• 한국해양공학회:학술대회논문집
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• 한국해양공학회 2001년도 춘계학술대회 논문집
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• pp.260-265
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• 2001

본 연구에서는 연속 웨이블렛 변환을 이용하여 Ringing 현상을 연구하였다. 사용되어진 웨이블렛은 Morlet 웨이블렛이었고, 실험은 파수조에서 수행되었다. 또한 Ringing 현상을 다루고자 쇄파를 발생시켰다. 실험에 쓰인 모델은 수면을 통과하여 수직으로 고정된 원주 실린더였고, 이 실린더에 작용된 힘과 파고가 측정되어졌다. 이들은 연속 웨이블렛 변환으로 분석되어졌고, 이러한 분석으로 얻어진 scalogram 들은 고주파 성분이 쇄파 충격시 만들어진다는 사실을 시간영역상에서 보여주었다. 이는 기존의 스펙트럼 분석에서는 찾기 힘든 것이다. Coherence 분석도 위의 결론을 뒷받침해 주었다.

• Journal of Electrical Engineering and Technology
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• 제9권6호
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• pp.1812-1821
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• 2014

Wind has been a rapidly growing renewable power source for the last twenty years. Since wind behavior is chaotic in nature, its forecasting is not easy. At the same time, developing an accurate forecasting method is essential when wind farms are integrated into the power grid. In fact, wind speed forecasting tools can solve issues related to grid stability and reserve allocation. In this paper 30 hours ahead wind speed profile forecast is proposed using Adaptive Wavelet Neural Network (AWNN). The implemented AWNN uses a Mexican hat mother Wavelet, and Morlet Mother Wavelet for seven, eight and nine levels decompositions. For wind speed forecasting, the time series data on wind speed has been gathered from the National Renewable Energy Laboratory (NREL) website. In this work, hourly averaged 10-min wind speed data sets for the year 2004 in the Midwest ISO region (site number 7263) is taken for analysis. Data sets are normalized in the range of [-1, 1] to improve the training performance of forecasting models. Total 8760 samples were taken for this forecasting analysis. After the forecasting phase, statistical parameters are calculated to evaluate system accuracy, comparing different configurations.

• 대한원격탐사학회지
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• 제28권6호
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• pp.623-632
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• 2012

In this paper, we analyzed the effect of wavelet decomposition levels in feature extraction for anomaly detection from hyperspectral imagery. After wavelet analysis, anomaly detection was experimentally performed using the RX detector algorithm to analyze the detecting capabilities. From the experiment for anomaly detection using CASI imagery, the characteristics of extracted features and the changes of their patterns showed that radiance curves were simplified as wavelet transform progresses and H bands did not show significant differences between target anomaly and background in the previous levels. The results of anomaly detection and their ROC curves showed the best performance when using the appropriate sub-band decided from the visual interpretation of wavelet analysis which was L band at the decomposition level where the overall shape of profile was preserved. The results of this study would be used as fundamental information or guidelines when applying wavelet transform to feature extraction and selection from hyperspectral imagery. However, further researches for various anomaly targets and the quantitative selection of optimal decomposition levels are needed for generalization.

• 한국지능시스템학회논문지
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• 제12권5호
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• pp.397-404
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• 2002

본 논문에서는 퍼지와 웨이브렛 변환의 다해상도 분해(MRA)를 가진 퍼지 개념을 이용한 웨이브렛 신경망을 제안하고, 또한 이 시스템을 이용하여 임의의 비선형 함수 학습 근사화를 개선하고자 한다. 여기에서 퍼지 개념은 벨(bell)형 퍼지 소속함수를 사용하였다. 그리고 웨이브렛의 구성은 단일 크기를 가지고 있으며, 퍼지 개념을 이용한 웨이브렛 신경망의 학습을 위해 역전파 알고리즘을 사용하였다. 웨이브렛 변환의 다해상도 분해, 벨형 퍼지 소속 함수 그리고 학습을 위한 역전파 알고리즘을 이용한 이 구조는 기존의 알고리즘보다 근사화 성능이 개선됨을 모의 실험을 통하여 1차원, 2차원 함수에서 확인하였다.

• 한국수자원학회논문집
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• 제57권3호
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• pp.209-223
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• 2024

