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arXiv 2024年

作者： Qin, Chengbing Li, Yuanyuan Yan, Yu Li, Jiamin Li, Xiangdong Song, Yunrui Zhang, Xuedong Han, Shuangping Liu, Zihua Guo, Yanqiang Zhang, Guofeng Chen, Ruiyun Hu, Jianyong Yang, Zhichun Liu, Xinhui Xiao, Liantuan Jia, Suotang State Key Laboratory of Quantum Optics and Quantum Optics Devices Institute of Laser Spectroscopy Shanxi University Shanxi Taiyuan030006 China Collaborative Innovation Center of Extreme Optics Shanxi University Shanxi Taiyuan030006 China Department of Physics and Optoelectronics Taiyuan University of Technology Taiyuan030024 China Key Laboratory of Advanced Transducers and Intelligent Control System of Ministry of Education Taiyuan University of Technology Shanxi Taiyuan030024 China

Non-classical light sources emitting bundles of N-photons have essential applications ranging from fundamental tests to quantum information processing. However, high-order correlations, g(N)(0) (N≥2), are still limited to hundreds. Herein, we report the realizaiton of a super-bunching laser in a photonic crystal fiber with g(2)(0) reaching 5.86×104 and g(5)(0) reaching 2.72×108. This laser presented extreme photon-number fluctuations and ubiquitous multi-photon events, with the occurrence of 31 photons from a single light pulse at a mean of 1.99×10-4 photons per pulse. The probability of this extreme event has been enhanced 10139 times compared to a coherent laser with a Poissonian distribution. By varying the power of the pumping laser, both the photon number distributions and the corresponding high-order correlations could be substantially tailored from Poissonian to super-bunching distributions. Our study showcases the ability to achieve non-classical laser with giant high-order correlations and extreme photon-number fluctuations, paving the way for high-order correlation imaging, extreme nonlinear optical effects, hyperspectral image and multi-photon physics. © 2024, CC BY.

关键词： Photonic crystal fibers

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Cooperative Label-Free Moving Target Fencing for Second-Order Multi-Agent systems with Rigid Formation

arXiv

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arXiv 2023年

作者： Hu, Bin-Bin Zhang, Hai-Tao Shi, Yang School of Artificial Intelligence Automation Key Laboratory of Image Processing Intelligent Control State Key Lab of Digital Manufacturing Equipment and Technology Huazhong University of Science and Technology Wuhan430074 China Department of Mechanical Engineering University of Victoria VictoriaBCV8W 2Y2 Canada

This paper proposes a label-free controller for a second-order multi-agent system to cooperatively fence a moving target of variational velocity into a convex hull formed by the agents whereas maintaining a rigid formation. Therein, no label is predetermined for a specified agent. To attain a rigid formation with guaranteed collision avoidance, each controller consists of two terms: a dynamic regulator with an internal model to drive agents towards the moving target merely by position information feedback, and a repulsive force between each pair of adjacent agents. Significantly, sufficient conditions are derived to guarantee the asymptotic stability of the closed-loop systems governed by the proposed fencing controller. Rigorous analysis is provided to eliminate the strong nonlinear couplings induced by the label-free property. Finally, the effectiveness of the controller is substantiated by numerical simulations. Copyright © 2023, The Authors. All rights reserved.

关键词： Multi agent systems

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Towards improving detection performance for malware with a correntropy-based deep learning method

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Digital Communications and Networks 2021年第4期7卷 570-579页

作者： Xiong Luo Jianyuan Li Weiping Wang Yang Gao Wenbing Zhao School of Computer and Communication Engineering University of Science and Technology BeijingBeijing100083China Beijing Key Laboratory of Knowledge Engineering for Materials Science Beijing100083China Beijing Intelligent Logistics System Collaborative Innovation Center Beijing101149China China Information Technology Security Evaluation Center Beijing100085China Department of Electrical Engineering and Computer Science Cleveland State UniversityClevelandOH44115USA

With the rapid development of Internet of Things(IoT)technologies,the detection and analysis of malware have become a matter of concern in the industrial application of Cyber-Physical system(CPS)that provides various services using the IoT ***,many advanced machine learning methods such as deep learning are popular in the research of malware detection and analysis,and some achievements have been made so ***,there are also some *** example,considering the noise and outliers in the existing datasets of malware,some methods are not robust ***,the accuracy of malware classification still needs to be *** at this issue,we propose a novel method that combines the correntropy and the deep learning *** our proposed method for malware detection and analysis,given the success of the mixture correntropy as an effective similarity measure in addressing complex datasets with noise,it is therefore incorporated into a popular deep learning model,i.e.,Convolutional Neural Network(CNN),to reconstruct its loss function,with the purpose of further detecting the features of *** present the detailed design process of our ***,the proposed method is tested both on a real-world malware dataset and a popular benchmark dataset to verify its learning performance.

