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International Conference of the TC Electrimacs Committee, ELECTRIMACS 2024

作者： Carrasco-González, David Sarrias-Mena, Raúl Horrillo-Quintero, Pablo Llorens-Iborra, Francisco Fernández-Ramírez, Luis M. Cadiz Spain SURET Research Group Department of Engineering in Automation Electronics and Computer Architecture and Networks ETSI Algeciras Cadiz Spain

ISBN: (纸本)9783031739200

Nowadays, hybrid AC/DC microgrids (HMG) are growing in popularity because of human needs and their benefits. They are composed of the interconnection of a DC microgrid (MG) and an AC MG, and each MG is constituted by generation, energy storage, and consumption technologies. This study develops a new control strategy for an HMG consisting of a DC MG and an AC MG interconnected through a bidirectional power converter. The AC MG is composed of AC loads, a photovoltaic (PV) generator and a battery bank. The DC MG is made up of a hydrogen system, DC loads, wind turbine (WT) and ultracapacitor (UC). The HMG is evaluated under different conditions in an experimental setup constituted by an OPAL-RT 4512 unit and a Raspberry Pi microcontroller. This experimental setup permits to execute the HMG in real time and to validate the results obtained. Finally, the control system developed for this HMG presents a satisfactory behaviour under different working conditions. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Wind turbines

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Decentralized Game-based Auction Algorithm for Scheduling Robotic Chargers to Service EVs with Uncertain Demands

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年 1-14页

作者： Fang, Qiuyang Zhang, Jianlei Wang, Chen Xie, Guangming Zhang, Chunyan Department of Automation College of Computer and Control Engineering Nankai University Tianjin China National Engineering Research Center of Software Engineering Peking University Beijing China State Key Laboratory for Turbulence and Complex Systems Intelligent Biomimetic Design Lab College of Engineering Peking University Beijing China

In this study, we introduce a novel auction-based algorithm modeled as a decentralized coalition formation game, designed for the complex requirements of large-scale multi-robot task allocation under uncertain demand. This context is particularly illustrative in scenarios where robots are tasked to charge electric vehicles. The algorithm begins by partitioning a composite task sequence into distinct subsets based on spatial similarity principles. Subsequently, we employ a coalition formation game paradigm to coordinate the assembly of robots into cooperative coalitions focused on these distinct subsets. To mitigate the impact of unpredictable task demands on allocations, our approach utilizes the conditional value-at-risk to assess the risk associated with task execution, along with computing the potential revenue of the coalition with an emphasis on risk-related outcomes. Additionally, integrating consensus auctions into the coalition formation framework allows our approach to accommodate assignments for individual robot-task pairings, thus preserving the stability of individual robotic decision autonomy within the coalition structure and assignment distribution. Simulative analyses on a prototypical parking facility layout confirm that our algorithm achieves Nash equilibrium within the coalition structure in polynomial time and demonstrates significant scalability. Compared to competing algorithms, our proposal exhibits superior performance in resilience, task execution efficiency, and reduced overall task completion times. The results demonstrate that our approach is an effective strategy for solving the scheduling challenges encountered by multi-robot systems operating in complex environments. IEEE

关键词： Polynomial approximation

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Exploring the Capabilities and Limitations of Recommender System Models in the Electric Power Sector

Exploring the Capabilities and Limitations of Recommender Sy...

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2025 IEEE Texas Power and Energy Conference, TPEC 2025

作者： Li, Yifu Dehghanian, Payman Zhang, Xiaohua Su, Jinshun Zhang, Ruotan The George Washington University Department of Electrical and Computer Engineering WashingtonDC United States Changzhou University Department of Electrical Engineering and Automation Jiangsu Changzhou China

ISBN: (纸本)9798331541125

As the modern bulk power system (BPS) expands and electricity users increase, an ever-growing amount of power data needs to be processed. The recommender system (RS), as an artificial intelligence (AI) algorithm, leverage big data to suggest additional products to consumers, making them naturally suited for BPS analysis. This paper summarizes published works on RS applications in the electric power sector, explores the goals and objectives of each proposed use case, and analyzes the advantages and limitations of their capabilities. Furthermore, this paper evaluates the performance of three large language models (LLMs) on power system domain challenges to compare the differences between the models. Finally, the paper summarizes the current applications of RS models in the electric power sector and explores prospects for future development. © 2025 IEEE.

