• Communications for Statistical Applications and Methods
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• 제7권1호
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• pp.129-140
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• 2000

Wavelets constitute a new orthogonal system which has direct application in density estimation. We introduce a brief wavelet density estimation and summarize some asymptotic results. An application to mixture normal distributions is implemented with S-Plus.

• 한국통신학회논문지
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• 제29권7C호
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• pp.937-945
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• 2004

잡음 제거에 사용되는 이진 결정 모델은 단지 이분적인 구분만을 수행하기 때문에 잡음에 대한 신호의 정확한 비율을 측정하기 어려운 단점이 있다. 이러한 단점을 보완하기 위하여 복잡한 통계 모델 및 다운샘플링이 되지 않은 웨이블릿 변환을 사용하는 것이 일반적이다. 본 논문에서는 잡음 영상에서 잡음의 정도를 측정할 수 있는 다수준 결정 모델을 이용한 잡음 제거 방법을 제안한다. 제안 방법은 잡음에 대한 신호의 비율을 다수준 값의 형태로 계산할 수 있기 때문에 직교 웨이블릿 변환으로 좋은 잡음 제거 성능을 나타낼 수 있다. 모의실험 결과를 통하여 본 논문의 방법이 직교 웨이블릿 변환을 사용한 최신의 잡음 제거 방법보다 PSNR 측면에서 평균적으로 0.ldB 정도 우수한 성능을 나타낸다는 것을 보여준다.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2001년도 추계학술대회 학술발표 논문집
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• pp.247-250
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• 2001

In this paper, we propose an growing and pruning algorithm to design the adaptive structure of modular wavelet neural network(MWNN) with F-projection and geometric growing criterion. Geometric growing criterion consists of estimated error criterion considering local error and angle criterion which attempts to assign wavelet function that is nearly orthogonal to all other existing wavelet functions. These criteria provide a methodology that a network designer can constructs wavelet neural network according to one's intention. The proposed growing algorithm grows the module and the size of modules. Also, the pruning algorithm eliminates unnecessary node of module or module from constructed MWNN to overcome the problem due to localized characteristic of wavelet neural network which is used to modules of MWNN. We apply the proposed constructing algorithm of the adaptive structure of MWNN to approximation problems of 1-D function and 2-D function, and evaluate the effectiveness of the proposed algorithm.

• 한국자기공명학회논문지
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• 제4권2호
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• pp.103-115
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• 2000

Wavelet transforms are introduced as a new tool to distinguish real peaks from the noise contaminated NMR data in this paper. New algorithms of two wavelet transforms including Daubechies wavelet transform as a discrete and orthogonal wavelet transform (DWT) and Morlet wavelet transform as a continuous and nonorthogonal wavelet transform(CWT) were developed fer noise elimination. DWT and CWT method were successfully applied to the noise reduction in spectrum. The inevitable distortion of NMR spectral baseline and the imperfection in noise elimination were observed in DWT method while CWT method gives a better baseline ahape and a well noise suppressed spectrum.

• 전자공학회논문지
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• 제50권7호
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• pp.34-42
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• 2013

본 논문에서는 웨이블릿 OFDM (Orthogonal Frequency Division Multiplexing) 및 FD-OFDM(Frequency Diversity OFDM) 시스템과 일반 OFDM 시스템의 성능을 비교 분석 한다. 푸리에 변환에 기반하는 일반 OFDM방식과는 달리 웨이블릿 OFDM 방식은 웨이블릿 변환을 사용하며, CDM (Code Division Multiplexing)과 OFDM의 중간적인 특성을 통해 심볼간 간섭을 효과적으로 제거할 수 있으며 채널간 간섭 역시 최소화 할 수 있다. FD-OFDM 방식의 경우, 각 병렬 브랜치에 입력된 심볼들이 직교 시퀀스로 곱해진 뒤 모든 부반송파에 분배되고, 각 부반송파는 주어진 프레임에서 각 병렬 브랜치의 심볼 조각들의 합의형태로 이루어진 정보를 전달한다. FD-OFDM의 한 부반송파 내에 포함된 모든 심볼들은 직교 시퀀스에 의해 구별되며, 간섭에 강하며 주파수 다이버시티 특성을 가진다는 장점을 지닌다. 협대역 간섭과 하모닉 잡음 환경에서 BER (Bit Error Rate) 성능을 통해 시스템 간 성능 비교 분석을 진행하였으며, 이를 통해 웨이블릿 OFDM과 FD-OFDM의 성능이 일반 OFDM보다 간섭에 대해 강건하고 특히 하모닉 노이즈 환경에서는 웨이블릿 OFDM이 가장 강건한 특성을 보이는 것을 알 수 있다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2006년도 제37회 하계학술대회 논문집 D
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• pp.1832-1833
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• 2006

