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Annual Conference of Industrial Electronics Society

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

ISBN: (数字)9781665464543

ISBN: (纸本)9781665464550

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.

关键词： Surveys Industrial electronics automation Concept drift Intrusion detection Reinforcement learning Telecommunication traffic Security Anomaly detection Systematic literature review

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

arXiv

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arXiv 2024年

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

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 objective. 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://***/YuchenXia/LLMDrivenSimulation © 2024, CC BY.

关键词： Digital elevation model

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Personalized Comfort Features in software-defined Vehicles Using Federated Learning

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

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International Conference on Emerging Technologies and Factory automation (ETFA)

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

ISBN: (数字)9798350361230

ISBN: (纸本)9798350361247

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.

关键词： Temperature sensors Temperature Federated learning Prototypes Predictive models Regulation Real-time systems Temperature control Sensors Safety

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

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

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International Conference on Emerging Technologies and Factory automation (ETFA)

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

ISBN: (数字)9798350361230

ISBN: (纸本)9798350361247

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.

关键词： Training Industries Technological innovation Federated learning Focusing Sensors Temperature control Human activity recognition Manufacturing automation Automotive engineering

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A Soft Sensor with Light and Efficient Multi-scale Feature Method for Multiple Sampling Rates in Industrial Processing

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Machine Intelligence Research 2024年第2期21卷 400-410页

作者： Dezheng Wang Yinglong Wang Fan Yang Liyang Xu Yinong Zhang Yiran Chen Ning Liao School of Automation Southeast UniversityNanjing 210096China Software and Artificial Intelligence College Chongqing Institute of EngineeringChongqing 400056China Beijing National Research Center for Information Science and Technology(BNRist) Department of AutomationTsinghua UniversityBeijing 100084China Liangjiang International College Chongqing University of TechnologyChongqing 401135China Smart City College Beijing Union UniversityBeijing 100101China

In industrial process control systems,there is overwhelming evidence corroborating the notion that economic or technical limitations result in some key variables that are very difficult to measure *** data-driven soft sensor is an effective solution because it provides a reliable and stable online estimation of such *** paper employs a deep neural network with multiscale feature extraction layers to build soft sensors,which are applied to the benchmarked Tennessee-Eastman process(TEP)and a real wind farm *** comparison of modelling results demonstrates that the multiscale feature extraction layers have the following advantages over other ***,the multiscale feature extraction layers significantly reduce the number of parameters compared to the other deep neural ***,the multiscale feature extraction layers can powerfully extract dataset ***,the multiscale feature extraction layers with fully considered historical measurements can contain richer useful information and improved representation compared to traditional data-driven models.

关键词： Multi-scale feature extractor deep neural network(DNN) multirate sampled industrial processes prediction

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

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

引用

International Conference on Emerging Technologies and Factory automation (ETFA)

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

ISBN: (数字)9798350361230

ISBN: (纸本)9798350361247

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

关键词： Visualization Large language models Decision making Cognitive load Digital twins Usability Manufacturing automation Multi-agent systems Load modeling software development management

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

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

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International Conference on Emerging Technologies and Factory automation (ETFA)

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

ISBN: (数字)9798350361230

ISBN: (纸本)9798350361247

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.

关键词： Large language models software systems software product lines Complexity theory Manufacturing automation

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Control Industrial automation System with Large Language Models

arXiv

引用

arXiv 2024年

作者： Xia, Yuchen Jazdi, Nasser Zhang, Jize Shah, Chaitanya Weyrich, Michael Institute of Industrial Automation and Software Engineering University of Stuttgart Stuttgart70550 Germany

Traditional industrial automation systems require specialized expertise to operate and complex reprogramming to adapt to new processes. Large language models offer the intelligence to make them more flexible and easier to use. However, LLMs' application in industrial settings is underexplored. This paper introduces a framework for integrating LLMs to achieve end-to-end control of industrial automation systems. At the core of the framework are an agent system designed for industrial tasks, a structured prompting method, and an event-driven information modeling mechanism that provides real-time data for LLM inference. The framework supplies LLMs with real-time events on different context semantic levels, allowing them to interpret the information, generate production plans, and control operations on the automation system. It also supports structured dataset creation for fine-tuning on this downstream application of LLMs. Our contribution includes a formal system design, proof-of-concept implementation, and a method for generating task-specific datasets for LLM fine-tuning and testing. This approach enables a more adaptive automation system that can respond to spontaneous events, while allowing easier operation and configuration through natural language for more intuitive human-machine interaction. We provide demo videos and detailed data on GitHub: https://***/YuchenXia/LLM4IAS © 2024, CC BY.

关键词： Semantics

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Defect detection of gear parts in virtual manufacturing

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Visual Computing for Industry,Biomedicine,and Art 2023年第1期6卷 76-87页

作者： Zhenxing Xu Aizeng Wang Fei Hou Gang Zhao School of Mechanical Engineering&Automation Beihang UniversityBeijing 100191China State Key Laboratory of Computer Science Institute of SoftwareChinese Academy of SciencesBeijing 100190China University of Chinese Academy of Sciences Beijing 100049China

Gears play an important role in virtual manufacturing systems for digital twins;however,the image of gear tooth defects is difficult to acquire owing to its non-convex *** this study,a deep learning network is proposed to detect gear defects based on their point cloud *** approach mainly consists of three steps:(1)Various types of gear defects are classified into four cases(fracture,pitting,glue,and wear);A 3D gear dataset was constructed with 10000 instances following the aforementioned classification.(2)Gear-PCNet++introduces a novel Combinational Convolution Block,proposed based on the gear dataset for gear defect detection to effectively extract the local gear information and identify its complex topology;(3)Compared with other methods,experiments show that this method can achieve better recognition results for gear defects with higher efficiency and practicability.

关键词： Defect detection Gear surface Gear dataset Combinational Convolution Block

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Future Use Cases for Vehicular Communication based on Connected Functions

Future Use Cases for Vehicular Communication based on Connec...

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IEEE Conference on Vehicular Technology (VTC)

作者： Falk Dettinger Matthias Weiß Michael Weyrich Institute of Industrial Automation and Software Engineering (IAS) University of Stuttgart Stuttgart Germany

ISBN: (数字)9798331517786

ISBN: (纸本)9798331517793

As software-defined vehicles continue to dominate automotive research, the vision for a future of extensively connected vehicular environments, including vehicle-to-everything (V2X) communications and nationwide connected infrastructures, grows more pronounced. However, despite the existence of standards to support these advances, widespread adoption and integration into broader vehicular ecosystems remain ***, the scope of this paper is to explore potential connected vehicle functions of the future and outline potential paths toward their realization. Utilizing the Delphi method, the authors engaged with experts from the German automotive industry to gain insights into emerging and feasible connected vehicle functions. The interviews served as a primary source for identifying promising vehicle functions, which were subsequently compared against existing literature to detect related functions and to group them into coherent clusters. Finally, by combining these clusters and the acquired knowledge, visions for the future of mobility were *** findings of this study reveal that the trajectory toward implementing connected vehicle functions is unlikely to follow a linear path. Instead, development is expected to occur through multiple, parallel scenarios. The success and prevalence of specific use cases will largely depend on the development of viable monetization strategies and business models that can sustain these individual "islands" of innovation. The paper concludes that future research should focus on strategies to integrate these disparate islands, aiming to forge a unified and comprehensive vehicular communication system.

关键词： Industries Economics Technological innovation Connected vehicles Biological system modeling Transportation Interviews Vehicle dynamics Vehicle-to-everything Business

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