检索结果-内蒙古大学图书馆

第三十八届中国控制会议

作者： Al-wesabi Ibrahim Min Ding Xiaoling Jin Xianzhong Dai Mohammad Abu Sarhan Muhammed Badeaa Shafik Hao Zhou School of Automation China University of Geoscience Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex System State Grid Energy Research Institute School of electrical engineering and automation Wuhan university

Nowadays PV systems is suffering from two main problems: high production cost and low efficiency especially under variable weather conditions. Therefore, to reduce such associated problems, PV systems are being connected with various optimization controllers such as maximum power point tracking. In this paper, an artificial neural network maximum power point method has been designed to be linked between a PV system and DC-DC buck converter, and each part of the system is fully explained. The whole system was modeled under MATLAB/Simulink environment, simulation results demonstrated the efficiency of the proposed method. The proposed method has been examined to track the maximum power point under different weather conditions which shows a rapid and accurate tracking of the maximum power point(MPP) of PV system. The whole system which consists of PV arrays, Buck converter, ANN tracker, and load has been modeled using MATLAB/Simulink environment. This Simulink model has been examined using different levels of temperature and irradiation and the output results of both P-V and I-V characteristics, output power, and the system efficiency clearly demonstrate the validation of the modeled system. This demonstration has been done by comparing between the result of experimental platform and the results of simulated model which approves that ANN is highly recommended to be used in the field of maximum power point tracking of PV system.

关键词： Artificial neural network(ANN) Photovoltaic(PV) Maximum power point tracking(MPPT) Buck converter

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Artificial intelligence enhances the performance of chaos-based wireless communication

arXiv

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arXiv 2019年

作者： Ren, Hai-Peng Zhao, Hong-Er Bai, Chao Yin, Hui-Ping Grebogi, Celso Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xian University of Technology Xian710048 China Institute for Complex Systems and Mathematical Biology University of Aberdeen AB24 3UE United Kingdom

Some new findings for chaos-based wireless communication systems have been identified recently. First, chaos has proven to be the optimal communication waveform because chaotic signals can achieve the maximum signal to noise ratio at receiver with the simplest matched filter. Second, the information transmitted in chaotic signals is not modified by the multipath wireless channel. Third, chaos properties can be used to relief inter-symbol interference (ISI) caused by multipath propagation. Although recent work has reported the method of obtaining the optimal threshold to eliminate the ISI in chaos-based wireless communication, its practical implementation is still a challenge. By knowing the channel parameters and all symbols, especially the future symbol to be transmitted in advance, it is almost an impossible task in the practical communication systems. Owning to Artificial intelligence (AI) recent developments, Convolutional Neural Network (CNN) with deep learning structure is being proposed to predict future symbols based on the received signal, so as to further reduce ISI and obtain better bit error rate (BER) performance as compared to that used the existing sub-optimal threshold. The feature of the method involves predicting the future symbol and obtaining a better threshold suitable for time variant channel. Numerical simulation and experimental results validate our theory and the superiority of the proposed method. Copyright © 2019, The Authors. All rights reserved.

关键词： Signal to noise ratio

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A hybrid differential evolution and estimation of distribution algorithm for the multi-point dynamic aggregation problem 18

A hybrid differential evolution and estimation of distributi...

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2018 Genetic and Evolutionary Computation Conference, GECCO 2018

作者： Hao, Rong Zhang, Jia Xin, Bin Chen, Chen Dou, Lihua School of Automation Beijing Institute of Technology Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing China School of Automation Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing China

ISBN: (纸本)9781450357647

The multi-point dynamic aggregation (MPDA) is a typical task planning problem. In order to solve the MPDA problem efficiently, a hybrid differential evolution (DE) and estimation of distribution algorithm (EDA) called DE-EDA is proposed in this paper, which combines the merits of DE and EDA. The DE-EDA has been applied to multiple MPDA instances of different scales, and compared with EDA and two versions of DE in convergence speed and solution quality separately. The results demonstrate the DE-EDA can solve the MPDA problem effectively. © 2018 Copyright held by the owner/author(s).

