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arXiv 2025年

作者： Wu, Sicheng Liwang, Minghui Wang, Deqing Wang, Xianbin Wu, Chao Tang, Junyi Li, Li Jiao, Zhenzhen The School of Informatics Xiamen University Fujian China The Department of Control Science and Engineering The National Key Laboratory of Autonomous Intelligent Unmanned Systems Tongji University Shanghai China Frontiers Science Center for Intelligent Autonomous Systems Ministry of Education Tongji University Shanghai China The Department of Electrical and Computer Engineering Western University ON Canada The School of Public Affairs Zhejiang University Hangzhou310027 China School of Information Science and Engineering Lanzhou University Lanzhou China The iF-Labs Beijing Teleinfo Technology Co. Ltd. CAICT China

To facilitate responsive and cost-effective computing resource scheduling and service delivery over edge-assisted mobile networks, this paper investigates a novel two-stage double auction methodology via utilizing an interesting idea of resource overbooking to overcome dynamic and uncertain nature from edge servers (sellers) and demand from mobile devices (as buyers). The proposed auction integrates multiple essential factors such as social welfare maximization and decision-making latency (e.g., the time for determining winning seller-buyer pairs) reduction, by introducing a stagewise strategy: an overbooking-driven pre-double auction (OPDAuction) for determining long-term cooperations between sellers and buyers before practical resource transactions as Stage I, and a real-time backup double auction (RBDAuction) for handling residual resource demands during actual transactions. In particular, by applying a proper overbooking rate, OPDAuction helps with facilitating trading contracts between appropriate sellers and buyers as guidance for future transactions, by allowing the booked resources to exceed supply. Then, since pre-auctions may cause risks, our RBDAuction adjusts to real-time market changes, further enhancing the overall social welfare. More importantly, we offer an interesting view to show that our proposed two-stage auction can support significant design properties such as truthfulness, individual rationality, and budget balance. Through extensive experiments, we demonstrate good performance in social welfare, time efficiency, and computational scalability, outstripping conventional methods in dynamic edge computing settings. Copyright © 2025, The Authors. All rights reserved.

关键词： Budget control

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Privacy-Aware Joint DNN Model Deployment and Partition Optimization for Delay-Efficient Collaborative Edge Inference

arXiv

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arXiv 2025年

作者： Cheng, Zhipeng Xia, Xiaoyu Wang, Hong Liwang, Minghui Chen, Ning Fan, Xuwei Wang, Xianbin School of Future Science and Engineering Soochow University Suzhou215006 China School of Computing Technologies RMIT University MelbourneVIC Australia Department of Control Science and Engineering The National Key Laboratory of Autonomous Intelligent Unmanned Systems Tongji University Shanghai201804 China Frontiers Science Center for Intelligent Autonomous Systems Ministry of Education Tongji University Shanghai201804 China Qingdao China College of Computer and Information Sciences Fujian Agriculture and Forestry University Fuzhou350002 China Department of Electrical and Computer Engineering Western University ON Canada

Edge inference (EI) is a key solution to address the growing challenges of delayed response times, limited scalability, and privacy concerns in cloud-based Deep Neural Network (DNN) inference. However, deploying DNN models on resource-constrained edge devices faces more severe challenges, such as model storage limitations, dynamic service requests, and privacy risks. This paper proposes a novel framework for privacy-aware joint DNN model deployment and partition optimization to minimize long-term average inference delay under resource and privacy constraints. Specifically, the problem is formulated as a complex optimization problem considering model deployment, user-server association, and model partition strategies. To handle the NP-hardness and future uncertainties, a Lyapunov-based approach is introduced to transform the long-term optimization into a single-time-slot problem, ensuring system performance. Additionally, a coalition formation game model is proposed for edge server association, and a greedy-based algorithm is developed for model deployment within each coalition to efficiently solve the problem. Extensive simulations show that the proposed algorithms effectively reduce inference delay while satisfying privacy constraints, outperforming baseline approaches in various scenarios. Copyright © 2025, The Authors. All rights reserved.

