• Smart Structures and Systems
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• 제19권5호
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• pp.513-521
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• 2017

The purpose of this paper is to study the capabilities of the impulse response method in length and flaw detecting for concrete piles and provide a suggested method to find small-size flaws in piles. In this work, wavelet transform is used to decompose the recorded time domain signal into a series of levels. These levels are narrowband, so the mix of different dominant bandwidths can be avoided. In this study, the impulse response method is used to analyze the signal obtained from the wavelet transform to improve the judgment of the flaw signal so as to detect the flaw location. This study provides a new way of thinking in non-destructive testing detection. The results show that the length of a pile is easy to be detected in the traditional reflection time or frequency domain method. However, the small flaws within pile are difficult to be found using these methods. The proposed approach in this paper is able to greatly improve the results of small-size flaw detection within piles by reducing the effects of any noise and clarifying the signal in the frequency domains.

• Structural Engineering and Mechanics
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• 제22권2호
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• pp.185-195
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• 2006

Nowadays it is very necessary to generate artificial accelerograms because of lack of adequate earthquake records and vast usage of time-history dynamic analysis to calculate responses of structures. According to the lack of natural records, the best choice is to use proper artificial earthquake records for the specified design zone. These records should be generated in a way that would contain seismic properties of a vast area and therefore could be applied as design records. The main objective of this paper is to present a new method based on wavelet theory to generate more artificial earthquake records, which are compatible with target spectrum. Wavelets are able to decompose time series to several levels that each level covers a specific range of frequencies. If an accelerogram is transformed by Fourier transform to frequency domain, then wavelets are considered as a transform in time-scale domain which frequency has been changed to scale in the recent domain. Since wavelet theory separates each signal, it is able to generate so many artificial records having the same target spectrum.

• 지능정보연구
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• 제4권2호
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• pp.23-34
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• 1998

Travel time forecasting, especially public bus travel time forecasting in urban areas, is a difficult and complex problem which requires a prohibitively large computation time and years of experience. As the network of target area grows with addition of streets and lanes, computational burden of the forecasting systems exponentially increases. Even though the travel time between two neighboring intersections is known a priori, it is still difficult, if not impossible, to compute the travel time between every two intersections. For the reason, previous approaches frequently have oversimplified the transportation network to show feasibilities of the problem solving algorithms. In this paper, forecasting of the travel time between every two intersections is attempted based on travel time data between two neighboring intersections. The time stamps data of public buses which recorded arrival time at predetermined bus stops was extensively collected and forecast. At first, the time stamp data was categorized to eliminate white noise, uncontrollable in forecasting, based on wavelet conversion. Then, the radial basis neural networks was applied to remaining data, which showed relatively accurate results. The success of the attempt was confirmed by the drastically reduced relative error when the nodes between the target intersections increases. In general, as the number of the nodes between target intersections increases, the relative error shows the tendency of sharp increase. The experimental results of the novel approaches, based on wavelet conversion and neural network teaming mechanism, showed the forecasting methodology is very promising.

• 지능정보연구
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• 제5권1호
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• pp.103-123
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• 1999

Input filtering as a preprocessing method is so much crucial to get good performance in time series forecasting. There are a few preprocessing methods (i.e. ARMA outputs as time domain filters, and Fourier transform or wavelet transform as time-frequency domain filters) for handling time series. Specially, the time-frequency domain filters describe the fractal structure of financial markets better than the time domain filters due to theoretically additional frequency information. Therefore, we, first of all, try to describe and analyze specially some issues on the effectiveness of different filtering methods from viewpoint of the performance of a neural network based forecasting. And then we discuss about neural network model architecture issues, for example, what type of neural network learning architecture is selected for our time series forecasting, and what input size should be applied to a model. In this study an input selection problem is limited to a size selection of the lagged input variables. To solve this problem, we simulate on analyzing and comparing a few neural networks having different model architecture and also use an embedding dimension measure as chaotic time series analysis or nonlinear dynamic analysis to reduce the dimensionality (i.e. the size of time delayed input variables) of the models. Throughout our study, experiments for integration methods of joint time-frequency analysis and neural network techniques are applied to a case study of daily Korean won / U. S dollar exchange returns and finally we suggest an integration framework for future research from our experimental results.

• 한국소음진동공학회논문집
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• 제25권12호
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• pp.888-894
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• 2015

A new method for the simulation of the vehicle's interior road noise is proposed in the present study. The road noise model can synthesize road noise of a vehicle for varying driving speed within a range. In the proposed method, interior road noise is considered as a stochastic time-series, and is modeled by a nonstationary parametric model via two steps. First, each interior road noise signal, obtained from constant speed driving tests performed within a range of speed, is modeled as an autoregressive model whose parameters are estimated by using a standard method. Finally, the parameters obtained for different driving speeds are interpolated based on the varying driving speed to yield a time-varying autoregressive model. To model a full band road noise, audible frequency range is divided into an octave band using a wavelet filter bank, and the road noise in each octave band is modeled.

