• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.465-465
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• 2015

강우/무강우 정보는 재해의 관점에서는 중요성이 덜하지만 농업, 건설 등의 산업분야나 우리의 일상생활에 큰 영향을 미치는 인자이다. 본 연구에서는 레이더 자료를 이용하여 강우발생을 판단하는데 있어 그 정확성을 살펴보고, 아울러 이를 높이기 위한 방법으로 가강수량의 역할을 평가하였다. 본 연구에서는 관악산 레이더 자료, 관악산 레이더 반경 내 위치한 30개 AWS지점의 강우 자료를 분석하였다. 30개 AWS지점은 관악산 레이더 반경을 전체적으로 포괄할 수 있도록 임의로 선정하였다. 또한, 오산과 백령도 고층기상관측자료를 이용하여 산정한 가강수량을 레이더 강우발생 판단에 적용하여 정확성 개선을 검토하였다. 아울러 본 연구의 결과를 2차원 평면에 나타내어 공간적인 변화를 비교하였다.

• Proceedings of the Korea Information Processing Society Conference
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• 2012.11a
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• pp.282-283
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• 2012

가상메모리 시스템에서 페이지 부재(page fault)를 최소화하기 위해서는 미래에 사용될 페이지를 미리 예측하는 것이 중요하다. 미래에 사용될 페이지는 이미 입력된 페이지들의 패턴 분석을 통해 예측 가능하며 이를 통해 시스템 성능을 최대화 할 수 있는 페이지 교체 정책(page replacement policy)에 대한 연구가 필요하다. 본 논문에서는 동일한 교체 우선권을 가지는 페이지들 간의 예측 적중률을 높이기 위하여 기존의 NUR(not used recently) 정책에 참조횟수 비트를 추가하여 우선 순위를 판단하는 방법을 제안한다. 제안하는 정책은 C 언어를 기반으로 모델링 되었으며 시뮬레이션 결과 페이지 프레임의 크기와 무관하게 기존의 정책들에 비해 성능이 향상됨을 확인하였다.

• Proceedings of the Korea Information Processing Society Conference
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• 2012.04a
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• pp.151-154
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• 2012

최근 차세대 저장장치로서 비휘발생 플래시 메모리 기반 저장장치의 사용이 층가하고 있다. 본 논문에서는 플래시 메모리 기반 저장장치의 특생인 삭제 연산의 문제점을 고려하는 새로운 버퍼 캐시 교체 기법을 연구하였다. 제안한 클린 블록 우선 (Clean Block First) 기법은 버퍼를 플래시 메모리의 삭제 블록의 리스트로 관리하고 클린 페이지를 가진 블록을 우선적으로 교체하여 플래시 메모리의 삭제 연산 횟수를 줄인다. 트레이스 기반의 시뮬레이션을 수행하여 교체를 위해 검색하는 클린 블록 개수의 변화에 대한 캐시 적중률과 삭제 연산 횟수를 분석하였다.

• Proceedings of the Korea Information Processing Society Conference
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• 2013.11a
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• pp.307-309
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• 2013

미래 인터넷 기술 중 하나인 컨텐츠 중심 네트워킹 (CCN)은 컨텐츠의 이름을 이용해 사용자가 원하는 컨텐츠를 요청하고 이를 받음으로써 현재 위치 기반에서 이름 기반으로 변화하고 있는 컨텐츠 패러다임을 반영하고 있다. 사용자가 요청한 컨텐츠가 서버에서 라우터를 거쳐 이동할 때 각 라우터마다 내장되어 있는 캐시에 저장하여 관리하는 방식을 사용하기 때문에 같은 컨텐츠가 중복으로 저장되는 문제가 발생한다. 본 논문에서는 AS 외부에 위치하는 서버로부터 컨텐츠를 받을 때 주변 라우터를 그룹으로 묶어 캐시를 관리함으로써 중복 저장되는 빈도를 낮추고 캐시 적중률 향상과 지연 시간 감소를 이끄는 전략을 제안하고 실험적으로 검증한다.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2021.11a
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• pp.221-223
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• 2021

풍랑특보는 우리나라 부근을 항행하는 선박 및 해상 업무 종사자에게 큰 영향을 미친다. 이 연구에서는 최근 11년(2010-2020년) 동안 기상청에서 발표한 서해·남해·동해의 앞바다 및 먼바다, 제주도 앞바다의 풍랑특보와 주요 해양기상부이의 관측자료를 비교·분석하여 풍랑특보의 적절성을 평가하였다. 각 해역에 대한 풍랑특보와 해양기상부이 관측자료를 일별, 월별, 연별로 통계를 내어 연평균, 월평균, 계절별로 비교한 결과, 풍랑특보의 적중률이 전 해역에 걸쳐 매우 낮은 것으로 분석되었다. 해상에서의 풍랑특보가 어선의 어업활동, 여객선 운항 및 관광, 해상 레저활동 등에 미치는 영향을 고려할 때 해양기상 예·특보의 정확성을 개선할 필요가 있음을 확인하였다.

