• The Korean Journal of Applied Statistics
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• v.26 no.4
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• pp.595-610
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• 2013

Testing of order-restricted alternative hypothesis in $2{\times}k$ contingency tables can be applied to various fields of medicine, sociology, and business administration. Most testing methods have been developed based on a large sample theory. In the case of a small sample size or unbalanced sample size, the Type I error rate of the testing method (based on a large sample theory) is very different from the target point of 5%. In this paper, the exact testing method is introduced in regards to the testing of an order-restricted alternative hypothesis in categorical data (particularly if a small sample size or extreme unbalanced data). Power and exact p-value are calculated, respectively.

• Journal of KIISE:Software and Applications
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• v.37 no.7
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• pp.491-502
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• 2010

When human beings acquire and develop knowledge through education, their prior knowledge influences the next learning process. As this is a fact that should be considered in machine learning, we need to examine the effects of controlling the order of training sequence on machine learning. In this research, the role of the supervisor is extended to control the order of training samples, in addition to just instructing the target values for classification problems. The supervisor sequences the training examples categorized by SOM to the learning model which in this case is MLP. The proposed method is distinguished in that it selects the most instructive example from categories formed by SOM to assist the learning progress, while others use SOM only as a preprocessing method for training samples. The result shows that the method is effective in terms of the number of samples used and time taken in training.

• Journal of KIISE:Software and Applications
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• v.37 no.10
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• pp.723-735
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• 2010

Evaluating the level of achievement and providing the knowledge which is appropriate at the evaluated level have great influence in studying of the human beings. This shows the importance of the order of training and the training order should be considered in machine learning. In this research, to assess the influence of the order of training, we propose a method of controlling the order of training samples utilizing the experience of supervisor in the training of MLP. The supervisor finds out the current state of MLP using teaching experience and student evaluation, and then selects the most instructive sample for MLP in that state. We use CRF to represent and utilize the experience of supervisor. While the proposed method is similar to active learning in selecting samples, it is basically different in that selection is not to reduce the number of samples to be used but to assist the learning progress. The result from classification problem shows that the method is usually effective in terms of time taken in training in contrast to random selection.

• 한국공간정보시스템학회:학술대회논문집
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• 2004.12a
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• pp.119-124
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• 2004

LiDAR 자료를 이용한 지형 및 공간 정보 자료를 구축하기 위해서는 LiDAR 자료의 오차 보정, 건물영역 및 지면 분리, 건물 및 지형의 재구성 등의 과정이 필요하다. 그 중에서 건물영역 분리 과정은 막대한 양의 LiDAR 자료에 대한 직, 간접적인 처리를 필요로 하며 결과물의 품질에도 큰 영향을 미친다. 본 연구에서는 LiDAR 자료로부터 건물 영역을 분리해 내기 위하여 LiDAR 원 자료를 그대로 활용하는 방식을 제안하였다. 항공레이저측량은 스캔라인을 따라 취득되는 포인트정보를 순서대로 저장하여 제공하므로 LiDAR 자료로부터 연속적으로 입력되는 포인트들은 서로 인접할 가능성이 높다. 이와 같은 특성을 이용하여 유사한 고도 값을 갖는 인접 포인트들로 클래스를 형성하고 새로운 포인트가 속할 클래스를 검색하여 편입시킴으로써 건물영역을 분리해 낸다. 아울러 각 건물 클래스에 대한 레이블링도 자동적으로 수행하며 새로운 포인트가 편입될 클래스를 검색하는 방법에 있어서도, 클래스의 검색 순서와 클래스의 자료 구조를 효율적으로 운용함으로써 성능 향상을 도모하였다.

• The Korean Journal of Applied Statistics
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• v.33 no.1
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• pp.25-46
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• 2020

This paper presents ordinal probit semiparametric regression models using Bayesian Spectral Analysis Regression (BSAR) method. Ordinal probit regression is a way of modeling ordinal responses - usually more than two categories - by connecting the probability of falling into each category explained by a combination of available covariates using a probit (an inverse function of normal cumulative distribution function) link. The Bayesian probit model facilitates posterior sampling by bringing a latent variable following normal distribution, therefore, the responses are categorized by the cut-off points according to values of latent variables. In this paper, we extend the latent variable approach to a semiparametric model for the Bayesian ordinal probit regression with nonparametric functions using a spectral representation of Gaussian processes based BSAR method. The latent variable is decomposed into a parametric component and a nonparametric component with or without a shape constraint for modeling ordinal responses and predicting outcomes more flexibly. We illustrate the proposed methods with simulation studies in comparison with existing methods and real data analysis applied to a Korean National Health and Nutrition Examination Survey (KNHANES) 2016 for investigating nonparametric relationship between smoking behavior and coffee intake.

