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International Conference on Natural Computation (ICNC)

作者： Xiao-li Xu Tao Chen Shao-hong Wang Key Laboratory of Modern Measurement & Control Technology Ministry of Education Beijing Information Science and Technology University Beijing China School of Mechanical and Vehicular Engineering Beijing Institute of Technology Beijing China

Echo State Network is one pioneering recurrent neural network with the core structure of a randomly generated and unchangeable reservoir. This paper provides a Condition prediction model of flue gas turbine by applying Echo State Network and makes prediction of the overall vibration value. Compared to Elman neural network prediction model, examples show that Echo State Network has wonderful dynamic characteristics with smaller error and less prediction time. The model has a good performance in non-linear time series prediction, indicating that the model is feasible in the condition prediction of flue gas turbine.

关键词： Turbines Reservoirs Vibrations Artificial neural networks Recurrent neural networks Training Predictive models

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Single Sensor Information Fusion for Local Fault Prediction of Large Rotating Machinery

Single Sensor Information Fusion for Local Fault Prediction ...

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International Conference on Electrical and control engineering (ICECE)

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

Data process of large rotating machinery is in line with basic features of information fusion. A framework of fault diagnosis and prediction based on sensor information fusion is built. An improved extracting method of features is used to deal with the information fusion of single sensor, which raises the calculation efficiency and precision. The local fault prediction process is presented, and the fault deterioration trend is judged on the basis of dynamic weighted method. Actual example of Beijing Yanshan Petrochemical Co. shows the correction of conclusion.

关键词： Machinery Feature extraction Fault diagnosis Vibrations Accuracy Turbines Monitoring

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An EEG-based classification system of passenger's motion sickness level by using feature extraction/selection technologies

An EEG-based classification system of passenger's motion sic...

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2010 6th IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 International Joint Conference on Neural Networks, IJCNN 2010

作者： Yu, Yi-Hsin Lai, Pei-Chen Ko, Li-Wei Chuang, Chun-Hsiang Kuo, Bor-Chen Lin, Chin-Teng Institute of Electrical Control Engineering Brain Research Center National Chiao-Tung University 1001 Ta Hsueh Rd. Hsinchu 300 Taiwan Graduate Institute of Educational Measurement and Statistics National Taichung University 140 MinShen Rd. Taichung 403 Taiwan Department of Biological Science and Technology Brain Research Center National Chiao Tung University 1001 Ta Hsueh Rd. Hsinchu 300 Taiwan

ISBN: (纸本)9781424469178

Past studies reported that the main electrogastrography (EEG) dynamic changes related to motion sickness (MS) were occurred in occipital, parietal, and somatosensory brain area, especially in the power increasing of the alpha band (8-13 Hz) and theta band (4-7 Hz) which had positive correlation with the subjective MS level. Depend on these main findings correlated with MS, we attempt to develop an EEG based classification system to automatically classify subject's MS level and find the suitable EEG features via common feature extraction, selection and classifiers technologies in this study. If we can find the regulations and then develop an algorithm to predict MS occurring, it would be a great benefit to construct a safe and comfortable environment for all drivers and passengers when they are cruising in the car, bus, ship or airplane. EEG is one of the best methods for monitoring the brain dynamics induced by motion-sickness because of its high temporal resolution and portability. After collecting the EEG signals and subjective MS level in a realistic driving environment, we first do the data pre-processing part including ICA, component clustering analysis and time-frequency analysis. Then we adopt three common feature extractions and two feature selections (FE/FS) technologies to extract or select the correlated features such as principal component analysis (PCA), linear discriminate analysis (LDA), nonparametric weighted feature extraction (NWFE), forward feature selections (FFS) and backward feature selections (BFS) and feed the feature maps into three classifiers (Gaussian Maximum Likelihood Classifier (ML), k-Nearest-Neighbor Classifier (kNN) and Support Vector Machine (SVM)). Experimental results show that classification performance of all our proposed technologies can be reached almost over 95%. It means it is possible to apply the effective technology combination to predict the subject's MS level in the real life applications. The better combination in thi

关键词： Feature extraction

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Driver's cognitive state classification toward brain computer interface via using a generalized and supervised technology

Driver's cognitive state classification toward brain compute...

