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Data Driven control and Learning systems (DDCLS)

作者： Kaixin Liu Xin Zhong Ping Chen Yun Dai Yi Liu The State Key Lab for Electronic Testing Technology North University of China China Department of Information and Communication Engineering North University of China Taiyuan China College of Mechanical Engineering Nantong Vocational University Nantong China Institute of Process Equipment and Control Engineering Zhejiang University of Technology Hangzhou China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

In recent years, various methods for processing and analyzing thermographic data have been applied in non-destructive testing to improve the visibility of defects. Representatively, traditional linear dimensionality reduction methods (e.g. principal component analysis) have achieved good results in thermographic data analysis. In this work, a slow feature thermography (SFT) method for non-destructive evaluation of defects in composite materials. Thermographic data can be regarded as a continuous time series generated by an infrared imager at different moments. Slow feature analysis algorithm is subsequently used to extract slow features of the thermal image sequence. Slow features refer to features that are unchanged or slowly changing in time-series data, such as the background of a thermal image. By extracting the slow features of data, the inhomogeneous background, noise and defect features contained in thermal images can be effectively distinguished. Test results on carbon fiber reinforced polymer specimens demonstrate the feasibility and effectiveness of the SFT method.

关键词： Image recognition Composite materials Time series analysis Infrared imaging Feature extraction Thermal noise Thermal analysis

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Unsupervised Feature Enrichment and Fidelity Preservation Learning Framework for Skeleton-based Action Recognition

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IEEE Transactions on Circuits and systems for Video technology 2025年

作者： Li, Chuankun Li, Shuai Gao, Yanbo Gao, Xingyu Chen, Ping Li, Jian Li, Wanqing North University of China State Key Laboratory of Dynamic Testing Technology School of Information and Communication Engineering Taiyuan030051 China Shandong University School of Control Science and Engineering School of Software Jinan250100 China Chinese Academy of Sciences Institute of Microelectronics Beijing100029 China University of Wollongong Advanced Multimedia Research Lab WollongongNSW2522 Australia

Unsupervised skeleton-based action recognition has achieved remarkable progress recently. Existing unsupervised learning methods suffer from severe overfitting problem, and thus small networks are used, significantly reducing the representation capability. To address this problem, the overfitting mechanism behind the unsupervised learning for skeleton-based action recognition is first investigated. It is observed that skeleton is already a relatively high-level and low-dimension feature, but not in the same manifold as the features for action recognition. Simply applying the existing unsupervised learning method tends to produce features that discriminate the different samples rather than action classes, resulting in the overfitting problem. To address this problem, this paper proposes an Unsupervised spatial-temporal Feature Enrichment and Fidelity Preservation (U-FEFP) learning framework to generate rich distributed features that contain all the information of a skeleton sample. A spatial-temporal feature transformation subnetwork is developed using channel-wise topology refinement graph convolutional block and graph convolutional gated recurrent unit block as the basic feature extraction network. The unsupervised Bootstrap Your Own Latent-based learning is utilized to generate rich distributed features, and the unsupervised pretext task-based learning is employed to preserve the information contained in the skeleton. The two unsupervised learning ways are collaborated as U-FEFP to produce robust and discriminative representations. Experimental results on four widely used benchmarks, namely NTU-RGB+D-60, PKU-MMD, NTU-RGB+D-120 and UAV-Human dataset, demonstrate that the proposed U-FEFP obtains the best result compared with the state-of-the-art unsupervised learning methods. © 1991-2012 IEEE.

关键词： Convolution

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On the Gaussian Filtering for Nonlinear Dynamic systems Using Variational Inference

On the Gaussian Filtering for Nonlinear Dynamic Systems Usin...

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International Conference on information Fusion

作者： Yi Liu Xi Li Le Yang Lyudmila Mihaylova Ji Li Department of Electrical and Computer Engineering University of Canterbury Christchurch NZ State Key Lab of Complex Electromagnetic Environment Effects on Electronics and Information Systems National University of Defense Technology Changsha China Department of Automatic Control and System Engineering University of Sheffield Sheffield UK Locaris Electronic Technology Co. Ltd Zhengzhou China

ISBN: (数字)9781737749769

ISBN: (纸本)9798350371420

This paper introduces a new variational Gaussian filtering approach for estimating the state of a nonlinear dynamic system. We first assume that the predictive distribution of the state is Gaussian and derive an iterative method for updating the state posterior in the natural parameter space through KullbackLeibler divergence minimization. The obtained update rule is the same as that of the conjugate-computation variational inference technique in Bayesian learning. The derivation here is simpler and more insightful. We then impose a Wishart prior on the inverse of the state prediction covariance to take into account the impact of approximating the state predictive distribution using a Gaussian density on the state posterior estimation. The prediction covariance is identified jointly with the state using variational inference and the established state posterior update rule to achieve the desired Gaussian filtering. Simulation study examines the performance of the proposed filtering framework in target tracking based on bearing and range measurements.

