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第三十四届中国控制会议

作者： XIE Guo ZHANG Dan HEI Xinhong QIAN Fucai Xi'an University of Technology Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing

ISBN: (纸本)9781467374439

Nonlinear system identification is one of the most important topics all over the word. Until now, there are many of off-line identification methods which exhibit well performance. The online approach with non-Gaussian noise, however, is still a challenge. For a class of nonlinear systems where all of the candidate parameters are contained in a definite parameter set, an online parameters and sates estimation method is proposed based on particle filter and Bayes theorem as the following steps. Firstly, regarding all of the candidates, the states are estimated by particle filter(PF) algorithm. Secondly the posterior probabilities of all of candidates are calculated according to the Bayes theorem;then the weights of all of the candidates are obtained through normalization. Lastly, the parameters and sates are estimated ultimately according to the weighted sum of all of the candidates and states. Numerical illustrations are presented to exhibit the application of the method proposed herein, and the performance of the method is examined.

关键词： Nonlinear system,Particle filter,Parameter set,Online parameter identification

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Occupancy grid based urban localization using weighted point cloud

Occupancy grid based urban localization using weighted point...

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International Conference on Intelligent Transportation

作者： Lindong Guo Ming Yang Bing Wang Chunxiang Wang Department of Automation The Key Laboratory of System Control and Information Processing Shanghai China Chunxiang Wang is with the Robotics Institute Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9781509018901

Localization is considered as a key capability for autonomous vehicles act in urban environments. Though have been proved to be able to perform convictive results, localization methods using neither laser scanners nor vision sensors could achieve the goal about balancing between accuracy and cost. In this paper, an occupancy grid based localization framework is presented in order to obtain a precise positioning result with relatively low-cost sensor configuration in large scale urban environment. The proposed approach takes a prebuilt global grid map as prior knowledge for localization. Model based feature extraction method is introduced to provide laser points classification, with each extracted point allocated a specified weight to describe local characteristic. The prior grid map is generated from weighted point cloud to be able to describe the local metric features such as curbs and building facades. Localization function is then carried out with a weight point based maximum likelihood matching method to determine the correspondence between local point cloud and the reference grid map. There are also position initialization and reference map management modules to make the framework more practical and reliable. In the end, the proposed approach has been validated by promising experimental results with long distance tests in large urban environments.

关键词： Three-dimensional displays Feature extraction Sensors Laser modes Roads Vehicles

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AUV assisted Asynchronous Localization for Underwater Sensor Networks

AUV assisted Asynchronous Localization for Underwater Sensor...

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第35届中国控制会议

作者： Jing Yan Xiaoning Zhang Xiaoyuan Luo Yanlong Sun Xinping Guan Institute of Electrical Engineering Yanshan University Department of Automation Shanghai Jiao Tong Universityand Key Laboratory of System Control and Information ProcessingMinistry of Education of China

ISBN: (纸本)9781509009107

Underwater sensor networks(USNs) have been identifies as a promising technology to monitor and explore the underwater *** applications of USNs demand reliable and accurate localization for sensor ***,mobility and clock asynchronous characteristics on underwater sensor nodes make it challenging to achieve *** this paper,we are concerned with AUV assisted asynchronous localization problem for USNs,where AUVs run as mobile nodes and reference points.A tracking variance threshold is set to determine the occasion of localization *** node need to be localized performs the localization protocol and communicates with the anchor ***,a method of time difference is used to eliminate the effects of asynchronous *** the localization,a motion compensation method based on estimation is proposed to compensate the movements of *** addition,the localizing errors due to velocity estimation are analyzed in error *** last,simulation results confirm the efficiency of the proposed algorithm.

关键词： Underwater sensor networks asynchronous localization mobility

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Enhanced Feature Selection Method Based on ANN and GA for Coal Boiler Plants Using Real Time Plant Data

Enhanced Feature Selection Method Based on ANN and GA for Co...