This paper presents a design method of the wavelet neural network(WNN) controller based on a direct adaptive control scheme for the intelligent control of Autonomous Underwater Vehicle(AUV) steering systems. The neural network is constructed by the wavelet orthogonal decomposition to form a wavelet neural network that can overcome nonlinearities and uncertainty. In our control method, the control signals are directly obtained by minimizing the difference between the reference track and original signal of AUV model that is controlled through a wavelet neural network. The control process is a dynamic on-line process that uses the wavelet neural network trained by gradient-descent method. Through computer simulations, we demonstrate the effectiveness of the proposed control method.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1999년도 하계학술대회 논문집 G
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• pp.3224-3226
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• 1999

This paper introduces a nonlinear wavelet transform based on the lifting scheme, which is applied to signal denoising through the translation invariant wavelet transform. The wavelet representation using orthogonal wavelet bases has received widespread attention. Recently the lifting scheme has been developed for the construction of biorthogonal wavelets in the spatial domain. In this paper, we adaptively reduce the vanishing moments in the discontinuities to suppress the ringing artifacts and this customizes wavelet transforms providing an efficient framework for the translation invariant denoising. Special care has been given to the boundaries, where we design a set of different prediction coefficients to reduce the prediction error.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2003년도 하계학술대회 논문집 D
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• pp.2414-2416
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• 2003

In this raper, we present a Wavelet Neural Network(WNN) approach to the solution of the tracking problem for mobile robots that possess complexity, nonlinearity and uncertainty. The neural network is constructed by the wavelet orthogonal decomposition to form a wavelet neural network that can overcome the problems caused by local minima of optimization and various uncertainties. This network structure is helpful to determine the number of the hidden nodes and the initial value of weights with compact structure. In our control method, the control signals are directly obtained by minimizing the difference between the reference track and the pose of a mobile robot that is controlled through a wavelet neural network. The control process is a dynamic on-line process that uses the wavelet neural network trained by the gradient-descent method. Through computer simulations, we demonstrate the effectiveness and feasibility of the proposed control method.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2005년도 추계종합학술대회
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• pp.387-390
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• 2005

Parameter estimation for the probability model of wavelet coefficients is essential to the wavelet-domain interpolation. However, phase uncertainty, one well-known drawback of the orthogonal wavelet transforms, make it difficult to estimate parameters. In this paper, we exploit a phase shifting matrix in order to improve the accuracy of estimation. Nonlinear modeling to capture the interscale characteristics is also described. The experimental results show that the proposed method outperforms the previous wavelet-domain interpolation method as well as the conventional bicubic method.

• 한국군사과학기술학회지
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• 제4권1호
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• pp.147-154
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• 2001

The underwater explosion shock test is performed for the evaluation of the shock-resistant capability which is a very critical factor considering the survivability of the battle ship. Some measured signals have impulsive noise and gaussian white noise because of the unstable power supply system and the transient movement of cables during the underwater explosion shock test. The advanced shock signal analysis method which remove the noise of measured signal using the threshold policy of the median filter and the orthogonal wavelet coefficients are proposed. It is verified that the signal-to-noise ratio was improved about 30㏈ by the numerical simulation.