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International Journal of Digital Content Technology and its Applications 2012年第20期6卷 268-276页

作者： Liu, Jin Wu, Yangping Zhuo, Dan Hu, Qiping Wang, Yan Zhang, Wensheng State Key Lab of Software Engineering Computer School Wuhan University China International School of Software Wuhan University China State Key Lab of Management and Control for Complex Systems Institute of Automation Chinese Academy of Science China

One of challenges in GIS Web service is mass GIS data storage and processing. There are at least three obstacles that oppose dealing with this challenge: the mismatch between the frequent requirement of concurrent tasks for accessing data in GIS Web service and the poor performance of conventional storage mechanism such as relational databases;the insufficiency of relational databases to manage complex data types in GIS Web service;the horizontal scalability of storage for non-stop GIS Web service evolution. This paper proposes a distributed cached storage solution, as the mixture of MongoDB storage and Memcached cache, for GIS Web service. Both techniques are closely related to NoSQL that is a class of database management system identified by its non-adherence to the widely used relational database management system (RDBMS) model. A concise case that facilitates a better understanding of this work indicates its feasibility.

关键词： Websites

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A virtual-metrology-based machining state conjecture system

A virtual-metrology-based machining state conjecture system

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IEEE/ASME (AIM) International Conference on Advanced Intelligent Mechatronics

作者： Haw-Ching Yang Hao Tieng Yo-Yong Li Min-Hsiung Hung Fan-Tien Cheng Institute of System Information and Control National Kaohsiung First University of Science and Technology Kaohsiung Taiwan Institute of Manufacturing Engineering National Cheng Kung University Tainan Taiwan Department of Computer Science Chinese Culture University Taipei Taiwan

This study proposes a virtual-metrology-based control system for conjecturing machining states and suggesting controller operating modes. The two machining state conjecture models were integrated to validate the virtual metrology results using a dual-stage featuring scheme and a two-phase modeling procedure. The featuring scheme is to extract significant features from collecting data for modeling and to minimize the modeling features by using a genetic algorithm. The modeling procedure adopts two quality indicators to evaluate model effectiveness. Two case studies indicate that the system achieves a mean MAPE of precision estimation less than 8.5% and suggests the controller with operating modes in 1 s.

关键词： Machining Data models Feature extraction Maintenance engineering Hidden Markov models Metrology Predictive models

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Block-based Feature Extraction Model for Early Fire Detection

Block-based Feature Extraction Model for Early Fire Detectio...

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IEEE Symposium Series on Computational Intelligence

作者： Kuang-Pen Chou Mukesh Prasad Deepak Gupta Sharmi Sankar Ting-Wei Xu Suresh Sundaram Chin-Teng Lin Wen-Chieh Lin Institute of Computer Science and Engineering National Chiao-Tung University Hsinchu Taiwan FEIT University of Technology Sydney Australia Department of Computer science & Engineering National Institute of Technology Yupia Arunachal Pradesh India Department of Information Technology Ibri College of Applied Sciences Sultanate of Oman Institute of Electrical and Control Engineering National Chiao-Tung University Hsinchu Taiwan Nanyang Technological University Singapore Singapore SG Department of Computer Science National Chiao-Tung University Hsinchu Taiwan

Every year the fire disaster always causes a lot of casualties and property damage. Many researchers are involved in the study of related disaster prevention. Early warning systems and stable fire can significantly reduce the damage caused by fire. Many existing image-based early warning systems can perform well in a particular field. In this paper, we propose a general framework that can be applied in most realistic environments. The proposed system is based on a block-based feature extraction method, which analyses local information in separate regions leading to a reduction in computing data. Local features of fire block are extracted from the detailed characteristics of fire objects, which include fire color, fire source immobility, and disorder. Each local feature has high detection rate and filter out different false-positive cases. Global analysis with fire texture and non-moving properties are applied to further reduce false alarm rate. The proposed system is composed of algorithms with low computation. Through a series of experiments, it can be observed that Experimental results show that the proposed system has higher detection rate and low false alarm rate under various environment.

关键词： Video surveillance Feature extraction Disorder analysis

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Sliding mode technology for automatic generation control of single area power systems

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ICIC Express Letters 2011年第8 A期5卷 2415-2421页

作者： Qian, Dianwei Yi, Jianqiang Liu, Xiangjie School of Control and Computer Engineering North China Electric Power University No. 2. Beinong Road. Huilongguan Changping District Beijing 102206 China Institute of Automation Chinese Academy of Sciences Beijing 100190 China

Automatic generation control (AGC) is one of the most profitable ancillary services of power systems. The main goal of AGC is to maintain zero steady state errors for frequency deviation and good tracking of load demands in a power system. However, the system performance is often constrained by governor dead band nonlinearity. This paper addresses a sliding mode controller for a single area power system with governor dead band. Two RBF neural networks are employed in this presented method, where one network is designed to compensate the dead band and the other network is designed to approximate the output of the dead band. The weight update formulas of the two RBF networks are derived from Lyapunov direct method. Finally, simulation results show the feasibility of the presented method for the AGC problem of a single area power system.

