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3rd International Conference on Autonomous Unmanned systems, ICAUS 2023

作者： Yang, Xiaolong Deng, Hui Duan, Xuting Xia, Haiying School of Transportation Science and Engineering Beihang University Beijing100191 China State Key Lab of Intelligent Transportation System Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control Beijing China China Telecom Digital City Technology Co. Ltd. Hebei China Key Laboratory of Operation Safety Technology on Transport Vehicles Ministry of Transport Beijing100088 China

ISBN: (纸本)9789819711024

With the addition of UAVs, the perception and recognition of ground targets have also advanced significantly. UAV photography has gradually become an integral part of this field. Due to the flexibility and lightness of UAVs, they can freely adjust their height and angle. Moreover, multiple UAVs can perceive and recognize the same target from different directions, which has led to increased attention toward collab.rative perception using multiple viewpoints of UAVs. Given the diverse image information captured by UAVs from various angles, heights, and environments, i.e., multi-scale conditions, this paper addresses the challenge of target detection by leveraging the Local Feature Matching with Transformer (LoFTR) approach. The paper also explains the process of coordinating transformations for different images captured by multiple UAVs to ensure data consistency and synergy. Finally, experimental validation using the VisDrone dataset demonstrates that the developed detection method can reliably and efficiently operate on targets at different angles and altitudes. © Beijing HIWING Scientific and Technological Information Institute 2024.

关键词： Object detection

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GROUP-BASED HYPERSPECTRAL IMAGE DENOISING USING LOW RANK REPRESENTATION

GROUP-BASED HYPERSPECTRAL IMAGE DENOISING USING LOW RANK REP...

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IEEE International Conference on Image Processing

作者： Mengdi Wang Jing Yu Weidong Sun State Key Lab. of Intelligent Technology & Systems Tsinghua National Lab. for Information Science & Technology Dept. of Electronic Engineering Tsinghua University College of Computer Science and Technology Beijing University of Technology

ISBN: (纸本)9781479983407

For the hyperspectral image (HSI) denoising problem, we propose a group-based low rank representation (GLRR) method. A corrupted HSI is divided into overlapping patches and the similar patches are combined into a group. The group is denoised as a whole using low rank representation (LRR). Our method can employ both the local similarity within the patch and the nonlocal similarity across the patches within a group simultaneously, while nonlocal similar patches within the group can bring extra structure information for the corrupted patch, which makes the noise more significant to be detected as outliers. Since the uncorrupted patches have an intrinsic low-rank structure, LRR is employed for the denoising of the patch group. Both simulated and real data are used in the experiments. The effectiveness of our method is proved both qualitatively and quantitatively.

关键词： Denoising Hyperspectral image Low rank representation Nonlocal similarity Noise reduction hyperspectral imagery Patches images AKR1B10 gene outliers

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Multi-Heterogeneity Public Goods Evolutionary Game on Lattice

SSRN

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SSRN 2022年

作者： Xie, Kai Liu, Xingwen Wang, Huazhang Jiang, Yulian Lab. of Complex Systems & Intelligent Control Key Lab. of Electronic Information of State Ethnic Affairs Commission College of Electrical Engineering Southwest Minzu University Sichuan Chengdu610225 China College of Electrical Engineering Southwest Minzu University Sichuan Chengdu610225 China

The topic of how cooperation emerges and develops in the real world has been intensively studied. Many mechanisms and models have been proposed to explain this complex phenomenon with the idealized assumption that all players are homogeneous or have little heterogeneity. This paper investigates the evolution of the public goods game with multiple heterogeneities. Roughly speaking, the quantity of investment (by a cooperator), punishment (received by a defector), or cost (paid by a punisher) is not fixed but depends on specific evolutionary situation. Moreover, heterogeneity is also introduced in expected payoff of each player for the strategy updating rule. Multi-heterogeneity makes the considered model more realistic. The simulation experiment shows that the combination of modified punishment mechanism and strategy updating rule can promote cooperation density in both the multi-heterogeneity and homogeneity models. © 2022, The Authors. All rights reserved.

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A SPECTRAL UNMIXING METHOD BASED ON WAVELET WEIGHTED SIMILARITY

A SPECTRAL UNMIXING METHOD BASED ON WAVELET WEIGHTED SIMILAR...

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IEEE International Conference on Image Processing

作者： Qingyu Pang Jing Yu Weidong Sun State Key Lab. of Intelligent Technology & Systems Tsinghua National Lab. for Information Science & Technology Dept. of Electronic Engineering Tsinghua University College of Computer Science and Technology Beijing University of Technology

ISBN: (纸本)9781479983407

With the rapid development of the hyperspectral technology, spectra unmixing has received more and more attentions. In this paper, in order to measure the similarity between the extracted endmember spectra and the actual corresponding spectra of land covers and maintain the spectral absorption feature, a wavelet weighted similarity is presented. And by introducing it into the minimum distance constrained nonnegative matrix factorization method, a spectral unmixing method based on the wavelet weighted similarity is proposed. Base on the experiments with real hyperspectral image, the feasibility and real performance of our method has been examined and compared with that of unsupervised spectral unmixing method.

