• 대한전기학회:학술대회논문집
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• 대한전기학회 1992년도 하계학술대회 논문집 A
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• pp.311-313
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• 1992

Automated analysis of EEG invariably requires the inclusion of a signal conditioning filter. This paper investigates the EEG waveform distortions caused by the transient responses of the various types of signal conditioning filters. This study explicitly simulates the filter responses to the typical EEG waveform models, and compares the distortions.

• 한국해양공학회지
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• 제26권4호
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• pp.57-63
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• 2012

In this paper, a Bayesian classifier based on PCA (principle component analysis) is proposed to classify underwater transient signals using $16^{th}$ order LPC (linear predictive coding) coefficients as feature vector. The proposed classifier is composed of two steps. The mechanical signals were separated from biological signals in the first step, and then each type of the mechanical signal was recognized in the second step. Three biological transient signals and two mechanical signals were used to conduct experiments. The classification ratios for the feature vectors of biological signals and mechanical signals were 94.75% and 97.23%, respectively, when all 16 order LPC vector were used. In order to determine the effect of underwater noise on the classification performance, underwater ambient noise was added to the test signals and the classification ratio according to SNR (signal-to-noise ratio) was compared by changing dimension of feature vector using PCA. The classification ratios of the biological and mechanical signals under ocean ambient noise at 10dB SNR, were 0.51% and 100% respectively. However, the ratios were changed to 53.07% and 83.14% when the dimension of feature vector was converted to three by applying PCA. For correct, classification, it is required SNR over 10 dB for three dimension feature vector and over 30dB SNR for seven dimension feature vector under ocean ambient noise environment.

• 대한기계학회논문집
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• 제17권8호
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• pp.1910-1920
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• 1993

An experimental study to investigate the unconventional chip formation called triangulation of chip in cutting with a SPRT (self-propelled rotary tool) is performed using acoustic emission (AE) signal analysis. In doing that, a quantitative model of the AE RMS signal in triangulation with a SPRT is first developed. The predicted results from this model show good correlation between the AE RMS signal and the general characteristics of triangular chip formation. Then, effects of various process parameters such as cutting conditions (cutting speed, depth of cut, oblique angle and normal rake angle) and the work material properties on the chip formation in cutting with a SPRT are explored. Special attention is paid to the work material properties which are found to have significant effects on triangulation.

• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2001년도 추계종합학술대회
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• pp.349-352
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• 2001

본 논문에서는 1400nm 대역에서 이득을 갖는 TDFA의 증폭 특성과 과도응답 특성을 이론적으로 해석하였다. 수치모델은 밀도반전의 형성과정, 여기파워, 증폭기를 따라 변화하는 신호파워를 포함한다. 본 이론해석의 결과는 광섬유증폭기의 이득 포화와 복구시간을 예측할 수 있게 해주고, 펌핑율과 포화 율이 이득의 포화와 복구시간에 미치는 영향을 예측할 수 있게 한다.

• 한국음향학회지
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• 제19권5호
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• pp.65-70
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• 2000

라우드니스(loudness)는 음질 평가에 있어서 가장 중요한 음질 인자로 간주되고 있고, 그 계산을 위해 정상음에 대한 국제규격도 마련되어 있다. 본 연구에서는, 이의 일반화를 위해 라우드니스 계산 모델에 과도음 해석 과정을 포함한 새로운 방법을 제시하고자 한다. 이를 위하여 과도 신호의 대역 분할 및 대역별 음압 레벨 변화 예측을 위한 신호 처리 기법과 과도 음에 대한 청각 반응을 모델링한 포스트 마스킹(post-masking) 및 라우드니스 시간 적분 모델이 도입되었다. 또한 순음의 라우드니스 해석에서 기존 라우드니스 모델이 갖고 있는 신호 해석상의 문제점을 개선하기 위하여 임계 대역폭의 1/2 간격으로 배치된 총 47개의 임계 대역 필터를 이용하였다. 제안된 모델의 유효성을 확인하기 위하여 기존의 임상 실험 결과 비교하였고, 예측치와 임상치는 아주 좋은 일치 경향을 가짐을 확인하였다.

