• Smart Structures and Systems
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• v.19 no.5
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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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• v.22 no.2
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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.

• Journal of Intelligence and Information Systems
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• v.4 no.2
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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.

• Journal of Intelligence and Information Systems
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• v.5 no.1
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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.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.25 no.12
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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.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.26 no.5
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• pp.513-518
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• 2008

The research of global-scale mass redistribution and it changed by Earth gravity filed variation observations, including Earth's oblateness $J_2$(also called low degree spherical harmonic coefficient $C_{20}$), is in continuous progress. Recently, the comparative analysis of geodetic observation SLR can be made by the development of GRACE and other time-variable gravity measurements. In this study, $C_{20}$ time series changes in the value of comparative analysis was got by GRACE monthly Gravity filed model (CSR RL04) for the period April 2002 to May 2008. And comparative analysis the harmonic coefficients of $C_{20}$ was obtained from SLR observations. Signal analysis for two time-series data was made by wavelet transform, CWT(continuous wavelet transform), XWT(cross wavelet transform) and WTC(wavelet coherence) methods. The results indicate that GRACE and SLR values for $C_{20}$ had both decreasing trend, as well as SLR data represent the annual frequencies, and GRACE was semiannual variations. In addition, the results of GRACE and SLR had a strong correlation with the XWT and WTC in an annual cycle.

• Journal of the Korean Society of Marine Environment & Safety
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• v.27 no.7
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• pp.1088-1097
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• 2021

Vibration data of mechanical equipment inevitably have noise. This noise adversely af ects the maintenance of mechanical equipment. Accordingly, the performance of a learning model depends on how effectively the noise of the data is removed. In this study, the noise of the data was removed using the Denoising Auto Encoder (DAE) technique which does not include the characteristic extraction process in preprocessing time series data. In addition, the performance was compared with that of the Wavelet Transform, which is widely used for machine signal processing. The performance comparison was conducted by calculating the failure detection rate. For a more accurate comparison, a classification performance evaluation criterion, the F-1 Score, was calculated. Failure data were detected using the One-Class SVM technique. The performance comparison, revealed that the DAE technique performed better than the Wavelet Transform technique in terms of failure diagnosis and error rate.

• Smart Media Journal
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• v.12 no.11
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• pp.36-47
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• 2023

Industrial Control System(ICS), which controls facilities at major industrial sites, is increasingly connected to other systems through networks. With this integration and the development of intelligent attacks that can lead to a single external intrusion as a whole system paralysis, the risk and impact of security on industrial control systems are increasing. As a result, research on how to protect and detect cyber attacks is actively underway, and deep learning models in the form of unsupervised learning have achieved a lot, and many abnormal detection technologies based on deep learning are being introduced. In this study, we emphasize the application of preprocessing methodologies to enhance the anomaly detection performance of deep learning models on time series data. The results demonstrate the effectiveness of a Wavelet Transform (WT)-based noise reduction methodology as a preprocessing technique for deep learning-based anomaly detection. Particularly, by incorporating sensor characteristics through clustering, the differential application of the Dual-Tree Complex Wavelet Transform proves to be the most effective approach in improving the detection performance of cyber attacks.

• Ocean Systems Engineering
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• v.4 no.3
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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.

• Convergence Security Journal
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• v.7 no.1
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• pp.83-89
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• 2007

This paper is to suggest the method of embedding a signature to pictures secretly under the orthogonal wavelet transform which represents pictures as multi-resolution representations. As it is focused upon the differential output under the multi-resolution representation of pictures, this method can embed bit series to pictures. In doing so, it can compound approximately 6K byte of information with gray-level image $256{\times}256$. The method can include not only the database which designates copyright of pictures but also the author and usage of pictures, and the information of the picture itself. Therefore, this method can easily discriminate the inspection of picture database.