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International Conference on Industry, Information System and Material Engineering

作者： Chen, Lijuan Zou, Xiangjun Chen, BingBing Chen, Yan Li, Jing Zou, Haixin Xiong, Juntao S China Agr Univ Minist Educ Key Lab Key Technol Agr Machine & Equipment Guangzhou 510642 Guangdong Peoples R China Chinese Acad Sci Inst Comp Technol Beijing 100080 Peoples R China

ISBN: (纸本)9783037850275

There are most important features hiding in the higher-order statistics information of the images. They are extracted by classical fast-fixed independent component analysis (fastica algorithm) which requires a large amount of calculation and it is sensitive on the selection of initial point. To overcome the two shortcomings, an improved fastica algorithm is proposed and mathematical models are constructed. And they are applied to obtain the basic vectors from the images. Finally, take litchi fruit image in natural environment as an instance and experiment with Matlab software. The results show that there are less computation and stronger stability of the improved FashICA algorithm used to extract image features.

关键词： independent component analysis fastica algorithm image feature extraction

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NOISE SOURCE IDENTIFICATION AND EXPERIMENTAL RESEARCH OF ENGINE COMPARTMENT OF A FORKLIFT BASED ON FAST INDEPENDENT COMPONENT ANALYSIS AND SCAN & PAINT

NOISE SOURCE IDENTIFICATION AND EXPERIMENTAL RESEARCH OF ENG...

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ASME International Mechanical Engineering Congress and Exposition (IMECE2015)

作者： Zhang, Enlai Hou, Liang Yang, Weiping Xiamen Univ Dept Mech & Elect Engn Xiamen Fujian Peoples R China

ISBN: (纸本)9780791857564

The noise signals from the engine compartment of a forklift are wideband non-stationary random signals as their structure and working process are complex. In order to separate and identify the noise sources of the engine compartment, blind source identification analysis was carried out based on fast independent component analysis (fastica) algorithm and experimental research on noise sources localization were done by using Microfiown's Scan & Paint system. Firstly, a numerical analysis method for effectively achieving noise source identification was proposed. Secondly, the feasibility of fastica algorithm and the efficiency of the proposed method were verified through simulation. Thirdly, the statistical independence and Gaussian of noise signals were analyzed. The results show that noise signals meet the preconditions of independent component analysis (ICA). Then, noise signals were separated by the proposed method. The corresponding relation between independent components (ICs) and different noise sources was obtained. And the accuracy of the identification results was validated with Scan & Paint sound source localization system. The differences between experimental and numerical analysis results are less than 5%. Finally, de-noising methods are devised based on sound source characteristics.

关键词： Engine compartment Noise source identification fastica algorithm Scan & Paint system

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Recognition System of Finger Motion Pattern Based on AR Model Coefficient Estimation

Recognition System of Finger Motion Pattern Based on AR Mode...

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IEEE International Conference on Information and Automation (ICIA)

作者： Bai, Dianchun Zhang, Shouxian Yang, Junyou Jiang, Yinlai Yokoi, Hiroshi Shenyang Univ Technol Sch Elect Engn Shenyang Liaoning Provin Peoples R China Univ Electrocommun Dept Mech Engn & Intelligent Syst Chofu Tokyo Japan

ISBN: (纸本)9781509041022

A new type of finger articulation angle recognition system based on surface electromyography (sEMG) signals were established. Especially, velocities of fingers can be effectively predicted with a few sEMG channels. This paper mainly collected the sEMG signals during forefinger motions which are slow, medium and fast velocities, respectively. A new method were proposed which acquired more accurate estimate of the velocity variations of the finger. Firstly, fast Independent Component Analysis (fastica) algorithm based on the largest negative entropy was used to predict the acquired signals, and then separate effective operation of the signals. Then autoregressive (AR) parameter model U-C algorithm was used to extract characteristic coefficient. Finally, a RBF neural network was designed, and the input is computational AR characteristic coefficient, the outputs are three different velocities of fingers motion. The experiment data were collected from healthy subjects' four muscles including Index finger extensor(IFE), Middle finger extensor (MFE), Palmaris Longus (PL) and the flexor carpi (FC). RBF neural network was used as a classifier to classification and recognition different finger movement action, and a satisfactory results has achieved.

