• The KIPS Transactions:PartD
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• v.9D no.1
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• pp.75-80
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• 2002

Putnam develops SLIM (Software LIfecycle Management) model based upon the assumption that the manpower utilization during software project development is followed by a Rayleigh distribution. To obtain the manpower distribution, we have to be estimate the total development effort and difficulty ratio parameter. We need a way to accurately estimate these parameters early in the requirements and specification phase before investment decisions have to be made. Statistical tests show that system attributes are highly correlation (redundant) so that Putnam discards one and get a parameter estimator from the other attributes. But, different statistical method has different system attributes and presents different performance. To select the principle system attributes, this paper uses the principle component analysis (PCA) instead of Putnam's method. The PCA's results improve a 9.85 percent performance more than the Putnam's result. Also, this model seems to be simple and easily realize.

• Proceedings of the Korea Water Resources Association Conference
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• 2017.05a
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• pp.120-120
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• 2017

인천지역의 상수도공급은 팔당댐을 취수원으로 하여 도수, 송수관을 거쳐 인천지역 내 정수장을 통하여 각 급수지역까지 일원화된 관로시스템으로 공급되고 있다. 관망에서의 적절한 수압관리, 노후관로 교체사업 등은 급수관망 내 관로 사고위험을 줄일 수 있고, 누수량을 저감하여 무수율의 감소로 이어질 수 있다. 상수관망 내 누수에 영향을 주는 물리적, 운영적 요소를 파악하고, 이를 이용하여 누수해결을 위한 방법론을 제시하는 것은 매우 중요하다. 본 연구에서는 인천시 배수관망 데이터를 활용하여 통계분석 및 인공신경망을 통하여 무수율에 영향을 미치는 인자를 선별하고, 무수율과의 연관성을 분석하고자 하였다. 이를 위해 대상지역에 대한 시설현황 및 운영자료를 취득하고, 무수율 분석에 활용하였다. 인천시의 소블럭을 대상으로 관로노후도, 배수관연장, 평균관경, 급수전당 공급량, 누수발생 횟수, 용도지역, 관망구성 형태 등을 고려하여 무수율과의 관계분석을 위한 통계분석을 수행하였다. 특히 급수에 필요한 최소에너지와 관망에서 공급되는 에너지를 비교하기 위하여 관망해석 프로그램인 EPANET을 이용하여 관망내 절점에서의 수압과 수요량이 적용된 최소공급에너지를 활용하였고, 이를 통하여 블록 내 과잉공급에너지와 무수율의 영향성을 비교하였다. 최종적으로 산출된 주요인자에 대한 주성분분석, 분산분석, 다중회귀분석 등의 통계분석과 인공신경망에 의해 학습된 알고리즘을 통하여 산정된 무수율을 실측 무수율과 비교, 분석하였다. 인공신경망에 의해 산정된 무수율과 실측 무수율의 정확도를 평가하기 위하여 MAE, MSE, PBIAS 등의 정확도 평가와 산점도 분석을 수행하고, 상관계수를 도출하여 가장 정확한 방법을 결정하였다. 분석 결과 통계분석에 의한 다중회귀식으로 산출된 무수율 보다 인공신경망에 의한 무수율이 실측값에 더욱 근접한 것으로 나타났으며 이용된 뉴런의 수의 따라 산출결과가 상이하기 때문에 최적 뉴런의 수를 산정해야 할 필요가 있음을 확인하였다. 특히 사용된 상수관망 주요인자 중 주성분분석을 통하여 선정된 각 성분을 인공신경망에 적용시 더욱 정확한 무수율 예측이 가능한 것으로 나타났다.

• Journal of Digital Convergence
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• v.11 no.12
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• pp.201-211
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• 2013

This study compares Customer Satisfaction Index(CSI) and the weight for each dimension by applying various methods of weight calculation and attempts to suggest some implications. For the purpose, the study classified the methods of weight calculation into the subjective method and the statistical method. Constant sum scale was used for the subjective method, and the statistical method was again segmented into correlation analysis, principal component analysis, factor analysis, structural equation model. The findings showed that there is difference between the weights from the subjective method and the statistical method. The order of the weights by the analysis methods were classified with similar patterns. Besides, the weight for each dimension by different methods of weight calculation showed considerable deviation and revealed the difference of discrimination and stability among the dimensions. Lastly, the CSI calculated by various methods of weight calculation showed to be the highest in structural equation model, followed by in the order of regression analysis, correlation analysis, arithmetic mean, principal component analysis, constant sum scale and factor analysis. The CSI calculated by each method showed to have statistically significant difference.

• Korean Journal of Soil Science and Fertilizer
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• v.43 no.5
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• pp.748-754
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• 2010

The contents of soil organic matter (SOM) and $Al_o+1/2Fe_o$ in soils are important criteria for the classification of new Andisols in Soil Taxonomy system. There are many soil types in Jeju Island with various soil forming environments. This paper was conducted to estimate the contents of soil organic matter and the content of ammonium oxalate extracted Al and Fe ($Al_o+1/2Fe_o$) using various environmental variables and to make soil property maps using a statistical analyses. The soil samples were collected from 321 locations and analyzed to measure the contents of SOM and $Al_o+1/2Fe_o$. It was analyzed the relationships among them and various environmental variables such as temperature, precipitation, net primary product, radiation, evapotranspiration, altitude, soil forming energy, topographic wetness index, elevation, difference surrounded area, and distances from the shore and the peak. We can exclude multi-collinearity among environmental variables with principal component analysis and reduce all the variables to 3 principal components. The contents of SOM and $Al_o+1/2Fe_o$ were estimated by multiple regression models and maps of them were made using the models.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.38 no.2
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• pp.183-191
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• 2018

