• ALGAE
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• 제30권2호
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• pp.89-101
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• 2015

Ecklonia cava Kjellman is a common kelp found in shallow subtidal in warm-temperate waters in the northwest Pacific Ocean. This species has shown substantial morphological variation along with subsistence in different locations and local environments. We quantified the magnitude of morphological variation of E. cava from six populations along ~700 km of coastline from Jeju Island to Dokdo in Korea. In addition, we examined genetic distance among the populations using random amplified polymorphic DNA (RAPD) analysis. Most morphological characteristics investigated were significantly different among locations. Multivariate analyses indicated two phenetically distinct groups (nearshore, sheltered vs. offshore, exposed), indicating wave exposure with turbidity are presumably major factors for the separation. With RAPD data, results of Nei's diversity (H) and AMOVA showed considerable variations in within- and between-populations. Pairwise ${\Phi}_{ST}$ and $N_m$ values indicated moderate gene flow between the six locations. Results of Nei's analysis revealed three genetically distinct groups, not consistent with the morphological groupings, indicating that a time gap may exist between morphological and genetic variations. This study also suggests dispersal distance of this kelp may be longer than what is commonly thought and genetic similarity in the populations was largely reflected by the direction of ocean current rather than just geographical distance.

• 한국환경보건학회지
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• 제26권2호
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• pp.34-40
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• 2000

The purpose of this study was to investigate the seasonal variation of PAHs and to estimate their source characteristics in Taegu area. To do this, four sampling sites were selected to represent an industrial, a traffic, a traffic & residential, and a residential area in Taegu. Total of 72 samples had been collected from January, 1999 to September, 1999 on glass micro fiber filters by high volume air sampler. The PAHs in the total suspended particulate were extracted by a soxhlet process with dichloromethane and analyzed by GC/MSD, GC/FID. A statistical analysis was performed for the PAHs data set using a principal component analysis to derive important factor inherent in the interactions among the variables. The specific conclusions of this research are: 1) There was a significant seasonal and local variation in the atmospheric concentration of PAHs. The seasonal variation is winter>spring>Fall>summer, and the local variation is industrial>traffic>graffic & residential>residential area. 2) To evaluate the correlation between a measured PAHs and other affecting factors such as air pollutant concentration and meterological data, statistical analysis was performed. PAHs and other affecting factors such as air pollutant concentration and meterological data, statistical analysis was performed. PAHs have negative correlation with temperature (r=-0.593, p<0.05), radiation(r=-0.535, p<0.05), and O3(r=-0.719, p<0.05), but have positive correlation with NO(r=0.615, p<0.05) 3)Finally, multivariate analysis was performed for the PAHs dat set to identify and to estimate the source contributions of PAHs. According to results of statistical analysis, it could be identifies as three factors such as vehicular/gasoline, vehicular/diesel, and combustion in Taegu area.

• 농업생명과학연구
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• 제45권3호
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• pp.25-33
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• 2011

후박나무의 유전자원보존을 위하여 10개 천연집단을 대상으로 9가지 엽의 형태적 특성에 대한 집단간 및 집단내 개체간 변이를 조사하고 다변량분석을 실시하였다. 엽의 평균 생장은 엽신장 9.8cm, 최대엽폭 4.0cm, 엽병길이 1.8cm, 엽맥수 8.4개, 엽저각 $67.9^{\circ}$, 엽두각 $78.0^{\circ}$로 나타났다. 각 형질특성에 대한 변이계수 값은 대체적으로 20% 내외의 비교적 유사한 특성을 나타냈다. Nested 분산분석 결과 모든 특성에서 집단간 및 개체간에 고도의 유의적인 차이가 인정되었으며, 전체 분산 가운데 집단간보다 집단내 개체간 차지하는 비율이 모든 특성들에서 높게 나타났다. 집단간 유연관계는 Euclidean distance 1.2 수준에서 크게 4개의 그룹으로 나뉘었으나 지리적 분포에 따른 특별한 경향은 나타나지 않았다. 유집군의 유형에 대한 주성분분석 결과 제3주성분까지가 누적변이 값이 92.8%로 나타났다. 제1주성분의 기여율은 40.3%로 대체적으로 최대엽폭, 제2주성분의 기여율은 28.7%로 엽신장, 제3주성분의 기여율은 23.8%로 엽병길이 특성의 기여도가 높게 나타나 후박나무 집단간 유연관계에 중요한 정보를 주는 요인으로 나타났다.

