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IEEE Globecom Workshops

作者： Qinyu Wang Ang Gao Yansu Hu Jiankang Zhang School of Electronics and Information Northwestern Polytechnical University Xi'an China Yangtze River Delta Research Institute of Northwest Polytechnic University Taicang China School of Electronic and Control Engineering Chang'an University China Department of Computing and Informatics Bournemouth University UK

This paper proposes a novel role switching scheme (RSS) to optimize the secure communication by the cooperation of multiple unmanned aerial vehicles (UAVs). Each UAV is enabled to switch its role during the flight, i.e., could be an aerial relay to serve ground users (GUs) or a friendly jammer to generate artificial noise (AN) to deteriorate the wiretapping of potential eavesdropper users (EUs). It's worthy to be noticed that the joint optimization for UAVs' trajectory and legitimate GUs' transmission power control with RSS is a non-convex mixed integer fractional programming problem. A multi-agent deep reinforcement learning (MADRL) combined successive convex approximate (SCA) algorithm is further designed to maximize the achievable secrecy energy efficiency (ASEE). Numerical results illustrate that compared with the role fixed scheme (RFS) and relaxation based SCA approaches, the proposed DRL-SCA algorithm endows UAVs the capacity to fly close enough to target users (both GUs and EUs) which brings a better achievable secrecy rate (ASR), less energy consumption and higher ASEE.

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

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

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Proceedings of the Digital Image computing: Technqiues and Applications (DICTA)

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

ISBN: (数字)9798350379037

ISBN: (纸本)9798350379044

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.

关键词： Training Performance evaluation Accuracy Image recognition Face recognition Computational modeling Modulation Benchmark testing Robustness Computational efficiency

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Adaptive Optimized Consensus control for a Class of Nonlinear Multi-Agent Systems with Asymmetric Input Saturation Constraints and Hybrid Faults

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SSRN 2023年

作者： Tang, Fanghua Wang, Huanqing Zhang, Liang Xu, Ning Ahmad, Adil M. College of Control Science and Engineering Bohai University Liaoning Jinzhou121013 China College of Mathematical Science Bohai University Liaoning Jinzhou121013 China Institute of Information and Control Hangzhou Dianzi University Hangzhou310018 China Department of Information Technology Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia

This article studies the adaptive optimized leader-follower consensus control problem for a class of discrete-time multi-agent systems with asymmetric input saturation constraints and hybrid faults based on the optimized backstepping technique. Different from the conventional saturation model, we consider an individual asymmetric saturation constraint for each actuator instead of a common upper and lower bound for all actuators. Besides, due to the complexity and scale of the actuators, a set of hybrid faults is also considered, with the main focus on the partial fault and bias fault for the multi-agent systems. To eliminate the effects of saturation and faults, a simplified smooth function is constructed to approximate the asymmetric saturation model, and the designed compensation signals are used to cope with the two main fault types such that the fault-tolerance and control performance of the multi-agent systems can be effectively improved. Subsequently, integrating all mechanisms into an adaptive optimized backstepping design framework, the long-term strategic utility functions and the virtual control signals generated in the backstepping procedure are approximated to the desired results by adopting the radial basis function neural network (RBFNN) and the actor-critic framework of reinforcement learning, and the weights in the actor-critic network are adjusted in the light of a classic gradient descent method. According to the forward difference Lyapunov function approach, it is proved that the closed-loop system can be stabilized and all errors are semiglobally uniformly ultimately bounded. Finally, the validity of the proposed control scheme is demonstrated through a simulation example. © 2023, The Authors. All rights reserved.

关键词： Multi agent systems

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A Model-Based Approach for Voltage and State-of-Charge Estimation of Lithium-ion Batteries

A Model-Based Approach for Voltage and State-of-Charge Estim...