关键词： Evolutionary algorithms

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Vehicle detection based on point cloud intensity and distance clustering

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Journal of Physics: Conference Series 2021年第4期1748卷

作者： Zhao Wang Xing Wang Bin Fang Kun Yu Jie Ma School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan 430074 China National key laboratory of multispectral information processing technology Huazhong University of Science and Technology Wuhan 430074 China Science and technology on complex system control and intelligent agent cooperation laboratory Beijing 100074 China

In the intelligent transportation system, vehicle detection is one of the essential technologies in obstacle avoidance and navigation, however the existing vehicle detection methods cannot meet the actual needs. This paper presents a vehicle detection method combines the intensity and distance information of point cloud, which improves the segmentation performance of nearby objects. Specifically, the data of point cloud collected by lidar is preprocessed first. Then the processed point cloud is clustered by combining its coordinate and intensity information. Finally, the clustered suspected targets are fed to the random forest classifier. Our method can efficiently detect and classify targets in large-scale disordered 3D point cloud with high accuracy. In the real-scanned Livox Mid-40 Lidar dataset, our proposed method improves the detection accuracy by 31% compared with the traditional Euclidean clustering.

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Adaptive robust control for four-motor driving servo system with uncertain nonlinearities

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control Theory and Technology 2017年第1期15卷 45-57页

作者： Wei ZHAO Xuemei REN Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing 100081 China

A novel adaptive robust control （ARC） is presented for the four-motor driving servo systems with the uncertain nonlinearities and actuation failures, such that the load tracking control is achieved with the proximate optimal-time. By applying the proposed scheme, several control objectives are achieved. First, the nonlinear synchronization algorithm is presented to maintain the velocity synchronization of each motor, which provides fast convergence without chatting. Moreover, the time-varying bias torque is applied to eliminate the effect of backlash and reduce the waste of energy. Then, the ARC is designed to achieve the proximate optimal-time output tracking with the transient performance in L2 norm, where the friction and actuation failures are addressed by the adaptive scheme based on the norm estimation of unknown parameter vector. Finally, the extensive simulated and experimental results validate the effectiveness of the proposed method.

关键词： Four-motor driving servo systems adaptive robust control velocity synchronization control friction backlash,actuation failures

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Target-Focused Video Stabilization for Human Computer Interaction 29

Target-Focused Video Stabilization for Human Computer Intera...

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第29届中国控制与决策会议

作者： Hao Zhu Qing You Wenjie Chen School of Automation Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex System

ISBN: (纸本)9781509046584

As an important task of video enhancement,a lot of video stabilization methods have been *** in many real-world applications,especially the systems with human-computer interactions,existing methods only remove camera motion in stabilized frames,the remaining object motion will also lead to deviations in manual *** this paper,we collect practical hand drawn bounding boxes which have been shown to contain serious *** we propose a target-focused video stabilization method consisting of a proposal-based detection component and a trackingbased motion estimation *** experiments demonstrate our method can remove camera jitter and target motion simultaneously,and also offer users a friendly and effective way to draw accurate target regions.

关键词： Video Stabilization Human-computer Interactive Object Proposal

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A survey on Lyapunov-based methods for stability of linear time-delay systems

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Frontiers of Computer Science 2017年第4期11卷 555-567页

作者： Jian SUN Jie CHEN School of Automation Beijing Institute of Technology Beijing 100081 China Key Laboratory of Intelligent Control and Decision of Complex System Beijing Institute of Technology Beijing 100081 China

Recently, stability analysis of time-delay systems has received much attention. Rich results have been obtained on this topic using various approaches and techniques. Most of those results are based on Lyapunov stability theories. The purpose of this article is to give a broad overview of stabil- ity of linear time-delay systems with emphasis on the more recent progress. Methods and techniques for the choice of an appropriate Lyapunov functional and the estimation of the derivative of the Lyapunov functional are reported in this ar- ticle, and special attention is paid to reduce the conservatism of stability conditions using as few as possible decision vari- ables. Several future research directions on this topic are also discussed.

