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IET International Radar Conference 2023, IRC 2023

作者： Gao, Silin Wang, Muhan Zhang, Zhe Zhang, Bingchen Wu, Yirong Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China Key Laboratory of Technology in Geo-spatial Information Processing and Application System Chinese Academy of Sciences Beijing100190 China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100049 China Key Laboratory of Intelligent Aerospace Big Data Application Technology Suzhou215123 China Suzhou Aerospace Information Research Institute Suzhou215123 China

ISBN: (纸本)9781839539954

This paper is centered on gridless direction of arrival (DoA) estimation for single-snapshot data collected by non-uniform linear arrays (NLAs) in the context of automotive applications. While recent single-snapshot DoA estimation algorithms rooted in grid-based compressed sensing (CS) offer super-resolution capabilities, they are notably sensitive to discrepancies between the assumed sparsity basis and the actual basis. Furthermore, the existing atomic soft thresholding (AST) algorithm, a promising gridless sparse recovery technique within the Toeplitz model, is constrained to uniform linear arrays (ULAs) exhibiting Vandermonde structure in their array manifolds. However, in automotive scenarios, it becomes imperative to efficiently apply gridless DoA estimation to NLAs featuring non-Vandermonde array manifolds. In this paper, we introduce the non-Vandermonde atomic soft thresholding (NVAST) algorithm and employ it for DoA estimation in NLAs. The novel algorithm establishes a link between Vandermonde and non-Vandermonde atoms by decomposing non-Vandermonde vectors and addressing them using semi-definite programming (SDP). Simulation and measurement experiments conducted with automotive radars underscore the remarkable performance of our proposed method. © The Institution of Engineering & Technology 2023.

关键词： Automotive radar

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AN AUTOFOCUS NETWORK FOR MULTI-CHANNEL PHASE ERRORS WITH APPLICATION TO TOMOSAR IMAGING

AN AUTOFOCUS NETWORK FOR MULTI-CHANNEL PHASE ERRORS WITH APP...

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IET International Radar Conference 2023, IRC 2023

作者： Wang, Muhan Gao, Silin Zhang, Zhe Qiu, Xiaolan Key Laboratory of Technology in Geo-spatial Information Processing and Application System Chinese Academy of Sciences Beijing100190 China Key Laboratory of Intelligent Aerospace Big Data Application Technology Suzhou215123 China Suzhou Aerospace Information Research Institute Suzhou215123 China Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100049 China

ISBN: (纸本)9781839539954

Synthetic aperture radar (SAR) tomography (TomoSAR) has garnered significant attention due to its capability for three-dimensional reconstruction. Compressed sensing (CS) methods are widely employed to address the TomoSAR inversion challenge. Nevertheless, practical applications reveal phase errors among different channels, resulting in defocusing and blurring when relying solely on CS for 3D reconstruction. Current state-of-the-art autofocus techniques suffer from prohibitive computational complexity, limiting their applicability to large-scale 3D imaging. In pursuit of efficient TomoSAR 3-D autofocusing, we proposed ASAMP-Net, an innovative deep unfolding network. Operating within a two-step framework, each layer comprises two stages: phase error estimation and iterative scattering coefficient reconstruction using the sparse adaptive matching pursuit (SAMP) algorithm. Additionally, phase error estimation is obtained through mathematical derivation, while challenges associated with fixed sparsity and limited efficiency in conventional methods are mitigated through deep learning techniques. Simulation experiments and real data validation affirm the effectiveness and superiority of the proposed method. © The Institution of Engineering & Technology 2023.

关键词： Radar imaging

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A GENERALIZED PENALTY METHOD FOR DIFFERENTIAL VARIATIONAL-HEMIVARIATIONAL INEQUALITIES

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Acta Mathematica Scientia 2022年第1期42卷 247-264页

作者： Liang LU Lijie LI Mircea SOFONEA Guangxi Key Laboratory of Cross-border E-commerce Intelligent Information Processing Guangxi University of Finance and EconomicsNanning 530003China Guangxi Colleges and Universities Key Laboratory of Complex System Optimization and Big Data Processing Yulin Normal UniversityYulin 537000China Laboratoire de Mathématiques et Physique Universitéde Perpignan Via Domitia52 Avenue Paul Alduy66860 PerpignanFrance

We consider a differential variational-hemivariational inequality with constraints,in the framework of reflexive Banach *** existence of a unique mild solution of the inequality,together with its stability,was proved in[1].Here,we complete these results with existence,uniqueness and convergence results for an associated penalty-type *** this end,we construct a sequence of perturbed differential variational-hemivariational inequalities governed by perturbed sets of constraints and penalty *** prove the unique solvability of each perturbed inequality as well as the convergence of its solution to the solution of the original ***,we consider a mathematical model which describes the equilibrium of a viscoelastic rod in unilateral *** weak formulation of the model is in a form of a differential variational-hemivariational inequality in which the unknowns are the displacement field and the history of the *** apply our abstract penalty method in the study of this inequality and provide the corresponding mechanical interpretations.