• 조명전기설비학회논문지
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• 제18권5호
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• pp.69-73
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• 2004

본 논문은 낙뢰나 스위칭 등에 의해 건물 내에서 발생할 수 있는 과도 현상에 대하여, 기준신호그리드와 절연/독립접지 방식을 비교한 것이다. 전자계 해석프로그램인 HIFREQ을 이용함으로써 정량적인 해석결과에 근거하여 건물내 접지방식의 차이에 따라 접지점에서 어떠한 과도현상이 발생하는지의 고찰이 가능하였다. 본 논문에서 시도한 건축물 내 과도현상 해석방법 및 사례연구 결과는 정보화 건축물에 필요한 접지 방식의 선정을 위한 자료로 활용될 것으로 기대된다.

• 전력전자학회논문지
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• 제13권6호
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• pp.447-452
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• 2008

본 논문은 단상 그리드전압의 동기화에 가장 적합한 곱형 PLL 시스템을 설계한다. 소신호 해석방법으로 PLL 시스템을 모델링하고, 동기 과도 응답특성뿐만 아니라 동기신호의 왜곡을 고려하여 저역필터의 차단주파수 및 이득의 최적 값을 유도한다. 설계의 성능을 검증하기 위하여, 시뮬레이션 및 실험결과로 차단주파수 및 이득의 변화에 동기신호의 과도응답과 리플성분을 관찰한다.

• 전기학회논문지P
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• 제57권2호
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• pp.91-95
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• 2008

The large AC generator fault may lead to large impacts or perturbations in power system. The generator protection control systems in Korea have been imported and operated through a turn-key from overseas entirely. Therefore a study of the generator protection field has in urgent need for a stable operation of the imported goods. In present, the algorithm using the current ratio differential relaying based DFT for stator winding protection or a fault detection had been applied that of internal fault protection of a generator. the DFT used for the analysis of transient state signal conventionally had defects losing a time information in the course of transforming a target signal to frequency domain. In this paper, the discrete wavelet transform (DWT) was applied a fault detection of the generator being superior to a transient state signal analysis and being easy to real time realization. The fault signals after executing a terminal fault modeling collect using a MATLAB package, and calculate the wavelet coefficients through the process of a muiti-level decomposition (MLD). The proposed algorithm for a fault detection using the Daubechies WT (wavelet transform) was executed with a C language and the commend line function for the real time realization after analyzing MATLAB's graphical interface. The advanced technique had improved faster a speed of fault discrimination than a conventional DFR based on DFT.

• 대한기계학회논문집A
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• 제27권4호
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• pp.537-543
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• 2003

Transient vibration generated by developing localized fault in gear can be used as indicators in gear fault detection. This vibration signal suffers from the background noise such as gear meshing frequency and its harmonics and broadband noise. Thus in order to extract the information about the only gear fault from the raw vibration signal measured on the gearbox this signal is processed to reduce the background noise with many kinds of signal-processing tools. However, these signal-processing tools are often very complex and time waste. Thus. in this paper. we propose a novel approach detecting the damage of gearbox and analyzing its pattern using the raw vibration signal. In order to do this, the residual signal. which consists of the sideband components of the gear meshing frequent) and its harmonics frequencies, is extracted from the raw signal by the power spectral density (PSD) to obtain the information about the fault and is used as the input data of the artificial neural network (ANN) for analysis of the pattern of gear fault. This novel approach has been very successfully applied to the damage analysis of a laboratory gearbox.

• 한국마린엔지니어링학회:학술대회논문집
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• 한국마린엔지니어링학회 2002년도 춘계학술대회논문집
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• pp.219-223
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• 2002

Voltage and current signals during impulse tests on transformer are treated as non-stationary signals. A new method incorporating signal-processing method such as Wavelets and courier transform is proposed for failure identification. It is now possible to distinguish failure during impulse tests. The method is experimentally validated on a transformer winding. The wavelet transforms enables the detection of the time of occurrence of switching or failure events. After establishing the time of occurrence, the original waveform is split into two or more sections. The wavelet transform has ability to analysis the failure signal on time domain as well as frequency domain. Therefore, the wavelet transform is superior than courier transform to analysis the failure signal. In this paper, the fact was proved by real data which was achieved.