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IEEE Transactions on Green communications and Networking 2025年

作者： Han, Shuai Wang, Jinming Xu, Sai Wang, Shiji Li, Cheng Harbin Institute of Technology School of Electronics and Information Engineering China 5GIC and 6GIC GuildfordGU2 7XH United Kingdom Southeast University National Mobile Communications Research Laboratory Nanjing211111 China Beijing Aerospace Measurement and control Technology CO.LTD Beijing China Simon Fraser University School of Engineering Science BurnabyBCV5A 1S6 Canada

Data collection schemes assisted by unmanned aerial vehicle (UAV) play a key role in Internet-of-Things (IoT) networks. Nonetheless, the massive IoT devices bring new challenges, especially the high energy consumption. The backscatter communication (BackCom) technique can utilize ambient signals for passive transmission, thus being applicable to IoT systems. Due to its numerous reflecting elements, Intelligent Reflecting Surface (IRS) can accomplish backscatter with high beamforming gain. Thus, IRS is suitable to alleviate the double-fading effect, which significantly restrains the development of BackCom networks. This paper utilizes the IRS-based BackCom technique to achieve efficient IoT data collection with the help of a UAV. The UAV provides carriers for multiple IRSs, which can emit data through backscatter. Then, the UAV collects the backward data dynamically. To maximize the collected data, the UAV's transmit power, trajectory, and the IRS's beamformers are jointly optimized. The optimization is realized through an alternative algorithm based on block coordinate descent (BCD) and reinforcement learning (RL). The Lagrangian dual transformation and semi-definite relaxation (SDR) approaches are employed to address the non-convexity of the problem effectively. Finally, simulation results demonstrated the effectiveness and feasibility of the proposed optimization scheme in the introduced system model. © 2017 IEEE.

关键词： Reinforcement learning

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A Power Split Strategy for a Vehicular Hybrid Energy Storage System Using Adaptive Neuro Fuzzy Inference System 8

A Power Split Strategy for a Vehicular Hybrid Energy Storage...

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8th IEEE International Conference on Advanced Robotics and Mechatronics, ICARM 2023

作者： Li, Jiao Zhang, Lijun Yang, Longzhi Wang, Bo School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Huazhong University of Science and Technology Belt and Road Joint Laboratory on Measurement and Control Technology Wuhan430074 China Northumbria University Faculty of Engineering and Environment Department of Computer Science and Digital Technologies Newcastle upon TyneNE1 8ST United Kingdom

ISBN: (纸本)9798350300178

This paper presents a real-time power split strategy for a battery-supercapacitor hybrid energy storage system. The objective of the proposed strategy is to alleviate battery degradation through effective supercapacitor utilization. The proposed strategy employs an adaptive neural fuzzy inference system, which is capable of learning a power split strategy from offline optimization results and distributing power in real-time. The performance of the strategy proposed and a low pass filtering technique is compared through simulations. The results demonstrate that the proposed method can effectively reduce battery capacity loss and perform well under unknown driving cycles in real time. © 2023 IEEE.

关键词： Supercapacitor

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Security Enhancement of UAV Swarm Empowered Downlink Transmission with Integrated Sensing and Communication

Security Enhancement of UAV Swarm Empowered Downlink Transmi...

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IEEE International Conference on Trust, Security and Privacy in Computing and communications (TrustCom)

作者： Runze Dong Buhong Wang Jiang Weng Kunrui Cao Jiwei Tian Tianhao Cheng School of Information and Navigation Air Force Engineering University Xi'an China School of Information and Communications National University of Defense Technology Wuhan China School of Air Traffic Control and Nav. Air Force Engineering University Xi'an China

