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第三十八届中国控制会议

作者： Xiaohang Zhao Ke Zhang Yi Chai Complex system safety and control Key Laboratory of Ministry of Education School of Automation Chongqing University IEEE Department of Electrical and computer engineering Duke University

Hydraulic systems is a class of nonlinear complex *** are many typical characteristics with the systems:multiple functional components,multiple operation modes,space-time coupling work,and monitoring signals for faults are multivariate time series data,*** of the characteristics,fault diagnosis for Hydraulic systems is not *** fault diagnosis methods mostly ignore the multivariable timing characteristics of monitoring signals,it has made many detection and diagnosis(especially for multiple fault)can not keep high accuracy,and some of the methods are not even be able to multiple fault *** at the problem,a multivariate time series classification based diagnosis method is ***,extracting timing characteristics(transformed features)from the time series data collected via sensors by 1-NN ***,training the transformed features by multi-class OVO-SVM to classify multivariate time *** of the method contains single fault and multiple faults conditions,the results show that the method has high accuracy,it can complete multiple-faults classification.

关键词： fault diagnosis hydraulic systems multivariate time series analysis classification

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Mem Brain: An Easy-to-Use Online Webserver for Transmembrane Protein Structure Prediction

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Nano-Micro Letters 2018年第1期10卷 12-19页

作者： Xi Yin Jing Yang Feng Xiao Yang Yang Hong-Bin Shen Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Department of Computer Science Shanghai Jiao Tong University Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering

Membrane proteins are an important kind of proteins embedded in the membranes of cells and play crucial roles in living organisms, such as ion channels,transporters, receptors. Because it is difficult to determinate the membrane protein's structure by wet-lab experiments,accurate and fast amino acid sequence-based computational methods are highly desired. In this paper, we report an online prediction tool called Mem Brain, whose input is the amino acid sequence. Mem Brain consists of specialized modules for predicting transmembrane helices, residue–residue contacts and relative accessible surface area of a-helical membrane proteins. Mem Brain achieves aprediction accuracy of 97.9% of ATMH, 87.1% of AP,3.2 ± 3.0 of N-score, 3.1 ± 2.8 of C-score. Mem BrainContact obtains 62%/64.1% prediction accuracy on training and independent dataset on top L/5 contact prediction,respectively. And Mem Brain-Rasa achieves Pearson correlation coefficient of 0.733 and its mean absolute error of13.593. These prediction results provide valuable hints for revealing the structure and function of membrane *** Brain web server is free for academic use and available at ***/bioinf/Mem Brain/.

关键词： Transmembrane a-helices Structure prediction Machine learning Contact map prediction Relative accessible surface area

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Data-Driven Modeling & Analysis of Dynamic Wake for Wind Farm control: A Comparison Study

Data-Driven Modeling & Analysis of Dynamic Wake for Wind Far...

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Chinese Automation Congress (CAC)

作者： Zhenyu Chen Bart M Doekemeijer Zhongwei Lin Zhen Xie Zongming Si Jizhen Liu Jan-Willem van Wingerden State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources School of Control Computer and Engineering North China Electric Power University Beijing China Delft Center for Systems and Control (DCSC) Faculty of Mechanical Maritime and Materials Engineering (3mE) Delft University of Technology The Netherlands CHN ENERGY (Shandong) New Energy Co. Ltd Jinan PR China

ISBN: (数字)9781728176871

ISBN: (纸本)9781728176888

For the study of wind farm and wake effect, the steady-state wake models like FLORIS were proposed and used during wind farm operations to achieve higher wind power utilization and conversion. However, the dynamic performance of the wake should also be involved in favor of better optimizations. In this paper, a data-driven analysis and modeling method, dynamic mode decomposition (DMD), is used to construct dynamic flow model with high-fidelity flow data. Two DMD-derived models are constructed based on flow data in three-dimensional and two-dimensional spaces, respectively. The obtained models and respective flows are compared in the time and frequency domain. Results show that both models have apparent differences in the frequency domain, but the dominant wake characteristics' consistency is maintained.

关键词： Wind turbines Wind farms Analytical models Mathematical model Computational modeling Solid modeling Wind speed

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Structural Performance Analysis of Wall Climbing Robot 2

Structural Performance Analysis of Wall Climbing Robot

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2019 2nd International Conference on Advanced Materials, Intelligent Manufacturing and Automation, AMIMA 2019

作者： Huang, Li Liao, Shangchun College of Computer Science and Technology Wuhan University of Science and Technology Wuhan Hubei430081 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology Wuhan Hubei430081 China Key Laboratory of Metallurgical Equipment and Control Technology Ministry of Education Wuhan University of Science and Technology Wuhan Hubei430081 China

