• Fourth Industrial Review
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• v.2 no.2
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• pp.1-9
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• 2022

Purpose - The purpose is to present an efficient library-use education model in the form of flip learning, reflecting traditional teaching methods and gamification elements even in such non-face-to-face and face-to-face situations after COVID-19. Research design, data, and methodology - Research on library use education, research on ubiquitous environment and gamification instructional design, flip the learning, and gamification elements are classified, compared, and analyzed to present educational models for library education, COVID-19 Pandemic situation, and subsequent library use education. Result - We propose an e-learning content development strategy for flipped learning-based library education. First, benchmark and use the existing educational contents. Second, a user-friendly interface is configured so that learners can flexibly organize their learning contents. Third, it allows learners to experience it directly or indirectly in a virtual space. Conclusion - If the e-learning environment can be standardized to the level of schools or educational institutions, a good educational model that can be used not only in library user education but also in other fields will be possible.

• Journal of The Korean Digital Architecture Interior Association
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• v.12 no.3
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• pp.101-109
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• 2012

Recently, the roof garden of the large commercial facilities has been changed as a complex cultural space follow the users' diverse needs. It is focused on large commercial facilities on Seoul and Gyeonggi area but large commercial facilities of the province still has not been able to respond to the needs of users. Thus, the present condition of roof gardens was compared space components with physical environmental factors in Seoul, Gyeonggi and Daejeon, Chungcheong. Throughout the comparison, roof gardens in Daejeon and Chungcheong area were focused on finding ways to improve As a result, roof gardens in Seoul and Gyeonggi area were applied to a variety of factors in space components experience, learning, performance, exhibition and etc. It is composed to take place of various acts in the roof garden. On the other hand, roof gardens in Daejeon and Chungcheong were not applied a variety space components by comparing Seoul and Gyeonggi area. Physical environmental factors also were insufficient by comparing Seoul and Gyeonggi area the landscape, awning facilities, amenities for handicapped. As described above, space components and physical environmental factors in Daejeon and Chungcheong area roof gardens were insufficient. To perform the role of the roof garden changing as a complex cultural space and to confront to the users' needs space component such as learning, performance, exhibition should be considered during planning. And physical environmental factors supportable space components should be considered together.

• Journal of Korean Home Economics Education Association
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• v.13 no.1
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• pp.55-72
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• 2001

This study is to develop teaching aids in the area of Clothing and Textiles based on cooperative learning as an alternative to increase the quality of Home Economics class. It is very difficult to control students because of noise coming from activities. when teachers teach students with cooperative learning strategy a classroom with restricted space. Sometimes the activities will disturb other classes in the school. which will harm the real purpose of cooperative learning. However. we have more positive results from cooperative learning method. because students have more chance to improve their personal skills. easily solve a problem together and openly express their opinions. Moreover. we found that they understand the subjects and increase their attention much better because they are tested right after learning. We anticipate that the developed teaching aids will be a great help to improve middle school Home Economics class.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.6
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• pp.565-570
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• 2015

Supervised machine learning has become popular in discovering context descriptions from sensor data. However, collecting a large amount of labeled training data in order to guarantee good performance requires a great deal of expense and time. For this reason, semi-supervised learning has recently been developed due to its superior performance despite using only a small number of labeled data. In the existing semi-supervised learning algorithms, unlabeled data are used to build a graph Laplacian in order to represent an intrinsic data geometry. In this paper, we represent the unlabeled data as the spatial-temporal dataset by considering smoothly moving objects over time and space. The developed algorithm is evaluated for position estimation of a smartphone-based robot. In comparison with other state-of-art semi-supervised learning, our algorithm performs more accurate location estimates.

• Journal of Korea Multimedia Society
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• v.21 no.4
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• pp.464-470
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• 2018

With the introduction of the e-learning since 2000, the place of the education has not been limited to off-line, but the range of it has become broader in online. The e-learning market has evolved steadily over time. With the advent of the term "Edu-tech", which means a combination of education and technology, various IT technologies have incorporated education. Particularly, the Korean education market collects patterns by computerizing the learning history in classes taught according to curriculums. Because of that environment, various personalized learning services have been developed which maximize the effect of the learning. These services have qualitative differences depending on how many data is accumulated and algorithms are developed for the precise analysis. The purpose of this study is to recognize and data-ize OMR marking by the most suitable method to convert analog data into digital data without harming the Korean education system.

