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An image compressed sensing algorithm based on adaptive nonlinear network

Chinese Physics B 2020年第5期29卷 243-253页

作者： Yuan Guo Wei Chen Shi-Wei Jing School of Computer and Control Engineering Qiqihar UniversityQiqihar 161006China

Traditional compressed sensing algorithm is used to reconstruct images by iteratively optimizing a small number of measured *** computation is complex and the reconstruction time is *** deep learning-based compressed sensing algorithm can greatly shorten the reconstruction time,but the algorithm emphasis is placed on reconstructing the network part *** random measurement matrix cannot measure the image features well,which leads the reconstructed image quality to be improved *** kinds of networks are proposed for solving this *** first one is ReconNet’s improved network IReconNet,which replaces the traditional linear random measurement matrix with an adaptive nonlinear measurement *** reconstruction quality and anti-noise performance are greatly *** the measured values extracted by the measurement network also retain the characteristics of image spatial information,the image is reconstructed by bilinear interpolation algorithm(Bilinear)and dilate *** a second network USDCNN is *** the BSD500 dataset,the sampling rates are 0.25,0.10,0.04,and 0.01,the average peak signal-noise ratio(PSNR)of USDCNN is 1.62 dB,1.31 dB,1.47 dB,and 1.95 dB higher than that of *** show the average reconstruction time of USDCNN is 0.2705 s,0.3671 s,0.3602 s,and 0.3929 s faster than that of ***,there is also a great advantage in anti-noise performance.

关键词： compressed sensing deep learning bilinear interpolation dilate convolution

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Viewpoint Optimization Method for Flying Robot Inspecting Transmission Towers Based on Point Cloud Model

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Chinese Journal of Electronics 2018年第5期27卷 976-984页

作者： LIU Chang'an DONG Ruifang JIN Lu WU Hua School of Control and Computer Engineering North China Electric Power University

This paper presents a method to derive optimal viewpoints for flying robot based transmission tower inspection, which applies point cloud model. A safe envelope is established according to safe transmission tower inspection rules. Essential inspection factors are proposed to evaluate the quality of candidate viewpoints, which includes visibility, dimensionality feature of point cloud as well as the distance between viewpoint and transmission power. A score function is constructed to quantify candidate viewpoints, while multiple attribute decision theory is applied to calculate the weight of each factor. Particle swarm optimization(PSO) is used to find the optimal viewpoints set. Both simulation experiment and practical observations are carried out. The results prove that optimal viewpoints have a great contribution for accurate transmission tower inspection. Final results are compared to patch-based method and proved to be feasible.

关键词： Transmission tower inspection Viewpoint optimization Point cloud model Particle swarm optimization(PSO) Flying robot

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A dynamic algorithm for trust inference based on double DQN in the internet of things

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Digital Communications and Networks 2024年第4期10卷 1024-1034页

作者： Xiaodong Zhuang Xiangrong Tong School of Computer and Control Engineering Yantai UniversityYantai264005China

The development of the Internet of Things(IoT)has brought great convenience to ***,some information security problems such as privacy leakage are caused by communicating with risky *** is a challenge to choose reliable users with which to interact in the ***,trust plays a crucial role in the IoT because trust may avoid some *** usually choose reliable users with high trust to maximize their own interests based on reinforcement ***,trust propagation is time-consuming,and trust changes with the interaction process in social *** track the dynamic changes in trust values,a dynamic trust inference algorithm named Dynamic Double DQN Trust(Dy-DDQNTrust)is proposed to predict the indirect trust values of two users without direct contact with each *** proposed algorithm simulates the interactions among users by double ***,CurrentNet and TargetNet networks are used to select users for *** users with high trust are chosen to interact in future ***,the trust value is updated dynamically until a reliable trust path is found according to the result of the ***,the trust value between indirect users is inferred by aggregating the opinions from multiple users through a Modified Collaborative Filtering Averagebased Similarity(SMCFAvg)aggregation *** are carried out on the FilmTrust and the Epinions *** with TidalTrust,MoleTrust,DDQNTrust,DyTrust and Dynamic Weighted Heuristic trust path Search algorithm(DWHS),our dynamic trust inference algorithm has higher prediction accuracy and better scalability.

