• Journal of Korea Society of Industrial Information Systems
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• v.17 no.4
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• pp.71-85
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• 2012

This paper aims to develop an event-driven failure analysis and prognosis system that is able to monitor ship status in real time, and efficiently react unforeseen system failures. In general, huge amount of recorded sensor data must be effectively interpreted for failure analysis, but unfortunately noise and redundant information in the gathered sensor data are obstacles to a successful analysis. This paper therefore applies 'Equal-frequency binning' and 'Entropy' techniques to extract only important information from the raw sensor data while minimizing information loss. The efficiency of the developed failure analysis system is demonstrated with the collected sensor data from a marine diesel engine.

• Journal of Internet Computing and Services
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• v.7 no.3
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• pp.155-167
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• 2006

It is desirable if users surfing on the Internet could find Web pages related to their interests as closely as possible. Toward this ends, this paper presents a topic specific Web crawler computing the degree of relevance. collecting a cluster of pages given a specific topic, and refining the preliminary set of related web pages using term frequency/document frequency, entropy, and compiled rules. In the experiments, we tested our topic specific crawler in terms of the accuracy of its classification, crawling efficiency, and crawling consistency. First, the classification accuracy using the set of rules compiled by CN2 was the best, among those of C4.5 and back propagation learning algorithms. Second, we measured the classification efficiency to determine the best threshold value affecting the degree of relevance. In the third experiment, the consistency of our topic specific crawler was measured in terms of the number of the resulting URLs overlapped with different starting URLs. The experimental results imply that our topic specific crawler was fairly consistent, regardless of the starting URLs randomly chosen.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2006.11a
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• pp.237-238
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• 2006

The electrical properties such as dielectric constants and dielectric losses in the spinel samples of $LiGaTiO_4$, Li(Ga,Eu)$TiO_4$, $Li(Ga.Yb)TiO_4$ have been characterized by varying measuring temperature and frequency. The long range order structures are analyzed by rietveld refinement method. and local atomic disorder structures are analyzed by MEM (maximum entropy method). The relation between the crystal structure and dielectric properties are discussed. $LiGaTiO_4$ spinel has the IMMA with lattice constant, a = 5.86333, b=17.5872. c = 8.28375 ${\AA}$, Li-sites are partially substituted by Ga or Ti. Two crystallographic oxygen sites are partially occupied(40~50%). The dielectric constants of $LiGaTiO_4$, $LiYbTiO_4$, and $LiGa_{2/6}Eu_{1/6}Ti_{1.5}O_4$ ceramics were 127, 75 and 272, respectively at 100 kHz. The dielectric relaxation were observed in the $LiGaTiO_3$ ceramics and the temperature where dielectric loss shows maximum was $390^{\circ}C$ at 1 kHz and increased with increasing the measuring frequency.

• Korean Journal of Cognitive Science
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• v.23 no.3
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• pp.295-322
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• 2012

In this paper, we cluster similar word senses applying vector space model and HAL (Hyperspace Analog to Language). HAL measures corelation among words through a certain size of context (Lund and Burgess 1996). The similarity measurement between a word pair is cosine similarity based on the vector space model, which reduces distortion of space between high frequency words and low frequency words (Salton et al. 1975, Widdows 2004). We use PCA (Principal Component Analysis) and SVD (Singular Value Decomposition) to reduce a large amount of dimensions caused by similarity matrix. For sense similarity clustering, we adopt supervised and non-supervised learning methods. For non-supervised method, we use clustering. For supervised method, we use SVM (Support Vector Machine), Naive Bayes Classifier, and Maximum Entropy Method.

• Proceedings of the KOSOMBE Conference
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• v.1997 no.11
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• pp.323-326
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• 1997

In this study, we investigated the circardian rhythm of complexity of cardiac dynamics in humans. Dynamic 24-hour electrocardiographic recordings were obtained from 30 healthy ambulant subjects aged 41 to 50 years. or each recordings, normalized low frequency (0.04-0.1 hertz) and high frequency (>0.15 hertz) component are calculated. our different indexes obtained from separate algorithms of nonlinear dynamics - approximate entropy, correlation dimension, Lyapunov exponent and fractal dimension - were calculated. During early morning, low frequency component rose rapidly with concomitant withdrawl of high frequency component. All the our indexes of nonlinear dynamics showed remarkably same circardian rhythm: an early morning dip preceded by a steep decline during late night, a gradual recovery during evening and a peak around midnight. These data indicate that the simultansous losses of all of the our different mechanisms of nonlinear control of heart rate during early morning, concomitent with the surge of symapathetic activity and reduction of vagal activity, may contribute to the increased incidence of cardiovascular events during morning hours.