关键词： Spectral unmixing Wavelet weighted similarity Nonnegative matrix factorization

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Design of Simultaneous state and Unknown Input Filtering Algorithm for a Class of Nonlinear Stochastic systems with Multiple Sensors 8

Design of Simultaneous State and Unknown Input Filtering Alg...

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8th IEEE International Conference on Cloud Computing and Intelligence systems, CCIS 2022

作者： Hu, Zhibin Hu, Jun Chen, Cai Du, Junhua Harbin University of Science and Technology Department of Applied Mathematics Harbin150080 China Harbin University of Science and Technology School of Automation Harbin150080 China Harbin University of Science and Technology Heilongjiang Prov. Key Lab. of Optimization Control and Intelligent Analysis for Complex Systems Harbin150080 China Qiqihar University College of Science Qiqihar161006 China

ISBN: (纸本)9781665477352

In this paper, we investigate the simultaneous state and unknown input (SUI) filtering issue for a class of multi-sensor networked systems (MSNSs). The unknown input with no prior knowledge is introduced in the system state and output. The focus of this paper is to design the local filters with regard to the SUI, which can yield that the local upper bounds of the filtering error covariance for the SUI are derived at each instant. Moreover, the local filter gains of the SUI are designed such that the obtained upper bounds can be minimized. Finally, the proposed joint SUI algorithm is verified by using the simulation example. © 2022 IEEE.

关键词： Stochastic systems

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Data-driven resilient predictive control under denial-of-service

arXiv

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arXiv 2021年

作者： Liu, Wenjie Sun, Jian Wang, Gang Bullo, Francesco Chen, Jie State Key Lab of Intelligent Control and Decision of Complex Systems The School of Automation Beijing Institute of Technology Beijing100081 China State Key Lab of Intelligent Control and Decision of Complex Systems The School of Automation Beijing Institute of Technology Beijing100081 China The Beijing Institute of Technology Chongqing Innovation Center Chongqing401120 China Mechanical Engineering Department The Center of Control Dynamical Systems and Computation UC Santa Barbara CA93106-5070 United States Department of Control Science and Engineering Tongji University Shanghai201804 China State Key Lab of Intelligent Control and Decision of Complex Systems The School of Automation Beijing Institute of Technology Beijing100081 China

The study of resilient control of linear time-invariant (LTI) systems against denial-of-service (DoS) attacks is gaining popularity in emerging cyber-physical applications. In previous works, explicit system models are required to design a predictor-based resilient controller. These models can be either given a priori or obtained through a prior system identification step. Recent research efforts have focused on data-driven control based on pre-collected input-output trajectories (i.e., without explicit system models). In this paper, we take an initial step toward data-driven stabilization of stochastic LTI systems under DoS attacks, and develop a resilient model predictive control (MPC) scheme driven purely by data-dependent conditions. The proposed data-driven control method achieves the same level of resilience as the model-based control method. For example, local input-to-state stability (ISS) is achieved under mild assumptions on the noise and the DoS attacks. To recover global ISS, two modifications are further suggested at the price of reduced resilience against DoS attacks or increased computational complexity. Finally, a numerical example is given to validate the effectiveness of the proposed control method. © 2021, CC BY.

关键词： Model predictive control

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Color object tracking in five degrees of freedom stereo vision system

International Journal of Digital Content Technology and its ...

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International Journal of Digital Content Technology and its Applications 2012年第20期6卷 43-52页

作者： Xin, Du Xiaoguang, Zhao Yu, Huang Jingjing, Ye School of Electrical Engineering Beijing Jiaotong University Beijing China State Key Lab. of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

From the view of combination of stereo vision and robot control, an active stereo vision for object tracking and location system is built to work on flexible manufacturing system. A method based on local HSV image and shape of object to recognize object is used for robot tracking. After the color segment, use the knowledge of shape of objects to recognize objects. And a technique for camera calibration based on plane square is proposed. It only requires the camera to observe a square at two (or more) different unknown orientations. Use the result of object recognize and camera calibration the robot can position and track the object. At the last of the paper, the results of object tracking experiments prove that the method is correct.

关键词： Stereo vision

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Traveling waves based faulted feeder selector and its field application

Traveling waves based faulted feeder selector and its field ...