With the development of artificial intelligence technology, robotics technology has become more and more mature. Ground walking robots not only develop rapidly, but also have been applied in actual production and life. However, the development of wall climbing robot technology is still in the laboratory research and small-scale application. We live in a world where progress is continuing. Large-scale buildings, bridges and ships are becoming more and more common. In these places, it is inevitable to involve the construction, maintenance and clarity of high-rise buildings and ships. In the case of dangerous and inefficient manpower work, the application of wall climbing robots can play a very good role. Therefore, the development of wall climbing robots is of vital importance both now and in the future. Starting from the performance characteristics of wall climbing robot, this paper studies and summarizes the moving mode, control mode, conditions to be satisfied and various adsorption forms of wall climbing robot, and introduces the basic research situation in the field of wall climbing robot. © Published under licence by IOP Publishing Ltd.

关键词： Robot applications

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An unsupervised hourly weather status pattern recognition and blending fitting model for PV system fault detection

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Applied Energy 2022年 319卷

作者： Jiaqi Qu Zheng Qian Yan Pei Lu Wei Hamidreza Zareipour Qiang Sun School of Instrumentation and Optoelectronic Engineering Beihang University Beijing China School of Electronics and Information Engineering Beihang University Beijing China The Department of Electrical and Computer Engineering University of Calgary Calgary Canada State Key Laboratory of Control and Simulation of Power System and Generation Equipment Department of Electrical Engineering Tsinghua University Beijing China

Detecting PV system faults in a timely fashion is important to ensure the safe operation of equipment and reduce their impact on the economy of the PV systems. It is necessary to further improve the time-sensitive performance evaluation of the system. However, the hourly weather scenario segmentations are seldom considered during the hour-level online monitoring process. Therefore, a hybrid method based on unsupervised hourly weather status pattern recognition and blending fitting model is proposed for hourly fault detection to improve the performance evaluation of PV systems. The proposed solution includes three parts, firstly, in the data preprocessing stage, the measured power with the errors and noise under normal operation situation caused by the environment changes is corrected by monthly linear fitting. Secondly, an unsupervised hourly weather status pattern recognition method is constructed using the measured radiation data, including unsupervised clustering and the Multiclass-GBDT-LR classification process. Finally, after eliminating the anomalies and errors, the blending fitting model of the hourly sub-weather status is established. Through the analysis of power plants in Australia and China, the proposed solutions are validated and evaluated to be superior to existing data-driven solutions in terms of fitting accuracy, detection validity, and response time. Numerical results of case studies indicate that the developed methodology under sub-weather has improved the detection accuracy up to 97.71% and 99.29% compared to benchmark models.

关键词： Photovoltaic systems performance Hourly fault detection Unsupervised hourly weather status pattern recognition Blending fitting model

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A Novel Person Re-ID Method based on Multi-Scale Feature Fusion

A Novel Person Re-ID Method based on Multi-Scale Feature Fus...

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Chinese control Conference (CCC)

作者： Yuanyuan Li Xiaofei Wang Zhiqin Zhu Xinghua Huang Penghua Li Guanqiu Qi Yujun Rong Chongqing University of Posts and Telecommunications Chongqing China Key Laboratory of Complex System Safety and Control Ministry of Education Chongqing University Chongqing China Computer Information Systems Department Buffalo State College Buffalo NY USA China Mobile (Hangzhou) Information Technology Co. Ltd. Hangzhou China

ISBN: (数字)9789881563903

ISBN: (纸本)9781728165233

Person re-identification (Re-ID) is a challenging but significant research topic. Previous methods based on global features are not pay much attention to the fine-grained feature information. While the methods based on local features strictly rely on the accurate pedestrian detection bounding-boxes to align the image pairs. In this paper, the Multi-Scale Feature Fusion Network is proposed which can extract global and local features simultaneously and to learn jointly. The network structure consists of two branches. One is a global branch for global feature learning, in this branch, we extract the whole body feature information of human body to express the pedestrian image as a whole. The other is a local branch for local feature learning. In the local branch, according to the characteristics of pedestrian's human structure, we segment the image into 6 horizontal strips to bring the local feature representation of the pedestrian image. In addition, we use a novel integration of multiple loss functions to further improve the recognition accuracy of the network. Experiments on Market-1501 and DukeMTMC-reID datasets show that our proposed method can achieve state-of-the-art results.

关键词： Feature extraction Task analysis Training Measurement Smoothing methods Electronic mail Machine learning

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A TextCNN Based Approach for Multi-label Text Classification of Power Fault Data

A TextCNN Based Approach for Multi-label Text Classification...