关键词： Internet of things Information security Reinforcement learning Trust propagation Trust inference

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TRNet: Two-Tier Recursion Network for Co-Salient Object Detection

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Cong, Runmin Yang, Ning Liu, Hongyu Zhang, Dingwen Huang, Qingming Kwong, Sam Zhang, Wei Shandong University School of Control Science and Engineering Jinan250061 China Ministry of Education Key Laboratory of Machine Intelligence and System Control Jinan250061 China Beijing Jiaotong University Institute of Information Science Beijing100044 China Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing100044 China Northwestern Polytechnical University Brain and Artificial Intelligence Laboratory School of Automation Xi'an710129 China University of Chinese Academy of Sciences School of Computer Science and Technology Beijing101408 China Lingnan University Hong Kong

Co-salient object detection (CoSOD) is to find the salient and recurring objects from a series of relevant images, where modeling inter-image relationships plays a crucial role. Different from the commonly used direct learning structure that inputs all the intra-image features into some well-designed modules to represent the inter-image relationship, we resort to adopting a recursive structure for inter-image modeling, and propose a two-tier recursion network (TRNet) to achieve CoSOD in this paper. The two-tier recursive structure of the proposed TRNet is embodied in two stages of inter-image extraction and distribution. On the one hand, considering the task adaptability and inter-image correlation, we design an inter-image exploration with recursive reinforcement module to learn the local and global inter-image correspondences, guaranteeing the validity and discriminativeness of the information in the step-by-step propagation. On the other hand, we design a dynamic recursion distribution module to fully exploit the role of inter-image correspondences in a recursive structure, adaptively assigning common attributes to each individual image through an improved semi-dynamic convolution. Experimental results on five prevailing CoSOD benchmarks demonstrate that our TRNet outperforms other competitors in terms of various evaluation metrics. The code and results of our method are available at https://***/rmcong/TRNetTCSVT2025. © 1991-2012 IEEE.

关键词： HTTP

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Motion Forecasting for Autonomous Vehicles: A Survey

arXiv

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arXiv 2025年

作者： Shi, Jianxin Chen, Jinhao Wang, Yuandong Sun, Li Liu, Chunyang Xiong, Wei Wo, Tianyu Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China Information Engineering College Capital Normal University Beijing100048 China School of Control and Computer Engineering North China Electric Power University Beijing102206 China Didi Chuxing Beijing100094 China

In recent years, the field of autonomous driving has attracted increasingly significant public interest. Accurately forecasting the future behavior of various traffic participants is essential for the decision-making of Autonomous Vehicles (AVs). In this paper, we focus on both scenario-based and perception-based motion forecasting for AVs. We propose a formal problem formulation for motion forecasting and summarize the main challenges confronting this area of research. We also detail representative datasets and evaluation metrics pertinent to this field. Furthermore, this study classifies recent research into two main categories: supervised learning and self-supervised learning, reflecting the evolving paradigms in both scenario-based and perception-based motion forecasting. In the context of supervised learning, we thoroughly examine and analyze each key element of the methodology. For self-supervised learning, we summarize commonly adopted techniques. The paper concludes and discusses potential research directions, aiming to propel progress in this vital area of AV technology. © 2025, CC BY.

关键词： Self-supervised learning

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STARS Assisted Semi-Grant-Free NOMA Communications

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IEEE Transactions on Wireless Communications 2025年

作者： Xie, Jin Yue, Xinwei Zhao, Jingjing Zou, Yixuan Liu, Yuanwei Liu, Rongke Ding, Zhiguo Key Laboratory of Information and Communication Systems China Beijing Information Science and Technology University Key Laboratory of Modern Measurement and Control Technology Ministry of Education Beijing100101 China Beihang University School of Electronic and Information Engineering Beijing100191 China Queen Mary University of London School of Electronic Engineering and Computer Science LondonE1 4NS United Kingdom The University of Hong Kong Department of Electrical and Electronic Engineering Hong Kong Hong Kong Kyung Hee University Department of Electronic Engineering Gyeonggi-do Yongin-si17104 Korea Republic of Khalifa University Department of Computer and Information Engineering Abu Dhabi United Arab Emirates