• Proceedings of the Korea Information Processing Society Conference
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• 2008.11a
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• pp.1367-1370
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• 2008

본 논문은 P2P 네트워크 환경에서 대규모 VOD 시스템을 위한 새로운 시간 기반 캐싱 기법을 제안한다. 제안된 캐싱 기법은 각 피어들이 요구 시작 시간을 기준으로 비디오의 서로 다른 부분을 분산 저장 관리함으로써 피어의 저장 용량을 최소화하고, 피어간 데이터 요구에 대한 캐시 적중률을 향상시켜 서버의 부하를 최소화 한다. 또한 요구 시간을 기준으로 인접한 피어들을 하나의 클러스터로 그룹핑하여 피어의 탐색 법위를 최소화하고, 이에 따른 네트워크 트래픽을 최소화 한다. 시뮬레이션을 통한 성능 평가에서 피어의 참여와 탈퇴 중가에 따른 서버 부하의 증가가 기존의 P2VOD와 비교하여 크게 감소함을 보인다.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.125-140
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• 2013

We call a data set in which the number of records belonging to a certain class far outnumbers the number of records belonging to the other class, 'imbalanced data set'. Most of the classification techniques perform poorly on imbalanced data sets. When we evaluate the performance of a certain classification technique, we need to measure not only 'accuracy' but also 'sensitivity' and 'specificity'. In a customer churn prediction problem, 'retention' records account for the majority class, and 'churn' records account for the minority class. Sensitivity measures the proportion of actual retentions which are correctly identified as such. Specificity measures the proportion of churns which are correctly identified as such. The poor performance of the classification techniques on imbalanced data sets is due to the low value of specificity. Many previous researches on imbalanced data sets employed 'oversampling' technique where members of the minority class are sampled more than those of the majority class in order to make a relatively balanced data set. When a classification model is constructed using this oversampled balanced data set, specificity can be improved but sensitivity will be decreased. In this research, we developed a hybrid model of support vector machine (SVM), artificial neural network (ANN) and decision tree, that improves specificity while maintaining sensitivity. We named this hybrid model 'hybrid SVM model.' The process of construction and prediction of our hybrid SVM model is as follows. By oversampling from the original imbalanced data set, a balanced data set is prepared. SVM_I model and ANN_I model are constructed using the imbalanced data set, and SVM_B model is constructed using the balanced data set. SVM_I model is superior in sensitivity and SVM_B model is superior in specificity. For a record on which both SVM_I model and SVM_B model make the same prediction, that prediction becomes the final solution. If they make different prediction, the final solution is determined by the discrimination rules obtained by ANN and decision tree. For a record on which SVM_I model and SVM_B model make different predictions, a decision tree model is constructed using ANN_I output value as input and actual retention or churn as target. We obtained the following two discrimination rules: 'IF ANN_I output value <0.285, THEN Final Solution = Retention' and 'IF ANN_I output value ${\geq}0.285$, THEN Final Solution = Churn.' The threshold 0.285 is the value optimized for the data used in this research. The result we present in this research is the structure or framework of our hybrid SVM model, not a specific threshold value such as 0.285. Therefore, the threshold value in the above discrimination rules can be changed to any value depending on the data. In order to evaluate the performance of our hybrid SVM model, we used the 'churn data set' in UCI Machine Learning Repository, that consists of 85% retention customers and 15% churn customers. Accuracy of the hybrid SVM model is 91.08% that is better than that of SVM_I model or SVM_B model. The points worth noticing here are its sensitivity, 95.02%, and specificity, 69.24%. The sensitivity of SVM_I model is 94.65%, and the specificity of SVM_B model is 67.00%. Therefore the hybrid SVM model developed in this research improves the specificity of SVM_B model while maintaining the sensitivity of SVM_I model.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.61 no.4
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• pp.257-263
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• 2016

