• 제목/요약/키워드: disjoint principal component analysis

검색결과 4건 처리시간 0.114초

분산주성분 분석을 이용한 실내환경 중 PM-10 오염의 패턴분류 (Pattern Classification of PM -10 in the Indoor Environment Using Disjoint Principal Component Analysis)

  • 남보현;황인조;김동술
    • 한국대기환경학회지
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    • 제18권1호
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    • pp.25-37
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    • 2002
  • The purpose of the study was to survey the distribution patterns of inorganic elements of PM-10 in the various indoor environments and analyze the pollution patterns of aerosol in various places of indoor environment using a pattern recognition method based on cluster analysis and disjoint principal component analysis. A total of 40 samples in the indoor had been collected using mini-vol portable samplers. These samples were analyzed for their 19 bulk inorganic compounds such as B, Na, Mg, Al, K, Ca, Ti, V, Cr, Fe, Ni, Cu, Zn, As, Se, Cd, Ba, Ce, and Pb by using an ICP-MS. By applying a disjoint principal component analysis, four patterns of the indoor air pollutions were distinguished. The first pattern was identified as a group with high concentrations of PM-10, Na, Mg, and Ca. The second pattern was identified as a group with high concentrations B, Mg, At, Ca, Fe, Cu, and Ba. The third pattern was a group of sites with high concentrations of K, Zn. Cd. The fourth pattern was a group with low concentrations PM-10 and all inorganic elements. This methodology was found to be helpful enough to set the criteria standard of indoor air quality, corresponding pollutants, and classification of indoor environment categories when making an indoor air quality law.

분산주성분 분석을 이용한 고등학교교실 내 오염패턴분류에 관한 연구 (Classification of Pollution Patterns in High School Classrooms using Disjoint Principal Component Analysis)

  • 장철순;이태정;김동술
    • 한국대기환경학회지
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    • 제22권6호
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    • pp.808-820
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    • 2006
  • In regard to indoor air quality patterns, the government introduced various polices that were about managing and monitoring quality of indoor air as a major assignment, and also executed 'Indoor Air Quality Management Act' which was presented in the May, 2004. However, among the multi-usage facilities controlled by the Act, the school was not included yet. This study goal was to investigate PM 10 pollution patterns of the high school classrooms using a pattern recognition method based on cluster analysis and disjoint principal component analysis, and further to survey levels of inorganic elements in May, June, and September, 2004. A hierarchical clustering method was examined to obtain possible objects in pseudo homogeneous sample classes by transformation raw data and by applying various distance. Following the analysis, the disjoint principal component analysis was used to define homogeneous sample class after deleting outliers. Then three homogeneous Patterns were obtained as follows: the first class had been separated and objects in the class were considered to be sampled under semi-open condition. This class had high concentration of Ca, Fe, Mg, K, Al, and Na which are related with a soil and a chalk compounds. The second class was obtained in which objects were sampled while working air-conditioners and was identified low concentration of PM 10 and elements. Objects in the last class were assigned during rainy day. A chalk, soil element and various types of anthropogenic sources including combustions and industrial influenced the third class. This methodology was thought to be helpful enough to classify indoor air quality patterns and indoor environmental categories when controlling an indoor air quality.

군집분석법과 분산주성분분석법을 이용한 대기분진시료의 분류 (Classification of Ambient Particulate Samples Using Cluster Analysis and Disjoint Principal Component Analysis)

  • 유상준;김동술
    • 한국대기환경학회지
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    • 제13권1호
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    • pp.51-63
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    • 1997
  • Total suspended particulate matters in the ambient air were analyzed for eight chemical elements (Ca, Co, Cu, Fe, Mn, Pb, Si, and Zn) using an x-ray fluorescence spectrometry (XRF) at the Kyung Hee University - Suwon Campus during 1989 to 1994. To use these data as basis for source identification study, membership of each sample was selected to represent one of the well defined sample groups. The data sets consisting of 83 objects and 8 variables were initially separated into two groups, fine (d$_{p}$<3.3 ${\mu}{\textrm}{m}$) and coarse particle groups (d$_{p}$>3.3 ${\mu}{\textrm}{m}$). A hierarchical clustering method was examined to obtain possible member of homogeneous sample classes for each of the two groups by transforming raw data and by applying various distances. A disjoint principal component analysis was then used to define homogeneous sample classes after deleting outliers. Each of five homogeneous sample classes was determined for the fine and the coarse particle group, respectively. The data were properly classified via an application of logarithmic transformation and Euclidean distance concept. After determining homogeneous classes, correlation coefficients among eight chemical variables within all the homogeneous classes for calculated and meteorological variables (temperature. relative humidity, wind speed, wind direction, and precipitation) were examined as well to intensively interpret environmental factors influencing the characteristics of each class for each group. According to our analysis, we found that each class had its own distinct seasonal pattern that was affected most sensitively by wind direction.ion.

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Pattern Recognition을 이용한 지하상가에서의 대기오염물질의 농도 분석에 관한 연구 (The Air Quality Analysis in Underground Shopping Centers Using Pattern Recognition)

  • 김동술;김형석
    • 한국대기환경학회지
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    • 제6권1호
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    • pp.1-10
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    • 1990
  • The purpose of the study was to analyze air quality in underground shopping centers using pattern recognition methods. In order to perform this, the concentraion of air pollutants such as $CO, NO_2, NO_x, SO_2$, and particulate matters was measured at the 11 different shopping centers in Seoul metropolitan area and the total of 47 samples were obtained at random based on the size of shopping centers. To introduce a new concept of the "average concentration" for the indoor air quality analyses, the various multivariate statistical analyses have been studied. Thus, a cluster analysis was applied to separate the samples into pseudo-patterns and a disjoint principal component analysis was used to generate homogeneous patterns after removing outliers from the pseudo-patterns. The 6 homogeneous patterns were then obtained as follows:the first pattern was a group of clean sites;the second a group of sites having high dust concentration;the third a group of sites having high dust and $NO_x$ concentration;the fourth a group of sites having low dust and $SO_2$ concentraion and high CO concentration;the fifth a group of sites having high $NO_2 and SO_2$ concentration;and the final a group of miscellaneous sites. Thus, the average concentration could be estimated for each pattern.h pattern.

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