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Parallel intelligent education with ChatGPT

Frontiers of Information Technology & Electronic engineering 2024年第1期25卷 12-18页

作者： Jiacun WANG Ying TANG Ryan HARE Fei-Yue WANG Department of Computer Science and Software Engineering Monmouth UniversityNew Jersey 07764USA Department of Electrical and Computer Engineering Rowan UniversityNew Jersey 08028USA Institute of Automation Chinese Academy of SciencesBeijing 100190China

This paper presents a framework for parallel intelligent education that involves physical and virtual learning for a personalized learning *** especially focus on Chat Generative Pre-trained Transformer(ChatGPT)owing to its considerable potential to supplement regular class *** address the strengths and weaknesses of learning with ***,we discuss the challenges and solutions of the proposed parallel intelligent education with ChatGPT.

关键词： education GPT intelligent

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Case Study: ROS-Based Fault Injection for Risk Analysis of Robotic Manipulator

Case Study: ROS-Based Fault Injection for Risk Analysis of R...

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IEEE International Conference on automation Science and engineering (CASE)

作者： Yuliang Ma Philipp Grimmeisen Andrey Morozov Institute of Industrial Automation and Software Engineering University of Stuttgart Germany

Industrial cyber-physical systems (ICPS) are becoming more complex due to increasing behavioral and structural complexity. This increases the likelihood of faults, errors, and failures. This can lead to economic losses and even hazardous events. Fault injection is an efficient method to estimate the potential risk of safety-critical ICPS. In this paper, we propose a new fault injection-based risk analysis method for Robot Operating System (ROS) and demonstrate its applicability with a robot manipulator case study. We conducted extensive fault injection experiments using a pick-and-place task. We injected two types of sensor signal faults: bias and noise. First, fault injections were implemented on a ROS/Gazebo model of the manipulator with randomly selected fault parameters such as fault type, location, magnitude, and duration. The experiments helped to identify potential failure scenarios and to find critical fault locations. The most important factor contributing to system failures was the operational phase during which the faults were injected. We then tested our fault injection method on a real Franka Emika Panda collaborative manipulator to validate the effectiveness of the proposed ROS-based fault injection method. We observed that the digital model showed similar behavior to the real manipulator.

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Parameter Optimization via Reinforcement Learning for the Regulation of Swarms 9

Parameter Optimization via Reinforcement Learning for the Re...

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9th International Conference on Information, Cybernetics, and Computational Social Systems, ICCSS 2023

作者： Wu, Qizhen Liu, Gaoxiang Liu, Kexin Chen, Lei School of Automation Science and Electrical Engineering Beihang University Beijing China Environment School of Automation Science and Electrical Engineering Beihang University The State Key Laboratory of Software Development Beijing China Advanced Research Institute of Multidisciplinary Sciences Beijing Institute of Technology Beijing China Yangtze Delta Region Academy of Beijing Institute of Technology Jiaxing China

ISBN: (纸本)9798350342239

The bird-oid object (Boids) model proposes a control algorithm to make the positions between agents achieve cooperative stability. By changing the parameters of cohesion and repulsion in the algorithm, the agents in the swarm can be made to converge to different positions, causing expansion and contraction of the formation. But it is often more difficult to select the appropriate parameters to form the ideal formation. Therefore, this paper proposes a method to improve the cohesive and repulsive parameters in the Boids model based on Q-learning network to achieve a simulation scenario with continuous obstacle avoidance and maximum coverage of space. © 2023 IEEE.

关键词： Reinforcement learning

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Video Self-Supervised Cross-Pathway Training Based on Slow and Fast Pathways

Video Self-Supervised Cross-Pathway Training Based on Slow a...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Li, Jie Yang, Jing Jiang, Zhou Chen, Yuehai Du, Shaoyi School of Automation Science and Engineering Xi'an Jiaotong University Xi'an China School of Software Engineering Xi'an Jiaotong University Xi'an China Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University Xi'an China

ISBN: (纸本)9798350337020

In the field of video self-supervised learning, contrastive instance learning methods suffer from a lack of semantic information, resulting in inadequate generalization in downstream tasks. Although optical flow can provide some semantic information, it requires significant computational cost prior to training. To address this, we propose a Video self-supervised Cross-pathway training model based on Slow and Fast pathways (VCSF). This model separately extracts temporal and spatial features from pure RGB video frames, and uses the complementary representations of the two pathways to conduct cross-pathway training. Additionally, we propose a motion perception module in the low-frame-rate space to enhance the network's ability to perceive rapidly changing human motion. We conducted extensive experiments in downstream missions of UCF101 and HMDB51, and obtained state-of-the-art results in models using the UCF101 data set for self-supervised pre-training, including motion recognition and nearest neighbor retrieval. © 2023 IEEE.