ISBN: (数字)9798331506209

ISBN: (纸本)9798331506216

As a promising technique for the next generation communication network, integrated sensing and communication (ISAC) has attracted incremental research attentions due to its capabilities in spectrum sharing, cost saving, and data collecting. In this paper we utilize unmanned aerial vehicle (UAV) swarm to perform downlink ISAC transmission to serve multiple terrestrial legitimate users and sensing targets. To accommodate more practical application scenarios, we assume that there are also multiple malicious eavesdroppers in the network attempting to eavesdrop on the confidential signal. In order to enhance the security of the downlink transmission while maintaining sufficient sensing performance, we propose a joint optimization of the centralized trajectory of UAV swarm, the transmit beamforming on each UAV, and the ISAC schedule, which is eventually formulated as an average secrecy rate (ASR) maximization problem. A deep reinforcement learning (DRL) based algorithm is developed to solve the considered optimization problem and its effectiveness is validated via experimental simulations, which also proves its superiority over benchmark methods.

关键词： Schedules Privacy Array signal processing Physical layer security Autonomous aerial vehicles Integrated sensing and communication Downlink Deep reinforcement learning Security Trajectory optimization

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Aero-Engine Remaining Useful Life Prediction Using A Multi-Dimensional Convolutional Feature Fusion Framework

Aero-Engine Remaining Useful Life Prediction Using A Multi-D...

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Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS), CAA Symposium on

作者： Liangcheng Wang Yang Wang Hong Zhang Yanwei Wang Ying Zheng The Belt and Road Joint Laboratory on Measurement and Control Technology School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China School of Mechanical & Electrical Engineering Wuhan Institute of Technology Wuhan Hubei

Accurate prediction of aero-engine remaining useful life (RUL) is essential for providing reliable maintenance or alarm decisions. The extraction of degraded features has a great impact on the accuracy of RUL prediction. This paper proposes a feature fusion framework that relies on a multi-dimensional convolutional neural network (MD-CNN). First, the data of each working condition is normalized separately to extract degradation features more effectively. Subsequently, the temporal features are extracted using the one-dimensional convolutional neural network (1D-CNN), while the spatial local features are captured through the utilization of the two-dimensional convolutional neural network (2D-CNN). Finally, the long short-term memory network (LSTM) is utilized as the prediction model to establish the mapping relationship from fusion features to RUL. The result shows that the performance in predicting using the proposed method surpasses that of the existing methods.

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Spatio-temporal characteristics of genotoxicity in the Yangtze River under the background of COVID-19 pandemic

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Frontiers of Environmental Science & engineering 2024年第11期18卷 121-132页

作者： Xinge Wang Na Li Yingnan Han Xiao Li Weixiao Qi Jian Li Kaifeng Rao Zijian Wang Yanjie Wei Mei Ma Key Laboratory of Drinking Water Science and Technology Research Center for Eco-Environmental SciencesChinese Academy of SciencesBeijing 100085China State Key Laboratory of Environmental Aquatic Chemistry Research Center for Eco-Environmental SciencesChinese Academy of SciencesBeijing 100085China Yangtze Eco-Environment Engineering Research Center China Three Gorges CorporationBeijing 100038China National Engineering Research Center of Industrial Wastewater Detoxication and Resource Recovery Beijing 100085China University of Chinese Academy of Sciences Beijing 100049China Center for Water and Ecology State Key Joint Laboratory of Environment Simulation and Pollution ControlSchool of EnvironmentTsinghua UniversityBeijing 100084China Engineering Research Center of Groundwater Pollution Control and Remediation Ministry of EducationCollege of Water SciencesBeijing Normal UniversityBeijing 100875China Key Laboratory of Environmental Protection in Water Transport Engineering Ministry of Communications Tianjin Research Institute of Water Transport EngineeringTianjin 300456China

