scholarly journals Identification of Automotive Seat Rattle Noise Using an Independent Component Analysis-Based Coherence Analysis Technique

2020 ◽  
Vol 10 (20) ◽  
pp. 7027
Author(s):  
Kookhyun Yoo ◽  
Un-Chang Jeong
Keyword(s):  
Independent Component Analysis ◽  
Evaluation Method ◽  
Signal Amplitude ◽  
Component Analysis ◽  
Independent Component ◽  
Coherence Analysis ◽  
Evaluation Technique ◽  
Noise Sources ◽  
Original Source ◽  
Analysis Technique

This study proposed a contribution evaluation through the independent component analysis (ICA) method. The necessity of applying ICA to the evaluation of contribution was investigated through numerical simulation. Moreover, the estimation of the number of input sources, the labeling of signals, and the restoration of the signal amplitude were considered to perform the ICA-based coherence evaluation. The contribution evaluation was performed using the coherence evaluation method and by applying the established ICA-based coherence evaluation method to the seat rattle noise of the vehicle. According to the result of the evaluation, with the coherence evaluation technique it was difficult to calculate the contribution in identifying noise sources that overlap in both spatially and in frequency, because it was challenging to distinguish between the two measured signals. By contrast, the ICA-based coherence evaluation was able to restore the original source and investigate the contribution.

Blind Separation of Excavator Noise Signals in Frequency Domain

2011 ◽  
Vol 105-107 ◽  
pp. 723-728
Author(s):  
Li Da Liao ◽  
Qing Hua He ◽  
Zhong Lin Hu
Keyword(s):  
Independent Component Analysis ◽  
Modal Analysis ◽  
Frequency Domain ◽  
Time Domain ◽  
Component Analysis ◽  
Independent Component ◽  
Blind Separation ◽  
Noise Sources ◽  
Amplitude Spectra ◽  
Complex Valued

In order to identify noise sources of an excavator in non-library environment, a complex-valued algorithm in frequency domain was applied. Firstly, an acoustic camera was used to acquire excavator’s noise signals, which were convolutive mixtures in time domain interfered by echo. Secondly, signals in time domain transformed into frequency domain by FT, turned to be complex-valued mixtures. Then, independent components of noise signals were obtained through separation of complex-valued mixtures using complex-valued algorithm based on independent component analysis. Finally, according to noise of diesel with muffler was mainly consist of surface noise, the relationship between principal frequencies and structrual parts was founded by comparing frequency-amplitude spectra and modal analysis in Ansys. Research shows that complex-valued algorithm based on fast fixed-point independent component analysis can effectively separate noise signals from an excavator in time domain, and noise sources can be well ascertained by comparing the modal analysis with blind separation components.

scholarly journals Reduction of Multidimensional Image Characteristics Based on Improved KICA

2014 ◽  
Vol 2014 ◽  
pp. 1-6
Author(s):  
Jia Dongyao ◽  
Ai Yanke ◽  
Zou Shengxiong
Keyword(s):  
Independent Component Analysis ◽  
Evaluation Method ◽  
Principal Component ◽  
Characteristic Parameter ◽  
Component Analysis ◽  
Independent Component ◽  
Average Correlation ◽  
Analysis Algorithm ◽  
Correlation Degree ◽  
Kernel Independent Component Analysis

The domestic and overseas studies of redundant multifeatures and noise in dimension reduction are insufficient, and the efficiency and accuracy are low. Dimensionality reduction and optimization of characteristic parameter model based on improved kernel independent component analysis are proposed in this paper; the independent primitives are obtained by KICA (kernel independent component analysis) algorithm to construct an independent group subspace, while using 2DPCA (2D principal component analysis) algorithm to complete the second order related to data and further reduce the dimension in the above method. Meanwhile, the optimization effect evaluation method based on Amari error and average correlation degree is presented in this paper. Comparative simulation experiments show that the Amari error is less than 6%, the average correlation degree is stable at 97% or more, and the parameter optimization method can effectively reduce the dimension of multidimensional characteristic parameters.

scholarly journals Data-driven re-referencing of intracranial EEG based on independent component analysis (ICA)

2017 ◽  
Author(s):  
Sebastian Michelmann ◽  
Matthias S. Treder ◽  
Benjamin Griffiths ◽  
Casper Kerrén ◽  
Frédéric Roux ◽  
...  
Keyword(s):  
Independent Component Analysis ◽  
Signal To Noise Ratio ◽  
Brain Activity ◽  
Real Data ◽  
Component Analysis ◽  
Independent Component ◽  
Electrode Spacing ◽  
Noise Sources ◽  
Linear Superposition ◽  
Depth Electrodes

AbstractIntracranial recordings from patients implanted with depth electrodes are a valuable source of information in cognitive neuroscience. They allow for the unique opportunity to record brain activity with a high spatial and temporal resolution. To extract the local signal of interest in stereotactic EEG (S-EEG) data, a common pre-processing choice is to re-reference the data with a bipolar montage.With bipolar reference, each channel is subtracted from its neighbour in order to reduce commonalities between channels and isolate activity that is spatially confined. We here challenge the assumption that bipolar reference can effectively perform this task. We argue that in order to extract local activity, the distribution of the signal source of interest, as well as the distribution of interfering distant signals and noise sources need to be considered. Those can have a variable spatial extent and are modulated by electrode spacing, location and anatomical characteristics. Those factors are not accounted for by a fixed referencing scheme and bipolar reference can therefore not only decrease the signal to noise ratio (SNR) of the data, but also lead to mislocalization of activity and consequently to misinterpretation of results.We promote the perspective of regarding referencing as a spatial filtering operation with fixed coefficients. As an alternative, we propose to use Independent Component Analysis (ICA), to derive filter coefficients that reflect the statistical dependencies of the data at hand. We argue that ICA performs the same task that bipolar referencing pursues, namely undoing the linear superposition of activity and can therefore be used to identify activity that is local. We first describe and demonstrate this procedure on human S-EEG recordings. In a simulation with real data, we then quantitatively show that ICA outperforms the bipolar referencing operation in sensitivity and importantly in specificity when it comes to revealing local time series from the superposition of neighbouring channels.

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