• Journal of Ecology and Environment
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• 제43권4호
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• pp.332-340
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• 2019

Background: Studies of morphological variation within and among populations provide an opportunity to understand local adaptation and potential patterns of gene flow. To study the evolutionary divergence patterns of Schlegel's Japanese gecko (Gekko japonicus) across its distribution, we analyzed data for 15 morphological characters of 324 individuals across 11 populations (2 in China, 4 in Japan, and 5 in Korea). Results: Among-population morphological variation was smaller than within-population variation, which was primarily explained by variation in axilla-groin length, number of infralabials, number of scansors on toe IV, and head-related variables such as head height and width. The population discrimination power was 32.4% and in cluster analysis, populations from the three countries tended to intermix in two major groups. Conclusion: Our results indicate that morphological differentiation among the studied populations is scarce, suggesting short history for some populations after their establishment, frequent migration of individuals among the populations, and/or local morphological differentiation in similar urban habitats. Nevertheless, we detected interesting phenetic patterns that may predict consistent linkage of particular populations that are independent of national borders. Additional sampling across the range and inclusion of genetic data could give further clue for the historical relationship among Chinese, Japanese, and Korean populations of G. japonicus.

• 한국동물학회지
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• 제37권1호
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• pp.49-57
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• 1994

In the course of an investigation on the morphological variation of Korean striped bittering, Acheilognathus vamatsutae Mori, two kinds of morrholoflical types were found. Some morphological characters were compared between them and multivariate analysis was conducted. Since the difference of the barbel length between them was very significant (p < 0.01,1-test), they should be designated as L-and 5-type Hshes: fishes with long barbels and those with should barbels, respectively. Lateral line scales, snout length and prepectoral length showed a tendency that the observation could be distinguished from each other (p < 0.05, Duncan's multiple range tests. There were no individuals over 70 mm BL in S-type and so S-type fishes were somewhat dwarf than L-type fishes. The two types were also clearly distinguished by multivariate analyses using cluster and discriminant analyses. According to the observation on the four populations of the Naktong River, the blue-green stripe on the body side and the white band on the margin of anal fin in males of S-type fishes were well developed through the year but those of males of L-type fishes completely disappeared or became slender during nonbreedins season as that of females. Of the seven localities surveved, there were no places that the two types cohabit. S-type fishes are limited only in the Kumho and Wichon River of the Naktong River system but L-type fishes are distributed more widely In the Hongchon, Mangvons, Somjin, Mirang and Panbyon River.

• 한국근적외분광분석학회:학술대회논문집
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• 한국근적외분광분석학회 2001년도 NIR-2001
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• pp.1244-1244
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• 2001

In linear discriminant analysis there are two important properties concerning the effectiveness of discriminant function modeling. The first is the separability of the discriminant function for different classes. The separability reaches its optimum by maximizing the ratio of between-class to within-class variance. The second is the stability of the discriminant function against noises present in the measurement variables. One can optimize the stability by exploring the discriminant variates in a principal variation subspace, i. e., the directions that account for a majority of the total variation of the data. An unstable discriminant function will exhibit inflated variance in the prediction of future unclassified objects, exposed to a significantly increased risk of erroneous prediction. Therefore, an ideal discriminant function should not only separate different classes with a minimum misclassification rate for the training set, but also possess a good stability such that the prediction variance for unclassified objects can be as small as possible. In other words, an optimal classifier should find a balance between the separability and the stability. This is of special significance for multivariate spectroscopy-based classification where multicollinearity always leads to discriminant directions located in low-spread subspaces. A new regularized discriminant analysis technique, the principal discriminant variate (PDV) method, has been developed for handling effectively multicollinear data commonly encountered in multivariate spectroscopy-based classification. The motivation behind this method is to seek a sequence of discriminant directions that not only optimize the separability between different classes, but also account for a maximized variation present in the data. Three different formulations for the PDV methods are suggested, and an effective computing procedure is proposed for a PDV method. Near-infrared (NIR) spectra of blood plasma samples from mastitic and healthy cows have been used to evaluate the behavior of the PDV method in comparison with principal component analysis (PCA), discriminant partial least squares (DPLS), soft independent modeling of class analogies (SIMCA) and Fisher linear discriminant analysis (FLDA). Results obtained demonstrate that the PDV method exhibits improved stability in prediction without significant loss of separability. The NIR spectra of blood plasma samples from mastitic and healthy cows are clearly discriminated between by the PDV method. Moreover, the proposed method provides superior performance to PCA, DPLS, SIMCA and FLDA, indicating that PDV is a promising tool in discriminant analysis of spectra-characterized samples with only small compositional difference, thereby providing a useful means for spectroscopy-based clinic applications.

• 한국근적외분광분석학회:학술대회논문집
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• 한국근적외분광분석학회 2001년도 NIR-2001
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• pp.1042-1042
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• 2001

