• Proceedings of the Korea Multimedia Society Conference
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• 2002.05c
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• pp.249-253
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• 2002

레이더 기술은 획득된 신호를 바탕으로 물체(object)를 추출, 추적함으로써 자동항해시스템, 항공기 충돌방지시스템 둥의 각종 첨단 분야에 두루 활용되고 있으며, 산업 전반에 걸쳐 눈부신 발전을 거듭해 왔다. 본 논문에서는 레이더로부터 획득한 신호로부터 효율적인 물체를 추출, 추적하기 위한 전처리 단계로서 레이더 이미지를 구성하는 방법에 대해 제안한다. 특히, 불완전한 데이터 시퀀스를 갖는 신호를 양질의 레이더 이미지로 복원하는 방법을 제안하고 결과 영상을 통해 제안하는 방법의 우수성을 검증하였다.

• 한국데이터정보과학회:학술대회논문집
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• 2003.05a
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• pp.145-158
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• 2003

불완전 데이터 즉, 결측값을 가지는 데이터를 분석할 경우 결측데이터에 대해서 어떠한 처리를 해야할 필요가 있다. 결측데이터에 대한 처리로서 주로 이용되어온 방법으로는 결측값을 포함한 관측값(case)을 제외하는 방법이었다. 이후 여러 방법들이 제안되어 EM알고리즘이나 회귀알고리즘에 의한 추정을 바탕으로 결측값에 대한 추정을 해서 그 추정값으로 결측값을 대치하는 방법을 사용할 수 있게되었다. 본 논문에서는 복수 개의 데이터세트를 생성해서 대치하는 다중대입 소프트인 SOLAS를 소개한다.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.6A
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• pp.589-599
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• 2004

Most models assume the complete debugging environments by requiring a complete software correction in quantitative evaluation of software reliability. But, in many case, new faults are involved in debugging works, for complete software correction is impossible. In this paper, software growth model is proposed about incomplete debugging environments by considering the possibility of new faults involvements, and software faults occurrence status are also mentioned about NHPP by considering software faults under software operation environments and native faults owing to the randomly involved faults in operation before test. While, effective quantitative measurements are derived in software reliability evaluation, applied results are suggested by using actual data, and fitnesswith existing models are also compared and analyzed.

• Journal of the Korean Data and Information Science Society
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• v.24 no.2
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• pp.333-339
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• 2013

This paper discusses a method for getting a basis set of estimable functions of less than full rank linear model. Since model parameters are not estimable estimable functions should be identified for making inferences proper about them. So, it suggests a method of using full rank factorization of model matrix to find estimable functions in easy way. Although they might be obtained in many different ways of using model matrix, the suggested full rank factorization technique could be one of much easier methods. It also discusses how to use projection matrix to identify estimable functions.

• Journal of the Korean Society for Railway
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• v.14 no.3
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• pp.216-221
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• 2011

An important problem in reliability analysis for repairable systems is to model the maintenance effect. The most of researches have assumed two extreme cases; one is perfect maintenance and the other is minimal maintenance. However, many of maintenances performed by domestic subway operators are imperfect maintenances which have the effect between both of two extreme cases. This article deals with the problem determining the imperfect preventive maintenance intervals based on failure data in units of the PDS(passenger door system) in Metro EMU. This paper deals with a case study on determining imperfect maintenance interval by using the level of maintenance effect through reliability analysis of PDS.

• Journal of the Korean Data and Information Science Society
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• v.21 no.2
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• pp.231-240
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• 2010

If only fixed effects exist in a 4 $\times$ 4 balanced incomplete block design, powers of FR statistic for testing a main effect show the highest level with a few replications. Under the exponential and double exponential distributions, FR statistic shows relatively high powers with big differences as compared with the F statistic. Further in a traditional balanced incomplete block design, powers of FR statistic having a fixed main effect and a random block effect show superior preference for all situations without regard to the effect size of a main effect, the parameter size and the type of population distributions of a block effect. Powers of FR statistic increase in a high speed as replications increase. Overall power preference of FR statistic for testing a main effect is caused by unique characteristic of a balanced incomplete block design having one main and block effect with missing observations, which sensitively responds to small increase of main effect and sample size.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.1
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• pp.53-62
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• 2010

This paper introduces a classification algorithm which can be applied to a learning problem with incomplete data sets, missing variable values or a class value. This algorithm uses a data expansion method which utilizes weighted values and probability techniques. It operates by extending a classifier which are considered to be in the optimal projection plane based on Fisher's formula. To do this, some equations are derived from the procedure to be applied to the data expansion. To evaluate the performance of the proposed algorithm, results of different measurements are iteratively compared by choosing one variable in the data set and then modifying the rate of missing and non-missing values in this selected variable. And objective evaluation of data sets can be achieved by comparing, the result of a data set with non-missing variable with that of C4.5 which is a known knowledge acquisition tool in machine learning.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.03a
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• pp.11-15
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• 1998

일반적으로 러프집합은 지식베이스 시스템에서 근사공간을 이용한 불확실한 데이터의 분류, 추론 및 의사결정 등에 사용된다. 지식베이스 시스템의 데이터 중에서 연속적인 구간 특성을 갖는 정량적 속성값이 불연속적일 때 중복 또는 불일치 등의 불확실성이 발생된다. 본 논문은 러프집합의 정량적 속성값들의 정성적 속성으로 변환시킬 때 식별 불가능 영역에 있는 정량적 속성값들을 명확한 경계를 갖는 보조구간으로 분리하여 불확실성을 제거함으로써 러프집합의 분류능력을 향상시키는 방법을 제안한다.

• Journal of the Korea Convergence Society
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• v.12 no.11
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• pp.91-97
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• 2021

This paper uses a data extension technique originally designed for the rule refinement problem to handling incomplete data. This technique is characterized in that each event can have a weight indicating importance, and each variable can be expressed as a probability value. Since the key problem in this paper is to find the probability that is closest to the missing value and replace the missing value with the probability, three different algorithms are used to find the probability for the missing value and then store it in this data structure format. And, after learning to classify each information area with the SVM classification algorithm for evaluation of each probability structure, it compares with the original information and measures how much they match each other. The three algorithms for the imputation probability of the missing value use the same data structure, but have different characteristics in the approach method, so it is expected that it can be used for various purposes depending on the application field.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.18 no.6
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• pp.11-30
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• 2019

Smartcards are used as the basic data for utilizing the various transportation policies and evaluations, etc. and provided the transportation basic statistics index. However, the main problem of the smartcard data is that the most of users do not take the alighting tag at the stop, so there is a limit to the scope of use for the total O-D trip data because incomplete O-D traffic data of transportation card users. In this study, a decision tree of learning model is estimated for the alighting stop of smartcard users. The model estimation accuracy in range less than 2 stops interval was 89.7% on average. By eliminating the incompleteness alighting stop of smartcard data through this model, it is expected to be used as the basic data for various transportation analyses and evaluations.