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Sustainable Power and Energy Conference (iSPEC)

作者： Milad Andalibi Seyed Saeed Madani Carlos Ziebert Farshid Naseri Mojtaba Hajihosseini Department of Control and Computer Engineering Faculty of Electrical Engineering and Computing University of Zagreb Zagreb Croatia Institute of Applied Materials-Applied Materials Physics Karlsruhe Institute of Technology Karlsruhe Germany Electrical and Computer Engineering Department Electrical Energy Technology Aarhus University Aarhus Denmark

ISBN: (纸本)9781665485234

Electric vehicles are equipped with a large number of lithium-ion battery cells. To achieve superior performance and guarantee safety and longevity, there is a fundamental requirement for a Battery Management System (BMS). In the BMS, accurate prediction of the State-of-Charge (SOC) is a crucial task. The SOC information is needed for monitoring, controlling, and protecting the battery, e.g. to avoid hazardous over-charging or over-discharging. Nonetheless, the SOC is an internal cell variable and cannot be straightforwardly obtained. This paper presents a Kalman Filter (KF) approach based on an optimized second-order Rc equivalent circuit model to carefully account for model parameter changes. An effective machine learning technique based on Proximal Policy optimization (PPO) is applied to train the algorithm. The results confirm the high robustness of the proposed method to varying operating conditions.

关键词： Lithium-ion batteries Machine learning algorithms Computational modeling Estimation Voltage Safety State of charge

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Optimal Design and Operation of Long-Distance Deep-Water HVAC Transmission for Offshore WECS Integration With FPSO Unit

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IEEE Transactions on Industry Applications 2025年

作者： Schmitz, Lenon Viglus, Francisco Jose de Andrade, Jessika Melo Asta, Matheus Schramm Dall Evald, Paulo Jefferson Dias de Oliveira Heldwein, Marcelo Lobo Lazzarin, Telles Brunelli Federal University of Santa Catarina Department of Computing Araranguá Brazil Federal Technological University of Paraná Academic Department of Electronics Campo Mourão Brazil Federal University of Santa Catarina Department of Mobility Eng. Joinville Brazil Federal University of Santa Catarina Department of Electrical and Electronics Engineering Florianopólis Brazil Federal University of Pelotas Int. Sys. and Control Group Pelotas Brazil Technical University of Munich High-Power Converter Systems Munich Germany

This paper evaluates the technical feasibility of integrating an offshore wind energy conversion system (WECS) into a floating production storage and offloading (FPSO) unit. The WECS is situated closer to the Brazilian mainland, requiring a 150 km long subsea umbilical cable for the integration with the FPSO unit. High voltage alternating current (HVAC) transmission is mandatory due to the substantial water depths, introducing challenges such as power losses and significant reactive capacitive current generated by the cable. This paper proposes a comprehensive approach to dealing with these challenges. Firstly, the umbilical cable section is sized based on ampacity and maximum current under short-circuit conditions. A power flow optimization is thus employed to determine the optimal transmission voltage that minimizes power losses. The optimization results define the reactive power that the back-to-back converter of the WECS must absorb based on the active power. This relationship is represented as a lookup table control, which ensures efficient operation by adjusting the reactive power reference in the WECS power control unit. Using this approach, the WECS back-to-back converter compensates for a portion of the reactive power generated by the umbilical, reducing the need for additional infrastructure such as shunt reactors or reactive compensation substations. Each component of the system is modeled, and a complete simulation is conducted using MATLAB/Simulink to validate the proposal. The results demonstrate the effectiveness of this approach in enhancing system efficiency while significantly reducing carbon dioxide emissions during oil and gas exploration through the integration of WECS with FPSO units. © 1972-2012 IEEE.

关键词： Floating production storage and offloading

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Decentralized Goal Function-Based Primary control of Microgrids

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SSRN 2022年

作者： Isakov, Ivana Vekić, Marko Rapaić, Milan Todorović, Ivan Grabić, Stevan University of Novi Sad Department of Power Electronic and Telecommunication Engineering Trg Dositeja Obradovica 6 Novi Sad21000 Serbia University of Novi Sad Department of Computing and Control Engineering Trg Dositeja Obradovica 6 Novi Sad21000 Serbia