关键词： sEMG fastica algorithm AR parameter model RBF neural network

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Recognition System of Finger Motion Pattern Based on AR Model Coefficient Estimation

Recognition System of Finger Motion Pattern Based on AR Mode...

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IEEE International Conference on Information and Automation

作者： Dianchun Bai Shouxian Zhang Junyou Yang Yinlai Jiang Hiroshi Yokoi School of Electrical Engineering Shenyang University of Technology Shenyang Liaoning Province China Department of Mechanical Engineering and Intelligent Systems The University of Electro-Communications Chofu Tokyo Japan

ISBN: (纸本)9781509041039

A new type of finger articulation angle recognition system based on surface electromyography (sEMG) signals were established. Especially, velocities of fingers can be effectively predicted with a few sEMG channels. This paper mainly collected the sEMG signals during forefinger motions which are slow, medium and fast velocities, respectively. A new method were proposed which acquired more accurate estimate of the velocity variations of the finger. Firstly, fast Independent Component Analysis (fastica) algorithm based on the largest negative entropy was used to predict the acquired signals, and then separate effective operation of the signals. Then autoregressive (AR) parameter model U-C algorithm was used to extract characteristic coefficient. Finally, a RBF neural network was designed, and the input is computational AR characteristic coefficient, the outputs are three different velocities of fingers motion. The experiment data were collected from healthy subjects' four muscles including Index finger extensor (IFE), Middle finger extensor (MFE), Palmaris Longus (PL) and the flexor carpi (FC). RBF neural network was used as a classifier to classification and recognition different finger movement action, and a satisfactory results has achieved.

关键词： sEMG fastica algorithm AR parameter model RBF neural network

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Trajectory optimisation method by using independent component analysis for MIMO-OTHR target tracking

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ELECTRONICS LETTERS 2015年第13期51卷 1020-1021页

作者： Luo, Yang Zhao, Zhiqin Univ Elect Sci & Technol China Sch Elect Engn Chengdu 611731 Peoples R China

Traditional sky-wave over-the-horizon radar (OTHR) target tracking algorithms, such as multipath probabilistic data association, can achieve good performance if the multipath environment clutter is not strong enough. The computation complexity and tracking performance of these algorithms can be further improved by multiple-input multiple-output (MIMO) radar technology owing to its capability to utilise transmitting and receiving diversities. However, multipath propagation is still considered to degrade the performance because of the strong ionosphere and sea clutter. To deal with the interference of strong clutter in a multipath environment, a tracking trajectory optimisation method is proposed by applying the fast and robust fixed-point independent component analysis algorithm which uses the projection pursuit approach to separate clutter and a true target moving trajectory from the received signals. Numerical simulations demonstrate the feasibility of the method, and it also shows that the multipath measurements can be utilised for MIMO-OTHR target tracking.

关键词： independent component analysis optimisation target tracking MIMO radar probability trajectory optimisation method MIMO-OTHR target tracking algorithm traditional sky-wave over-the-horizon radar target tracking algorithms multipath probabilistic data association MPDA multiple-input multiple-output radar technology multipath propagation fastica algorithm projection pursuit approach fixed-point independent component analysis algorithm

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A New fastica algorithm of Newton's Iteration

A New FastICA Algorithm of Newton's Iteration

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2010 2nd International Conference on Education Technology and Computer(第二届IEEE教育技术与计算机国际会议 ICETC 2010)

作者： Ji Ce Yu Yang Yu Peng Department of Information Science and Engineering Northeast University Shenyang Liaoning ProvinceChina

ISBN: (纸本)9781424463671;9781424463688;9781424463701

This paper introduces the basic theory of ICA and the fastica *** order to increase the algorithm convergence rate and reduce the running time,the paper which is based on fastica algorithm proposes an improved independent component analysis algorithm with Newton's iteration-the "Newton Type" iteration algorithm with third-order *** simulation results of the image signal separation show that the improved algorithm have the same separate effect as conventional fastica algorithm and can reduce the times of iterations.