关键词： Differential privacy

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PATH PLANNING FOR MOBILE MANIPULATORS FOR MULTIPLE TASK EXECUTION

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IEEE TRANSACTIONS ON ROBOTICS AND AUTOMATION 1991年第3期7卷 403-408页

作者： CARRIKER, WF KHOSLA, PK KROGH, BH Laboratory for Automated Systems and Information Processing Department of Electrical and Computer Engineering Carnegie Mellon University Pittsburgh PA USA

We consider the planning problem for a mobile manipulator system that must perform a sequence of tasks defined by position, orientation, force, and moment vectors at the end-effector. Each task can be performed in multiple configurations due to the redundancy introduced by mobility. We formulate the planning problem as an optimization problem in which the decision variables for mobility (base position) are separated from the manipulator joint angles in the cost function. The resulting numerical problem is nonlinear with nonconvex, unconnected feasible regions in the decision space. Simulated annealing is proposed as a general solution method for obtaining near-optimal results. The problem formulation and numerical solution by simulated annealing are illustrated for a manipulator system with three degrees of freedom mounted on a base with two degrees of freedom. These results are compared with results obtained by conventional nonlinear programming techniques customized for the particular example system.

关键词： Path planning Simulated annealing Cost function Numerical simulation Space stations Robotics and automation Manipulators Trajectory Constraint optimization Ear

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Short-term wind power forecast with turning weather based on DBSCAN-RFE-LightGBM

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Renewable Energy 2025年 251卷

作者： Wu, Zhenlong Fan, Xinyu Bian, Guibin Liu, Yanhong Zhang, Xiaoke Chen, YangQuan School of Electrical and Information Engineering Zhengzhou University Zhengzhou450001 China State Key Laboratory of Intelligent Agricultural Power Equipment Zhengzhou University Luoyang471000 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China Electric Power Research Institute of State Grid Henan Electric Power Company Zhengzhou450052 China Lab Department of Mechanical Engineering School of Engineering University of California MercedCA95343 United States

At present, the wind power prediction technology is becoming more and more mature, however, the turning weather brings a decrease in prediction accuracy due to its suddenness and instability. In this paper, a wind power prediction method considering the turning period and non-turning period respectively is proposed to weaken the adverse effects caused by turning weather. Firstly, the prediction effects of multiple models are compared, density-based spatial clustering of applications with noise (DBSCAN) is selected as the outlier processing method, recursive feature elimination (RFE) is used as the feature selection method, and Light gradient boosting machine (LightGBM) is used for prediction. The combined prediction method based on DBSCAN-RFE-LightGBM can reduce the influence of abnormal data and redundant features and improve the prediction effect. Then, the sliding window is set to detect the turning period. Considering that the turning weather is an emergency and does not happen frequently, the amount of data is small, which leads to the inability to train the model well. Generative adversarial networks (GAN) are applied to expand the turning period data. The LightGBM is trained and predicted by using the expended turning period data. Finally, the time-division prediction results are merged. Using the data collected from wind farms for short-term power prediction experiments, the time-segment prediction method proposed in this paper with GAN reduces MAE by 1.913 and RMSE by 3.351 on a single unit compared with the non-differentiated period. © 2025 Elsevier Ltd

关键词： Merging

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Investigation on Insulating Properties of Epoxy Resin Impregnated Paper Affected by Curing Agents and Accelerator Contents

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IEEE Transactions on Dielectrics and electrical Insulation 2025年

作者： Teng, Chenyuan Ding, Yichao Zhou, Yuanxiang Zhao, Lei Zhang, Yunxiao Chen, Haoran Zhang, Youbing Zhejiang University of Technology College of Information Engineering Hangzhou310023 China Nankai University China and College of Electronic Information and Optical Engineering Tianjin300350 China Zhejiang Testing & Inspection Institute for Mechanical and Electrical Products Quality Co. Ltd Hangzhou310051 China Tsinghua University State Key Laboratory of Control and Simulation of Power Systems and Generation Equipment Department of Electrical Engineering Beijing100084 China State Grid Xinxiang Electric Power Supply Company Xinxiang453002 China

The curing formula is vital to the performance of epoxy resin impregnated paper (RIP) material. This work explored the effect of curing agents and accelerator contents on the thermal and electrical properties of RIP material. It was found that the RIP material cured with 1phr accelerator has the optimal glass transition temperature, electrical conductivity, dielectric loss and DC breakdown strength compared with RIP material cured by 0.5phr, 2phr, and 3phr accelerator. The RIP material cured with MEHHPA has better thermal and insulating properties compared with the RIP material cured with METHPA. The existence of the crepe paper amplifies the influence of accelerator and curing agent for the hydroxyl groups affect the curing process as well as the chain segment arrangement. This study provides the research support for the design of the curing formula for RIP insulation used in DC bushing. © 1994-2012 IEEE.