• Smart Structures and Systems
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• v.33 no.3
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• pp.201-215
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• 2024

Spline chirplet transform and local maximum synchrosqueezing are introduced to present a novel structural instantaneous frequency (IF) identification method named local maximum synchrosqueezing spline chirplet transform (LMSSSCT). Namely spline chirplet transform (SCT), a transform is firstly introduced based on classic chirplet transform and spline interpolated kernel function. Applying SCT in association with local maximum synchrosqueezing, the LMSSSCT is then proposed. The index of accuracy and Rényi entropy show that LMSSSCT outperforms the other time-frequency analysis (TFA) methods in processing analytical signals, especially in the presence of noise. Numerical examples of a Duffing nonlinear system with single degree of freedom and a two-layer shear frame structure with time-varying stiffness are used to verify the effectiveness of structural IF identification. Moreover, a nonlinear supported beam structure test is conducted and the LMSSSCT is utilized for structural IF identification. Numerical simulation and experimental results demonstrate that the presented LMSSSCT can effectively identify the IFs of nonlinear structures and time-varying structures with good accuracy and stability.

• Journal of the Korean Society of Safety
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• v.6 no.3
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• pp.12-20
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• 1991

The dielectric properties that effect to electric safety for unsaturated Pelvester resin were studied over the temperature range of 30-40［$^{\circ}C$］ and the frequency range of 110~3$\times$10$^{6}$ ［Hz］. According to the results of the study, it has been observed that two kinds of dielectric losses in electric safety were existed by the molecular movement of C-H and OH group. When the ,adio of hardener was 1.5［%］, the characteristics of specimens were excellent in view of the electrical materical. In the range of measuring frequency the dielectric constants and the dielectric losses were 3.5~5.5 and 10$^2$~10$^3$ respectly. The activation energy of loss spectrum in aspect of electric safety and the entropy appeared at the 30［$^{\circ}C$］ were 33.4［Kacl/mole］ and 54.9［cal/mole.deg］ respectly.

• Proceedings of the KOSOMBE Conference
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• v.1996 no.11
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• pp.225-228
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• 1996

ECG data are used for the diagnostic purposes with many clinical situations, especially heart disease. In this paper, an efficient ECG data compression technique by wavelet transform and high-speed vector quantization on PMS-B algorithm is proposed. In general, ECG data compression techniques are divided into two categories: direct and transform methods. The direct data compression techniques are AZTEC, TP, CORTES, FAN and SAPA algorithms, besides the transform methods include K-L, Fourier, Walsh, and wavelet transforms. In this paper, we applied wavelet analysis to the ECG data. In particular, vector quantization on PMS-B algorithm to the wavelet coefficients in the higher frequency regions, but scalar quantized in the lower frequency regions by PCM. Finally, the quantized indices were compressed by LZW lossless entropy encoder. As the result of simulation, it turns out to get sufficient compression ratio while keeping clinically acceptable PRD.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.5C
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• pp.520-529
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• 2007

In this paper, we study the characteristics of parameters which are related to performance of MCTF which is a key technique for wavelet-based scalable video coding, and propose an improved MCTF method. The proposed MCTF method adopts the motion estimation of which motion vector field is distributed more uniformly using variable block sizes. By using the proposed method, the decomposition performance of temporal filter is improved, and the energy in high-frequency frames is reduced. It can help the entropy coder to generate lower bitrate. From simulation results, we verify the decomposed energy on high-frequency frame using the proposed method is reduced by 25.86% at the most in terms of variance of the high-frequency frame.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.55 no.3
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• pp.131-137
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• 2006

The secondary system of nuclear power plants consists of sophisticated piping systems operating in very aggressive erosion and corrosion environments, which make a piping system vulnerable to the wear and degradation due to the several chemical components and high flow rate (~10 m/sec) of the coolant. To monitor the wear and degradation on a pipe, the vibration signals are measured from the pipe with an accelerometer For analyzing the vibration signal the time-frequency analysis (TFA) is used, which is known to be effective for the analysis of time-varying or transient signals. To reduce the inteferences (cross-terms) due to the bilinear structure of the time-frequency distribution, an adaptive cone-kernel distribution (ACKD) is proposed. The cone length of ACKD to determine the characteristics of distribution is optimally selected through an adaptive algorithm using the normalized Shannon's entropy And the ACKD's are compared with the results of other analyses based on the Fourier Transform (FT) and other TFA's. The ACKD shows a better signature for the wear/degradation within a pipe and provides the additional information in relation to the time that any analysis based on the conventional FT can not provide.