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arXiv 2024年

作者： Engelaar, Maico H.W. Zhang, Zengjie Vlahakis, Eleftherios E. Dimarogonas, Dimos V. Lazar, Mircea Haesaert, Sofie Department of Electrical Engineering Control Systems Group Eindhoven University of Technology Netherlands Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden

This paper addresses the control synthesis of heterogeneous stochastic linear multi-agent systems with real-time allocation of signal temporal logic (STL) specifications. Based on previous work, we decompose specifications into sub-specifications on the individual agent level. To leverage the efficiency of task allocation, a heuristic filter evaluates potential task allocation based on STL robustness, and subsequently, an auctioning algorithm determines the definitive allocation of specifications. Finally, a control strategy is synthesized for each agent-specification pair using tube-based model predictive control (MPC), ensuring provable probabilistic satisfaction. We demonstrate the efficacy of the proposed methods using a multi-shuttle scenario that highlights a promising extension to automated driving applications like vehicle routing. © 2024, CC BY-NC-ND.

关键词： Stochastic systems

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The Method of Verification of Sphere Kinematics by Two Mouse Image Sensors 4

The Method of Verification of Sphere Kinematics by Two Mouse...

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4th IEEE International Conference on Electrical, Computer, and Energy Technologies, ICECET 2024

作者： Kimura, Kenji Ishii, Kazuo National Institute of Technology Matsue College Department of Control Engineering 14-4 Nishi-ikuma-cho Matsue Matsue Shimane690-8518 Japan Graduate School of Life Science and engineering Kyushu Institute of Technology 2-4 Hibikino Wakamatsu-ku Kitakyushu-shi Fukuoka808-0196 Japan

ISBN: (纸本)9798350395914

In the past, omni-directional wheels, including omni-rollers, have been studied. The sphere has attracted attention as a shape with better running and overweight characteristics than this. Because of its excellent symmetry, it has been used as a wheel in omni-directional moving mechanisms. Spherical robots driven by two constrained rollers have been studied extensively, not only to derive their kinematics but also to study their validation methods. In this study, we propose a new method for verifying sphere kinematics, in which the rotational motion of spheres driving two rollers by two mouse sensors and used for estimation calculations. © 2024 IEEE.

关键词： Spheres

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The tangible impacts of air pollution on built heritage during COVID-19 period on the Historical Peninsula of Istanbul, Turkey

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Environmental science and Pollution Research 2025年 2025 May 21页

作者： Sahin, Hasret Sözen, Adnan Department of Energy Systems Engineering Graduate School of Natural and Applied Sciences Gazi University Ankara Turkey Department of Mechanical Engineering Adana Alparslan Türkeş Science and Technology University Adana Turkey

The degree of corrosion of the built cultural heritage site is examined considering the changes in air pollution trends during the COVID-19 pandemic, with the Historical Peninsula of Istanbul as a case study area. Key findings indicate that restricted mobility during stringent lockdown measures contributed to a noticeable reduction in PM10, SO2, NO2, and O3. This causes a temporary decrease in the surface recession rates and corrosion depths of the materials. The maximum reduction in surface recession rate for Portland limestone and White Mansfield dolomitic sandstone reaches to 7.2% and 7.5%, respectively while for cast bronze and zinc, this ratio continues to 17.1% for cast bronze and 4.8% for zinc. Except for the Sultanahmet Urban Archaeological Site, carbon steel and copper exhibit modest variances in corrosion depth, remaining below 2%. Despite the reduction during the pandemic period, Portland limestone surface recession rate and the corrosion depth values of copper, and zinc have already surpassed the 2050 thresholds, indicating that the materials are already experiencing severe damage. This implies that the Istanbul Historical Peninsula necessitates comprehensive conservation strategy plans considering the potential for increased urban and traffic mobility rather than temporary solutions. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Air quality

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Development and control of Underwater Gliding Robots:A Review

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IEEE/CAA Journal of Automatica Sinica 2022年第9期9卷 1543-1560页

作者： Jian Wang Zhengxing Wu Huijie Dong Min Tan Junzhi Yu the State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190 the School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China the State Key Laboratory for Turbulence and Complex System Department of Mechanics and Engineering ScienceBIC-ESATCollege of EngineeringPeking UniversityBeijing 100871China

As one of the most effective vehicles for ocean development and exploration,underwater gliding robots(UGRs)have the unique characteristics of low energy consumption and strong ***,by borrowing the motion principles of current underwater robots,a variety of novel UGRs have emerged with improving their maneuverability,concealment,and environmental friendliness,which significantly broadens the ocean *** this paper,we provide a comprehensive review of underwater gliding robots,including prototype design and their key *** the perspective of motion characteristics,we categorize the underwater gliding robots in terms of traditional underwater gliders(UGs),hybrid-driven UGs,bio-inspired UGs,thermal UGs,and ***,their buoyancy driven system,dynamic and energy model,and motion control are concluded with detailed ***,we have discussed the current critical issues and future *** review offers valuable insight into the development of next-generation underwater robots well-suited for various oceanic applications,and aims to gain more attention of researchers and engineers to this growing field.

