• 말소리와 음성과학
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• 제7권1호
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• pp.21-26
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• 2015

The aim of this study is to synthesize pathological breathy voice and to make a cepstral peak prominence (CPP) table following breathiness ranks by cepstral analysis to supplement reliability of the perceptual auditory judgment task. KlattGrid synthesizer included in Praat was used. Synthesis parameters consist of two groups, i.e., constants and variables. Constant parameters are pitch, amplitude, flutter, open phase, oral formant and bandwidth. Variable parameters are breathiness (BR), aspiration amplitude (AH), and spectral tilt (TL). Five hundred sixty samples of synthetic breathy vowel /a/ for male were created. Three raters participated in ranking of the breathiness. 217 were proved to be inadequate samples from perceptual judgment and cepstral analysis. Finally, 343 samples were selected. These CPP values and other related parameters from cepstral analysis are classified under four breathiness ranks (B0~B3). The mean and standard deviation of CPP is $16.10{\pm}1.15$ dB(B0), $13.68{\pm}1.34$ dB(B1), $10.97{\pm}1.41$ dB(B2), and $3.03{\pm}4.07$ dB(B3). The value of CPP decreases toward the severe group of breathiness because there is a lot of noise and a small quantity of harmonics.

• 한국의학물리학회지:의학물리
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• 제9권1호
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• pp.23-28
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• 1998

최근에 당뇨병이 말초혈관 혈류흐름에 치명적인 영향을 준다는 의학보고에 따라 기존의 혈액체취를 통한 혈액 속의 당치를 측정하는 방식 대신에 고분해능을 가진 LDF(Laser Dopller Flowmeter)를 이용하여 말초혈관 혈류를 측정하여 정상인과 당뇨병환자와의 차이점에 대하여 연구하였다. 여러 가지 분석 방법중, 본 연구에서는 주파수영역에서의 ASD방법을 통하여 그 차이를 분석하였다. 실험대상은 음성 신경병증, 양성 신경병증, 갑상선 기능 항진증, 갑상선 기능 저하증 등 4개의 당뇨병환자군과 당뇨병에 대한 정상군에 대하여 LDF를 측정하였다. 그 결과 모든 당뇨환자군은 정상인에 대하여 모든 진동수영역에서 낮은 ASD를 가져 당뇨병이 말초혈관 혈류운동에 손실을 가져옴을 확인하였고, 신경병증을 가진 환자와 갑상선기능저항증 당뇨환자군은 거의 모든 진동수 영역에서 낮은 ASD를 가졌다.

• 한국전산구조공학회:학술대회논문집
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• 한국전산구조공학회 1998년도 가을 학술발표회 논문집
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• pp.439-446
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• 1998

One of the most important roles in the nonlinear dynamic structural analysis is to select a proper ground excitation, which dominates the response of a structure. Because of the lack of recorded accelerograms in Korea, a stochastic model of ground excitation with various dynamic properties rather than recorded accelerograms is necessarily required. If all information is not available at site, the information from other sites with similar features can be used by the procedure of seismic hazard analysis. Eliopoulos and Wen identified the parameters of the ground motion model by the empirical relations or expressions developed by Trifunac and Lee. Because the relations used in the parameter identification are largely empirical, it is required to apply the artificial neural networks instead of the empirical model. Additionally, neural networks have the advantage of the empirical model that it can continuously re-train the new recorded data, so that it can adapt to the change of the enormous data. Based on the redefined traditional processes, three neural-networks-based models (FAS_NN, PSD_NN and INT_NN) are proposed to individually substitute the Fourier amplitude spectrum, the parameter identification of power spectral density function and intensity function. The paper describes the first half of the research for the development of Neural-Networks-based model for the generation of an Artificial earthquake and a Response Spectrum(NNARS).

• 융합신호처리학회논문지
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• 제5권1호
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• pp.1-5
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• 2004

유성음의 피치주파수 궤적을 추정할 수 있는 새로운 방법을 제시하였다. 이 방법은 에너지연산자［1］를 두 번 적용하는데 기초하고 있다. Kaiser의 에너지연산자는 정현파의 진폭과 주파수 정보를 추출하는 기능을 가지고 있다. 변조모형에 의하면 유성음은 피치 신호로 변조된 포만트들의 합성으로 파악될 수 있으므로 이 파형의 진폭 포락선을 추출해서 피치 신호와 유사한 파형을 얻는다. 이 파형의 평균 주파수를 검출하여 피치 주파수를 구하는 것이다. 앞부분은 Gopalan의 접근법［9］과 마찬가지이나, 뒷부분의 LPC-스펙트럼 분석등의 과정 대신 또 한번 에너지 연산자를 적용하도록 하여 매우 단순화되고 온라인 적용이 가능한 알고리듬을 얻었다. 추정 결과는 거친 편이지만 온라인으로 피치 궤적의 일반적 스케치를 얻는데 유용할 것으로 기대된다.