In this study a traffic volume dependent displacement estimation model for Gwangan Bridge was developed using bridge monitoring big data. Traffic volume data for four different vehicle types and the vertical displacement data in the central position of the Gwangan Bridge were used to develop and validate the estimation model. Two statistical estimation models were developed using multiple regression analysis (MRA) and principal component analysis (PCA). Estimation performance of those two models were compared with actual values. The results show that both the MRA and the PCA based models are successfully estimating the vertical displacement of Gwangan Bridge. Based on the results, it is concluded that the developed model can effectively be used to predict the traffic volume dependent displacement behavior of Gwangan Bridge.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.3
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• pp.293-298
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• 2015

The vessel encounter data collected from the vessel trajectories in the maritime traffic situation is possible to analyze vessel collision and near-collision risk using statistical method. In this study, analyzing variables extracted from the vessel encounter data using factor analysis, we determine main factors effecting vessel collision risk from vessel encounter data. In order to calculate each factor, it used principal component analysis for factor analysis after normalization and standardization of vessel encounter variables. As a result of the factor analysis, main effect factors are summarized into the vessel approach factor and collision avoidance variance factor.

• The Korean Journal of Applied Statistics
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• v.19 no.3
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• pp.431-445
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• 2006

In this paper we review some methods for analyzing functional data and illustrate real application of functional data analysis. Representing methods for functional data by using basis function, analyzing functional variation by functional principal component analysis and functional linear models are reviewed. For a real application, we use temperature and precipitation data measured in Korea from the January of 1970 to the May of 2004. We apply functional principal component analysis for each data and test the significance of regional division done by using shining hours. We also estimate functional regression model for temperature and precipitation.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37B no.9
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• pp.837-848
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• 2012

This study has conducted the economic effects of R&D investment in the IT industry using multi-regression analysis with three independent variables; capital stock, labor input and R&D stock. In this study, the IT industry has been categorized into three sub-industries; broadcasting communication appliances, information appliances and electronic components industry. Our analysis has found that auto-correlation shows considerable levels whereas figures of t-value and R-square show significant levels among all the IT sub-industries. Meanwhile, the values of R&D stock in the information appliances industry and that of labor input coefficients in the electronic components industry were minus, thus multi-collinearity was suspected. We have solved the problems regarding auto-correlation and multi-collinearity through Cochrane-Orcutt estimation and principal components analysis. This paper has derived the implications that R&D investment in the broadcasting communication industry is much more influential than any other IT sub-industry.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.38 no.1
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• pp.31-37
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• 1993

To analyze and classify the varietal variation of alkali digestibility in detail, which is closely connected with the gelatinization temperature and physical characteristics of cooked rice, the patterns of alkali decomposition changed along the alkali concentration were investigated for thirty three Korean leading rice cultivars and new breeding lines(japonica : 25, Tongil-type:8) including five glutinous rice. Principal component analysis was used to condense the information and to classify rice materials according to decomposed reaction pattern at several levels of potassium hydroxide(KOH) concentration. Thirty three rice varieties were classified largely into four groups by the distribution on the plane of upper two principal component scores which contained above 92% of total informations. Group I was consisted of one variety, Dobongbyeo, which owned almost same strong resistance to alkali digestion at the range of 0.8% to 1.6% KOH solutions. Group II included three japonica and Tongil-type glutinous rice varieties, which revealed medium alkali digestion value(ADV) at 1.4% KOH solution and intermediate change in ADV from 0.8% to 1.6% KOH solutions. Most of Tongil-type and early-maturity japonica rice, which exhibited medium-high ADV at 1.4% of KOH concentration and large ADV difference between low and high alkali solutions, were contained in Group III. Group N included most of medium or medium-late-maturity japonica, which showed high ADV at 1.4% KOH and medium or intermediate-high ADV change between low and high alkali solutions. The 1st principal component indicated the average index of ADV through 0.8-1.6% KOH solutions and the 2nd principal component pointed out the factor related with ADV difference between low and high alkali solutions or regression coefficients of ADV change along with the KOH concentrations.

• The Korean Journal of Applied Statistics
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• v.21 no.1
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• pp.19-33
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• 2008

Linear regression method, proposed by Haseman and Elston(1972), for detecting linkage to a quantitative trait of sib pairs is a linkage testing method for a single locus and a single trait. However, multivariate methods for detecting linkage are needed, when information from each of several traits that are affected by the same major gene are available on each individual. Amos et al. (1990) extended the regression method of Haseman and Elston(1972) to incorporate observations of two or more traits by estimating the principal component linear function that results in the strongest correlation between the squared pair differences in the trait measurements and identity by descent at a marker locus. But, it is impossible to control the probability of type I errors with this method at present, since the exact distribution of the statistic that they use is yet unknown. In this paper, we propose a multivariate nonparametric trend test for detecting linkage to multiple traits. We compared with a simulation study the efficiencies of multivariate nonparametric trend test with those of the method developed by Amos et al. (1990) for quantitative traits data. For multivariate nonparametric trend test, the results of the simulation study reveal that the Type I error rates are close to the predetermined significance levels, and have in general high powers.