• The Journal of Korean Association of Computer Education
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• 제21권1호
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• pp.43-50
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• 2018

The 2015 Revised National Curriculum emphasizes software education to develop creative and convergent talents in preparation of the Fourth Industrial Revolution. Accordingly, it is necessary to develop competency-based training programs for informatics teachers in a rapidly changing educational environment. In this background, this study selects a framework to analyze the content of in-service training for informatics teachers through review of previous studies. By analyzing the current training programs to strengthen competencies required for informatics teachers in secondary schools, the study aims to develop implications for future in-service training programs. To this end, the study conducted a questionnaire survey of experts who participated in the development of in-service training textbooks and consulted them, then analyzed the elements of competency-based training program content and the relative importance of each competency element using the analytical hierarchy process (AHP). According to the results of the analysis, the content was relatively concentrated on the competency of "Understanding and Reconstructing the National Curriculum" required for general and informatics teachers as well, which accounted for 47% of all, or 7 hours out of the total 15 hours. In contrast, the content structure lacked the competency of highly relative importance by competency element "Establishing and Using Teaching-Learning Strategies for Informatics," which took up the highest portion of 27%. These findings will be used as basic data for understanding and reflecting the areas that fall short of the development of in-service training programs for informatics teachers.

• Journal of the Korea Computer Graphics Society
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• 제26권3호
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• pp.111-121
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• 2020

Realistic training contents utilizing intensive immersion of virtual reality are being used in various fields such as industry, education, and medical care. High-risk, high-cost education training, in particular, is difficult to conduct in reality, but it can be applied with the latest virtual reality technology that enhances educational effectiveness by efficiently and safely experiencing it in an environment similar to reality. This study introduces a management system that systematically manages realistic virtual training contents and visualizes training results in schematic pictures based on defined evaluation elements. The management system can store the information generated from the content in the database and manage the training records of each trainee in a practical way. In addition, a content-based scenario can be created in multiple scenarios by setting training goals, number of participants, and methods for applying evaluation elements. This paper describes the management system's production method and the results based on the virtual reality training content as an application example.

• Journal of Digital Contents Society
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• 제13권3호
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• pp.335-342
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• 2012

The purpose of the study was to develop a computer access assessment system for improving computer access of individuals with physical disabilities for the first time in Korea. Korea-Computer Access Assessment System (K-CAAS) presented in this article tests five user skills of aim, drag, menu, scan, and switch. Skill levels of the five test areas could be set as low, middle, and high according to individuals' characteristics. All tests have their default set in each level, and skill levels can be selected and controlled by the abilities and goals of a user. Tests could be selected for users' training and their results could be traced and shown. Therefore, the K-CAAS is a training program to improve computer access skills as well as an assessing program. It would help users with physical disabilities operate a computer by themselves as improving their computer access skills.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권6호
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• pp.2709-2729
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• 2016

Representation based classification, kernel method and sparse representation have received much attention in the field of face recognition. In this paper, we proposed an improved kernel principal component analysis method based on sparse representation to improve the accuracy and robustness for face recognition. First, the distances between the test sample and all training samples in kernel space are estimated based on collaborative representation. Second, S training samples with the smallest distances are selected, and Kernel Principal Component Analysis (KPCA) is used to extract the features that are exploited for classification. The proposed method implements the sparse representation under ℓ₂ regularization and performs feature extraction twice to improve the robustness. Also, we investigate the relationship between the accuracy and the sparseness coefficient, the relationship between the accuracy and the dimensionality respectively. The comparative experiments are conducted on the ORL, the GT and the UMIST face database. The experimental results show that the proposed method is more effective and robust than several state-of-the-art methods including Sparse Representation based Classification (SRC), Collaborative Representation based Classification (CRC), KCRC and Two Phase Test samples Sparse Representation (TPTSR).

• 한국정보교육학회:학술대회논문집
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• 한국정보교육학회 2010년도 하계학술대회
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• pp.51-57
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• 2010

When you look at computer training techniques, generally you will see an unbalanced focus towards the software applications being used. Office programs, including software applications, or simply the ability to operate has been assigned in the attempt to develop more information. Omitted, while a students thought process dealing with computer applications is usually clear and effective, the functional oriented tasks involved are time consuming. In order to keep up with the pace of today changing requirements, creativity and problems solving ability is a necessity. These are the areas in which both our training techniques are inefficient, and the resulting ability of student's is unsatisfactory. In this study of 5thand6thgradelevelcomputerteachingtechniques.

