• Journal of The Korean Association of Information Education
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• v.10 no.1
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• pp.143-152
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• 2006

The aim of this study was to analyze effects of a blended learning program based on case study approach and to suggest implications in appropriately evaluating blended learning in practices for corporate education and training. In order to achieve the goal, issues such as the ones related to blended learning including development and status quo of blended learning programs in the field of corporate education and training and operation models for the blended learning were reviewed. Then, the outcomes of a blended learning program were completely analyzed through systems approach. The methodology of the study was a mixed research method which was comprised of quantitative and qualitative approaches. The results of quantitative analysis showed that blended learning itself seemed to have significant effects on the leadership capability in general assessment and self assessment. The most viable effects of blended learning in leadership training are said to be actual change of actions and activities in leadership capability of the participants followed by changes in their job tasks contributing to improving the managerial performance of the company, good transfer to current job tasks, and implementation of the practice plans.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.1
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• pp.30-45
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• 2024

This study introduces an effective method for predicting individual local tax delinquencies using prevalent machine learning and deep learning algorithms. The evaluation of credit risk holds great significance in the financial realm, impacting both companies and individuals. While credit risk prediction has been explored using statistical and machine learning techniques, their application to tax arrears prediction remains underexplored. We forecast individual local tax defaults in Republic of Korea using machine and deep learning algorithms, including convolutional neural networks (CNN), long short-term memory (LSTM), and sequence-to-sequence (seq2seq). Our model incorporates diverse credit and public information like loan history, delinquency records, credit card usage, and public taxation data, offering richer insights than prior studies. The results highlight the superior predictive accuracy of the CNN model. Anticipating local tax arrears more effectively could lead to efficient allocation of administrative resources. By leveraging advanced machine learning, this research offers a promising avenue for refining tax collection strategies and resource management.

• Journal of Korea Game Society
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• v.13 no.3
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• pp.141-152
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• 2013

This research aims at suggesting a case of designing theme-based integrated learning for usage smart media. New approach and direction for developing a gaming instructional design was suggested which can encourage learners to participate in. Quest-based learning, the learning environment design where learners conduct various learner-centered activities, plays an important role of reinforcing the motivation, promoting experiential and cooperative learning based on social interaction. The design using QR codes has been proved to be able to offer the learner-centered learning environment which includes social interaction strategy required for learners expanding their cognitive structure, motivation enhancing strategy encouraging their consistent participation in learning, complex problematic situation and scaffolding strategy emphasized by constructivism. And it is expected to contribute to promoting the design of theme-based integrated learning which is being demanded in the educational environment recently by combining systematic design process and strategies.

• Journal of the Korea Institute of Building Construction
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• v.17 no.1
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• pp.101-109
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• 2017

This study aims to quantitatively analyze impacts of the team-based learning (TBL) method on learning of architectural engineering students. A TBL-based education program, consisted of preparation, readiness assurance, and application phases, was proposed by considering characteristics of architectural engineering education in South Korea and was applied in building construction classes. In order to measure learning effects and satisfaction levels of the proposed TBL-based education program, a set of questionnaires was conducted with students who took the building construction classes. As the results, learning effects and satisfaction levels of the TBL-based approach were higher than those of traditional approaches. Individual and team readiness assurance tests in the readiness assurance phase were the most effective and satisfactory items, while assessment in the application phase was the least effective and satisfactory item.

• Smart Structures and Systems
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• v.24 no.5
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• pp.567-585
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• 2019

In the past two decades, structural health monitoring (SHM) systems have been widely installed on various civil infrastructures for the tracking of the state of their structural health and the detection of structural damage or abnormality, through long-term monitoring of environmental conditions as well as structural loadings and responses. In an SHM system, there are plenty of sensors to acquire a huge number of monitoring data, which can factually reflect the in-service condition of the target structure. In order to bridge the gap between SHM and structural maintenance and management (SMM), it is necessary to employ advanced data processing methods to convert the original multi-source heterogeneous field monitoring data into different types of specific physical indicators in order to make effective decisions regarding inspection, maintenance and management. Conventional approaches to data analysis are confronted with challenges from environmental noise, the volume of measurement data, the complexity of computation, etc., and they severely constrain the pervasive application of SHM technology. In recent years, with the rapid progress of computing hardware and image acquisition equipment, the deep learning-based data processing approach offers a new channel for excavating the massive data from an SHM system, towards autonomous, accurate and robust processing of the monitoring data. Many researchers from the SHM community have made efforts to explore the applications of deep learning-based approaches for structural damage detection and structural condition assessment. This paper gives a review on the deep learning-based SHM of civil infrastructures with the main content, including a brief summary of the history of the development of deep learning, the applications of deep learning-based data processing approaches in the SHM of many kinds of civil infrastructures, and the key challenges and future trends of the strategy of deep learning-based SHM.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.11
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• pp.1456-1462
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• 2016

