scholarly journals Independent Component Analysis in Frequency Domain and Its Application in Structural Vibration Signal Separation

2011 ◽  
Vol 16 ◽  
pp. 511-517 ◽  
Author(s):  
Cao Junhong ◽  
Wei Zhuobin
Keyword(s):  
Independent Component Analysis ◽  
Frequency Domain ◽  
Vibration Signal ◽  
Component Analysis ◽  
Independent Component ◽  
Signal Separation ◽  
Structural Vibration

Denoising in Steel Structural Impulsive Vibration Signal Based on Independent Component Analysis

2011 ◽  
Vol 219-220 ◽  
pp. 1337-1341 ◽  
Author(s):  
Jun Hong Cao ◽  
Zhuo Bin Wei
Keyword(s):  
Independent Component Analysis ◽  
Steel Structure ◽  
Vibration Signal ◽  
Component Analysis ◽  
Independent Component ◽  
Structural Vibration ◽  
Vibration Signals ◽  
Structure Vibration

The analysis of structure vibration signals is influenced by noise mixed in the signals. Independent component analysis (ICA) method is introduced to denoise the vibration signals in this paper. The representative algorithms: FastICA and JADE are told in detail. The algorithms are applied to separate steel structural vibration signals. The denoising performances in impulsive vibration signals generated by steel structure demonstrate the effectiveness and good robustness of ICA method.

Mixed Vibration Signal Separation and Moving Object Detection Based on Independent Component Analysis

2011 ◽  
Vol 328-330 ◽  
pp. 2113-2116
Author(s):  
Ning Qiang ◽  
Fang Xiang
Keyword(s):  
Independent Component Analysis ◽  
Sensor Array ◽  
Vibration Signal ◽  
Component Analysis ◽  
Spatial Spectrum ◽  
Independent Component ◽  
Signal Separation ◽  
Moving Targets ◽  
Spectrum Function ◽  
Multiple Moving Targets

This article briefly describes the basic theory of independent component analysis (ICA) and algorithms. Independent component analysis (ICA) method is employed to separate the mixed vibration signal, measured from linear sensor array. By calculating the spatial spectrum function, identification and tracking of multiple moving targets achieved. The results show that, ICA can successfully detect and track multiple targets.

INDEPENDENT COMPONENT ANALYSIS (ICA) DAN SPARSE COMPONENT ANALYSIS (SCA) DALAM PEMISAHAN VOKAL DAN INSTRUMEN PADA SENI GEGUNTANGAN

2019 ◽  
Vol 8 (1) ◽  
pp. 105
Author(s):  
Angga Pramana Putra ◽  
Ni Wayan Wiantari ◽  
Putu Mira Novita Dewi ◽  
I Dewa Made Bayu Atmaja Darmawan
Keyword(s):  
Independent Component Analysis ◽  
Mean Square Error ◽  
Blind Source Separation ◽  
Component Analysis ◽  
Independent Component ◽  
Signal Separation ◽  
Blind Signal Separation ◽  
Mean Square ◽  
Blind Signal ◽  
Sparse Component Analysis

Geguntangan adalah pesantian dalam upacara keagamaan yang diiringi dengan gamelan. Indra  pendengaran manusia cenderung memiliki keterbatasan, yang menyebabkan tidak semua vokal yang  tercampur dengan gamelan bisa didengar jelas. Oleh karena itu diperlukan suatu sistem yang dapat digunakan untuk memisahkan vokal dengan gamelan pada geguntangan. Pemisahan sumber suara ini dikategorikan sebagai Blind Source Separation (BSS) atau disebut juga Blind Signal Separation yang  artinya sumber tidak dikenal. Algoritma yang digunakan untuk menangani BSS adalah algoritma Independent Component Analysis (ICA) dan Sparse Component Analysis (SCA) dengan berfokus  pada pemisahan sinyal suara pada file suara berformat *.wav. Algoritma SCA dan ICA digunakan  untuk proses pemisahan suara dengan parameter nilai yang digunakan adalah Mean Square Error (MSE) dan Signalto Interference Ratio(SIR). Dari hasil simulasi menunjukkan Hasil perhitungan MSE dan SIR dengan dengan menggunakan mixing matriks [0.3816, 0.8678], [0.8534, -0.5853] didapatkan untuk metode ICA nilai MSE sebesar 4.169380402433175 x 10-6 untuk instrumennya dan 2.884749383815846 x 10-5 untuk vokalnya dan didapatkan nilai SIR sebesar 53.79928479270223 untuk instrumennya dan 45.39891910741724 untuk vokalnya. Selanjutnya untuk metode SCA, nilai MSE sebesar 3.382207103335018 x 10-5 untuk instrumennya dan 3.099942460987607 x 10-5 untuk vokalnya dan didapatkan nilai SIR sebesar 44.707998026869014 untuk instrumennya dan 45.08646367168143 untuk vokalnya.

A Periodic Noise Separation Method Research of well Field Three-Dimensional Drill String Vibration Signal

2013 ◽  
Vol 318 ◽  
pp. 27-32
Author(s):  
Hao Cheng Wu ◽  
Yong Shou Dai ◽  
Wei Feng Sun ◽  
Li Gang Li ◽  
Ya Nan Zhang
Keyword(s):  
Independent Component Analysis ◽  
Vibration Signal ◽  
Component Analysis ◽  
Drill String ◽  
Independent Component ◽  
Analysis Algorithm ◽  
String Vibration ◽  
Periodic Noise ◽  
Well Field ◽  
Independent Component Analysis Algorithm

Periodic noise is an important manifestation of the drill string vibration signal noise. In order to extract the characteristics of the signals which reflect the situation of the tools in drilling, the periodic components which influence the original drill string vibration signal in the well field were researched and the independent component analysis algorithm which is on the basis of negative entropy for periodic vibration noise separation was adopted. At the same time, the effect of algorithm demixing was improved where periodic noise components which existed in three directions of drill string vibration signals were used, combining with the improved particle swarm optimization algorithm to seek the optimal mixed matrix by which the multi-channel mixed-signal of independent component analysis algorithm could be structured. This method in operation was fast. And after separation each signal was of high similarity. Through the experimental simulation, the method was proven effective in the drill string vibration periodic noise signal separation.

Sign in / Sign up

Export Citation Format

Share Document