to all mothers, it is full of vivid emotions, tender moments. We understand that our goal is to help focus on these moments. Anxiety and daily routine can be very distracting. Focusing on the target audience and relying on the brand identity of Baby K'TAN, i have developed an online store redesign for the regional branch. The research was conducted in several stages and for each a target focus group was assembled – women preparing to become mothers (45%), mothers with children aged 1-2 years (45%) and fathers. We believe this proportion most accurately reflects the user. Based on the Baby K’Tan data, in addition to working with the focus group, the main principles of the design concept were highlighted: minimalism, gender neutrality, maximum ease of navigation and simple but relevant animation of element. What is the essence of the product or service? What are the core benefits for the target group? Your little one depends on you for everything, and at times this can feel overwhelming. But for the last nine months, you were her whole world, and in a few short years, she’ll want down instead of “up, up!” The truth is, for right now just the beat of your heart under hers is enough. Relish it while you can with an intuitive carrier that wraps her up tightly, close against you. The user journey begins with the moment the purchase decision is made and ends with the moment when the mother can truly say that her hands are free and her child is comfortable. The essence of the project is that the process of choosing a carrier, selecting the right size, understanding how to use it correctly, it is quite complicated, because we are talking about the health of the child. The difficulty also lies in the fact that users (mothers) are faced with one of the most serious changes in their lives, which also affects them at different levels. In this regard, it was decided to move in the direction of simplifying navigation as much as possible. Also, according to our research, mothers who decide to purchase a baby carrier after the birth of their child often conduct their research at night, when the child is already sleeping and this is the only time free for them for tasks of this kind. In this regard, a color concept was developed so as not to increase the load on the girl’s tired body. In conclusion I can said that heartfelt approach as important as data and metrics. While technological prowess and design innovation remain integral, it is the sincere approach that truly resonates with users. Empathizing with your customers is the first step in creating a successful e-commerce user experience (UX). In our case, we consider the role of empathy in the context of e-commerce in a narrower niche – baby carrier for newborn children. Baby K'tan is a solution that will help parents be closer to their children during the first years of their lives. Baby wearing allows caregivers to keep the baby close and safe while keeping both hands free. Focusing on the target audience and adhering to the authenticity of the Baby K'tan brand, the updated regional online store helps parents focus on what is most important to them - the safety and benefits for the child, while reducing the time spent searching for goods and choosing it. A particular challenge for in this case is maximum responsibility, since we are talking about the health of a newborn child and his mother. We know that during the postpartum period, all the mother's attention is focused only on the child. And this fragile balance must not be disturbed. The goal was to make navigation as simple as possible. And at the same time, maintain consistency in presenting relevant information throughout the user journey. The complexity of the product lies in the need to accompany it with a large amount of information describing its properties and confirming its reliability. At the stage of initial communication and further testing with a focus group, it was possible to divide this information into three levels of importance and develop a system for its presentation. At the initial stages of developing the project concept, there was a low point due to the difficulty of combining two goals: important information should be transmitted to the user quickly and easily accessible, but without losing the quality of the information and important aspects. The key was empathy and immersion in the lifestyle and needs of modern parents after the birth of a child. After prototype testing when we saw that this puzzle finally came together, there were emotions that can be compared to an “aha! moment.” To make the most empathic UX design, you need to try to understand in what conditions, at what time and under what circumstances your user will interact with your design. The main users are mothers who are preparing for the birth of a child and mothers of newborn babies and are looking for devices to ease the postpartum period. The main problem she faces is the lack of time that she can spend studying the issue and the solution should be easy and obvious The main task is to emphasize expert opinion. The period after birth is extremely important to all mothers, it is full of vivid emotions, tender moments. We understand that our goal is to help focus on these moments. Anxiety and daily routine can be very distracting. Focusing on the target audience and relying on the brand identity of Baby K'TAN, i have developed an online store redesign for the regional branch. The research was conducted in several stages and for each a target focus group was assembled – women preparing to become mothers (45%), mothers with children aged 1-2 years (45%) and fathers. We believe this proportion most accurately reflects the user. Based on the Baby K’Tan data, in addition to working with the focus group, the main principles of the design concept were highlighted: minimalism, gender neutrality, maximum ease of navigation and simple but relevant animation of element. What is the essence of the product or service? What are the core benefits for the target group? Your little one depends on you for everything, and at times this can feel overwhelming. But for the last nine months, you were her whole world, and in a few short years, she’ll want down instead of “up, up!” The truth is, for right now just the beat of your heart under hers is enough. Relish it while you can with an intuitive carrier that wraps her up tightly, close against you. The user journey begins with the moment the purchase decision is made and ends with the moment when the mother can truly say that her hands are free and her child is comfortable. The essence of the project is that the process of choosing a carrier, selecting the right size, understanding how to use it correctly, it is quite complicated, because we are talking about the health of the child. The difficulty also lies in the fact that users (mothers) are faced with one of the most serious changes in their lives, which also affects them at different levels. In this regard, it was decided to move in the direction of simplifying navigation as much as possible. Also, according to our research, mothers who decide to purchase a baby carrier after the birth of their child often conduct their research at night, when the child is already sleeping and this is the only time free for them for tasks of this kind. In this regard, a color concept was developed so as not to increase the load on the girl’s tired body. In conclusion I can said that heartfelt approach as important as data and metrics. While technological prowess and design innovation remain integral, it is the sincere approach that truly resonates with users.

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