• Journal of the Korean Geophysical Society
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• v.2 no.2
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• pp.143-152
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• 1999

We have studied a denoising technique involving wavelet transform for improving the quality of geophysical data during the preprocessing stage. To assess the effectiveness of this technique, we have made synthetic data contaminated by random noises and compared the results of denoising with those obtained by conventional low-pass filtering. The low-pass filtering of the sinusoidal signal having a sharp discontinuity between the first and last sample values shows apparent errors related to Gibbs' phenomena. For the case of bump signal, the low-pass filtering induces maximum errors on peak values by removing some high-frequency components of signal itself. The wavelet transform technique, however, denoises these signals with much less adverse effects owing to its pertinent properties on locality of wavelet and easy discrimination of noise and signal in the wavelet domain. The field data of gravity tide are denoised by using soft threshold, which shrinked all the wavelet coefficients toward the origin, and the G-factor is determined by comparing the denoised data and theoretical data.

• Journal of the Korean Chemical Society
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• v.53 no.5
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• pp.585-608
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• 2009

The aim of this study was to research the characteristics of science teachers' thinking related to dissolution and diffusion. For this study, five science teachers who majored in chemistry or chemistry education were selected and interviewed. These teachers have been teaching on average for 2$\sim$6 years. From the study, it was discovered that the science teachers didn't recognize the necessity of sorting out diffusion from dissolution. The teachers divided in various mixing phenomena with diverse criteria. The science teachers had difficulty in sorting out diffusion from dissolution based on solubility and Gibbs' energy. The teachers didn't see the linkage between the contents that were divided into chapters, and didn't find omitted contents themselves in the chapters that introduced applicable principles. During the interviews, the teachers felt the need to understand the principles for understanding phenomena. But they did not have the ability to teach these principles after learning about the principles themselves. Therefore, it is necessary to develop teacher education programs, as well as a science curriculum, that helps in linking the knowledge between natural phenomena and principles.

• The KIPS Transactions:PartD
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• v.9D no.3
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• pp.389-398
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• 2002

Software reliability growth models are used in testing stages of software development to model the error content and time intervals between software failures. This paper presents a stochastic model for the software failure phenomenon based on a nonhomogeneous Poisson process(NHPP) and performs Bayesian inference using prior information. The failure process is analyzed to develop a suitable mean value function for the NHPP ; expressions are given for several performance measure. Actual software failure data are compared with several model on the constant reflecting the quality of testing. The performance measures and parametric inferences of the suggested models using Rayleigh distribution and Laplace distribution are discussed. The results of the suggested models are applied to real software failure data and compared with Goel model. Tools of parameter point inference and 95% credible intereval was used method of Gibbs sampling. In this paper, model selection using the sum of the squared errors was employed. The numerical example by NTDS data was illustrated.

• Journal of Electrical Engineering and Technology
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• v.9 no.2
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• pp.695-706
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• 2014

Partial discharge (PD) measurements have emerged as a dominant investigative tool for condition monitoring of insulation in high voltage equipment. But the major problem behind them the PD signal is severely polluted by several noises like White noise, Random noise, Discrete Spectral Interferences (DSI) and the challenge lies with removing these noise from the onsite PD data effectively which leads to preserving the signal for feature extraction. Accordingly the paper is mainly classified into two parts. In first part the PD signal is artificially simulated and mixed with white noise. In second part the PD is measured then it is subjected to the proposed denoising techniques namely Translation Invariant Wavelet Transform (TIWT). The proposed TIWT method remains the edge of the original signal efficiently. Additionally TIWT based denoising is used to suppress Pseudo Gibbs phenomenon. In this paper an attempt has been made to review the methodology of denoising the PD signals and shows that the proposed denoising method results are better when compared to other wavelet-based approaches like Fast Fourier Transform (FFT), Discrete Wavelet Transform (DWT), by evaluating five different parameters like, Signal to noise ratio, Cross-correlation coefficient, Pulse amplitude distortion, Mean square error, Reduction in noise level.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.12
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• pp.2327-2334
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• 2007

This paper presents a method for improvement of communication speed and reduction of data storage space in gas turbine monitoring system to acquire, save, and display combustion vibration data. The proposed method implements FFT from sampled raw data. The FFT result data are encoded to be transferred to monitoring PC for storage. By this way, it can reduce data storage space. To display the received data, it needs inverse FFT to reconstruct original signal. To verify the validity and efficiency of the proposed scheme, computer-aided simulation are carried out. It includes the analyzed results the relationship between FFT's order and Gibb's Phenomenon. Finally, high-performance of the proposed method is proved by combustion experiment results using a prototype gas turbine.