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IEEE International Conference on Human-Machine Systems

作者： Feng Guo Xiwang Guo Jiacun Wang Weitian Wang Jinrui Cao Imane Bigaume Information and Control Engineering Liaoning Petrochemical University Fushun China Computer Science and Software Eng. Monmouth University Wes Long Branch New Jersey USA School of Computing Montclair State University Montclair USA Computer Science Department New Jersey City University Jersey City USA

ISBN: (数字)9798350315790

ISBN: (纸本)9798350315806

In the remanufacturing process, the efficient disassembly of end-of-life (EOL) products assumes paramount significance for recycling endeavors. This work introduces a novel disassembly model aiming at enhancing disassembly efficiency within a hybrid disassembly line through the strengths of humanrobot collaboration. In order to optimize the generation of disassembly sequences, this work proposes an improved fireworks optimization algorithm (IFW) that combines uniform mutation and two-point crossover methods. Afterwards, the model’s validity and algorithmic accuracy are substantiated using the CPLEX solver (a commercial solver). Furthermore, a comparative analysis and evaluation against alternative algorithms, such as salp swarm algorithm (SSA) and whale optimization algorithm (WOA), demonstrate a better stability of the proposed algorithm in the human-robot collaborative hybrid disassembly line balancing problem.

关键词： Accuracy Human-machine systems Collaboration Programming Stability analysis Whale optimization algorithms Recycling

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Intellectual lidar-based object classification for V2V communication technology implementation 22

Intellectual lidar-based object classification for V2V commu...

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Proceedings of the 6th International Conference on Future Networks & Distributed Systems

作者： Ansaf Abdulnagimov Ekaterina Lopukhova Gleb Alektorov Nail Klyavlin Department of Automated Control Systems Ufa University of Science and Technology Russian Federation Department of Telecommunication Systems Ufa University of Science and Technology Russian Federation Department of Computational Mathematics and Engineering Cybernetics Ufa University of Science and Technology Russian Federation Department of High Performance Computing Systems and Technology Ufa University of Science and Technology Russian Federation

ISBN: (纸本)9781450399050

The deep learning techniques have been shown to make a traffic objects classification system for V2V communications to ensure traffic safety and traffic flow prediction. The robust classifier on the base of MLP and PointNet are explored to recognize the traffic objects from lidar point clouds. The features of a lidar sensor, the lidar point cloud coordinate system and its complex properties for creation a smart traffic object detection and recognition model are described. The best configuration of PointNet architecture with hyperparameters are shown, which is more efficient and robust with respect to input perturbation and corruption of lidar point clouds.

关键词： PointNet

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Distributed time-varying formation control with uncertainties based on an event-triggered mechanism

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Science China(Information Sciences) 2021年第3期64卷 167-176页

作者： Xiaoduo LI Yumeng BAI Xiwang DONG Qingdong LI Zhang REN Science and Technology on Aircraft Control Laboratory School of Automation Science and Electrical EngineeringBeihang University Department of Electrical and Computer Engineering University of Wisconsin-Madison Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University

This paper investigates the distributed time-varying formation(TVF) problems for general linear multi-agent systems(MASs) subject to matched bounded uncertainties based on an adaptive event-triggered mechanism. A TVF protocol was designed with an event-triggered mechanism by introducing adaptive weights into the formation control protocol and triggering condition, and the large chattering phenomenon was avoided by the σ-modification adaptive law. According to the Lyapunov stability theory, proof has been established that the MASs in the presence of uncertainties can realize the expected formation that satisfies the given feasible condition. Finally, an example is provided to verify the effectiveness of the proposed algorithm.

关键词： formation control multi-agent systems event-triggered adaptive control

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A Modified P&O-MPPT Technique Using Fuzzy Logic controller for PV Systems

A Modified P&O-MPPT Technique Using Fuzzy Logic Controller f...

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Emerging Technologies (GlobConET), IEEE IAS Global Conference on

作者： Rafah Ibraheem Jabbar Karam Khairullah Mohammed Saad Mekhilef Marizan Mubin Mehdi Seyedmahmoudian Alex Stojcevski Ben Horan Mohamed Becherif Department of Electrical Engineering Universiti Malaya Kuala Lumpur Malaysia Mechatronics Engineering Department Universiti Teknologi Malaysia Control Johor Bahru Malaysia School of Science Computing and Engineering Technologies Swinburne University of Technology Hawthorn VIC Australia School of Engineering Deakin University Geelong VIC Australia FEMTO-ST Institute Univ. Bourgogne Franche- Comté UTBM CNRS Rue Ernest Thierry Mieg Belfort France

The perturb and observe (P&O) algorithm is the most popular of the numerous maximum power point tracking (MPPT) algorithms due to its simplicity and ease of implementation. However, the method has limitations regarding convergence speed, steady-state oscillation, and drift problem under sudden irradiance variations. This paper presents an improved P&O-MPPT algorithm to enhance the tracking convergence speed using adaptive duty cycle perturbation step fuzzy logic (FL-∆D) size. Moreover, a new strategy is added to the algorithm to stop the steady-state oscillation once MPP is tracked. The proposed algorithm was verified using MATLAB/Simulink under constant and varied irradiance levels. The results showed that the proposed algorithm with an adaptive FL-∆D size could efficiently track an accurate MPP with zero-state oscillation at different irradiance levels. Moreover, the algorithm achieves fast tracking under a rapid change in irradiance. The proposed algorithm was compared with the traditional P&O algorithm and the modified P&O algorithms that have recently been published. The comparison results validated the effectiveness of the proposed algorithm.

