• 음성과학
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• 제9권3호
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• pp.3-16
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• 2002

We examined some problems in speaker identification procedures: transformation of acoustic parameters into auditory scales, invalid measurement values, and comparability of spectral energy values across the frequency range. To resolve those problems, we analyzed the acoustic spectral energy of three Korean numbers produced by ten female students from narrow-band spectrograms at 19 proportional time points of each voiced segment. Then, cells of the first five spectral matrices were averaged to form a matrix model for each speaker. The correlation coefficients and sum of the absolute amplitude difference in each pair of the spectral models of the ten subjects were obtained. Also, some individual matrix models were compared to those of the same subject or the other subject with a similar spectral model. Results showed that in numbers '2' and '9' subjects could not be clearly distinguished from the others but in number '4' it shed some possibility of setting threshold values for speaker identification if we employed the coefficients and the sum of absolute difference. Further studies would be desirable on various combinations of the range of long-term average spectra and the degree of signal pre-emphasis.

• 한국융합학회논문지
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• 제11권8호
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• pp.1-6
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• 2020

수박의 맛을 평가하는 다양한 방식이 있으나, 기존의 방법들은 주관적 방식, 평가 비용, 대상의 손상 등과 같은 평가 방식의 한계점이 있다. 최근에는 이러한 단점들을 해소하기 위해 소리를 이용하여 수박을 평가하는 연구들이 진행되고 있다. 본 연구에서는 수박을 두드렸을 때 나는 반향 소리를 AI기반의 기계 학습을 이용하여 수박의 당도를 예측하는 모델을 개발 하였다. 수박의 당도가 높을수록 높은 주파수 성분이 특이점으로 나타나며, 따라서 반향소리 시간-주파수 특이점에 기반 하여 기계 학습 방법을 개발하였다. 2개의 수박 당도별 그룹을 구분 시에 83.2%, 3개의 그룹을 구분시에 59.6%의 정확도로 당도를 예측 할 수 있었다.

• 대한의용생체공학회:의공학회지
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• 제7권2호
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• pp.139-146
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• 1986

본 논문은 tms 32010이라는 디지탈 신호 처리용소자를 사용하여 초음하 펄스 도플러 장치를 구현한 것이다. 도플러 장피란 초음파 신호의 송수신 과정에서 발생하는 도플러 효과를 이용하여 혈류의 속도를 측정하는 장치이다. 한 점에서의 속도를 측정하는 단일채널 도플러 장치에서는 실시간 고속 푸리에 변환기를 구현하여 도플러 주하수의 스펙트럼을 측정함으로서 혈류속도를 측정하며 초음파 빔의 일직선상에서의 여러점을 동시에 측정하는 다중채널 도플러 장치에서는 영점교차검출기를 구현하여 평균주파수를 측정하였다. 자중채널 장치는 직렬처리법을 사용하여 하드웨어를 간단히 하였으며 8점에서의 속도를 측정할 수 있도록 하였다.

• 대한전기학회논문지:시스템및제어부문D
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• 제55권5호
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• pp.240-247
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• 2006

Frequent arousals during sleep degrade the quality of sleep and result in sleep fragmentation. Visual inspection of physiological signals to detect the arousal events is cumbersome and time-consuming work. The purpose of this study is to develop an automatic algorithm to detect the arousal events. The proposed method is based on time-frequency analysis and the support vector machine classifier using single channel electroencephalogram (EEG). To extract features, first we computed 6 indices to find out the informations of a subject's sleep states. Next powers of each of 4 frequency bands were computed using spectrogram of arousal region. And finally we computed variations of power of EEG frequency to detect arousals. The performance has been assessed using polysomnographic (PSG) recordings of twenty patients with sleep apnea, snoring and excessive daytime sleepiness (EDS). We could obtain sensitivity of 79.65%, specificity of 89.52% for the data sets. We have shown that proposed method was effective for detecting the arousal events.

• 대한임베디드공학회논문지
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• 제15권3호
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• pp.119-127
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• 2020

The detection and recognition of abnormal driving becomes crucial for achieving safety in Intelligent Transportation Systems (ITS). This paper presents a feature extraction method based on spectral data to train a neural network model for driving behavior recognition. The proposed method uses a two stage signal processing approach to derive time-saving and efficient feature vectors. For the first stage, the feature vector set is obtained by calculating variances from each frequency bin containing the power spectrum data. The feature set is further reduced in the second stage where an intersection method is used to select more significant features that are finally applied for training a neural network model. A stream of live signals are fed to the trained model which recognizes the abnormal driving behaviors. The driving behaviors considered in this study are weaving, sudden braking and normal driving. The effectiveness of the proposed method is demonstrated by comparing with existing methods, which are Particle Swarm Optimization (PSO) and Convolution Neural Network (CNN). The experiments show that the proposed approach achieves satisfactory results with less computational complexity.

