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63rd IEEE Conference on Decision and control, CDC 2024

作者： Yi, Yuhao Shan, Liren Pare, Philip E. Johansson, Karl H. Sichuan University Institute of Clinical Pathology West China Hospital College of Computer Science Chengdu China Toyota Technological Institute at Chicago United States Purdue University School of Electrical and Computer Engineering West LafayetteIN United States Kth Royal Institute of Technology and They Are Also Affiliated with Digital Futures Division of Decision and Control Systems School of Electrical Engineering and Computer Science StockholmSE-100 44 Sweden

ISBN: (纸本)9798350316339

This paper studies efficient algorithms for dynamic curing policies and the corresponding network design problems to guarantee fast extinction of epidemic spread in a Markov process-based susceptible-infected-susceptible (SIS) model. We provide a computationally efficient curing algorithm based on the curing policy proposed by Drakopoulos, Ozdaglar, and Tsitsiklis (2014). Since the corresponding optimization problem is NP-hard, finding optimal policies is intractable for large graphs. We provide approximation guarantees on the curing budget of the proposed dynamic curing algorithm. To avoid the waiting period included in the original curing policy, we study network design problems to reduce the total infection rate by deleting edges or reducing the weight of edges. To this end, we provide algorithms with provable guarantees. In summary, the proposed curing and network design algorithms together provide an effective and computationally efficient approach that mitigates SIS epidemic spread in networks. © 2024 IEEE.

关键词： Markov processes

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Crowdsourcing Task Recommendation Method Based on Heterogeneous Graph Feature Anomaly Detection 18th

Crowdsourcing Task Recommendation Method Based on Heterogen...

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18th China Conference on Wireless Sensor Networks, CWSN 2024

作者： Wei, Kai Pan, Qingxian Yu, Song Fan, Zequn Li, Jinru School of Computer and Control Engineering Yantai University Yantai264005 China School of Computer and Information Science Southwest University Chongqing400715 China

ISBN: (纸本)9789819621859

The crowdsourcing task recommendation method addresses worker selection overload by intelligently matching workers with tasks. However, existing research often overlooks the inherent characteristics of workers and tasks, the long-term behavioral preferences of workers, and abnormal information about workers and tasks, which impacts the accuracy of recommendations. To address these issues, we propose a novel crowdsourcing task recommendation method based on anomaly detection using multi-attribute heterogeneous graph features. This method first establishes the connection relationships between crowdsourcing workers and tasks to construct a worker-task heterogeneous graph. DeepWalk generates initial node features, and the message-passing mechanism in the graph neural network is employed to explore the feature information of workers and task nodes. During message transmission, the graph convolutional autoencoder drives the detection of worker and task feature information. Finally, Bi-GRU captures the long-term behavioral preferences of workers, and link prediction constructs links based on node similarity to complete the recommendation of sensing tasks. We evaluate nine real datasets, and the experimental results demonstrate that the proposed method outperforms the baseline method. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Graph neural networks

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Propagation Characteristics of Partial Discharges in an Oil-Filled Power Transformer 17

Propagation Characteristics of Partial Discharges in an Oil-...

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17th IEEE International Conference on Compatibility, Power Electronics and Power engineering, CPE-POWERENG 2023

作者： Kameli, Sayed Mohammad Refaat, Shady S. Ghrayeb, Ali Abu-Rub, Haitham Guzinski, Jaroslaw Texas AandM University at Qatar Electrical and Computer Engineering Doha Qatar University of Hertfordshire School of Physics Engineering and Computer Science Department of Engineering United Kingdom Gdansk University of Technology Electrical and Control Engineering Faculty ul. Gabriela Narutowicza 11/12 Gdansk80-233 Poland

ISBN: (纸本)9798350300048

Power transformers are among the most important assets in the power transmission and distribution grid. However, they suffer from degradation and possible faults causing major electrical and financial losses. Partial discharges (PDs) are used to identify the insulation health status and their degradation level. PDs are incipient, low-magnitude faults caused by localized dielectric breakdown. Those activities emit signals in many forms, including electrical, chemical, acoustic, electromagnetic, and optical, facilitating various detection methods. This paper provides a theoretical basis for the condition evaluation of an oil-filled power transformer and clarifies the relationship between the operating voltage, void location, and electric-field intensity within the void. This was achieved by investigating the propagation characteristics of partial discharge signals in an oil-filled power transformer using a 3D finite element method (FEM) based simulation. Moreover, the characterization of simulated PD sources at different positions is investigated in this paper. The simulation results are curried out to show that air voids near the windings are subject to greatest peak electric field intensity. © 2023 IEEE.