关键词： Malware detection Mixture correntropy Deep learning Convolutional neural network(CNN)

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Soccer match broadcast video analysis method based on detection and tracking

Soccer match broadcast video analysis method based on detect...

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作者： Li, Hongyu Yang, Meng Yang, Chao Kang, Jianglang Suo, Xiang Meng, Weiliang Li, Zhen Mao, Lijuan Sheng, Bin Qi, Jun School of Information science and Technology Beijing Forestry University Beijing China Engineering Research Center for Forestry-Oriented Intelligent Information Processing National Forestry and Grassland Administration Beijing China School of Athletic Performance Shanghai University of Sport Shanghai China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Department of Computer Science and Engineering Shanghai Jiao Tong Univerity Shanghai China Department of Computing Xi'an JiaoTong-Liverpool University Suzhou China

We propose a comprehensive soccer match video analysis pipeline tailored for broadcast footage, which encompasses three pivotal stages: soccer field localization, player tracking, and soccer ball detection. Firstly, we introduce sports camera calibration to seamlessly map soccer field images from match videos onto a standardized two-dimensional soccer field template. This addresses the challenge of consistent analysis across video frames amid continuous camera angle changes. Secondly, given challenges such as occlusions, high-speed movements, and dynamic camera perspectives, obtaining accurate position data for players and the soccer ball is non-trivial. To mitigate this, we curate a large-scale, high-precision soccer ball detection dataset and devise a robust detection model, which achieved the (Formula presented.) of 80.9%. Additionally, we develop a high-speed, efficient, and lightweight tracking model to ensure precise player tracking. Through the integration of these modules, our pipeline focuses on real-time analysis of the current camera lens content during matches, facilitating rapid and accurate computation and analysis while offering intuitive visualizations. © 2024 John Wiley & Sons Ltd.

关键词： Visualization

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Indirect effects among biodiversity loss of mutualistic ecosystems

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National Science Open 2022年第2期1卷 8-18页

作者： Guangwei Wang Xueming Liu Guanrong Chen Hai-Tao Zhang School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent ControlHuazhong University of Science and TechnologyWuhan 430074China State Key Lab of Digital Manufacturing Equipment and Technology Huazhong University of Science and TechnologyWuhan 430074China Department of Electrical Engineering City University of Hong KongHong KongChina

Drastic reduction in biodiversity has been a severe threat to ecosystems,which is exacerbated when losing few species leads to disastrous and even irreparable ***,revealing the mechanism underlying biodiversity loss is of uttermost *** this study,we show that abundant indirect interactions among mutualistic ecosystems are critical in determining species’*** topological and ecological characteristics,we propose an indicator derived from a dynamic model to identify keystone species and quantify their influence,which outperforms widely-used indicators like degree in realistic and simulated ***,we demonstrate that networks with high modularity,heterogeneity,biodiversity,and less intimate interactions tend to have larger indirect effects,which are more amenable in predicting decline of biodiversity with the proposed *** findings shed some light onto the influence of apposite biodiversities,paving the way from complex network theory to ecosystem protection and restoration.

关键词： biodiversity indirect effect complex network mutualism

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Characterization of plasma discharge channel in EDM using photography and spectroscopy

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Procedia CIRP 2022年 113卷 184-189页

作者： Ling Qin Chen Liu Ruixue Zhang Ya-ou Zhang Xiaodong Yang Wansheng Zhao State Key Laboratory of Mechanical System and Vibration China School of Mechanical Engineering Shanghai Jiao Tong University 800 Dongchuan Road Shanghai 200240 China Department of Mechanical Engineering and Automation Harbin Institute of Technology Harbin 150001 China

