• 융합신호처리학회논문지
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• 제5권4호
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• pp.263-270
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• 2004

최근 웨이블릿 변환은 많은 분야에서 다양하게 적용된다. 본 논문에서 tasks뇌파의 중요한 몇가지 특성파 검출을 위한 다비치 웨이블릿은 뇌파분석에 필요하다. 우리가 제안한 시스템은 다른 방법보다는 특성파 검출에 높은 성능을 가졌다. 본 연구의 뉴럴시스템의 구조는 하나의 은닉층과 3계층 피드포워드층은 오류 BP 학습알고리즘을 적용하였다. 4명의 피험자에게 알고리즘을 적용하여 92% 분류율을 보였다. 제안된 시스템은 웨이블릿과 신경망으로 tasks 뇌파의 보다 정확하게 분석함을 보였다. 모의실험결과 tasks 뇌파는 의사의 노동력을 줄일수 있고 정량적 해석이 가능함을 보였다.

• Journal of Information Processing Systems
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• 제13권5호
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• pp.1135-1148
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• 2017

Speech recognition is one of the fascinating fields in the area of Computer science. Accuracy of speech recognition system may reduce due to the presence of noise present in speech signal. Therefore noise removal is an essential step in Automatic Speech Recognition (ASR) system and this paper proposes a new technique called combined thresholding for noise removal. Feature extraction is process of converting acoustic signal into most valuable set of parameters. This paper also concentrates on improving Mel Frequency Cepstral Coefficients (MFCC) features by introducing Discrete Wavelet Packet Transform (DWPT) in the place of Discrete Fourier Transformation (DFT) block to provide an efficient signal analysis. The feature vector is varied in size, for choosing the correct length of feature vector Self Organizing Map (SOM) is used. As a single classifier does not provide enough accuracy, so this research proposes an Ensemble Support Vector Machine (ESVM) classifier where the fixed length feature vector from SOM is given as input, termed as ESVM_SOM. The experimental results showed that the proposed methods provide better results than the existing methods.

• 융합신호처리학회 학술대회논문집
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• 한국신호처리시스템학회 2000년도 하계종합학술대회논문집
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• pp.269-272
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• 2000

It is need to develop and apply a human pulse diagnosis system providing a quantitative and automatic analysis in the the oriental medicine. In order to analyze quantitatively the characteristic of pulsation, each of points had to be recognized accurately notifying the existence and the position of feature point in the wave form. And getting the period of human pulse. Thus, in this paper, it is proposed the preprocessing method of human pulse and the detection method of period by Wavelet Transformation. The human pulse is seprated from each band through Wavelet Transformation and feature points can be recognized through over the fact, and then the parameter of proposed Mac-Jin parameter is measured. Commonly, Human pulse signal has often various noises which are baseline drift, high frequency noise and so on. So it is significant to remove that noises. Thus, in this paper, the one period of human pulse is deciede and the feature points are detected after doing the preprocessing by wavelet transformation. As a result, it could be confirmed that this method is effective as a real program for the auto-diagnosis of human pulse.

• 한국멀티미디어학회:학술대회논문집
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• 한국멀티미디어학회 1998년도 춘계학술발표논문집
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• pp.110-115
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• 1998

웨이브릿 해석에서 CWT(continuous wavelet transform)에서는 Plancherel 형태의 복원 정리가 성립하고, 웨이브릿 급수는 frame 이론과 다해상도 이론(multiresolution analysis)을 활용한 이산복원정리가 성립한다. 복원정리가 만들어짐에 따라 이에 상응하는 웨이브릿이 생성되는데, CWT에서는 허용조건(admissibility condition)을 만족하는 basic wavelet이고, 웨이브릿 급수에서는 MRA를 이용한 Daubechies 웨이브릿, frame 이론을 이용한 Meyer 웨이브릿 등을 생각할 수 있다. 본 연구에서는 CWT에서 사용한 허용조건을 자연스럽게 확장함으로써 기존의 것보다 간편하고 활용도가 우수한 이산복원정리를 발견하고, 이에 상응하는 보다 만들기 쉬운 새로운 형태의 L1 웨이브릿군을 개발함을 목적으로 한다. 본 연구에 개발한 새로운 웨이브릿을 사용하여 시간-주파수에서의 신호 복원 및 분석에 응용한다.

• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2000년도 추계종합학술대회
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• pp.459-462
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• 2000

The analysis of distribution line faults is essential to the proper protection of power system. A high impedance fault(HIF) dose not make enough current to cause conventional protective device operating. so it is well hon that undesirable operating conditions and certain types of faults on electric distribution feeders cannot be detected by using conventional protection system. In this paper, we prove that the nature of the high impedance faults is indeed a deterministic chaos, not a random motion Algorithms for estimating Lyapunov spectrum and the largest Lyapunov exponent are applied to various fault currents detections in order to evaluate the orbital instability peculiar to deterministic chaos dynamically, and fractal dimensions of fault currents which represent geometrical self-similarity are calculated. Wavelet transform analysis is applied the time-scale information to fault signal. Time-scale representation of high impedance faults can detect easily and localize correctly the fault waveform.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2006년도 하계종합학술대회
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• pp.731-732
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• 2006

Limitations on the Linear Discriminant Analysis (LDA) for face recognition, such as the loss of generalization and the computational infeasibility, are addressed and illustrated for small number of samples. The Principal Component Analysis (PCA) followed by the LDA mapping may be an alternative that can overcome this limitation. We also show that processing time is reduced by wavelet transform.

• Journal of Information Processing Systems
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• 제8권3호
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• pp.483-494
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• 2012

Many recent events, such as terrorist attacks, exposed defects in most sophisticated security systems. Therefore, it is necessary to improve security data systems based on the body or behavioral characteristics, often called biometrics. Together with the growing interest in the development of human and computer interface and biometric identification, human face recognition has become an active research area. Face recognition appears to offer several advantages over other biometric methods. Nowadays, Principal Component Analysis (PCA) has been widely adopted for the face recognition algorithm. Yet still, PCA has limitations such as poor discriminatory power and large computational load. This paper proposes a novel algorithm for face recognition using a mid band frequency component of partial information which is used for PCA representation. Because the human face has even symmetry, half of a face is sufficient for face recognition. This partial information saves storage and computation time. In comparison with the traditional use of PCA, the proposed method gives better recognition accuracy and discriminatory power. Furthermore, the proposed method reduces the computational load and storage significantly.

• 한국지진공학회:학술대회논문집
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• 한국지진공학회 2003년도 추계 학술발표회논문집
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• pp.74-81
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• 2003

During the 1995 Hyogoken-Nambu earthquake, many caisson-type quay walls in Kobe Port moved several meters towards the seaside due to liquefaction and subsequent ground flow, To investigate the mechanism of quay wall damage, we carried out the numerical simulation using the 2-D effective stress analysis. Input earthquake motions used for the analyses are original Dip wave and the component wave in each compact support of wavelet transformation. The results suggested that the shear failure occurred in the foundation soil underneath the caisson type quay wall due to the deformation of the caisson type quay wall.

• 한국통신학회논문지
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• 제30권11C호
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• pp.1053-1059
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• 2005

컴퓨터 생성 홀로그램(CGH, Computer Generated Hologram)은 광 홀로그램의 간섭 패턴 대신 3차원 영상을 재생하는데 필요한 정보만을 컴퓨터로 설계 및 제작하기 때문에 물리적으로 존재하지 않는 가상의 물체의 합성 및 생성이 가능하다. 하지만 CGH를 통해 생성된 fringe 영상은 그 데이터양이 방대하기 때문에 저장, 전송 및 처리를 위해서는 데이터양을 줄일 필요성이 있다. 하나의 객체를 나타내기 위한 Fringe 영상의 데이터양을 줄이는 가장 효율적인 방법은 부호화 과정이다. 본 논문에서는 효과적인 부호화를 위해 fringe 영상을 2차원 영상으로 가정한 후에 DCT(Discrete Cosine Transform)에 비해서 좋은 주파수 변환 특성을 보이는 DWT(Discrete Wavelet Transform)을 도입하여 Fringe 영상의 주파수 특성을 분석하였다. 그리고 분석된 주파수 특성을 기반으로 Fringe 영상을 웨이블릿 기반의 코덱들을 이용해 압축한 결과 Yoshikawa(2)나 Thomas(3)에 의한 방법에 비해 최대 약 2배의 압축율을 가질 수 있어 Fringe 패턴을 압축하는 좋은 방법이 될 수 있다는 것을 확인하였다.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2013년도 추계학술대회 논문집
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• pp.331-337
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• 2013

Acoustic Emission technique is widely applied to develop the early fault detection system, and the problem about a signal processing method for AE signal is mainly focused on. In the signal processing method, envelope analysis is a useful method to evaluate the bearing problems and Wavelet transform is a powerful method to detect faults occurred on rotating machinery. However, exact method for AE signal is not developed yet. Therefore, in this paper two methods which are Hilbert transform and DET for feature extraction. In addition, we evaluate the classification performance with varying the parameter from 2 to 15 for feature selection DET, 0.01 to 1.0 for the RBF kernel function of SVR, and the proposed algorithm achieved 94% classification accuracy with the parameter of the RBF 0.08, 12 feature selection.