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2010 International Conference on Intelligent Computation Technology and Automation, ICICTA 2010

作者： Sun, Jianghong Zuo, Yunbo Xu, Xiaoli School of Electro Mechanical Engineering Beijing Information Science and Technology University Beijing China Beijing Institute of Technology School of Mechanical and Vehicular Engineering Beijing China Beijing Key Laboratory -Measurement and Control of Mechanical and Electrical System Beijing China

ISBN: (纸本)9780769540771

Data process of large rotating machinery is in line with basic features of information fusion. The fault deterioration is extracted from the pattern spectrum as the fault feature , and its trend is predicted by the information fusion which is based on the dynamic weighted method for single-sensor and multi-sensors respectively. Actual example of Beijing Yanshan Petrochemical Co. shows the correction of conclusion. © 2010 IEEE.

关键词： Information fusion

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Design and Implementation of General Source Simulation system Architecture

Design and Implementation of General Source Simulation Syste...

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2017 International Conference on Applied Mathematics, Modeling and Simulation (AMMS2017)

作者： Wei Chu Songyi Liu Key Laboratory of Information Systems Engineering Department of China Satellite Launch Measurement and Control System

Equipment development and daily training of C2 system need simulation intelligence which are provided by simulation system. This paper put forward a whole system architecture and integration framework which can improve the develop efficiency of source simulation system on the basis of the using demand analysis of simulation system. This may satisfy the demand of system flexibility and rapid deployment.

关键词： simulation system system architecture integration framework

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Non-convex Optimization Based On Deep Koopman Operator

Non-convex Optimization Based On Deep Koopman Operator

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Youth Academic Annual Conference of Chinese Association of Automation (YAC)

作者： Jun Guo Yubin Jia Panxiao Yong Jun Zhou Zhimin Liu School of Automation Southeast University Nanjing China Key Laboratory of Measurement and Control of Complex System of Engineering Ministry of Education Southeast University Nanjing China

Non-convex optimization problems are prevalent in various fields, and finding the global optimal solution for such problems remains challenging. This paper proposes an optimization method based on deep Koopman operator, which leverages the power of deep neural networks to provide a data-driven architecture for the Koopman function. The Koopman operator is used to linearly represent non-convex problems, and the proposed method utilizes model predictive control to optimize the resulting linear systems. Simulation results show that this method can solve the non-convex optimization problem effectively, quickly and accurately, while obtaining the global optimal solution.

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Evaluation method of spindle performance degradation based on VMD and random forests

Evaluation method of spindle performance degradation based o...

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7th International Symposium on Test Automation and Instrumentation, ISTAI 2018

作者： Wei, Pangshi Wang, Hongjun School of Mechanical and Electrical Engineering Beijing Information Science and Technology University Beijing100192 China Key Laboratory of Modern Measurement and Control Technology Beijing Information Science and Technology University Beijing100192 China Beijing Key Laboratory of Mechanical-Electronic System Measurement and Control Beijing Information Science and Technology University Beijing100192 China

As one of the core components of the machine tool, the reliability of spindle is very important for improving machine tool quality. To effectively evaluate the performance degradation degree of the spindle, a method of degradation evaluation of the spindle performance based on variational mode decomposition (VMD) and random forests (RFs) was proposed. Firstly, VMD is used to process the current signal to obtain several modal components. Then, the time domain and frequency domain features of each modes component are calculated as eigenvalues. Finally, the RFs algorithm is used to classify the eigenvalues. The experimental results show that VMD can decompose the signal better and avoid the phenomenon of the modal mixture. The combination of VMD and RFs can accurately and effectively evaluate the performance degradation of the spindle. © 2019 Institution of engineering and Technology. All rights reserved.

关键词： Variational mode decomposition

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Data-Driven Frequency Regulation Method for Multi-Terminal HVDC Grids

Data-Driven Frequency Regulation Method for Multi-Terminal H...