关键词： Target tracking Smoothing methods Filtering Estimation Filtering algorithms Prediction algorithms Minimization Nonlinear dynamical systems Bayes methods Iterative methods

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Dynamic space-covered broadcast algorithm based on neighbor-degree in mobile ad hoc wireless networks

Dynamic space-covered broadcast algorithm based on neighbor-...

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2020 International Conference on Machine Learning and Intelligent systems, MLIS 2020

作者： Lou, Qiaoqiao Zhao, Zhijin School of Telecommunication Engineering Hangzhou Dianzi University China State Key Lab of Information Control Technology in Communication System of No.36 Research Institute China Electronic Technology Corporation China

ISBN: (纸本)9781643681368

Broadcast is an important link in mobile ad hoc wireless networks (MANETs). In order to improve broadcast coverage, reduce forwarding probability and broadcast collision, a dynamic space-covered broadcast algorithm based on neighbor-degree in MANETs is proposed. The concepts of neighbor-degree, available-angel and available-distance are introduced. The neighbor-degree is used to generate the initial forwarding probability of a node. Based on the available-angle and available-distance, the node weight is proposed to calculate the final forwarding probability, thereby realizing the dynamic selection of forwarding node and reflecting the dynamic space-covered of node. The forwarding strategy is proposed to reduce broadcast collision. Simulation results show that, compared with dynamic probability broadcast and node location based space-covered broadcast, proposed broadcast algorithm reduces the broadcast collision, improves the broadcast coverage, and reduces the forwarding probability when the number of nodes is large. © 2020 The authors and IOS Press.

关键词： Probability

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Physical-layer key distribution using synchronous complex dynamics of DBR semiconductor lasers

arXiv

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arXiv 2023年

作者： Wang, Anbang Du, Yicheng Li, Qingtian Wang, Longsheng Jia, Zhiwei Qin, Yuwen Wang, Yuncai Institute of Advanced Photonics Technology School of Information Engineering Guangdong University of Technology Guangzhou51006 China Key Laboratory of Photonic Technology for Integrated Sensing and Communication Ministry of Education of China Guangdong University of Technology Guangzhou51006 China Guangdong Provincial Key Laboratory of Information Photonics Technology Guangdong University of Technology Guangzhou51006 China Key Lab of Advanced Transducers and Intelligent Control System Ministry of Education Taiyuan University of Technology Taiyuan030024 China College of Optoelectronics Taiyuan University of Technology Taiyuan030024 China

Common-signal-induced synchronization of semiconductor lasers with optical feedback inspired a promising physical key distribution with information-theoretic security and potential in high rate. A significant challenge is the requirement to shorten the synchronization recovery time for increasing key rate without sacrificing operation parameter space for security. Here, open-loop synchronization of wavelength-tunable multi-section distributed Bragg reflector (DBR) lasers is proposed as a solution for physical-layer key distribution. Experiments show that the synchronization is sensitive to two operation parameters, i.e., currents of grating section and phase section. Furthermore, fast wavelength-shift keying synchronization can be achieved by direct modulation on one of the two currents. The synchronization recovery time is shortened by one order of magnitude compared to close-loop synchronization. An experimental implementation is demonstrated with a final key rate of 5.98 Mbit/s over 160 km optical fiber distance. It is thus believed that fast-tunable multi-section semiconductor lasers opens a new avenue of high-rate physical-layer key distribution using laser synchronization. Copyright © 2023, The Authors. All rights reserved.

关键词： Synchronization

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The 2023 report of the synergetic roadmap on carbon neutrality and clean air for China: Carbon reduction, pollution mitigation, greening, and growth

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Environmental Science and Ecotechnology 2025年第1期23卷 1-18页

