• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.2
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• pp.245-250
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• 2005

With rapid development of Internet, new paradigm creation requirement about the education environment and method is increasing and also the e-learning to apply traditional education industry was introduced in many field of education. The research about a learner's satisfaction of the e-learning, aided by effort to spread this e-learning, have been processed much but most of these researches were intended for the enterprise and there are few for the high school. Therefore, in this study we proposed a model for evaluating the education satisfaction of the e-learning and analyzed the consciousness structure about the e-learning education satisfaction of the high school students using Fuzzy Structural Modeling method. Also, constructing an evaluation model considered the results of consciousness structure analysis, we evaluated the e-learning education satisfaction and showed a method which improved it by the sensitivity analysis.

• Journal of the Korea Fashion and Costume Design Association
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• v.18 no.2
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• pp.145-159
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• 2016

This study developed the learning criteria for Science, Technology, Engineering, Arts & Mathematics to establish the theoretical background of the education pursued by STEAM. The learning criteria was developed on a basis of 6 kinds of Technology Home Economics textbooks by 2009 Amended Curriculum, and the factors of STEAM were extracted according to related contents. From the results of this study, the unit 'Dress and Self-expression' assimilated T.E.A.M with learning related to clothing psychology, consumer behavior, fashion design, and Korean fashion. The unit 'eco-friendly clothing and fixing clothes' was found to assimilate S.T.E.A.M. with learning related to clothes science and dress structure. Accordingly we can understand this unit also consists of the S. T. E. A. M assimilation such as clothes science, fashion marketing, dress structure, dress aesthetics, design and so on. Both units 'dress and self-expression' and 'eco-friendly clothing and fixing clothes' were found to consist of suggesting situations, creative planning and emotional experience following the learning criteria of STEAM. Therefore, these units will be the basic material for developing STEAM programs centering upon 'Home Economics' among the curriculum.

• Journal of The Korean Association of Information Education
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• v.3 no.2
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• pp.85-93
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• 2000

The good courseware must have the me끼ts of computer and must be designed according to principles of instruction. Therefore, we propose a model of learning structure for tutorial web courseware on the basis of elaboration theory that can give us the suitable design strategies of contents for web courseware. This model is suited to the level learning and individual learning because it is constructed of six factors - epitome, precedence learning, basis learning, depth learning, summarizer, synthesizer. We also make a tutorial web courseware to apply this model and evaluate effects in comparison with the existing tutorial web coursewares.

• Computers and Concrete
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• v.32 no.3
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• pp.313-326
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• 2023

The seismic safety of the shear wall structure can be assessed through seismic fragility analysis, which requires high computational costs in estimating seismic demands. Accordingly, machine learning methods have been applied to such fragility analyses in recent years to reduce the numerical analysis cost, but it still remains a challenging task. Therefore, this study uses the ensemble machine learning method to present an improved framework for developing a more accurate seismic demand model than the existing ones. To this end, a rank-based selection method that enables determining an excellent model among several single machine learning models is presented. In addition, an index that can evaluate the degree of overfitting/underfitting of each model for the selection of an excellent single model is suggested. Furthermore, based on the selected single machine learning model, we propose a method to derive a more accurate ensemble model based on the bagging method. As a result, the seismic demand model for which the proposed framework is applied shows about 3-17% better prediction performance than the existing single machine learning models. Finally, the seismic fragility obtained from the proposed framework shows better accuracy than the existing fragility methods.

• Journal of Digital Convergence
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• v.14 no.2
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• pp.245-258
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• 2016

This paper aims to present the way to bring about significant results through performance improvement of learning algorithm in the research applying to machine learning. Research papers showing the results from machine learning methods were collected as data for this case study. In addition, suitable machine learning methods for each field were selected and suggested in this paper. As a result, SVM for engineering, decision-making tree algorithm for medical science, and SVM for other fields showed their efficiency in terms of their frequent use cases and classification/prediction. By analyzing cases of machine learning application, general characterization of application plans is drawn. Machine learning application has three steps: (1) data collection; (2) data learning through algorithm; and (3) significance test on algorithm. Performance is improved in each step by combining algorithm. Ways of performance improvement are classified as multiple machine learning structure modeling, $+{\alpha}$ machine learning structure modeling, and so forth.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.5
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• pp.728-733
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• 2018

Deep learning has been actively studied for predicting protein secondary structure based only on the sequence information of the amino acids constituting the protein. In this paper, we compared the performances of the convolutional neural networks of various structures to predict the protein secondary structure. To investigate the optimal depth of the layer of neural network for the prediction of protein secondary structure, the performance according to the number of layers was investigated. We also applied the structure of GoogLeNet and ResNet which constitute building blocks of many image classification methods. These methods extract various features from input data, and smooth the gradient transmission in the learning process even using the deep layer. These architectures of convolutional neural networks were modified to suit the characteristics of protein data to improve performance.

• International Journal of Contents
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• v.9 no.4
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• pp.11-15
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• 2013

"Protein Folding Problem" is considered to be one of the "Great Challenges of Computer Science" and prediction of disordered protein is an important part of the protein folding problem. Machine learning models can predict the disordered structure of protein based on its characteristic of "learning from examples". Among many machine learning models, we investigate the possibility of multilayer perceptron (MLP) as the predictor of protein disorder. The investigation includes a single hidden layer MLP, multi hidden layer MLP and the hierarchical structure of MLP. Also, the target node cost function which deals with imbalanced data is used as training criteria of MLPs. Based on the investigation results, we insist that MLP should have deep architectures for performance improvement of protein disorder prediction.

• Journal of the Korea Society for Simulation
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• v.3 no.2
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• pp.69-76
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• 1994

Traditionally, simulation-based learning games which are known as flight-simulators have been constructed as a black-box game. Within a black-box game, game-players can view and modify only a part of model parameters. Game-players cannot change the structure of a simulation model. In a black-box game, game-players cannot understand and learn the system structure which is responsible for the system behavior. In this paper, the multi-tasking at the level of operating systems is exploited to enhance the transparency of simulation-based learning game. The white-box game or transparent-box game allows game-players ot view and modify the model structure. The multi-tasking solution for white-box learning game is implemented with Smalltalk language on MS-/windows operating system.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.35C no.2
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• pp.48-55
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• 1998

Structural learning methods of MLP classifiers for a given application using genetic algorithms have been studied. In the methods, however, the search space for an optimal structure is increased exponentially for the physical application of high diemension-multi calss. In this paperwe propose a method of MLP classifiers using species genetic algorithm(SGA), a modified GA. In SGA, total search space is divided into several subspaces according to the number of hidden units. Each of the subdivided spaces is called "species". We eliminate low promising species from the evoluationary process in order to reduce the search space. experimental results show that the proposed method is more efficient than the conventional genetic algorithm methods in the aspect of the misclassification ratio, the learning rate, and the structure.structure.

• Journal of Digital Convergence
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• v.17 no.2
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• pp.47-57
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• 2019

This study analyze whether the organizational learning culture affects the firm's dynamic capacity and whether the dynamic capacity mediates the relationship between organizational learning culture and management performance. Respectively. First, "The more organizational structure is organic, the higher the integrated relocation capacity and learning capacity. Organizations with organic organizational structures were found to have the ability to successfully adapt to external changes because there is a practice that is not tied to formal processing or procedures. Second, it can be seen that there is a positive (+) influence on the relocation capacity among the dynamic competence of the learning culture of the corporate organization. Third, both sub-factors of dynamic competence have positive (+) influence on business performance. Also, there was no mediating effect of dynamic competence related to learning culture.