This paper investigates the performance of simultaneously transmitting and reflecting surface (STARS) assisted semi-grant-free non-orthogonal multiple access network with randomly distributed users. By deploying STARS, the transmit signals of grant-based user (GBU) and grant-free users (GFUs) can be exquisitely adjusted to reduce interference. We propose a maximum channel scheduling (MCS) protocol that allows a GFU to access GBU’s channel with the assistance of STARS. In particular, the impacts of perfect/imperfect successive interference cancellation (pSIC/ipSIC) on MCS protocol are taken into account. To characterize the performance of STARS aided MCS (STARS-MCS) network, we derive the expressions of outage probability for GBU and GFU with pSIC/ipSIC. By applying convolution theorem and Laplace transform, the asymptotic expressions of outage probability and diversity orders for GBU and GFU are attained. We further design a STARS-based power control (SPC) strategy to eliminate the outage probability error floor and improve the outage performance. Numerical results show that: 1) The performance of STARS-MCS outperforms the existing benchmarks in terms of outage probability and system throughput;2) The SPC strategy can effectively improve the performance of the STARS-MCS network and eliminate the outage probability error floor at high signal-to-noise ratios;and 3) By adjusting reflection and transmission coefficients of STARS, the outage performance of GBU and GFU can be greatly enhanced. © 2025 IEEE. All rights reserved.

关键词： Laplace transforms

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Geo2ComMap: Deep Learning-Based MIMO Throughput Prediction Using Geographic Data

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IEEE Wireless Communications Letters 2025年

作者： Lin, Fan-Hao Huang, Tzu-Hao Wen, Chao-Kai Duong, Trung Q. National Sun Yat-sen University Institute of Communications Engineering Kaohsiung80424 Taiwan National Yang Ming Chiao Tung University Institute of Electrical Control Engineering Hsinchu300 Taiwan Memorial University Faculty of Engineering and Applied Science St. John'sNLA1C 5S7 Canada Queen's University Belfast School of Electronics Electrical Engineering and Computer Science Belfast United Kingdom

Accurate communication performance prediction is crucial for wireless applications such as network deployment and resource management. Unlike conventional systems with a single transceiver antenna, throughput (Tput) estimation in antenna array-based multiple-output multiple-input (MIMO) systems is computationally intensive, i.e., requiring analysis of channel matrices, rank conditions, and spatial channel quality. These calculations impose significant computational and time burdens. This paper introduces Geo2ComMap, a deep learning-based framework that leverages geographic databases to efficiently estimate multiple communication metrics across an entire area in MIMO systems using only sparse measurements. To mitigate extreme prediction errors, we propose a sparse sampling strategy. Extensive evaluations demonstrate that Geo2ComMap accurately predicts full-area communication metrics, achieving a median absolute error of 27.35 Mbps for Tput values ranging from 0 to 1900 Mbps. © 2012 IEEE.

关键词： Antenna arrays

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Comparative Analysis of Different Sliding Mode control Approaches for Load Frequency control in Edge of the Grid System

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IET Generation, Transmission and Distribution 2025年第1期19卷

作者： Aich, Pritom Ahmed, Anis Shah, Rakibuzzaman Islam, Md Rabiul Roy, Naruttam Kumar Nasir, Ali Department of Electrical and Electronic Engineering Daffodil International University Dhaka Bangladesh Department of Electrical and Electronic Engineering Khulna University of Engineering & Technology Khulna Bangladesh Federation University Australia Mt HelenVIC Australia School of Electrical Computer and Telecommunications Engineering University of Wollongong WollongongNSW Australia Control and Instrumentation Engineering Department King Fahd University of Petroleum and Minerals Dhahran Saudi Arabia Interdisciplinary Research Center for Intelligent Manufacturing and Robotics King Fahd University of Petroleum and Minerals Dhahran Saudi Arabia