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Youth Academic Annual Conference of Chinese Association of Automation (YAC)

作者： Panxiao Yong Yubin Jia Jun Guo Jun Zhou Zhimin Liu School of Automation Southeast University Nanjing China Key Laboratory of Measurement and Control of Complex System of Engineering Ministry of Education Southeast University Nanjing China

High voltage direct current (HVDC) lines allow low-loss transmission over long distances. The multi-terminal HVDC network consists of multiple terminals connected by HVDC lines, and each area shares the frequency reserve. Through secondary frequency control, the frequency of AC and DC areas can be regulated back to the set point when the system frequency has deviated. In this paper, a data-driven model predictive control (MPC) strategy is proposed. The Kooman operator is used to obtain the high-dimensional linear model of a nonlinear system, and the Deep Learning method is used to approximate the infinite-dimensional Koopman operator. The nominal MPC is used for optimization and control, and the optimal problem is solved for the Koopman linearized system. The optimal control is applied to the original nonlinear system. Simulation results show the effectiveness of the proposed method.

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Emissions reduction potential and feasibility of vehicle-to-grid for Beijing’s future electric vehicles

Cell Reports Physical Science

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Cell Reports Physical Science 2025年

作者： Jiaxin Xu Jiahui Chen Hongyi Wei Yinan Zhao Bowen Tian Yifan Wen Haiyang Jiang Ning Zhang Shaojun Zhang Ye Wu School of Environment Tsinghua University Beijing 100084 China Center for Sustainable Systems School for Environment and Sustainability University of Michigan Ann Arbor MI 48109 USA School of Electrical and Power Engineering Power System Research Institute Tsinghua University Beijing 100084 China State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex Beijing 100084 China Beijing Laboratory of Environmental Frontier Technologies School of Environment Tsinghua University Beijing 100084 China State Key Laboratory of Regional Environment and Sustainability Tsinghua University Beijing 100084 China

Electric vehicles (EVs) offer significant potential to reduce greenhouse gas (GHG) emissions from road transport. However, nighttime charging increases peak demand and thermal power reliance. This study evaluates vehicle-to-grid (V2G) as a solution by integrating GPS-based EV travel data with hourly grid modeling for 2 million EVs in Beijing. Results show V2G boosts renewable energy use in charging to 66%, up from under 4% in uncoordinated charging. V2G cuts vehicle uncoordinated charging emissions by 20.6% and reduces the grid’s GHG footprint by replacing thermal power with discharging electricity. Commuter vehicles and the summer months offer the highest V2G emissions reduction potential. The study finds battery degradation manageable and highlights synergies between V2G, renewable capacity expansion, and economic incentives, while emphasizing the need for robust infrastructure and local grid networks to support large-scale EV adoption. This approach therefore offers a promising pathway to accelerate sustainable energy solutions amid increasing vehicle electrification and renewable integration.

关键词： electric vehicles vehicle to grid V2G GHG emissions travel pattern grid optimization

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Research on UAV Detection Technology of Gm-APD Lidar Based on YOLO Model

Research on UAV Detection Technology of Gm-APD Lidar Based o...

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2021 IEEE International Conference on Unmanned systems, ICUS 2021

作者： DIng, Yuanxue Qu, Yanchen Zhang, Qing Tong, Jiahui Yang, Xianhui Sun, Jianfeng Harbin Institute of Technology National Key Laboratory of Science and Technology on Tunable Laser Institution of Opto-Electronics Harbin China Beijing Electromechanical Engineering Institute Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China

ISBN: (纸本)9780738146577

In recent years, unmanned aerial vehicle (UAV) technology has developed rapidly, which plays an important role in both military and civil fields. While it brings convenience to all walks of life, there are also a lot of security problems. Therefore, it is necessary to study anti-UAV technology, but the detection of UAV is an important basis of anti-UAV technology. In this paper, by combining the three-dimensional range profile collected by Gm-APD (Geiger mode Avalanche Photo Diode) lidar with the deep learning method, a UAV detection method based on the improved YOLOv3 (You Only Look Once v3) network is proposed. Firstly, the data of UAV in low altitude real scene is collected by Gm-APD lidar, and range profile information is generated. Then, the YOLOv3 network is improved, and the SPP module can be added to combine the local features of the target with the global features to improve the detection accuracy. It is suitable for the detection of UAVs. Finally, the trajectory of the UAV is drawn based on the distance information. The experimental results show that the improved method can effectively detect UAV targets, and is better than the previous method, YOLOv3-Tiny method and YOLOv4 method. The farthest detection distance is 238m, the detection speed is fast, and the purpose of real-time detection and positioning can be achieved. Therefore, this method can be applied to anti-UAV technology and has important research significance. © 2021 IEEE.

