• Journal of architectural history
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• v.17 no.4
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• pp.129-152
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• 2008

Early Buddhist rock cave temples of India, in spite of being an origin of Buddhist temples, has little been studied in Korea. After field studies and an interpretation of their forms in conjunction with religious life, precedent theories are supplemented and refuted as follows. Starting from the 2nd century B,C., Buddhist ascetic disciples digged residential rock caves, called vihara, for protection from monsoon rain and hot weather, A typical arrangement was settled -a courtyard type, with 3 side rows of tiny one-person bedroom and a front veranda with columns. Also digged were Chaitya caves, in line with viharas, to worship, which is the tumulus of Buddha's relics. I suggest that the original type of chaitya a simple circle cave with a stupa, suitable for circumambulating ceremonies. I refute the existing theory presenting Barabar caves of Ajivika as a chaitya origin, featuring empty circular room without a stupa. I also interpret a typical apsidal plan as being a simple result of adding a place of worshipping rites in front of the stupa. Enclosing columns around a cylindrical stupa is a result of reinforcing both the divine space and circumambulating ceremonies, with elongation toward hall. Finally the chaitya came to have a grandeur apsidal plan with high vault ceiling nave and a side aisle as in Western cathedrals with large frontal horseshoe arch windows. The Buddha image, which had become a new worshipping object, was integrated into the stupa and interior surface. First the stupa and then the statue was introduced to residential Viharas. Therefore, I suggest that the vihara should be renamed as 'chaitya' as a worshipping place, by establishing statue rooms without bedrooms at all. The functionally changed vihara is similar in form to a 'rectangular type of chaitya', little known and developed in different routes. A columned inner courtyard gradually becama an offering place, like Hindu mandapa, Buddhist caves ware changed to a kind of Tantric and Hindu temple by means of statue worshipping offering rituals.

• The Bulletin of The Korean Astronomical Society
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• v.35 no.2
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• pp.49.1-49.1
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• 2010

The study of pores, small penumbraless sunspots, can give us a chance to understand how strong magnetic fields interact with convective motions in the photosphere. For a better understanding of this interaction, we investigate the temporal variation of several tiny pores smaller than 2". These pores were observed by the Solar Optical Telescope (SOT) onboard Hinode on 2006 December 29. We have analyzed the high resolution spectropolarimetric (SP) data and the G-band filtergrams taken during the observation. Magnetic flux density and Doppler velocities of the pores are estimated by applying the center of gravity (COG) method to the SP data. The horizontal motions in and around the pores are tracked by adopting the Nonlinear Affine Velocity Estimator (NAVE) method to the G-band filter images. As results, we found the followings. (1) Darkness of pores is positively correlated with magnetic flux density. (2) Downflows always exist inside and around the pores. (3) The speed of downflows inside the pores is negatively correlated with their darkness. (4) The pores are surrounded by strong downflows. (5) Brightness changes of the pores are correlated with the divergence of mass flow (correlation coefficient > 0.9). (6) The pores in the growing phase are associated with the converging flow pattern and the pores in the decay phase with the diverging flow pattern. Our results support the idea that a pore grows as magnetic flux density increases due to the convergence of ambient mass flow and it decays with the decrease of the flux density due to the diverging mass flow.

• Journal of architectural history
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• v.23 no.6
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• pp.7-20
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• 2014

The purpose of this paper is to study on the architecture and extension project for the Church of St. Lorenzo. This study is composed of a through analysis of the extension elements on the church building and extension type for extension project of the Church of St. Lorenzo. The results of study are as follows: 1. The extension project for the Church of St. Lorenzo was basically consisted of the concept of a horizontal extension plan through both the interior and exterior space of the church building. This project was the plan of concept of additional or affixing extension through existing spaces with necessary spaces to make up for the previous simple space and form as well as functional aspect. 2. The unit spaces of the nave, the aisle, the small chapels bilaterally adorning the aisles, the transept and the chapels around transepts, and the sacristies by the extension project for the Church of St. Lorenzo is composed of regulation, balance and harmony as geometric space composition according to simple proportion system on whole and parts as well as parts and parts, through the definite articulation by each space zoning. 3. The most important innovative aspect different to previous churches in the extension project for the Church of St. Lorenzo is the compositive system of each other symmetrical spaces through long centrical axis of central part, applying the module system for horizontal arrangement. In particular, the use of regular module on repetition and proportion of rhythm from architectural composition system was precisely composed with the alter that consists of visual focus to express more accurate perspective.

• Journal of Sasang Constitutional Medicine
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• v.10 no.1
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• pp.65-80
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• 1998

Proceeding an objective Study of sasang constitution diagnosis by Sound Analysis which uses Computed Sound lab(CSL), we verified the confidence level of Questionnaire of Sasang Constitution classification II(QSCC II) and the first results of Sound Analysis for verifying correlation between the physical character and Sound character are as follows. 1. The confidence level of QSCC II is 70.8% to Soeumin, 60.8% to Soyangin, 74.5% to Taeumin, and 70.08% in total. But, the actual results of verifying the confidence level after making 100 persons an object of study, are that the confidence level of that is 55.10% to Soeumin, 30.77% to Soyangin, 80.00% to Taeumin, and 55.29% in total. So it doesn't coincide with the confidence lecel of QSCC II 70.8%. 2. The results of verifying the confidence level about other 134 persons after enough explanation before the constitutional diagnosis by QSCC II are that the confidence of that is 71.08 to Soeumin, 54.76% to Soyangin 81.82% to Taeumin, and 69.22% in total. 3. The results of verifying the correlation between B.M.I. and Sasang Costitution are that there are significant differences below P<0.001 between Taeumin and Soeumin, and between Taeumin and Soyangin. 4. Height and Weight influence on a fundamental frequency and formant frequency. 5. There are differences for every constitutions in a amplitude when we nave a Sound analysis. As aboves, it is considered that we can find the differences among the constitutional groups, if we have a Sound analysis of the constitutional Sound characters.

