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International Conference on Information Fusion

作者： Xi Li Le Yang Lyudmila Mihaylova Fucheng Guo Min Zhang School of Electronic Science National University of Defense Technology Changsha China Department of ECE University of Canterbury Christchurch New Zealand Department of Automatic Control & System Engineering University of Sheffield Sheffield UK

The Gaussian mixture model (GMM) has been extensively investigated in nonlinear/non-Gaussian filtering problems. This paper presents two enhancements for GMM-based nonlinear filtering techniques, namely, the adaptive ordering of the measurement update and normalized innovation square (NlS)-based mixture component management The first technique selects the order of measurement update by maximizing the marginal measurement likelihood to improve performance. The second approach takes the filtering history of a mixture component into account and prunes those components with NIS larger than a threshold to eliminate their impact on the filtering posterior. The advantage of the proposed enhancements is illustrated via simulations that consider source tracking using the time difference of arrival (TDOA) and frequency difference of arrival (FDOA) measurements received at two unmanned aerial vehicles (UAVs). A GMM-cubature quadrature Kalman filter (CQKF) is implemented and its performances with different measurement update and mixture component management strategies are compared. The superior performance obtained via the use of the two proposed techniques is demonstrated.

关键词： Time measurement Covariance matrices Noise measurement Frequency measurement Weight measurement Merging Kalman filters

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Traffic parameters prediction using a three-channel convolutional neural network 2nd

Traffic parameters prediction using a three-channel convolut...

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2nd IFIP TC 12 International Conference on Intelligence Science, ICIS 2017

作者： Zang, Di Wang, Dehai Cheng, Jiujun Tang, Keshuang Li, Xin Department of Computer Science and Technology Tongji University Shanghai China The Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai China Department of Transportation Information and Control Engineering Tongji University Shanghai China Shanghai Lujie Electronic Technology Co. Ltd. PudongShanghai China

ISBN: (纸本)9783319681207

Traffic three elements consisting of flow, speed and occupancy are very important parameters representing the traffic information. Prediction of them is a fundamental problem of Intelligent Transportation systems (ITS). Convolutional Neural Network (CNN) has been proved to be an effective deep learning method for extracting hierarchical features from data with local correlations such as image, video. In this paper, in consideration of the spatiotemporal correlations of traffic data, we propose a CNN-based method to forecast flow, speed and occupancy simultaneously by converting raw flow, speed and occupancy (FSO) data to FSO color images. We evaluate the performance of this method and compare it with other prevailing methods for traffic prediction. Experimental results show that our method has superior performance. © IFIP International Federation for Information Processing 2017.

关键词： Forecasting

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Using convolutional neural network with asymmetrical kernels to predict speed of elevated highway 2nd

Using convolutional neural network with asymmetrical kernels...

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2nd IFIP TC 12 International Conference on Intelligence Science, ICIS 2017

作者： Zang, Di Ling, Jiawei Cheng, Jiujun Tang, Keshuang Li, Xin Department of Computer Science and Technology Tongji University Shanghai China The Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai China Department of Transportation Information and Control Engineering Tongji University Shanghai China Shanghai Lujie Electronic Technology Co. Ltd. Pudong Shanghai China

ISBN: (纸本)9783319681207

In this paper, we present a deep learning based approach to performing the whole-day prediction of the traffic speed for the elevated highway. In order to learn the temporal features of traffic speed data in a hierarchical way, an improved convolutional neural network (CNN) with asymmetric kernels is proposed. Speed data are collected from loop detectors of Yan’an elevated highway of Shanghai. To test the performance of the presented method, we compare it with some conventional approaches of traffic speed estimation. Experimental results demonstrate that our method outperforms all of them. © IFIP International Federation for Information Processing 2017.

关键词： Speed

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High field dielectric properties of low density polyethylene with rough surface under AC ramp electric field application

High field dielectric properties of low density polyethylene...

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Conference on Electrical Insulation and Dielectric Phenomena, (CEIDP)

作者： Yurika Sano Kazuyuki Tohyama Multidisciplinary Engineering National Institute of Technology Numazu Japan Department of Electronic Control System Eng. Numazu Institute of Technology Numazu Japan

ISBN: (纸本)9781538611951

Latest power cables like cross-linked polyethylene (XLPE) have taken measures to prevent water tree deterioration and improve the performance of the insulator itself. Thus, not only its operating electric field, but also its stability and reliability are getting higher. However, due to operating for long duration, it is possible that there is a risk of sudden breakdown from the electrical tree which started from a very small defect or contaminant in the insulator. Thus, it is very important to elucidation of electric conduction mechanism that the blank period before the electrical tree initiation. In previous study, the dielectric properties of LDPE with mechanical damage on film's surface under AC high field was reported. However, it was pointed out that the heating at the damaged film sample's preparation process affected to the experimental result of dielectric properties. In this study, 3 kinds of damaged LDPE films were prepared without the heating process. The estimation of the influences of mechanical damage on film surface to the high field dielectric properties were investigated by the results of simultaneous observation of dissipation current and electroluminescence(EL). Since the conduction mechanisms under ac high field have time dependence, it is very important to make the electric field application history equal in each sample. So, a simultaneous observation under an AC ramp electric field application was introduced to control it [1]. From the results of the simultaneous observations of these damaged samples, the influences of mechanical damage to the dielectric properties are discussed.

