• Journal of the Korea Society of Computer and Information
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• v.27 no.3
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• pp.209-216
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• 2022

In this paper, we propose an effective software education program to promote employment of the disabled and verify the effectiveness of SW education through pilot operation. In this SW education program, we develop a SW curriculum consisting of the basic course, Unity programming course, and the advanced course, AR/VR digital content development course. The SW education achievement standard develops the basic and advanced course achievement standards in consideration of the level of the virtual reality content production job of the National Competency Standards(NCS) and the SW education achievement standards of youth with visual, hearing, and physical disabilities. SW education materials are developed on a project basis so that one AR/VR digital content can be implemented step by step according to the intellectual level of the disabled based on Unity. SW education pilot training is conducted as online education based on Blended Learning due to COVID-19. In order to derive the SW education effect and each learner's individual SW education academic achievement for the SW education pilot training, a survey is conducted on learners, and the results are analyzed. In the basic course, 77.3% of learners achieved academic achievement above excellent(80-90), and in the advanced course, 48.8% of learners achieved academic achievement above excellent(80-90). These results verify that the SW education program for the disabled developed in this paper is effective in activating employment for the disabled.

• Composites Research
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• v.35 no.5
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• pp.303-308
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• 2022

PIC (Piecewise Integrated Composite) is a new concept for designing a composite structure with mosaically assigning various types of stacking sequences in order to improve mechanical properties of laminated composites. Also, machine learning is a sub-category of artificial intelligence, that refers to the process by which computers develop the ability to continuously learn from and make predictions based on data, then make adjustments without further programming. In the present study, the tapered box beam type PIC robot arm for carrying and transferring wide and thin LCD display was designed based on the machine learning in order to increase structural stiffness. Essential training data were collected from the reference elements, which were intentionally designated elements among finite element models, during preliminary FE analysis. Additionally, triaxiality values for each finite element were obtained for judging the dominant external loading type, such as tensile, compressive or shear. Training and evaluating machine learning model were conducted using the training data and loading types of elements were predicted in case the level accuracy was fulfilled. Three types of stacking sequences, which were to be known as robust toward specific loading types, were mosaically assigned to the PIC robot arm. Henceforth, the bending type FE analysis was carried out and its result claimed that the PIC robot arm showed increased stiffness compared to conventional uni-stacking sequence type composite robot arm.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.514-517
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• 2021

The purpose of this study is to discover the occupational identity by examining the narrative of the life of a vocational training teacher with self-esteem in programming fields. The following six types of occupational identity were found: 'a positive image of a vocational training teacher(fits oneself)', 'I feel proud of myself while doing vocational training activities.', 'a teacher who continues to develop him/herself as an expert in the subject class', 'a teacher who immerses him/herself as an expert on student change and growth', 'a teacher engaged in leading activities to create opportunities for vocational training', and 'a teacher of continuous pursuit'. This study has significance in exploring the structure of occupational identity recognition and experience of its formation of a self-esteemed vocational training teacher in programming fields, which have not been studied.

• Journal of Gifted/Talented Education
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• v.25 no.2
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• pp.339-358
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• 2015

This study was to examine perceptions of information technology(IT) competencies among gifted and non-gifted students(i.e., information science high school students and technical high school students). Of the 370 high school students surveyed from 3 high schools(i.e., gifted academy, information science high school, and technical high school) in three metropolitan cities, Korea, 351 students completed and returned the questionnaires yielding a total response rate of 94.86%. High school students recognized the IT professional competence as being most important when recruiting IT employees. And they considered that practice-oriented education was the most importantly needed to improve their IT skills. In addition, the most important sub-factors of IT core competencies among gifted academy students and information science high school students were basic software skills. Also Technical high school students responded that the main network and security capabilities were the most importantly needed to do so. Finally, the most appropriate training courses for enhancing IT competencies were recognized differently among gifted and non-gifted students. Gifted academy students responded that the 'algorithm' was the mostly needed for enhancing IT competencies, whereas information science high school students responded that 'data structures' and 'computer architecture' were mostly needed to do. For technical high school students, they responded that a 'programming language' course was the most needed to do so. Results are discussed in relations to IT corporate and school settings.

