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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Cederberg, Daniel Larsson, Erik G. Johansson, Mikael Department of Electrical Engineering Stanford University Stanford United States Department of Electrical Engineering Linköping University Linköping Sweden Division of Decision and Control Systems KTH Royal Institute of Technology Stockholm Sweden

ISBN: (纸本)9798350368741

This paper presents a comprehensive approach to federated learning in wireless networks. We discuss communication strategies that address packet loss and bitrate limitations in both uplink and downlink transmissions, and introduce FABLE, a novel optimization algorithm designed to operate effectively under these network constraints. The algorithm also supports non-smooth regularizers and accommodates heterogeneous data distributions across clients. We provide theoretical convergence guarantees under gradient compression and asynchronous operation, and demonstrate the efficiency of our approach through numerical experiments. © 2025 IEEE.

关键词： Federated learning optimization algorithms wireless communications

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Bilateral Primary Frequency Support and AC Voltage Regulation of LCC-HVDC systems for Asynchronously Interconnected systems

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IEEE Transactions on Power systems 2025年第3期40卷 2600-2612页

作者： Li, Zhixuan Xue, Ying Chen, Yiping Chen, Nan Yang, Conghuan Huang, Jiyu South China University of Technology School of Electric Power Engineering Guangzhou510641 China China Southern Power Grid Power Dispatching and Control Center Guangzhou510663 China University of Birmingham Department of Electronic Electrical and Systems Engineering School of Engineering BirminghamB15 2TT United Kingdom Guangzhou Maritime University Guangzhou510725 China

Modern power systems require significant grid flexibility. As the AC transmission lines are passive and cannot be controlled, grid flexibility in the transmission system can only be provided by actively controlling High-Voltage Direct Current (HVDC) systems, particularly the dominant LCC-HVDC systems. Although grid-flexibility support using LCC-HVDC has been studied, existing methods fail to achieve bidirectional frequency support while maintaining AC voltage. Therefore, a coordinated control method is proposed for the LCC-HVDC system to achieve bilateral frequency support (BFS) and AC voltage regulation (AVR) for asynchronously interconnected systems. The level of frequency support and the coupled dynamics of sending-end (SE) and receiving-end (RE) system frequencies are quantified by the developed low-order frequency response model, different from frequency models in previous research for only one end of the system. With the developed model, the control parameters of the proposed method are analytically calculated. This ensures an appropriate level of primary frequency support without jeopardizing the frequency response of the system at the remote end. The effectiveness of the proposed method is demonstrated through real-time simulation. © 1969-2012 IEEE.

关键词： HVDC power transmission

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GY-YOLO: ghost separable YOLO for pedestrian detection

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Neural Computing and Applications 2025年 1-27页

作者： Elhenidy, Ali M. Labib, Labib M. Haikal, Amira Y. Saafan, Mahmoud M. Computers and Control Systems Engineering Department Faculty of Engineering Mansoura University Mansoura Egypt Faculty of Engineering Mansoura National University Gamasa Egypt

In recent years, there has been impressive development in human detection. The main challenge in pedestrian detection is the training data. To assess detectors in crowd scenarios more effectively, a novel dataset in this study called the HEP dataset (Hybrid Egyptian Pedestrian dataset) is introduced. The HEP dataset is extensive, has comprehensive annotations, and is highly diverse. The dataset images are collected by two different means. Most of the images are collected from different mobile cameras for people crossing the street in high crowded streets in Egypt, and the rest of the images are collected from the web. That is why the dataset is called hybrid. The collected dataset is more suitable for pedestrian detection as the whole images focus on pedestrian scenarios for people outdoors crossing the street. This outperforms the previous benchmarks such as CrowdHuman and WiderPerson which collect data from the web and surveillance cameras with lots of images for indoor people. GS-YOLO also is proposed to address the real-time performance and the occlusion in the crowd scenes issues. GS-YOLO is a novel pedestrian detection model that utilizes efficient Ghost and depth separable convolution modules. GS-YOLO replaces all the convolution layers in the backbone and the head of the original YOLOv8 with Ghost and depth separable modules, respectively. A deformable to-features module is proposed to enrich features for the different feature pyramid networks. GS-YOLO is trained and tested over the collected dataset and other benchmarks like CrowdHuman and WiderPerson datasets. GS-YOLO achieves competitive results over the state-of-the-art models such as YOLOv5 and YOLOv8. GS-YOLO achieves 92.8% mAp on the HEP dataset, while YOLOv5 achieves 90.3% mAp and YOLOv8 achieves 91.1% mAp.