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Channel and space-based joint rate allocation algorithm

Channel and space-based joint rate allocation algorithm

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Dayong Wang Chao Yuan Yu Sun Xin Lu Hui Guo Frederic Dufaux Ce Zhu Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and Telecommunications Guangxi Key Laboratory of Machine Vision and Intelligent Control Wuzhou University Chongqing Key Laboratory of Image Cognition Chongqing University of Posts and Telecommunications China Department of Computer Science University of Central Arkansas Faculty of Computing Engineering and Media (CEM) De Montfort University UK Université Paris-Saclay CNRS CentraleSupélec Laboratoire Des Signaux et Systèmes France School of Information and Communication Engineering University of Electronic Science and Technology of China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Rate control is a critical component for image and video compression Particularly under limited network bandwidth conditions, bitrate control is essential to ensure efficient image transmission by effectively allocation channel resources. In this research, since both Channel and Spatial have relationship with rate allocation, we first propose a joint Channel-wise and Spatial-wise Quantization scheme to determine optimal quantization parameters. Subsequently, we develop a quantization step estimation network to obtain parameters to efficiently allocate rate according to target rate. Experiments demonstrate that our algorithm significantly improve compressed image quality with minimal bitrate distortion and achieve accurate rate control with nearly 3% average bitrate error.

关键词： Image quality Quantization (signal) Image coding Bit rate Signal processing algorithms Estimation Rate-distortion Video compression Resource management Speech processing

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Semi-supervised Domain Adaptation on Graphs with Contrastive Learning and Minimax Entropy

arXiv

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arXiv 2023年

作者： Xiao, Jiaren Dai, Quanyu Shen, Xiao Xie, Xiaochen Dai, Jing Lam, James Kwok, Ka-Wai Department of Mechanical Engineering The University of Hong Kong Hong Kong Department of Computing The Hong Kong Polytechnic University Hong Kong School of Computer Science and Technology Hainan University Haikou China Department of Automation Harbin Institute of Technology Shenzhen China Institute for Automatic Control and Complex Systems University of Duisburg-Essen Duisburg Germany

Label scarcity in a graph is frequently encountered in real-world applications due to the high cost of data labeling. To this end, semi-supervised domain adaptation (SSDA) on graphs aims to leverage the knowledge of a labeled source graph to aid in node classification on a target graph with limited labels. SSDA tasks need to overcome the domain gap between the source and target graphs. However, to date, this challenging research problem has yet to be formally considered by the existing approaches designed for cross-graph node classification. This paper proposes a novel method called SemiGCL to tackle the graph Semi-supervised domain adaptation with Graph Contrastive Learning and minimax entropy training. SemiGCL generates informative node representations by contrasting the representations learned from a graph’s local and global views. Additionally, SemiGCL is adversarially optimized with the entropy loss of unlabeled target nodes to reduce domain divergence. Experimental results on benchmark datasets demonstrate that SemiGCL outperforms the state-of-the-art baselines on the SSDA tasks. © 2023, CC BY-NC-ND.

关键词： Entropy

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Deep Learning based Defect Detection Algorithm for Solar Panels

Deep Learning based Defect Detection Algorithm for Solar Pan...

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WRC Symposium on Advanced Robotics and Automation (WRC SARA)

作者： Jiaqi Li Hongxu Li Yifan Wu Hailiang Zhou Luigi Manfredi Peng Li Hong Zhang Department of Key Lab of Industrial Computer Control Engineering Hebei Province Yanshan University Qinhuangdao China Department of Computing Science University of Alberta Edmonton AB Canada Hebei Baoding Jiasheng Photovoltaic Technology Co. Ltd. Department of Institute for Medical Science and Technology (IMSaT) University of Dundee United Kingdom Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen China

Defect detection of solar panels plays an essential role in guaranteeing product quality within automated production lines. However, traditional manual inspection of solar panel defects suffers from low efficiency. This paper proposes an enhanced YOLOv5 algorithm (EL-YOLOv5) fused with the CBAM hybrid attention module to ensure product quality. The algorithm focuses on detecting five common types of defects that frequently appear on photovoltaic production lines, namely hidden cracks, scratches, broken grids, black spots, and short circuits. This study utilizes publicly available solar panel datasets, as well as datasets collected from actual photovoltaic production lines. These datasets are annotated accordingly and used to train the proposed algorithm. The experimental results demonstrate that the proposed algorithm achieves good performance on both the public and actual solar panel defect datasets. Particularly in actual datasets, where defect features are often less apparent and defects are smaller in size, the proposed algorithm can still detect even minor black spots.