• The Korean Journal of Pesticide Science
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• v.4 no.3
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• pp.34-39
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• 2000

A new fifteen 2,3-dihydro-2-ethyl-2,4,6,7-tetramethylbenzofuran-5-yl substituents in 5-benzofuryl-2-[1-(alkoxyimino)alkyl]-3-hydroxycyclohex-2-en-1-one derivatives as substrate were synthesized and their herbicidal activities against rice plant (Oryza sativa L.) and barnyard grass (Echinochloa crus-galli) with post emergence were measured under submerged conditions. The $R_{1}$=methyl substituents, $1{\sim}8$ showed the higher herbicidal activity to the rice plant and barnyard grass. The structure-activity relationships (SARs) on tile herbicidal activity of $R_{1}$ and $OR_{2}$ groups on the azomethine bond in substrates were analysized. From tile results of dicussed SAR, the herbicide activities against rice plant would depend largely on the steric factor, Whereas, in case of barnyard grass, the activities were governed by the hydrophobicity factor. The conditions of selective herbicide activity between the two plant species are assumed that the substrates should nave optimal hydrophobicity ( $(logP)_{opt.}=6.0$), a $R_{1}$ groups of small and a long $OR_{2}$ groups.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.23-45
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• 2020

Big data is creating in a wide variety of fields such as medical care, manufacturing, logistics, sales site, SNS, and the dataset characteristics are also diverse. In order to secure the competitiveness of companies, it is necessary to improve decision-making capacity using a classification algorithm. However, most of them do not have sufficient knowledge on what kind of classification algorithm is appropriate for a specific problem area. In other words, determining which classification algorithm is appropriate depending on the characteristics of the dataset was has been a task that required expertise and effort. This is because the relationship between the characteristics of datasets (called meta-features) and the performance of classification algorithms has not been fully understood. Moreover, there has been little research on meta-features reflecting the characteristics of multi-class. Therefore, the purpose of this study is to empirically analyze whether meta-features of multi-class datasets have a significant effect on the performance of classification algorithms. In this study, meta-features of multi-class datasets were identified into two factors, (the data structure and the data complexity,) and seven representative meta-features were selected. Among those, we included the Herfindahl-Hirschman Index (HHI), originally a market concentration measurement index, in the meta-features to replace IR(Imbalanced Ratio). Also, we developed a new index called Reverse ReLU Silhouette Score into the meta-feature set. Among the UCI Machine Learning Repository data, six representative datasets (Balance Scale, PageBlocks, Car Evaluation, User Knowledge-Modeling, Wine Quality(red), Contraceptive Method Choice) were selected. The class of each dataset was classified by using the classification algorithms (KNN, Logistic Regression, Nave Bayes, Random Forest, and SVM) selected in the study. For each dataset, we applied 10-fold cross validation method. 10% to 100% oversampling method is applied for each fold and meta-features of the dataset is measured. The meta-features selected are HHI, Number of Classes, Number of Features, Entropy, Reverse ReLU Silhouette Score, Nonlinearity of Linear Classifier, Hub Score. F1-score was selected as the dependent variable. As a result, the results of this study showed that the six meta-features including Reverse ReLU Silhouette Score and HHI proposed in this study have a significant effect on the classification performance. (1) The meta-features HHI proposed in this study was significant in the classification performance. (2) The number of variables has a significant effect on the classification performance, unlike the number of classes, but it has a positive effect. (3) The number of classes has a negative effect on the performance of classification. (4) Entropy has a significant effect on the performance of classification. (5) The Reverse ReLU Silhouette Score also significantly affects the classification performance at a significant level of 0.01. (6) The nonlinearity of linear classifiers has a significant negative effect on classification performance. In addition, the results of the analysis by the classification algorithms were also consistent. In the regression analysis by classification algorithm, Naïve Bayes algorithm does not have a significant effect on the number of variables unlike other classification algorithms. This study has two theoretical contributions: (1) two new meta-features (HHI, Reverse ReLU Silhouette score) was proved to be significant. (2) The effects of data characteristics on the performance of classification were investigated using meta-features. The practical contribution points (1) can be utilized in the development of classification algorithm recommendation system according to the characteristics of datasets. (2) Many data scientists are often testing by adjusting the parameters of the algorithm to find the optimal algorithm for the situation because the characteristics of the data are different. In this process, excessive waste of resources occurs due to hardware, cost, time, and manpower. This study is expected to be useful for machine learning, data mining researchers, practitioners, and machine learning-based system developers. The composition of this study consists of introduction, related research, research model, experiment, conclusion and discussion.