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TextGeo-SLAM: A LiDAR SLAM With Text Semantics and Geometric-Constraint-Based Loop Closure

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IEEE Internet of Things Journal 2025年第9期12卷 12021-12033页

作者： Chen, Shoubin Li, Chunyu Jiang, Qi Zhuang, Xuebin Zhang, Bo Zhou, Baoding Li, Qingquan Shenzhen518060 China Sun Yat-sen University School of Systems Science and Engineering Guangzhou510275 China National University of Singapore Queenstown Singapore Shenzhen University College of Mechatronics and Control Engineering Shenzhen City Joint Laboratory of Autonomous Unmanned Systems and Intelligent Manipulation Shenzhen518060 China Shenzhen518060 China Shenzhen University Department of Shenzhen Key Laboratory of Spatial Smart Sensing and Services Shenzhen518060 China

A robust back-end module with loop closure detection is crucial for accurate positioning and mapping in LiDAR-based simultaneous localization and mapping (SLAM) systems, particularly in Internet of Things (IoT) environments where multiple devices collaborate. Traditional methods that rely on images or point clouds often fail in environments with similar structures or textures, leading to incorrect loop closures. To address this, we propose a novel LiDAR SLAM system that integrates a front-end odometry module, a loop closure detection module using text semantics and geometric constraints, and a global optimization module. By using cameras on an unmanned ground vehicle (UGV), the system captures text information from the environment, enabling semantic matching to identify potential loops. Geometric constraints help eliminate erroneous loops caused by identical text in different locations. Evaluations on datasets with similarly structured environments, such as indoor parking lots, outdoor campus areas, and mixed indoor–outdoor scenes, show that our method significantly improves loop closure detection accuracy and global precision compared to existing state-of-the-art approaches. Our research can support autonomous IoT systems and multiagent systems that rely on accurate positioning and mapping, with potential applications in embodied intelligence, self-driving cars, and smart cities. © 2014 IEEE.

关键词： Multi agent systems

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Achieving an Accurate Random Process Model for PV Power Using Cheap Data:Leveraging the SDE and Public Weather Reports

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CSEE Journal of Power and Energy systems 2025年第1期11卷 124-135页

作者： Yiwei Qiu Jin Lin Zhipeng Zhou Ningyi Dai Feng Liu Yonghua Song College of Electrical Engineering Sichuan UniversityChengdu610065China State Key Laboratory of the Control and Simulation of Power Systems and Generation Equipment Tsinghua UniversityBeijing 100087China State Key Laboratory of Internet of Things for Smart City University of MacaoMacao999078China Department of Electrical Engineering Tsinghua UniversityBeijing100087China

Stochastic differential equation (SDE)-based random process models of renewable energy sources (RESs) jointly capture evolving probability distribution and temporal correlation in continuous time. It enabled recent studies to remarkably improve performance of power system dynamic uncertainty quantification and optimization. However, considering the non-homogeneous random process nature of PV, there still remains a challenging question: how can a realistic and accurate daily SDE model for PV power be obtained that reflects its weather-dependent and non-Gaussian uncertainty in operation, especially when high-resolution numerical weather prediction (NWP) or sky imager is unavailable for many distributed plants? To fill this gap, this article finds that an accurate SDE model for PV power can be constructed only using the data from low-resolution public weather reports. Specifically, for each day, an hourly parameterized Jacobi diffusion process recreates temporal patterns of PV volatility. Its parameters are mapped from the day's public weather reports to reflect varying weather conditions using a simple learning model. The SDE model jointly captures intraday and intrahour volatility. Statistical examination shows that the proposed approach outperforms a selection of the latest deep learning-based time series models on real-world data collected in Macao.