关键词： Dielectric losses

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ON PETRI NET MODELS OF INFINITE STATE SUPERVISORS

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IEEE TRANSACTIONS ON AUTOMATIC control 1992年第2期37卷 274-277页

作者： SREENIVAS, RS KROGH, BH Laboratory for Automated Systems and Information Processing Department of Electrical Engineering and Computer Engineering Carnegie Mellon University Pittsburgh PA USA

We consider a class of supervisory control problems that require infinite state supervisors Petri nets with inhibitor arcs (PN's) to model the supervisors. We compare this PN-based approach to supervisory control to automata-based approaches. The primary advantage of a PN-based supervisory controller is that a PN-based controller provides a finite representation of an infinite state supervisor. For verification, implementation, and testing reasons, a finite PN-based representation of an infinite state supervisor is preferred over an automata-based supervisor. We show that this modeling advantage is accompanied by a decision disadvantage, in that in general the controllability of a language that can be generated by the closed-loop system is undecidable.

关键词： Supervisory control Automatic control Petri nets Inhibitors control systems Automata Discrete event systems control system synthesis Automatic testing

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Dexterous Manipulation through Imitation Learning: A Survey

arXiv

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arXiv 2025年

作者： An, Shan Meng, Ziyu Tang, Chao Zhou, Yuning Liu, Tengyu Ding, Fangqiang Zhang, Shufang Mu, Yao Song, Ran Zhang, Wei Hou, Zeng-Guang Zhang, Hong School of Electrical and Information Engineering Tianjin University Tianjin300072 China School of Control Science and Engineering Shandong University Jinan250061 China State Key Laboratory of General Artificial Intelligence Beijing100086 China Department of Mechanical and Process Engineering ETH Zurich Zurich8092 Switzerland Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen518055 China School of Informatics University of Edinburgh EH8 9AB United Kingdom Department of Computer Science University of Hong Kong 999077 Hong Kong State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China CASIA-MUST Joint Laboratory of Intelligence Science and Technology Institute of Systems Engineering Macau University of Science and Technology 999078 China

Dexterous manipulation, which refers to the ability of a robotic hand or multi-fingered end-effector to skillfully control, reorient, and manipulate objects through precise, coordinated finger movements and adaptive force modulation, enables complex interactions similar to human hand dexterity. With recent advances in robotics and machine learning, there is a growing demand for these systems to operate in complex and unstructured environments. Traditional model-based approaches struggle to generalize across tasks and object variations due to the high-dimensionality and complex contact dynamics of dexterous manipulation. Although model-free methods such as reinforcement learning (RL) show promise, they require extensive training, large-scale interaction data, and carefully designed rewards for stability and effectiveness. Imitation learning (IL) offers an alternative by allowing robots to acquire dexterous manipulation skills directly from expert demonstrations, capturing fine-grained coordination and contact dynamics while bypassing the need for explicit modeling and large-scale trial-and-error. This survey provides an overview of dexterous manipulation methods based on imitation learning (IL), details recent advances, and addresses key challenges in the field. Additionally, it explores potential research directions to enhance IL-driven dexterous manipulation. Our goal is to offer researchers and practitioners a comprehensive introduction to this rapidly evolving domain. Copyright © 2025, The Authors. All rights reserved.