The usage of distributed generation sources and segmentation of the power systems into the microgrids brings several advantageous features, such as energy independency, lower transmission losses, higher flexibility, etc. Still, the expected steady abolition of traditional centralized networks and their centralized control schemes will introduce several operational difficulties, that can compromise (micro)grid’s stability and reliability. Consequently, several approaches to how microgrids should be operated have been proposed, albeit with several salient deficiencies, such as power-sharing, control of frequency and voltage excursions, applicability on different grids, etc. This paper proposes a novel, goal function-based, decentralized control that addresses the mentioned problems and guarantees the microgrid’s stability by constraining the frequency and node voltage deviations, simultaneously supporting the desired active power exchange between prosumers. Unlike droop-based control schemes, the proposed approach aims to inherently preserve the frequency and voltage levels within the limits defined by the grid operator or future microgrid codes, thus preventing the generation unit’s unwanted disconnection during transients and disturbances. In addition, the control algorithm is independent of network topology. To confirm the effectiveness of the proposed control strategy, simulation results are presented and discussed. © 2022, The Authors. All rights reserved.

关键词： Electric power transmission networks

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Optimal control of linear Gaussian quantum systems via quantum learning control

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Physical Review A 2024年第6期109卷 063508-063508页

作者： Yu-Hong Liu Yexiong Zeng Qing-Shou Tan Daoyi Dong Franco Nori Jie-Qiao Liao Key Laboratory of Low-Dimensional Quantum Structures and Quantum Control of Ministry of Education Key Laboratory for Matter Microstructure and Function of Hunan Province Department of Physics and Synergetic Innovation Center for Quantum Effects and Applications Theoretical Quantum Physics Laboratory Cluster for Pioneering Research Quantum Computing Center Key Laboratory of Hunan Province on Information Photonics and Freespace Optical Communication College of Physics and Electronics School of Engineering and Information Technology Department of Physics Institute of Interdisciplinary Studies

Efficiently controlling linear Gaussian quantum (LGQ) systems is a significant task in both the study of fundamental quantum theory and the development of modern quantum technology. Here, we propose a general quantum-learning-control method for optimally controlling LGQ systems based on the gradient-descent algorithm. Our approach flexibly designs the loss function for diverse tasks by utilizing first- and second-order moments that completely describe the quantum state of LGQ systems. We demonstrate both deep optomechanical cooling and large optomechanical entanglement using this approach. Our approach enables the fast and deep ground-state cooling of a mechanical resonator within a short time, surpassing the limitations of sideband cooling in the continuous-wave driven strong-coupling regime. Furthermore, optomechanical entanglement could be generated remarkably fast and surpass several times the corresponding steady-state entanglement, even when the thermal phonon occupation reaches one hundred. This work will not only broaden the application of quantum learning control, but also open an avenue for optimal control of LGQ systems.

关键词： Optomechanics Quantum optics

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A Causality-Inspired Capsule Network for Domain Generalization in Cross-Domain Fault Diagnosis of Rolling Bearing

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IEEE Transactions on Instrumentation and Measurement 2025年 74卷

作者： Sun, Zhizheng Zhu, Xiaomin Liu, Jian Zhao, Xiangkun Wang, Hanxi Zhang, Runtong Beijing Jiao Tong University School of Mechanical Electronic and Control Engineering Beijing100044 China Tianjin Jinhang Computing Technology Research Institute Rail Transit Department Tianjin300000 China Li Auto Inc Beijing101399 China Beijing Jiao Tong University School of Economics and Management Beijing100044 China

Cross-domain fault diagnosis (CDFD) leveraging domain generalization (DG) has attracted increasing attention due to its potential to generalize diagnostic knowledge to unseen data domains. However, the majority of current DG methods primarily model statistical dependencies, which limits their generalization performance in unobserved domains. To address this limitation, causal learning offers a natural advantage in revealing the causal linkages within diagnostic knowledge, providing a novel perspective for diagnostic guidance. Inspired by this strength, this study proposes a causality-inspired capsule network (CICN) for cross-domain fault diagnosis in the bearing field. The CICN employs a capsule network to extract features that encapsulate rich fault-related information. During the dynamic routing process, a causal gated unit is introduced to emphasize the potential causal diagnostic knowledge embedded in multi-source domain fault data, enabling the model to leverage richer causal information. Furthermore, the method disentangles causal features from noncausal features and enhances the robustness of causal invariant features through causal intervention, improving the model’s performance against domain interference. The effectiveness of the CICN model is validated through cross-domain fault diagnosis experiments on five datasets. The results demonstrate the superior generalization capability of the proposed approach across diverse working conditions and different machine settings. © 1963-2012 IEEE.

