• Korean Journal of Cognitive Science
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• v.20 no.2
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• pp.155-181
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• 2009

Psychophysiologists are often interested in the EEG signals that accompany certain psychological events. When one is interested in a time series of event-related changes in EEG, one focuses on examining how the waveforms recorded at individual electrode sites vary over time across one or more experimental conditions. This is an analysis of event-related potentials (ERPs). In addition to such a classical EEG analysis in the time domain, the EEG measures can be investigated in the frequency domain. Moreover, it has been demonstrated that spectral analyses can often yield significant insight into the functional cognitive correlations of the signals. Therefore, this review paper tries to summarize essential concepts (e.g. phase-locking) and conventional methods (e.g. wavelet transformation) for understanding spectral analyses of brain oscillatory activity. Phase-coherence is also introduced in relation to functional connectivity of the brain.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.4
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• pp.959-966
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• 2010

Quantitative ultrasound analysis provides fundamental information of various ultrasound parameters using spectral information of the short-gated radiofrequency(RF) data. Therefore, accurate extraction of spectral information from backscattered RF signal is crucial for further analysis of medical ultrasound parameters. In this paper, we propose two techniques for calculating a more accurate power spectrum which are based on the phase-compensation using the normalized cross-correlation to minimize estimation errors due to phase variations, and the weighted averaging technique to maximize the signal-to-noise ratio(SNR). The simulation results demonstrate that the proposed method estimates better results with 10% smaller estimation variances compared to the conventional methods.

• Proceedings of the Korea Institutes of Information Security and Cryptology Conference
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• 2006.06a
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• pp.141-147
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• 2006

부채널 정보를 이용한 공격이 제안된 이후, 스마트카드와 RFID와 같은 저 전력 정보보호 장치 내부의 암호 프로세서에서 소비되는 전력을 분석하여 암호 연산에 사용된 비밀 정보를 알아내는 공격 방법은 가장 위협적인 물리적 공격 방법으로 알려져 있다. 하지만 측정된 소비 전력 신호를 시간 영역에서 분석하는 기존의 분석 기법들은 암호 연산 시점이 시간 축 상에서 동일하여야 된다는 단점을 가지고 있다. 제안하는 주파수 분석 공격 방법은 공격이 적용되는 장치에서 측정된 시간 영역에서 정렬되지 않는 신호를 Fourier 변환을 하여 주파수 영역에서 분석함으로써 기존 전력 분석 공격이 시간 영역에서 정렬된 소비 전력 신호를 필요로 하는 문제점을 해결하였다. UC Berkeley에서 제작된 능동형 RFID 모듈에 국내 표준인 ARIA 알고리즘을 응용프로그램으로 탑재하여 기존 전력 분석 공격과 제안하는 주파수 분석 공격을 적용하여 결과를 분석 하였다.

• Journal of the Korean Society for Railway
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• v.12 no.4
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• pp.465-471
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• 2009

In this paper, a relationship between the noise and vibration of a high-speed train's wheelset is examined by using time-varying frequency analysis with random data analysis which together contributes to a reduction in the number of experimental running. The noise and vibration of the wheelset is mainly caused by an interaction between the wheel and railway which shows in non-stationary characteristics. For the analysis, they are measured by some microphones and accelerometers, and those signals are post-processed by time-varying frequency analysis with random data analysis. From the analysis, their methods are useful for analyzing the noise and vibration of high-speed train's wheelset.

• Journal of IKEEE
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• v.23 no.3
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• pp.893-898
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• 2019

Time-frequency representations are methods to display the magnitude or energy density of a signal on the two dimensional plane of both time and frequency. They are useful in analyzing the characteristics of time-varying signals. Music is a typical time-varying signal, and it can be analyzed by time-frequency representations. Recently, it is popular to change the sound quality by attaching a safety sounder to an instrument. It is performed to improve perception subjectively by spending little cost and modifying sound quality. In time domain, it is difficult to notify the difference between music signals with and without the sounder. But, it is easy to find the difference in frequency domain or in time-frequency domain. In this paper, the music signal from a flute with sounder is analyzed both in the frequency domain and in the time-frequency domain. It is confirmed that the frequency components in the mid-frequency range of 500~2500 are reinforced.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2010.05a
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• pp.578-579
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• 2010