关键词： Heating systems Electric fields Dielectrics Presses Morphology Electroluminescence

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Optimization Algorithms for Predictive control Approach to Networked Bilinear systems

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自动化学报 2017年第7期43卷 1234-1240页

作者： Binglin Wang Yu Kang Jiahu Qin Yanmei Li Department of Automation University of Science and Technology of China Hefei 230027 China State Key Laboratory of Fire Science Department of Automation Institute of Advanced Technology University of Science and Technology of China Hefei 230027 China and also with the Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Chi- nese Academy of Sciences Beijing 100190 China Physics and Electronic Engineering Anqing Normal University Anqing 246011 China

This paper is concerned with the networked predictive control of discrete-time bilinear *** deal with the network-induced communication delay that exists in both forward channel（controller to actuator）and feedback channel（sensor to controller）,a bilinear networked predictive control scheme is *** a non-convex optimization problem of solving the predictive control sequence is presented,for which two gradually-optimized algorithms are proposed based on the special structure of bilinear system dynamics *** numerical simulation indicates that the resulting predictive control sequence can compensate for the network-induced issues actively,which proves the effectiveness of the proposed predictive control strategy.

关键词：离散双线性系统预测控制策略逐步优化算法网络化系统动力学模型控制序列反馈通道通信延迟

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A deep learning-based remaining useful life prediction approach for bearings

arXiv

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arXiv 2018年

作者： Cheng, Cheng Ma, Guijun Zhang, Yong Sun, Mingyang Teng, Fei Ding, Han Yuan, Ye The Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China The State Key Laboratory of Digital Manufacturing Equipment and Technology Huazhong University of Science and Technology Wuhan430074 China The School of Mechanical Science and Engineering Huazhong University of Science and Technology Wuhan430074 China School of Information Science and Engineering Wuhan University of Science and Technology Wuhan430081 China The College of Control Science and Engineering Zhejiang University Hangzhou310007 China The Department of Electrical & Electronic Engineering Imperial College London LondonSW7 2AZ United Kingdom

In industrial applications, nearly half the failures of motors are caused by the degradation of rolling element bearings (REBs). Therefore, accurately estimating the remaining useful life (RUL) for REBs is of crucial importance to ensure the reliability and safety of mechanical systems. To tackle this challenge, model-based approaches are limited by the complexity of mathematical modeling. Conventional data-driven approaches, on the other hand, require massive efforts to extract the degradation features and construct the health index. In this paper, a novel data-driven framework is proposed to exploit the adoption of deep convolutional neural networks (CNN) in predicting the RULs of bearings. More concretely, raw vibrations of training bearings are first processed using the Hilbert-Huang transform to construct a novel nonlinear degradation energy indicator which can be used as the training label. The CNN is then employed to identify the hidden pattern between the extracted degradation energy indicator and the raw vibrations of training bearings, which makes it possible to estimate the degradation of the test bearings automatically. Finally, testing bearings’ RULs are predicted through using a ǫ-support vector regression model. The superior performance of the proposed RUL estimation framework, compared with the state-of-the-art approaches, is demonstrated through the experimental results. The generality of the proposed CNN model is also validated by performance test on other bearings undergoing different operating conditions. Copyright © 2018, The Authors. All rights reserved.

关键词： Hilbert-Huang transform

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Automatic Sleep Spindle Detection with EEG Based on Complex Demodulation Method and Decision Tree Model

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Journal of Biomedical Science and engineering 2017年第5期10卷 10-17页

作者： Jiabin Li Bei Wang Takenao Sugi Yu Zhang Masatoshi Nakamura Department of Automation East University of Science and Technology Shanghai China Department of Electrical and Electronic Engineering Saga University Saga Japan Research Institute of Systems Control Institute for Advanced Research and Education Saga University Saga Japan

Sleep spindle is the characteristic waveform of electroencephalogram (EEG) which is important for clinical diagnosis. In this study, an automatic sleep spindle detection method was developed. The EEG signals were recorded based on the standard polysomnogram (PSG) measurement. A preprocessing procedure is introduced to exclude the unnecessary data segments and normalized the necessary data segments. Complex demodulation method is adopted to detect the candidate sleep spindle waveforms and calculate the features. The sleep spindles are recognized based on a decision tree model. Finally, the detected sleep spindles were utilized to amend the sleep stage recognition results. The sleep EEG data from 3 patients with sleep disorders were analyzed. The obtained results showed that the detected sleep spindles in EEG signal improved the accuracy of sleep stage recognition.