The plasma discharge channel of electrical discharge machining (EDM) brings a temperature of several thousands of degrees Celsius into the gap between the anode and cathode, resulting in the material removal by means of melting and evaporating. To investigate the characteristics of the plasma discharge channel, high-speed imaging technology is adopted to observe the dimension of the plasma. A spectrometer is used to calibrate the temperature of the plasma as well. Then the particle-in-cell with Monte Carlo collision (PIC-MCC) simulation is conducted to analyze the variation and distribution of the plasma and heat flux. The experimental results demonstrate that the discharge channels’ morphology obtained is stable after a few microseconds of expansion after breakdown, and the diameter of discharge channel and the current density increases with the increase of discharge current. Besides, the emission intensity of plasma increases with the increase of electrical parameters. However, the temperature of plasma decreases after high current, which means debris particles make machining condition deteriorated. Finally, the simulation results demonstrate that despite the current density increases, the maximum heat flux at the center of electrode reduces rapidly with the expansion of the discharge channel. The distribution of heat flux upon electrode evolves towards "wider and flatter", leading to larger debris particles than exceptional size.

关键词： EDM plasma discharge channel spectrometer high-speed photography PIC-MCC

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Muscle-Synergy-Based Planning and Neural-Adaptive Control for a Prosthetic Arm

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2021年第5期2卷 424-436页

作者： Li, Guoxin Li, Zhijun Li, Junjun Liu, Yueyue Qiao, Hong The Department of Automation University of Science and Technology of China Hefei230026 China The Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei230026 China The School of Automation Science and Engineering South China University of Technology Guangzhou511442 China The State Key Laboratory of Management and Control for Complex System Institute of Automation Chinese Academy of Science Beijing100190 China The School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China The Center for Excellence in Brain Science and Intelligence Technology Institute of Neuroscience Chinese Academy of Science Shanghai200031 China

Upper limb loss has significant effects on the individual's quality of life. Artificial prosthetic limbs as an alternative to the lost limb are designed to allow amputees to regain motor function. Motion classification via extracted surface electromyogram (sEMG) signals is widely utilized to realize a friendly human-robot interface in the control of the prosthesis. However, limited classification of discrete motion patterns from sEMG prevents intuitive motor control. Thus, instead of using discrete patterns, decoding the human intention continuously from sEMG would significantly benefit the prosthesis control. In this article, we propose a muscle-synergy-based intention decoding and motion planning that can model a broad set of complex upper limb movements as a combination of motor primitives. A novel muscle activation-to-force mapping model is developed to detect muscular effort of the healthy side to drive the affected side. A neural-network-approximation-based controller is developed for the bionic neuroprosthetic arm to execute the movement. Operational experiments with prosthetic movement control were performed on four healthy participants and an upper limb amputee participant. Results of controlling prosthetic arm (0.94 ± 0.02 of R2 in the horizontal reaching tasks and 0.95 ± 0.01 in the vertical reaching tasks for the healthy subjects, 0.95 and 0.97 for the amputee) demonstrate that our control method could successfully capture human movement intention and effectively control the movement of prosthesis. Impact statement-Powered robotic prostheses have enabled individuals with limb amputations to regain motor function via motion intention decoding from extracting surface electromyogram signals. Most research works have provided human-robot interaction through motion classification obtained from pattern recognition using surface electromyogram signals and simple movement control. However, decoding the human intent continuously from surface electromyogram is con

关键词： Matrix algebra

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Resource Allocation for Coupled Slices in Edge-Assisted Industrial IoT systems

Resource Allocation for Coupled Slices in Edge-Assisted Indu...

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IEEE International Conference on Communications in China (ICCC)

作者： Xiaojing Wen Cailian Chen Cheng Ren Ling Lyu Xinping Guan Department of Automation Shanghai Jiao Tong University and Key Laboratory of System Control and Information Processing Ministry of Education of China and Shanghai Engineering Research Center of Intelligent Control and Management Shanghai P. R. China School of Information Science and Technology Dalian Maritime University Dalian China