关键词： Buoyancy driven motion control oceanic applications system development underwater gliding robots

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Risk-Aware Real-Time Task Allocation for Stochastic Multi-Agent systems under STL Specifications

Risk-Aware Real-Time Task Allocation for Stochastic Multi-Ag...

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IEEE Conference on Decision and control

作者： M. H. W. Engelaar Z. Zhang E. E. Vlahakis D.V. Dimarogonas M. Lazar S. Haesaert Department of Electrical Engineering (Control Systems Group) Eindhoven University of Technology The Netherlands Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

This paper addresses the control synthesis of heterogeneous stochastic linear multi-agent systems with realtime allocation of signal temporal logic (STL) specifications. Based on previous work, we decompose specifications into subspecifications on the individual agent level. To leverage the efficiency of task allocation, a heuristic filter evaluates potential task allocation based on STL robustness, and subsequently, an auctioning algorithm determines the definitive allocation of specifications. Finally, a control strategy is synthesized for each agent-specification pair using tube-based model predictive control (MPC), ensuring provable probabilistic satisfaction. We demonstrate the efficacy of the proposed methods using a multi-shuttle scenario that highlights a promising extension to automated driving applications like vehicle routing.

关键词： Vehicle routing Stochastic processes Probabilistic logic Prediction algorithms Real-time systems Robustness Resource management Vehicle dynamics Multi-agent systems Predictive control

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Closer through commonality: Enhancing hypergraph contrastive learning with shared groups

arXiv

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arXiv 2025年

作者： Roh, Daeyoung Han, Donghee Kim, Daehee Han, Keejun Yi, Mun Graduate School of Data Science KAIST Korea Republic of School of Computer Engineering Hansung University Korea Republic of Department of Industrial and Systems Engineering KAIST Korea Republic of

Hypergraphs provide a superior modeling framework for representing complex multidimensional relationships in the context of real-world interactions that often occur in groups, overcoming the limitations of traditional homogeneous graphs. However, there have been few studies on hypergraph-based contrastive learning, and existing graph-based contrastive learning methods have not been able to fully exploit the high-order correlation information in hypergraphs. Here, we propose a Hypergraph Fine-grained contrastive learning (HyFi) method designed to exploit the complex high-dimensional information inherent in hypergraphs. While avoiding traditional graph augmentation methods that corrupt the hypergraph topology, the proposed method provides a simple and efficient learning augmentation function by adding noise to node features. Furthermore, we expands beyond the traditional dichotomous relationship between positive and negative samples in contrastive learning by introducing a new relationship of weak positives. It demonstrates the importance of fine-graining positive samples in contrastive learning. Therefore, HyFi is able to produce high-quality embeddings, and outperforms both supervised and unsupervised baselines in average rank on node classification across 10 datasets. Our approach effectively exploits high-dimensional hypergraph information, shows significant improvement over existing graph-based contrastive learning methods, and is efficient in terms of training speed and GPU memory cost. The source code is available at https://***/Noverse0/***. © 2025, CC BY.

关键词： Contrastive Learning

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End-to-End Visuomotor Learning from Virtual Environment to Real Robot

End-to-End Visuomotor Learning from Virtual Environment to R...

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IEEE International Conference on Automation science and engineering (CASE)

作者： Kei Higuchi Constantin Uhde Gordon Cheng Ixchel G. Ramirez-Alpizar Gentiane Venture Natsuki Yamanobe Graduate School of Engineering Mechanical Systems Engineering Tokyo University of Agriculture and Technology Japan National Institute of Advanced Industrial Science and Technology (AIST) Japan Institute for Cognitive Systems Technical University of Munich Germany Department of Mechanical Engineering Faculty of Engineering The University of Tokyo Japan

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

Robots can acquire skills to accomplish a target task by learning human manipulations. In this study, we build an end-to-end visuomotor learning system for a robot to learn multiple tasks in a virtual environment, and then perform the same tasks in a real environment without re-training. We use domain randomization to improve the generalization performance of the learning model. To effectively tackle this challenge, we build an integrated learning system that jointly learns robot motions and visual features of the task. The experimental results show that our system can perform multiple tasks with a high success rate and is able to successfully bridge the Sim2Real gap, compared to learning motion and visual features separately.

关键词： Learning systems Robot motion Bridges Visualization Image recognition Computer aided software engineering Virtual environments Motors Robots Image reconstruction

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Deep Reinforcement Learning Based Traffic Signal control: A Comparative Analysis 14

Deep Reinforcement Learning Based Traffic Signal Control: A ...

