• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2019.11a
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• pp.106-109
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• 2019

최근 Convolutional Neural Network (CNN)에 영상이 아닌 비학습적 알고리즘으로부터 도출된 특징맵을 입력함으로써 영상처리 성능 및 계산자원 효율성 향상을 이룬 보고가 늘어나고 있다. 본 논문에서는 이러한 점을 바탕으로 가보웨이블릿 특징맵을 입력으로 하는 CNN 기반 영상잡음제거기를 제안하고 그 성능 및 특징을 고찰하였다. 즉 기존의 CNN 에서는 일반적인 영상을 입력하는 반면에 본 논문에서는 영상으로부터 추출한 웨이블릿 계수들을 입력하였고, 이를 통하여 기존의 방법에 비하여 성능을 유지하면서 계산량을 줄일 수 있는 가능성을 확인하였다.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.50 no.7
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• pp.350-353
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• 2001

In general, the Least Square Error method is used for signal classification to measure distance in the $l^2$ metric or the $L^2$ metric space. A defect of the Least Square Error method is that it does not classify properly some waveforms, which is due to the property of the Least Square Error method: the global analysis. This paper proposes a new linear operator, the Integra-Normalizer, that removes the problem. The Integra-Normalizer possesses excellent property that measures the degree of relative similarity between signals by expanding the functional space with removing the restriction on the functional space inherited by the Least Square Error method. The Integra-Normalizer shows superiority to the Least Square Error method in measuring the relative similarity among one dimensional waveforms.

• Proceedings of the IEEK Conference
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• 2001.09a
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• pp.25-28
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• 2001

A theory of binary wavelets has been recently proposed by using two-band perfect reconstruction filter banks over binary field . Binary wavelet transform (BWT) of binary images can be used as an alternative to the real-valued wavelet transform of binary images in image processing applications such as compression, edge detection, and recognition. The BWT, however, requires large amount of computations since its operation is accomplished by matrix multiplication. In this paper, a fast BWT algorithm which utilizes filtering operation instead or matrix multiplication is presented . It is shown that the proposed algorithm can significantly reduce the computational complexity of the BWT. For the decomposition and reconstruction or an N ${\times}$ N image, the proposed algorithm requires only 2LN$^2$ multiplications and 2(L-1)N$^2$addtions when the filter length is L, while the BWT needs 2N$^3$multiplications and 2N(N-1)$^2$additions.

• Proceedings of the Korean Nuclear Society Conference
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• 1998.05b
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• pp.705-710
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• 1998

We describe a multigrid wavelet-based natural pixel (WNP) method for image reconstruction in emission computed tomography (ECT). The ECT is used to identify the tagged radioactive material's position in the body for detection of abnormal tissue such as tumor or cancer, as in SPECT and PET. With ECT methodology in parallel beam mode, we formulate a matrix-based reconstruction method for radionuclide sources in the human body. The resulting matrix for a practical problem is very large and nearly singular. To overcome this ill-conditioning, wavelet transform is considered in this study. Wavelets have inherent de-noising and multiscale resolution properties. Therefore, the multigrid wavelet-based natural pixel (WNP) method is very efficient to reconstruct image from projection data that is noisy and incomplete. We test this multigrid wavelet natural pixel (WNP) reconstruction method with the MCNP generated projection data for diagnosis of the simulated cancerous tumor.

• Journal of the Korea Institute of Military Science and Technology
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• v.4 no.1
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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.

• Journal of Electrical Engineering and Technology
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• v.4 no.2
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• pp.168-174
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• 2009

An application of wavelet analysis to power system transient generated signals is presented in this paper. With the time-frequency localisation characteristics embedded in wavelets, the time and frequency information of a waveform can be presented as a visualised scheme. This feature is very important for non-stationary signals analysis such as the ones generated from power system disturbances. Unlike the Fourier transform, the wavelet transform approach is more efficient in monitoring fault signals as time varies. For time intervals where the function changes rapidly, this method can zoom in on the area of interest for better visualisation of signal characteristics.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.8
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• pp.1580-1586
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• 2009

Wavelet transform is utilized to the field of the signal processing and the digital communication. In this paper, the performance for wavelets is analyzed for Haar and Daubechies series in the wavelet shift keying. It is mainly utilized to Haar, Daubechies 4tap, 8tap and 12tap in this paper. The analysis scheme is utilized by the eye pattern and the error probability. As a results of simulation, we confirmed that the proposed scheme was superior to performance when the number of the filler coefficient is small.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2002.05a
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• pp.16-19
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• 2002

Random and/or periodic defects occur in all spun yarns. These irregularities can often lead to defects in finished fabric. Yarn evenness tests are used to obtain statistical data about yarn properties, such as CV%, which is useful in comparing several sets of similar data that differ in mean value but may have some commonality in relative variation. Although this statistical data is helpful in determining relative yarn Quality, accurate predictions of how the yarn will appear in fabric form are still difficult to obtain. As an promising alterative, wavelet analysis has been employed to localize yam defect so as to predict the visual qualifies of the fabrics.

• Science of Emotion and Sensibility
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• v.5 no.3
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• pp.35-38
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• 2002

Random and/or periodic defects occur in all spun yarns. These irregularities can often lead to defects in finished fabric. Yarn evenness tests are used to obtain statistical data about yarn properties, such as CV%, which is useful in comparing several sets of similar data that differ in mean value but may have some commonality in relative variation. Although this statistical data is helpful in determining relative yam quality, accurate predictions of how the yarn will appear in fabric form are still difficult to obtain. As an promising alterative, wavelet analysis has been employed to localize yarn defect so as to predict the visual qualities of the fabrics.

• Proceedings of the Korea Multimedia Society Conference
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• 1998.04a
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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.