Multiple types of sensors are utilized for remote monitoring of single industrial process to compensate for the limited accuracy of a single type of sensor. Network slicing technology is considered promising for collecting data from heterogeneous sensors and meeting diverse Quality of Service (QoS) requirements. However, existing research on network slicing optimization mainly focuses on inter-slice resource constraints based on slice heterogeneity, neglecting slice coupling feature. In this regard, this paper proposes a novel network slicing framework for edge-assisted industrial IoT systems. The framework includes a slice coupling model and network resource allocation (transmission and computing) to address the problem of improving estimation performance while ensuring system convergence. The slice coupling model is primarily composed of the estimation error covariance of two slices, calculated using the Age of Task (AoT) as a bridge, which reflects the accuracy and freshness of the estimation. To reduce the complexity of the direct solution, the optimization problem is scaled down and decoupled. The iterative Particle Swarm Optimization (PSO) algorithm is proposed to determine the allocation rates of inter-slice transmission and computing resources. Simulation results demonstrate that the proposed algorithm achieves a flexible balance under heterogeneous estimation weights and exhibits near-optimal performance.

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A Many-Objective Evolutionary Algorithm Assisted by Ideal Hyperplane

SSRN

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SSRN 2022年

作者： Zhang, Zhixia Wen, Jie Wang, Hui Cai, Xingjuan Cui, Zhihua Zhang, Wensheng School of Computer Science and Technology Taiyuan University of Science and Technology China School of Information Engineering Nanchang Institute of Technology Nanchang330099 China State Key Laboratory of Intelligent Control and Management of Complex System Institute of Automation Chinese Academy of Sciences China State Key Lab. for Novel Software Technology Nanjing University China

In many-objective optimization problems (MaOPs), it is a difficult task for evolutionary algorithms to balance convergence and diversity while rapidly converging to the Pareto front. As the number of objectives increases, the difficulty of evolutionary algorithms increases with individual selection pressure. To address above problems, we propose a many-objective evolution algorithm assisted by an ideal hyperplane (MaOEA-IH). Firstly, the ideal hyperplane as a reference surface to guide the individual to search the Pareto front, which is established from the extremum of each dimension of the objective space. Secondly, a parallel p -norm mating selection strategy is proposed to improve convergence. In addition, two factors are considered in this strategy: 1) different p -norm are used to measure different spatial scales;2) individual selection uncertainty is defined by incorporating a probabilistic perturbation mechanism. Thirdly, the improved shift-based density estimation strategy is used as the evaluation criterion for environmental selection to improve the convergence and diversity of solution sets and enhance selection pressure. To verify the effectiveness of the proposed algorithm, MAOEA-IH is compared with seven excellent algorithms in three benchmark sets (DTLZ, WFG and MaF). The results show that the proposed MAOEA-IH is highly competitive in solving MaOPs. © 2022, The Authors. All rights reserved.

关键词： Geometry

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Spatio-Temporal Correlation-Based Surface Solar Irradiance Retrieval Approach Using Satellite Data Under Different Weather Conditions

SSRN

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SSRN 2024年

作者： Li, Na Zhen, Zhao Wang, Fei Department of Electrical Engineering North China Electric Power University Baoding071003 China State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power University Beijing102206 China Hebei Key Laboratory of Distributed Energy Storage and Microgrid North China Electric Power University Baoding071003 China Department of Automation Taiyuan Institute of Technology Taiyuan030008 China

Wide-area, high temporal-spatial resolution surface solar irradiance data (SSI) is critical for solar energy resource assessment, PV power station site selection, and PV power forecasting. However, it is evidently impractical to obtain the aforementioned irradiance data by deploying a large number of high-density observation devices on the ground. Satellite remote sensing, with global coverage and high spatio-temporal resolution, provides detailed surface information, widely applied in the retrieval of meteorological parameters and surface irradiance studies. Currently, most satellite data-based SSI retrieval studies only consider satellite cloud images as a representation of cloud cover, without fully utilizing the multi-channel spectral information from satellite. Furthermore, these studies not only overlook the impact of satellite cloud image spatial scale under different weather conditions on SSI retrieval but also neglect the correlation between pixels of satellite cloud images. To solve the above problems, this study proposes a spatio-temporal correlation-based SSI retrieval approach using satellite data under different weather conditions. The SSI retrieval approach mainly consists of three parts: weather classification and feature selection, SSI retrieval based on satellite single-pixel data, and SSI retrieval based on spatial correlation. The experiments not only confirm the feasibility of the proposed satellite single-pixel data-based SSI retrieval in this study but also validate the necessity of considering weather types in establishing spatio-temporal correlations for SSI retrieval. © 2024, The Authors. All rights reserved.

关键词： Solar energy

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