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14th International Conference on Ambient systems, Networks and Technologies Networks, ANT 2023 and The 6th International Conference on Emerging Data and Industry 4.0, EDI40 2023

作者： Wu, Chunliang Kim, Inhi Ma, Zhenliang Cho Chun Shik Graduate School of Mobility Korea Advanced Institute of Science and Technology Daejeon34051 Korea Republic of Department of Civil and Architectural Engineering KTH Royal Institute of Technology Stockholm11428 Sweden

More recently, the advancement of deep learning techniques enables reinforcement learning (RL) to handle high-dimensional decision-making problems. It brings increasing attention in transport areas. Some work has applied them to solve the intractable traffic signal control (TSC) problem and achieved promising results. However, very few research comprehensively investigates the impacts of key design elements, including state definitions, reward functions and deep reinforcement learning (DRL) methods, on TSC performance. To fill this research gap, this paper first selects commonly used design elements from existing literature. Then, we compare their learning stability and control performance at an isolated intersection under different scenarios via simulation experiments. The experimental results show that the quantitative state (e.g., the number of vehicles on lanes) and image-like state (e.g., vehicle position and speed) have no significant impact on the performance under different traffic demands. However, the impact of various reward functions on performance is vivid, especially under high traffic demand. Also, high-resolution vehicular network data may not be possible to improve control performance versus ordinary camera data. In addition, the value-based DRL algorithms outperform the policy-based algorithm and traditional TSC control methods. The findings of this research would provide insights and guidance for transport engineers to design an efficient DRL-based TSC system in a real traffic environment. © 2023 Elsevier B.V.. All rights reserved.

关键词： Reinforcement learning

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Optimal Strategies Trajectory with Multi-Local-Worlds Graph

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Computers, Materials & Continua 2023年第4期75卷 2079-2099页

作者： Xiang Yu Chonghua Wang Xiaojing Zheng Chaoyu Zeng Brij BGupta School of Electronics and Information Engineering Taizhou UniversityTaizhouZhejiang318000China China Industrial Control Systems Cyber Emergency Response Team Beijing100040China School of Economics and Management Weifang University of Science and TechnologyShouguangShandong262700China National Computer System Engineering Research Institute of China Beijing102699China Department of Computer Science and Information Engineering Asia UniversityTaiwanTaichung413China Research and Innovation Department Skyline University CollegeSharjahP.O.Box 1797United Arab Emirates Staffordshire University Stoke-on-TrentST42DEUK

This paper constructs a non-cooperative/cooperative stochasticdifferential game model to prove that the optimal strategies trajectory ofagents in a system with a topological configuration of a Multi-Local-Worldgraph would converge into a certain attractor if the system’s configuration isfixed. Due to the economics and management property, almost all systems aredivided into several independent Local-Worlds, and the interaction betweenagents in the system is more complex. The interaction between agents inthe same Local-World is defined as a stochastic differential cooperativegame;conversely, the interaction between agents in different Local-Worldsis defined as a stochastic differential non-cooperative game. We construct anon-cooperative/cooperative stochastic differential game model to describethe interaction between agents. The solutions of the cooperative and noncooperativegames are obtained by invoking corresponding theories, and thena nonlinear operator is constructed to couple these two solutions *** last, the optimal strategies trajectory of agents in the system is proven toconverge into a certain attractor, which means that strategies trajectory arecertainty as time tends to infinity or a large positive integer. It is concluded thatthe optimal strategy trajectory with a nonlinear operator of cooperative/noncooperativestochastic differential game between agents can make agentsin a certain Local-World coordinate and make the Local-World paymentmaximize, and can make the all Local-Worlds equilibrated;furthermore, theoptimal strategy of the coupled game can converge into a particular attractorthat decides the optimal property.

关键词： Multi-Local-world graph stochastic differential game optimal strategy attractor

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Deep learning-based defect detection on livestock operations 22

Deep learning-based defect detection on livestock operations

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2022 Conference on Research in Adaptive and Convergent systems, RACS 2022

作者： Kim, Hyun Soo Muallifah, Nabilah Cho, Yesung Lee, Bumho Yi, Mun Yong Department of Civil Environmental Engineering Kaist Daejeon Korea Republic of Graduate School of Data Science Kaist Daejeon Korea Republic of Department of Industrial and Systems Engineering Kaist Daejeon Korea Republic of

ISBN: (纸本)9781450393980

The recent growth of computer vision system research has contributed to the advancement of livestock operations including meat processing. However, the defect detection of livestock products has not been actively studied due to the scarcity of dataset despite its significance. In the real world, the ratio of the defects in the livestock dataset is extremely low, compared to the normal products, resulting in insufficient model training. To address this problem, this study propose a deep learning-based anomaly detection method for an extremely unbalanced dataset. Adopting an anomaly detection framework from prior research, we suggest an adversarial autoencoder, which includes loss inversion to optimize reconstruction error for training skewed data, and a linear perturbation method called the Fast Gradient Sign Method (FGSM) to generate noises as anomaly cases. The model was then evaluated on the real-world dataset acquired from a livestock factory to detect defective chicken carcass (e.g., broken leg, injured skin, bent wing). The experiment results show that the proposed model outperforms existing models used for anomaly detection problems (i.e. DevNet and DeepSAD) and loss inversion and FGSM successfully improve the model performance when detecting livestock defects, even if the dataset contains extremely few defects. © 2022 ACM.

关键词： Defects

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