• 한국실내디자인학회논문집
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• 제19권4호
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• pp.3-10
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• 2010

The purpose of this study is to select and analyze words that represent various emotional states towards a spatial environment. Selecting appropriate words for the sensibility evaluation of a spatial environment is a process of constructing sensibility indicators, so that an accurate selection of sensibility words is very important. To collect basic words for this study, words for expressing sensation, emotional states, and sensibility regarding a spatial environment have been collected first via free association and a literature review of previous studies. In the second stage, the selected words are refined. Fifteen evaluators have participated in the first process of refining words, 140 college students participated in the second process, and than the final list of 277 refined words has been selected. During the third stage, 15 specialists were asked to evaluate the appropriateness of sensibility evaluation words, for that 7 point-scale has been applied. Then, 99 words with an average point of 4.55 or more and a standard deviation of 1.55 or lower were selected. After investigating the similarity in the meanings of the selected words, 55 pairs of contrasting words have been selected as a final list of sensibility evaluation words. During this last stage, 307 college students majoring in related fields were asked to evaluate the appropriateness of sensibility evaluation words for a spatial environment, and 7 point-scale was obtained. A factor analysis, cluster analysis, and multidimensional analysis have been conducted on the data obtained from these survey. According to the results of the factor analysis, the eight important factors are obtained from the sensibility evaluation criteria of a spatial environment(form, texture, function, value, comfort, aesthetics, atmosphere, and affinity). The factors obtained from this study can be used in the beginning stage of evaluating the sensibility factors of a spatial environment. In addition, the results of this study can be used as basic data when constructing a list of evaluation indicators to select various complex sensibility words for a space; or as general indicators when evaluating various spatial design factors.

• 정보학연구
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• 제5권2호
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• pp.27-34
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• 2002

CLDC를 지원하는 자바가상머신인 KVM의 가비지 콜렉터는 일반적으로 단순한 마크-회수 알고리즘에 기반을 두고 있다. 그러나 이 알고리즘은 단편화 없이 다양한 크기의 객체를 다루기 어렵다는 문제점을 가진다. 본 논문에서는 객체의 크기에 따라 자유 기억 공간의 할당 위치를 결정하는 메모리 할당 방법을 적용한 개선된 마크-회수 가비지 콜렉터를 설계하고 구현하였다. 실험을 통해 단편화 문제가 감소되었고 실행시간의 개선이 있었음을 확인하였다.

• 한국정보과학회논문지:컴퓨팅의 실제 및 레터
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• 제14권6호
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• pp.599-603
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• 2008

플래시 메모리는 기존 저장 매체와는 달리 읽기 연산에 비해 쓰기 연산의 수행비용이 매우 크고, 저장된 데이타에 대한 직접 갱신이 불가능한 고유의 특성이 있다. 본 논문에서는 플래시 메모리 환경이 클러스터링 방법과 비클러스터링 방법에 미치는 영향을 분석한다. 이를 통해 디스크 환경과는 달리 플래시 메모리 환경에서는 비 클러스터링 방법이 더 적합하다는 것을 보인다. 또한, 플래시 메모리 환경에서 비 클러스터링 방법이 가진 문제점을 지적하고, 이를 기반으로 플래시 메모리 환경에서 레코드 관리방법의 설계 시 고려해야 할 사항들을 제안한다.

• 천문학회보
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• 제42권2호
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• pp.59.5-60
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• 2017

We present the relationship between vector magnetic field parameters and solar major flare occurrence rate. Based on this, we are developing a forecast model of major flare (M and X-class) occurrence rate within a day using hourly vector magnetic field data of Space-weather HMI Active Region Patch (SHARP) from May 2010 to April 2017. In order to reduce the projection effect, we use SHARP data whose longitudes are within ${\pm}60$ degrees. We consider six SHARP magnetic parameters (the total unsigned current helicity, the total photospheric magnetic free energy density, the total unsigned vertical current, the absolute value of the net current helicity, the sum of the net current emanating from each polarity, and the total unsigned magnetic flux) with high F-scores as useful predictors of flaring activity from Bobra and Couvidat (2015). We have considered two cases. In case 1, we have divided the data into two sets separated in chronological order. 75% of the data before a given day are used for setting up a flare model and 25% of the data after that day are used for test. In case 2, the data are divided into two sets every year in order to reduce the solar cycle (SC) phase effect. All magnetic parameters are divided into 100 groups to estimate the corresponding flare occurrence rates. The flare identification is determined by using LMSAL flare locations, giving more numbers of flares than the NGDC flare list. Major results are as follows. First, major flare occurrence rates are well correlated with six magnetic parameters. Second, the occurrence rate ranges from 0.001 to 1 for M and X-class flares. Third, the logarithmic values of flaring rates are well approximated by two linear equations with different slopes: steeper one at lower values and lower one at higher values. Fourth, the sum of the net current emanating from each polarity gives the minimum RMS error between observed flare rates and predicted ones. Fifth, the RMS error for case 2, which is taken to reduce SC phase effect, are smaller than those for case 1.