关键词： Power transformers

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GLOBAL PATH PLANNING OF CLIMBING ROBOT FOR WELD QUALITY INSPECTION IN LARGE SCALE STORAGE TANKS

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International Journal of Robotics and Automation 2025年第3期40卷 242-253页

作者： Wang, Ruifeng Gu, Shichao Liu, Xiaoguang Zhang, Hao Xu, Zhihao Pan, Jia Zhou, Xuefeng Institute of Intelligent Manufacturing Guangdong Academy of Sciences Guangdong Key Laboratory of Modern Control Technology GuangDong Guangzhou510070 China School of Rail Transportation Wuyi University GuangDong Jiangmen529020 China School of Mechanical Automotive Engineering South China University of Technology Guangdong Guangzhou510006 China Department of Computer Science University of Hong Kong 999077 Hong Kong

Climbing robots are considered efficient solutions for conducting quality inspections of welds on large storage tanks. Nevertheless, planning the global shortest path along spatially complex cross distributed welds poses a significant challenge for climbing robots. This paper introduces a novel graph-based global shortest climbing path planning method, facilitating swift inspect all welds by the robot. The three-dimensional (3D) weld lines undergo conversion into a two-dimensional (2D) weighted connected graph using a 3D model equivalence mapping approach. The dimensionality-reduced connectivity map is required to be an Euler graph;otherwise, the adoption of the Floyd and KM algorithms is necessary to construct an undirected Euler graph with the minimum total weight. Subsequently, an Euler circuit is searched in the Euler graph, considering the presence of plus edges, through an improved Fleury algorithm to obtain the shortest global path for the climbing robot. The paper also introduces the idea of Euler graph segmentation to address the challenge of Euler circuits in large or symmetric models. Simulations are conducted to validate the effectiveness and performance of the proposed planners. The results demonstrate that our method effectively reduces the length global path and enhances inspection efficiency. © 2025 Acta Press. All rights reserved.

关键词： Motion planning

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ShadowNets: Efficient and Accurate Face Recognition for Resource-Constrained Devices 25

ShadowNets: Efficient and Accurate Face Recognition for Reso...

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25th International Conference on Digital Image Computing: Techniques and Applications, DICTA 2024

作者： Mirza, Inzela Iqbal, Shahzaib Alhaqbani, Bandar Alshammari, Bandar Khan, Tariq Razzak, Imran Department of Computing Islamabad Pakistan Tcc Research and Development Labs Technology Control Company Riyadh Saudi Arabia Computer and Information Sciences College Jouf University Saudi Arabia School of Computer Science and Engineering University of New South Wales Sydney Australia

ISBN: (纸本)9798350379037

Over recent years, deep convolutional neural networks have advanced the field of face recognition in both verification and identification applications. In this paper, we present ShadowNets, a highly efficient and compact face recognition network. We employed multiple shadow blocks in a cascading manner where the output of each shadow block is given as the input to the next shadow block. By integrating the advantages of Focal Modulation Blocks, Shadow Block and Ghost Modules, our proposed ShadowNets achieves superior face recognition performance, optimized specifically for edge devices. This network architecture not only minimizes computational costs and storage needs but also delivers high accuracy, making it suitable for deployment in edge environments. Our extensive experiments on challenging benchmark face datasets showcase the outstanding performance by achieving accuracy 98.85% on LFW, 84.37% on CFP-FP, and 91.23% on AgeDB-30 which validates the effectiveness of ShadowNets over state-of-the-art lightweight and deep face recognition models. © 2024 IEEE.

