• Journal of Korea Multimedia Society
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• v.22 no.6
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• pp.706-715
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• 2019

This research implemented grammatical rules for relationship extraction from class diagram candidate. The problem statement is generated by our algorithm to yield class diagram and candidate relationship candidates. The relationships of class diagrams are extracted automatically from the problem statement by using Natural Language Processing (NLP). The extraction used the grammatical rules that obtained from various sources and translated into our algorithm. The performance evaluation of the extraction algorithm used ATM problem statements. The application captures the problem statement and draws automatically the relations of class diagrams using Forced-Directionality Graph algorithm. The performance evaluations show refining methods for class diagram and relationships extraction improve recall score.

• Journal of the Korean Institute of Rural Architecture
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• v.12 no.4
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• pp.21-28
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• 2010

As the issues such as safety, hygiene, convenience of school facility from the perspective of living environment for students and teachers arise as an important task in recent, there is increasing concern for resting convenience facility in school. In recent, the government is planning to change middle high school into the departmental classroom system. The departmental classroom system refers to the school operation system in which the classroom is structured as a subject-specialized classroom and the students move to the classroom according to their class schedules. This departmental classroom system is advantageous in that each subject can be equipped with professional facilities or preparations; the usage rate of the classroom can be enhanced; the common study space or living space can be used substantially; the school can plan and operate its own specialized space. On the contrary, it has the weaknesses that there is need to prepare the home base for the guidance of the class or student guidance; there is need for the facilities including personal lockers. Thus, the departmental classroom system requires the space expansion for home base or the resting convenience space for students and thus has to provide various spaces. Under this background, it is also important to check and plan the resting space for the schools. Therefore, this study is aimed to examine and analyze the arrangement location and space Layout of the resting convenience space among school facilities to propose the architectural data for future resting/convenience spaces for the schools.

• Journal of the Architectural Institute of Korea Planning & Design
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• v.35 no.4
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• pp.61-68
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• 2019

As high schools implement credit completion system these days, concerns about the dissolution of classes, which are the original stable groups of studying and living, and the instability of the basal space, is growing due to the extended operation of moving optional classes. The purpose of this research is to understand the effect that environmental features of the basal space within the school and the operation method have on the students' space use and formation of attachment place within the school. For this, the main activity places, attachment places, school life satisfaction and others were investigated at 2 Japanese credit-based comprehensive high schools, which are different in the physical environmental features of school buildings. Based on this, a quantitative analysis about the distribution of activity places and attachment places was implemented. The space use features for each student attribute were compared, and the school life satisfaction for each type of attachment place formation was analyzed. As a result, the change of the territorial consciousness about the class space according to the implementation of moving optional classes could be understood. And it was confirmed that the students' space using behavior and place evaluation change according to the physical environmental feature of the class space and common space, and that this is affecting the life satisfaction of students.

• Bulletin of the Korean Space Science Society
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• 2004.04a
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• pp.95-95
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• 2004

The objectives of this study are to perform extensive analysis on internal mass motion for a wider parameter space and to provide suitable design criteria for a broader applicability for the class of spinning spacecraft. In order to examine the stability criterion determined by an analytical method, some numerical simulations will be performed and compared at various parameter points. (omitted)

• Korean Journal of Mathematics
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• v.6 no.1
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• pp.47-66
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• 1998

In this paper, we introduce an $L_p$ analytic Fourier-Feynman transformation, show the existence of the $L_p$ analytic Fourier-Feynman transforms for a certain class of cylinder functionals on an abstract Wiener space, and investigate its interesting properties. Moreover, we define a convolution product for two functionals on the abstract Wiener space and establish the relationships between the Fourier-Feynman transform for the convolution product of two cylinder functionals and the Fourier-Feynman transform for each functional.

• Communications of the Korean Mathematical Society
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• v.27 no.1
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• pp.137-148
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• 2012

Recently Ding [4, 5, 8] gives examples of his FC-spaces which are not L-spaces due to Ben-El-Mechaiekh et al. [1]. We show that they are actually L-spaces. We also clarify that all statements in [5] can be stated in corrected and generalized forms for the class of abstract convex spaces beyond FC-spaces.

• Honam Mathematical Journal
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• v.40 no.2
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• pp.293-304
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• 2018

In this paper, we study ${\varphi}$-recurrent almost cosymplectic (${\kappa},{\mu}$)-space and prove that it is an ${\eta}$-Einstein manifold with constant coefficients. Next, we show that a three-dimensional locally ${\varphi}$-recurrent almost cosymplectic (${\kappa},{\mu}$)-space is the space of constant curvature.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.8
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• pp.3984-4005
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• 2017

Image recognition has become an increasingly important topic for its wide application. It is highly challenging when facing to large-scale database with large variance. The recognition systems rely on a key component, i.e. the low-level feature or the learned mid-level feature. The recognition performance can be potentially improved if the data distribution information is exploited using a more sophisticated way, which usually a function over hidden variable, model parameter and observed data. These methods are called generative score space. In this paper, we propose a discriminative extension for the existing generative score space methods, which exploits class label when deriving score functions for image recognition task. Specifically, we first extend the regular generative models to class conditional models over both observed variable and class label. Then, we derive the mid-level feature mapping from the extended models. At last, the derived feature mapping is embedded into a discriminative classifier for image recognition. The advantages of our proposed approach are two folds. First, the resulted methods take simple and intuitive forms which are weighted versions of existing methods, benefitting from the Bayesian inference of class label. Second, the probabilistic generative modeling allows us to exploit hidden information and is well adapt to data distribution. To validate the effectiveness of the proposed method, we cooperate our discriminative extension with three generative models for image recognition task. The experimental results validate the effectiveness of our proposed approach.

• Journal of KIISE
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• v.42 no.6
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• pp.817-822
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• 2015

Relation extraction is an important information extraction technique that can be widely used in areas such as question-answering and knowledge population. Previous studies on relation extraction have been based on supervised machine learning models that need a large amount of training data manually annotated with relation categories. Recently, to reduce the manual annotation efforts for constructing training data, distant supervision methods have been proposed. However, these methods suffer from a drawback: it is difficult to use these methods for collecting negative training data that are necessary for resolving classification problems. To overcome this drawback, we propose a one-class classification model that can be trained without using negative data. The proposed model determines whether an input data item is included in an inner category by using a similarity measure based on lexical information and syntactic patterns in a vector space. In the experiments conducted in this study, the proposed model showed higher performance (an F1-score of 0.6509 and an accuracy of 0.6833) than a representative one-class classification model, one-class SVM(Support Vector Machine).

• Bulletin of the Korean Space Science Society
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• 2006.10a
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• pp.61-61
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• 2006