• International Journal of Advanced Culture Technology
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• 제8권3호
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• pp.316-324
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• 2020

It has experienced how difficult People with foreign language learning, it is to pronounce a new language different from the native language. The goal of various foreigners who want to learn Korean is to speak Korean as well as their native language to communicate smoothly. However, each native language's vocal habits also appear in Korean pronunciation, which prevents accurate information transmission. In this paper, the pronunciation of Chinese learners was compared with that of Korean. For comparison, the fundamental frequency and its variation of the speech signal were examined and the spectrogram was analyzed. The Formant frequencies known as the resonant frequency of the vocal tract were calculated. Based on these characteristics parameters, the classifier of the Supporting Vector Machine was found to classify the pronunciation of Koreans and the pronunciation of Chinese learners. In particular, the linguistic proposition was scientifically proved by examining the Korean pronunciation of /ㄹ/ that the Chinese people were not good at pronouncing.

• 음성과학
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• 제15권3호
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• pp.39-51
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• 2008

Formant measurements are one of the most important factors to objectively test cross-linguistic differences among vowels produced by speakers of any given languages. However, many speech analysis softwares present erroneous estimates and some researchers use them without any verification procedures. The purposes of this paper are to examine formant measurements of complex waves which were synthesized from the average formant values of five Korean vowels using three default methods in Praat and to verify the measured values of the five vowels produced by 20 students using one of the methods. Variances along the time axis are discussed after determining absolute difference sum from the 1/3 vowel duration point. Results show that there were smaller measurement errors by the burg method. Also, greater errors were observed in the sl or lpc methods mostly caused by the inappropriate formant settings. Formant measurement deviations were greater in those vowels produced by the female students than those of the male students, which were mostly attributed to the settings for the vowels /o, u/. Formant settings can best be corrected by changing the number of formants to the number of visible dark bands on the spectrogram. Those results suggest that researchers should check the validity of the estimates from the speech analysis software. Further studies are recommended on the perception test of the original sound with the synthesized sound by the estimated formant values.

• 대한임베디드공학회논문지
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• 제14권6호
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• pp.287-294
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• 2019

With the automation of production lines in the manufacturing industry, the importance of real-time fault diagnosis of facility is increasing. In this paper, we propose a fault diagnosis algorithm of LM (Linear Motion)-guide based on deep learning using vibration signals. Generally, in order to guarantee the performance of the deep learning, it is necessary to have a sufficient amount of data, but in a manufacturing industry, it is often difficult to obtain enough data due to physical and time constraints. To solve this problem, we propose a convolutional neural networks (CNN) model based on transfer learning. In addition, the spectrogram image is input to the CNN to reflect the frequency characteristic of the vibration signals with time. The performance of fault diagnosis according to various load condition and transfer learning method was compared and evaluated by experiments. The results showed that the proposed algorithm exhibited an excellent performance.

• 한국광학회지
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• 제15권3호
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• pp.278-284
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• 2004

지속시간이 펨토초 수준에 이르는 빛펄스의 전기장 및 위상의 시간적 변화를 잴 수 있는 "이차조화파 발생 프로그(SHG FROG: Second Harmonic Generation Frequency Resolved Optical Gating)" 장치 및 소프트웨어를 개발하고 성능을 확인하였다. 이 장치를 써서 잰 실험값으로부터 빛펄스를 복원하는데는 "주요성분 일반 투영(PCGP: Principal Component Generalized Projection)" 방식에 더하여 프로그 궤적의 주파수 및 시간지연 "한계값(marginal)"과 이차조화파의 분광분포에 대한 조건을 덧붙임으로써 복원과정의 안정성과 수렴속도를 개선하였다.

• 한국소음진동공학회논문집
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• 제16권3호
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• pp.255-262
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• 2006

Frequency spectrum using the conventional Fourier analysis gives adequate information about the dynamic characteristics of the slider air bearing for the linear and stationary cases. The intermittent contacts for the extremely low flying height, however, generate nonlinear and nonstationary vibration at the instant of contact. Nonlinear dynamic model should be developed to simulate the impulse response of the air bearing during slider-disk contact. Time-frequency analysis is widely used to investigate the nonstationary signal. Several time-frequency analysis methods are employed and compared for the slider vibration signal caused by the impact against an artificially induced scratch on the disk. The representative Wigner-Ville distribution leads to the severe interference problem by cross terms even though it gives good resolution both in time and frequency. The smoothing process improves the interference problem at the expense of resolution. In order to get the results with good resolution and little interference, the reassignment method is proposed. Among others the reassigned Gabor spectrogram shows the best resolution and readability with negligible interference.