关键词： differential variational-hemivariational inequality generalized penalty method Mosco convergence viscoelastic rod unilateral constraint

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Feedforward Dual-Mode Repetitive Control for Single-Phase V2G Inverters in Residential Grids

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IEEE Transactions on Transportation Electrification 2025年

作者： Zhao, Qiangsong Zhou, Guohui Xia, Yuanqing Zhu, Yongsheng Yu, Dongdong Zhang, Hongwei Zhongyuan University of Technology School of Automation and Electrical Engineering Zhengzhou450007 China Beijing Institute of Technology School of Automation Beijing100081 China Zhejiang University of Technology Data-Driven Intelligent Systems Laboratory College of Information Engineering Hangzhou310023 China

Vehicle-to-grid (V2G) technology enhances the utilization of renewable energy by enabling electric vehicles to inject stored renewable power into the grid. However, in singlephase residential grids, the grid background harmonics and grid frequency variations directly affect the feed-in grid current quality of the V2G inverter system. To address this issue, this paper proposes a novel feedforward dual-mode repetitive control (FDMRC) scheme, which adjusts adaptively the repetitive control (RC) gains according to the harmonics distribution of feed-in grid current. By introducing the feedforward channels into the dual-mode repetitive control, the FDMRC enlarges the open-loop gains at the frequencies of interest, while enhancing the dynamic performance of the system. In addition, the adaptive RC gains improve the harmonics suppression performance of the FDMRC system under varied grid background harmonics. Furthermore, stability analysis and detailed parameters optimization design for the FDMRC system are presented. The experimental platform of a single-phase V2G inverter is established to verify the effectiveness of the proposed FDMRC scheme, and the experimental results demonstrates that the proposed FDMRC scheme possesses enhanced harmonics suppression performance, strong frequency robustness, and fast dynamic performance under the scenarios of varied grid background harmonics and grid frequency variations. © 2015 IEEE.

关键词： Feedforward control

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Tensor and Minimum Connected Dominating Set based Confident information Coverage Reliability Evaluation for IoT

IEEE Transactions on Sustainable Computing

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IEEE Transactions on Sustainable Computing 2024年第3期10卷 547-561页

作者： Xiao, Ziheng Zhu, Chenlu Feng, Wei Liu, Shenghao Deng, Xianjun Lu, Hongwei Yang, Laurence T. Park, Jong Hyuk Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Hubei Wuhan430074 China Artificial Intelligence and Intelligent Transportation Joint Technical Center of HUST and Hubei Chutian Intelligent Transportation Company Ltd United States Network and Industrial Control Information Security Technology Department China Nuclear Power Operation Technology Company Ltd. 1011 Xiongchu Avenue Hubei Wuhan430070 China The Department of Computer Science and Engineering Seoul National University of Science and Technology Seoul01811 Korea Republic of

Internet of Things (IoT) reliability evaluation contributes to the sustainable computing and enhanced stability of the network. Previous algorithms usually evaluate the reliability of IoT by enumenating the states of nodes and networks, which are difficult to handle IoT with hundreds of nodes because the computational cost. In this paper, a novel algorithm, TMCRA, is proposed to evaluate the reliability of IoT in complex network environment, which consider both coverage and connectivity. For coverage, TMCRA employs the Confident information Coverage (CIC) model to divide the target area into independent grids and calculates the coverage rate. In terms of connectivity, TMCRA forming the Virtual Backbone Network (VBN) based on two proposed methods: TMA and MGIN, and evaluate connectivity by analyzing the VBN rather than the whole network. The TMA and MGIN are two algorithms for constructing Minimum Connected Dominant Sets (MCDS), which are suitable for different scale networks. Finally, based on the data of coverage and connectivity, TMCRA utilizes tensors for the unified modeling and representation of network structure, and calculates IoT reliability based on the tensors. Simulations are carried out for various sizes of IoT to show the advantages and effectiveness of the proposed approach in reliability evaluation. © 2016 IEEE.

