• Proceedings of the Korean Nuclear Society Conference
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• 2003.05a
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• pp.78.1-78.1
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• 2003

• Review of Korean Society for Internet Information
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• v.5 no.2
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• pp.89-95
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• 2004

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.4
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• pp.855-860
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• 2014

Currently, aircraft and automobile simulator for training provides a variety of training by making hypothetical situation on a simulator installed on the ground currently. And the instructor maximizes the effectiveness of the training by monitoring training and instructing the required training. When trainees are boarding the aircraft or automobile. The Instructor in the ground is not able to monitoring aircraft, automobile. The assessment of the training is not easy after the end of the training. Therefore, it is difficult to provide high quality of education to the students. In this paper, simulation system is to develop the following. Collecting GPS and real-time information for aircraft, automobile $\grave{a}$implementing 3D simulation. Implementing current image of the aircraft or automobile in the screen by 3D real-time monitoring of training situation at the control center utilizing for training saving 3D video files analysis, evaluation on training after the end of the training.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.18 no.36
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• pp.175-182
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• 1995

Industrial safety management program can be divided three part that is education, technology, and management. The effectiveness of a industrial safety management program depends on the ability to manage hardware which is technology and software, education and management, In this research, it will be described that how to design and develop Computer Integrated Safety System and Computer Based Training System for Press operations which is how to integrated industrial safety program wi th production planning and control in order to control efficiently using personnel computer system.

• International Journal of Computer Science & Network Security
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• v.22 no.4
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• pp.299-309
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• 2022

Human factor represents a very challenging issue to organizations. Human factor is responsible for many cybersecurity incidents by noncompliance with the organization security policies. In this paper we conduct a comprehensive review of the literature to identify strategies to address human factor. Security awareness, training and education program is the main strategy to address human factor. Scholars have consistently argued that importance of security awareness to prevent incidents from human behavior.

• Proceedings of the Korea Database Society Conference
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• 1997.10a
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• pp.24-48
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• 1997

. Supported byㆍDARPA′s image Understanding (IU) program under "Video Retrieval Based on Language and image Analysis" project.DARPA′s Computer Assisted Education and Training Initiative program (CAETI)ㆍObjective: Develop practical systems for automatic understanding and indexing of video sequences using both audio and video tracks(omitted)

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2010.05a
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• pp.943-944
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• 2010

• Proceedings of the Korean Institute of Communication Sciences Conference
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• 2004.07a
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• pp.225-225
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• 2004

• Journal of the Korea Society of Computer and Information
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• v.18 no.4
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• pp.19-26
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• 2013

Pseudo sample neural network (PSNN) is a variant of traditional neural network using pseudo samples to mitigate the local-optima-convergence problem when the size of training samples is small. PSNN can take advantage of the smoothed solution space through the use of pseudo samples. PSNN has a focus on the quantity problem in training, whereas, methods stressing the quality of training samples is presented in this paper to improve further the performance of PSNN. It is evident that typical samples and highly correlated features help in training. In this paper, therefore, kernel density estimation is used to select typical samples and correlation factor is introduced to select features, which can improve the performance of PSNN. Debris flow data set is used to demonstrate the usefulness of the proposed methods.

• Journal of the Korea Society of Computer and Information
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• v.19 no.9
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• pp.125-139
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• 2014

In this paper, I propose a response modeling with a Semi-Supervised Support Vector Regression (SS-SVR) algorithm. In order to increase the accuracy and profit of response modeling, unlabeled data in the customer dataset are used with the labeled data during training. The proposed SS-SVR algorithm is designed to be a batch learning to reduce the training complexity. The label distributions of unlabeled data are estimated in order to consider the uncertainty of labeling. Then, multiple training data are generated from the unlabeled data and their estimated label distributions with oversampling to construct the training dataset with the labeled data. Finally, a data selection algorithm, Expected Margin based Pattern Selection (EMPS), is employed to reduce the training complexity. The experimental results conducted on a real-world marketing dataset showed that the proposed response modeling method trained efficiently, and improved the accuracy and the expected profit.