In linear discriminant analysis there are two important properties concerning the effectiveness of discriminant function modeling. The first is the separability of the discriminant function for different classes. The separability reaches its optimum by maximizing the ratio of between-class to within-class variance. The second is the stability of the discriminant function against noises present in the measurement variables. One can optimize the stability by exploring the discriminant variates in a principal variation subspace, i. e., the directions that account for a majority of the total variation of the data. An unstable discriminant function will exhibit inflated variance in the prediction of future unclassified objects, exposed to a significantly increased risk of erroneous prediction. Therefore, an ideal discriminant function should not only separate different classes with a minimum misclassification rate for the training set, but also possess a good stability such that the prediction variance for unclassified objects can be as small as possible. In other words, an optimal classifier should find a balance between the separability and the stability. This is of special significance for multivariate spectroscopy-based classification where multicollinearity always leads to discriminant directions located in low-spread subspaces. A new regularized discriminant analysis technique, the principal discriminant variate (PDV) method, has been developed for handling effectively multicollinear data commonly encountered in multivariate spectroscopy-based classification. The motivation behind this method is to seek a sequence of discriminant directions that not only optimize the separability between different classes, but also account for a maximized variation present in the data. Three different formulations for the PDV methods are suggested, and an effective computing procedure is proposed for a PDV method. Near-infrared (NIR) spectra of blood plasma samples from daily monitoring of two Japanese cows have been used to evaluate the behavior of the PDV method in comparison with principal component analysis (PCA), discriminant partial least squares (DPLS), soft independent modeling of class analogies (SIMCA) and Fisher linear discriminant analysis (FLDA). Results obtained demonstrate that the PDV method exhibits improved stability in prediction without significant loss of separability. The NIR spectra of blood plasma samples from two cows are clearly discriminated between by the PDV method. Moreover, the proposed method provides superior performance to PCA, DPLS, SIMCA md FLDA, indicating that PDV is a promising tool in discriminant analysis of spectra-characterized samples with only small compositional difference.

• Journal of the Korean Data and Information Science Society
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• 제19권4호
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• pp.1201-1210
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• 2008

The goal of this paper is to investigate the trend of stream quality and the quality of water in Nakdong river by the method of factor analysis. It used the fourteen different monthly time series data such as pH, BOD, COD, SS, TN and etc. of the thirty four of Nakdong River measurement points from Jan. 1998 to Dec. 2006. The result of factor analysis is that the factor 1 results from organic water pollution is occupied 29.288% such as BOD, COD, TN and EC, and the factor 2 explained from sewage and a seasonal variation is occupied 16.467% such as SS.

• 대한수의학회지
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• 제53권1호
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• pp.7-10
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• 2013

A study was conducted to determine the interdependence among the conformation traits of 28 "Pallaresa" cows using principal component analysis. Originally 21 body linear measurements were obtained, from which eight traits are subsequently eliminated. From the principal components analysis, with raw varimax rotation of the transformation matrix, two principal components were extracted, which accounted for 65.8% of the total variance. The first principal component alone explained 51.6% of the variation, and tended to describe general size, while the second principal component had its loadings for back-sternal diameter. The two extracted principal components, which are traits related to dorsal heights and back-sternal diameter, could be considered in selection programs.

• KDI Journal of Economic Policy
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• 제14권1호
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• pp.121-145
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• 1992

본고(本稿)에서는 현재의 경제상황을 잘 반영하는 건설투자활동(建設投資活動)의 단기예측모형(短期豫測模型)을 정립하고자 먼저 관련 시계열자료의 안정성(安定性) 여부(與否)와 순환성(循環性), 계절성(季節性)의 특성을 살펴본 후 여러 단기모형의 예측력(豫測力), 정합성(整合性), 설명력(說明力)을 비교 검토했다. 단위근(單位根) 검정(檢定)과 자기상관계수(自己相關係數) 스펙트랄 밀도함수 분석의 결과, 건설관련 시계열자료들이 대체로 단위근(單位根)을 갖지 않음으로써 안정적이고 주기적인 순환변동을 하고 있으며, 시차변수의 설명력이 높은 특성을 나타내었다. 또한 건설투자자료의 특성이 선행지표(先行指標)인 건축허가연면적(建築許可延面積) 및 건설수주액(建設受注額)과 아주 유사하여 건설투자 단기예측에 있어서 두 지표 사이의 시차관계(時差關係) 파악이 중요함을 알 수 있었다. 제(第)III장(章)에서는 단변량(單變量) 시계열모형(時系列模型)으로 ARIMA모형(模型)과 승법선형추세예측모형(乘法線型趨勢豫測模型)을, 다변량(多變量) 시계열모형(時系列模型)으로는 첫째, 선행지표(先行指標)를 이용한 1차자기회귀모형(次自己回歸模型), VAR모형(模型), 둘째 GNP자료를 이용한 거시경제모형의 단순한 축약형모형(縮約型模型)과 VAR모형(模型)을 제시하고 이들을 비교 평가하였다. 이에 따르면 단변량 시계열모형보다는 다변량 시계열모형이 시간이 경과할수록 예측오차(豫測誤差)가 커지지 않는다는 점에서 우수한 것으로 나타났으며, 다변량모형 중에서도 벡터자기회귀모형이 여타 모형보다 절대예측오차평균(絶對豫測誤差平均), 평균자승근(平均自乘根) 퍼센트 오차(誤差), 결정계수(決定係數) 등 모든 면에서 우수한 것으로 평가되었다. 이는 최근 건설투자가 추세에서 벗어난 급증세를 지속하고 있음을 고려할 때 타당한 결론이라 생각된다.