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34th CIRP Design Conference, CIRP 2024

作者： Stümpfle, Johannes Jazdi, Nasser Weyrich, Michael Institute of Industrial Automation and Software Engineering University of Stuttgart Germany

software product lines (SPL) have emerged as a pivotal paradigm in software engineering, enabling the efficient development of variant-rich software systems. Consistently updating these systems, often through over-the-air updates, enables the continuous integration of new features and bug fixes, ensuring the system remains up to date throughout its entire lifecycle. However, evolving such complex systems is an error prone task, leading to a phenomenon known as erosion. This phenomenon significantly impacts the efficiency and longevity of software systems, presenting a formidable challenge for manufacturers of variant-rich software systems, such as in the automotive domain. While existing studies concentrate on the evolutionary planning of variant-rich software systems, there is a noticeable lack of research addressing the problem of erosion. In this paper, we conduct an in-depth exploration of the erosion phenomena within variant-rich software systems. We begin by highlighting the significance of controlling erosion in extensive variant-rich software systems. Subsequently, we address the current challenges regarding tackling erosion, including issues such as the lack of a consensus on understanding and defining erosion, as well as the early detection and elimination. Finally, we outline a first approach aimed at tackling erosion in variant-rich software systems. © 2024 The Authors. Published by Elsevier B.V.

关键词： Design: challenges & Innovation Erosion software Product Line

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ADAPTIVE AND SCALABLE APPROACH FOR TIME SERIES ANOMALY DETECTION USING TRANSFORMER

ADAPTIVE AND SCALABLE APPROACH FOR TIME SERIES ANOMALY DETEC...

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ASME 2024 International Mechanical engineering Congress and Exposition, IMECE 2024

作者： Hu, Jieyi Ding, Sheng Grimmeisen, Philipp Morozov, Andrey Institute of Industrial Automation and Software Engineering University of Stuttgart Germany

ISBN: (纸本)9780791888698

This paper introduces an innovative approach to time series anomaly detection, focusing on addressing the challenge of adapting to concept drift within input time series. Traditional time series anomaly detection methods often assume a stable environment for modeling. However, in practical applications, time series data may be influenced by concept drift, where the data distribution and features change over time. To effectively tackle this issue, we propose a dynamic adaptive model capable of real-time detection and adaptation to concept drift within time series data. Our algorithm leverages advanced time series anomaly detection techniques, particularly a Transformer-based online learning model, utilizing prediction errors as the basis for anomaly scores. Notably, the model incorporates a mechanism for concept drift detection and adaptation, continuously monitoring changes in the time series to adjust the learning rate accordingly. Additionally, our approach enhances the self-attention mechanism within the Transformer model by employing sparse self-attention, improving computational efficiency. Experimental results demonstrate that our algorithm exhibits superior accuracy in the presence of concept drift compared to traditional time series anomaly detection methods. This makes our approach particularly valuable in practical applications, especially in dynamic environments requiring adaptability. The combination of online learning, concept drift adaptation, and optimized self-attention contributes to the algorithm's effectiveness in handling real-world time series data. Copyright © 2024 by ASME.

关键词： Time series

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Personalized Comfort Features in software-defined Vehicles Using Federated Learning 29

Personalized Comfort Features in Software-defined Vehicles U...

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29th IEEE International Conference on Emerging Technologies and Factory automation, ETFA 2024

作者： Gül, Baran Can Devarakonda, Neeharika Jazdi, Nasser Weyrich, Michael Institute of Industrial Automation and Software Engineering University of Stuttgart Stuttgart Germany

ISBN: (纸本)9798350361230

Most of the existing vehicle comfort features operate solely based on user input, lacking consideration for individual preferences, and environmental conditions. This manual adjustment while driving can lead to potential distractions, and jeopardizing user safety. On the other hand, implementing a system where the vehicle control unit learns individual preferences and autonomously adjusts accordingly would significantly enhance the driving experience. In this paper, we examine the thermal comfort of users as one of the comfort features within software-defined vehicles. Given that thermal comfort is influenced by both physiological and external factors, and people have diverse individual preferences, this paper proposes leveraging personalized federated learning to automate and personalize temperature regulation, thereby enhancing thermal comfort of passengers in the vehicle cabin. To validate this concept, we conducted experiments employing a prototype equipped with sensors to collect real-time data, which is then used to train a predictive model. The model's accuracy was assessed using metrics and compared against a centralized approach. In addition, we used a simulator to visualize the potential improvement in thermal comfort with the predicted values. Our findings indicate that temperature control in the vehicle cabin, utilizing federated learning for individualized regulation, outperforms conventional learning approaches, thus yielding significant improvement for thermal comfort of passengers. © 2024 IEEE.

