scholarly journals Ultrasonic acoustic health monitoring of ball bearings using neural network pattern classification of power spectral density

2010 ◽  
Author(s):  
William Kirchner ◽  
Steve Southward ◽  
Mehdi Ahmadian
Keyword(s):  
Neural Network ◽  
Spectral Density ◽  
Power Spectral Density ◽  
Pattern Classification ◽  
Health Monitoring ◽  
Ball Bearings ◽  
Power Spectral ◽  
Network Pattern

scholarly journals The Time-Robustness Analysis of Individual Identification Based on Resting-State EEG

2021 ◽  
Vol 15 ◽  
Author(s):  
Yang Di ◽  
Xingwei An ◽  
Wenxiao Zhong ◽  
Shuang Liu ◽  
Dong Ming
Keyword(s):  
Spectral Density ◽  
Power Spectral Density ◽  
Resting State ◽  
Individual Identification ◽  
Frequency Range ◽  
Cross Spectrum ◽  
Power Spectral ◽  
Phase Lags ◽  
Individual Features

An ongoing interest towards identification based on biosignals, such as electroencephalogram (EEG), magnetic resonance imaging (MRI), is growing in the past decades. Previous studies indicated that the inherent information about brain activity may be used to identify individual during resting-state of eyes open (REO) and eyes closed (REC). Electroencephalographic (EEG) records the data from the scalp, and it is believed that the noisy EEG signals can influence the accuracies of one experiment causing unreliable results. Therefore, the stability and time-robustness of inter-individual features can be investigated for the purpose of individual identification. In this work, we conducted three experiments with the time interval of at least 2 weeks, and used different types of measures (Power Spectral Density, Cross Spectrum, Channel Coherence and Phase Lags) to extract the individual features. The Pearson Correlation Coefficient (PCC) is calculated to measure the level of linear correlation for intra-individual, and Support Vector Machine (SVM) is used to obtain the related classification accuracy. Results show that the classification accuracies of four features were 85–100% for intra-experiment dataset, and were 80–100% for fusion experiments dataset. For inter-experiments classification of REO features, the optimized frequency range is 13–40 Hz for three features, Power Spectral Density, Channel Coherence and Cross Spectrum. For inter-experiments classification of REC, the optimized frequency range is 8–40 Hz for three features, Power Spectral Density, Channel Coherence and Cross Spectrum. The classification results of Phase Lags are much lower than the other three features. These results show the time-robustness of EEG, which can further use for individual identification system.

Flank Wear Estimation in Turning Through Wavelet Representation of Acoustic Emission Signals

1997 ◽  
Vol 122 (1) ◽  
pp. 12-19 ◽  
Author(s):  
S. V. Kamarthi ◽  
S. R. T. Kumara ◽  
P. H. Cohen
Keyword(s):  
Neural Network ◽  
Acoustic Emission ◽  
Fourier Transform ◽  
Spectral Density ◽  
Power Spectral Density ◽  
Flank Wear ◽  
Wavelet Representation ◽  
Power Spectral ◽  
Frequency Components ◽  
Ae Signals

This paper investigates a flank wear estimation technique in turning through wavelet representation of acoustic emission (AE) signals. It is known that the power spectral density of AE signals in turning is sensitive to gradually increasing flank wear. In previous methods, the power spectral density of AE signals is computed from Fourier transform based techniques. To overcome some of the limitations associated with the Fourier representation of AE signals for flank wear estimation, wavelet representation of AE signals is investigated. This investigation is motivated by the superiority of the wavelet transform over the Fourier transform in analyzing rapidly changing signals such as AE, in which high frequency components are to be studied with sharper time resolution than low frequency components. The effectiveness of the wavelet representation of AE signals for flank wear estimation is investigated by conducting a set of turning experiments on AISI 6150 steel workpiece and K68 (C2) grade uncoated carbide inserts. In these experiments, flank wear is monitored through AE signals. A recurrent neural network of simple architecture is used to relate AE features to flank wear. Using this technique, accurate flank wear estimation results are obtained for the operating conditions that are within in the range of those used during neural network training. These results compared to those of Fourier transform representation are much superior. These findings indicate that the wavelet representation of AE signals is more effective in extracting the AE features sensitive to gradually increasing flank wear than the Fourier representation. [S1087-1357(00)71401-8]

scholarly journals Sleep State Classification Using Power Spectral Density and Residual Neural Network with Multichannel EEG Signals

2020 ◽  
Vol 10 (21) ◽  
pp. 7639
Author(s):  
Md Junayed Hasan ◽  
Dongkoo Shon ◽  
Kichang Im ◽  
Hyun-Kyun Choi ◽  
Dae-Seung Yoo ◽  
...  
Keyword(s):  
Neural Network ◽  
Spectral Density ◽  
Power Spectral Density ◽  
Sleep Stage ◽  
Human Subjects ◽  
Sleep Stages ◽  
Eeg Signals ◽  
Healthy Human ◽  
Power Spectral ◽  
Eeg Data

This paper proposes a classification framework for automatic sleep stage detection in both male and female human subjects by analyzing the electroencephalogram (EEG) data of polysomnography (PSG) recorded for three regions of the human brain, i.e., the pre-frontal, central, and occipital lobes. Without considering any artifact removal approach, the residual neural network (ResNet) architecture is used to automatically learn the distinctive features of different sleep stages from the power spectral density (PSD) of the raw EEG data. The residual block of the ResNet learns the intrinsic features of different sleep stages from the EEG data while avoiding the vanishing gradient problem. The proposed approach is validated using the sleep dataset of the Dreams database, which comprises of EEG signals for 20 healthy human subjects, 16 female and 4 male. Our experimental results demonstrate the effectiveness of the ResNet based approach in identifying different sleep stages in both female and male subjects compared to state-of-the-art methods with classification accuracies of 87.8% and 83.7%, respectively.

scholarly journals An approach to spectral analysis of psychologically influenced speech

2017 ◽  
Vol 7 (1.2) ◽  
pp. 66
Author(s):  
Bhagyalaxmi Jena ◽  
Sudhansu Sekhar Singh
Keyword(s):  
Spectral Analysis ◽  
Fourier Transform ◽  
Spectral Density ◽  
Fast Fourier Transform ◽  
Information Content ◽  
Power Spectral Density ◽  
Speech Signal ◽  
Power Spectral ◽  
Normal Speech

The significant part of any speech signal lies in the information content and the emotions contents like stress or fatigue at a particular period of time. The classification of various types of stress and their effects are defined here. To analyze the changes in stressed speech than that of the normal speech, a database has been created which has investigated the stress among students during the examination in our college. In this paper, the spectral analysis of speech is done where emphasis has been given in the parameters like Fast Fourier Transform (FFT), spectrogram and Power Spectral Density (PSD). These parameters have been simulated using MATLAB codes. The comparison of the mentioned parameters is also done between a normal speech and a psychological stressed speech.

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