• Structural Monitoring and Maintenance
• /
• v.1 no.1
• /
• pp.69-87
• /
• 2014

A new output only modal analysis method is developed in this paper. This method uses continuous wavelet transform to modify a popular blind source separation algorithm, second order blind identification (SOBI). The wavelet modified SOBI (WMSOBI) method replaces original time domain signal with selected time-frequency domain wavelet coefficients, which overcomes the shortcomings of SOBI. Both numerical and experimental studies on bridge models are carried out when there are limited number of sensors. Identified modal properties from WMSOBI are analyzed and compared with fast Fourier transform (FFT), SOBI and eigensystem realization algorithm (ERA). The comparison shows WMSOBI can identify as many results as FFT and ERA. Further case study of structural health monitoring (SHM) on an arch bridge verifies the capability to detect damages by combining WMSOBI with incomplete flexibility difference method.

• International Journal of Control, Automation, and Systems
• /
• v.6 no.4
• /
• pp.607-612
• /
• 2008

This paper proposes a robot visual servo approach based on image appearance and a wavelet function neural network. The inputs of the wavelet neural network are changes of image features or the elements of image appearance vector, and the outputs are changes of robot joint angles. Image appearance vector is calculated by using eigen subspace transform algorithm. The proposed approach does not need a priori knowledge of the robot kinematics, hand-eye geometry and camera models. The experiment results on a real robot system show that the proposed method is practical and simple.

• Proceedings of the Korean Society for Technology of Plasticity Conference
• /
• 2003.05a
• /
• pp.27-33
• /
• 2003

In closed-die forging process, blocker has been used to fill and distribute metal well in finisher die. Generally, the blocker shape was determined by an expert with many experiences. However, the manual blocker design process takes much time and efforts, so various automatic methods for the blocker design process have been suggested for the last three decades. The method with filtering in FFT (Fast Fourier Transform) for the blocker design provides general solution than other methods. But, due to the properties of FFT in time-frequency domain, this method has some drawbacks such as long calculation time, difficulty of local control and additional boundary process after filtering. In this study, DWT (Discrete Wavelet Transform), which is more flexible and is more wildly used than FFT, is applied to the blocker design. The method with filtering in DWT is very proper to design blocker in both 2-D and 3-D shapes. To verify the efficiency of this method, blockers of some models are designed and the results show that blocker design with DWT is effective fer the blocker designs

• Transactions of Materials Processing
• /
• v.12 no.4
• /
• pp.277-283
• /
• 2003

In a closed-die forging process, blocker has been used to fill and distribute metal well in finisher die. Generally, the blocker shape was determined by an expert with many experiences. However, the manual blocker design process takes much time and efforts, so various automatic methods for the blocker design process have been suggested for the last three decades. The method with filtering in FFT (Fast Fourier Transform) for the blocker design provides general solution than other methods. But. due to the properties of FFT in time-frequency domain, this method has some drawbacks such as long calculation time, difficulty of local control and additional boundary process after filtering. In this study. DWT (Discrete Wavelet Transform), which is more flexible and is more wildly used than FFT, is applied to the blocker design. The method with filtering in DWT is very proper to design blocker in both 2-D and 3-D shapes. To verify the efficiency of this method, blockers of some models are designed and the results show that blocker design with DWT is effective for the blocker designs.

• Journal of Biosystems Engineering
• /
• v.38 no.1
• /
• pp.48-54
• /
• 2013

Purpose: This study evaluated the feasibility of using a discrete wavelet transform (DWT) method as a preprocessing tool for visible/near-infrared spectroscopy (VIS/NIRS) with a spectroscopic transmittance dataset for predicting the internal quality of cherry tomatoes. Methods: VIS/NIRS was used to acquire transmittance spectrum data, to which a DWT was applied to generate new variables in the wavelet domain, which replaced the original spectral signal for subsequent partial least squares (PLS) regression analysis and prediction modeling. The DWT concept and its importance are described with emphasis on the properties that make the DWT a suitable transform for analyzing spectroscopic data. Results: The $R^2$ values and root mean squared errors (RMSEs) of calibration and prediction models for the firmness, sugar content, and titratable acidity of cherry tomatoes obtained by applying the DWT to a PLS regression with a set of spectra showed more enhanced results than those of each model obtained from raw data and mean normalization preprocessing through PLS regression. Conclusions: The developed DWT-incorporated PLS models using the db5 wavelet base and selected approximation coefficients indicate their feasibility as good preprocessing tools by improving the prediction of firmness and titratable acidity for cherry tomatoes with respect to $R^2$ values and RMSEs.

• Structural Engineering and Mechanics
• /
• v.37 no.6
• /
• pp.575-592
• /
• 2011

For seismic resistant design of critical structures, a dynamic analysis, either response spectrum or time history is frequently required. Owing to the lack of recorded data and the randomness of earthquake ground motion that may be experienced by structure in the future, usually it is difficult to obtain recorded data which fit the requirements (site type, epicenteral distance, etc.) well. Therefore, the artificial seismic records are widely used in seismic designs, verification of seismic capacity and seismic assessment of structures. The purpose of this paper is to develop a numerical method using Artificial Neural Network (ANN) and wavelet packet transform in best basis method which is presented for the decomposition of artificial earthquake records consistent with any arbitrarily specified target response spectra requirements. The ground motion has been modeled as a non-stationary process using wavelet packet. This study shows that the procedure using ANN-based models and wavelet packets in best-basis method are applicable to generate artificial earthquakes compatible with any response spectra. Several numerical examples are given to verify the developed model.