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2011 International Conference on Advanced Power System Automation and Protection, APAP 2011

作者： Shi, Shenxing Wang, Jun Li, Aiming Du, Xinchun Cao, Xiaoxia Du, Yajun Dong, Xinzhou State Key Lab. of Control and Simulation of Power Systems and Generation Equipments Dept. of Electrical Engineering Tsinghua University Haidian District Beijing 100084 China Yulin Power Company Shaanxi 719000 China China Jiuquan Satellite Launch Center Plumbing Electrical Station Jiuquan 732750 China

ISBN: (纸本)9781424496198

The paper proposes faulted feeder selector in power distribution system with neutral non-effectively grounded. At first, the paper presents basic principle of single-phase-to-ground fault feeder selection based on traveling waves, which compares amplitude and polarity of the initial current traveling waves in all feeders. Then the implementation scheme, including hardware design and software flowchart is presented. At last field application of faulted feeder selector is introduced. The field operation results validated the correctness of faulted feeder selector based on transient traveling wave. © 2011 IEEE.

关键词： Electric grounding

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Positive-sequence component based three-phase unbalanced power flow solution for distribution system with PV nodes

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Zhongguo Dianji Gongcheng Xuebao/Proceedings of the Chinese Society of Electrical Engineering 2012年第1期32卷 115-121页

作者： Li, Hongwei Sun, Hongbin Zhang, An'an Liu, Qingyou State Key Laboratory of Oil and Gas Reservoirs Geology and Exploitation Southwest Petroleum University Chengdu 610500 Sichuan Province China State Key Lab. of Control and Simulation of Power Systems and Generation Equipments Dept. of Electrical Engineering Tsinghua University Haidian District Beijing 100084 China

The traditional power flow methods aren't appropriate to the electrical distribution systems with distributed generators (DG). Therefore, a direct unbalanced three-phase power flow algorithm was presented in this paper. The proposed approach which was developed based on the path matrix, utilized the special topological structure of distribution systems and established the incident matrix of node voltages and injection currents. Based on the assumption that the positive-sequence voltage magnitudes of PV nodes are invariable, the relation of reactive power and compensation voltage for the PV nodes had been derived by the positivesequence component and path matrix. As a result, a new approach handling PV nodes of DG had been proposed and could be integrated into the proposed power flow method. It needn't introduce any additional active power at PV nodes and kept the positive-sequence voltage magnitudes on their scheduled values, considering that reactive power limits were not violated. The results of the 6-bus and the 69-bus test systems verified the validity and universality of the proposed methods. © 2012 Chin. Soc. for Elec. Eng.

关键词： Reactive power

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Variational Bayesian Learning based Joint Localization and Channel Parameter Estimation in Wireless Sensor Networks with Distance-dependent Noise

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IEEE Transactions on Vehicular Technology 2024年第5期74卷 6877-6891页

作者： Li, Yunfei Luo, Yiting Tan, Weiqiang Li, Chunguo Ma, Shaodan Yang, Guanghua Anhui Polytechnic University Department of Electrical Engineering Wuhu China Guangzhou University School of Computer Science and Cyber Engineering Guangzhou China Southeast University School of Information Science and Engineering Nanjing China Purple Mountain Laboratories Nanjing China University of Macau State Key Lab. of Internet of Things for Smart City and the Dept. of Elec. and Computer Engineering Taipa China Jinan University School of Intelligent Systems Science and Engineering Zhuhai519070 China

This paper focuses on the challenge of jointly optimizing location and path loss exponent (PLE) in distancedependent noise. Departing from the conventional independent noise model used in localization and path loss exponent estimation problems, we consider a more realistic model incorporating distance-dependent noise variance, as revealed in recent theoretical analyses and experimental results. The distance-dependent noise introduces a complex noise model with unknown noise power and PLE, resulting in an exceptionally challenging nonconvex and nonlinear optimization problem. In this study, we address a joint localization and path loss exponent estimation problem encompassing distance-dependent noise, unknown parameters, and uncertainties in sensor node locations. To surmount the intractable nonlinear and non-convex objective function inherent in the problem, we introduce a variational Bayesian learning-based framework that enables the joint optimization of localization, path loss exponent, and reference noise parameters by leveraging an effective approximation to the true posterior distribution. Furthermore, the proposed joint learning algorithm provides an iterative closed-form solution and exhibits superior performance in terms of computational complexity compared to existing algorithms. Computer simulation results demonstrate that the proposed algorithm approaches the performance of the Bayesian Cramer-Rao bound (BCRB), achieves localization performance comparable to the (maximum likelihood-Gaussian message passing) ML-GMP algorithm in some cases, and outperforms the other comparison algorithm in all cases. © 1967-2012 IEEE.

关键词： Iterative methods

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