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IEEE International Conference on Cloud Computing and Big Data Analysis (ICCCBDA)

作者： Qiang Zhang Rongrong Zheng Ziyan Zhao Bo Chai Jiangui Li Artificial Intelligence on Electric Power System State Grid Corporation Joint Laboratory (GEIRI) Global Energy Interconnection Research Institute Co. Ltd Beijing China State Grid Information & Telecommunication Co. Ltd School of Control and Computer Engineering North China Electric Power University Beijing China

ISBN: (数字)9781728160245

ISBN: (纸本)9781728160252

With the increasing number of power terminal devices in power systems, fault diagnosis based on textual fault data has become a challenging task. Classification technology plays an important role in power system fault diagnosis. In this paper, we propose a TextCNN based approach to multi-label text classification for analyzing fault text data of power terminal devices. First, the main category labels of faults and failure were constructed. The dataset was preprocessed for feature representation. Next, we adopted the TextCNN based model for multi-label classification of power fault texts. The experimental results show that the proposed method is better than the typical multi-label classification models for power fault text multi-label classification. The constructed label categories were also experimentally evaluated, and these labels reflect the main types of power faults and failure.

关键词： Text categorization Internet of Things Power system faults Feature extraction Fault diagnosis

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Opacity of Nondeterministic Transition systems: A (Bi)Simulation Relation Approach

Opacity of Nondeterministic Transition Systems: A (Bi)Simula...

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作者： Zhang, Kuize Yin, Xiang Zamani, Majid ACCESS Linnaeus Center School of Electrical Engineering KTH Royal Institute of Technology Stockholm10044 Sweden Department of Automation Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University Shanghai200240 China Computer Science Department University of Colorado Boulder BoulderCO80309 United States

In this paper, we propose several opacity-preserving (bi)simulation relations for nondeterministic transition systems (NTSs) in terms of initial-state opacity, current-state opacity, K-step opacity, and infinite-step opacity. We also show how one can leverage quotient constructions to compute such relations. As a result, although the opacity verification problem for infinite NTSs is generally undecidable, if one can find such an opacity-preserving relation from an infinite NTS to a finite one, the (lack of) opacity of the infinite NTS can be easily verified over the finite one, which is decidable. © 1963-2012 IEEE.

关键词： Opacity

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Multi-objecive LPV control of wind energy conversion system under wide-scale operating conditions

Multi-objecive LPV control of wind energy conversion system ...

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8th Renewable Power Generation Conference (RPG 2019)

作者： Chenzhi Qu Zhongwei Lin Zhen Xie Yilin Li Shangang Zhang Xiongwei Li Zhenqing Duan State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources School of Control and Computer Engineering North China Electric Power University Beijing 102206 PR China CHNENERGY (Shandong) New Energy Co. Ltd Jinan 250099 PR China Guodian New Energy Technology Research Institute Co. Ltd Beijing 102209 PR China

ISBN: (数字)9781839531248

ISBN: (纸本)9781839531255

Wind energy conversion system (WECS) is essentially a time-varying system with high nonlinear characteristics, which leads to the difficulty of guaranteeing the control performance in a wide-scale operating conditions for the conventional linear control strategies. In this paper, a linear-parameter-varying (LPV) gain-scheduled controller is proposed for variable-speed-variable-pitch wind turbines above rated wind speed, which improves both steady-and dynamic-response performances under wide-scale operating conditions. Firstly, the H 2 /H ∞ technique is adopted to reduce the rated power tracking errors, while enhancing the robustness against high-frequency turbulence. Secondly, the regional pole placement technique is applied on WECS, which builds the connections to the dynamic response performance. To verify the effectiveness of the proposed method, the simulation is implemented on the NREL 5 MW wind turbine, which is compared with the conventional local linear control strategy with a full analysis on the related performances.

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A pre-allocation design for cost minimization and delay constraint in vehicular offloading system

arXiv

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arXiv 2019年

作者： Chen, Zhijie Yang, Bo Chen, Cailian Guan, Xinping Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China China

To accommodate exponentially increasing traffic demands of vehicle-based applications, operators are utilizing offloading as a promising technique to improve quality of service (QoS), which gives rise to the application of Mobile Edge Computing (MEC). While the conventional offloading paradigms focus on delay and energy tradeoff, they either fail to find efficient models to represent delay, especially the queueing delay, or underestimate the role of MEC Server. In this paper, we propose a novel Pre-Allocation Design for vehicular Offloading (PADO). A task delay queue is constructed based on an allocate-execute separate (AES) mechanism. Due to the dynamics of vehicular network, we are inspired to utilize Lyapunov optimization to minimize the execution cost of each vehicle and guarantee task delay. The MEC Server with energy harvesting devices is also taken into consideration of the system. The transaction between vehicles and server is decided by a Stackelberg Game framework. We conduct extensive experiments to show the property and superiority of our proposed framework. Copyright © 2019, The Authors. All rights reserved.

关键词： Energy harvesting

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