• 한국정보통신학회논문지
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• 제4권2호
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• pp.461-468
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• 2000

디지털 기술 발전에 따른 오디오 데이터의 증가는 여러 컴퓨터 응용에 사용되면서 데이터를 관리하고 사용하기 위해, 내용기반 질의와 유사성 검색과 같은 새로운 기능을 갖는 데이터베이스 시스템의 개발이 불가피하게 됐다. 내용 기반 질의를 위한 빠르고 정확한 검색은 이러한 응용 시스템들에 필요하다. 효율적인 내용기반 색인과 유사성 검색의 설계는 관련성 있는 데이터의 빠른 검색을 제공하기 위한 주된 요소이다. 본 논문에서는 소파(Wavelet) 변환을 이용한 한국 전통 음악 데이터베이스의 오디오 색인을 위한 방법을 제안한다. 또한 소파 변환을 이용해 오디오 데이터에 대한 색인의 가능성을 보인다.

• 대한의용생체공학회:학술대회논문집
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• 대한의용생체공학회 1998년도 추계학술대회
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• pp.70-71
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• 1998

In this paper, We proposed fast ultrasound image compression based on characteristics of ultrasound images. In the proposed method, wavelet transform is performed for non-zero coefficients selectively. It codes zero-tree symbols using conditional pdf (probability density function) as orientation of bands. It normalizes wavelet coefficients with threshold of each wavelet band and encodes those using a uniform quantizer. Experimental results show that the proposed method is the proposed method is superior in PSNR to LuraTech's method by about 1.0 dB, to JPEG by about 5.0 dB for $640\times480$ 24bits color ultrasound image.

• Journal of Information Processing Systems
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• 제13권4호
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• pp.668-676
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• 2017

Recently, the importance of big data has been emphasized with the development of smartphone, web/SNS. As a result, MapReduce, which can efficiently process big data, is receiving worldwide attention because of its excellent scalability and stability. Since big data has a large amount, fast creation speed, and various properties, it is more efficient to process big data summary information than big data itself. Wavelet histogram, which is a typical data summary information generation technique, can generate optimal data summary information that does not cause loss of information of original data. Therefore, a system applying a wavelet histogram generation technique based on MapReduce has been actively studied. However, existing research has a disadvantage in that the generation speed is slow because the wavelet histogram is generated through one or more MapReduce Jobs. And there is a high possibility that the error of the data restored by the wavelet histogram becomes large. However, since the wavelet histogram generation system based on the MapReduce developed in this paper generates the wavelet histogram through one MapReduce Job, the generation speed can be greatly increased. In addition, since the wavelet histogram is generated by adjusting the error boundary specified by the user, the error of the restored data can be adjusted from the wavelet histogram. Finally, we verified the efficiency of the wavelet histogram generation system developed in this paper through performance evaluation.

• 대한안전경영과학회지
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• 제3권4호
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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.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2002년도 추계학술대회 및 정기총회
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• pp.427-430
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• 2002

Recently the subject of "wavelet analysis" has be drawn by both mathematical and engineering application fields such as Signal Processing, Compression/Decomposition, Wavelet-Neural Network, Statistics and etc. Even though its similar to Fourier analysis, wavelet is a versatile tool with much mathematical content and great potential for applications. Especially, wavelet transform uses localizable various mother wavelet functions in time-frequency domain. Therefore, wavelet transform has good time-analysis ability for high frequency component, and has good frequency-analysis ability for low frequency component. Using the discriminative ability is more easy method than other conventional techniques. In this paper, Morlet wavelet transform was applied to discriminate the kind of line fault by acquired data from real power transformation network. The experimental result presented that Morlet wavelet transform is easier,and more useful method than the FFT (Fast Fourier Transform).

• 한국음향학회:학술대회논문집
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• 한국음향학회 2000년도 하계학술발표대회 논문집 제19권 1호
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• pp.165-168
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• 2000

A fast estimation method using wavelet transform for a time delay system is proposed. Main point of this method is to get the wavelet transform of the correlation between the input signal and delayed signal using transformed signals. But wavelet transform using Haar wavelet functions has basis with different phases and can offers a bisection method to estimate a time delay of a signal. Selective computation of the transform of correlation is performed and the computational complexity is reduced. Computational order of this method is O(N log N) and it is much love. than a simple correlation esimation when the length of signal is long.

• Journal of Welding and Joining
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• 제21권6호
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• pp.33-39
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• 2003

Wavelet transform analysis results show a spectrum energy distribution of CWT along scale factors distinguish the partial, full and over penetration in a electron beam welding by analyzing the curve of spectrum energy at small scale, middle and large scale range, respectively. Two types of signals collected by Ion collector and x-ray sensors and analyzed. The acquired signals from sensors are very complicated since these signals are very closely related the dynamics of keyhole which interact the very high density energy with materials during welding. The results show the wavelet transform is more effective to diagnosis than Fourier Transform, further for the general welding defects which are not a periodic based, but a transient, non-stationary and time-varying phenomena.

• 소음진동
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• 제10권5호
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• pp.769-774
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• 2000

The purpose of the work is to present successful applications of a modified wavelet shrinkage method for the accurate and fast estimation of a transfer function. Although the experimental process of determining a transfer function introduces not only Gaussian but also non-Gaussian noises, most existing estimation methods are based only on a Gaussian noise model. To overcome this limitation, we propose to employ a modified wavelet shrinkage method in which L1 -based median filtering and L2 -based wavelet shrinkage are applied repeatedly. The underlying theory behind this approach is briefly explained and the superior performance of this modified wavelet shrinkage technique is demonstrated by a numerical example.

• Journal of Information Processing Systems
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• 제16권5호
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• pp.1129-1144
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• 2020

The thangka image inpainting method based on wavelet transform is not ideal for contour curves when the high frequency information is repaired. In order to solve the problem, a new image inpainting algorithm is proposed based on edge structural constraints and wavelet transform coefficients. Firstly, a damaged thangka image is decomposed into low frequency subgraphs and high frequency subgraphs with different resolutions using wavelet transform. Then, the improved fast marching method is used to repair the low frequency subgraphs which represent structural information of the image. At the same time, for the high frequency subgraphs which represent textural information of the image, the extracted and repaired edge contour information is used to constrain structure inpainting in the proposed algorithm. Finally, the texture part is repaired using texture synthesis based on the wavelet coefficient characteristic of each subgraph. In this paper, the improved method is compared with the existing three methods. It is found that the improved method is superior to them in inpainting accuracy, especially in the case of contour curve. The experimental results show that the hierarchical method combined with structural constraints has a good effect on the edge damage of thangka images.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1999년도 하계학술대회 논문집 G
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• pp.2905-2907
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• 1999

The meaningful speech sound block classification provides very important information in the speech recognition. The following technique of the classification is based on the DWT (discrete wavelet transform), which will provide a more fast algorithm and a useful, compact solution for the pre-processing of speech recognition. The algorithm is implemented to the unvoiced/voiced classification and the denoising.