关键词： Cross-pathway training Self-supervised learning Slow and Fast pathways

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Multi-View Dynamic Kernelized Evidential Clustering

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IEEE/CAA Journal of Automatica Sinica 2024年第12期11卷 2435-2450页

作者： Jinyi Xu Zuowei Zhang Ze Lin Yixiang Chen Weiping Ding the Software Engineering Institute East China Normal UniversityShanghai 200062 Shanghai Key Laboratory of Trustworthy Computing East China Normal UniversityShanghai 200062China IEEE the School of Automation Northwestern Polytechnical UniversityXi’an 710072China the School of Artificial Intelligence and Computer Science Nantong UniversityNantong 226019 the Faculty of Data Science City University of MacaoMacao 999078China

It is challenging to cluster multi-view data in which the clusters have overlapping *** multi-view clustering methods often misclassify the indistinguishable objects in overlapping areas by forcing them into single clusters,increasing clustering *** solution,the multi-view dynamic kernelized evidential clustering method(MvDKE),addresses this by assigning these objects to meta-clusters,a union of several related singleton clusters,effectively capturing the local imprecision in overlapping *** offers two main advantages:firstly,it significantly reduces computational complexity through a dynamic framework for evidential clustering,and secondly,it adeptly handles non-spherical data using kernel techniques within its objective *** on various datasets confirm MvDKE's superior ability to accurately characterize the local imprecision in multi-view non-spherical data,achieving better efficiency and outperforming existing methods in overall performance.

关键词： Evidential clustering imprecision characterizing kernel technique multi-view clustering

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Flexible Co-Simulation Approach for Model Adaption in Digital Twins of Power-to-X Platforms

Flexible Co-Simulation Approach for Model Adaption in Digita...

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International Conference on Emerging Technologies and Factory automation (ETFA)

作者： Daniel Dittler David Stauss Philipp Rentschler Johannes Stümpfle Nasser Jazdi Michael Weyrich Institute of Industrial Automation and Software Engineering University of Stuttgart Stuttgart Germany Institute for Micro Process Engineering Karlsruhe Institute of Technology Karlsruhe Germany

ISBN: (数字)9798350361230

ISBN: (纸本)9798350361247

A Digital Twin of a production plant comprises a variety of simulation models. When these models are coupled for co-simulation, they can reflect the behavior of the entire production plant and be used in various application scenarios. To leverage the benefits of the Digital Twin and simulation during the operational phase, manual efforts in model adaption must be automated. This contribution presents a flexible co-simulation approach that enables automated selection of simulation tool interfaces, parameterization, and execution of different model configurations. Finally, application scenarios in the context of Power-to-X production are discussed, and the current implementation of the approach is presented.

关键词： Adaptation models Production Digital twins Manufacturing automation

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User Linguistic Style Awareness and Interest-Driven Conversational Recommender Systems 4

User Linguistic Style Awareness and Interest-Driven Conversa...

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4th IEEE International Conference on Digital Twins and Parallel Intelligence, DTPI 2024

作者： Du, Hongkai Li, Haozhou Peng, Qinke Fu, Laiyi Xi'an Jiaotong University School of Software Engineering Xi'an710049 China Xi'an Jiaotong University School of Automation Science and Engineering Xi'an710049 China Research Institute of Xi'an Jiaotong University Hangzhou311200 China Sichuan Digital Economy Industry Development Research Institute Chengdu610036 China

