• Journal of Korea Multimedia Society
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• v.21 no.4
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• pp.450-463
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• 2018

Sound based machine fault diagnosis is the process consisting of detecting automatically the damages that affect the machines by analyzing the sounds they produce during their operating time. The collected sounds being inevitably corrupted by random disturbance, the most important part of the diagnosis consists of discovering the hidden elements inside the data that can reveal the faulty patterns. This paper presents a novel feature extraction methodology that combines various digital signal processing and pattern recognition methods for the analysis of the sounds produced by the drills. Using the Fourier analysis, the magnitude spectrum of the sounds are extracted, converted into two-dimensional vectors and uniformly normalized in such a way that they can be represented as 8-bit grayscale images. Histogram equalization is then performed over the obtained images in order to adjust their very poor contrast. The obtained contrast enhanced images will be used as the features of our diagnosis system. Finally, principal component analysis is performed over the image features for reducing their dimensions and a nonlinear classifier is adopted to produce the final response. Unlike the conventional features, the results demonstrate that the proposed feature extraction method manages to capture the hidden health patterns of the sound.

• Earthquakes and Structures
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• v.11 no.5
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• pp.779-807
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• 2016

Ground Motion Prediction Equations (GMPEs) are essential tools in seismic hazard analysis. With the introduction of probabilistic approaches for the estimation of seismic response of structures, also known as, performance based earthquake engineering framework; new tasks are defined for response spectrum such as the reference criterion for effective structure-specific selection of ground motions for nonlinear time history analysis. One of the recent efforts to introduce a high quality databank of ground motions besides the corresponding selection scheme based on the broadband spectral consistency is the development of SIMBAD (Selected Input Motions for displacement-Based Assessment and Design), which is designed to improve the reliability of spectral values at all natural periods by removing noise with modern proposed approaches. In this paper, a new global GMPE is proposed by using selected ground motions from SIMBAD to improve the reliability of computed spectral shape indicators. To determine regression coefficients, 204 pairs of horizontal components from 35 earthquakes with magnitude ranging from Mw 5 to Mw 7.1 and epicentral distances lower than 40 km selected from SIMBAD are used. The proposed equation is compared with similar models both qualitatively and quantitatively. After the verification of model by several goodness-of-fit measures, the epsilon values as the spectral shape indicator are computed and the validity of available prediction equations for correlation of the pairs of epsilon values is examined. General consistency between predictions by new model and others, especially, in short periods is confirmed, while, at longer periods, there are meaningful differences between normalized residuals and correlation coefficients between pairs of them estimated by new model and those are computed by other empirical equations. A simple collapse assessment example indicate possible improvement in the correlation between collapse capacity and spectral shape indicators (${\varepsilon}$) up to 20% by selection of a more applicable GMPE for calculation of ${\varepsilon}$.

• The Journal of the Acoustical Society of Korea
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• v.23 no.1
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• pp.61-74
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• 2004

In this paper, a contort- and speaker-dependent cepstrum extraction method and a channel normalization method for minimizing the loss of speaker characteristics in the cepstrum were proposed for a robust speaker recognition system over the channel. The proposed extraction method creates a cepstrum based on the pitch synchronous analysis using the inherent pitch of the speaker. Therefore, the cepstrum called the 〃pitch synchronous cepstrum〃 (PSC) represents the impulse response of the vocal tract more accurately in voiced speech. And the PSC can compensate for channel distortion because the pitch is more robust in a channel environment than the spectrum of speech. And the proposed channel normalization method, the 〃formant-broadened pitch synchronous CMS〃 (FBPSCMS), applies the Formant-Broadened CMS to the PSC and improves the accuracy of the intraframe processing. We compared the text-independent closed-set speaker identification on 56 females and 112 males using TIMIT and NTIMIT database, respectively. The results show that pitch synchronous km improves the error reduction rate by up to 7.7% in comparison with conventional short-time cepstrum and the error rates of the FBPSCMS are more stable and lower than those of pole-filtered CMS.