关键词： Convolutional neural networks

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Detection and Identification of Power Quality Disturbance Signals in New Power System Based on Quantum Classic Hybrid Convolutional Neural Networks 2nd

Detection and Identification of Power Quality Disturbance ...

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2nd International Conference on Data Security and Privacy Protection, DSPP 2024

作者： Li, Yue Li, Xinhao Jia, Haopeng Liu, Anjiang Wang, Qingle Hao, Shuqing Liu, Hao Electric Power Research Institute of Guizhou Power Grid Co. Ltd. Guiyang550002 China School of Control and Computer Engineering North China Electric Power University Beijing102206 China School of Electrical and Electronic Engineering North China Electric Power University Beijing102206 China

ISBN: (纸本)9789819785452

With the rapid development of modern power systems, power quality disturbances are a growing concern, impacting system reliability. Effective detection and identification of PQDs in new distribution systems with high renewable energy integration are crucial. In this paper, we propose a quantum-classical hybrid convolutional neural network for detecting and identifying power quality disturbances using a quantum-classical hybrid convolutional neural network (PQDs-QC-CNN). The PQDs-QC-CNN model includes quantum convolutional layers, fully connected layers, and softmax regression. The proposed PQDs-QC-CNN achieves a certain speed with the time complexity of O(poly(N)) and the space complexity of O(N), where N is the number of qubits. Compared to classical convolutional neural networks, PQDs-QC-CNN leverages quantum algorithms, offering the advantage of lower computational complexity. According to the IEEE Std 1159-2019 standard, the experimental results of our PQDs-QC-CNN achieves 100% detection accuracy. Following the seven types of single disturbance signals, the identification is 99.25%. PQDs-QC-CNN represents a significant exploration of the application of quantum algorithms in the power system. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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On-Device Learning with Raspberry Pi for GCN-Based Epilepsy EEG Classification

On-Device Learning with Raspberry Pi for GCN-Based Epilepsy ...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： He, Zhuoli Wang, Chuansheng Huang, Jiayan Grau, Antoni Guerra, Edmundo Yan, Jiaquan Fuzhou University College of Computer and Big Data Fuzhou China Polytechnic University of Catalonia Department of Automatic Control Barcelona Spain Putian University College of New Engineering Industry Putian China Minjiang University School of Computer and Big Data Fuzhou China

ISBN: (纸本)9798350386226

Epilepsy is a chronic brain disease characterized by recurrent and transient seizures, which is accompanied by super-synchronous abnormal discharge of electroencephalogram (EEG) signals. As a non-invasive auxiliary diagnostic technique, EEG is currently important means of seizure detection. However, due to ignoring the spatial topological relationship between electrodes, existing data-driver methods fail to fully reflect the interaction between signals. Meanwhile, their models usually be designed with a large number of redundant parameters, making it difficult to deploy to micro-embedded devices with limited-resources. In this paper, we propose a on-device learning with edge device for epilepsy EEG classification network based on GCN (oDLGCN-EEG). Specifically, to analyze the spatial relationships between various electrodes and their temporal dependencies, we design a Brain Topology Network (BTN) for the spatiotemporal dependency feature map construction. To capture the internal activity during epilepsy seizures, we design a Neural Feature Extraction Module (NFEM) for the neural activity feature map construction. Besides, we propose a pruning scheme to optimize the model, which successfully deploys the optimized oDLGCN-EEG on the embedded Raspberry Pi device for efficient and low-power consumption intelligent epilepsy classification. Experiments in comparison with state-of-the-art methods show that oDLGCN-EEG achieves the best classification accuracy and with the smallest parameter number on baseline EEG dataset. The code is available at https://***/cathnat/Epileptic-Classification. © 2024 IEEE.

关键词： Topology

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Distributed Optimization with Finite Bit Adaptive Quantization for Efficient Communication and Precision Enhancement 63

Distributed Optimization with Finite Bit Adaptive Quantizati...

