• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2004.10a
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• pp.98-107
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• 2004

The objectives of this research are to establish the criteria of peel occurrence considering the shape of bond terminus and to compare the strength properties of some adhesive joints. The criteria of feel occurrence at the bond terminus was suggested. Peel loads of some adhesive joint(butt joint, T -shape specimen, single lap joint) were determined from tensile tests. Principal stress distributions of these joints were determined from finite element method analysis. Then, peel occurrence was estimated with intensity of stress singularity ' $K_{prin.}$' when the terminus shape was square, with average principal stress when the terminus shape was rounded. The conclusions are summarized as follows; (1) In the non-filleted model(e.g., butt joint, T-shape specimen), principal stress shows singularity at the bond terminus, intensity of stress(principal stress) singularity ' $K_{prin.}$&apso; can use as the criteria of peel occurrence at the bond terminus. (2) In the filleted model(e.g., single lap joint), principal stress doesn't show singularity at the bond terminus. Average principal stress can use as the criteria of peel occurrence at the bond terminus.'t show singularity at the bond terminus. Average principal stress can use as the criteria of peel occurrence at the bond terminus.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.50 no.1
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• pp.60-66
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• 2005

Pod shape of twenty soybean (Glycine max L. Merrill) genotypes was evaluated quantitatively by image analysis using elliptic Fourier descriptors and their principal components. The closed contour of each pod projection was extracted, and 80 elliptic Fourier coefficients were calculated for each contour. The Fourier coefficients were standardized so that they were invariant of size, rotation, shift, and chain code starting point. Then, the principal components on the standardized Fourier coefficients were evaluated. The cumulative contribution at the fifth principal component was higher than $95\%$, indicating that the first, second, third, fourth, and fifth principal components represented the aspect ratio of the pod, the location of the pod centroid, the sharpness of the two pod tips and the roundness of the base in the pod contour, respectively. Analysis of variance revealed significant genotypic differences in these principal components and seed number per pod. As the principal components for pod shape varied continuously, pod shape might be controlled by polygenes. It was concluded that principal component scores based on elliptic Fourier descriptors yield seemed to be useful in quantitative parameters not only for evaluating soybean pod shape in a soybean breeding program but also for describing pod shape for evaluating soybean germplasm.

• Korean Journal of Agricultural Science
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• v.8 no.2
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• pp.139-151
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• 1981

To obtain breeding materials 57 Korean local corn lines collected were assessed and classified by the application of principal component analysis. The results obtained were as follows. 1. In the result of principal component analysis for 27 characters, 67.1% and 88.6% of total variation could be appreciated by the first four and fir st ten principal component respectively. 2. According to the value of characters and principal components, contribution of characters to principal components were very variable. 3. Biological meaning of the principal component and plant type corresponded to the each principal component were explained clear by the correlation coefficients between principal components and characters. 4. 57 lines were classified into 4 lineal groups by the taxonomic distances.

• Transactions of the Korean Society of Mechanical Engineers
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• v.19 no.12
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• pp.3372-3381
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• 1995

An equilibrium analysis was carried out to determine principal species of heavy metals in waste incineration and their behaviors with variation of temperature, chlorine concentration, excess air ratio, and C/H ratio. The waste was assumed as a compound of hydrocarbon fuel, chlorine, and metals. Calculated results showed that the most important parameter to determine the principal species was temperature. Chlorine concentration also affected on mole fractions of the principal species. Generally principal species at high temperature were chlorides while there were some metals of which principal species were oxides. At low temperature mole fractions of the principal species increased, but at high temperature mole fractions of some metal species decreased. C/H ratio of the hydrocarbon fuel and excess air ratio had little effect on mole fractions of the metal species, compared to the temperature and chlorine concentration.

• Journal of The Korean Society of Agricultural Engineers
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• v.48 no.4
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• pp.33-40
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• 2006

In present study, the principal stress condition needed to conduct cubical large-scale triaxial test which can reflect three dimensional stress condition (or plain strain condition) in a dam was investigated by performing model test and numerical analysis and the principal stress ratio varying with the height of CFRD was examined. Also, the principal stress ratio in CFRD body was investigated from the monitoring results of horizontal and vertical earth pressure gages, installed in the center zone and lower part of transition zone of the dam body, respectively, in order to consider the principal stress condition in the large-scale triaxial test to model the behavior of CFRD. The result of the study indicated that the principal stress ratio decreased gradually from the lower to the upper part in the dam body for its center axis and was about 0.5 and 0.2 in the lower and upper part, respectively.

