• Bulletin of the Korean Chemical Society
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• v.24 no.9
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• pp.1265-1268
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• 2003

The estimation of retention factors by correlation equations with physico-chmical properties maybe helpful in chromatographic work. The physico-chemical properties were water solubility (S), hydrophobicity (P), total energy ($E_t$), connectivity index 1 ($^1{\chi}$), hydrophilic-lipophlic balance (x) and hydrophilic surface area (h) of isoflavones. The retention factors were experimentally measured by RP-HPLC. Especially, the empirical regulations of water solubility and hydrophobicity were expressed in a linear form. The equation between retention factors and various physico-chemical properties of isoflavones was suggested as $k = a_0 + a_1\;log S + a_2log\;P^Q + a_3(E_t) + a_4(^1{\chi}) + a_5(x) + a_6(h)$, and the correlation coefficients estimated were relatively higher than 0.95. The empirical equations might be successfully used for a prediction of the various chromatographic characteristics of substances, with a similar chemical structure.

• Phonetics and Speech Sciences
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• v.1 no.1
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• pp.55-62
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• 2009

This paper proposes an improved method to verify a keyword segment that results from a word spotting system. First a baseline word spotting system is implemented. In order to improve performance of the word spotting systems, we use a two-pass structure which consists of a word spotting system and an utterance verification system. Using the basic likelihood ratio test (LRT) based utterance verification system to verify the keywords, there have been certain problems which lead to performance degradation. So, we propose a method which uses phone-level log-likelihood ratios (PLLR) patterns in computing confidence measures for each keyword. The proposed method generates weights according to the PLLR patterns and assigns different weights to each phone in the process of generating confidence measures for the keywords. This proposed method has shown to be more appropriate to word spotting systems and we can achieve improvement in final word spotting accuracy.

• Journal of Industrial Technology
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• v.27 no.B
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• pp.115-119
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• 2007

Currently, geophysical method is applied for understanding the subsurface geologic structure economically and systematically, but there exists some limitations on recognizing complex subsurface structures precisely by a single geophysical method. In order to understand the complex subsurface structures, we applied various geophysical methods including seismic refraction survey, two-dimensional resistivity survey, seismic tomography survey, suspension-ps log, and understood distribution of low velocity, low resistivity range of resistivity survey and correlation of an intersecting point, velocity distribution of seismic tomography survey.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.3 no.1
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• pp.29-34
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• 1999

In the paper, the extened Huygens-Fresnel principle has been used to make an analysis of the statistics the received intensity for speckle propagation though the turbulent atmosphere. The results of these formulations include of the log-amplitude covariance as well as the wave structure functions, and It was found that the normalized variance is dependent on the turblence strength and rises above unity.

• Bulletin of the Korean Chemical Society
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• v.10 no.1
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• pp.1-5
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• 1989

The nonempirical molecular connectivity indexes of a number of mono- and disubstituted purines were calculated. Very good correlations were obtained between anticancer activity (log 1/c) and toxic activity (log 1/) of tIhese compounds and their molecular connectivity indexes and physicochemical constants. Our structure-activity relationship is discussed briefly in relation to theories of general QSAR.

• Journal of Information Processing Systems
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• v.17 no.4
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• pp.772-786
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• 2021

The process of tracking suspicious behavior manually on a system and gathering evidence are labor-intensive, variable, and experience-dependent. The system logs are the most important sources for evidences in this process. However, in the Microsoft Windows operating system, the action events are irregular and the log structure is difficult to audit. In this paper, we propose a model that overcomes these problems and efficiently analyzes Microsoft Windows logs. The proposed model extracts lists of both common and key events from the Microsoft Windows logs to determine detailed actions. In addition, we show an approach based on the proposed model applied to track illegal file access. The proposed approach employs three-step tracking templates using Elastic Stack as well as key-event, common-event lists and identify event lists, which enables visualization of the data for analysis. Using the three-step model, analysts can adjust the depth of their analysis.

• East Asian Economic Review
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• v.18 no.4
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• pp.367-394
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• 2014

In this study, we examined the fractal structure of the Nikkei225, HangSeng, Shanghai Stock Exchange and Straits Times Index of Singapore. Empirical analysis was performed via non-parametric, semi-parametric long memory tests and also fractal dimension calculations. In order to avoid spurious long memory features, besides the Detrended Fluctuations Analysis (DFA), we also used Smith's (2005) modified GPH method. As for fractal dimension calculations, they were conducted via Box-Counting and Variation (p=1) tests. According to the results, while there is no long memory property in log returns of any index, we found evidence for long memory properties in the volatility of the HangSeng, the Shanghai Stock Exchange and the Straits Times Index. However, we could not find any sign of long memory in the volatility of Nikkei225 index using either the DFA or modified GPH test. Fractal dimension analysis also demonstrated that all raw index prices have fractal structure properties except for the Nikkei225 index. These findings showed that the Nikkei225 index has the most efficient market properties among these markets.

• International journal of advanced smart convergence
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• v.10 no.3
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• pp.51-58
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• 2021

In this paper, we propose a deep learning classifier for estimating the number of layers in the Earth's structure. When installing a grounding system, knowledge of the subsurface in the area is absolutely necessary. The subsurface structure can be modeled by the earth parameters. Knowing the exact number of layers can significantly reduce the amount of computation to estimate these parameters. The classifier consists of a feedforward neural network. Apparent resistivity curves were used to train the deep learning classifier. The apparent resistivity at 20 equally spaced log points in each curve are used as the features for the input of the deep learning classifier. Apparent resistivity curve data sets are collected either by theoretical calculations or by Wenner's measurement method. Deep learning classifiers are coded by Keras, an open source neural network library written in Python. This model has been shown to converge with close to 100% accuracy.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.26 no.4
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• pp.263-279
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• 2022

This paper presents an automatic inspection of defects in semiconductor images. We devise a statistical method to find defects on homogeneous background from the observation that it has a log-normal distribution. If computer aided design (CAD) data is available, we use it to construct a signed distance function (SDF) and change the pixel values so that the average of pixel values along the level curve of the SDF is zero, so that the image has a homogeneous background. In the absence of CAD data, we devise a hybrid method consisting of a model-based algorithm and two neural networks. The model-based algorithm uses the first right singular vector to determine whether the image has a linear or complex structure. For an image with a linear structure, we remove the structure using the rank 1 approximation so that it has a homogeneous background. An image with a complex structure is inspected by two neural networks. We provide results of numerical experiments for the proposed methods.

• Korean Journal of Cognitive Science
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• v.20 no.4
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• pp.535-554
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• 2009

Information collected on mobile devices might be utilized to support user's memory, but it is difficult to effectively retrieve them because of the enormous amount of information. In order to organize information as an episodic approach that mimics human memory for the effective search, it is required to detect important event like landmarks. For providing new services with users, in this paper, we propose the prediction model to find landmarks automatically from various context log information based on attributed Bayesian networks. The data are divided into daily and weekly ones, and are categorized into attributes according to the source, to learn the Bayesian networks for the improvement of landmark prediction. The experiments on the Nokia log data showed that the Bayesian method outperforms SVMs, and the proposed attributed Bayesian networks are superior to the Bayesian networks modelled daily and weekly.