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Biosignal-Based Attention Monitoring for Evaluating Train Driver Safety-Relevant Tasks

SSRN

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SSRN 2024年

作者： Kim, Jung Hwan Kim, Yeongjun Cho, Younggeol Kim, Tae Kyun Jang, Tongil Park, Chanwoo Kang, Seongkeun Advanced Instrumentation & Control Research Section Korea Atomic Energy Research Institute Daejeon Korea Republic of Department of Nuclear & Quantum Engineering Korea Advanced Institute of Science and Technology Daejeon Korea Republic of School of Computing Korea Advanced Institute of Science and Technology Daejeon Korea Republic of Department of Railroad Electrical & Electronic Engineering Korea National University of Transportation Uiwang Korea Republic of System Safety Research Department Korea Railroad Research Institute Uiwang Korea Republic of

This study explores the impact of phone usage on train driver performance, a critical aspect influencing the safety of railroad operations. The persistent problem of phone distractions, which severely impair situational awareness and contribute to accidents, necessitates innovative solutions to enhance operational safety. Leveraging advancements in technology, this study introduces a novel approach combining electroencephalogram (EEG)-based analysis with deep learning algorithms to assess the effects of phone usage on the attention states of train drivers. Utilizing a full-type train simulator to replicate real-life scenarios, this study conducts an experimental design involving scenarios with and without phone engagement, aiming to provide a comprehensive assessment of phone usage on performance in critical situations. The deep learning model employed achieved an impressive classification accuracy of approximately 85.6% in distinguishing attention states among 25 participants, with high precision and recall values indicating its efficacy in detecting phone interaction. Moreover, findings reveal that phone usage leads to a 1.4 times increase in response time to situational awareness, significantly affecting driver performance. These findings highlight the significant impact of phone usage on train driver attention and underscore the potential of EEG-based analysis and deep learning in improving railroad safety measures. This study contributes to the growing body of safety literature by focusing on train drivers, offering fresh insights and practical implications for enhancing the safety and efficiency of railroad operations. © 2024, The Authors. All rights reserved.

关键词： Railroads

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A Survey of Fault Tolerance in Cloud computing

A Survey of Fault Tolerance in Cloud Computing

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Systems, Advanced Technologies and Knowledge (ISSATK), International Symposium of

作者： Om Kolthoum Elhajjej Nouha Alyaoui Samia Dardouri Karim Chabir Research Laboratory Modeling Aanalysis Control of System (MACS) LR16ES22 National engineering school of Gabes (ENIG) University of Gabes Tunisia Computer Science and Telecommunications Department Laboratory of Modeling Analysis and Control of Systems (MACS) Higher Institute of Applied Sciences and Technology Zarroug Gafsa Gafsa University Tunisia Department of Computer Science College of Computing and Information Technology Shaqra University Saudi Arabia InnoV’COM Laboratory-Sup’Com University of Carthage Tunisia

ISBN: (数字)9798331540173

ISBN: (纸本)9798331540180

This paper is designed to provide a comprehensive overview of the latest developments in fault tolerance methods for cloud computing. Maintaining high availability and reliability of cloud environments requires fault tolerance. This paper explores fault tolerance in the context of cloud computing and discusses recent challenges and innovations in the field. Moreover, it examines the ongoing research efforts to improve fault-tolerance architectures. At the end of the paper, the paper presents system-level metrics that are relevant to fault tolerance.

关键词： Measurement Surveys Cloud computing Fault tolerance Technological innovation Scalability Fault tolerant systems Computer architecture Software Software reliability

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Cluster Synchronization of Kuramoto Oscillators via Pacemakers and Mean-phase Feedback control

Cluster Synchronization of Kuramoto Oscillators via Pacemake...

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IEEE Conference on Decision and control

作者： Ryota Kokubo Mitsuaki Matsubara Rui Kato Hideaki Ishii Department of Computer Science Tokyo Institute of Technology Yokohama Japan Department of Systems and Control Engineering Tokyo Institute of Technology Yokohama Japan Laboratoire d’Automatique de Génie des Procédés et de Génie Pharmaceutique (LAGEPP) Université Claude Bernard Lyon 1 Villeurbanne France Department of Information Physics and Computing The University of Tokyo Tokyo Japan

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

Brain networks typically exhibit characteristic synchronization patterns where several synchronized clusters coexist. On the other hand, neurological disorders are considered to be related to pathological synchronization such as excessive synchronization of large populations of neurons. Motivated by these phenomena, this paper presents two approaches to control the cluster synchronization of Kuramoto oscillators. One is based on the use of pacemakers to the clusters, and the other is based on feeding back the mean phases to the clusters. We first show conditions on the pacemaker weights and the feedback gains for the network to achieve cluster synchronization. Then, we propose a method to find optimal feedback gains through convex optimization. A numerical example demonstrates the effectiveness of the proposed methods.

关键词： Neurological diseases Hands Pathology Design methodology Neurons Pacemakers Convex functions Feedback control Synchronization Oscillators

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