作者： Jicheng Gong Zhicong Yin Yu Lei Xi Lu Qiang Zhang Cilan Cai Qimin Chai Huopo Chen Renjie Chen Wenhui Chen Jing Cheng Xiyuan Chi Hancheng Dai Zhanfeng Dong Guannan Geng Jianlin Hu Shan Hu Cunrui Huang Tiantian Li Wei Li Xiaomei Li Yongsheng Lin Jun Liu Jinghui Ma Yue Qin Weiqi Tang Dan Tong Jiaxing Wang Lijuan Wang Qian Wang Xuhui Wang Xuying Wang Libo Wu Rui Wu Qingyang Xiao Yang Xie Xiaolong Xu Tao Xue Haipeng Yu Da Zhang Li Zhang Ning Zhang Shaohui Zhang Shaojun Zhang Xian Zhang Zengkai Zhang Hongyan Zhao Bo Zheng Yixuan Zheng Tong Zhu Huijun Wang Jinnan Wang Kebin He State Key Joint Laboratory for Environment Simulation and Pollution Control College of Environmental Sciences and Engineering and Center for Environment and HealthPeking UniversityBeijing100871China Key Laboratory of Meteorological Disaster Ministry of Education/Joint International Research Laboratory of Climate and Environment Change(ILCEC)/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters(CIC-FEMD)Nanjing University of Information Science&TechnologyNanjing210044China Center of Air Quality Simulation and System Analysis Chinese Academy of Environmental PlanningBeijing100041China Center for Carbon Neutrality Chinese Academy of Environmental PlanningBeijing100041China State Key Joint Laboratory of Environment Simulation and Pollution Control School of EnvironmentTsinghua UniversityBeijing100084China Institute for Carbon Neutrality Tsinghua UniversityBeijing100084China Ministry of Education Key Laboratory for Earth System Modelling Department of Earth System ScienceTsinghua UniversityBeijing100084China National Center for Climate Change Strategy and International CooperationBeijing100035China Nansen-Zhu International Research Centre Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing100029China School of Public Health Key Lab of Public Health Safety of the Ministry of Education and Key Lab of Health Technology Assessment of the Ministry of HealthFudan UniversityShanghai200032China School of Economics and Management Beijing University of Chemical TechnologyBeijing100029China Department of Earth System Science University of CaliforniaIrvineIrvineCA92697USA National Meteorological Center China Meteorological AdministrationBeijing100081China College of Environmental Sciences and Engineering Peking UniversityBeijing100871China Institute of Eco-Environmental Management and Policy Chinese Academy of Environmental PlanningBeijing100041China Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Contr

The response to climate change and air pollution control demonstrates strong synergy across scientific mechanisms,targets,strategies,and governance *** report,based on a monitoring indicator system for coordinated governance of air pollution and climate change,employs an interdisciplinary approach combining natural and social *** establishes 20 indicators across five key areas:air pollution and climate change,governance systems and practices,structural transformation and technologies,atmospheric components and emission reduction pathways,and health impacts and *** report tries to provide actionable insights into the interconnectedness of air pollution and climate *** highlights key policy gaps,presents updated indicators,and offers a refined monitoring framework to track progress toward China's dual goals of reducing emissions and improving air *** to previous editions,this year's report has updated four key indicators:meteorological impacts on air quality,climate change and its effects,governance policies,and low-carbon building energy *** aim is to further refine the monitoring framework,track progress,and establish a comprehensive theory for collaborative governance while identifying challenges and proposing solutions for China's pathway to carbon neutrality and clean *** report comprises six *** executive summary chapter is followed by analyzing air pollution and climate change *** systems and practices are discussed in the third chapter,focusing on policy implementation and local *** fourth chapter addresses structural transformations and emission reduction technologies,including energy and industrial shifts,transportation,low-carbon buildings,carbon capture and storage,and power *** fifth chapter outlines atmospheric component dynamics and emission pathways,presenting insights into emission drivers and future *** sixth chapter assesses he

关键词： Synergetic roadmap Carbon neutrality Clean air

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A Hybrid Model for Building-level Day Ahead Power Load Forecasting Based on Gated Recurrent Unit Networks

A Hybrid Model for Building-level Day Ahead Power Load Forec...

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2021 IEEE Sustainable Power and Energy Conference, iSPEC 2021

作者： Zhang, Hualu Deng, Boya Shi, Jie Northeast Electric Power University Jilin Key Laboratory of Modern Power System Simulation Control and Green Power New Technology Ministry of Education Jilin China Nanjing Nanrui Information Communication Technology Co. Ltd. Nari Group Corporation State Grid Electric Power Research Institute Nanjing China

ISBN: (纸本)9781665414395

Accurate forecasting of day-ahead electrical load is a key to optimize the operation of the integrated energy system (IES) and a fundamental function of the cyber-physical system (CPS) of IES. Due to the characteristics of different features, data collected by CPS often have different sampling frequencies, resulting in mining data trends inadequately when making a load forecast. This paper proposes an GRU-based model to combine historical data with different sampling frequencies in forecasting day-ahead building-level load. First, day-level features are clustered by K-means. Hour-levels features are then partitioned according to clustering results and inputted separately into a gated recurrent unit (GRU) based neural network to capture relationships between external features and load fluctuations. The temporal dependencies of minute-level data are captured by the GRU network directly without any external features. Finally, the least square method is used to combine forecasting results at each hour of two models. The simulation results present that the proposed model performs better than a single model when applying to real-world data. © 2021 IEEE.

