• Journal of Ocean Engineering and Technology
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• v.17 no.3 s.52
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• pp.1-6
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• 2003

This study presents the results of series of studies, which are mainly devoted to the application of wavelet transforms to various problems in ocean engineering. Both continuous and discrete wavelet transforms were used. These studies attempted to solve detection of wave directionality, detection of wave profile, and decoupling of the rolling component from free roll decay tests. The results of these analysis, using wavelet transform, demonstrated that the wavelet transform can be a useful tool in analyzing many problems in the filed of ocean engineering.

• Journal of the Korea Society for Simulation
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• v.19 no.2
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• pp.127-135
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• 2010

The validation of a simulation model is a key to demonstrate that the simulation model is reliable. However, among various validation methods have been introduced, it is very poor to research the specific techniques for the time series data. Therefore, this paper suggests the methodology to verify the simulation using the time series data by Wavelet Transform, Power Spectrum and Coherence. This method performs 2 steps as followed. Firstly, we get spectrum using the Wavelet transform available for non-periodic signal separation. Secondly, we compare 2 patterns of output data from simulation model and actual system by Coherence Analysis. As a result of comparing it with other validation techniques, the suggested way can judge simulation model accuracy more clearly. By this way, we can make it possible to perform the simulation validation test under various situations using detailed sectional validation method, which has been impossible using a single statistics for the whole model.

• Proceedings of the KIEE Conference
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• 2009.07a
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• pp.2142_2143
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• 2009

Many waveform characteristics of arc signal support the detection of hazardous arc faults. The shoulder is one of main characteristics of arc signal. This paper provide a method for the detection of shoulder from series arc fault based on discrete wavelet transform. The simulation results show that the proposed method is reliable and simple.

• International Journal of Control, Automation, and Systems
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• v.6 no.5
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• pp.639-650
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• 2008

Electricity price forecasting has become an integral part of power system operation and control. In this paper, a wavelet transform (WT) based neural network (NN) model to forecast price profile in a deregulated electricity market has been presented. The historical price data has been decomposed into wavelet domain constitutive sub series using WT and then combined with the other time domain variables to form the set of input variables for the proposed forecasting model. The behavior of the wavelet domain constitutive series has been studied based on statistical analysis. It has been observed that forecasting accuracy can be improved by the use of WT in a forecasting model. Multi-scale analysis from one to seven levels of decomposition has been performed and the empirical evidence suggests that accuracy improvement is highest at third level of decomposition. Forecasting performance of the proposed model has been compared with (i) a heuristic technique, (ii) a simulation model used by Ontario's Independent Electricity System Operator (IESO), (iii) a Multiple Linear Regression (MLR) model, (iv) NN model, (v) Auto Regressive Integrated Moving Average (ARIMA) model, (vi) Dynamic Regression (DR) model, and (vii) Transfer Function (TF) model. Forecasting results show that the performance of the proposed WT based NN model is satisfactory and it can be used by the participants to respond properly as it predicts price before closing of window for submission of initial bids.

• Journal of Korea Water Resources Association
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• v.45 no.11
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• pp.1187-1199
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• 2012

A hybrid forecasting scheme based on wavelet decomposition coupled to a support vector machine model is presented for water demand series that exhibit nonlinear behavior. The use of wavelet transform followed by the SVM model of each leading component is explored as a model for water demand data. The proposed forecasting model yields better results than a traditional ARIMA time series forecasting model in terms of self-prediction problem as well as reproducing the properties of the observed water demand data by making use of the advantages of wavelet transform and SVM model. The proposed model can be used to substantially and significantly improve the water demand forecasting and utilized in a real operation.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.28 no.1
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• pp.70-78
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• 2004

This paper presents study of efficiency of wavelet transformation for on-line chatter detection during hard fuming process. From comparison with other time series and statistical methods such as fast fourier transformation (FFT), Kurtosis and standard deviation (STD), wavelet transform is better than others in on-line chatter detection. With using wavelet function with pseudo frequency corresponding to chatter frequency, chatter could be detected more sensitively. And for both force signal from dynamometer and displacement signal from capacitance type cylindrical sensor (CCS), wavelet transform with DB2 function on level 4 could be well used for chatter detection in hard turning process.

• Computers and Concrete
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• v.2 no.6
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• pp.481-498
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• 2005

A wavelet based approach is proposed for structural damage detection in beams, plate and delamination of composite plates. Wavelet theory is applied here for crack identification of a beam element with a transverse on edge non-propagating open crack. Finite difference method was used for generating a general displacement equation for the cracked beam in the first example. In the second and third example, damage is detected from the deformed shape of a loaded simply supported plate applying the wavelet theory. Delamination in composite plate is identified using wavelet theory in the fourth example. The main concept used is the breaking down of the dynamic signal of a structural response into a series of local basis function called wavelets, so as to detect the special characteristics of the structure by scaling and transformation property of wavelets. In the light of the results obtained, limitations of the proposed method as well as suggestions for future work are presented. Results show great promise of wavelet approach for damage detection and structural health monitoring.

• Proceedings of the Korea Water Resources Association Conference
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• 2006.05a
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• pp.1450-1454
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• 2006

Empirical mode decomposition (EMD) is applied to analyze time series characterized with nonlinearity and nonstationarity. This decomposition could be utilized to construct finite and small number intrinsic mode functions (IMF) that describe complicated time series, while admitting the Hilbert transformation properties. EMD has the capability of being adaptive, capture local characteristics, and applicable to nonlinear and nonstationary processes. Unlike discrete wavelet transform (DWT), IMF eliminates spurious harmonics and retains meaningful instantaneous frequencies. Examples based on data representing natural phenomena are given to demonstrate highlight the power of this method in contrast and comparison of other ones. A presentation of the energy-frequency-time distribution of these signals found to be more informative and intuitive when based on Hilbert transformation.

• Journal of applied mathematics & informatics
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• v.17 no.1_2_3
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• pp.719-736
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• 2005

An understanding of Fourier series and their generalization is important for physics and engineering students, as much for mathematical and physical insight as for applications. Students are usually confused by the so-called Gibbs' phenomenon, an overshoot between a discontinuous function and its approximation by a Fourier series as the number of terms in the series becomes indefinitely large. In this paper we give short story of Gibbs phenomenon in chronological order.

• Proceedings of the Korean Information Science Society Conference
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• 2004.10b
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• pp.85-87
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• 2004

유클리드 거리에 기반하여 유사한 시퀀스 검색을 하는 기법들은 각 시퀀스에서 특징을 추출하여 차원을 감소시킨 후, R-tree 같은 다차원 인덱싱 기법을 사용하여 검색을 수행한다. 본 논문에서는 시계열 데이터 베이스에서의 유사 검색 성능 향상을 위한 새로운 특징 추출 기법인 Polar Wavelet 기법을 제안한다. 이 기법은 유사 검색 시 후보 시퀀스의 개수를 줄임으로써 검색 성능을 향상시킬 수 있고, 특징 추출을 위해 시퀀스의 길이를 2$^n$으로 만들 필요가 없는 장점을 갖고 있다.