4차 산업혁명 시대에 접어들어 데이터 기반의 의사결정이 보편화되고 있다. 하지만 데이터 품질이 확보되지 않은 채 수행되는 데이터 분석은 왜곡된 결과를 낳을 가능성이 존재한다. 수자원 관리의 기초가 되는 수위 데이터도 마찬가지로 결측, 스파이크, 잡음 등 다양한 품질 문제를 가진다. 본 연구에서는 잡음으로 인해 발생하는 데이터 품질 문제를 해결하고자 하였다. 잡음은 데이터의 트렌드 분석을 어렵게 하고 비정상적인 이상치를 생성할 가능성이 있다. 본 연구는 이러한 문제를 해결하기 위해 Wavelet Transform을 이용한 잡음 제거 접근 방안을 제안한다. Wavelet Transform은 신호처리에 주로 사용되는 방법으로 잡음 제거에 효과적인 것으로 알려져 있으며 수집된 데이터의 정답 데이터(True value) 수집을 요구하지 않으므로 시간과 비용을 줄일 수 있다는 점에서 적용이 용이한 편이다. 본 연구는 Wavelet Transform의 성능 평가를 위해 대표적인 머신러닝 기반 잡음 제거 방법인 Denoising Autoencoder와 성능 비교를 수행하였다. 그 결과 Wavelet Transform 중 Coiflets 함수는, Denoising Autoencoder에 비해 Mean Absolute Error, Mean Absolute Percentage Error, Mean Squared Error 등 모든 측면에서 우수한 성능을 보이는 것으로 나타났다. 이러한 결과는 환경에 맞는 적절한 웨이블릿 함수의 선택을 통한 잡음 문제를 효과적으로 해결할 수 있음을 시사한다. 본 연구는 수위 데이터의 품질을 향상시켜 수자원 관리 결정의 신뢰성에 기여하는 강력한 도구로서 Wavelet Transform의 잠재력을 확인한 의의가 있다.

• Structural Engineering and Mechanics
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• 제33권2호
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• pp.179-191
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• 2009

This paper is aimed at combining wavelet multiresolution analysis and nonstationary Kanai-Tajimi model for the simulation of earthquake accelerograms. The proposed approach decomposes earthquake accelerograms using wavelet multiresolution analysis for the simulation of earthquake accelerograms. This study is on the basis of some Iranian earthquake records, namely Naghan 1977, Tabas 1978, Manjil 1990 and Bam 2003. The obtained results indicate that the simulated records preserve the significant properties of the actual accelerograms. In order to investigate the efficiency of the model, the spectral response curves obtained from the simulated accelerograms have been compared with those from the actual records. The results revealed that there is a good agreement between the response spectra of simulated and actual records.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1997년도 추계학술대회 논문집 학회본부
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• pp.603-605
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• 1997

A new algorithm using 1-dimensional wavelet transform for autofocusing of optical instrument has been developed. Previous studies based on the conventional frequency analysis have shown that as the lens-object distance approaches the optimum value, the high frequency energy in the corresponding image shows a consistent increase. However, as conventional frequency analysis techniques hide spatial distribution of each band energy, shape information in the original signal cannot be easily utilized. In this paper, a newly devised wavelet based focus measuring scheme is presented. Unlike other frequency domain analysis techniques that simply produce "frequency-only" spectra, wavelet analysis provides a "time-frequency" localized view of a given signal. As a result, both frequency band filtering and spatial distribution filtering can easily be realized. Depending on the proposed focus quality measuring algorithm, a fast and reliable automatic focus adjustment of optical devices could be implemented.

• 한국조명전기설비학회:학술대회논문집
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• 한국조명전기설비학회 2000년도 학술대회논문집
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• pp.183-187
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• 2000

This paper deals with multiresolution analysis of wavelet transform for partial discharge(PD), both corona and surface discharge. Multiresolution analysis was used for performing discrete wavelet transform. PD signals was decomposed into "approximation" and "detail" components until 4 levels by using discrete wavelet analysis. In this paper, daubechies family is adopted for the research of the characteristics of PD signals. The results show that in corona discharge the segment 7, 8, 9, 10, 11 values of defined variable is increased with discharge process, so phase distribution is characterized by 210~330 ranges. In case surface discharge in expoxy insulator inserted, defined variable values is fairly symmetric discharge pattern because coupled both corona and dielectric bounded discharges. We can confirmly discriminate the type PD source. the type PD source.

• East Asian Economic Review
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• 제19권2호
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• pp.189-220
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• 2015

Multi-scale representations are effective in characterising the time-frequency characteristics of financial return series. They have the capability to reveal the properties not evident with typical time domain analysis. Given the aforesaid, this study derives crucial insights from multi scale analysis to investigate the co-movements between Indian and emerging Asian equity markets using wavelet correlation and wavelet coherence measures. It is reported that the Indian equity market is strongly integrated with Asian equity markets at lower frequency scales and relatively less blended at higher frequencies. On the other hand the results from cross correlations suggest that the lead-lag relationship becomes substantial as we turn to lower frequency scales and finally, wavelet coherence demonstrates that this correlation eventually grows strong in the interim of the crises period at lower frequency scales. Overall the findings are relevant and have strong policy and practical implications.

• Structural Engineering and Mechanics
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• 제57권2호
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• pp.281-294
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• 2016

The paper is devoted to study a mesh-free analysis method of structural elements of engineering structures based on B-spline Wavelet Basis Function. First, by employing the moving-least square method and the weighted residual method to solve the structural displacement field, the control equations and the stiffness equations are obtained. And then constructs the displacement field of the structure by using the m-order B-spline wavelet basis function as a weight function. In the end, the paper selects the plane beam structure and the structure with opening hole to carry out numerical analysis of deformation and stress. The Finite Element Method calculation results are compared with the results of the method proposed, and the calculation results of the relative error norm is compared with Gauss weight function as weight function. Therefore, the clarification verified the validity and accuracy of the proposed method.