The global spread of viruses can lead to the release of large amounts of disinfectants or antiviral drugs into the water *** resulting disinfection byproducts(DBPs)and residual antiviral drugs,acting as genotoxic substances or their precursors,may pose risks to aquatic animals and drinking water sources;however,to date,no studies have analyzed the changes in genotoxicity in the Yangtze River before and after the *** the present study,water and sediment samples from the Yangtze River were collected during different seasons,just before and after the outbreak of COVID-19,and were assessed using the SOS/umu test(with and without liver S9).The results indicated that water samples exhibited more pronounced genotoxicity than did sediments,with direct genotoxicity being the primary ***,there were significant regional differences,with notably greater genotoxicity observed in the upper Yangtze River than in the lower reaches before the COVID-19 ***,this trend was reversed six to ten months later,suggesting the accumulation of DBPs or antiviral drugs after the COVID-19 ***,the risk quotient indicated that 65%of the water samples posed a high risk for Paramecium caudatum,whereas 71%of the samples posed a medium risk for Danio rerio,thereby representing a potential threat to the ecological security of the Yangtze *** conclusion,this study,at the basin scale,revealed the impacts of COVID-19 on the Yangtze River,highlighting the need to prevent DBPs and pharmaceutical pollution during similar events in the future.

关键词： Yangtze River COVID-19 Genotoxicity Spatio-temporal characteristics

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Autonomous Localization Method for Railway Trains Based on Multi-Source Information Fusion

Autonomous Localization Method for Railway Trains Based on M...

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Electronic engineering and Information Systems (EEISS), International Conference on

作者： Hanxuan Zhang Ju Huo Muyao Xue Jianbao Zhou School of Electrical Engineering and Automation Harbin Institute of Technology Harbin China Shanghai Space Propulsion Technology Research Institute Shanghai China Harbin Nuoxin University of Technology Measurement and Control Technology Co. Ltd. Harbin China

ISBN: (数字)9798350351033

ISBN: (纸本)9798350351040

Aiming at the problems of high cost and difficult maintenance in the practical application of the traditional "vehicle-ground cooperation" train positioning technology in special section railway projects. In this paper, a method for autonomous positioning of railway trains based on multisource information fusion is proposed. First, develop a kilometer mark detection thread based on VINS Fusion, and build a lightweight optical symbol recognition system to identify the character area of the kilometer mark to obtain the position information of the electronic map. Secondly, based on the LSD line detection algorithm combined with the way of dynamically expanding the line, the coordinates of the vertex coordinates of the kilometer mark are extracted. Then, a global optimization objective function containing the position information constraints of the kilometer mark is established, and the global positioning accuracy is improved through the graph optimization method. This solves the problem that existing intelligent positioning of railway trains cannot eliminate accumulated errors in the absence of loop closure detection. Finally, the semi-physical simulation results show that the positioning error of the original positioning system can be reduced by more than 57%.

关键词： Location awareness Costs Simulation Symbols Optimization methods Linear programming Rail transportation

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Network Intrusion Detection Based on Graph Neural Network and Ensemble Learning

Network Intrusion Detection Based on Graph Neural Network an...

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Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS), CAA Symposium on

作者： Lina Zhang Yang Wang Minyue Wu Yanwei Wang Ying Zheng The Belt and Road Joint Laboratory on Measurement and Control Technology School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China School of Mechanical & Electrical Engineering Wuhan Institute of Technology Wuhan China

Network technology has become an inseparable part of daily life, which is easily attacked by some criminals. Therefore, ensuring the security of network becomes an urgent problem. In the past few years, various deep learning techniques have been put forward for identifying network intrusions. However, the majority of these approaches overlook the non-Euclidean nature of relationships within network intrusion data. In this paper, graph neural network (GNN) is used to learn the relationship between data. The sample balancing module is to address the significant disparity between the number of intrusive samples and normal samples. The results obtained from the experiments indicate that the suggested approach can enhance the effectiveness of the intrusion detection system.

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Physics-Informed LSTM Network-Based Nonlinear Model Predictive control

Physics-Informed LSTM Network-Based Nonlinear Model Predicti...