Recently, an interest has developed in the use of whole green grains as functional food materials. The present study was conducted to provide the baseline data for the stable production of whole green grains in 20 cultivars of wheat by investigating the greenness of grain with maturation (from $20^{th}$ to $41^{st}$ day after heading, at an interval of 3 days). On the $20^{th}$ day after heading, the grains were dark green with a wrinkled long-oval shape. After the $35^{th}$ day of heading, the grains turned almost yellow with an oval shape. Their redness ('a' value of chromaticity) increased from the $20^{th}$ to $41^{st}$ days after heading, indicating a negative value up to the $32^{nd}$ day after heading. A significant decrease in their chlorophyll content was observed with maturation. The yield of whole green wheat grain (including greenish yellow grain) was the highest from the $32^{nd}$ to $35^{th}$ after heading. Therefore, we concluded that the optimal harvesting period for whole green wheat grain was from the $32^{nd}$ to $35^{th}$ day after heading. The heading time of various cultivars ranged from April 28 to May 5, the time of Jopummil cultivar grew the fastest among them, such as Gurumil, Alchanmil, but Dahongmil got the latest in heading time. The greenness of seven cultivars (Jeokjungmil, Keumkangmil, Jogyeongmil, Jopummil, Baekjungmil, Yeonbaekmil, and Milseongmil) was relatively higher than that of the others. The yield of greenish whole grain was relatively high in six cultivars (Alchanmil, Baekjungmil, Eunpamil, Yeonbaekmil, Dahongmil, and Urimil). Based on their greenness and yield, the Baekjungmil and Yeonbaekmil cultivars have been considered to be optimal for the production of whole green wheat grain.

• Journal of Hospice and Palliative Care
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• v.7 no.2
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• pp.200-213
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• 2004

Purpose: The purpose of this study was to create an electronic nursing record form to build a hospice nursing process database to be used in the u-hospital EMR system. Specific aims of the study were: 1. To generate a complete, accurate, and simple electronic nursing record form. 2. To verify its appropriateness following documentation with the standardized hospice protocol. 3. To verify its validity and finalize the hospice nursing process database through discussion among hospice professionals. Methods: Nursing records from three independent hospice organizations were collected and analyzed by five expert hospice nurses with more than 10 years of experience, and a nursing record database was developed. This database was applied to 81 hospice patients at three hospice organizations to verify its completeness. Results: 1. An electronic nursing record form with completeness, accuracy, and simplicity was developed. 2. The completeness of the standardized home hospice service protocol was 95.86 percent. 3. The hospice nursing process database contains 18 items on health problems, 79 items on related causes and major symptoms, and 229 items on nursing interventions. Conclusion: The new nursing record form and database will reduce documentation time and articulate and streamline the working process among team members. They can also improve the quality of hospice services, and ultimately enable us to estimate hospice service costs.

• Journal of the Korean Society of Child Welfare
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• no.56
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• pp.253-281
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• 2016

The purpose of this study was to verify the clinical utility of th Korea Child Behavior Checklist 16-18(K-CBCL 6-18) in diagnosing ADHD among children with psychological disorders in child welfare institutions. The participants were 509 elementary school children(309 boys and 200 girls) who lived in child welfare institutions. They were assessed using the Korean ADHD Rating Scale(K-ARS) and K-CBCL 6-18. Only five scales of the K-CBCL 6-18 related with attention were used for analysis: syndrom total, externalizing total, aggressive behavior, attention problems and DSM-oriented ADHD scales. The results were as follows. First, K-ARS and K-CBCL 6-18 had significantly positive correlations with all five scales. Second, as a result of a t-test on the ADHD and the non-ADHD groups, which were divided using K-ARS, the mean scores of ADHD group were significantly higher than the non-ADHD group for all five scales of the K-CBCL 6-18. The hit rate of all five scales of the K-CBCL 6-18 was 60 to 70 percent. The syndrom total and externalizing total scales had high sensitivity, whereas the aggressive behavior, attention problems, and the DSM-oriented ADHD scales had high specificity. In addition, all scales had high positive predictive values. Third, as the result of a t-test on the ADHD group and the emotional disorder group, there were significant difference in the mean scores of the attention problems and the DSM-oriented ADHD scales. The attention problems and the DSM-oriented ADHD scales had a similar percentage of hit rate, high specificity and low sensitivity. Especially, the DSM-oriented ADHD scale revealed higher specificity than the attention problems scale. The results of this study suggested that the five scales related to attention of the K-CBCL 6-18 are useful in diagnosing ADHD in child welfare institutions.