• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 2005년도 춘계학술대회 논문집
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• pp.342-348
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• 2005

This study was carried out fusion-welded butt joints in SWS 490A high strength steel subjected to tensile test that load-deflection curve. The windowed or short-time Fourier transform (WFT or SIFT) makes possible for the analysis of non-stationary or transient signals into a joint time-frequency domain and the wavelet transform (WT) is used to decompose the acoustic emission (AE) signal into various discrete series of sequences over different frequency bands. In this paper, for acoustic emission signal analysis to use a continuous wavelet transform, in which the Gabor wavelet base on a Gaussian window function is applied to the time-frequency domain. A wavelet transform is demonstrated and the plots are very powerful in the recognition of the acoustic emission features. As a result, the technique of acoustic emission is ideally suited to study variables which control time and stress dependent fracture or damage process in metallic materials.

• 응용통계연구
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• 제22권6호
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• pp.1203-1214
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• 2009

본 논문에서는 시계열자료의 군집분석을 위해 시간영역과 진동수영역에서의 군집 방법들을 소개하고 각 방법들의 장단점에 대해 논의하였다. KOSPI 200에 속한 15개 기업의 일별 주가자료률 이용한 비교분석 결과 비모수적인 방법인 웨이블릿을 이용한 군집분석이 가장 좋은 결과를 보였다. 비정상 시계열자료의 경우 차분 보다는 EMD를 이용하여 추세를 제거하는 방법이 스펙트럼 밀도함수를 이용한 군집분석에 더 효율적이었다.

• Smart Structures and Systems
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• 제22권2호
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• pp.175-183
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• 2018

Time-series data often contain one of the most valuable pieces of information in many fields including manufacturing. Because time-series data are relatively cheap to acquire, they (e.g., vibration signals) have become a crucial part of big data even in manufacturing shop floors. Recently, deep-learning models have shown state-of-art performance for analyzing big data because of their sophisticated structures and considerable computational power. Traditional models for a machinery-monitoring system have highly relied on features selected by human experts. In addition, the representational power of such models fails as the data distribution becomes complicated. On the other hand, deep-learning models automatically select highly abstracted features during the optimization process, and their representational power is better than that of traditional neural network models. However, the applicability of deep-learning models to the field of prognostics and health management (PHM) has not been well investigated yet. This study integrates the "residual fitting" mechanism inherently embedded in the wavelet transform into the convolutional neural network deep-learning structure. As a result, the architecture combines a signal smoother and classification procedures into a single model. Validation results from rotor vibration data demonstrate that our model outperforms all other off-the-shelf feature-based models.

• 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.

• 한국정밀공학회지
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• 제30권1호
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• pp.67-76
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• 2013

We present an innovative real-time laser welding monitoring technique employing the correlation analysis of the plasma plume optical emission generated during the process. The plasma optical radiation emitted during Nd:YAG laser welding of S45C steel samples has detected with a Photodiode and analyzed under different process conditions. The discrete DC voltage difference, filter methods and wavelet transform has been used to decompose the optical signal into various discrete series of sequences over different frequency bands. Considering that wavelet analysis can decompose the optical signals, extract the characteristic information of the signals and define the defects location accurately, it can be used to implement process-control of laser welding.

• Journal of applied mathematics & informatics
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• 제39권1_2호
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• pp.239-249
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• 2021

This study attempts to predict the price of gold futures, a real financial product, using ARIMA and LSTM. The wavelet analysis was applied to the data to predict the price of gold futures through LSTM and ARIMA. As results, it is confirmed that the prediction performance of the existing model of predict was improved. the case of predict of price of gold futures, we confirmed that the use of a deep learning model that is not affected by the non-stationary series data is suitable and the possibility of improving the accuracy of prediction through wavelet analysis.

• Structural Engineering and Mechanics
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• 제45권1호
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• pp.95-110
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• 2013

In the recent decade, practical of wavelet technique is being utilized in various domain of science. Particularly, engineers are interested to the wavelet solution method in the time series analysis. Fundamentally, seismic responses of structures against time history loading such as an earthquake, illustrates optimum capability of systems. In this paper, a procedure using particularly discrete Haar wavelet basis functions is introduced, to solve dynamic equation of motion. In the proposed approach, a straightforward formulation in a fluent manner is derived from the approximation of the displacements. For this purpose, Haar operational matrix is derived and applied in the dynamic analysis. It's free-scaled matrix converts differential equation of motion to the algebraic equations. It is shown that accuracy of dynamic responses relies on, access of load in the first step, before piecewise analysis added to the technique of equation solver in the last step for large scale of wavelet. To demonstrate the effectiveness of this scheme, improved formulations are extended to the linear and nonlinear structural dynamic analysis. The validity and effectiveness of the developed method is verified with three examples. The results were compared with those from the numerical methods such as Duhamel integration, Runge-Kutta and Wilson-${\theta}$ method.

• 한국해안·해양공학회논문집
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• 제20권6호
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• pp.583-588
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• 2008

2008년 2월에 동해안에서 발생하였던 너울 관측 자료에 대하여 웨이블릿 방법을 사용하여 분석하였다. 시간에 따른 파군, 첨두 주파수 및 스펙트럼의 변화를 볼 수 있었으며 그 결과를 시간에 따라 평균하여 푸리에 스펙트럼과 비교한 결과 시간에 따른 형태나 첨두 주기의 변화는 유사하게 나왔으나 첨두 주파수 에너지와 유의 파고에 있어서는 차이를 나타냈다. 웨이블릿 분석 방법은 주파수 뿐 만 아니라 시간에 따른 스펙트럼의 변화를 볼 수 있어서 이상 파랑이나 갑작스러운 너울과 같은 일시적이고 불규칙적인 현상 연구에 효과적 것인으로 보이며 향후 우리나라 파랑 자료에 대한 많은 적용과 분석 연구가 필요하다.

• 상하수도학회지
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• 제35권1호
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• pp.15-25
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• 2021

In this study, a method of leakage detection was proposed to locate leak position for a reservoir pipeline valve system using wavelet coherence analysis for an injected pressure wave. An unsteady flow analyzer handled nonlinear valve maneuver and corresponding experimental result were compared. Time series of pressure head were analyzed through wavelet coherence analysis both for no leak and leak conditions. The leak information can be obtained through either time domain reflectometry or the difference in wavelet coherence level, which provide predictions in terms of leak location. The reconstructed pressure signal facilitates the identification of leak presence comparing with existing wavelet coherence analysis.

• 한국환경과학회지
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• 제15권9호
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• pp.865-870
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• 2006

Recently, Total Organic Carbon (TOC) which can be measured instantly can be used as an organic pollutant index instead of BOD or COD due to the diversity of pollutants and non-degradable problem. The primary purpose of the present study is to reveal the properties of time series data for TOC which have been measured by real-time monitoring in Juam Lake and, in particularly, to understand the long- and short-term characteristics with the extraction of the respective components based on the different return periods. For the purpose, we proposed Discrete Wavelet Transform (DWT) as the methodology. The results from the DWT showed that the different components according to the respective periodicities could be extracted from the time series data for TOC and the variation of each component with respect to time could emerge from the return periods and the respective energy ratios of the decomposed components against the raw data.