关键词： Sleep Spindle Detection Complex Demodulation Method Decision Tree Mod-el EEG

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The Ellipsoidal Invariant Set of Fractional Order systems Subject to Actuator Saturation:The Convex Combination Form

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IEEE/CAA Journal of Automatica Sinica 2016年第3期3卷 311-319页

作者： Kai Chen Junguo Lu Chuang Li School of Mechanical and Electrical Engineering Hainan University Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China

The domain of attraction of a class of fractional order systems subject to saturating actuators is investigated in this paper. We show the domain of attraction is the convex hull of a set of ellipsoids. In this paper, the Lyapunov direct approach and fractional order inequality are applied to estimating the domain of attraction for fractional order systems subject to actuator saturation. We demonstrate that the convex hull of ellipsoids can be made invariant for saturating actuators if each ellipsoid with a bounded control of the saturating actuators is invariant. The estimation on the contractively invariant ellipsoid and construction of the continuous feedback law are derived in terms of linear matrix inequalities (LMIs). Two numerical examples illustrate the effectiveness of the developed method. © 2014 Chinese Association of automation.

关键词： Algebra Computational geometry Linear matrix inequalities Numerical methods Saturation (materials composition)

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A modified DTC with capability of regenerative braking energy in BLDC driven electric vehicles using adaptive control theory

arXiv

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arXiv 2017年

作者： Geraee, Shiva Mohammadbagherpoor, Hamed Shafiei, Mehdi Valizadeh, Majid Montazeri, Farshad Feyzi, Mohammad Reza Faculty of Electrical and Computer Engineering University of Tabriz Faculty of Electrical and Computer Engineering North Carolina State University United States Department of Electrical and Electronic Engineering and Computer Science Queensland University of Technology Department of Electrical and Electronic Engineering University of Ilam Center of Excellence for Power System Automation and Operation Electronic Engineering Iran University of Science and Technology

This paper represents a novel regenerative braking approach for the Electric Vehicles. The proposed method solves the short-range problem which is corresponding to the charge of the battery pack. The DTC switching algorithm has been modified to recover the electrical energy from Electrical Vehicle (EV), driven by Brushless DC motor, without using the additional power converter or the other electrical energy storage devices. During regenerative braking process, different switching pattern is applied to the inverter to convert the mechanical energy to the electrical energy through the reverse diodes. This switching pattern is different from the normal operation due to the special arrangement of voltage vectors which is considered to convert the mechanical energy to electrical energy. The state of charge of the battery is used as a performance indicator of the method. Simultaneously, a model reference adaptive system has been designed to tune the system's parameters. Several simulations are carried out to validate the performance and effectiveness of the proposed methods. The results show the high capability and performance of the designed method. Copyright © 2017, The Authors. All rights reserved.

关键词： Brushless DC motors

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Automatic Sleep Spindle Detection with EEG Based on Complex Demodulation Method and Decision Tree Model

Automatic Sleep Spindle Detection with EEG Based on Complex ...

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The 11th International Conference on Bioinformatics and Biomedical engineering (ICBBE 2017)

作者： Jiabin Li Bei Wang Takenao Sugi Yu Zhang Masatoshi Nakamura Department of Automation East University of Science and Technology Department of Electrical and Electronic Engineering Saga University Research Institute of Systems Control Institute for Advanced Research and Education Saga University

Sleep spindle is the characteristic waveform of electroencephalogram(EEG) which is important for clinical diagnosis. In this study, an automatic sleep spindle detection method was developed. The EEG signals were recorded based on the standard polysomnogram(PSG) measurement. A preprocessing procedure is introduced to exclude the unnecessary data segments and normalized the necessary data segments. Complex demodulation method is adopted to detect the candidate sleep spindle waveforms and calculate the features. The sleep spindles are recognized based on a decision tree model. Finally, the detected sleep spindles were utilized to amend the sleep stage recognition results. The sleep EEG data from 3 patients with sleep disorders were analyzed. The obtained results showed that the detected sleep spindles in EEG signal improved the accuracy of sleep stage recognition.

关键词： Sleep Spindle Detection Complex Demodulation Method Decision Tree Model EEG

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