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arXiv 2025年

作者： Ren, Junhao Zhao, Kai Zhang, Guangxiao Liu, Xinghua Zhai, Chao Xiao, Gaoxi School of Electrical and Electronic Engineering Nanyang Technological University Singapore639798 Singapore Institute of Catastrophe Risk Management Nanyang Technological University Singapore639798 Singapore Future Resilient Systems Singapore-ETH Centre Singapore138602 Singapore School of Electrical Engineering Xi’an University of Technology Xi’an710048 China Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems The Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China

Parallel cyber-physical attacks (PCPA) refer to those attacks on power grids by disturbing/cutting off physical transmission lines and meanwhile blocking transmission of measurement data to dwarf or delay the system protection and recovery actions. Such fierce hostile attacks impose critical threats to the modern power grids when there is a fusion of power grids and telecommunication technologies. In this paper, we investigate the fault diagnosis problem of faulty transmission lines under a broader spectrum of PCPA for a linearized (or DC) power flow model. The physical attack mechanism of PCPA includes not only disconnection but also admittance value modification on transmission lines, for example, by invading distributed flexible AC transmission system (D-FACTS). To tackle the problem, we first recover the information of voltage phase angles within the attacked area. Using the information of voltage phase angle and power injection of buses, a graph attention network-based fault localization (GAT-FL) algorithm is proposed to find the locations of the physical attacks. By capitalizing on the feature extraction capability of the GAT on graph data, the fault localization algorithm outperforms the existing results when under cyber attacks, e.g., denial of service (DoS) attacks. A line state identification algorithm is then developed to identify the states of the transmission lines within the attacked area. Specifically, the algorithm restores the power injection of buses within the attacked area and then identities the state of all the transmission lines within the attacked area by solving a linear programming (LP) problem. Experimental simulations are conducted on IEEE 30/118 bus standard test cases to demonstrate the effectiveness of the proposed fault diagnosis algorithms. Copyright © 2025, The Authors. All rights reserved.

关键词： Cyber attacks

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Learning to Find Good Correspondences Based on Global and Local Attention Mechanism

Learning to Find Good Correspondences Based on Global and Lo...

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2021 China Automation Congress, CAC 2021

作者： Chen, Jun Gu, Yue Luo, Linbo School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China School of Mechanical Engineering and Electronic Information China University of Geosciences Wuhan China

ISBN: (纸本)9781665426473

We are committed to designing a method for establishing a set of reliable correspondences between two images in this paper. Previous work proposes an outlier removal network based on global and local attention mechanism. It normalizes the feature map under the guidance of attention weight, as a result the robustness to outliers is improved and inliers are better preserved. However, element-wise multiplication of these two different attentions makes them unable to perform their original functions. In order to overcome this limitation, a network based on attention mechanism has been designed in our work. Firstly, global attention is employed to compute a global weight which can help normalization focus on inliers and reduce the influence of outliers on context information. Secondly, to extract channel information, we use local attention to learn a channel weight for each correspondence. Finally, the above structures and residual network modules are combined for correspondence establishment. Experimental results show that our method is better than other SOTA methods. © 2021 IEEE

关键词： Computer vision

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An Improved Task Planning Method for Unmanned Swarms with Coupled Physical and Logical Constraints

An Improved Task Planning Method for Unmanned Swarms with Co...

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IEEE International Conference on Unmanned systems (ICUS)

作者： Xiubin Chen Lei Zhang Fang Xu Weiran Yao School of Astronautics Harbin Institude of Technology Harbin China Wuhan 2nd Ship Design and Research Institute Wuhan China Marine Military Intelligence Innovation Institute CSSC Systems Engineering Research Institute Beijing China National Key Laboratory of Complex System Control and Intelligent Agent Cooperation Beijing China

ISBN: (数字)9798350384185

ISBN: (纸本)9798350384192

Traditional task allocation methods for unmanned swarm systems ignore the effects of actual paths, resulting in estimation accuracy reduction. This paper formulates task planning problem by incorporating physical and logical constraints, and establishes an integrated framework of task allocation and path planning. Conflict-based search method is used to address path planning with physical constraints. A genetic algorithm is employed to solve multi-traveling salesman allocation problem. A bounded suboptimal optimization, a data dictionary, and an island model are introduced to accelerate the convergence speed of the genetic algorithm. The experiments verify that compared to the decoupled task planning methods, the proposed method improves task execution efficiency and remains adaptability to various complex spatial maps.