The integration of renewable energy sources (RESs) at the edge of the grid plays a contributory role in advancing the transition to a sustainable energy future. However, this transition introduces significant variability, which challenges the maintenance of frequency stability in power systems. To address the impacts of RES variability, various robust control strategies have been developed. This paper compares the performance of different advanced sliding mode control (SMC) approaches, i.e., super twisting (ST) algorithm-based second order sliding mode control (STSOSMC), second order sliding mode control (SOSMC), double integral sliding mode control (DISMC) and integral sliding mode control (ISMC) with conventional sliding mode control (SMC) for load frequency control with stochastic RESs such as biogas generators (BG) and wind-based doubly fed induction generators (DFIG). The impact of the advanced sliding mode controllers on the voltage and rotor angle stability of the system is also explored. The efficacy of the controllers is tested under different load disturbances, considering nonlinearities such as communication delay (CD), sensor inaccuracy (SI), governor dead band (GDB), and generation rate constraint (GRC). The results demonstrate that STSOSMC achieves superior frequency regulation with minimal chattering and outperforms other controllers in terms of settling time, overshooting, and undershooting in frequency deviation. The result also demonstrates the effectiveness of STSOSMC in managing the variability and disturbances of RESs. Therefore, it could be considered the most robust control mechanism from the edge of the grid system with high penetration of RESs. © 2025 The Author(s). IET Generation, Transmission & Distribution published by John Wiley & Sons Ltd on behalf of The Institution of engineering and Technology.

关键词： Robust control

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Intermittently Dynamic Fuzzy Learning-Based Tracking control of Amplitude Signals for Vibratory Gyroscopes With Composite Identification

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IEEE Transactions on Instrumentation and Measurement 2025年 74卷

作者： Xu, Zeyuan Ge, Shuzhi Sam Liu, Weihao Yi, Guoxing Xie, Zhongliang Li, Kuo National University of Singapore Department of Chemical and Biomolecular Engineering Queenstown 117585 Singapore University of Pavia Department of Electrical Computer and Biomedical Engineering Pavia27100 Italy National University of Singapore Department of Electrical and Computer Engineering Queenstown 119077 Singapore Harbin Institute of Technology Department of Control Science and Engineering Harbin150001 China Northwestern Polytechnical University Department of Engineering Mechanics Xi’an710072 China Yanshan University School of Electrical Engineering Qinhuangdao066004 China

This article investigates an intermittently dynamic fuzzy learning-based tracking control (IDFL-TC) method of amplitude signals for vibratory gyroscopes. Compared with learning-based TC (L-TC) and fuzzy L-TC (FL-TC) methods, the IDFL-TC method with dynamic pruning technology can improve the identification accuracy of gyroscope dynamics and reduce calculation times by intermittently updating the output of nodes and dynamically recruiting or removing nodes. Subsequently, a composite identification mechanism is introduced into the control method to further improve the identification accuracy. A novel tracking controller is developed to well track the reference amplitude signal and guarantee the boundedness of the tracking error. Finally, comparative experimental results demonstrate that the proposed IDFL-TC method can obtain higher control precision of amplitude signals for vibratory gyroscopes with fewer calculation time in comparison to the L-TC and FL-TC methods. © 1963-2012 IEEE.

关键词： Identification (control systems)

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A Profit-Oriented Attack by Virtual Power Plants for Economic Risk Transfer: Profitability Analysis and Defense Strategy

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IEEE Systems Journal 2025年

作者： Wang, Luyu Wu, Jiping Ding, Zhetong Zhang, Kaifeng Sikdar, Biplab Southeast University Key Laboratory of Measurement and Control of CSE School of Automation Nanjing 210096 China National University of Singapore Department of Electrical and Computer Engineering 119077 Singapore NARI Technology Limited Company Nanjing 211100 China

Virtual power plants (VPP) may face the risk of economic losses arising from the deviation between day-ahead schedule and real-time energy delivery. This article introduces a novel profit-oriented attack by some greedy VPP (G-VPP) for the first time, which aims to transfer its risk of economic losses to other normal VPPs (N-VPPs) and obtain a risk-free profit. Particularly, the G-VPP launches the load-altering attack (LAA) on N-VPPs to fail their real-time (RT) generation schedule via modifying the generation instructions of the managed resources. The optimal LAA strategy considering stealthiness is proposed to avoid priori detection and the optimal bidding strategy using CVaR is proposed to quantify bidding risk and maximize profit. To defend against such profit-oriented attacks, this article constitutes the first attack traceability research. A defense scheme with attack detection and attacker identification is proposed, which is executed by VPPs and the independent system operator (ISO). VPPs employ an attack detection algorithm based on complex features, and if any, report it to the ISO. The ISO constructs a knowledge graph of electricity market transactions and embeds a rules-based identification algorithm to identify suspected attackers. Simulation results show the effectiveness of the defense scheme and Neo4j is used to visualize. © 2007-2012 IEEE.

关键词： Cyber-physical system security electricity market profit-oriented cyberattack

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