• Journal of the Institute of Convergence Signal Processing
• /
• v.14 no.1
• /
• pp.13-20
• /
• 2013

A combined DWT and DCT based watermarking technique of low frequency watermarking with weighted correction is proposed. The DWT has excellent spatial localization, frequency spread and multi-resolution characteristics, which are similar to the theoretical models of the human visual system (HVS). The DCT based watermarking techniques offer compression while DWT based watermarking techniques offer scalability. These desirable properties are used in this combined watermarking technique. In the proposed method watermark bits are embedded in the low frequency band of each DCT block of selected DWT sub-band. The weighted correction is also used to improve the imperceptibility. The extracting procedure reverses the embedding operations without the reference of the original image. Compared with the similar approach by DCT based approach and DWT based approach, the experimental results show that the proposed algorithm apparently preserves superiori mage quality and robustness under various attacks such as JPEG compression, cropping, sharping, contrast adjustments and so on.

• Proceedings of the Korea Database Society Conference
• /
• 1999.06a
• /
• pp.175-186
• /
• 1999

Detecting the features of significant patterns from their own historical data is so much crucial to good performance specially in time-series forecasting. Recently, a new data filtering method (or multi-scale decomposition) such as wavelet analysis is considered more useful for handling the time-series that contain strong quasi-cyclical components than other methods. The reason is that wavelet analysis theoretically makes much better local information according to different time intervals from the filtered data. Wavelets can process information effectively at different scales. This implies inherent support fer multiresolution analysis, which correlates with time series that exhibit self-similar behavior across different time scales. The specific local properties of wavelets can for example be particularly useful to describe signals with sharp spiky, discontinuous or fractal structure in financial markets based on chaos theory and also allows the removal of noise-dependent high frequencies, while conserving the signal bearing high frequency terms of the signal. To date, the existing studies related to wavelet analysis are increasingly being applied to many different fields. In this study, we focus on several wavelet thresholding criteria or techniques to support multi-signal decomposition methods for financial time series forecasting and apply to forecast Korean Won / U.S. Dollar currency market as a case study. One of the most important problems that has to be solved with the application of the filtering is the correct choice of the filter types and the filter parameters. If the threshold is too small or too large then the wavelet shrinkage estimator will tend to overfit or underfit the data. It is often selected arbitrarily or by adopting a certain theoretical or statistical criteria. Recently, new and versatile techniques have been introduced related to that problem. Our study is to analyze thresholding or filtering methods based on wavelet analysis that use multi-signal decomposition algorithms within the neural network architectures specially in complex financial markets. Secondly, through the comparison with different filtering techniques' results we introduce the present different filtering criteria of wavelet analysis to support the neural network learning optimization and analyze the critical issues related to the optimal filter design problems in wavelet analysis. That is, those issues include finding the optimal filter parameter to extract significant input features for the forecasting model. Finally, from existing theory or experimental viewpoint concerning the criteria of wavelets thresholding parameters we propose the design of the optimal wavelet for representing a given signal useful in forecasting models, specially a well known neural network models.

• Proceedings of the Korea Inteligent Information System Society Conference
• /
• 1999.03a
• /
• pp.175-186
• /
• 1999

Detecting the features of significant patterns from their own historical data is so much crucial to good performance specially in time-series forecasting. Recently, a new data filtering method (or multi-scale decomposition) such as wavelet analysis is considered more useful for handling the time-series that contain strong quasi-cyclical components than other methods. The reason is that wavelet analysis theoretically makes much better local information according to different time intervals from the filtered data. Wavelets can process information effectively at different scales. This implies inherent support for multiresolution analysis, which correlates with time series that exhibit self-similar behavior across different time scales. The specific local properties of wavelets can for example be particularly useful to describe signals with sharp spiky, discontinuous or fractal structure in financial markets based on chaos theory and also allows the removal of noise-dependent high frequencies, while conserving the signal bearing high frequency terms of the signal. To data, the existing studies related to wavelet analysis are increasingly being applied to many different fields. In this study, we focus on several wavelet thresholding criteria or techniques to support multi-signal decomposition methods for financial time series forecasting and apply to forecast Korean Won / U.S. Dollar currency market as a case study. One of the most important problems that has to be solved with the application of the filtering is the correct choice of the filter types and the filter parameters. If the threshold is too small or too large then the wavelet shrinkage estimator will tend to overfit or underfit the data. It is often selected arbitrarily or by adopting a certain theoretical or statistical criteria. Recently, new and versatile techniques have been introduced related to that problem. Our study is to analyze thresholding or filtering methods based on wavelet analysis that use multi-signal decomposition algorithms within the neural network architectures specially in complex financial markets. Secondly, through the comparison with different filtering techniques results we introduce the present different filtering criteria of wavelet analysis to support the neural network learning optimization and analyze the critical issues related to the optimal filter design problems in wavelet analysis. That is, those issues include finding the optimal filter parameter to extract significant input features for the forecasting model. Finally, from existing theory or experimental viewpoint concerning the criteria of wavelets thresholding parameters we propose the design of the optimal wavelet for representing a given signal useful in forecasting models, specially a well known neural network models.

• 유체기계공업학회:학술대회논문집
• /
• 2002.12a
• /
• pp.346-353
• /
• 2002

The steam generator feedwater flow rate in a nuclear power plant was estimated by means of artificial neural networks with the wavelet analysis for enhanced information extraction. The fouling of venturi meters, used for steam generator feedwater flow rate in pressurized water reactors, may result in unnecessary plant power derating. The backpropagation network was used to generate models of signals for a pressurized water reactor. Multiple-input single-output heteroassociative networks were used for evaluating the feedwater flow rate as a function of a set of related variables. The wavelet was used as a low pass filter eliminating the noise from the raw signals. The results have shown that possible fouling of venturi can be detected by neural networks, and the feedwater flow rate can be predicted as an alternative to existing methods. The research has also indicated that the decomposition of signals by wavelet transform is a powerful approach to signal analysis for denoising.