• Journal of Korea Multimedia Society
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• v.20 no.11
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• pp.1793-1802
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• 2017

In the recent years, IT and Network Technology has rapidly advanced environment in accordance with the needs of the times, the usage of the smart learning service is increasing. Smart learning is extended from e-learning which is limited concept of space and place. This system can be easily exposed to the various security threats due to characteristic of wireless service system. Therefore, this paper proposes the improvement methods of smart learning system security by use of faults analysis methods such as the FTA(Fault Tree Analysis) and FMECA(Failure Mode Effects and Criticality Analysis) utilizing the consolidated analysis method which maximized advantage and minimized disadvantage of each technique.

• Journal of The Korean Association of Information Education
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• v.7 no.1
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• pp.1-10
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• 2003

Web provides a rich learning environment for learners mainly due to the hypertext technology and the high interactivity. It provides a learner-centered learning environment. In this environment, students are to develop their own paths in non-sequential and flexible ways according to their needs and their demands. However, in many cases students who use this environment are easily exposed to difficulties in their choices of learning paths, disorientation, and cognitive overload in the cyberspace. Consequently, they have trouble in maintaining their motivation and interest in their learning. This study presents the design and the use of 3D-based space navigation system in order to maintain student's interest in educational web contents.

• The Journal of the Acoustical Society of Korea
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• v.39 no.4
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• pp.246-254
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• 2020

In this paper, we propose a learning based compressive sensing algorithm for the purpose of side scan sonar image denoising. The proposed method is based on Iterative Shrinkage and Thresholding Algorithm (ISTA) framework and incorporates a powerful strategy that reinforces the non-linearity of deep learning network for improved performance. The proposed method consists of three essential modules. The first module consists of a non-linear transform for input and initialization while the second module contains the ISTA block that maps the input features to sparse space and performs inverse transform. The third module is to transform from non-linear feature space to pixel space. Superiority in noise removal and memory efficiency of the proposed method is verified through various experiments.

• Database Research
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• v.34 no.3
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• pp.3-13
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• 2018

Text classification is one of the text mining technologies that classifies a given textual document into its appropriate categories and is used in various fields such as spam email detection, news classification, question answering, emotional analysis, and chat bot. In general, the text classification system utilizes machine learning algorithms, and among a number of algorithms, naïve Bayes and support vector machine, which are suitable for text data, are known to have reasonable performance. Recently, with the development of deep learning technology, several researches on applying deep neural networks such as recurrent neural networks (RNN) and convolutional neural networks (CNN) have been introduced to improve the performance of text classification system. However, the current text classification techniques have not yet reached the perfect level of text classification. This paper focuses on the fact that the text data is expressed as a vector only with the word dimensions, which impairs the semantic information inherent in the text, and proposes a neural network architecture based upon the semantic tensor space model.

• Journal of the Korea Society of Computer and Information
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• v.14 no.3
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• pp.139-147
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• 2009

The memory based reasoning just stores in the memory in the form of the training pattern of the representative pattern. And it classifies through the distance calculation with the test pattern. Because it uses the techniques which stores the training pattern whole in the memory or in which it replaces training patterns with the representative pattern. Due to this, the memory in which it is a lot for the other machine learning techniques is required. And as the moreover stored training pattern increases, the time required for a classification is very much required. In this paper, We propose the EAS(Evaluation And Selection) algorithm in order to minimize memory usage and to improve classification performance. After partitioning the training space, this evaluates each partitioned space as MDL and PM method. The partitioned space in which the evaluation result is most excellent makes into the representative pattern. Remainder partitioned spaces again partitions and repeat the evaluation. We verify the performance of Proposed algorithm using benchmark data sets from UCI Machine Learning Repository.