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Data Driven control and Learning Systems (DDCLS)

作者： Yujing Chen Qilin Qui Hong Zhang Yanwei Wang Ying Zheng The Belt and Road Joint Laboratory on Measurement and Control Technology School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China School of Mechanical Electrical Engineering Wuhan Institute of Technology Wuhan China

To address the problems of poor physical interpretability and huge sample size requirement when using neural networks to fit nonlinear control system models for state prediction, this paper proposes a model predictive control algorithm based on a physics-informed long short-term memory(LSTM) network. Firstly, the neural network incorporating physical information is extended to model the ordinary differential equations with variable initial states and external control quantities, which makes the network adaptable to the control task and makes the training model physically interpretable. Secondly, a network structure with a mixture of fully connected layers and LSTM layers is built by using the good learning ability of LSTM for time-series data, and the loss function is designed according to the system characteristics and prediction requirements. The trained neural network model is then used as an internal prediction model to construct a nonlinear model predictive control algorithm. Finally, taking the continuous stirring reactor system as an example, the method is verified to be able to fit the system model highly and reduce the time to reach the steady state with a small number of samples.

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A Novel All-phase Fast Fourier Transform-based Parameters Identification of High Frequency Oscillations

A Novel All-phase Fast Fourier Transform-based Parameters Id...

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Annual Meeting Industrial and Commercial Power Systems Technical Conference

作者： Feiyu Sun Dongsheng Cai Zhenyuan Zhang Qi Huang College of Nuclear Technology and Automation Engineering Chengdu University of Technology China Sichuan Provincial Key Lab of Power System Wide-Area Measurement and Control School of Mechanical and Electrical Engineering University of Electronic Science and Technology of China Chengdu China School of Information Engineering Southwest University of Science and Technology Mianyang P.R. China

ISBN: (数字)9798350345308

ISBN: (纸本)9798350345315

Wind and solar energy inverter-based resources (IBRs) has been employed extensively with the aim of carbon neutrality. Power systems also make extensive use of power electronic converters, leading to interactions between IBRs and with the grid, causing high frequency oscillation (HFO) problems. It damages equipment and jeopardizes the security of the system. To identify HFO components above 100 Hz, this research use Phasor measurement Unit (PMU) instantaneous value data to determine the HFO associated characteristics. To reduce the influence of spectral leakage and the fence effect, as well as to improve the identification accuracy of the related parameters, this paper adopts the RV self-convolutional window based Discrete Fourier Transform (DFT) and the Hanning double-window based all-phase Fast Fourier Transform (apFFT) hybrid algorithm to identify the HFO modal parameters. We take into account the identification accuracy in complex cases, such as non-nominal frequencies, with noise effects and multiple HFO modes. The simulation results show that the proposed method has high identification bandwidth, accuracy and strong flexibility.

关键词： Accuracy Wind energy Simulation Discrete Fourier transforms Noise Bandwidth Hafnium oxide

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Parallel Power Distribution control Strategy of Wind-Solar Storage Multi-micro-grid Virtual Synchronous Machines 1

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18th Chinese Intelligent Systems Conference, CISC 2022

作者： Yang, Chengdong Lv, Shuaishuai Wang, Hui Meng, Xiangping Li, Zijie School of Electrical and information Engineering Changchun Institute of Technology NO. 395 Kuanping Road Chaoyang District Changchun130012 China National and Local Joint Engineering Research Center for Measurement Control and Safe Operation of Intelligent Distribution Networks Changchun130012 China

ISBN: (数字)9789811962264

ISBN: (纸本)9789811962257

Aiming at the difficulty in decision-making of coordinated power allocation of multiple wind-solar storage micro-grids, a power allocation control strategy for virtual synchronous machines in parallel is proposed considering the wind-solar capacity of each micro-grid. First, lithium batteries and Super capacitor are introduced to stabilize fluctuations to achieve DC bus voltage regulation in the micro-grid, and then according to the different wind and solar capacities in each micro-grid, the inverters are controlled by virtual synchronous machine multi-machine parallel adaptive power distribution to achieve real-time Finally, the validity of the proposed control strategy is verified through the analysis of computing power. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Lithium batteries

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