• The Korean Journal of Applied Statistics
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• v.23 no.5
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• pp.967-975
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• 2010

Since the least squares estimation is not appropriate when multicollinearity exists among the regressors of the linear regression model, the principal components regression is used to deal with the multicollinearity problem. This article suggests a new procedure for the selection of suitable principal components. The procedure is based on the condition index instead of the eigenvalue. The principal components corresponding to the indices are removed from the model if any condition indices are larger than the upper limit of the cutoff value. On the other hand, the corresponding principal components are included if any condition indices are smaller than the lower limit. The forward inclusion method is employed to select proper principal components if any condition indices are between the upper limit and the lower limit. The limits are obtained from the linear model which is constructed on the basis of the conjoint analysis. The procedure is evaluated by Monte Carlo simulation in terms of the mean square error of estimator. The simulation results indicate that the proposed procedure is superior to the existing methods.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.3
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• pp.79-84
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• 2021

A feature extraction method capable of reflecting features well while mainaining the properties of data is required in order to process high-dimensional data. The principal component analysis method that converts high-level data into low-dimensional data and express high-dimensional data with fewer variables than the original data is a representative method for feature extraction of data. In this study, we propose a principal component analysis method based on adaptive correlation when selecting principal component variables in principal component analysis for data feature extraction when the data is high-dimensional. The proposed method analyzes the principal components of the data by adaptively reflecting the correlation based on the correlation between the input data. I want to exclude them from the candidate list. It is intended to analyze the principal component hierarchy by the eigen-vector coefficient value, to prevent the selection of the principal component with a low hierarchy, and to minimize the occurrence of data duplication inducing data bias through correlation analysis. Through this, we propose a method of selecting a well-presented principal component variable that represents the characteristics of actual data by reducing the influence of data bias when selecting the principal component variable.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.35 no.3
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• pp.243-249
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• 1999

Specificity of catches has been analyzed to japanese tuna purse seine A principle component analysis was used to improve the efficiency of fishing and increase sustainable production and productivity of Korean tuna purse seine.The result are as follows;From the principal component analysis of the fish catches, the first principal component(Z1) to promote principal component score was skipjack Kastsuwonus Pelamis, LINNAEUS and yellowfin tuna Thunnus Albacares, BONNATERRE (Small : smaller than 10kg) and proportion was 86.8% of total. The second principal component(Z2) to increase principal component score was yellowfin tuna (Large : larger than 10kg) and proportion was 9.5%.On the other hand, fish operating that have caught skipjack and yellowfin tuna (Small and Larger) was not so much. Fish catches for one species raised volume of the catches while catches for multi-species decreased it since principal composition score for one species and both species together has been increased.Fish school could be divided into three groups of schools each of which was associated with drift objects, payaho and ship, school associated with shark, whale and porpoise and school of breezing, feeding and jumping from proportion of principal component analysis for fish catches of school types. However, the biological pattern is different among school associated with ship, payaho and school associated with drift objects for analysis eigen vector. School associated with ship, payaho and school associated with drifting object associated is judged as school which be assembled to vessel and drifted log temporary.

• The Korean Journal of Applied Statistics
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• v.30 no.3
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• pp.311-321
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• 2017

Principal component analysis is a popular statistical method to reduce the dimension of the high dimensional climate data and to extract meaningful climate patterns. Based on the principal component analysis, we can further apply a regression approach for the linear prediction of future climate, termed as principal component regression (PCR). In this paper, we develop a new PCR method based on the regularized principal component analysis for spatial data proposed by Wang and Huang (2016) to account spatial feature of the climate data. We apply the proposed method to temperature prediction in the East Asia region and compare the result with conventional PCR results.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.14 no.5
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• pp.62-73
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• 2005

The objects of this research are to establish the criteria of peel occurrence considering the shape of bond terminus and to compare the strength properties of adhesive joint of different three type such as butt joint, T-shape, and single lap Joints. The criteria of peel occurrence at the bond terminus was suggested. Peel loads of three type adhesive joint (butt Joint, T-shape specimen, single lap joint) were determined from tensile tests. Principal stress distributions of these joints were determined from finite element method analysis. Then, peel occurrence was estimated with stress singularity factor$(K_{prin})$ when the terminus shape was square, with average principal stress when the terminus shape was rounded. The conclusions are summarized as follows; (1) In the non-filleted model(e.g., butt joint, T-shape specimen), principal stress shows singularity at the bond terminus, intensity of stress(principal stress) singularity $(K_{prin})$ can use as the criteria of peel occurrence at the bond terminus. (2) In the filleted model(e.g., single lap joint), principal stress has not affected singularity at the bond terminus. Average principal stress$(K_{av})$ can use as the criteria of peel occurrence at the bond terminus.