• 한국지반공학회:학술대회논문집
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• 한국지반공학회 2003년도 봄 학술발표회 논문집
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• pp.249-256
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• 2003

To investigate the safety and stability of the concrete lining, numerous studies have been conducted over the years and several methods have been developed. Most signal processing method of NDT techniques has based on the Fourier analysis. However, the application of Fourier analysis to analyze recorded signal shows results only in frequency domain, it is not enough to analyze transient waves precisely. In this study, a new NDT technique .using the wavelet theory was employed for the analysis of non-stationary wave propagation induced by mechanical impact in the concrete lining. The wavelet transform of transient signals provides a method for mapping the frequency spectrum as a function of time. To verify the availability of wavelet transform as a time- frequency analysis tool, model experiments have been conducted on the concrete lining model. From this study, it was found that the contour map by Wavelet transform provides more distinct results than the power spectrum by Fourier transform and it was concluded that Wavelet transform was an effective tool for the experimental analysis of dispersive waves in concrete structures.

• Computational Structural Engineering : An International Journal
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• 제2권1호
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• pp.43-50
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• 2002

This paper presents a method to predict dynamic responses of floating bodies in the time domain. Because of the frequency-dependence of the radiation wave forces, the memory effect must be taken into account when the responses are evaluated in the time domain. Although the formulations firstly developed by Cummins (1962) have been well-known for this purpose, the effective numerical procedure has not been established yet. This study employs FFT (Fast Fourier Transform) algorithm to evaluate the memory effect function, and the equations of motion of an integro-differential type are solved by Newmark-β method. Numerical examples for a truncated circular cylinder have indicated the effectiveness of the proposed numerical procedure.

• 대한기계학회논문집A
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• 제25권8호
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• pp.1253-1260
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• 2001

Transient thermal analysis of a three-dimensional axisymmetric automotive disk brake is presented in this paper. Temperature fields are obtained using a hybrid FFT-FEM scheme that combines Fourier transform techniques and finite element method. The use of a fast Fourier transform algorithm can avoid singularity problems and lead to inexpensive computing time. The transformed problem is solved with finite element scheme for each frequency domain. Inverse transforms are then performed for time domain solution. Numerical examples are presented for validation tests. Comparisons with analytical results show very good agreement. Also, a 3-D simulation, based upon an automotive brake disk model is performed.

• 한국음향학회지
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• 제35권1호
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• pp.8-15
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• 2016

Fractional 푸리에 변환(Fractional Fourier Transform : FrFT)은 기존의 푸리에 변환의 일반화된 형태로서, 변환차수 ${\alpha}$에 따라 임의의 시간-주파수 영역에서의 신호해석이 가능하다. FrFT는 특히 LFM(Linear Frequency Modulation) 신호의 분석에 있어 잡음에 강한 특성으로 인해 많은 장점을 가진다. 본 논문에서는 능동소나에서 표적에 맞고 반향된 수신신호의 FrFT 스펙트럼으로부터 표적과의 거리를 추정하는 새로운 방법을 제안하였다. 합성한 표적 신호를 통해 제안한 방법의 타당성을 검증하였으며, 실제 수중 실험을 통해 얻은 잡음 및 잔향 환경에서의 신호에서도 신뢰성 있는 표적과의 거리 추정이 가능함을 확인하였다.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제7권2호
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• pp.102-108
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• 2007

The issues of preventive and condition-based maintenance, online monitoring, system fault detection, diagnosis, and prognosis are of increasing importance. This study introduces a technique to detect and identify faults in induction motors. Stator currents were measured and stored by time domain. The time domain is not suitable for representing current signals, so wavelet transform is used to convert the signal; onto frequency domain. The raw signals can not show the significant feature, therefore difference values are applied. The difference values were transformed by wavelet transform and the features are extracted from the transformed signals. The dynamic time warping method was used to identify the four fault types. This study describes the results of detecting fault using wavelet analysis.

• Coupled systems mechanics
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• 제1권2호
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• pp.183-204
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• 2012

A method for the analysis of wave scattering in 3-D elastic full space is developed by means of the coupled boundary-volume integral equation, which takes into account the effects of both the boundary of inclusions and the uctuation of the wave field. The wavenumber domain formulation is used to construct the Krylov subspace by means of FFT. In order to achieve the wavenumber domain formulation, the boundary-volume integral equation is transformed into the volume integral equation. The formulation is also focused on this transform and its numerical implementation. Several numerical results clarify the accuracy and effectiveness of the present method for scattering analysis.

• Steel and Composite Structures
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• 제18권4호
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• pp.889-907
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• 2015

This paper aims to present an alternative analytical method for transient vibration analysis of doubly-curved laminated shells subjected to dynamic loads. In the method proposed, the governing differential equations of laminated shell are derived using the dynamic version of the principle of virtual displacements. The governing equations of first order shear deformation laminated shell are obtained by Navier solution procedure. Time-dependent equations are transformed to the Laplace domain and then Laplace parameter dependent equations are solved numerically. The results obtained in the Laplace domain are transformed to the time domain with the help of modified Durbin's numerical inverse Laplace transform method. Verification of the presented method is carried out by comparing the results with those obtained by Newmark method and ANSYS finite element software. Also effects of number of laminates, different material properties and shell geometries are discussed. The numerical results have proved that the presented procedure is a highly accurate and efficient solution method.

• International Journal of Control, Automation, and Systems
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• 제6권5호
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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.

• 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.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1993년도 추계학술대회 논문집
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• pp.363-368
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• 1993

Hilbert Transform has been used for detection of nonlinearity in modal analysis. HTD(Hilbert Transform Describers) are used to quantify and identify nonlinearity. Mottershead and Stanway method for identification of N-th power velocity nonlinear damping are extended to P-th power displacement stiffness, N-th power velocity damping and dry friction. Time domain and frequency domain data are used and HTD and Mottershead methods are combined for identification of nonlinear parameters in this paper. Computer simulations and experimental results are shown to verify nonlinear structure identification methods.