关键词： Roller bearings

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Characterizing Human Collective Behaviors During COVID-19 — Hong Kong SAR, China, 2020

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China CDC weekly 2023年第4期5卷 71-75,I0001-I0003页

作者： Zhanwei Du Xiao Zhang Lin Wang Sidan Yao Yuan Bai Qi Tan Xiaoke Xu Sen Pei Jingyi Xiao Tim K.Tsang Qiuyan Liao Eric H.YLau Peng Wu Chao Gao Benjamin J.Cowling WHO Collaborating Centre for Infectious Disease Epidemiology and Control School of Public HealthLi Ka Shing Faculty of MedicineThe University of Hong KongHong Kong Special Administrative RegionChina Laboratory of Data Discovery for Health Hong Kong Science and Technology ParkNew TerritoriesHong Kong Special Administrative RegionChina Department of Genetics University of CambridgeCambridgeCB23EHUK Institute of High Performance Computing(IHPC) Agency for ScienceTechnology and Research(A*STAR)Singapore College of Information and Communication Engineering Dalian Minzu UniversityDalian CityLiaoning ProvinceChina Department of Environmental Health Sciences Mailman School of Public HealthColumbia UniversityNew York CityNYUSA School of Artificial Intelligence Opticsand Electronics(iOpen)Northwestern Polytechnical UniversityXi’an CityShaanxi ProvinceChina

Summary What is already known about this topic?People are likely to engage in collective behaviors online during extreme events,such as the coronavirus disease 2019(COVID-19)crisis,to express awareness,take action,and... 详细信息

关键词： collective likely extreme

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Application of Extreme Learning Machine for Shunt Faults Detection and Classification in Three-phase Transmission Line Systems

Application of Extreme Learning Machine for Shunt Faults Det...

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Signal, control and Communication (SCC), International Conference on

作者： Khaoula Assadi Jihane Ben Slimane Salah Salhi University of Tunis El Manar National Engineering School of Tunis LR11ES20 Analysis Design and Control of Systems Laboratory Tunis Tunisia Department of Computer Sciences Faculty of Computing and InformationTechnology Northern Border University Rafha Saudi Arabia

ISBN: (数字)9798350326390

ISBN: (纸本)9798350326406

The electrical power transmission lines play a crucial role in maintaining a continuous electricity supply. However, the exposed environment of these lines increases the risks of faults occurrence, necessitating prompt detection, and classification, to eliminate them within a specified time. In light of this, the present study introduces a methodology for the detection and classification of faults in a three-phase transmission line system employing the Extreme Learning Machine (ELM). A 220 kV, 300 km three-phase transmission line system was simulated in MATLAB/Simulink software, where the current and voltage signals were generated under different fault conditions including fault types, fault inception time, and fault resistance. The fundamental components of these signals were used as inputs in the ELM model. Different shunt fault types, including line-to-ground, line-to-line, double-line-to-ground, triple-line, and triple-line-to-ground were applied for fault detection and classification framework. Furthermore, the optimum number of hidden neurons in the ELM model was investigated in this study. In terms of the Mean Squared Error (MSE) and correlation coefficient, four ELM variants based on Sine, Sigmoid, Radial Basis, and Triangular Basis transfer functions were assessed for each module. The outcome of these metrics indicates that the ELM with the sigmoid model demonstrated superior performance compared to the other ELM models. Based on statistical metrics MSE, Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE), and also the computational time, the efficiency of ELM-Sigmoid and state-of-the-art methods of Multi-Layer Perceptron Neural Network (MLPNN) and Radial Basis Function Neural Network (RBFNN) were compared and analyzed during training and testing phases. The results obtained revealed that the ELM-Sigmoid model outperforms the other models in the detection and classification of shunt faults with significant accuracy and fast computational speed.

关键词： Measurement Training Power transmission lines Extreme learning machines Computational modeling Fault detection Neurons

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