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A novel component separation method for deformation monitoring of rockfill dams

STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL 2024年

作者： Li, Yajiao Chen, Chen Zhang, Han Zhang, Liming Zhao, Wenjian Zhang, Pengtao Xue, Jianghan Tang, Xiyang Sichuan Univ Coll Water Resource & Hydropower State Key Lab Hydraul & Mt River Engn 24 South Sect 1Yihuan Rd Chengdu Peoples R China HKUST Shenzhen Hong Kong Collaborat Innovat Res In Shenzhen Peoples R China

High dams are constructed in mountain valley areas with extremely complex geomorphologies, geologies, and operating environments. The dam body and foundation are subjected to large hydraulic loadings and high stress levels, where many factors affect the structural deformation of the dam, making dam safety monitoring and risk assessment difficult. Since dam deformation is critical for evaluating safe operation, a novel dam deformation monitoring model based on the component separation method is proposed, which can provide scientific significance and high application value for operational safety control. The deformation monitoring data include deformation caused by the water level, the temperature, and the time effect. The homologous monitoring sequence points are selected, and a safety monitoring model is proposed based on the physical causes and the independent component signal decomposition technology. First, the aging component can be extracted by using the correlation consistency of a specific measurement point, where the environment is almost the same at the same time of year, such as the temperature and the water level. Then, fast independent component analysis algorithm is applied to further extract the dam deformation induced by the water level and the temperature via correlation analysis. Finally, a deformation monitoring model based on the component separation can be successfully constructed by combining models of the three components. The proposed dam deformation model can overcome problems of large covariance of deformation component, large collinear interference of periodic component separation, and unstable separation. It is further applied to a high face rockfill dam, and the results shows that the multiple correlation coefficient is greater than 0.995 and the mean absolute percentage error is less than 1.37%, demonstrating the high accuracy and stable separation of the components produced by using this model.

关键词： Face rockfill dams Dam deformation monitoring fast ica algorithm Component separation Safety monitoring and risk assessment

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Research of improved fast independent component analysis algorithm in rectal diagnosis signal preprocessing

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INTERNATIONAL JOURNAL OF ARTIFICIAL ORGANS 2019年第11期42卷 636-644页

作者： Zan, Peng Xue, Yingjie Chang, Meihan Shanghai Univ Sch Mechatron Engn & Automat Shanghai 200072 Peoples R China Shanghai Key Lab Power Stn Automat Technol Shanghai Peoples R China

With the maturity of artificial organ technology, the use of artificial anal sphincters was proposed to help patients who suffered anal incontinence for various causes reconstruct rectal perception, monitor rectal pressure and diagnose rectal lesion. Aimed at the lack of signal pretreatment in the artificial anal sphincter system, we find a way to solve it, that is, the multi-dimensional reconstruction of the intestinal one-dimensional pressure signal sequence by using phase space reconstruction, and the separation of the reconstructed signal by using the improved fast independent component analysis algorithm. We did some relevant experiments, further extracted the features of the isolated rectal signal, and used back propagation neural network to diagnose the rectal lesions. Experiments show that the method can pretreat the rectal signal, and further analyze the separated signal to diagnose of rectal function. The improved fast independent component analysis algorithm has few iterations, fast convergence, short run time, low requirements on initial weights and good diagnosis. This study lays a foundation for the diagnosis of rectal function by using artificial anal sphincters.

关键词： Artificial anal sphincter PSR fast ica algorithm BP neural network rectal function diagnosis

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Analysis of EEG Signals and Biomedical Changes due to Meditation on Brain by using ica for Feature Extraction 2

Analysis of EEG Signals and Biomedical Changes due to Medita...

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2nd International Conference on Intelligent Computing and Control Systems (ICICCS)

作者： Korde, Kajal S. Paikrao, P. L. Jadhav, N. S. Govt Coll Engn Dept Elect & Telecommun Engn Amravati India Dr BA Technol Univ Dept Elect & Telecommun Engn Lonere India

ISBN: (纸本)9781538628423

Meditation imparts relaxation to the human being. Nowadays due to worrying and tense atmosphere, the meditation has become demanding solution to reduce physical and mental stress. The current experimentation aims at investigating the biomedical changes of meditation on the human brain. The term meditation refers to an extremely wide variety of practices. The central goal of these meditation practices is to alter mental states. However, the manner in which this goal is pursued in the variety of practices differs from person to person each so that as of today no clear definition of necessary and sufficient criteria for meditation exists that has achieved universal or widespread acceptance. Meditation can be characterized by concentration of attention on the breath, the recitation of a mantra, the state of calmness, mental freedom and the cultivation of a compassionate state. The major focus of fundamental research on meditation is to observe and analyze the changes in brain activation, functioning and anatomy induced by mental training. In this research work, 11 subjects with Mindfulness meditation practice and 11 subjects of non-meditation group are considered. The EEG signals are obtained by using 14 electrode systems. The pre-processing technique used is Daubechies wavelet transform before calculating the statistical features. The statistical features calculated are variance, kurtosis, energy and entropy. Along with these four features, the three features are calculated by using fast Independent Component Analysis (fast ica). These features are averaged and plot in order to categorize between meditational and non-meditational group.

关键词： EEG (Electroencephalography) k-nn (k-nearest neighbor classification algorithm) fast ica algorithm DWT (Discrete Wavelet Transform) Confusion Matrix (CM)

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Analysis of EEG Signals and Biomedical Changes due to Meditation on Brain by using ica for Feature Extraction

Analysis of EEG Signals and Biomedical Changes due to Medita...