In this paper, we apply a chord progression learning model to a rule-based generation of a four-part chorus. The proposed system is given a 32-note melody line and completes the four-part chorus based on the rule of harmonics, predicting the chord progression with the CRBM model. The data for the training model was collected from various harmony textbooks, and chord progressions were extracted with key-independent features so as to utilize the given data effectively. It was shown that the output piece obtained with the proposed learning model had a more natural progression than the piece that used only the rule-based approach.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.5
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• pp.400-405
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• 2001

This paper addresses an effective approach of training neural networks classifiers for credit card fraud detection. The proposed approach uses evolutionary programming to trails the neural networks classifiers based on maximization of the detection rate of fraudulent usages on some ranges of the rejection rate, loot minimization of mean square error(MSE) that Is a common criterion for neural networks learning. This approach enables us to get classifier of satisfactory performance and to offer a directive method of handling various conditions and performance measures that are required for real fraud detection applications in the classifier training step. The experimental results on "real"credit card transaction data indicate that the proposed classifiers produces classifiers of high quality in terms of a relative profit as well as detection rate and efficiency.

• Journal of the Korean School Mathematics Society
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• v.5 no.1
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• pp.13-19
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• 2002

In this experiment we emphasized the cooperative small group learning and the members of my group worked together to succeed and communicate their mathematics ideas freely. The researcher(teacher) became an observer and facilitator of small group interaction, paying attention to the ongoing learning process, Sometimes the researcher suggested some investigation approach(or discovery)being written by computer software or papers. In this experiment we provided 6 activities as follows : (1) changing the conditions in given problem. (2) operating the meaningful heuristics with the problem sets. (3) creating the problem situations related to understanding (4) creating the Modeling situations. (5) creating the problem related to combinatorial thinking in real world. (6) posing some real problem from real world. we could observed several conjectures First, Attitude and chility to interpret the problem setting is highly important to pose the problem effectively. Second, Generating the understanding can be a great tool to pose the problem effectively. Third, Sometimes inquiry approach represented by software or programmed book could be some motivation to enhance the posing activities. Forth, The various posing activities relate to one concept could give the students some opportunity to be adaptable and flexible in the their approach to unfamiliar problem sets.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 1999.06a
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• pp.29-34
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• 1999

We present a practical method for removing biotches and restoring their mission data. To detect blotches, we employ a robust approach of local analysis of spatiotemporal anisotropic brightness continuity Our approach uses first-order spatiotemporal directional derivatives to select the smoothest direction for each examined pixel, and puts out the incorruption probability that he examined pixel may not be corrupted by blotches. As the restoration filter, were employ a spatiotemporal fuzzy filter whose response is adaptively controlled according to a fuzzy rule defined by the incorruption probability. The fuzzy filter is composed of the two different filter of the identity filter and the spatiotemporal directional-weighted-mean filter, and will put out an intermediate value between the original input brightness and the directional-weighted-mean brightness. We design the fuzzy rule in advance by a standard supervised learning fuzzy rule in advance by a standard supervised learning method. The computer simulations are presented.

• Journal of Electrical Engineering and Technology
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• v.11 no.6
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• pp.1872-1879
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• 2016

The techniques to recycle and reuse plastics attract public attention. These public attraction and needs result in improving the recycling technique. However, the identification technique for black plastic wastes still have big problem that the spectrum extracted from near infrared radiation spectroscopy is not clear and is contaminated by noise. To overcome this problem, we apply Raman spectroscopy to extract a clear spectrum of plastic material. In addition, to improve the classification ability of fuzzy Radial Basis Function Neural Networks, we apply supervised learning based clustering method instead of unsupervised clustering method. The conditional fuzzy C-Means clustering method, which is a kind of supervised learning based clustering algorithms, is used to determine the location of radial basis functions. The conditional fuzzy C-Means clustering analyzes the data distribution over input space under the supervision of auxiliary information. The auxiliary information is defined by using k Nearest Neighbor approach.