关键词： Dictionaries Search methods Estimation Path planning Data models Planning Resource management Optimization Genetic algorithms Convergence

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A fuzzy control and neural network based rotor speed controller for maximum power point tracking in permanent magnet synchronous wind power generation system

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Global Energy Interconnection 2023年第5期6卷 554-566页

作者： Min Ding Zili Tao Bo Hu Meng Ye Yingxiong Ou Ryuichi Yokoyama School of Automation China University of GeosciencesWuhan 430074P.R.China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan 430074P.R.China Global Energy Interconnection Group Co. LtdSTATE GRID Corporation of ChinaBeijing 100032P.R.China School of Environment and Energy Engineering Waseda UniversityTokyo169-8050Japan

When the wind speed changes significantly in a permanent magnet synchronous wind power generation system,the maximum power point cannot be easily determined in a timely *** study proposes a maximum power reference signal search method based on fuzzy control,which is an improvement to the climbing search method.A neural network-based parameter regulator is proposed to address external wind speed fluctuations,where the parameters of a proportional-integral controller is adjusted to accurately monitor the maximum power point under different wind speed ***,the effectiveness of this method is verified via Simulink simulation.

关键词： Maximum wind power tracking Fuzzy control Neural network

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Non-Markovian Dynamics: The Governing Equations Of Probability Density Functions for Nonlinear Dynamical systems with Combined Fractional Gaussian and Gaussian White Noises

SSRN

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SSRN 2024年

作者： Pei, Bin Feng, Lifang Li, Yunzhang Xu, Yong School of Mathematics and Statistics Northwestern Polytechnical University Xi’an710072 China Research & Development Institute Northwestern Polytechnical University in Shenzhen Shenzhen518057 China Research Institute of Intelligent Complex Systems Fudan University Shanghai200433 China MOE Key Laboratory for Complexity Science in Aerospace Northwestern Polytechnical University Xi’an710072 China

This paper aims to investigate the non-Markovian dynamics. The governing equations are derived for probability density functions (PDFs) of non-Markovian stochastic responses to nonlinear dynamical systems (NDSs) excited by combined fractional Gaussian noises (FGNs) and Gaussian white noises (GWNs). The main difficulty here, is that, NDSs excited by FGNs cannot be augmented by filters excited by GWNs, leading to the inapplicability of It\^o stochastic calculus theory. Thus, in the present work, based on fractional Wick Itô Skorohod integral and rough path theory, new non-Markovian probability density evolution methods (PDEMs) are established to derive theoretically the fractional-power time varying term-dependent governing equations (FPTV-GEs) for PDFs of non-Markovian stochastic responses to NDSs excited by FGNs and GWNs, which is a breakthrough to stochastic dynamics. Then, we extend an efficient algorithm, the local discontinuous Galerkin method, to numerically solve the FPTV-GEs. Remarkably, this proposed method attains a higher accuracy compared to the prevalent methods such as finite difference, path integral and Monte Carlo methods, and boasts a broader applicability than the path integral method, which fails to solve the FPTV-GEs for PDFs of non-Markovian stochastic responses. Finally, several numerical examples are illustrated to verify the proposed scheme. © 2024, The Authors. All rights reserved.

关键词： Probability density function

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Generative Adversarial Network Based NDVI Super-resolution Reconstruction

Generative Adversarial Network Based NDVI Super-resolution R...

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IEEE Global Conference on Consumer Electronics (GCCE)

作者： Zhengbao Sun Hongyu Pan Kun Yue School of Engineering Yunnan University Kunming China Aerospace Information Reasearch Institute Chinese Academy of Sciences Beijing China School of Information Science and Engineering Yunnan University Kunming China Yunnan Key Laboratory of Intelligent Systems and Computing Yunnan University Kunming China

Low resolution satellite remote sensing images remains a challenge for obtaining high-precision Normalized Difference Vegetation Index (NDVI), and contain some important vegetation cover information for supporting environmental analyses of the surface in large-scale regions, since high-resolution satellite remote sensing observations are still incomplete nowadays. In this paper, we consider the spatial resolution and time series characteristics of remote sensing images, and propose a super-resolution reconstruction algorithm for NDVI fused with Time features by optimizing the Super-Resolution Generative Adversarial Network (TSRGAN). Empirical research in the Salween River estuary area shows that the proposed approach has performance significant advantages in terms of PSNR and SSIM.