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第35届中国控制会议

作者： Zeeshan Haider Cheng Yin Weidong Zhang Lizong Zhang Mohammad Yousaf Nasir Ali Department of Automation Shanghai Jiaotong University Key Laboratory of System Control and Information Processing Ministery of Education State Grid Shaoxing Power Supply Company Shaoxing

ISBN: (纸本)9781509009107

In this paper a novel feature extraction algorithm is proposed which uses Genetic Algorithm(GA) inorder to optimize the output node from Trained artificial neural network(ANN).Basically this algorithm does not change the training process and nor does modify the *** only extract the relevant features and discard the redundant *** weights between the input node to hidden node and from the hidden node to output node are extracted and a general nonlinear optimization objective function is *** function only depends on the input because weights obtained are *** GA is used to obtain the relevant features which maximizes the objective *** dominant features of that class represents the dominant feature of all *** algorithm is applied for the feature extraction of coal-boiler *** results are compared to other methods like the sensitivity *** is proved that proposed algorithm reduces the dimensionality by 41.23%and require less time to train the neural network.

关键词： Neural network Genetic Algorithm Coal-boiler Plant Mutation Cross Over

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Fuzzy comprehensive evaluation of economic performance on thermal power plant using modified grey clustering

Fuzzy comprehensive evaluation of economic performance on th...

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Chinese control Conference (CCC)

作者： Xiaoying Huang Jingcheng Wang Qi Xia Bohui Wang Key Laboratory of System control and Information Processing Ministry of Education of China Shanghai Jiao Tong University Shanghai CN Shanghai Institute of Laser Technology China

Real-time comprehensive evaluation of economic performance on thermal power plant unit is helpful to improve benefits of power plant. Due to the complexity of power plant, this paper proposes a three-layer evaluation framework to solve the real-time economic evaluation problem. The top layer only has a comprehensive evaluation objective. The second layer is block layer with four economic blocks, in which each block is achieved by their corresponding indexes of the third layer. The index selection of the evaluation framework is completed with PCA. Moreover, this paper proposes a combined weight strategy which is based on AHP and entropy method to determine element weight of each layer. As for evaluation strategy, this paper proposes a modified grey clustering for block evaluation. Based on grey clustering results and combined weight, an improved fuzzy comprehensive evaluation method is used to complete economic comprehensive assessment. The real data from a 1000MW power plant is used to train and test the evaluation model. Evaluation results are basically accord with real condition, which proves the effectiveness of the evaluation method.

关键词： Indexes Power generation Economics Principal component analysis Real-time systems Entropy Data models

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Ultrathin Solar Cells: Quasi-Omnidirectional Ultrathin Silicon Solar Cells Realized by Industrially Compatible Processes (Adv. Electron. Mater. 3/2019)

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Advanced Electronic Materials 2019年第3期5卷

作者： Yan Li Sihua Zhong Yufeng Zhuang Lifei Yang Fanying Meng Wenjie Wang Zhengping Li Wenzhong Shen Institute of Solar Energy Key Laboratory of Artificial Structures and Quantum Control (Ministry of Education) Department of Physics and Astronomy Shanghai Jiao Tong University Shanghai 200240 P. R. China GCL System Integration Technology Co. Ltd. Suzhou 215000 P. R. China Research Center for New Energy Technology Shanghai Institute of Microsystem and Information Technology (SIMIT) Chinese Academy of Sciences Shanghai 200050 P. R. China Collaborative Innovation Center of Advanced Microstructures Nanjing 210093 P. R. China

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Grain storage state detection based on support vector machine and granary bottom pressure

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Journal of Computational and Theoretical Nanoscience 2016年第9期13卷 5777-5780页

作者： Zhang, Miao Zhang, De-Xian Department of Information Science and Engineering Henan University of Technology Zhengzhou 450001 ChinaKey Laboratory of Processing and Control on Grain Information Zhengzhou 450001 China

The pressure sensor technology and support vector machine (SVM) algorithm are combined to recognize different grain storage states of granaries. Data from the pressure sensors on the inner and outer rings of granary bottoms are used as study object. SVM algorithm is used to study the grain storage states of different granaries and different grain varieties. Experimental results show that the proposed method can meet the demand of online remote surveillance of grain storage states of granaries. Copyright © 2016 American Scientific Publishers All rights reserved.