최근 내연 기관에서 엔진상태 및 잠재적인 결함의 발견을 위해 연료분사, 실린더 내 연소과정, 실린더 내부와 피스톤 마모상태, 흡 배기 밸브 및 과급기 (turbocharger) 등의 상태 모니터링을 통해서 결함에 대한 진단 및 경향관리에 대한 연구들이 진행되고 있다. 본 연구에서는 기 개발된 선박용 대형디젤엔진에 대한 상시 모니터링 시스템을 이용하여 엔진상태 및 잠재적인 결함을 진단하고자 하였으며, 일차적으로 가속도 신호를 이용하여 실린더 내 연소과정에서 정상적인 상태와 착화실패에 따른 진동 양상을 비교 분석하였다. 신호 분석 기법으로 시간-주파수 분산 기법들은 충격파 신호인 엔진 폭발신호를 분석하는데 적합하였으며, 그 기법들 중 Short Time Fourier Transform 기법과 Wavelet Transform 기법을 이용하였다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.3 no.1
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• pp.83-95
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• 1999

Wavelet transform has localization for time or frequency. It is useful to analyze a nonstationary signal. Basic function on wavelet transform is generated dilating and translating the original wavelet(mother wavelet). In this paper, time-frequency analysis method using linear combination wavelet transform is proposed. And it is applied to diagnostic monitoring system using the proposed linear combination wavelet transform. The stationary and nonstationary signal is used linear chirp signal, fan noise signal, a sinusoid signal from revolution body, electronic signal. Transform applied to signal analysis use fast Fourier transform (FFT), Daubechies, Haar and proposed linear combination method. The result of time-frequency analysis using linear combination wavelet transform is suited for portraying nonstationary time signal as well as stationary signal. Also the diagnostic monitoring system carry out the effective the signal analysis.

• Journal of Biomedical Engineering Research
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• v.25 no.6
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• pp.581-587
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• 2004

Conventional power spectrum methods based on FFT, AR method are not appropriate for analyzing biomedical signals whose spectral characteristics change rapidly. On the other hand, time-frequency analysis has more desirable characteristics of a time-varying spectrum. In this study, we investigated the spectral components of heart rate variability(HRV) in time-frequency domain using time frequency analysis methods. In the various time-frequency kernels functions, we studied the suitable kernels for the analysis of HRV using synthetic HRV signals. First, we evaluated the time/frequency resolution and cross term reduction of various kernel functions. Then, from the instantaneous frequency, obtained from time-frequency distribution, the method extracting frequency components of HRV was proposed. Subjects were 17 healthy young men. A coin-stacking task was used to induce mental stress. For each subjects, the experiment time was 3 minutes. Electrocardiogram, measured during the experiment, was analyzed after converted to HRV signal. In the results, emotional stress of subjects produced an increase in sympathetic activity. Sympathetic activation was responsible for the significant increase in the LF/HF ratio. Subjects were divided into two groups with task ability. Subjects who have higher mental stress have lack of task ability.

• Proceedings of the KIEE Conference
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• 2009.07a
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• pp.1990_1991
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• 2009

본 논문은 뇌파와 같이 측정을 위해서 많은 수의 채널이 필요한 계측 장치에서 채널에 따른 증폭률의 차이를 보정하기 위해 동일한 입력을 가한 후 측정된 시간 영역의 신호를 주파수 영역으로 변환하고 주파수 영역에서의 신호를 분석하여 각 채널의 증폭률의 차이를 유도하고 유도된 증폭률의 차이를 보정하는 알고리즘을 소개한다. 본 논문은 다채널 시스템에서 측정된 신호를 주파수 스펙트럼으로 변환하는 단계와 스펙트럼에서 각 채널의 이득률을 분석하는 단계를 포함하는 다채널 시스템에서 채널간 이득률을 보정하는 방법을 제안한다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2002.12a
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• pp.423-426
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• 2002

Fourier 변환은 연속이며, 무한 회 미분 가능한 함수인 반면, Wavelet 변환은 시간적으로도, 주파수적으로도 국재화 시키는 함수를 사용하는 점에서 차이가 있다. Fourier 변환에 비해 Wavelet 변환은 고주파 성분에 대해서는 시간 분해능이 높고, 저주파 성분에 대해서는 주파수 분해능이 높아지므로, 주어진 신호에 대한 지식이 없이도 시간-주파수 해석이 가능하고, 고주파 성분을 갖는 특이점의 검출에도 적합하다. 그러므로 Wavelet 변환의 이러한 성질을 이용하여 변압기의 출고전 권선의 완전한 절연을 검증하고자 한다. 이때 시행하는 뇌 임펄스 내전압 시험 시 나타난 푸리에 변환과 Wavelet 변환 곡선을 비교 분석 및 해석을 통하여 유의수준 및 신뢰 구간 둥의 통계학적 분석을 이용, 해석하여 보다 정확한 그래프를 산출하므로서 과도구간에 대한 정밀한 해석을 하고자 한다.