• Journal of Industrial Convergence
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• v.17 no.3
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• pp.39-49
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• 2019

This study, as a study on building platforms for smart campus ecosystem, took an approach that reflected the needs of various stakeholders of smart campus, and focused on functions to help them strengthen their competitiveness and advance into society by focusing on the learning of the most important university student users, college life, and social connection. First, we looked at the theories related to smart campus construction through prior research, and next, through domestic and international environmental analysis and trend analysis, we designed and presented a target model for e-portfolio focusing on core competency development and support system for Industry-Academic Cooperation, and proposed the main point for continuous smart campus development model.

• Journal of Convergence for Information Technology
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• v.12 no.5
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• pp.40-50
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• 2022

This study is a qualitative study in which nursing students analyzed the contents of nursing skills recorded on a structured video observation record sheet while observing a colleague team's real-time video of chronic obstructive pulmonary disease scenario implementation during simulation practice. As a result of the analysis using the content analysis method, categories and topics of effective and ineffective aspects were derived in the areas of observation record: accuracy of procedures, adherence to aseptic technique, consideration of safety and safety, explanation and education, and purpose explanation and method education. This study is meaningful in that it presents factors that can increase the efficiency of nursing education through simulation-based practice.

• Journal of KIISE:Software and Applications
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• v.29 no.3
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• pp.156-166
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• 2002

Decision tree induction is a kind of useful machine learning approach for extracting classification rules from a set of feature-based examples. According to the partitioning style of the feature space, decision trees are categorized into univariate decision trees and multivariate decision trees. Due to observation error, uncertainty, subjective judgment, and so on, real-world data are prone to contain some errors in their feature values. For the purpose of making decision trees robust against such errors, there have been various trials to incorporate fuzzy techniques into decision tree construction. Several researches hove been done on incorporating fuzzy techniques into univariate decision trees. However, for multivariate decision trees, few research has been done in the line of such study. This paper proposes a fuzzy decision tree induction method that builds fuzzy multivariate decision trees named fuzzy oblique decision trees, To show the effectiveness of the proposed method, it also presents some experimental results.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.3
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• pp.43-50
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• 2021

Recently, researches and projects of companies based on artificial intelligence have been actively carried out. Various services and systems are being grafted with artificial intelligence technology. They become more intelligent. Accordingly, interest in deep learning, one of the techniques of artificial intelligence, and people who want to learn it have increased. In order to learn deep learning, deep learning theory with a lot of knowledge such as computer programming and mathematics is required. That is a high barrier to entry to beginners. Therefore, in this study, we designed and implemented a web-based deep learning platform called DeepBlock, which enables beginners to implement basic models of deep learning such as DNN and CNN without considering programming and mathematics. The proposed DeepBlock can be used for the education of students or beginners interested in deep learning.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.4
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• pp.835-842
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• 2012

Operating Systems is a discipline which handles abstract concepts and techniques. However, most of OS courses have been textbook-oriented theoretical classes. Theoretical classes lead to the decline in the understanding of a lecture, and hurt their concentration. Many instructors have tried to make use of educational tools to help students understand lectures and arouse interests. This paper describes the design and implementation of a CPU scheduling simulator which shows the operation of process scheduling algorithms visually. The academic achievement evaluation for 2010's students and 2011's and t-test results show that the differences of the correct answer ratio for the exam about CPU scheduling algorithms are meaningful. The survey shows that the simulator is useful as an educational tool which causes the interests and enhances the understanding of a lecture, this teaching method is effective to develop problem solving skills.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.1A
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• pp.35-40
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• 2011

The complex-valued blind algorithm based on a set of randomly generated symbols and Euclidean distance can take advantage of information theoretic learning and cope with the channel phase rotation problems. On the algorithm, in this paper, the effect of kernel size has been studied and a kernel-modified version of the algorithm that rearranges the forces between the information potentials by kernel-modification has been proposed. In simulation results for 16 QAM and complex-channel models, the proposed algorithm show significantly enhanced performance of symbol-point concentration and no phase rotation problems caused by the complex channel models.

• Journal of The Korean Association of Information Education
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• v.9 no.1
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• pp.147-158
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• 2005

Computer education was started first step as the subject matter at 7th curriculum. Up to the present, It grown up the research scope continuously bat has several issues about education as subject matter. This paper discusses the issues related to speciality development of computer subject matter of elementary school. It make inquiries into the present state about it and is proposed theoretical base for systematic frameworks of it First of all, It is analyzed the curriculum, information infrastructure, human resource, learner, support system, contents related to computer subject matter of elementary school. Based on it, It is proposed concept, structure, scopes of the computer subject matter and a reform measure of teacher education program.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.16 no.11
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• pp.1201-1209
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• 1991

Generally, the conventional tracking algorithms are very limited in the practical applications because of its exponential increase in the required computation time for the number of targets being tracked. Therefore, in this paper, a new real-time moving target tracking system is proposed, which is based on the neural networks with massive parallel processing capabilities. Through the theoretical and experimental results, the target tracking system based on neural network algorithm is analyzed to be computationally independent of the number of objects being tracked and performs the optimized tracking through its massive parallel computation and learning capabilities. And this system also has massive matched filtering effects because the moving target data can be compactly stored in the interconnection weights by learning. Accordingly, a possibility of the proposed neural network target tracking system can be suggested to the fields of real-time application.

• The Journal of Korean Association of Computer Education
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• v.10 no.3
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• pp.1-8
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• 2007

In this paper, we introduce a new measure called surprisal that estimates the informativeness of transactional instances and attributes in the item response dataset and improve the quality of association rules. In order to this, we set artificial dataset and eliminate noisy and uninformative data using the surprisal first, and then generate association rules between items. And we compare the association rules from the dataset after surprisal-based pruning with support-based pruning and original dataset unpruned. Experimental result that the surprisal-based pruning improves quality of association rules in question item response datasets significantly.

• The Transactions of the Korea Information Processing Society
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• v.6 no.7
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• pp.1825-1831
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• 1999

In this paper, to reduce misrecognized characters, we propose the new method that extract features from character to apply to the character recognition using features from character to apply to the character recognition using fractal dimensions and attractors. Firstly, to reduce the load of recognizer we classify the characters. For the classified character, we extract the features for Box-counting dimensions. Natural Measures, Information dimensions then recognize characters. With histogram, we generate attractors and calculate dimensions from attractors. Then we recognize characters with dimensions of characters and attractors. An experimental result that the overall recognition rates for the training data and testing data are 96.03% and 91.74% respectively. This result shows the effectiveness of proposed method.