• Proceedings of the Korean Society Of Semiconductor Equipment Technology
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• 2006.10a
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• pp.72-76
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• 2006

본 연구에서는 웨이브릿과 신경망을 결합하여 플라즈마 고장을 감시하기 위한 시계열 모델을 개발하였다. 본 기법은 플라즈마 증착장비에 의해 수집된 18 개의 센서정보에 적용하여 평가하였다. 이산치 웨이브릿(Discrete Wavelet Transformation)은 장비에서 수집된 센서정보의 전 처리를 위해 이용되었다. 시계열 모델의 성능은 과거와 미래정보의 함수로 평가하였다. 수집된 18 개의 센서정보에 대한 모델성능 비교를 위해 표준화된 성능평가지표가 적용되었다. 평가결과, 본 기법에 의해 개발된 시계열 모델은 대략 4% 정도의 예측에러를 보였다.

• Journal of Korea Multimedia Society
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• v.7 no.9
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• pp.1246-1254
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• 2004

This paper presents a image coding method that is well adaptive to close-range video transmission because of its low titrate and simple coding procedure. At first, it reduces temporal redundancies by performing image DPCM between previous frame and current frame, and makes wavelet transformed image of this difference image. Then, the coefficients are quantized selectively by using the coefficient values of base level and mid-frequency level because inter-level redundancies are widely exists in multi-resolution images. Finally quantized coefficients are made iron the function that implies the target bitrate, the average coefficient energy, and the value of the level. The proposed method shows the effective Performance in the experiments using the continuous motion images and transition images.

• Proceedings of the Korea Multimedia Society Conference
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• 2002.05d
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• pp.886-889
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• 2002

본 논문에서는 보다 강인한 워터마크의 은닉을 위하여 웨이브릿 변환영역에서 정상상태 일반화 가우스(generalized Gaussian) 모델을 이용한 적응 워터마크 은닉 기술을 제안한다. 워터마크는 고주파 영역에서 연속 부대역 양자화(successive subband quantization: SSQ)를 이용하여 다해상도 영상의 웨이브릿 계수 중에서 시각적 중요 계수(perceptually significant coefficients: PSC)에만 은닉한다. 워터마크를 은닉하기 위한 지각모델은 정상상태의 통계적 특성을 이용한다. 이것은 국부영상 특성을 갖는 NVF(noise visibility function) 함수에 의하여 계산되어진다. 은닉모델은 다해상도내의 각 서브밴드별 분산과 형상계수(shape parameter)를 사용한다. 여러 가지 공격 실험결과 우수한 비가시성과 강인성을 확인하였다.

• Journal of the Korea Safety Management & Science
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• v.3 no.4
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• pp.91-101
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• 2001

This paper is to develop replacement models under minimal repair with exponential polynomial wavelet failure rate function. Wavelets have good time-frequency localization, fast algorithms and parsimonious representation. Also this study is presented along with numerical examples using sensitivity analysis for exponential polynomial trigonometric failure rate function.

• Journal of the Institute of Convergence Signal Processing
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• v.9 no.4
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• pp.253-260
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• 2008

Image denoising as one of image enhancement methods has been studied a lot in the spatial and transform domain filtering. Recently wavelet transform which has an excellent energy compaction and a property of multiresolution has widely used for image denoising. But a transform based on human visual system is visually useful if an end user is human beings. Therefore, Gabor cosine and sine transform which is considered as human visual filter is applied to image denoising areas in this paper. Denoising performance of the proposed transform is compared with those of the derivatives of Gaussian transform being another human visual filter and of discrete wavelet transform in terms of PSNR. With three levels of various noises, experimental results for real images show that the proposed transform has better PSNR performance of 0.41dB than DWT and 0.14dB than DGT.

• Journal of the Institute of Convergence Signal Processing
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• v.11 no.1
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• pp.1-8
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• 2010

Noise removal methods for noisy images have been studied a lot in the domain of spatial and transform filtering. Low pass filtering was initially applied in the spatial domain. Recently, discrete wavelet transform has widely used for image denoising as well as image compression due to an excellent energy compaction and a property of multiresolution. In this paper, Gabor cosine and sine transform which is considered as human visual filter is applied to image denoising areas using soft thresholding technique. GCST is compared with excellent wavelet transform which uses existing soft thresholding methods from PSNR point of view. Resultant images removed noises are also visually compared. Experimental results with adding four different standard deviation levels of Gaussian distributed noises to real images show that the proposed transform has better PSNR performance of a maximum of 1.18 dB and visible perception than wavelet transform.

• Proceedings of the Korea Information Processing Society Conference
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• 2001.04a
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• pp.421-424
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• 2001

본 연구는 디지털 오디오데이터에 워터마크를 삽입하고 추출하는 알고리즘을 제시한다. 삽입방법은 3단계의 웨이브릿 변환을 행한 후 가장 낮은 주파수대역으로부터 2번째 주파수 대역에 워터마크를 삽입하는데 그 워터마크는 키에 의한 의사난수를 이용한다. 추출시는 상관함수(correlation function)을 이용하여 추출한다. 잡음에 대한 필터 특성 또한 보인다.

• Journal of Korea Multimedia Society
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• v.6 no.3
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• pp.508-517
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• 2003

This paper presents perceptual model with a stochastic multiresolution characteristic that can be applied with watermark embedding in the biorthogonal wavelet domain. The perceptual model with adaptive watermarking algorithm embeds at the texture and edge region for more strongly embedded watermark by the SSQ. The watermark embedding is based on the computation of a NVF that has local image properties. This method uses non- stationary Gaussian and stationary Generalized Gaussian models because watermark has noise properties. The particularities of embedding in the stationary GG model use shape parameter and variance of each subband regions in multiresolution. To estimate the shape parameter, we use a moment matching method. Non-stationary Gaussian model uses the local mean and variance of each subband. The experiment results of simulation were found to be excellent invisibility and robustness. Experiments of such distortion are executed by Stirmark 3.1 benchmark test.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.05a
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• pp.195-198
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• 2000

We propose a new wavelet neural network structure, for which we apply new recurrent nodes to the network, in this paper for the dynamic system identification and control. We will construct the wavelet neural network by using wavelet frame function. The function does not have the best approximation property, but it may be possible to apply some modification to the structure of the network because the constriction of orthogonality is loosened a little. This wavelet neural network we propose can obtain previous state information by its structure of the network without any addition of input, though the conventional wavelet network needs additional previous state input for the improvement of the dynamic performance. In numerical experience, the performance of the new wavelet neural network we propose in the nonlinear system with uncertainity of parameter Is equal to that of the wavelet network which used the additional previous information input, superior to that of the conventional wavelet network.

• Journal of Broadcast Engineering
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• v.4 no.1
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• pp.1-11
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• 1999

This paper introduces orthogonally multiplexed modulation and demodulation methods based on Wavelet Packet Bases and particularly describes Wavelet Packet Modulation (WPM) techniques that provide the designer of transmission signal set in time-frequency domain with tree structural information which can be adapted to given channel characterristics. Multi-dimensional signaling methods are also contrasted to common and different characteristics of conventional QAM. multi-tone modulation methods. The paper addresses the mothod how to find a best tree structure that has more adaptivity to impulse and narrowband tone pulse noises using a tunning algorithm which arbitrarily partitions the time-frequency space and makes a suitable orthogonal signaling waveforms. Simulation results exhibits a favorable performance over existing mod/demod methods specially for narrowband tone pulse and impulse interferences.