关键词： data reliability

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PDEGAN: A Panoramic Style Transfer Based on Generative Adversarial Networks

Journal of Network Intelligence

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Journal of Network Intelligence 2024年第4期9卷 2112-2121页

作者： Wang, Qinghua Long, Xinling Huang, Jingwei Chen, Yang Yang, Lirong Zhang, Fuquan College of Computer and Big Data Minjiang University Fuzhou350108 China Fuzhou Technology Innovation Center of intelligent Manufacturing information System Minjiang University Fuzhou350108 China Fujian Province University Fuzhou350300 China College of Computer and Control Engineering Minjiang University Fuzhou350108 China College of Computer and Big Data Fuzhou University Fuzhou China School of Mechanical and Automotive Engineering Fujian University of Technology Fuzhou China College of foreign languages Dalian Jiaotong University Dalian116028 China Digital Media Art Key Laboratory of Sichuan Province Sichuan Conservatory of Music Chengdu610021 China

Panoramic image plays an extremely important role in the application of 3D, but in some special scenarios, the depth information is also needed as an auxiliary, but to obtain the panoramic depth information needs a high cost, it is extremely difficult to obtain. Therefore, this paper proposes a panoramic depth estimation method called PDEGAN (Panoramic Depth Estimation Generative Adversarial Network). This method first enhances the style migration, transfers the input panoramic image to a style closer to the panoramic depth image domain, and then combines the conversion results with the original panoramic image as the input data for further depth estimation training. By using the style closer to the depth estimation, the texture, color and other characteristics of the image can be adjusted, so as to better highlight the depth information of the object in the image, so that the generator network is more focused on performing the image translation task, reduce the impact of style differences on the image translation task, and improve the generalization ability of the model. At the same time, the model will also use multi-scale feature fusion, combined with the attention mechanism, to better capture the depth information of different scales, viewing angles and channels. Not only that, but also by introducing depth gradient loss, surface normal loss to improve the accuracy and stability of depth estimation. To this end, we also made a set of related datasets for the related experiments of PDEGAN, and named it PDE Images. Finally, it is shown that the method proposed in this paper has more accurate estimation results than the traditional Pix2Pix image conversion model. © 2024, Taiwan Ubiquitous information CO LTD. All rights reserved.

关键词： Generative adversarial networks

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Prescribed-Time Tracking Control for Robotic systems with Uncertain Dynamics

Prescribed-Time Tracking Control for Robotic Systems with Un...

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data driven Control and Learning systems (DDCLS)

作者： Hang Yang Yingbo Huang Jing Na Qiang Chen Faculty of Mechanical and Electrical Engineering Kunming University of Science and Technology Kunming China Yunnan International Joint Laboratory of Intelligent Control and Application of Advanced Equipment Kunming China Data-Driven Intelligent Systems Laboratory College of Information Engineering Zhejiang University of Technology Hangzhou China

This paper presents a prescribed-time tracking control scheme for robotic systems with unknown dynamics. One salient feature is that the robotic systems can achieve prescribed-time stability with the prescribed transient performance, which is different with the traditional work that achieves uniformly ultimately bounded (UUB) and/or asymptotic stability. To realize this purpose, the prescribed performance dynamics (PPD) instead of the traditional prescribed performance function (PPF) is suggested, by which both the transient and steady-state tracking performance can be guaranteed within a specified region in-priori. To avoid using the function approximators (i.e., neural networks (NNs), fuzzy logic systems (FLSs)), an approximation-free-based prescribed-time controller is established with the inertial matrix of robotic systems and the finite-time function merely, which can not only address the system unknown uncertainties but also regulate the control error to zero in a prescribed-time. Furthermore, the Lyapunov-based theoretical analysis is conducted to prove the prescribed-time stability of the closed-loop system. Finally, comparative numerical simulation results are provided to demonstrate the effectiveness of the proposed method.

