• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.7 no.4
• /
• pp.11-21
• /
• 1997

This paper introduces a statistical approach of intrusion detection and tunes an intrusion detection model using fuzzy ste. We describel the method of applying fuzzy set for NIDES intensity measure. By using fuzzy set, we improve the algorithm for evaluating score value of NIDES, and present a possibility of intrusion detection system.

• International Journal of Reliability and Applications
• /
• v.10 no.1
• /
• pp.59-62
• /
• 2009

We comment on a new testing procedure for testing exponentiality against different ageing classes. We show that the proposed test is inappropriate at least for two alternatives. We point out the subtle flaw in their argument.

• Journal of Institute of Control, Robotics and Systems
• /
• v.11 no.6
• /
• pp.550-557
• /
• 2005

The multivariate statistical analysis methods, using both multiple linear regression(MLR) and partial least square(PLS), have been applied to predict the reactor properties and the product quality of a direct esterification reactor for polyethylene terephthalate(PET) synthesis. On the basis of the set of data including the flow rate of water vapor, the flow rate of EG vapor, the concentration of acid end groups of a product and other operating conditions such as temperature, pressure, reaction times and feed monomer mole ratio, two multi-variable analysis methods have been applied. Their regression and prediction abilities also have been compared. The prediction results are critically compared with the actual plant data and the other mathematical model based results in reliability. This paper shows that PLS method approach can be used for the reasonably accurate prediction of a product quality of a direct esterification reactor in PET synthesis process.

• Communications for Statistical Applications and Methods
• /
• v.15 no.6
• /
• pp.977-991
• /
• 2008

This study is about the k-nearest neighbor-based approach for the estimation of mutual information when the type of target variable is categorical and continuous. The results of Monte-Carlo simulation and experiments with real-world data show that k=1 is preferable. In practical application with real world data, our study shows that jittering and bootstrapping is needed.

• Journal of the Korean Wood Science and Technology
• /
• v.44 no.6
• /
• pp.809-820
• /
• 2016

The goal of this study was to predict lamina yield from logs of different diameter for production of cross laminated timber. Log characteristics of red pine (Pinus densiflora) and Japanese cedar (Cryptomeria japonica), including diameter, length, volume, and defects were used for statistical and geometrical analyses, along with the lamina characteristics, including width, thickness, and defects. Based on the data obtained, the strong factors influencing the yield and grade of lamina from the two species were statistically evaluated. A geometrical approach was used for analysis of the yield from logs of given diameters. Statistical analysis showed that lamina yield was dependent on target lamina size but the grade of lamina was not related to any of the log characteristics. The suggested yield equations from the geometrical approach indicated an accuracy of less than 20% difference.

• Journal of Korea Water Resources Association
• /
• v.36 no.6
• /
• pp.985-997
• /
• 2003

The objective of this study is to propose Runoff Curve Numbers(CNs) for land cover and treatment classification of satellite image. For this purpose, land cover classifications by using satellite image in addition to the exiting SCS's land cover and treatment classifications studies and land cover classifications suggested by Ministry of Environment are selected to provide CNs depending on the classifications. CNs estimation method is statistical approach that is suggested by Hjelmfelt(1991). Result of this study may contribute to use efficiently for the estimation of CNs in using satellite image.

• Geomechanics and Engineering
• /
• v.11 no.3
• /
• pp.361-372
• /
• 2016

The soil-concrete interface shear strength, although has been extensively studied, is still difficult to predict as a result of the dependence on many factors such as normal stresses, surface roughness, particle sizes, moisture contents, dilation angles of soils, etc. In this study, a well-known rigorous statistical learning approach, namely the least squares support vector machine (LS-SVM) realized in a ubiquitous spreadsheet platform is firstly used in estimating the soil-structure interface shear strength. Instead of studying the complicated mechanism, LS-SVM enables to explore the possible link between the fundamental factors and the interface shear strengths, via a sophisticated statistic approach. As a preliminary investigation, the authors study the expansive soils that are found extensively in most countries. To reduce the complexity, three major influential factors, e.g., initial moisture contents, initial dry densities and normal stresses of soils are taken into account in developing the LS-SVM models for the soil-concrete interface shear strengths. The predicted results by LS-SVM show reasonably good agreement with experimental data from direct shear tests.

• The Korean Journal of Applied Statistics
• /
• v.22 no.6
• /
• pp.1265-1275
• /
• 2009

In this paper, we investigated the composite covariance structure models for repeated measures data with multiple repeat factors. When the number of repeat factors is more than three, it is infeasible to fit the composite covariance models using the existing statistical packages. In order to fit the composite covariance structure models to real data, we proposed two approaches: the dimension reduction approach for repeat factors and the random effect model approximation approach. Our proposed approaches were illustrated by using the blood pressure data with three repeat factors obtained from 883 subjects.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.35 no.8C
• /
• pp.661-666
• /
• 2010

In this paper, we apply a likelihood ratio test (LRT) to a non-negative matrix factorization (NMF) based voice activity detection (VAD) to find optimal threshold. In our approach, the NMF based VAD is expressed as Euclidean distance between noise basis vector and input basis vector which are extracted through NMF. The optimal threshold each of noise environments depend on NMF results distribution in noise region which is estimated statistical model-based VAD. According to the experimental results, the proposed approach is found to be effective for statistical model-based VAD using LRT.

• Journal of the Korean Statistical Society
• /
• v.33 no.1
• /
• pp.79-97
• /
• 2004

We propose a permutation approach to the classic Behrens-Fisher problem of comparing two means in the presence of unequal variances. It is motivated by the observation that a paired test is valid whether or not the variances are equal. Rather than using a single arbitrary pairing of the data, we average over all possible pairings. We do this in both a parametric and nonparametric setting. When the sample sizes are equal, the parametric version is equivalent to referral of the unpaired t-statistic to a t-table with half the usual degrees of freedom. The derivation provides an interesting representation of the unpaired t-statistic in terms of all possible pairwise t-statistics. The nonparametric version uses the same idea of considering all different pairings of data from the two groups, but applies it to a permutation test setting. Each pairing gives rise to a permutation distribution obtained by relabeling treatment and control within pairs. The totality of different mean differences across all possible pairings and relabelings forms the null distribution upon which the p-value is based. The conservatism of this procedure diminishes as the disparity in variances increases, disappearing completely when the ratio of the smaller to larger variance approaches 0. The nonparametric procedure behaves increasingly like a paired t-test as the sample sizes increase.