关键词： Radial basis function networks

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A Bayesian k-vector estimation method for electromagnetic waves in magnetized cold plasma

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URSI Radio Science Bulletin 2023年第378期2021卷 77-82页

作者： Yuji Tanaka Mamoru Ota Shoya Matsuda Yoshiya Kasahara Division of Electrical Engineering Graduate School of Engineering Kyoto University Kyotodaigaku-Katsura Nishikyo-ku Kyoto Kyoto Japan Department of Electrical and Control Systems Engineering National Institute of Technology Toyama College 13 Hongo-machi Toyama Toyama Japan Division of Electrical Engineering and Computer Science Graduate School of Natural Science and Technology Kanazawa University Kakuma Kanazawa Ishikawa Japan

Using the plasma wave characteristics and remote sensing technology, the k-vector direction of plasma waves can provide important information for understanding the global features of space plasma. In this study, we proposed a Bayesian k-vector estimation method in magnetized cold plasma based on the wave distribution function method. The proposed method can be applied to various types of sensors with easy visualization and calculation of the estimation accuracy. We verified the effectiveness of the proposed method through simulations.

关键词： Estimation Plasmas Electromagnetics Mathematical models Magnetic sensors Bayes methods Plasma waves

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Simultaneous Fault Detection Of Heat Exchangers

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IFAC Proceedings Volumes 1998年第10期31卷 365-370页

作者： Gcibor Szederkényi Erik Weyer Katalin M. Hangos Dept. of Computer Science University of Veszprem H-8201 Veszprem Egyetem u. 3. Hungary Dept. of Electrical and Electronic Engineering The University of Melbourne Parkville VIC 3052 Australia Systems and Control Research Laboratory Computer and Automation Institute HAS H-1518 Budapest PO Box 63 Hungary

A grey box model-based method for fault diagnosis is proposed in this paper. The method is based on a first principle model of the process unit: a heat exchanger, and on a grey box model of the faults: the deterioration of the heat transfer surface by aging and the leaking of the outer container. The deterioration of the heat transfer surface is due to material settling, and in old heat exchangers pieces of this material can break off and cause damage in the equipment. A recursive least squares estimator with forgetting factor is used to track both the heat transfer coefficients and the cold side volume. The heat transfer coefficients are estimated both from the hot and cold side equations. The settled material breakage fault is detected via detection of abrupt positive jumps in the estimated heat transfer coefficients using a detector based on a cumulative sum (CUSUM) test. The proposed method enables simultaneous detection of the two fault types considered. Fault localization along the equipment length is also possible when temperature measurements are available along the length.

关键词： Fault detection Fault isolation Fault location Process model Grey box modelling Identification

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Bayesian Neural Networks Uncertainty Quantification with Cubature Rules

Bayesian Neural Networks Uncertainty Quantification with Cub...

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International Joint Conference on Neural Networks (IJCNN)

作者： Peng Wang Nidhal C. Bouaynaya Lyudmila Mihaylova Jikai Wang Qibin Zhang Renke He Automatic Control & Systems Engineering Dept. The University of Sheffield Information and Navigation College Air Force Engineering University The 32nd Research Institute of CETC Department of Automation University of Science and Technology of China Department of Electrical and Computer Engineering Rowan University

ISBN: (数字)9781728169262

ISBN: (纸本)9781728169279

Bayesian neural networks are powerful inference methods by accounting for randomness in the data and the network model. Uncertainty quantification at the output of neural networks is critical, especially for applications such as autonomous driving and hazardous weather forecasting. However, approaches for theoretical analysis of Bayesian neural networks remain limited. This paper makes a step forward towards mathematical quantification of uncertainty in neural network models and proposes a cubature-rule-based computationally-efficient uncertainty quantification approach that captures layer-wise uncertainties of Bayesian neural networks. The proposed approach approximates the first two moments of the posterior distribution of the parameters by propagating cubature points across the network nonlinearities. Simulation results show that the proposed approach can achieve more diverse layer-wise uncertainty quantification results of neural networks with a fast convergence rate.