关键词： Temperature control

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LLM experiments with simulation: Large Language Model Multi-Agent System for Simulation Model Parametrization in Digital Twins 29

LLM experiments with simulation: Large Language Model Multi-...

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29th IEEE International Conference on Emerging Technologies and Factory automation, ETFA 2024

作者： Xia, Yuchen Dittler, Daniel Jazdi, Nasser Chen, Haonan Weyrich, Michael Institute of Industrial Automation and Software Engineering University of Stuttgart Stuttgart Germany

ISBN: (纸本)9798350361230

This paper presents a novel design of a multi-agent system framework that applies large language models (LLMs) to automate the parametrization of simulation models in digital twins. This framework features specialized LLM agents tasked with observing, reasoning, decision-making, and summarizing, enabling them to dynamically interact with digital twin simulations to explore parametrization possibilities and determine feasible parameter settings to achieve an obj ective. The proposed approach enhances the usability of simulation model by infusing it with knowledge heuristics from LLM and enables autonomous search for feasible parametrization to solve a user task. Furthermore, the system has the potential to increase user-friendliness and reduce the cognitive load on human users by assisting in complex decision-making processes. The effectiveness and functionality of the system are demonstrated through a case study, and the visualized demos and codes are available at a GitHub Repository: https://***/LLMDrivenSimulation © 2024 IEEE.

关键词： Multi agent systems

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GENERATION OF MATLAB SIMSCAPE MODELS USING LARGE LANGUAGE MODELS FOR TRAINING RELIABILITY EVALUATION METHODS

GENERATION OF MATLAB SIMSCAPE MODELS USING LARGE LANGUAGE MO...

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ASME 2024 International Mechanical engineering Congress and Exposition, IMECE 2024

作者： Hummel, Patrick Grimstad, Joachim Xia, Yuchen Morozov, Andrey Institute of Industrial Automation and Software Engineering University of Stuttgart Stuttgart Germany

ISBN: (纸本)9780791888698

The approach presented in this paper makes use of the natural language processing capabilities of large language models in order to generate MATLAB Simscape models based on specification texts. This process is based on a well-defined state machine and begins with the creation of abstract system models that may be iterated on using natural language. The abstract models are then converted to more detailed models by adding parameters and blocks required for simulation. Fault tolerance may optionally be improved by automatically applying design patterns that increase redundancy using voter and comparator blocks, among others. The abstract models are then converted into MATLAB Simscape models using traditional algorithms. The generated models can be used to train artificial intelligence systems designed for reliability evaluation. A selection of publicly available large language models is systematically evaluated based on capability regarding this task. A software prototype demonstrates the feasibility of using generative artificial intelligence to assist with the modeling of systems in MATLAB Simscape as a proof-of-concept. Current limitations and future research potential is discussed. Copyright © 2024 by ASME.

关键词： MATLAB

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Enhance FMEA with Large Language Models for Assisted Risk Management in Technical Processes and Products 29

Enhance FMEA with Large Language Models for Assisted Risk Ma...

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29th IEEE International Conference on Emerging Technologies and Factory automation, ETFA 2024

作者： Xia, Yuchen Jazdi, Nasser Weyrich, Michael Institute of Industrial Automation and Software Engineering University of Stuttgart Stuttgart Germany

ISBN: (纸本)9798350361230

This paper presents a novel application of Large Language Models (LLMs) to improve Failure Mode and Effect Analysis (FMEA) for managing risks in technical processes and products. We designed an LLM multi-agent system that utilizes Retrieval Augmented Generation (RAG) for risk analysis and text generation. This system interfaces directly with an FMEA knowledge database to dynamically extract relevant information, perform reasoning based on user input and retrieved information, and generate targeted text recommendations for completing FMEA spreadsheets. By automating the analysis and synthesis of risk information, this system can provide expert knowledge and reduces the cognitive load on users, speeding up the FMEA process. The effectiveness and functionality of this LLM-enhanced FMEA application is demonstrated through a prototype, accessible via a GitHub Repository at: https://***/YuchenXia/LLMRiskAnalyzer. © 2024 IEEE.

关键词： Multi agent systems

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Federated Learning for Comfort Features in Vehicles with Collaborative Sensing: A Review 29

Federated Learning for Comfort Features in Vehicles with Col...

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29th IEEE International Conference on Emerging Technologies and Factory automation, ETFA 2024

作者： Gül, Baran Can Dittler, Daniel Jazdi, Nasser Weyrich, Michael Institute of Industrial Automation and Software Engineering University of Stuttgart Stuttgart Germany

ISBN: (纸本)9798350361230

The rapid innovation in the automotive industry highlights the increasing importance of user comfort, especially when integrated with advanced learning scenarios. However, there is a noticeable gap in research focusing on vehicle cabin comfort, particularly in the context of learning and personalization of features. This study conducts a systematic literature review to assess the current state of research in this area. By utilizing federated learning with personalization, a novel and promising technique, various use cases related to vehicle interior comfort are explored. These use cases help derive the requirements needed to address the research question. The methodology of the systematic literature review is detailed, including the evaluation of specific prerequisites. The key finding reveals that no existing study meets all the predefined requirements, underscoring the need for further research in this domain. © 2024 IEEE.

