• Proceedings of the Korean Geotechical Society Conference
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• 2008.03a
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• pp.599-604
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• 2008

The dispersive phase velocity of a wave propagating through a system is an important parameter and carries valuable information in non-destructive tests related to multilayered systems such as a soil site. The dispersive phase velocity of a wave can be determined using the phase spectrum, which is easily evaluated through the cross power spectrum. However, the phase spectrum as determined using the cross power spectrum is sensitive to background noise which always exists in the field. This causes difficulties in the determination of the dispersive phase velocities. In this paper, a new method to evaluate the phase spectrum using the harmonic wavelet transform is proposed. The proposed method can successfully remove background noise effects. To evaluate the validity of the proposed method, numerical simulations of multi-layered systems were performed. Phase spectrums by the proposed method were found to be in good agreement with the actual phase spectrums under conditions characterized by heavy background noise. This shows the potential of the proposed method.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.47 no.8
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• pp.85-90
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• 2010

In this paper, a blind hop timing and duration estimation scheme for FHSS (Frequency Hopping Spread Spectrum) systems based on digital frequency discriminator and wavelet transform is proposed. The proposed scheme not only requires the lower hardware complexity but also shows superior estimation performance compared to the previously proposed temporal correlation function based hop timing estimation scheme.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.6 no.1
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• pp.27-32
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• 2006

Verbal communication is the most commonly used mean of communication. A spoken word carries a lot of informations about speakers and their emotional states. In this paper we designed a model to recognize emotional states in a speech, a first phase of two phases in developing a toy machine that recognizes emotional states in a speech. We conducted an experiment to extract and analyse the emotional state of a speaker in relation with speech. To analyse the signal output we referred to three characteristics of sound as vector inputs and they are the followings: frequency, intensity, and period of tones. Also we made use of eight basic emotional parameters: surprise, anger, sadness, expectancy, acceptance, joy, hate, and fear which were portrayed by five selected students. In order to facilitate the differentiation of each spectrum features, we used the wavelet transform analysis. We applied ANFIS (Adaptive Neuro Fuzzy Inference System) in designing an emotion recognition model from a speech. In our findings, inference error was about 10%. The result of our experiment reveals that about 85% of the model applied is effective and reliable.

• Journal of the Society of Naval Architects of Korea
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• v.38 no.3
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• pp.117-122
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• 2001

The shock test of shipboard equipments is performed for the evaluation of the shock-resistant. capability by analyzing the maximum acceleration, the effective time duration and the shock response spectrum, etc. But some measured signals have impulsive noise and gaussian white noise because of the ambient noise, the acquisition equipment error and the transient movement of cables during the shock test. The improved transient signal analysis method which removes the noise of measured signal using the threshold policy of the median filter and the orthogonal wavelet coefficients is proposed. It was verified that the signal-to-noise ratio was improved about 30dB by the numerical simulation. And the shock response spectrum was extracted using the denoised shock response signal which was applied by this proposed method.

• Earthquakes and Structures
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• v.8 no.6
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• pp.1463-1480
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• 2015

This research aims to develop an empirical model for simulation of time-varying frequency in earthquake ground motion so as to be used easily in engineering applications. Briefly, 10545 recordings of the Next Generation Attenuation (NGA) global database of accelerograms from shallow crustal earthquakes are selected and binned by magnitude, distance and site condition. Then the wavelet spectrum of each acceleration record is calculated by using one-dimensional continuous wavelet transform, and the frequencies corresponding to the maximum values of the wavelet spectrum at a series of sampling time, named predominant frequencies, are extracted to analyze the variation of frequency content of seismic ground motions in time. And the time-variation of the predominant frequencies of 178 magnitude-distance-site bins for different directions are obtained by calculating the mean square root of predominant frequencies within a bin. The exponential trigonometric function is then use to fit the data, which describes the predominant frequency of ground-motion as a function of time with model parameters given in tables for different magnitude, distance, site conditions and direction. Finally, a practical frequency-dependent amplitude envelope function is developed based on the time-varying frequency derived in this paper, which has clear statistical parameters and can emphasize the effect of low-frequency components on later seismic action. The results illustrate that the time-varying predominant frequency can preferably reflect the non-stationarity of the frequency content in earthquake ground motions and that empirical models given in this paper facilitates the simulation of ground motions.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.4 no.3
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• pp.653-660
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• 2000