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2010 6th IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 International Joint Conference on Neural Networks, IJCNN 2010

作者： Chuang, Chun-Hsiang Lai, Pei-Chen Ko, Li-Wei Kuo, Bor-Chen Lin, Chin-Teng Institute of Electrical Control Engineering Brain Research Center National Chiao-Tung University 1001 Ta Hsueh Rd. Hsinchu 300 Taiwan Department of Biological Science and Technology Brain Research Center National Chiao Tung University 1001 Ta Hsueh Rd. Hsinchu 300 Taiwan Graduate Institute of Educational Measurement and Statistics National Taichung University 140 MinShen Rd. Taichung 403 Taiwan

ISBN: (纸本)9781424469178

Growing numbers of traffic accidents had become a serious social safety problem in recent years. The main factor of the high fatalities was the obvious decline of the driver's cognitive state in their perception, recognition and vehicle control abilities while being sleepy. The key to avoid the terrible consequents is to build a detecting system for ongoing assessment of driver's cognitive state. A quickly growing research, brain-computer interface (BCI), offers a solution offering great assistance to those who require alternative communicatory and control mechanisms. In this study, we propose an alertness/drowsiness classification system based on investigating electroencephalographic (EEG) brain dynamics in lane-keeping driving experiments in a virtual reality (VR) driving environment with a motion platform. The core of the classification system is composed of dimension reduction technique and classifier learning algorithm. In order to find the suitable method for better describing the data structure, we explore the performances using different feature extraction and feature selection methods with different classifiers. Experiment results show that the accuracy is over 80% in most combinations and even near 90% under Principal Component Analysis (PCA) and Nonparametric Weighted Feature Extraction (NWFE) going with Gaussian Maximum Likelihood classifier (ML) and k-Nearest-Neighbor classifier (kNN), respectively. In addition, this developed classification system can also solve the individual brain dynamic differences caused from different subjects and overcome the subject dependent limitation. The optimized solution with better accuracy performance out of all combinations can be considered to implement in the kernel brain-computer interface. © 2010 IEEE.

关键词： Brain computer interface

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The Application of Discrete Particle Swarm Optimization In Attitude Determination

The Application of Discrete Particle Swarm Optimization In A...

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WASE International Conference on Information engineering, ICIE

作者： Chang Chuanwen Mao Wenshen Xia Na Gu Jing No.28 Research Institute China Electronics Technology Group Corporation Nanjing China School of Computer and Information Hefei University of Technology Hefei China Engineering Research Center of Safety Critical Industrial Measurement and Control Technology Ministry of Education Hefei China

Attitude determination with navigation satellites is a key technology in aviation, marine and land navigation. In the method based on carrier phase difference, the double difference integer ambiguity solving is important and difficult. This paper adopts Discrete Particle Swarm Optimization to search for the optimal integer ambiguity directly without the decorrelation of ambiguity, and calculates the baseline vector consequently. This method can improve the efficiency of integer ambiguity solving, so as to be applied in dynamic attitude determination. The experimental results show the validity of this method.

关键词： Particle swarm optimization Position measurement Global Positioning System Satellites Algorithm design and analysis Equations

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The circular-scanning algorithm in laser marking based on mathematical morphology: Design and implementation

The circular-scanning algorithm in laser marking based on ma...

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International Conference on Modelling, Identification and control (ICMIC)

作者： Ling Liu Huaiyu Wu Lei Cheng Yongji Wang Quanmin Zhu Department of Information Wuhan University of Science and Technology Wuhan China Engineering Research Center of Metallurgical Automation and Measurement Technology Ministry of Education in China Wuhan University of Science and Technology Wuhan China Department of Control Science and Engineering Huazhong University of Science and Technology Wuhan China Bristol Institute of Technology University of West of England Bristol UK

Laser processing has excellent processing performance, making the laser processing technology used widely, bringing huge economic benefits. An efficient algorithm of circular scanning and the corresponding design and implementation in laser marking will be introduced in this article. The algorithm is based on mathematical morphological, and realized on Visual c++ and GDI+ platform. The distance of the scanning line we got in the original graphic is in accordance with our expectation, and the scanning time is not more than 0.1s. Such stability and accuracy is originated from the core portion of the algorithm that is one-step scanning which makes each closed contour widen inspired by the thought of corrosion expansion algorithm and is aplicable to any curves so that assure reliability, such widen size is in accordance to scanning distance setted by users. This article can be divided into three parts: basics introduction, algorithm design, implementation. The results of numerous circular-scanning examples verify the effectiveness of the algorithm.

