• Journal of Broadcast Engineering
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• v.2 no.1
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• pp.45-55
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• 1997

In this paper, we propose a fast algorithm with adaptive thresholding for the wavelet transform (WT) based blocking artifact reduction. In the fast algorithm, all processings that are equivalent to the processing in WT domain of the first and second scale are performed in spatial domain. In the adaptive thresholding, the threshold values used to classify the block boundary are selected adaptively according to each input image by using the statistical properties of the WT of the coded signal at block boundary and at block center, which can be obtained in spatial domain. Experimental results showed that the proposed fast algorithm is about 10 times faster than the WT-based algorithm. It also was found that the postprocessing with proposed adaptive thresholding yields some PSNR improvement and better subjective quality over that with nonadaptive thresholding which has best performance at high compression ratios of a certain .image, even at low compression ratios.

• Journal of the Korean Society for Nondestructive Testing
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• v.24 no.2
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• pp.142-150
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• 2004

A preliminary study of the behavior of ultrasonic guided wave mode in a pipe using a comb transducer for maintenance inspection of power plant facilities has been verified experimentally. The mode identification has been carried out in a pipe using the time-frequency analysis methods such as the wavelet transform(WT) and the short time Fourier transform (STFT), compared with theoretically calculated group velocity dispersion curves for longitudinal and flexural modes. The results are in good agreement with analytical predictions and show the effectiveness of using the time-frequency analysis method to identify the individual modes. It was found out that the longitudinal mode(0,1) is less affected by mode conversion compared with the other modes. Therefore, L(0,1) is selected as an optimal mode for the evaluation of the surface defect in a pipe.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1127-1127
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• 2001

The transfer of predictive models using various chemometric techniques has been reported for FTNIR and scanning-grating based NIR instruments with respect relatively dry samples (<10% water). Some of the currently used transfer techniques include slope and bias correction (SBC), direct standardization (DS), piecewise direct standardization (PDS), orthogonal signal correction (OSC), finite impulse transform (FIR) and wavelet transform (WT) and application of neural networks. In a previous study (Greensill et at., 2001) on calibration transfer for wet samples (intact melons) across silicon diode array instrumentation, we reported on the performance of various techniques (SBC, DS, PDS, double window PDS (DWPDS), OSC, FIR, WT, a simple photometric response correction and wavelength interpolative method and a model updating method) in terms of RMSEP and Fearns criterion for comparison of RMSEP. In the current study, we compare these melon transfer results to a similar study employing pairs of spectrometers for non-invasive prediction of soluble solid content of peaches.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.35S no.2
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• pp.70-78
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• 1998

Spike detection in long-term EEG monitoring forepilepsy by wavelet transform(WT), artificial neural network(ANN) and the expert system is presented. First, a small set of wavelet coefficients is used to represent the characteristics of a singlechannel epileptic spikes and normal activities. In this stage, two parameters are also extracted from the relation between EEG activities before the spike event and EEG activities with the spike. then, three-layer feed-forward network employing the error back propagation algorithm is trained and tested using parameters obtained from the first stage. Spikes are identified in individual EEG channels by 16 identical neural networks. Finally, 16-channel expert system based on the context information of adjacent channels is introducedto yield more reliable results and reject artifacts. In this study, epileptic spikes and normal activities are selected from 32 patient's EEG in consensus among experts. The result showed that the WT reduced data input size and the preprocessed ANN had more accuracy than that of ANN with the same input size of raw data. Ina clinical test, our expert rule system was capable of rejecting artifacts commonly found in EEG recodings.

• Journal of Korea Society of Industrial Information Systems
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• v.22 no.1
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• pp.41-51
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• 2017

This Paper Describes a Method for the Evaluation of Sleep Apnea, Namely, the Peak Signal-to-noise ratio (PSNR) of Wavelet Transformed Electroencephalography (EEG) Data. The Purpose of this Study was to Investigate EEG Properties with Regard to Differences between Sleep Spindles and K-complexes and to Characterize Obstructive Sleep Apnea According to Sleep Stage. We Examined Non-REM and REM Sleep in 20 Patients with OSA and Established a New Approach for Detecting Sleep Apnea Base on EEG Frequency Changes According to Sleep Stage During Sleep Apnea Events. For Frequency Bands Corresponding to A3 Decomposition with a Sampling Applied to the KC and the Sleep Spindle Signal. In this Paper, the KC and Sleep Spindle are Ccalculated using MSE and PSNR for 4 Types of Mother Wavelets. Wavelet Transform Coefficients Were Obtained Around Sleep Spindles in Order to Identify the Frequency Information that Changed During Obstructive Sleep Apnea. We also Investigated Whether Quantification Analysis of EEG During Sleep Apnea is Valuable for Analyzing Sleep Spindles and The K-complexes in Patients. First, Decomposition of the EEG Signal from Feature Data was Carried out using 4 Different Types of Wavelets, Namely, Daubechies 3, Symlet 4, Biorthogonal 2.8, and Coiflet 3. We Compared the PSNR Accuracy for Each Wavelet Function and Found that Mother Wavelets Daubechies 3 and Biorthogonal 2.8 Surpassed the other Wavelet Functions in Performance. We have Attempted to Improve the Computing Efficiency as it Selects the most Suitable Wavelet Function that can be used for Sleep Spindle, K-complex Signal Processing Efficiently and Accurate Decision with Lesser Computational Time.