• Journal of the Korean Geographical Society
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• v.39 no.4
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• pp.643-664
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• 2004

This study aims to review what geographers have contributed to GIS industries and national needs. To-be-geographers and geographers are expected to meet the gap between what we have teamed in school and what we have to do after graduation. The characteristics of GIS industry in the 1990 are summarized with approximate evaluation of the contribution of geographers in each stage. Author introduced the requirement for the licenses of geomatics and geospatial engineering experts and the other licenses, which are important to get a job in GIS industry from 2003 to 2004. A set of questionnaire on the user's requirements was given to GIS people in private companies and public GIS research centers and analyzed. Author found that they put an emphasis on hands-on experiences and programming skills. no advantages or geography such as capability or integration and inter-disciplinary collaboration were not appreciated. The prospects for the GIS tend to be positive but the reflectance of the prospect was not accompanied by at the same degree of preference for geography. Most government strategies for the next ten years' GIS focus on new-growth leading industries. SWOT(strength, weakness, opportunity, threat) analysis of geography for GIS industry will give some directions such as telematics, regional marketing strategies with web-based GIS technology, location based service. That means intra-disciplinary study in geography will evoke the potentiality of GIS, compared with interdisciplinary studies.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.1-19
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• 2019

Artificial intelligence (AI) is one of the main driving forces leading the Fourth Industrial Revolution. The technologies associated with AI have already shown superior abilities that are equal to or better than people in many fields including image and speech recognition. Particularly, many efforts have been actively given to identify the current technology trends and analyze development directions of it, because AI technologies can be utilized in a wide range of fields including medical, financial, manufacturing, service, and education fields. Major platforms that can develop complex AI algorithms for learning, reasoning, and recognition have been open to the public as open source projects. As a result, technologies and services that utilize them have increased rapidly. It has been confirmed as one of the major reasons for the fast development of AI technologies. Additionally, the spread of the technology is greatly in debt to open source software, developed by major global companies, supporting natural language recognition, speech recognition, and image recognition. Therefore, this study aimed to identify the practical trend of AI technology development by analyzing OSS projects associated with AI, which have been developed by the online collaboration of many parties. This study searched and collected a list of major projects related to AI, which were generated from 2000 to July 2018 on Github. This study confirmed the development trends of major technologies in detail by applying text mining technique targeting topic information, which indicates the characteristics of the collected projects and technical fields. The results of the analysis showed that the number of software development projects by year was less than 100 projects per year until 2013. However, it increased to 229 projects in 2014 and 597 projects in 2015. Particularly, the number of open source projects related to AI increased rapidly in 2016 (2,559 OSS projects). It was confirmed that the number of projects initiated in 2017 was 14,213, which is almost four-folds of the number of total projects generated from 2009 to 2016 (3,555 projects). The number of projects initiated from Jan to Jul 2018 was 8,737. The development trend of AI-related technologies was evaluated by dividing the study period into three phases. The appearance frequency of topics indicate the technology trends of AI-related OSS projects. The results showed that the natural language processing technology has continued to be at the top in all years. It implied that OSS had been developed continuously. Until 2015, Python, C ++, and Java, programming languages, were listed as the top ten frequently appeared topics. However, after 2016, programming languages other than Python disappeared from the top ten topics. Instead of them, platforms supporting the development of AI algorithms, such as TensorFlow and Keras, are showing high appearance frequency. Additionally, reinforcement learning algorithms and convolutional neural networks, which have been used in various fields, were frequently appeared topics. The results of topic network analysis showed that the most important topics of degree centrality were similar to those of appearance frequency. The main difference was that visualization and medical imaging topics were found at the top of the list, although they were not in the top of the list from 2009 to 2012. The results indicated that OSS was developed in the medical field in order to utilize the AI technology. Moreover, although the computer vision was in the top 10 of the appearance frequency list from 2013 to 2015, they were not in the top 10 of the degree centrality. The topics at the top of the degree centrality list were similar to those at the top of the appearance frequency list. It was found that the ranks of the composite neural network and reinforcement learning were changed slightly. The trend of technology development was examined using the appearance frequency of topics and degree centrality. The results showed that machine learning revealed the highest frequency and the highest degree centrality in all years. Moreover, it is noteworthy that, although the deep learning topic showed a low frequency and a low degree centrality between 2009 and 2012, their ranks abruptly increased between 2013 and 2015. It was confirmed that in recent years both technologies had high appearance frequency and degree centrality. TensorFlow first appeared during the phase of 2013-2015, and the appearance frequency and degree centrality of it soared between 2016 and 2018 to be at the top of the lists after deep learning, python. Computer vision and reinforcement learning did not show an abrupt increase or decrease, and they had relatively low appearance frequency and degree centrality compared with the above-mentioned topics. Based on these analysis results, it is possible to identify the fields in which AI technologies are actively developed. The results of this study can be used as a baseline dataset for more empirical analysis on future technology trends that can be converged.