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63rd IEEE Conference on Decision and control, CDC 2024

作者： Rikos, Apostolos I. Jiang, Wei Charalambous, Themistoklis Johansson, Karl H. Artificial Intelligence Thrust of the Information Hub Guangzhou China The Hong Kong University of Science and Technology Department of Computer Science and Engineering Clear Water Bay Hong Kong Aalto University School of Electrical Engineering Department of Electrical Engineering and Automation Espoo Finland University of Cyprus School of Engineering Department of Electrical and Computer Engineering Nicosia1678 Cyprus Kth Royal Institute of Technology Division of Decision and Control Systems StockholmSE-100 44 Sweden

ISBN: (纸本)9798350316339

In realistic distributed optimization scenarios, individual nodes possess only partial information and communicate over bandwidth constrained channels. For this reason, the development of efficient distributed algorithms is essential. In our paper we addresses the challenge of unconstrained distributed optimization. In our scenario each node's local function exhibits strong convexity with Lipschitz continuous gradients. The exchange of information between nodes occurs through 3-bit bandwidth-limited channels (i.e., nodes exchange messages represented by a only 3 -bits). Our proposed algorithm respects the network's bandwidth constraints by leveraging zoom-in and zoom-out operations to adjust quantizer parameters dynamically. We show that during our algorithm's operation nodes are able to converge to the exact optimal solution. Furthermore, we show that our algorithm achieves a linear convergence rate to the optimal solution. We conclude the paper with simulations that highlight our algorithm's unique characteristics. © 2024 IEEE.

关键词： Bandwidth

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Review on guidance and control of aerospace vehicles: recent progress and prospect

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Aerospace Systems 2024年第2期7卷 175-192页

作者： Wang, Zian Cai, Pengcheng Gong, Zheng Zhang, Chengxi Zhao, Shunyi Wu, Jin Dong, Peng Department of Aerospace Engineering Nanjing University of Aeronautics and Astronautics Nanjing210016 China Key Laboratory of Advanced Control for Light Industry Processes Ministry of Education Jiangnan University Wuxi214122 China Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Kowloon Hong Kong School of Aeronautics and Astronautics Shanghai Jiao Tong University Shanghai China

This paper expounds on the development status and relevant works of control and guidance methods of the aerospace vehicle in recent years. The control difficulties and the solutions in the related results are introduced briefly. Moreover, the guidance methods are then expounded in detail according to the flight phases of the whole flight mission. Guidance methods are usually included in each phase, and the corresponding trajectory design theories are also introduced where necessary. In addition, the potential future development direction prospects. Based on the above, a brief conclusion is then made as a summary. © Shanghai Jiao Tong University 2024.

关键词： Space flight

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Research on the Expert Database System of Characteristic Species in Wuling Mountain Area 21

Research on the Expert Database System of Characteristic Spe...

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21st International computer Conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2024

作者： Yujun, Yang Yimei, Yang Wang, Zhou Liyun, Li Ling, Peng Jin, Xiang Huixia, Shu College of Computer and Artificial Intelligence Software College Huaihua University Huaihua418008 China Hunan Provincial Key Laboratory of Ecological Agriculture Intelligent Control Technology Huaihua418008 China School of Computer and Software Engineering Xihua University Chengdu610039 China

ISBN: (纸本)9798331519254

The Wuling Mountains Area(WMA) is one of the important ecological protection areas in central China, known for its rich biodiversity and unique ecological environment. To effectively protect the species resources in this area and strengthen ecological monitoring and research, this paper proposes and designs the 'Wuling Mountains Region Characteristic Species Expert Database System.' By integrating expert resources, species data, ecological environment information, and modern information technologies, an intelligent system has been developed that combines data management, expert support, species identification, and research analysis. This paper discusses in detail the system's requirements analysis, design concept, implementation technologies, and potential applications, aiming to provide technological support for biodiversity conservation and sustainable ecological development in the region. © 2024 IEEE.

关键词： Characteristic species Expert database system Intelligent system Species data Wuling Mountain Area

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