关键词： Power systems computer aided design

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Scheduling Bi-Objective Lot-Streaming Hybrid Flow Shops with Consistent Sublots via an Enhanced Artificial Bee Colony Algorithm

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Complex System Modeling and Simulation 2025年第1期5卷 46-67页

作者： Benxue Lu Kaizhou Gao Peiyong Duan Adam Slowik School of Computer Science Liaocheng UniversityLiaocheng 252000China Macao Institute of Systems Engineering Macao University of Science and TechnologyMacao 999078China School of Information and Automation Engineering Qilu University of TechnologyJinan 250353China Department of Electronics and Computer Science Koszalin University of TechnologyKoszalin 75-453Poland

This work addresses bi-objective hybrid flow shop scheduling problems considering consistent sublots(Bi-HFSP_CS).The objectives are to minimize the makespan and total energy ***,the Bi-HFSP_CS is formalized,followed by the establishment of a mathematical ***,enhanced version of the artificial bee colony(ABC)algorithms is proposed for tackling the Bi-HFSP_***,fourteen local search operators are employed to search for better *** different Q-learning tactics are developed to embed into the ABC algorithm to guide the selection of operators throughout the iteration ***,the proposed tactics are assessed for their efficacy through a comparison of the ABC algorithm,its three variants,and three effective algorithms in resolving 95 instances of 35 different *** experimental results and analysis showcase that the enhanced ABC algorithm combined with Q-learning(QABC1)demonstrates as the top performer for solving concerned *** study introduces a novel approach to solve the Bi-HFSP_CS and illustrates its efficacy and superior competitive strength,offering beneficial perspectives for exploration and research in relevant domains.

关键词： hybrid flow shop scheduling Q-learning lot streaming local search

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Fusion of optimal cross-stage partial net with context-aware segmentation for enhanced damage detection in civil infrastructures using thermal images

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Journal of Supercomputing 2025年第8期81卷 1-23页

作者： Shahzadi, Maryam Khan, Muhammad Zeeshan Khan, Asim Ali, Umar Hassan, Muhammad Arshad Shehzad Department of Computer Science The University of Faisalabad Faisalabad38000 Pakistan German Institute of Engineering Chongqing College of Mobile and Communication P.R. Chongqing401520 China School of Electrical Engineering and Automation Chongqing College of Mobile and Communication Chongqing China

Crack detection plays a critical role in maintaining the structural integrity and longevity of civil infrastructure, yet computer vision-based methods face limitations from inconsistent lighting, environmental noise, and complex backgrounds, which can hinder accurate crack detection. To address these challenges, this work introduces an enhanced crack detection algorithm, CSP-CAS, which incorporates a compact inverted block to optimize the cross-stage partial (CSP) net and integrates it with context-aware segmentation (CAS) in the neck and head of the You Only Look Once (YOLO) v9 architecture. We collected an initial dataset comprising 2000 RGB and 2000 thermal images from multiple historical sites in Pakistan, which was further expanded to 6000 images each, using augmentation techniques. CSP-CAS shows a precision improvement of 8.04%, a recall increase of 17.9%, and an accuracy boost of 9.2% on the RGB dataset as compared to YOLOv9. Further, we conducted a comparative evaluation between thermal and RGB datasets to determine the impact of using multispectral images for crack detection in civil infrastructures. The thermal dataset shows a 13.9% boost in precision, a 20% rise in mAp@0.5, and an 18.4% increase in recall, proving that thermal imaging is much more effective than RGB for finding cracks in civil infrastructure. These results validate the effectiveness of the proposed approach, making it a promising solution for accurate and efficient crack detection in civil infrastructure. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Thermography (imaging)

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Event-Triggered Optimized Anti-Disturbance Dynamic Positioning for Marine Intelligent Vehicles Via Reinforcement Learning

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年 1-12页

作者： Gao, Xiaoyang Li, Tieshan Li, Meilin Chen, C.L.P. School of Maritime Economics and Management Dalian Maritime University Dalian China School of Automation Engineering University of Electronic Science and Technology of China Chengdu China School of Computer Science and Engineering South China University of Technology Guangzhou China

The dynamic positioning of marine intelligent vehicles is gradually becoming the core technique to perform herculean tasks in complex sea conditions. To cope with the composite ocean disturbances and the constraint of communication resources, this paper proposes an event-triggered anti-disturbance dynamic positioning control strategy with performance optimization via reinforcement learning. Firstly, to overcome the composite disturbances caused by external environmental disturbance and internal uncertainties, the disturbance estimator with a fuzzy logic system is established. By constructing an event-triggered mechanism that relies on position and heading information, an event-triggered optimized dynamic positioning controller is designed by combining modified backstepping and reinforcement learning methods. The designed controller consists of a composite anti-disturbance controller and an optimized compensator. Thus, unnecessary communication can be avoided and the performance index is minimized simultaneously. Furthermore, the errors of the closed-loop system are rigorously proved to be bounded. At last, the validity of the proposed control scheme for a marine intelligent vehicle is illustrated by simulation and comparison studies. IEEE

关键词： Reinforcement learning

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Blockchain based federated learning for intrusion detection for Internet of Things

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Frontiers of computer Science 2024年第5期18卷 109-119页

作者： Nan SUN Wei WANG Yongxin TONG Kexin LIU School of Cyber Science and Technology Beihang UniversityBeijing 100191China School of Automation Science and Electrical Engineering Beihang UniversityBeijing 100191China Zhongguancun Laboratory Beijing 100094China School of Computer Science and Engineering Beihang UniversityBeijing 100191China

In Internet of Things(loT),data sharing among different devices can improve manufacture efficiency and reduce workload,and yet make the network systems be more vulnerable to various intrusion *** has been realistic demand to develop an efficient intrusion detection algorithm for connected *** of existing intrusion detection methods are trained in a centralized manner and are incapable to identify new unlabeled attack *** this paper,a distributed federated intrusion detection method is proposed,utilizing the information contained in the labeled data as the prior knowledge to discover new unlabeled attack ***,the blockchain technique is introduced in the federated learning process for the consensus of the entire *** results are provided to show that our approach can identify the malicious entities,while outperforming the existing methods in discovering new intrusion attack types.