关键词： Corrosion

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New enhancement of infrared image based on human visual system

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Chinese Optics Letters 2009年第3期7卷 206-209页

作者：于天河李秋明戴景民 Department of Automation Measurement and Control Engineering Harbin Institute of Technology Computer Center Harbin University of Science and Technology School of Astronautics Harbin Institute of Technology

Infrared images are firstly analyzed using the multifractal theory so that the singularity of each pixel can be extracted from the images. The multifractal spectrum is then estimated, which can reflect overall characteristic of an infrared image. Thus the edge and texture of an infrared image can be accurately extracted based on the singularity of each pixel and the multifractal spectrum. Finally the edge pixels are classified and enhanced in accordance with the sensitivity of human visual system to the edge profile of an infrared image. The experimental results obtained by this approach are compared with those obtained by other methods. It is found that the proposed approach can be used to highlight the edge area of an infrared image to make an infrared image more suitable for observation by human eyes.

关键词： Imaging systems Infrared imaging Infrared spectroscopy Pixels

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An EEG-based classification system of Passenger's motion sickness level by using feature extraction/selection technologies

An EEG-based classification system of Passenger's motion sic...

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International Joint Conference on Neural Networks (IJCNN)

作者： Yi-Hsin Yu Pei-Chen Lai Li-Wei Ko Chun-Hsiang Chuang Bor-Chen Kuo Chin-Teng Lin Institute of Electrical Control Engineering and Brain Research Center National Chiao Tung University Hsinchu Taiwan Graduate Institute of Educational Measurement and Statistics National Taichung University Taichung Taiwan Department of Biological Science and Technology and Brain Research Center National Chiao Tung University Hsinchu Taiwan

关键词： Feature extraction Electroencephalography Support vector machines Principal component analysis Classification algorithms Iron Accuracy

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Driver's cognitive state classification toward brain computer interface via using a generalized and supervised technology

Driver's cognitive state classification toward brain compute...

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International Joint Conference on Neural Networks (IJCNN)

作者： Chun-Hsiang Chuang Pei-Chen Lai Li-Wei Ko Bor-Chen Kuo Chin-Teng Lin Institute of Electrical Control Engineering and Brain Research Center National Chiao Tung University Hsinchu Taiwan Graduate Institute of Educational Measurement and Statistics National Taichung University Taichung Taiwan Department of Biological Science and Technology and Brain Research Center National Chiao Tung University Hsinchu Taiwan

关键词： Feature extraction Driver circuits Electroencephalography Classification algorithms Brain modeling Principal component analysis Support vector machines

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Fault diagnosis of rocket engine ground testing bed with self-organizing maps(SOMs)

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Journal of Harbin institute of technology(New Series) 2009年第2期16卷 204-208页

作者：朱宁冯志刚王祁 Dept.of Automatic Measurement and Control Harbin Institute of Technology School of Automation Shenyang Institute of Aeronautical Engineering

To solve the fault diagnosis problem of liquid propellant rocket engine ground testing bed,a fault diagnosis approach based on self-organizing map(SOM)is *** SOM projects the multidimensional ground testing bed data into a two-dimensional *** of the SOM is used to cluster the ground testing bed *** out map of the SOM is divided to several *** region is represented for one fault *** fault mode of testing data is determined according to the region of their labels belonged *** method is evaluated using the testing data of a liquid-propellant rocket engine ground testing bed with sixteen fault *** results show that it is a reliable and effective method for fault diagnosis with good visualization property.

关键词： fault diagnosis self-organizing map （SOM） U-matrix visualization

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