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EventHDR: from Event to High-Speed HDR Videos and Beyond

arXiv

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arXiv 2024年

作者： Zou, Yunhao Fu, Ying Takatani, Tsuyoshi Zheng, Yinqiang MIIT Key Laboratory of Complex-field Intelligent Sensing Beijing Institute of Technology Beijing China School of Computer Science and Technology Beijing Institute of Technology Beijing China Institute of Systems and Information Engineering The University of Tsukuba Tsukuba Japan Next Generation Artificial Intelligence Research Center The University of Tokyo Tokyo113-8656 Japan

Event cameras are innovative neuromorphic sensors that asynchronously capture the scene dynamics. Due to the event-triggering mechanism, such cameras record event streams with much shorter response latency and higher intensity sensitivity compared to conventional cameras. On the basis of these features, previous works have attempted to reconstruct high dynamic range (HDR) videos from events, but have either suffered from unrealistic artifacts or failed to provide sufficiently high frame rates. In this paper, we present a recurrent convolutional neural network that reconstruct high-speed HDR videos from event sequences, with a key frame guidance to prevent potential error accumulation caused by the sparse event data. Additionally, to address the problem of severely limited real dataset, we develop a new optical system to collect a real-world dataset with paired high-speed HDR videos and event streams, facilitating future research in this field. Our dataset provides the first real paired dataset for event-to-HDR reconstruction, avoiding potential inaccuracies from simulation strategies. Experimental results demonstrate that our method can generate high-quality, high-speed HDR videos. We further explore the potential of our work in cross-camera reconstruction and downstream computer vision tasks, including object detection, panoramic segmentation, optical flow estimation, and monocular depth estimation under HDR scenarios. © 2024, CC BY.

关键词： Convolutional neural networks

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Averaging principle for semilinear slow-fast rough partial differential equations

arXiv

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arXiv 2024年

作者： Li, Miaomiao Li, Yunzhang Pei, Bin Xu, Yong School of Mathematics and Statistics Northwestern Polytechnical University Xi’an710072 China Research Institute of Intelligent Complex Systems Fudan University Shanghai200433 China Research and Development Institute Northwestern Polytechnical University in Shenzhen Shenzhen518057 China MOE Key Laboratory of Complexity Science in Aerospace Northwestern Polytechnical University Xi’an710072 China

In this paper, we investigate the averaging principle for a class of semilinear slow-fast stochastic partial differential equations driven by finite-dimensional rough multiplicative noise. Specifically, the slow component is driven by a general random rough path, while the fast component is driven by a Brownian rough path. Using controlled rough path theory and the classical Khasminskii’s time discretization scheme, we demonstrate that the slow component converges strongly to the solution of the corresponding averaged equation under the Hölder topology. © 2024, CC BY.

关键词： Stochastic systems

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The influence of color temperature mismatch in star simulator on positioning accuracy and magnitude measurement by star sensor

The influence of color temperature mismatch in star simulato...

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2021 International Conference on Computer Technology, Information Engineering and Electron Materials, CTIEEM 2021

作者： Wang, Zhiyong Liu, Shishen Sun, Hao Zhang, Jingjing Li, Ruyou Liu, Chen School of Automation China University of Geosciences Hubei Wuhan430074 China Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration China Shanghai Institute of Technical Physics Chinese Academy of Sciences Shanghai200083 China

During the calibrating of star sensor, the calibration accuracy is greatly affected by the mismatch between the color temperature of the light and the to-be-measured star, which further affects the attitude measurement accuracy. This paper studied the near-infrared spectra of stars with different color temperatures, and analyzed the errors on star positioning and magnitude measurement of star sensor due to the color temperature mismatch. The results showed that in the central field of view, the spot centroid deviation caused by spectral mismatch is smaller than that in the edge field of *** the defocus of the imaging surface also affects the spot centroid deviation. Besides, when calibrating with 6000K color temperature light, the maximum measurement error can reach -1.9126 magnitude. © Content from this work may be used under the terms of the Creative Commons Attribution 3.0 Licence.

关键词： Stars

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Non-Markovian dynamics: the memory-dependent probability density evolution equations

arXiv

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arXiv 2024年

This paper aims to investigate the non-Markovian dynamics. The governing equations are derived for the probability density functions (PDFs) of non-Markovian stochastic responses to Langevin equation excited by combined fractional Gaussian noise (FGN) and Gaussian white noise (GWN). The main difficulty here is that the Langevin equation excited by FGN cannot be augmented by a filter excited by GWN, leading to the inapplicability of Itô stochastic calculus theory. Thus, in the present work, based on the fractional Wick Itô Skorohod integral and rough path theory, a new non-Markovian probability density evolution method is established to derive theoretically the memory-dependent probability density evolution equation (PDEEs) for the PDFs of non-Markovian stochastic responses to Langevin equation excited by combined FGN and GWN, which is a breakthrough to stochastic dynamics. Then, we extend an efficient algorithm, the local discontinuous Galerkin method, to numerically solve the memory-dependent PDEEs. Remarkably, this proposed method attains a higher accuracy compared to the prevalent methods such as finite difference, path integral (PI) and Monte Carlo methods, and boasts a broader applicability than the PI method, which fails to solve the memory-dependent PDEEs. Finally, several numerical examples are illustrated to verify the proposed scheme. © 2024, CC BY.

关键词： Stochastic systems

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