• Journal of Information Technology Services
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• v.6 no.3
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• pp.111-128
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• 2007

The purpose of the research is to analyze the status and problems of the e-Learning quality assurance system on e-Learning contents and service provider(institutes) in the field of enterprise education. In addition, the research is to suggest the direction and strategies for revising and developing the system. The research put emphasis on two systems of the e-Learning quality assurance(contents, service provider) which directly influence financial support of government. This study depended mostly on literature review, supplemented by expert panel meetings. In the case of the quality assurance system on e-Learning contents, the followings are suggested; (1)admitting the contents made of the combination of modules in the approved module set, (2)making easier the qualifying of modified contents for maintenance, (3)revising evaluation criteria, (4)providing substantial feedback. In the field of service provider, the followings are requested; (1)differentiating of qualifying system by industry and scale of company, (2)extending the qualifying cycle, (3)improving the feedback and sharing system.

• Journal of Engineering Education Research
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• v.14 no.3
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• pp.61-71
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• 2011

Korea has introduced the levy-grand system in the vocational learning finance. The standard unit price system of training cost was utilized in the distribution of training budget and the reimbursement system including total or partial training cost return has been operated in the corporate training after completing the learning course particularly. The standard unit price was calculated in the base of analyzing on supporting budget by the government per training institutions and corporate payment decision to learning institutions. The proposing standard unit price system of training cost was analyzed in the current standard price unit of training cost and then an improvement policy and the implication are derived from it. At the result of this study, the current government supporting level to e-learning and postal distance learning indicates good status.

• Journal of the Semiconductor & Display Technology
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• v.19 no.4
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• pp.121-125
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• 2020

With miniaturization of semiconductor, the manufacturing process become more complex, and undetected small changes in the state of the equipment have unexpectedly changed the process results. Fault detection classification (FDC) system that conducts more active data analysis is feasible to achieve more precise manufacturing process control with advanced machine learning method. However, applying machine learning, especially in supervised learning criteria, requires an arduous data labeling process for the construction of machine learning data. In this paper, we propose a semi-supervised learning to minimize the data labeling work for the data preprocessing. We employed equipment status variable identification (SVID) data and optical emission spectroscopy data (OES) in silicon etch with SF6/O2/Ar gas mixture, and the result shows as high as 95.2% of labeling accuracy with the suggested semi-supervised learning algorithm.

• Journal of Korea Multimedia Society
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• v.23 no.8
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• pp.1059-1065
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• 2020

In hospitals, nurses are subjectively determining the urine status to check the kidneys and circulatory system of patients whose statuses are related to patients with kidney disease, critically ill patients, and nursing homes before and after surgery. To improve this problem, this paper proposes a urine spectrum analysis system which clusters urine test results based on a hybrid machine learning model consists of unsupervised learning and supervised learning. The proposed system clusters the spectral data using unsupervised learning in the first part, and classifies them using supervised learning in the second part. The results of the proposed urine spectrum analysis system using a mixed model are evaluated with the results of pure supervised learning. This paper is expected to provide better services than existing medical services to patients by solving the shortage of nurses, shortening of examination time, and subjective evaluation in hospitals.

• Progress in Medical Physics
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• v.30 no.2
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• pp.39-48
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• 2019

Deep learning has been applied to various medical data. In particular, current deep learning models exhibit remarkable performance at specific tasks, sometimes offering higher accuracy than that of experts for discriminating specific diseases from medical images. The current status of deep learning applications to molecular imaging can be divided into a few subtypes in terms of their purposes: differential diagnostic classification, enhancement of image acquisition, and image-based quantification. As functional and pathophysiologic information is key to molecular imaging, this review will emphasize the need for accurate biomarker acquisition by deep learning in molecular imaging. Furthermore, this review addresses practical issues that include clinical validation, data distribution, labeling issues, and harmonization to achieve clinically feasible deep learning models. Eventually, deep learning will enhance the role of theranostics, which aims at precision targeting of pathophysiology by maximizing molecular imaging functional information.

• Journal of Information Technology Applications and Management
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• v.21 no.3
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• pp.53-64
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• 2014

It is important to keep learners' feeling positive during learning to enhance learning performance. According to flow theory,challenge-skill balance is a precondition for flow experience: Learners feel anxiety when the challenge of learning is higher than their ability, feel boredom when the challenge of learning is lower than learners' ability, and engage in flow status when the challenge of learning matches the learners' ability. However, the current empirical study reveals that emotions related to enjoyment may appear when the learners' skill is equal to or higher than the learning challenge. Nevertheless, boredom emotion may appear when learners perceive the courses are difficult but unimportant. These empirical survey results revealed the necessary of rethinking the appearance of boredom and enjoyment emotions in computer education.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.2
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• pp.283-287
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• 2020

In this paper, we implemented a control for ventilation system based on IoT. It can on/off of system and monitoring current status through the smartphone app. We applied linear regression, one of machine learning algorithm. It autonomously collects data about temperature, humidity in home and works diagnosing system status. Using this proposed control method, the energy efficiency can be improved. It is expected to be used in energy efficiency and convenience.

• Journal of Engineering Education Research
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• v.18 no.3
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• pp.3-12
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• 2015

The aim of this paper is to provide teacher awareness and the status of robot based instruction(RBI) by focusing on the school curriculum. To gather that information, we conducted a questionnaire survey composed of six items to 116 teachers who have had experiences on RBI. The questions are about the fit school year for RBI, the fit subjects for it, the possibility of applying it to regular subject, the fit students' learning levels for it, the fit learning styles for it and effective methods to apply it to regular subject teachers. The result is as follows: (1) RBI is suitable for fifth and sixth grade in elementary school and all grades in high school. (2) It is suitable for all regular subjects in all schools. (3) It is more effective for the students who have average learning level. (4) It fits into introverted students more than the other style of learners. (5) It is likely to be more effective in supporting of learning and understanding of the contents than merely assisting the teachers' instruction. (6) The teachers showed positive awareness on applying RBI to subject of creative activities. The results are significant in relation to the following two views. First, we can get the positive possibility in applying school curriculum using RBI. Second we can foresee that RBI will provide an innovative paradigm to school curriculum. In addition, the results of this paper can be used as preliminary information for developing models and programs on RBI.

• International Journal of Internet, Broadcasting and Communication
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• v.14 no.4
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• pp.173-180
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• 2022

In the naked eye observation, the health of livestock can be controlled by the range of activity, temperature, pulse, cough, snot, eye excrement, ears and feces. In order to confirm the health of livestock, this paper uses calf face image data to classify the health status by image shape, color and texture. A series of images that have been processed in advance and can judge the health status of calves were used in the study, including 177 images of normal calves and 130 images of abnormal calves. We used GLCM calculation and Convolutional Neural Networks to extract 6 texture attributes of GLCM from the dataset containing the health status of calves by detecting the image of calves and learning the composite image of Convolutional Neural Networks. In the research, the classification ability of GLCM-CNN shows a classification rate of 91.3%, and the subsequent research will be further applied to the texture attributes of GLCM. It is hoped that this study can help us master the health status of livestock that cannot be observed by the naked eye.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.8 no.6
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• pp.1673-1681
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• 2007

The u-Learning just begins to emerge as the next-generation knowledge-based business. Since it has a great potential to become a high value-added industry, there is much attention paid in this field. In this work, we first summarized the concept of the u-Learning where the architecture of various u-learning areas has been identified. Then we investigated the current status and problems of the u-Learning industry. Through the SWOT analysis, we have extracted the political strategies that will be essential for the rapid industrialization of u-Learning which will, in turn, contribute much to enhance the competitiveness of national economy.