关键词： Automotive industry

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Automating software Product Line Adoption Based on Feature Models Using Large Language Models 29

Automating Software Product Line Adoption Based on Feature M...

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29th IEEE International Conference on Emerging Technologies and Factory automation, ETFA 2024

作者： Stümpfle, Johannes Baum, Sebastian Dittler, Daniel Jazdi, Nasser Weyrich, Michael Institute of Industrial Automation and Software Engineering University of Stuttgart Stuttgart Germany

ISBN: (纸本)9798350361230

software-intensive systems emerge in a multitude of variations to meet diverse customer requirements. To develop such variant-rich software systems, software product line (SPL) engineering has emerged as a key strategy for managing the variability. However, the adoption of SPLs is highly complex due to the diversity of feature model formats and specifications, and the complexity of implementing variability. Leveraging the capabilities of powerful large language models (LLMs) can facilitate the adoption of SPLs. Nonetheless, these LLMs often lack knowledge of the various specifications of potential feature models as well as efficient implementation of different variability mechanisms. To address these challenges, we propose a novel method based on retrieval-augmented generation. This method generates reusable artefacts and a corresponding feature mapping based on a given feature model, thereby aiding system engineers in adopting an SPL. © 2024 IEEE.

关键词： Specifications

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A survey about self-adaptive anomaly-detection in software-defined systems 50

A survey about self-adaptive anomaly-detection in software-d...

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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Weiß, Matthias Thich, Stefan Artelt, Maurice Weyrich, Michael Institute of Industrial Automation and Software Engineering University of Stuttgart Stuttgart Germany

ISBN: (纸本)9781665464543

The rise of software-defined automation systems is accompanied by an increase in interconnected services and networks. Among other implications, this leads to an expansion of the attack surface for cyber threats, which in turn can lead to significant security issues and financial losses. Against this background, this paper investigates the effectiveness of anomaly-based intrusion detection systems (IDS) for detecting and preventing new and evolving cyber threats. The paper especially considers the challenge of concept drift, which describes the phenomenon that the statistical properties of the network traffic or behavior patterns that an IDS is trying to detect change over time. To accomplish this, a comprehensive literature review, prioritizing recent studies since 2019, highlights the challenges of concept drift and the application of reinforcement learning in IDS. The results suggest that the integration of adaptive strategies and reinforcement learning in IDS enables significant improvements in anomaly detection. © 2024 IEEE.

关键词： Network intrusion

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OCRBench: on the hidden mystery of OCR in large multimodal models

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Science China(Information Sciences) 2024年第12期67卷 23-35页

作者： Yuliang LIU Zhang LI Mingxin HUANG Biao YANG Wenwen YU Chunyuan LI Xu-Cheng YIN Cheng-Lin LIU Lianwen JIN Xiang BAI School of Artificial Intelligence and Automation Huazhong University of Science and Technology School of Electronic and Information Engineering South China University of Technology Microsoft Research School of Computer & Communication Engineering University of Science and Technology Beijing Institute of Automation Chinese Academy of Sciences School of Software Engineering Huazhong University of Science and Technology

Large models have recently played a dominant role in natural language processing and multimodal vision-language learning. However, their effectiveness in text-related visual tasks remains relatively unexplored. In this paper, we conducted a comprehensive evaluation of large multimodal models, such as GPT4V and Gemini, in various text-related visual tasks including text recognition, scene text-centric visual question answering(VQA), document-oriented VQA, key information extraction(KIE), and handwritten mathematical expression recognition(HMER). To facilitate the assessment of optical character recognition(OCR) capabilities in large multimodal models, we propose OCRBench, a comprehensive evaluation benchmark. OCRBench contains 29 datasets, making it the most comprehensive OCR evaluation benchmark available. Furthermore, our study reveals both the strengths and weaknesses of these models, particularly in handling multilingual text, handwritten text, non-semantic text, and mathematical expression *** importantly, the baseline results presented in this study could provide a foundational framework for the conception and assessment of innovative strategies targeted at enhancing zero-shot multimodal *** evaluation pipeline and benchmark are available at https://***/Yuliang-Liu/Multimodal OCR.

关键词： large multimodal model OCR text recognition scene text-centric VQA document-oriented VQA key information extraction handwritten mathematical expression recognition

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