ISBN: (纸本)9798350349252

Generation-based conversational recommender systems (CRSs) are tailored to individual user needs, employing cutting-edge text generation techniques for generating contextually relevant and fluent responses. Nevertheless, current CRSs, including advanced general language models like ChatGPT, often overlook the subtle differences in users' linguistic styles and the evolving nature of their interests, which leads to a tendency for uniform responses at the onset of dialogues and unnecessarily prolonged interaction cycles. To overcome these challenges, our approach leverages user historical data and information from similar user profiles to more accurately capture the user linguistic feature within dialogue subtasks. We introduce an innovative multi-factor cross-attention mechanism, specifically designed for the efficient integration of diverse quantitative relationships. In the recommendation subtask, our strategy involves the intuitive representation of user interest migration trajectories and the stereotype. This not only allows for a more granular understanding of the trajectory of user interests but also alleviates data sparsity for new users. Our proposed framework, User Linguistic Style Awareness and Interest-Driven CRSs (LSAID), has undergone extensive testing on well-established CRS datasets such as ReDial and INSPIRED. The empirical results underscore LSAID's effectiveness, showcasing its state-of-the-art performance. Our code will be released at http://***/zhizhaixingchen/LSAID. © 2024 IEEE.

关键词： Recommender systems

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REINFORCEMENT LEARNING AND GRAPH NEURAL NETWORKS FOR PROBABILISTIC RISK ASSESSMENT

arXiv

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arXiv 2024年

作者： Grimstad, Joachim Morozov, Andrey Institute of Industrial Automation and Software Engineering University of Stuttgart Pfaffenwaldring 47 Stuttgart70550 Germany

This paper presents a new approach to the solution of Probabilistic Risk Assessment (PRA) models using the combination of Reinforcement Learning (RL) and Graph Neural Networks (GNN). The paper introduces and demonstrates the concept using one of the most popular PRA models - Fault Trees. This paper’s original idea is to apply RL algorithms to solve a PRA model represented with a graph model. Given enough training data, or through RL, such an approach helps train generic PRA solvers that can optimize and partially substitute classical PRA solvers that are based on existing formal methods. Such an approach helps to solve the problem of the fast-growing complexity of PRA models of modern technical systems. © 2024, CC BY-SA.

关键词： Reinforcement learning

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Collective Behavior Regulation in Drone Swarms via Pinning Strategies 7th

Collective Behavior Regulation in Drone Swarms via Pinning...

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7th Chinese Conference on Swarm Intelligence and Cooperative Control, CCSICC 2023

作者： Liu, Gaoxiang Liu, Kexin Chen, Lei School of Automation Science and Electrical Engineering Beihang University Beijing100191 China The State Key Laboratory of Software Development Environment Beihang University Beijing100191 China Advanced Research Institute of Multidisciplinary Sciences Beijing Institute of Technology Beijing100081 China

ISBN: (纸本)9789819733354

In this paper, we discuss the collective behavior of drone swarms and remark that the swarm behavior can be realized by pinning a few nodes. Additionally, the selection of pinning nodes effects the global characteristic. Compared with other methods, the uniform degree selection of pinning nodes in a swarm exhibits better performance. In order to verify the effectiveness of our pinning strategy, we conduct simulations in a self-driven drone swarm, in which the experimental results confirm the availability of our conclusion. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Drones

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Towards autonomous system: flexible modular production system enhanced with large language model agents

Towards autonomous system: flexible modular production syste...

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International Conference on Emerging Technologies and Factory automation (ETFA)

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

In this paper, we present a novel framework that combines large language models (LLMs), digital twins and industrial automation system to enable intelligent planning and control of production processes. We retrofit the automation system for a modular production facility and create executable control interfaces of fine-granular functionalities and coarse-granular skills. Low-level functionalities are executed by automation components, and high-level skills are performed by automation modules. Subsequently, a digital twin system is developed, registering these interfaces and containing additional descriptive information about the production system. Based on the retrofitted automation system and the created digital twins, LLM-agents are designed to interpret descriptive information in the digital twins and control the physical system through service interfaces. These LLM-agents serve as intelligent agents on different levels within an automation system, enabling autonomous planning and control of flexible production. Given a task instruction as input, the LLM-agents orchestrate a sequence of atomic functionalities and skills to accomplish the task. We demonstrate how our implemented prototype can handle un-predefined tasks, plan a production process, and execute the operations. This research highlights the potential of integrating LLMs into industrial automation systems in the context of smart factory for more agile, flexible, and adaptive production processes, while it also underscores the critical insights and limitations for future work. Demos at: https://***/YuchenXia/GPT4Industrialautomation

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