关键词： Photovoltaic(PV)power random process stochastic differential equation(SDE) stochastic optimization time series modeling uncertainty quantification weather report

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Fault Diagnosis Based on the Updating Strategy of Interval-Valued Belief Structures

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Chinese Journal of Electronics 2025年第4期23卷 753-760页

作者： Xiaobin Xu Ping Liu Yanbo Sun Chenglin Wen Institute of Systems Science and Control Engineering School of Automation Hangzhou Dianzi University Hangzhou China Department of Control Science and Engineering School of Astronautics Harbin Institute of Technology Harbin China

This paper presents the dynamic method for fault diagnosis based on the updating of Interval-valued belief structures (IBSs). The classical Jeffrey's updating rule and the linear updating rule are extended to the framework of IBSs. Both of them are recursively used to generate global diagnosis evidence with the form of Interval basic belief assignment (IBBA) by updating the previous evidence with the incoming evidence. The diagnosis decision can be made by global diagnosis evidence. In the process of evidence updating, the similarity factors of evidence are used to determine switching between the extended Jeffrey's and linear updating rules, and to calculate the linear combination weights. The diagnosis examples of machine rotor show that the proposed method can provide more reliable and accurate results than the diagnosis methods based on Dempster-Shafer evidence theory.

关键词： Fault diagnosis Accuracy Evidence theory Rotors Switches Reliability theory Real-time systems

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Mechanical and Geometric Properties of Nanosprings Based on Carbon Nanotubes with Dislocation Dipoles

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ACS Applied Nano Materials 2025年第18期8卷 9192-9200页

作者： Lei, Xiao-Wen Koyane, Jumpei Lu, Tong Matsuzaki, Kenichiro Yan, Yi Shi, Jin-Xing Fujii, Toshiyuki School of Materials and Chemical Technology Institute of Science Tokyo 2-12-1 Ookayama Meguro-ku Tokyo152-8550 Japan Department of Engineering Mechanics MIIT Key Laboratory of Dynamics and Control of Complex Systems Northwestern Polytechnical University Xi’an710072 China Department of Production Systems Engineering and Sciences Komatsu University Nu 1-3 Shicyomachi Ishikawa Komatsu923-8511 Japan

Carbon nanotubes (CNTs) are lightweight materials with excellent mechanical, electrical, and thermal conductivity, which make helical CNTs promising candidates for applications in mechanical components such as nanosprings. In this study, based on the continuous dislocation theory, we introduce dislocation dipoles into CNT with discrete crystal structures to design nanosprings, propose a method that integrates mechanics and geometry, and analyze the mechanical and geometric properties of nanosprings. In detail, this study investigated the feasibility of designing helical CNTs through spontaneous deformation induced by dislocation dipoles separated from Stone-Wales defects. Analytical models were constructed by systematically introducing dislocation dipoles into (4,4) CNTs of varying lengths. We combined mechanical and geometric methods, performed molecular dynamics simulations to obtain the stable structure of CNTs with dislocation dipoles, and employed differential geometry to investigate the Willmore energy of helically shaped CNTs. The results showed that when dislocation dipoles were introduced into straight CNTs, energy relaxation calculations demonstrated spontaneous deformation, leading to the formation of a spring-shaped CNT. The CNT length and dislocation type significantly affected the spring constant. Longer CNTs exhibited lower spring constants, and an increase in dislocation density led to a higher average potential energy. This study provides unique insights into the control of the CNT shape and mechanical and geometrical properties, contributing to the design of functional materials such as nanosprings. © 2025 The Authors. Published by American Chemical Society.

关键词： Geometry

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TCIP: Transformed Colour Intensity Profiles analysis for fault detection in power line insulators

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Multimedia Tools and Applications 2025年第8期84卷 4503-4534页

作者： Tomaszewski, Michal Gasz, Rafal Kasana, Singara Singh Osuchowski, Jakub Singh, Simranjit Zator, Slawomir Department of Computer Science Faculty of Electrical Engineering Automatic Control and Informatics Opole University of Technology Prószkowska 76 Opole45-758 Poland Department of Computer Science and Information Technology Central University of Haryana Haryana Mahendergarh123031 India Department of Information Technology Dr B. R. Ambedkar National Institute of Technology Punjab Jalandhar India Department of Safety Engineering and Technical Systems Faculty of Production Engineering and Logistics Opole University of Technology Prószkowska 76 Opole45-758 Poland