关键词： Forecasting

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Research on Multimodal Algorithms for Multi-routes Planning Based on Niche Technique

Research on Multimodal Algorithms for Multi-routes Planning ...

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2020 International Conference on Culture-Oriented Science and technology, ICCST 2020

作者： Ren, Hui Shen, Xiaochen Jia, Xiaojun Communication University of China Ministry of Culture and Tourism Beijing Key Laboratory of Modern Entertainment Technology Communication University of China School of Information and Communication Engineering State Key Laboratory of Media Convergence of Communication Communication University of China Key Laboratory of Acoustic Visual Technology and Intelligent Control System Beijing100024 China

ISBN: (纸本)9781728181387

Multiple routes strategy, classified as a typical multimodal optimization, is an effective method for AGV to tackle the real-world problem. This article majorly explores the traditional optimization algorithm and the evolutionary algorithm for the routes planning problem. Compared with the traditional path planning method, the evolutionary algorithm based on niche technique can successfully avoid the particle interference in the multimodal condition, thereby ensuring the solution diversity. However, various restrictions may be produced when the niche technique cooperates with the evolutionary algorithm. Hence, this paper summarizes some niche techniques with evolutionary algorithms to analyze their merits for solving the multimodal problem. Also, several benchmark functions are involved for further discussing the effectiveness of the niche technology on the multimodal problem and some improvements are conducted for the algorithm to achieve a more effective work. © 2020 IEEE.

关键词： Evolutionary algorithms

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On the Approximation of the Quotient of Two Gaussian Densities for Multiple-Model Smoothing

On the Approximation of the Quotient of Two Gaussian Densiti...

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International Conference on information Fusion

作者： Yi Liu Xi Li Le Yang Lyudmila Mihaylova Yanbo Xue Department of Electrical and Computer Engineering University of Canterbury Christchurch NZ State Key Lab of Complex Electromagnetic Environment Effects on Electronics and Information Systems National University of Defense Technology Changsha China Department of Automatic Control and System Engineering University of Sheffield Sheffield UK Career Science Lab Beijing China

The quotient of two multivariate Gaussian densities can be written as an unnormalized Gaussian density, which has been applied in some recently developed multiple-model fixed-interval smoothing algorithms. However, this expression is invalid if instead of being positive definite, the covariance of the unnormalized Gaussian density is indefinite (i.e., it has both positive and negative eigenvalues) or undefined (i.e., computing it requires inverting a singular matrix). This paper considers approximating the quotient of two Gaussian densities in this case using two different approaches to mitigate the caused numerical problems. The first approach directly replaces the indefinite covariance of the unnormalized Gaussian density with a positive definite matrix nearest to it. The second approach computes the approximation through solving, using the natural gradient, an optimization problem with a Kullback-Leibler divergence-based cost function. This paper illustrates the application of the theoretical results by incorporating them into an existing smoothing method for jump Markov systems and utilizing the obtained smoothers to track a maneuvering target.

关键词：

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Robust Beamforming Based on Covariance Matrix Reconstruction and ADMM

Robust Beamforming Based on Covariance Matrix Reconstruction...

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2021 CIE International Conference on Radar, Radar 2021

作者： Gong, Pengcheng Wang, Zhaobin Xu, Kaiyan School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu611731 China School of Computer Science and Engineering Wuhan Institute of Technology Wuhan430205 China Hubei University of Technology Hubei Key Lab. for High-efficiency Utiliz. of Solar Ener. and Oper. Control of Energy Storage System Wuhan430068 China

ISBN: (纸本)9781665498142

Considering at the issue that the performance of the traditional beamforming algorithm will decrease sharply when the disturbance occurs at the disturbed position and the steering vector mismatch occurs, a new robust beamforming method based on covariance matrix reconstruction and Alternating Direction Method of Multipliers (ADMM) is proposed. Firstly, based on the maximum output power criterion of beamformers, an optimization model is designed to solve the optimal steering vector. Then, the covariance matrix is reconstructed by using the defined interference range to widen the nullity and enhance the anti-motion interference ability of the system. Then, for the quadratic inequality constraint problem of the steering vector, this paper uses ADMM to solve the model iteratively, and obtains the specific solution of the steering vector in each iteration. In addition, the complexity of the algorithm is also analyzed. The experimental results show that, compared with the existing beamforming algorithms, the proposed method Widen the width of zero notch in the interference area, improves the anti-interference performance of the beam, and can well correct the mismatched steering vector. © 2021 IEEE.

关键词： Alternating Direction Method of Multipliers Beamforming Covariance matrix reconstruction Null widening Quadratic constraint

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