• Geomechanics and Engineering
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• 제21권1호
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• pp.35-51
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• 2020

In this study, the seismic response analysis of embankment dams was investigated through numerical modeling. The seismic behavior of dams under main earthquake records and wavelet-based records were studied. Earthquake records were decomposed using de-noising method (DNM) and down-sampling method (DSM) up to five levels. In decomposition process, low and high frequencies of the main earthquake record were separated into two signals. Acceleration response, spectral acceleration, and Fourier amplitude spectrum at the crest of embankment dams under different decomposition levels were evaluated. The seismic behavior under main and decomposed earthquake records was compared. The results indicate an acceptable agreement between the seismic responses of embankment dams under wavelet-based decomposed records and main earthquake motions. Dynamic analyses show that the DNM-based decomposed earthquake records have a better performance compared to DSM-based records. DNM-based records up to level 4 and DSM-based records up to level 2 have a high accuracy in assessment of seismic behavior of embankment dams. The periods corresponding to the maximum values of acceleration spectra and the frequencies corresponding to the maximum values of Fourier amplitude spectra of embankment dam crest under main and decomposed records are in good agreement. The results demonstrate that the main earthquake records can be replaced by wavelet-based decomposed records in seismic analysis of embankment dams.

• International Journal of Naval Architecture and Ocean Engineering
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• 제8권4호
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• pp.312-319
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• 2016

Measured wave data covering two years simultaneously at 3 locations along the eastern Arabian Sea reveals the presence of long-period (peak wave period > 18 s) low-amplitude waves (significant wave height < 1 m) and the characteristics of these waves are described in this article. In a year, 1.4-3.6% of the time, the low-amplitude long-period swells were observed, and these waves were mainly during the nonmonsoon period. The wave spectra during these long-period swells were multi-peaked with peak wave period around 18.2 s, the secondary peak period around 13.3 s and the wind-sea peak period at 5 s. The ratio of the spectral energy of the wind-sea peak and the primary peak (swell) was slightly higher at the northern location (0.2) than that at the southern location (0.15) due to the higher wind speed present at the northern location.

• 한국전자파학회논문지
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• 제23권12호
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• pp.1365-1372
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• 2012

본 논문에서는 임펄스 발생기로부터 계측 시스템으로 전달되는 펄스 신호의 주파수 성분을 매우 정밀하게 측정하고 관련 불확도를 평가하는 시스템과 방법을 다루었다. 임펄스 신호의 스펙트럼 진폭 불확도를 결정짓는 복잡한 요소들과 그 해석적 분석 방법 및 실측을 통한 평가 결과를 제시하였다. 측정 시스템의 확장 불확도는 0.015이고, 이는 국내 최고 수준이며 세계적 교정기관의 결과와도 대등한 수준이다.

• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2004년도 춘계종합학술대회
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• pp.682-684
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• 2004

Random Telegraph Signal(RTS)에 의한 1/f 잡음이 CMOS Ring Oscillator의 Phase Noise와 Jitter에 미치는 영향을 조사한다. 7단 Ring Oscillator의 각 노드에 병렬 연결된 10개의 Piece-Wise-Linear 전류원이 RTS 신호를 모델링 한다. RTS 전류원의 진폭과 Time Constant를 변화시키면서 Ring Oscillator 출력의 FFT 및 전력 스팩트럼 밀도, Jitter를 관찰한다. RTS 전류원의 진폭은 Phase Noise의 폭을 증가시키고 결과적으로 Jitter의 크기도 증가 시키는 것이 확인 되었다. 그리고 RTS Time Constant가 짧아질수록 출력 신호의 FFT peak의 폭이 커지고 Cycle to Cycle Jitter 값이 증가하였다.

• Smart Structures and Systems
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• 제23권5호
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• pp.507-520
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• 2019

In the structural health monitoring field, damage detection has been commonly carried out based on the structural model and the engineering features related to the model. However, the extracted features are often subjected to various errors, which makes the pattern recognition for damage detection still challenging. In this study, an automated damage identification method is presented for hanger cables in a tied-arch bridge using a convolutional neural network (CNN). Raw measurement data for Fourier amplitude spectra (FAS) of acceleration responses are used without a complex data pre-processing for modal identification. A CNN is a kind of deep neural network that typically consists of convolution, pooling, and fully-connected layers. A numerical simulation study was performed for multiple damage detection in the hangers using ambient wind vibration data on the bridge deck. The results show that the current CNN using FAS data performs better under various damage states than the CNN using time-history data and the traditional neural network using FAS. Robustness of the present CNN has been proven under various observational noise levels and wind speeds.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권2호
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• pp.713-725
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• 2022

Mostly, artificial intelligence does not show any definite change in emotions. For this reason, it is hard to demonstrate empathy in communication with humans. If frequency modification is applied to neutral emotions, or if a different emotional frequency is added to them, it is possible to develop artificial intelligence with emotions. This study proposes the emotion conversion using the Generative Adversarial Network (GAN) based voice frequency synthesis. The proposed method extracts a frequency from speech data of twenty-four actors and actresses. In other words, it extracts voice features of their different emotions, preserves linguistic features, and converts emotions only. After that, it generates a frequency in variational auto-encoding Wasserstein generative adversarial network (VAW-GAN) in order to make prosody and preserve linguistic information. That makes it possible to learn speech features in parallel. Finally, it corrects a frequency by employing Amplitude Scaling. With the use of the spectral conversion of logarithmic scale, it is converted into a frequency in consideration of human hearing features. Accordingly, the proposed technique provides the emotion conversion of speeches in order to express emotions in line with artificially generated voices or speeches.