In response to the escalating demand for electricity, the aging process and inherent failures in power lines have become unavoidable challenges in their operational integrity. This research addresses the imperative need for a robust method to detect damage in power insulator disc images, introducing a novel feature extraction technique grounded in color intensity profile transformation and ensemble learning. For the purposes of the research, a specific dataset was developed based on publicly available images of power insulators. The collection consists of 9232 images of insulator discs which included 2184 images of damaged discs. The discernible damages include breakages and burnouts. Two distinct analysis variants were employed. In the first variant (V1), two classification groups were selected: disc with damages and disc without damages. In the second variant (V2), three classification groups were selected: discs without damage, broken discs, and burnout discs. The described research was divided into two successive stages. In the first of them (S1), the transformed color intensity profiles for the prepared data set were built and various machine learning models for their classification were trained and evaluated. In the next stage (S2), previously developed, highest efficient machine learning models, were used to make the detection of various damaged types of overhead line insulator discs. For the first stage of research high-performance results and a high value of the F1 score parameters were obtained (F1 score = 0.999 for the first variant (S1V1) and F1 score = 0.992 for the second variant (S1V2). In the second stage of research and the first variant (S2V1), the highest result of the F1 score was 0.932 and for the second variant (S2V2) F1 score was 0.930. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Feature extraction

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Linear Convergent Distributed Nash Equilibrium Seeking With Compression

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IEEE Transactions on Automatic control 2025年第5期70卷 3316-3323页

作者： Chen, Xiaomeng Wu, Yuchi Yi, Xinlei Huang, Minyi Shi, Ling Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong Shanghai University School of Mechatronic Engineering and Automation Shanghai200444 China College of Electronics and Information Engineering Tongji University Department of Control Science and Engineering Shanghai201804 China Massachusetts Institute of Technology Lab for Information & Decision Systems CambridgeMA02139 United States Carleton University School of Mathematics and Statistics OttawaONK1S 5B6 Canada

Information compression techniques are majorly employed to reduce communication cost over peer-to-peer links. In this article, we investigate distributed Nash equilibrium (NE) seeking problems in a class of noncooperative games over directed graphs with information compression. To improve communication efficiency, a compressed distributed NE seeking (C-DNES) algorithm is proposed to obtain an NE for games, where the differences between decision vectors and their estimates are compressed. The proposed algorithm is compatible with a general class of compressors, including both unbiased and biased compressors. Moreover, our approach only requires the adjacency matrix of the directed graph to be row-stochastic, in contrast to past works that relied on balancedness or specific global network parameters. It is shown that C-DNES not only inherits the advantages of conventional distributed NE algorithms, achieving linear convergence rate for games with restricted strongly monotone mappings, but also saves communication costs in terms of transmitted bits. Finally, numerical simulations illustrate the advantages of C-DNES in saving communication cost by an order of magnitude under different compressors. © 1963-2012 IEEE.

关键词： Stochastic systems

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Scalable and Low-Cost Remote Lab Platforms: Teaching Industrial Robotics Using Open-Source Tools and Understanding Its Social Implications 16th

Scalable and Low-Cost Remote Lab Platforms: Teaching Indust...

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16th International Conference on Social Robotics, ICSR + AI 2024

作者： Kumar, Amit Jose, Jaison Jain, Archit Kulkarni, Siddharth Arya, Kavi Centre for Systems and Control Indian Institute of Technology Bombay Mumbai India Embedded Real-Time Systems / e-Yantra Lab Indian Institute of Technology Bombay Mumbai India Computer Science and Engineering Department Indian Institute of Technology Bombay Mumbai India

ISBN: (纸本)9789819635214

With recent advancements in industrial robots, educating students in new technologies and preparing them for the future is imperative. However, access to industrial robots for teaching poses challenges, such as the high cost of acquiring these robots, the safety of the operator and the robot, and complicated training material. This paper proposes two low-cost platforms built using open-source tools like Robot Operating System (ROS) and its latest version ROS 2 to help students learn and test algorithms on remotely connected industrial robots. Universal Robotics (UR5) arm and a custom mobile rover were deployed in different life-size testbeds, a greenhouse, and a warehouse to create an Autonomous Agricultural Harvester System (AAHS) and an Autonomous Warehouse Management System (AWMS). These platforms were deployed for a period of 7 months and were tested for their efficacy with 1,433 and 1,312 students, respectively. The hardware used in AAHS and AWMS was controlled remotely for 160 and 355 hours, respectively, by students over a period of 3 months. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Costs

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