• The Journal of the Korea Contents Association
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• v.9 no.9
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• pp.97-104
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• 2009

This paper describes comparison and analysis of methodology which enables us in order to search the projection technique of optimum for projection in the plane. For this methodology, we applies the high-dimensional facial motion capture data respectively in linear and nonlinear projection techniques. The one core element of the methodology is to applies the high-dimensional facial expression data of frame unit in PCA where is a linear projection technique and Isomap, MDS, CCA, Sammon's Mapping and LLE where are a nonlinear projection techniques. And another is to find out the methodology which distributes in this low-dimensional space, and analyze the result last. For this goal, we calculate the distance between the high-dimensional facial expression frame data of existing. And we distribute it in two-dimensional plane space to maintain the distance relationship between the high-dimensional facial expression frame data of existing like that from the condition which applies linear and nonlinear projection techniques. When comparing the facial expression data which distribute in two-dimensional space and the data of existing, we find out the projection technique to maintain the relationship of distance between the frame data like that in condition of optimum. Finally, this paper compare linear and nonlinear projection techniques to projection high-dimensional facial expression data in low-dimensional space and analyze it. And we find out the projection technique of optimum from it.

• The Journal of the Korea Contents Association
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• v.19 no.1
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• pp.524-534
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• 2019

This study was to analyze the students' engagement in regular curriculum and extra-curriculum and its effects on learning outcomes in higher education. Students' engagement was analysed by high order learning, reflective and integrative learning, learning strategies, collaborative learning, discussions with diverse others, and high impact activities. To achieve the purpose of this study, 392 students joined in K-NSSE were participated. To analyze the datum, frequency analysis, ANOVA, correlation analysis, and regression analysis were performed using IBM SPSS 25.0 program. The following results were obtained. First, students' engagement was generally very low, especially in high impact activities which has an effect on the students' achievement. And compared to the students' engagement in the college of humanity and social science, the students' engagement in engineering college were very low. Learning outcomes were influenced by the high impact activities, high-order learning, and discussions with diverse others. So to reinforce students' engagement in learning process, this study proposed a curriculum-extracurriculum integrated system. And to improvement students' engagement, teaching and learning support programs including high impact activities. high order learning, and discussions with diverse others were proposed to be developed and operated.

• Proceedings of the Korean Society of Applied Pharmacology
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• 1994.04a
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• pp.214-214
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• 1994

본 실험실에서 개발한 대표적인 함아연 가수분해 효소인 carboxypeptidase A에 선택적으로 작용하는 mechanism-based inactivator의 설계법을 ACE 억제제 개발에 적용하여 고혈 압강하효과가 있을 것으로 기대되는 새로운 형태의 ACE 억제제들을 합성하였다. 그 대표적인 합성경로는 아래와 같다. (Figure Omitted)

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2010.05a
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• pp.219-220
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• 2010

• Proceedings of the Korean Information Science Society Conference
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• 2008.06a
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• pp.333-334
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• 2008

• Proceedings of the Korean Information Science Society Conference
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• 2007.06a
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• pp.23-24
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• 2007

• The Korean Journal of Applied Statistics
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• v.26 no.5
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• pp.747-758
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• 2013

The covariance matrix is important in multivariate statistical analysis and a sample covariance matrix is used as an estimator of the covariance matrix. High dimensional data has a larger dimension than the sample size; therefore, the sample covariance matrix may not be suitable since it is known to perform poorly and event not invertible. A number of covariance matrix estimators have been recently proposed with three different approaches of shrinkage, thresholding, and modified Cholesky decomposition. We compare the performance of these newly proposed estimators in various situations.

• Journal of Digital Contents Society
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• v.19 no.4
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• pp.731-737
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• 2018

The recommendation system is actively researched for the purpose of suggesting information that users may be interested in in many fields such as contents, online commerce, social network, advertisement system, and the like. However, there are many recommendation systems that propose based on past preference data, and it is difficult to provide users with little or no data in the past. Therefore, interest in higher-order data analysis is increasing and Matrix Factorization is attracting attention. In this paper, we study and propose a comparison and replay of the Factorization Machines Leaning(FM) model which is attracting attention in the recommendation system and High-Order Factorization Machines Learning(HOFM) which is a high - dimensional data analysis.

• The Journal of Korean Association of Computer Education
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• v.9 no.3
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• pp.47-65
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• 2006

For the purpose of analysis, the supporting methods for inquiry process is divided into 3 types; when CSILE supports low-level of basic inquiry process, when CSILE supports high-level of integrated inquiry process and when CSILE supports both low-level and high-level of inquiry process. Strauss and Corbin's(1998) grounded theory was used to analyze inquiry process of learning groups. 48 elementary school students in 6th grade participated in this study. Those participants were assigned into 3 groups and each group consisted of 16 students. Then, participants studied a retarded unit in science subject cooperatively for 4 weeks using CSILE program. Through this extensive experiment, 3 types of inquiry model was revealed.

• The Korean Journal of Applied Statistics
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• v.33 no.2
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• pp.115-122
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• 2020

K-means clustering uses a spherical or elliptical metric to group data points; however, it does not work well for non-convex data such as the concentric circles. Spectral clustering, based on graph theory, is a generalized and robust technique to deal with non-standard type of data such as non-convex data. Results obtained by spectral clustering often outperform traditional clustering such as K-means. In this paper, we review spectral clustering and show important issues in spectral clustering such as determining the number of clusters K, estimation of scale parameter in the adjacency of two points, and the dimension reduction technique in clustering high-dimensional data.