关键词： intrusion detection federated learning new attacks discovering blockchain

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eHealth Mobile Application Using Fitbit Smartwatch 1

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8th International Conference on Advancements of Medicine and Health Care through Technology, MEDITECH 2022

作者： Pop, Adela Fanca, Alexandra Valean, Honoriu Gota, Dan-Ioan Stan, Ovidiu Roman, Marius Nicolae Clitan, Iulia Muresan, Vlad Faculty of Automation and Computer Science Department of Automation Technical University of Cluj Napoca Cluj Napoca Romania Faculty of Electrical Engineering Electrical Engineering and Measurements Department Technical University of Cluj Napoca Cluj Napoca Romania

ISBN: (数字)9783031511202

ISBN: (纸本)9783031511196

Health is one of the most precious aspects of every person's life. Whether we're talking about a well-functioning heart, nervous system, respiratory system, or simply body weight, each of these branches contributes to a person's overall health. It is important to monitor certain body functions and characteristics as often as possible so that we can prevent or, in unfortunate situations, treat health problems early. The developed mobile application facilitates the monitoring of heart rate, ideal weight, basal metabolic rate, and body mass index. The data used by the mobile app is collected by the FITBIT smartwatch. Based on the data, heart rate statistics and nutritional calculation results (body mass index, basal metabolic rate, and ideal body weight) are generated according to the user's gender, age, weight, and height. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Metabolism

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Enhanced YOLOv7 with three-dimensional attention and its application into underwater object detection

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Multimedia Tools and Applications 2024年 1-18页

作者： Qin, Yi Liang, Chen Mao, Yongfang Zhou, Mingliang College of Mechanical and Vehicle Engineering Chongqing University Chongqing400044 China School of Automation Chongqing University Chongqing400044 China College of Computer Science Chongqing University Chongqing400044 China

In recent years, with the increasing importance of marine resources, robots have been widely used in underwater environment exploration, seafood delivery and fishing. The visual perception system of the robot is equivalent to the "eyes" of the robot, which is an indispensable part of the robot to realize control and autonomous navigation. Object detection technology is an important part of the robot visual perception system, which determines the accuracy of the robot’s surrounding environment perception. YOLOv7 algorithm has the advantages of few parameters, fast detection speed and high accuracy. However, for underwater overlapping targets, YOLOv7 has the problems of missed detection and false detection and difficulty in separating target and background. Therefore, this paper proposes a new YOLO algorithm aiming at the missed detection problem of multiple overlapping targets. A three-dimensional attention convolution module is proposed to successfully identify multiple overlapping echinus and holothurian. Aiming at the difficulty of separating target and background, the upsampling process is redesigned to more fully retain the deep semantic information. Experiments were carried out on a dataset containing four kinds of underwater targets, and mAP was improved by 0.7% compared with YOLOv7. In order to verify the generalization performance of the model, a large number of ablation experiments have been carried out on other public datasets and the results on COCO and VOC show the superiority of the proposed model compared to other algorithms. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Machine vision

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Weighted LP -Norm Minimization Algorithm Based on Hybrid Internal and External Priors 5

Weighted LP -Norm Minimization Algorithm Based on Hybrid Int...

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5th International Conference on Sensing, Measurement and Data Analytics in the Era of Artificial Intelligence, ICSMD 2024

作者： Sun, Dong Zhang, Donghua Ning, Wan Hu, Yong Wang, Ru Gao, Qingwei School of Electrical Engineering and Automation Anhui University Hefei China School of Computer Science Anhui University Hefei China

ISBN: (纸本)9798331529192

Under the Bayesian restoration framework, this paper aims at the problem of the inadequate accuracy of the sparse solution under the traditional convex regularization constraint, which leads to the loss of texture details and excessive edge smoothing in the recovered image, a new method is proposed. Firstly, the internal nonlocal self-similarity of the degraded image and the external nonlocal self-similarity in the clean dataset are used to construct similar block groups, which are subject to structured sparse coding, and the sparse coefficients are constrained by a weighted l-p penalty function;Secondly, by combining the sparse coefficients obtained in the previous step, an alternating minimization optimization method is designed to iteratively resolve the image restoration equations for computing a reasonable estimation for the original restored image. Simulation experiments verify the correctness and effectiveness of the proposed scheme. © 2024 IEEE.

关键词： Restoration

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