• Proceedings of the Korean Statistical Society Conference
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• 2005.05a
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• pp.147-154
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• 2005

본 논문에서는 집단화된 자료의 분위수들을 계산하는 수정된 방법을 제시하였다. 제시된 방법은 각 계급구간 안의 자료들이 그 구간에 걸쳐 균등한 간격으로, 그리고 구간의 중간점에 관하여 대칭으로 분포하고 있다고 가정하고 분위수들을 계산하는 방법이다. 개개의 자료값들이 주어진 자료를 통하여, 제시된 방법과 기존의 방법을 비교하였다.

• Proceedings of the Korean Information Science Society Conference
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• 2001.10b
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• pp.595-597
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• 2001

World-Wide Web으로 인하여 인터넷 상의 다양하고 고품질의 자료들을 교육 자료에 손쉽게 활용할 수 있는 시대가 도래하였다. 그러나 이러한 자료들은 교육적 효과를 극대화시키기 위해서 좀 더 정제되고, 교육과정에 맞는 흐름을 가질 필요가 있다. 이러한 과정의 흐름을 제공하기 위해서는 웹 상에서 분산되어 독립적으로 존재하는 디지털 문서들을 교육 목적에 맞게 새로운 순서, 즉 문맥화된 순서를 가진 자료로 재구성할 수 있어야 하며, 문서간의 부드러운 내용 전개를 위해서 부가적인 설명이나 기존 문서에 빠져 있는 내용들을 보완할 수 있어야 한다. 본 논문의 연구과정에서 개발된 가상교육 시스템은 교사가 교육용 지식문서를 작성하여 면대면(face to face) 교육에서는 직접 학생들을 교육할 수 있는 교육 자료로 사용될 수 있을 뿐만 아니라 웹을 통해서는 학생 스스로가 부족한 부분을 원하는 시간에 학습할 수 있는 능동적인 교육 환경을 제공할 수 있다. 또한, 가상교육 시스템에 가상문서 개념을 도입함으로써 인터넷 상의 수많은 리소스들을 인용하는 것에 대한 부하를 막을 수 있다. 본 논문에서는 인터넷 상의 디지털 컨텐츠를 전문적인 지식을 가진 교사가 교육과정에 맞게 쉽게 재구성해 줄 수 있도록 가상교육 시스템을 설계 및 구현한 내용에 대해 기술한다.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.19 no.1
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• pp.73-80
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• 2007

The predominant factor considered in the design of harbors in the East Coast of Korea is wave propagating onshore. Also, the strong wave induced current on the east coast have the biggest influence on sediment transport around the harbor structure. Therefore, a consideration of wave induced current due to waves should take place on design when constructing a harbor on east coast. In this study, we studied on the influences of construction procedure on harbor siltation using annual coastal line data and bathymetry data near breakwater. And, this study focused on investigation of the construction procedure for the best way to decrease harbor siltation.

• The Korean Journal of Applied Statistics
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• v.7 no.2
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• pp.145-157
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• 1994

For multivariate distributions satisfying stochastic ordering, we suggest maximum likelihood estimation with incomplete data via an EM algorithm. In this paper we restrict our attention to the contingency tables with partially cross-classified observations. We may use the existing isotonic regression program to implement EM algorithm, and we illustrate the estimation process through an example.

• The KIPS Transactions:PartA
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• v.12A no.1 s.91
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• pp.23-30
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• 2005

Causal order of message delivery algorithm ensures that every transmitted message is delivered in causal order. It should be noted that control information should be transmitted with each message in order to enforce causal order. Hence, it is important to reduce this communication overhead because the impact of the overhead increases proportionally with the number of related processes. In this paper we propose and evaluate effective a ${\Delta}-causal$ order algorithm for multimedia data which have real-time property. To reduce transmission overhead, proposed algorithm eliminates redundant information as early as possible which is not explicitly required for preserving causal order. Average communication overhead of our algorithm is much smaller than other existing algorithms.