In this paper, an adaptive interference excision scheme with multiple adaptive filter bank using an adaptive algorithm is proposed. This scheme suppresses interference in the wavelet packet transform domain for the direct spread spectrum communication systems. Using the Monte-Carlo simulation, we analyze the performance of the direct spread spectrum communication systems with this excision scheme using LMS and RLS adaptive algorithms in AWGN and interference channel.

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

• Journal of Korea Multimedia Society
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• v.10 no.9
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• pp.1125-1134
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• 2007

In this paper, we propose a new approach for rotation invariant texture classification based on Gabor wavelet. Conventional methods have the low correct classification rate in large texture database. In our proposed method, we define two feature groups which are the global feature vector and the local feature matrix. The feature groups are output of Gabor wavelet filtering. By using the feature groups, we defined an improved discriminant and obtained high classification rates of large texture database in the experiments. From spectrum symmetry of texture images, the number of test times were reduced nearly 50%. Consequently, the correct classification rate is improved with $2.3%{\sim}15.6%$ values in 112 Brodatz texture class, which may vary according to comparison methods.

• Steel and Composite Structures
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• v.21 no.6
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• pp.1183-1210
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• 2016

Viscoelastic sandwich structures (VSSs) are widely used in mechanical equipment, but in the service process, they always suffer from aging which affect the whole performance of equipment. Therefore, aging state recognition of VSSs is significant to monitor structural state and ensure the reliability of equipment. However, non-stationary vibration response signals and weak state change characteristics make this task challenging. This paper proposes a novel method for this task based on adaptive second generation wavelet packet transform (ASGWPT) and multiwavelet support vector machine (MWSVM). For obtaining sensitive feature parameters to different structural aging states, the ASGWPT, its wavelet function can adaptively match the frequency spectrum characteristics of inspected vibration response signal, is developed to process the vibration response signals for energy feature extraction. With the aim to improve the classification performance of SVM, based on the kernel method of SVM and multiwavelet theory, multiwavelet kernel functions are constructed, and then MWSVM is developed to classify the different aging states. In order to demonstrate the effectiveness of the proposed method, different aging states of a VSS are created through the hot oxygen accelerated aging of viscoelastic material. The application results show that the proposed method can accurately and automatically recognize the different structural aging states and act as a promising approach to aging state recognition of VSSs. Furthermore, the capability of ASGWPT in processing the vibration response signals for feature extraction is validated by the comparisons with conventional second generation wavelet packet transform, and the performance of MWSVM in classifying the structural aging states is validated by the comparisons with traditional wavelet support vector machine.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.2B
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• pp.237-248
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• 2008

Precipitation variability in Korea is mainly influenced by climate circulation such as sea surface temperature, not a local convection. Therefore, this study investigates relationship between monthly precipitation of 61 station observed by Korea Meteorological Administration and global sea surface temperatures (SSTs). The main components of monthly precipitation in Korea are extracted by a method which consists of the principal analysis combined with the cluster analysis, to examine the correlation between monthly rainfalls and SSTs. The relationships between main components of monthly precipitation and SSTs exists in Pacific Ocean. At the result of Wavelet Transform analysis, The 2-4 year band have a strong wavelet power spectrum and the low frequency. the correlation coefficient between low frequency components of monthly rainfalls and SSTs calculated bigger then correlation coefficient between main components and SSTs. Hence, these results propose a prediction possibility of monthly precipitations using the varition of SSTs.