关键词： time-delay system,ity delay-dependent stability,Lyapunov-Krasovskii functionaldelay-independent stabil-linear matrix inequality,

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3D Semantic Mapping Based on Convolutional Neural Networks

3D Semantic Mapping Based on Convolutional Neural Networks

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Chinese control Conference

作者： Jing Li Yanyu Liu Junzheng Wang Min Yan Yanzhi Yao Key Laboratory of Complex System Intelligent Control and Decision Beijing Institute of Technology Beijing P. R. China

As an important part of environmental perception, maps guarantee the accuracy of intelligent robots in navigation, localization and path planning. The traditional 3D maps mainly focus on the spatial structure of the objects, which lacks the semantic information. A method is proposed in the paper, this method combines convolutional neural networks (CNNs) and Simultaneous Localization and Mapping (SLAM) to create global dense 3D semantic maps for indoor scenes. The deep neural network that includes convolution and deconvolution is designed to predict semantic category of every pixel. RGB-D camera is used to obtain scene information, accomplish localization and build 3D maps simultaneously. The semantic information is integrated into the 3D scene, we present an octree map method to replace traditional point clouds method, which can reduce the error from pose estimation and single frame labeling. By this method, the accuracy of semantic information is greatly improved.

关键词： Three-dimensional displays Semantics Cameras Simultaneous localization and mapping Octrees Feature extraction

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Load Curve Modeling Based on Behavior Analysis of Distributed Power Customers

Load Curve Modeling Based on Behavior Analysis of Distribute...

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2019 4th Asia Conference on Power and Electrical Engineering (ACPEE 2019)

作者： Xinyang HAN Yu ZHANG Yuqiao JIAO Yue ZHANG Yanjing QU Min DING State Grid Energy Research Institute School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex System State Grid Shanghai Municipal Electric Power Company

As renewable power generation directly affects the customers' traditional electricity behavior and then offsets the power load,this paper proposes a load curve modeling method for renewable power customers based on the behavior ***,customers' active behavior is represented by the quantity of active customer *** on the analysis of customer behaviors,a modeling method for the quantity of active customer households is proposed based on Markov Chain Monte Carlo ***,with the inputs as the quantity of active customer households and time of photovoltaic power generation,an inference model based on fuzzy logic is proposed to get the quantity of customer household starting electrical *** combing the average usage time of electrical appliances,load characteristics are analyzed based on usage state of electrical appliance of distributed power ***,the simulation results verify the effectiveness of the proposed method.

关键词： Carlo Monte Markov Load Curve Modeling Based on Behavior Analysis of Distributed Power Customers

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Identification of Protein complex Based on a Novel Multi-Objective Method 37

Identification of Protein Complex Based on a Novel Multi-Obj...

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37th Chinese control Conference, CCC 2018

作者： Yuan, Zhu Xiaoyu, Peng Chong, Wu School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex System China University of Geosciences Wuhan430074 China

ISBN: (纸本)9789881563941

systematic identification of protein complexes is an important application of data mining in bioscience. The existing computation methods of detecting protein complexes are usually based on the topological properties of protein-protein interaction(PPI) network data. However, limited by the inherent single structure of the PPI network, the mining of protein complexes may not be fully resolved. In this paper, we propose an original multi-objective optimization strategy based protein complex detection method, which is used Adaptive Multi-Objective Black Hole algorithm, called AMOBH. In the selection of objective function strategy, we integrate the topological structure of PPI network data with semantic similarity based on GO (Gene Ontology) annotation data. Experiments demonstrate that the method we adopt provide more convinced results and higher computational efficiency compared with the existing prediction measures. © 2018 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Gene Ontology

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