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An Encoder-Decoder Model Based On Spiking Neural Networks For Address Event Representation Object Recognition

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IEEE Transactions on Cognitive and Developmental systems 2025年

作者： Du, Sichun Zhu, Haodi Zhang, Yang Hong, Qinghui Hunan University College of Computer Science and Electronic Engineering Changsha418002 China Shenzhen University Computer Vision Institute School of Computer Science and Software Engineering National Engineering Laboratory for Big Data System Computing Technology Guangdong Key Laboratory of Intelligent Information Processing Shenzhen518060 China

Address event representation (AER) object recognition task has attracted extensive attention in neuromorphic vision processing. The spike-based and event-driven computation inherent in the spiking neural network (SNN) provides an energy-saving solution for AER object recognition. However, SNN with spike timing dependent plasticity (STDP) learning rule has not achieved satisfying AER object recognition performance. This work proposes an SNN-based encoder-decoder model to improve the recognition performance of AER objects. An STDP-based locally connected spiking neural network (LC-SNN) is proposed as an encoder to extract rich spatiotemporal features from AER event flows more flexibly. After the encoder extracts and learns primary features, we propose a fully connected spiking neural network (FC-SNN) based on the reward-modulated spike-timing-dependent plasticity (R-STDP) learning rule as a decoder to learn higher-level features for classification. In addition, we improved the winner-take-all (WTA) mechanisms and R-STDP learning rule in the decoder based on the reward and punish decision, enabling the network to perform better. The experiments are performed on the N-MNIST, MNIST-DVS, and the DVS Gesture datasets, improving the accuracy of the best existing plasticity-based SNN by 0.19%, 0.27% and 1.35%, respectively. © 2016 IEEE.

关键词： Decoding

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End-to-End Remote Sensing Image Scene Classification with Vision Transformers 23

End-to-End Remote Sensing Image Scene Classification with Vi...

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6th International Conference on Big data Technologies, ICBDT 2023

作者： Yuan, Hao Liu, Kun Shi, Jiechuan Wang, Can Wang, Weiwei School of Information Science and Engineering University of Jinan Jinan250022 China China High-resolution Earth Observation System Shandong Center of Data and Application University of Jinan Jinan250022 China Shandong Provincial Key Laboratory of Network Based Intelligent Computing University of Jinan Jinan250022 China QiLu Research Institute Jinan250200 China

ISBN: (纸本)9798400707667

In recent years, the development of deep learning technology has led to widespread attention on Vision Transformer (ViT) as an emerging image classification method. Remote sensing image classification is an important task in the field of remote sensing, with extensive application prospects. This paper aims to explore the remote sensing image classification method based on Vision Transformer, addressing the limitations of traditional convolutional neural networks in terms of global perception capability, context information retrieval, and positional encoding. The classification performance of the Vision Transformer model is evaluated and compared on remote sensing datasets. Vision Transformer is a deep neural network model based on self-attention mechanism that can capture the global context information in images and has achieved remarkable performance in various computer vision tasks. Furthermore, experimental results demonstrate that the remote sensing image classification method based on Vision Transformer exhibits outstanding accuracy and generalization ability. Compared to traditional convolutional neural networks, it can better capture the global features in remote sensing images and has better scalability when dealing with large-scale remote sensing image data. Experimental results on different remote sensing image datasets show that the model performs well compared to state-of-the-art methods. Specifically, Vision Transformer achieves average classification accuracies of 95.41%, 98.26%, 93.74% and 95.25% on the AID, UC-Merced, NWPU-RESISC45 and Optimal31 datasets, respectively. © 2023 ACM.

关键词： Neural network models

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Security Compressed Sensing Image Encryption Algorithm Based on Elliptic Curve 9th

Security Compressed Sensing Image Encryption Algorithm Based...

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9th International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2023

作者： Jin, Anan Li, Xiang Xiong, Qingzhi School of Information Engineering East China University of Technology Jiangxi Nanchang330013 China Jiangxi Engineering Technology Research Center of Nuclear Geoscience Data Science and System East China University of Technology Jiangxi Nanchang330013 China Jiangxi Key Laboratory of Cybersecurity Intelligent Perception East China University of Technology Jiangxi Nanchang330013 China

ISBN: (纸本)9789819959679

Aiming to address the problems of high costs associated with the storage and transmission of environmental monitoring images, as well as potential security risks, this paper proposes a security compressed sensing image encryption algorithm based on elliptic curve cryptography. The algorithm introduces elliptic curve encryption technology within a compressed sensing framework, using elliptic curve cryptography to encrypt the matrix during the compression perception acquisition process. This enables secure acquisition of environmental monitoring encrypted images. Therefore, this paper presents a security compressed sensing framework to address security gaps in the compression perception reconstruction process and improve the security of environmental monitoring images. Experimental results show that the proposed algorithm effectively encrypts and reconstructs environmental monitoring images, with high security and resistance to cracking. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Compressed sensing

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