关键词： Optical radar

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Low-Cost Approach for Improving Video Transmission Efficiency in WVSN

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Journal of Shanghai Jiaotong university(Science) 2020年第5期25卷 600-605页

作者： LIU Min DENG Bin TANG Ying WU Minghu WANG Juan Hubei Key Laboratory for High-Efficiency Utilization of Solar Energy and Operation Control of Energy Storage System.Hubei University of Technology Wuhan 430068.China Department of Electrical and Computer Engineering.Rowan University GlassboroNJ 08028USA State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China Institute of Smart Education Qingdao Academy of Intelligent IndustriesQingdao 266041ShandongChina

The wireless visual sensor network(WVSN)as a new emerged intelligent visual system,has been applied in many video monitoring ***,there is still great challenge because of the limited wireless network *** resolve the problem,we propose a real-time dynamic texture approach which can detect and reduce the temporal redundancy during many successive image ***,an adaptively learning background model is improved to discover successive similar image frames from the inputting video ***,the dynamic texture model based on the singular value decomposition is adopted to distinguish foreground and background element ***,a background discarding strategy based on visual motion coherence is proposed to determine whether each image frame is streamed or *** evaluate the trade-off performance of the proposed method,it is tested on the CDW-2014 dataset,which can accurately detect the first foreground frame when the moving objects of interest appear in the field of view in the most tested dynamic scenes,and the misdetection rate of the undetected foreground frames is near to *** to the original stream,it can reduce the occupied bandwidth a lot and its computational cost is relatively lower than the state-of-the-art methods.

关键词： wireless visual sensor network(WVSN) temporal redundancy foreground background dynamic texture

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Multi-dimensional Taylor Network Optimal control of Aircraft Flight

Multi-dimensional Taylor Network Optimal Control of Aircraft...

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2017 IEEE 第2届先进信息技术,电子与自动化控制国际会议(IAEAC 2017)

作者： Chun-Peng Hu Hong-Sen Yan Yu Zhou School of Automation Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education

This paper will introduce the design of the multidimensional Taylor network(MTN) optimal control in the flight control of aircraft. The MTN optimal control, which combines the classical architecture o

ISBN: (纸本)9781467389808

关键词： Aircraft control Multi-dimensional Taylor network (MTN) controller Attitude control Simulation Aircraft control attitude control Air Travel network optimization simulating Classical architecture Taylor flight control(aircraft) Feedback control systems Optimal control Stability analysis

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Finite Time Disturbance Observer-Based Tube Model Predictive control for Vehicle Platoon systems with Guaranteed L∞ String Stability

Finite Time Disturbance Observer-Based Tube Model Predictive...

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第34届中国控制与决策会议

作者： Qian Chen Jinhao Liu Jun Yang Shihua Li School of Automation Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education

This paper investigates optimal longitudinal control problems for a vehicle platoon in presence of parameter uncertainties and external ***,a multi-constraint multi-objective optimization model is developed,where physical limits,safety constraints,driving comfort,and fuel economy are taken into *** reduce communication burden and avoid network congestion,the preceding vehicle's acceleration is obtained by employing a finite time disturbance observer(FTDO).As for the parameter uncertainties as well as external disturbances,they are estimated as a lumped disturbance by exploiting a ***,under a predecessor following communication topology,a FTDO-based tube model predictive control method with explicit consideration of string stability is ***,numerical simulations illustrate the effectiveness and superiority of the proposed control approach.

关键词： Vehicle Platoon Longitudinal control Finite Time Disturbance Observer Model Predictive control Predecessor-follower L_∞ String Stability

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