In this paper the potential of the principal component analysis(PCA) technique for the application of detecting leaks in water pipe networks was evaluated. For this purpose the PCA was conducted to evaluate the relevance of the calculated outliers of a PCA model utilizing the recorded pipe flows and the recorded pipe leak incidents of a case study water distribution system. The PCA technique was enhanced by applying the computational algorithms developed in this study which were designed to extract a partial set of flow data from the original 24 hour flow data so that the effective outlier detection rate was maximized. The relevance of the calculated outliers of a PCA model and the recorded pipe leak incidents was analyzed. The developed algorithm may be applied in determining further leak detection field work for water distribution blocks that have more than 70% of the effective outlier detection rate. However, the analysis suggested that further development on the algorithm is needed to enhance the applicability of the PCA in detecting leaks by considering series of leak reports happening in a relatively short period.

In this paper, we investigate how the power consumption of a heat pump dryer depends on various factors in the drying process by analyzing variables that affect the power consumption. Since there are in general many variables that affect the power consumption, for a feasible analysis, we utilize the principal component analysis to reduce the number of variables (or dimensionality) to two or three. We find that the first component is correlated positively to the entrance temperature of various devices such as compressor, expander, evaporator, and the second, negatively to condenser. We then model the power consumption as a multiple regression with two and/or three transformed variables of the selected principal components. We find that fitted value from the multiple regression explains 80~90% of the observed value of the power consumption. This results can be applied to a more elaborate control of the power consumption in the heat pump dryer.

이 연구는 입목축적과 산림관리정책 간의 전이함수(transfer function model)를 도출하기 위한 선행연구로, 입목축적변화를 유도하는 산림사업 간 다중공선성의 문제를 해결하기 위해 주성분 분석을 실시하였다. 분석자료는 9개의 대표적인 산림관리정책에 대해 1977~2008년까지 32년간의 연도별 시계열데이터를 활용하였으며, 분석 결과 추출된 3개의 주성분에 대한 전체 설명력은 91.4％로 상당히 높게 나타났다. 요약된 3개의 성분은 양호한 산림관리·병해충관리·산불발생이라는 새로운 변수명으로 개념화하였다.

최근 선박의 배기가스 규제가 강화되면서 연료소비량을 저감하기 위한 많은 방안들이 검토되고 있다. 그중에서도 선박으로부터 수 집한 데이터를 활용하여 연료소모량을 예측하는 기계학습 모델을 개발하고자 하는 연구가 활발히 수행되고 있다. 하지만 많은 연구들이 학습 모델의 주요 변수 선정이나 수집데이터의 처리 방법에 대한 고려가 미흡하였으며, 무분별한 데이터의 활용은 변수 간의 다중공선성 문제를 야기할 수도 있다. 본 연구에서는 이러한 문제점을 해결하기 위하여 주성분 분석을 이용하여 선박의 연료소비를 예측하는 방법을 제시하였다. 13K TEU 컨테이너 선박의 운항데이터에 주성분 분석을 수행하였으며, 추출한 주성분으로 회귀분석을 수행하여 연료소비 예측모델을 구현하 였다. 평가용 데이터에 대한 모델의 설명력은 82.99%이었으며, 이러한 예측모델은 항해 계획 수립 시 운항자의 의사결정을 지원하고 항해 중 에너지 효율적인 운항상태 모니터링에 기여할 수 있을 것으로 기대된다.

To establish cultivation areas for the stable production of yacon, this study investigated the productivity and functional component contents of yacon in eight regions of Korea from 2011 to 2013. The results of principal component analysis using these data were as follows. A survey of 16 agricultural traits and meteorological data in the eight yacon cultivation areas showed that five factors (average temperature, maximum temperature, minimum temperature, frost-free days, and fructooligosaccharide content) were highly significant at the p < 0.001 level. Among the 16 agricultural traits and meteorological data used in the main component analysis of yacon cultivation areas, approximately eight contributed to the first principal component, and approximately four contributed to each of the second and third principal components. In particular, factors related to productivity, fructooligosaccharide content, and temperature change were considered important criteria for the classification of cultivation areas. The cultivation areas were divided into three groups by principal component analysis. In Group I, containing the Jinbu and Bonghwa areas in the mid-highland region at 500–560 m above sea level, the product yield was the highest at 2,622–3,196 kg/10a, the fructooligosaccharide content was also the highest at 9.04–9.62%, and the mean temperature was 17.3–18.5℃. In Group II, the areas Suncheon, Okcheon, Yeoju, and Gangneung, at 20–180 m above sea level, had the lowest yield, relatively lower fructooligosaccharide content, and the highest temperature. The areas in Group III showed values intermediate between those of Group I and Group II. For the different yacon cultivation areas, the product quantity and fructooligosaccharide content differed according to the environmental temperature, and the temperature conditions and number of frost-free days are considered important indicators for cultivation sites. Therefore, in terms of producing yacon with high quality, cultivation at 500–560 m is considered to give a higher yield and functional fructooligosaccharide content.