关键词： Neural networks Uncertainty Bayes methods Computational modeling Gaussian distribution Two dimensional displays Weather forecasting

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A Two-Step Fault Diagnosis Framework for Rolling Element Bearings with Imbalanced Data

A Two-Step Fault Diagnosis Framework for Rolling Element Bea...

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International Conference on Ubiquitous Robots and Ambient Intelligence

作者： Yuan Lan Weiwei Zong Xiaojian Ding Xiaoyan Xiong Xiaohang Han Jiahai Huang Bing Ma Key Laboratory of Ministry of Education in Advance Transducers and Intelligent Control System Institute of Mechatronics Engineering under School of Mechanical Engineering Taiyuan University of Technology Department of Computer Science Wayne State University Science and Technology on Information Systems Engineering Laboratory The Second Research Institute of China Electronic Technology Group Corporation

ISBN: (纸本)9781509008223

Rolling element bearings constitute the key parts on rotating machinery and their fault diagnosis are of great importance. In this paper, a novel Two-Step fault diagnosis framework is proposed to diagnose the status of rolling element bearings with imbalanced data. The Wavelet Packet Transform (WPT) is used to determine the feature vectors. 16-dimensional wavelet packet node energies were extracted from the original datasets as the feature vectors prepared to input to the classifiers. Next, our proposed framework consists of two steps for the fault diagnosis, where Step One makes use of Weighted Extreme Learning Machine (weighted ELM) in an effort to classify the normal or abnormal categories, and Step Two further diagnoses the underlying anomaly in details. The effectiveness of our proposed approach is testified on the raw data collected from the rolling element bearing experiments conducted in our institute, and the empirical results showed that our approach is really fast and can achieve the diagnosis accuracies more than 95%.

关键词： Two-Step fault diagnosis framework Weighed ELM ELM Bearing fault diagnosis

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Friction feedforward compensation and position feedforward in CNC machines

Friction feedforward compensation and position feedforward i...

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7th Annual International Conference on Electrical engineering/Electronics, computer, Telecommunications and Information Technology, ECTI-CON 2010

作者： Suteeratanapirom, Wijin Rattanawaorahirankul, Rapeepong Laosapan, Chutima Sanposh, Peerayot Chayopitak, Nattapon Tungpimolrut, Kanokvate Kunieda, Hiroaki TAIST Tokyo Tech ICTES Program Department of Electrical Engineering Kasetsart University 50 Phahonyothin Rd. Ladyao Jatujak Bangkok 10903 Thailand Intelligent Mechatronics Automation Robotics and Control Laboratory Department of Electrical Engineering Kasetsart University 50 Phahonyothin Rd. Ladyao Jatujak Bangkok 10903 Thailand Industrial Control and Automation Laboratory National Electronics and Computer Technology Center 112 Thailand Science Park Phahonyothin Rd. Klong 1 Klong Luang Pathumthani 12120 Thailand Department of Electrical and Electronic Engineering Tokyo Institute of Technology S3-65 2-12-1 Ookayama Meguro-ku Tokyo 152-8550 Japan

ISBN: (纸本)9789746724913

This research proposes a method to eliminate friction effect and reduced tracking error on CNC milling machines. A CNC milling machine has many connected mechanical components which have friction effects such as ball screws and rails. To control servo motors which are used to drive X-Y table in CNC milling machine, the conventional PID controller is widely used. In this research, Panasonic motor driver is set to the torque control mode. To compensate the friction effects, the friction feedforward compensation method is proposed. Moreover, to reduce the position tracking error, position feedforward controller is used. The simulation and implementation of position control in one axis of PID with Friction Feedforward Compensation and Position Feedforward controller (FFC-PFC) are presented.

关键词： Friction

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Event-triggered control for consensus of multi-agent systems with nonlinear output and directed topologies

arXiv

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arXiv 2019年

作者： Yi, Xinlei Zhang, Shengjun Yang, Tao Wu, Junfeng Johansson, Karl H. School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm100 44 Sweden Department of Electrical Engineering University of North Texas DentonTX76203 United States College of Control Science and Engineering Zhejiang University Hangzhou310027 China

We propose a distributed event-triggered control law to solve the consensus problem for multi-agent systems with nonlinear output. Under the condition that the underlying digraph is strongly connected, we propose some sufficient conditions related to the nonlinear output function and initial states to guarantee that the event-triggered controller realizes consensus. Then the results are extended to the case where the underlying directed graph contains a directed spanning tree. These theoretical results are illustrated by numerical simulations. Copyright © 2019, The Authors. All rights reserved.

关键词： Multi agent systems

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