关键词： Support vector machines

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Improvement of Resting-state EEG Analysis Process with Spectrum Weight-Voting based on LES

arXiv

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arXiv 2017年

作者： Ye, Yumeng Liu, Haichun Zhang, TianHong Pan, Changchun Yang, Genke Wang, JiJun Qiu, Robert C. Key Laboratory of System Control and Information Processing Shanghai Jiaotong University Ministry of Education of China Shanghai200240 China Department of Automation and Shanghai Key Laboratory of Navigation and Location Based Services Shanghai Key Laboratory of Psychotic Disorders Shanghai Mental Health Center Shanghai Jiaotong University School of Medicine Shanghai200030 China Brain Science and Technology Research Center Shanghai Jiao Tong University Shanghai200240 China Shanghai Jiaotong University Shanghai200240 China Department of Electrical Engineering Shanghai Jiaotong University Shanghai200240 China Department of Electrical and Computer Engineering Tennessee Technological University CookevilleTN38505 United States

Electroencephalography is a non-invasive technique for recording brain bioelectric activity, which has potential applications in various fields such as human-computer interaction and neuroscience. Among them, analysis of the risk of schizophrenia using EEG data is a relatively new research topic. However, there are many difficulties in analyzing EEG data, including its complex composition, low amplitude as well as low signal-to-noise ratio. Some of the existing methods of analysis are based on feature extraction and machine learning to differentiate the phase of schizophrenia (First-episode schizophrenia, Healthy controls or Clinical high-risk) that samples belong to. However, medical research requires the use of machine learning not only to give more accurate classification results, but also to give the results that can be applied to pathological studies. The main purpose of this study is to obtain the weight values as the representation of influence of each frequency band on the classification of schizophrenia phases on the basis of a more effective classification method using the LES feature extraction, and then the weight values are processed and applied to improve the accuracy of machine learning classification. We propose a method called weight-voting to obtain the weights of sub-bands features by using results of classification for voting to fit the actual categories of EEG data, and using weights for reclassification. Through this method, we can first obtain the influence of each band in distinguishing three schizophrenia phases, and analyze the effect of band features on the risk of schizophrenia contributing to the study of psychopathology. In addition, the weights applied to the original classifier can achieve the upgrade of the classification effect, which contributes to the BCI-assisted system of diagnosis and treatment. Our results show that there is a high correlation between the change of weight of low gamma band and the difference between HC, CHR an

关键词： Electroencephalography

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Action recognition with novel high-level pose features

Action recognition with novel high-level pose features

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IEEE International Conference on Multimedia and Expo Workshops (ICMEW)

作者： Jiayi Fan Zhengjun Zha Xinmei Tian University of Science and Technology of China CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System

ISBN: (纸本)9781509015535

Recently high-level pose features (HLPF) have been shown to be efficient for action recognition in joint-annotated tasks. However, the relative positions between pairs of joints in actual situations and the spatio-temporal information are not considered in constructing HLPF. To tackle their problems, we propose a set of novel high-level pose features (NHLPF). Specifically, considering that the distances between adjacent pairs of joints usually remain unchanged, we propose a horizontally relative position feature and a vertically relative position feature. In addition, a joint inner product feature is proposed to code the spatial information among each triplet of joints. To code temporal information, we calculate the trajectories of the above-mentioned three types of features as corresponding trajectory features. Furthermore, to combine the spatial and temporal information, we present a joint energy change feature, which is designed using observations of the magnitude and direction of the force between joints. We evaluate our NHLPF on a benchmark dataset. The results show that NHPLF are superior features for action recognition.

关键词： Trajectory Pose estimation Detectors Joining processes Hip Force Three-dimensional displays

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GUID-based mobile visual communication using NDN mechanism

GUID-based mobile visual communication using NDN mechanism

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IEEE Visual Communications and Image processing (VCIP)

作者： Yuanzun Zhang Xiaobin Tan Hao Liu Weiping Li CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China

ISBN: (纸本)9781509053179

With the explosive growth in the number of mobile terminals, the demand for visual communication with mobility is increasing. However, traditional solutions for mobility over IP network cannot always meet the demand of satisfying visual communication. Named Data Networking (NDN) is a new communication model that aims to replace IP model brings a different background to mobile visual communication problems. In this paper, we take advantage of the NDN model to realize seamless mobile visual communication. We introduce a delegate with calculation functions and a globally unique identifier (GUID) which can provide native identity indication into the NDN mechanism. The use of GUID benefits real-time applications like visual communication and further works with the delegate to decrease unnecessary routing update. We also specify the naming rule and design a FIB+ to support seamless mobile visual communication. To test the performance of our solutions, we build a proof-of-concept prototype and run experiments on it. The experiments demonstrate that our solution can provide real-time video communication with seamless mobility experience.

关键词： Routing Mobile communication Visual communication IP networks Artificial neural networks Mobile computing Real-time systems

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