• 한국측량학회지
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• 제26권5호
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• pp.513-518
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• 2008

지구 중력장의 경년변화 관측을 통한 전 지구 차원에서의 대규모 질량이동 및 그 변화에 대한 연구는 지속적으로 진행되고 있다. 여기에는 지구의 편평 정도를 나타내는 $J_2$(또는 지구 중력장모델의 $C_{20}$)에 대한 연구도 포함되며, 최근에는 GRACE를 비롯한 위성중력기술의 개발로 기존의 SLR 등 우주관측기술의 관측결과와 비교분석을 수행할 수 있게 되었다. 본 연구에서는 2002년 4월부터 2008년 5월사이의 GRACE 월별 중력장모델(CSR RL04)을 이용하여 저차항 중력장 구면조화 계수 $C_{20}$의 시계열 변화를 구하고 SLR 관측 자료로부터 얻어진 $C_{20}$ 값과 비교분석을 수행하였다.시계열 데이터의 분석에는 웨이블릿 변환 신호분석기법을 사용하였으며,구체적으로 연속 웨이블릿 변환,직교 웨이블릿 변환 및 웨이블릿 상관간계 분석을 수행하였다.분석 결과, GRACE와 SLR의 $C_{20}$ 결과는 모두 감소하는 추세를 나타내었으며, 1년 주기를 나타내는 SLR과는 달리 GRACE는 반년 주기에서 더욱 높은 강도를 보였다.또한,GRACE는SLR와의 직교 웨이블릿 스펙트럼 및 상관관계 분석에서도1년 주기에서 매우 강한 상관관계를 보여주었다.

• 해양환경안전학회지
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• 제27권7호
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• pp.1088-1097
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• 2021

기계 장비의 진동 데이터는 필연적으로 노이즈를 포함하고 있다. 이러한 노이즈는 기계 장비의 유지보수를 진행하는데 악영향을 끼친다. 그에 따라 데이터의 노이즈를 얼마나 효과적으로 제거해주냐에 따라 학습 모델의 성능을 좌우한다. 본 논문에서는 시계열 데이터를 전처리 함에 있어 특성추출 과정을 포함하지 않는 Denoising Auto Encoder 기법을 활용하여 데이터의 노이즈를 제거했다. 또한 기계 신호 처리에 널리 사용되는 Wavelet Transform과 성능 비교를 진행했다. 성능비교는 고장 탐지율을 계산하여 진행했으며 보다 정확한 비교를 위해 분류 성능 평가기준 중 하나인 F-1 Score를 계산하여 성능 비교를 진행했다. 고장을 탐지하는 과정에서는 One-Class SVM 기법을 활용하여 고장 데이터를 탐지했다. 성능 비교 결과 고장 진단율과 오차율 측면에서 Denoising Auto Encoder 기법이 Wavelet Transform 기법에 비해 보다 좋은 성능을 나타냈다.

• 스마트미디어저널
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• 제12권11호
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• pp.36-47
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• 2023

주요 산업현장에서 설비를 제어하는 산업제어시스템(ICS, Industrial Control System)이 네트워크로 다른 시스템과 연결되는 사례가 증가하고 있다. 또한, 이러한 통합과 함께 한 번의 외부 침입이 전체 시스템 마비로 이루어질 수 있는 지능화된 공격의 발달로, 산업제어시스템에 대한 보안에 대한 위험성과 파급력이 증가하고 있어, 사이버 공격에 대한 보호 및 탐지 방안의 연구가 활발하게 진행되고 있으며, 비지도학습 형태의 딥러닝 모델이 많은 성과를 보여 딥러닝을 기반으로 한 이상(Anomaly) 탐지 기술이 많이 도입되고 있다. 어어, 본 연구에서는 딥러닝 모델에 전처리 방법론을 적용하여 시계열 데이터의 이상 탐지성능을 향상시키는 것에 중점을 두어, 그 결과 웨이블릿 변환(WT, Wavelet Transform) 기반 노이즈 제거 방법론이 딥러닝 기반 이상 탐지의 전처리 방법론으로 효과적임을 알 수 있었으며, 특히 센서에 대한 군집화(Clustering)를 통해 센서의 특성을 반영하여 Dual-Tree Complex 웨이블릿 변환을 차등적으로 적용하였을 때 사이버 공격의 탐지성능을 높이는 것에 가장 효과적임을 확인하였다.

• Ocean Systems Engineering
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• 제4권3호
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• pp.201-213
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• 2014

Wave run-up is an important issue in offshore engineering, which is tightly related to the loads on the marine structures. In this study, a series of physical experiments have been performed to investigate the wave run-up around a vertical cylinder in transitional water depth. The wave run-ups of regular waves, irregular waves and focused waves have been presented and the characteristics in frequency domain have been investigated with the FFT and wavelet transform methods. This study focuses on the nonlinear features of the wave run-up and the interaction between the wave run-up and the cylinder. The results show that the nonlinear interaction between the waves and the structures might result wave run-up components of higher frequencies. The wave run-ups of the moderate irregular waves exhibit 2nd order nonlinear characteristics. For the focused waves, the incident waves are of strong nonlinearity and the wavelet coherence analysis reveals that the wave run-up at focal moment contains combined contributions from almost all the frequency components of the focused wave sequence and the contributions of frequency components up to 4th order harmonic levels are recommended to be included.

• 융합보안논문지
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• 제7권1호
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• pp.83-89
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• 2007

본 논문에서는 영상을 다중 해상도로 표현하는 직교 웨이브렛변환에서, 비밀스럽게 서명데이터를 영상에 삽입하는 방법을 제안한다. 그 원리는 영상의 다중해상도표현에 있어서 차분출력에 편중(집중)이 있음에 주목하여, 그 특징을 이용하여 서명비트계열을 화상에 삽입한다. 이 때, $256{\times}256$화소로 되는 농담화상으로 대략 6K바이트 정도의 문자정보를 합성할 수 있다. 이 방법은 영상의 저작권을 표시하는 서명데이터뿐만 아니라, 영상의 저자나 사용조건, 또는 영상 그 자체의 속성정보를 포함할 수 있어, 영상데이터베이스의 검색 등에 있어서도 유사영상의 식별을 용이하게 할 수 있는 등의 응용을 고려할 수 있다.