• 한국정보교육학회:학술대회논문집
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• 한국정보교육학회 2010년도 하계학술대회
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• pp.169-177
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• 2010

This study is to make a comparison with actual condition on the computer specialty and aptitude for elementary school of city and farm with questionnairing and to hit with improvement plan. They have several difference from supplying teachers, bankrolling educational expenses but several points of sameness on requiring professional ability, operating study and training, limiting the receiving. Accordingly, if leading the quality improvement of the computer specialty and aptitude by forming stable employing mood, operating various programs, bankrolling educational expenses, extending study and training opportunity in elementary school, students will get opportunities to develope their ability and aptitude.

• Journal of Multimedia Information System
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• 제6권4호
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• pp.225-234
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• 2019

Deep learning has become increasingly popular in both academic and industrial areas nowadays. Various domains including pattern recognition, Computer vision have witnessed the great power of deep neural networks. However, current studies on deep learning mainly focus on quality data sets with balanced class labels, while training on bad and imbalanced data set have been providing great challenges for classification tasks. We propose in this paper a method of data analysis-based data reduction techniques for selecting good and diversity data samples from a large dataset for a deep learning model. Furthermore, data sampling techniques could be applied to decrease the large size of raw data by retrieving its useful knowledge as representatives. Therefore, instead of dealing with large size of raw data, we can use some data reduction techniques to sample data without losing important information. We group PCB characters in classes and train deep learning on the ResNet56 v2 and SENet model in order to improve the classification performance of optical character recognition (OCR) character classifier.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 한국방송공학회 2009년도 IWAIT
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• pp.803-807
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• 2009

The bobsleigh is a winter sport which use a sled to slide down an ice-covered course. There is a big expectation for having a training environment and being able to train year round. At present, training is very limited due to the season or course facilities. A variety of VR (Virtual Reality) equipment has been developed in recent years, and it is beginning to spread. We have also made our contribution in bobsleigh simulation. The reactive force applied in our bobsleigh simulation is much smaller than that of a real bobsleigh. This paper proposes a method to enhance reactive force of bobsleigh simulation in real time. The reactive force is magnified instantly in the physically-based simulation. The Laplacian filter is applied to the sequence of reactive force, this technique is often used in the field of image processing. The simulation is comprised of four large scale surround screens and a 6-D.O.F. (Degree Of Freedom) motion system. We also conducted an experiment with several motion patterns to evaluate the effectiveness of enhancement. The experimental results proved useful in some cases.

• Journal of Information Processing Systems
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• 제9권3호
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• pp.395-404
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• 2013

In order to classify a web page as being benign or malicious, we designed 14 basic and 16 extended features. The basic features that we implemented were selected to represent the essential characteristics of a web page. The system heuristically combines two basic features into one extended feature in order to effectively distinguish benign and malicious pages. The support vector machine can be trained to successfully classify pages by using these features. Because more and more malicious web pages are appearing, and they change so rapidly, classifiers that are trained by old data may misclassify some new pages. To overcome this problem, we selected an adaptive support vector machine (aSVM) as a classifier. The aSVM can learn training data and can quickly learn additional training data based on the support vectors it obtained during its previous learning session. Experimental results verified that the aSVM can classify malicious web pages adaptively.

• Annual Conference on Human and Language Technology
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• 한국정보과학회언어공학연구회 2006년도 제18회 한글 및 한국어 정보처리 학술대회
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• pp.32-39
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• 2006

본 논문은 한국어정보처리 과정에서 구문 관계를 의미 관계로 사상하는 의미역 결정 문제에 대해 다루고 있다. 한국어의 경우 대량의 학습 말뭉치를 구하기 힘들며, 이를 구축하기 위해서는 많은 시간과 노력이 필요한 문제점이 있다. 따라서 본 논문에서는 학습 말뭉치를 직접 태깅하지 않고 격틀사전을 이용하여 자동으로 학습 말뭉치를 구축하고 간단한 확률모델을 적용하여 점진적으로 모델을 학습하는 수정된 self-training 알고리즘을 사용하였다. 실험 결과, 4개의 부사격 조사에 대해 평균적으로 81.81%의 정확률을 보였으며, 수정된 self-training 방법은 기존의 방법에 비해 성능 및 실행시간에서 개선된 결과를 보였다.