关键词： Grippers

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PATTERN RECOGNITION BY USING AN ASSOCIATIVE MEMORY

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IEEE TRANSACTIONS ON ELECTRONIC COMPUTERS 1966年第6期EC15卷 944-+页

作者： YAU, SS YANG, CC Information-Processing and Control Systems Laboratory and Department of Electrical Engineering Northwestern University Evanston IL USA Program of Information Science and the Department of Elecrical Engineering Washington State University Pullman USA

The purpose of this paper is to present a simple template-matching pattern recognition technique by using any general-purpose associative memory. The input patterns for recognition may have wide variations, provided that the distinct features of individual pattern classes can be extracted. Each pattern class is allowed to have deviations in size, style, orientation, etc. within certain limits. This pattern recognition technique is extremely efficient in handwritten character recognition, which is used for illustration in this paper. Because each input pattern is processed with all the pattern classes simultaneously, the speed of this pattern recognition technique is very high. It is found that most input patterns are recognized within first comparison process and no input patterns require more than two comparison processes for their recognition.

关键词： Pattern recognition Associative memory Character recognition Pattern classification Feature extraction Convergence Machine learning Piecewise linear techniques Parallel processing Random number generation

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SYNTHESIS OF FEEDBACK-control LOGIC FOR DISCRETE MANUFACTURING systems

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AUTOMATICA 1991年第4期27卷 641-651页

作者： KROGH, BH HOLLOWAY, LE Laboratory for Automated Systems and Information Processing Department of Electrical and Computer Engineering Carnegie Mellon University Pittsburgh PA 15213 United States

This paper concerns the supervisory coordination and control of concurrent activity cycles in automated manufacturing facilities. In contrast to commonly used simulation models which integrate the control policy with the system model, the state transition logic for the manufacturing equipment is represented by a class of controlled Petri nets (CtlPN) with external inputs to be determined by the control synthesis algorithm. We formulate the forbidden state control problem in the CtlPN context and present an algorithm for generating maximally permissive controls which guarantee the system will avoid the forbidden states while permitting a maximal amount of flexibility in the system operation. The problem formulation and control synthesis algorithm is illustrated for an example of AGV co-ordination, and several classes of manufacturing control problems which can be addressed within this framework are identified.

关键词： COMPUTER control control SYSTEM SYNTHESIS DISCRETE systems MANUFACTURING PROCESSES SUPERVISORY control SYNTHESIS METHODS

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Early Detection of Induction Machines Fault Using Vibration-Based Analysis under COMSOL Multiphysics within a Preventive Maintenance Framework

Early Detection of Induction Machines Fault Using Vibration-...

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Innovative Research in Applied Science, engineering and Technology (IRASET),Conference on

作者： Hamza Sabir Mohammed Ouassaid Nourdine Herbaz Nabil Ngote Mohamed El Khaili Ahmed Abid Laboratory of Engineering Sciences & Biosciences Faculty of Sciences and Techniques of Mohammedia Hassan II University Casablanca Morocco Engineering for Smart and Sustainable Systems Research Center Mohammadia School of Engineers Mohammed V University in Rabat Rabat Morocco Laboratory of Electronics Energy Automation & Information Processing (LEEA&TI). Faculty of Sciences and Techniques of Mohammedia Hassan II University Casablanca Morocco ENSMR Engineering School Rabat Morocco IESI Lab ENSET Mohammedia Hassan 2nd University of Casablanca Casablanca Morocco Electrical Engineering Department Faculty of Sciences and Techniques of Mohammedia Hassan II University Casablanca Morocco

ISBN: (数字)9798331532970

ISBN: (纸本)9798331532987

In this paper, a predictive maintenance strategy is developed to enhance the fault identification of induction machines using the analysis of residual mechanical vibrations. Induction machines are the most widely adopted machines in many industrial environments, owing to their high robustness, simplicity and economic efficiency. Nevertheless, these machines are susceptible to both electrical and mechanical failures, including stator phase deficiencies, which may result in significant downtime and expensive maintenance charges. The approach developed is designed by combining FFT (Fast Fourier Transform) and vibration signal analysis to detect faults at an early stage. The methodology has been validated by simulations performed with COMSOL Multiphysics. The results reveal a significant improvement over traditional maintenance methods based solely on vibration analysis in the early detection of failures, resulting in reduced downtime and lower maintenance costs.

关键词： Vibrations Fault diagnosis Machine learning algorithms Fast Fourier transforms Windings Stator windings Maintenance Wind turbines Induction machines Preventive maintenance

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