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International Conference on Intelligent Computing and Control Systems

作者： Kajal S. Korde P. L. Paikrao N. S. Jadhav Department of Electronics and Telecommunication Engineering Government College of Engineering Amravati India Department of Electronics and Telecommunication Engineering Dr. B. A. Technological University Lonere India

ISBN: (纸本)9781538628430;9781538628423

关键词： EEG (Electroencephalography) K-nn (k-nearest neighbor classification algorithm) fast ica algorithm DWT (Discrete wavelet transform) Confusion matrix (CM)

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Independent component analysis and clustering for pollution data

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ENVIRONMENTAL AND ECOLOGicaL STATISTICS 2015年第1期22卷 33-43页

作者： Chattopadhyay, Asis Kumar Mondal, Saptarshi Biswas, Atanu Univ Calcutta Kolkata India Indian Stat Inst Kolkata India

Independent component analysis (ica) is closely related to principal component analysis (PCA). Whereas ica finds a set of source variables that are mutually independent, PCA finds a set of variables that are mutually uncorrelated. Here we consider an objective classification of different regions in central Iowa, USA, in order to study the pollution level. The study was part of the Soil Moisture Experiment 2002. Components responsible for significant variation have been obtained through both PCA and ica, and the classification has been done by K-Means clustering. Result shows that the nature of clustering is significantly improved by the ica.

关键词： Circular data Distance fast ica algorithm Independent Component Analysis K-means clustering Negentropy Non-Gaussianity Principal Component Analysis

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Optimizing the algorithm of FECG Separation from MECG based on ica Rationale

Optimizing the Algorithm of FECG Separation from MECG based ...

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International Conference on Mechatronics and Semiconductor Materials (ICMSCM 2013)

作者： Zhou Hengyan Xu Yucong Luo Yuxi Gao Yubao Sun Yat Sen Univ Sch Engn Guangzhou 510006 Guangdong Peoples R China

ISBN: (纸本)9783037859391

The study presents a method to separate the fetal electrocardiograph(FECG) from concomitant maternal electrocardiograph (MECG) by using fast Independent component analysis(ica) algorithm of Blind Signal Separation. Current methods of extracting fetal ECG have defects and drawbacks. Traditional ica method has a persistent problem that the signal of FECG extracted from MECG was always mixed with the signal of MECG in diverse levels, and the order of MECG and FECG is uncertain, resulting in the decrease of its rate of convergence. To improve the rate of convergence, this research adopts fast ica algorithm. Experimental results indicate that this method is useful for extracting the fetal signal of ECG And a satisfactory signal to noise ratio (SNR) is obtained.

关键词： Fetal ECG signal fast ica algorithm ica algorithm

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The Study Of Face Recognition Based On Hybrid Principal Components Analysis and Independent Component Analysis

The Study Of Face Recognition Based On Hybrid Principal Comp...

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IEEE International Conference on Electronics, Communications and Control (ICECC)

作者： Zhou, Yanhong Cao, Shukai Wen, Dong Zhang, Huiyang Zhao, Liqiang Hebei Normal Univ Sci & Technol Coll Math & Informat Technol Qinhuangdao Peoples R China

ISBN: (纸本)9781457703218

In this paper a new method has been proposed based on the combination of principal component analysis(PCA) and independent component analysis (ica) for face recognition. In the process of face feature subspace extraction, the dimension of the date set has been reduced by the principal component analysis (PCA), and then the principal components are used to construct eigenface subspace as R. At the same time, the solution of mixed matrix W can be obtained by the fast fixed point algorithm (fast ica) iteration, thereby the face feature classification subspace (RW)-W-T can be got in the end. In the face classification, the recognition vector can be got when the training face set and testing face set have projected in face feature subspace respectively, then the classification results can be obtained while the vector is brought into the K-neighbor classifier. In this paper the ORL face database has been used to verify this method. The experiment result shows that the algorithm has a good recognition rate.

关键词： face recognition independent component analysis principal component analysis fast ica algorithm KNN classification

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Robust Method via Independent Component Analysis with Additive Noise

Robust Method via Independent Component Analysis with Additi...

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International Conference of Environment Materials and Environment Management

作者： Zhao, Yongjian Liu, Boqiang Wang, Hongrun Shandong Univ Inst Informat Engn Weihai Peoples R China Shandong Univ Control Sci & Engn Inst Shandong Peoples R China East Shangdong Univ Phys & Elect Technol Inst Shandong Peoples R China

ISBN: (纸本)9780878492596

Blind source separation via independent component analysis (ica) has received increasing attention because of its potential application in signal processing system. The existing ica methods can not give a consistent estimator of the mixing matrix because of additive noise. Based on interpretation and properties of the vectorial spaces of sources and mixtures, a new ica method is presented in this paper that may constructively reject noise so as to estimate the mixing matrix consistently. This procedure may capture the underlying source dynamics effectively even if additive noise exists. The simulation results show that this method has high stability and reliability in the process of revealing the undering group structure of extracted ica components.

关键词： ica fast ica algorithm probability density function estimator ECG

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Acceleration of FPGA-based ica processor for real-time processing

Acceleration of FPGA-based ICA processor for real-time proce...

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2010 IEEE International Symposium Antennas and Propagation/CNC-USNC/URSI Radio Science Meeting

作者： Fujio, Shunsuke Shiomi, Hidehisa Okamura, Yasuyuki Osaka Univ Grad Sch Engn Sci Suita Osaka 565 Japan

This paper describes about designing of the architecture of the fastica for FPGA, and it can check that it is possible to perform ica processing in real-time for transmission of the QPSK signals of more than 1 Mbps.

ISBN: (纸本)9781424449682

关键词： FPGA-based ica processor QPSK signals fast ica algorithm field programmable gate array field programmable gate arrays independent component analysis independent component analysis quadrature phase shift keying quadrature phase shift keying real-time processing

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