• Composites Research
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• v.13 no.1
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• pp.40-49
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• 2000

The objective of this research is to develop the impact monitoring techniques providing impact identification and damage diagnostics of smart composite laminates susceptible to impacts. This can be implemented simultaneously by using the acoustic waves by the impact loads and the acoustic emission waves from damage. In the previous research, we have discussed the impact location detection process in which impact generated acoustic waves are detected by PZT using the improved neural network paradigm. This paper describes the implementation of time-frequency analysis such as the Short-Time Fourier Transform (STFT) and the Wavelet Transform (WT) on the determination of the occurrence and the estimation of damage.

• Journal of the Korea Society of Computer and Information
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• v.29 no.5
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• pp.21-29
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• 2024

Reducing the radiation dose during CT scanning can lower the risk of radiation exposure, but not only does the image resolution significantly deteriorate, but the effectiveness of diagnosis is reduced due to the generation of noise. Therefore, noise removal from CT images is a very important and essential processing process in the image restoration. Until now, there are limitations in removing only the noise by separating the noise and the original signal in the image area. In this paper, we aim to effectively remove noise from CT images using the wavelet transform-based GAN model, that is, the WT-GAN model in the frequency domain. The GAN model used here generates images with noise removed through a U-Net structured generator and a PatchGAN structured discriminator. To evaluate the performance of the WT-GAN model proposed in this paper, experiments were conducted on CT images damaged by various noises, namely Gaussian noise, Poisson noise, and speckle noise. As a result of the performance experiment, the WT-GAN model is better than the traditional filter, that is, the BM3D filter, as well as the existing deep learning models, such as DnCNN, CDAE model, and U-Net GAN model, in qualitative and quantitative measures, that is, PSNR (Peak Signal-to-Noise Ratio) and SSIM (Structural Similarity Index Measure) showed excellent results.

• The Journal of the Acoustical Society of Korea
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• v.15 no.5
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• pp.21-29
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• 1996

Industrial equipment, such as power plant, is required to operate reliably, continuously and economically under rather severe conditions of temperature, stress, and enbironment. To test structural integrity and fitness, ultrasonic nondestructive testing is used because of effectiveness and simplicity. In this paper, wavelet transform based least mean square(LMS) algorithm is applied to reduce the influence of the interference occurring between randomly positioned small scatters. The RUN test is performed to check the nonstationarity of the speckle noise signal. The performance of this new approach is compared with that of the time domain LMS algorithm by means of condition numbers, signal-to-noise ratio and 3-D image. As a result, the wavelet transform based LMS algorithm shows better performance than the time domain LMS algorithm in this experiment.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.43 no.3 s.309
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• pp.1-8
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• 2006

In this paper, we propose a new post-processing method, based on the theory of the projection onto convex sets (POCS) to reduce the blocking artifacts in decoded images. We propose a few smoothness constraint set (SCS) and its projection operator in the wavelet transform (WT) domain to remove unnecessary high-frequency components caused by blocking artifacts. We also propose a new method to find and preserve the original high frequency components of the image edge. Experimental results show that the proposed method can not only achieve a significantly enhanced subjective quality, but also have the PSNR improvement in the output image.

• Composites Research
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• v.20 no.3
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• pp.31-36
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• 2007

A quantitative prediction method for initial crack length in a carbon/epoxy (CF/EP) composite beam using active piezoelectric transducers was established in this study. Wavelet Transform (WT)-based signal processing and identification technique in time-frequency domain was developed to facilitate the determination of damage presence and severity. Dynamic response of a CF/EP composites beam containing a continuously expanding crack, coupled with a pair of active piezoelectric disks, was examined under a narrow band excitation, and then applied with the proposed signal processing technique.