关键词： Independent component analysis fastica algorithm "Newton Type" iteration method Negentropy

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Study on gastric interdigestive pressure activity based on phase space reconstruction and fastica algorithm

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MEDICAL ENGINEERING & PHYSICS 2009年第3期31卷 320-327页

作者： Huang Biao Yan Guozheng Zan Peng Li Qianru Shanghai Jiao Tong Univ Dept Instrument Shanghai 200240 Peoples R China

To investigate the features of the gastric interdigestive pressure activity under normal physiological conditions, we have developed the wireless radiotelemetry capsule based on a telemetry technique. Twelve healthy volunteers participated in this study. Pressure activity data which are an important index of gastric motility can be obtained from the wireless radiotelemetry capsule. But the capsule only records single-dimensional pressure time series which may contain a few interdependent components simultaneously. Automated embedding phase space reconstruction algorithm is employed to reconstruct multi-dimensional phase space. Then the dominant and separated component of the gastric contractions is identified using fastica algorithm. Finally the use of Hilbert Huang transform method for analyzing the characters of gastric motility is investigated. The results show that the proposed method is an effective approach for the analysis of the gastric pressure series. (c) 2008 IPEM. Published by Elsevier Ltd. All rights reserved.

关键词： Radiotelemetry capsule Gastric motility Phase space reconstruction fastica algorithm

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Improved fastica algorithm using a sixth-order Newton's method

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IEICE ELECTRONICS EXPRESS 2009年第13期6卷 904-909页

作者： Kim, K. J. Zhang, S. Nam, S. W. Hanyang Univ Dept Elect & Comp Engn Seoul 133791 South Korea

In this paper, an improved fastica algorithm is proposed for blind source separation by using a sixth-order Newton's method for finding the solutions of the nonlinear equations much faster than other Newton-type iterative methods. In particular, the proposed method has comparable separation performance, fewer iterations, and faster convergence speed.

关键词： independent component analysis fastica algorithm sixth-order Newton's method

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A On-line Coherence Identifying Method Based on Independent Component Analysis

A On-line Coherence Identifying Method Based on Independent ...

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International Congress on Image and Signal Processing

作者： Jing Wang Ke Guo Sch. of Electr. Eng. Wuhan Univ. Wuhan China

ISBN: (纸本)9781479958368

For the large interconnected power system, dynamic equivalence could significantly reduce the computing load and manifest the dominant characteristics. Coherent method is one approach of dynamic equivalence and its core is the automatic identification of coherency. Based on the angle perturbed trajectory measured by WAMS, a new coherency recognition method based on Independent Component Analysis is proposed in this paper, which can take advantage of different fault types and the dynamic behavior of nonlinear systems varying characteristics into account. First of all, the power angle collected by WAMS were centralized and whiten and then the angle perturbed trajectory feature was obtained by the fastica algorithm and the coherent generator groups were identified consequently. This method avoids building of the system model, determining of the system parameters and the model of system operation. The simulations of New England 39-bus system prove the validity of the proposed method. It could give the clustering results accurately.

关键词： independent component analysis power system faults power system interconnection power system measurement fastica algorithm New England 39-bus system WAMS angle perturbed trajectory feature angle perturbed trajectory measurement automatic coherency identification coherency recognition method computing load reduction dominant characteristics manifestation dynamic equivalence fault types feature extraction interconnected power system nonlinear systems varying characteristics online coherence identifying method system parameter determination Clustering algorithms Generators Independent component analysis Nonlinear dynamical systems Power system dynamics Power system stability Trajectory coherent recognition

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Blind Separation of Mixed Audio Signals Based on Improved fastica

Blind Separation of Mixed Audio Signals Based on Improved Fa...

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6th International Congress on Image and Signal Processing (CISP)

作者： Li, Zhiming Yang, Genke Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Shanghai 200240 Peoples R China

ISBN: (纸本)9781479927630

Independent component analysis has become a predominant method for blind source separation problem. And the fastica algorithm is widely used due to its rapid convergence property. However, the performance of this algorithm is sensitive to its initial values for the input weight of separation matrix. This paper proposes approaches to improve this algorithm from two aspects. First, performance comparison is made by simulation with three different nonlinear functions used which results in choosing the optimal one. Then the optimal function is further improved by adjusting its parameter. Second, the initial values are calculated with the steepest descent method based on the improved optimal function instead of random choice. The results show that with these techniques we can solve the initial value sensitivity problem, avoid uneven convergence speed and improve the separation effect.

关键词： independent component analysis fastica algorithm steepest descent method initial value sensitivity negentropy

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