참나리 2, 3배체 복합 배수성 집단에서 형태적, 지리적 분화를 명확히 하기 위해서 173개 군락에서 수집된 참나리의 38개 형질에 대한 분산분석(ANOVA)과 주성분분석(PCA)을 수행하였다. 그 결과, 173개 참나리 개재들은 배수성에 의해 78개의 2배체와 95 개체의 3배체로 구분되었고, 3배체는 지리적 분포와 형태적 특성에 의해 73개의 내륙 3배체(남한 전지역)와 20개의 섬지역 3배체(백령도와 소청도)로 구분되었다. ANOVA분석에서 섬지역 3배체는 잎과 꽃의 여러 형질들에 의해 내륙의 3배체와 뚜렷한 형태적 차이를 나타내었다. 주성분 분석에 의한 누적기여율은 4차 주성분까지 44.1%를 나타내었고, 주성분분석에서 2배체는 긴 초장, 작은 꽃의 형질들, 높은 화분임성, 더 많은 기공 수 등의 형질에 의해 내륙의 3배체와 구분되었다. 섬지역 3배체도 약간의 중복은 있었으나 1, 2차 주성분에 대한 2차원 분포도에서 서로 구분되었다. 한편, 배수성이 다를지라도 섬지역 3배체와 2배체는 주성분 1과 2의 요인에 의해 완전한 중복을 나타내었다. 이러한 사실은 섬지역 3배체와 2배체가 외형적으로 유사하다는 것을 반영한다. 이와 같이 섬지역과 내륙의 3배체의 형태적 차이는 지리적 기원을 반영하는 것으로 생각된다.

The objectives of this study were to develop an evaluation method of regional vulnerability to agricultural drought and to classify the vulnerability patterns. In order to test the method, 24 city or county areas of Gyeonggi-do were chose. First, statistic data and digital maps referred for agricultural drought were defined, and the input data of 31 items were set up from 5 categories: land use factor, water resource factor, climate factor, topographic and soil factor, and agricultural production foundation factor. Second, for simplification of the factors, principal component analysis was carried out, and eventually 4 principal components which explain about 80.8% of total variance were extracted. Each of the principal components was explained into the vulnerability components of scale factor, geographical factor, weather factor and agricultural production foundation factor. Next, DVIP (Drought Vulnerability Index for Paddy), was calculated using factor scores from principal components. Last, by means of statistical cluster analysis on the DVIP, the study area was classified as 5 patterns from A to E. The cluster A corresponds to the area where the agricultural industry is insignificant and the agricultural foundation is little equipped, and the cluster B includes typical agricultural areas where the cultivation areas are large but irrigation facilities are still insufficient. As for the cluster C, the corresponding areas are vulnerable to the climate change, and the D cluster applies to the area with extensive forests and high elevation farmlands. The last cluster I indicates the areas where the farmlands are small but most of them are irrigated as much.

A study on the variational characteristics of water quality and phytoplankton biomass by principal component analysis(PCA) was carried out in Kogum-sudo from February to October in 1993. We analyzed PCA on biological factors such as chlorophyll α and phytoplankton cell numbers for centric and pennate diatoms, phytoflagellates, and total phytoplankton as well as physico-chemical factors as water temperature, salinity, transparency, dissolved oxygen(DO), saturation of DO, apparent oxygen utilization(AOU), chemical oxygen demand(COD), nutrient (ammonia, nitrite, nitrate, phosphate and silicate), N/P ratio and suspended solid(SS). The source of nutrients supply depended on the mineralization of organic matters and inputs of seawater from outside rather than runoff of freshwater. The phytoplankton biomass was changed within short interval period by nutrients change. And it was controlled by the combination of several environmental factors, especially of light intensity, ammonia and phosphate. The marine environmental characteristics were determined by the mineralization of organic matters in winter, by runoff of freshwater including high nutrients concentration in spring, by ammonia uptake and high phytoplankton productivity in summer, and phosphate supplied input seawater from outside of Kogum -sudo in autumn. And Kogum-sudo was separated with 2 regions by score distributions of PCA. That is to say, one region was middle parts of straits which was characterized by the mixing seawater and the accumulated organic matters, other one region was Pungnam Bay and the water around Kogum Island which was done by high phytoplankyon biomass and productivity year-round.

This study was conducted to evaluate water quality utilizing principal component analysis in the Nakdong River Estuary. From the results of analysis, water quality in the Nakdong River Estuary could be explained up to 65.3 percentage by three factors which were included in river loading(wastes from the Nakdong River and rainfalls ; 39.1%), sediment resuspension(13.7%) and metabolism(12.5%). In the eastern part of estuary in flowing the Nakdong River, river loading factor score(factor 1)was higher than that in western part. Sediment resuspension factor score(factor 2) was high in shallow water, while metabolism factor score(factor 3) was high in deeper water. For seasonal variations of factors score, factor 1 was highly related to rainfall season.

1979년에 조신력, 팔달, 진흥, 통일, 밀양2003등 5품종을 공시하여 질소수준을 0kg, 12kg, 18kg/10a에서 재배하여 15회에 걸쳐 조사한 엽후, 엽면적지수, 주당건물중과 수양 및 수양구성요소와의 관계를 주성분분석을 이용하여 분석한 결과는 다음과 같다. 1. 엽후, 엽면적지수, 주당건물중의 시계열데이타의 주성분분석에서 제 1주성분은 전기간의 평균적 크기의 대소를 표시하는 특성으로, 제 2주성분은 엽후, 엽면적지수에서는 변대의 위치, 주당건물중에서는 생육초기의 특성을 표시하는 종합특성치로 도출할 수 있었다. 2. 생장특성을 표시하는 종합특성치(주성분스코아)의 엽후는 품종간 차리가 엽면적지수 및 주당건물중은 질소수준간 차이가 인정되있다. 3. 수양 및 수량구성요소와의 관계에서 임실비율과 주당 수수가 엽면적 및 주당건물중의 스코아와 관계가 있었고 엽후와는 거의 관계가 없었으며 수양에 미치는 영향도 임실비율과 주당수수를 통해서라는 것을 알 수 있었다.