• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.11
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• pp.164-169
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• 2019

Defense R&D investment is expanding. This shows that major countries are preparing for future warfare by securing high-tech technologies and developing new concept weapons systems. In particular, it is expected to accelerate the development of the technology of the 4th Industrial Revolution in the future, and Korea needs its own ability to develop advanced weapons and medium- and long-term investment strategies to prepare for future warfare. The defense science and technology strategy will be established every five years. The strategy-dependent R&D drive has limitations in replacing the rapidly changing security environment and changes in science and technology. This study proposes an investment efficiency process to proactive incorporate information into R&D strategies with a focus on implementing policies and changing security threats, while maintaining continuity in which strategic and focused areas are linked to core technology development. The process can quickly reflect the needs of technological change, the security environment and defense policy. The process can be used to efficiently allocate defense R&D budgets and establish strategic investment directions.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.7
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• pp.537-543
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• 2020

In order to cultivate the talents needed in the 4th industrial revolution era, it is necessary to select gifted students and train them systematically. The affective characteristics of the gifted are self-concept, personality, sociality, motivation, morality, attitude and interest, and these are important factors that affect science achievement. In particular, computer scientific attitude is an important variable affecting computer science achievement. This study developed a computer scientific attitude test based on TOSRA developed by Fraser to measure the affective characteristics of information-gifted students. The computer scientific attitude test is composed of 7 areas: social implications of computer science, attitude to computer scientific inquiry, adoption of computer scientific attitudes, adoption of computer scientific attitudes, leisure interest in computer science, career interest in computer science, and normality of computer scientists. The relationship between computer scientific attitude and academic achievement of gifted students was analyzed using the developed test. To determine find out whether computer scientific attitude significantly predicts academic achievement, the results of a regression analysis showed that t = 2.543 and p = 0.025, indicating that the average of computer science attitude significantly predicted academic achievement.

• The Journal of Bigdata
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• v.6 no.1
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• pp.23-35
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• 2021

In the era of the 4th industrial revolution, smart factories have received great attention, where production and manufacturing technology and ICT converge. With the development of IoT technology and big data, automation of production systems has become possible. In the advanced manufacturing industry, production systems are subject to unscheduled performance degradation and downtime, and there is a demand to reduce safety risks by detecting and reparing potential errors as soon as possible. This study designs a model based on supervised and unsupervised learning for detecting anomalies. The accuracy of XGBoost, LightGBM, and CNN models was compared as a supervised learning analysis method. Through the evaluation index based on the confusion matrix, it was confirmed that LightGBM is most predictive (97%). In addition, as an unsupervised learning analysis method, MD, AE, and LSTM-AE models were constructed. Comparing three unsupervised learning analysis methods, the LSTM-AE model detected 75% of anomalies and showed the best performance. This study aims to contribute to the advancement of the smart factory by combining supervised and unsupervised learning techniques to accurately diagnose equipment failures and predict when abnormal situations occur, thereby laying the foundation for preemptive responses to abnormal situations. do.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2022.06a
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• pp.391-393
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• 2022

In recent years, technologies forecasting demand based on deep learning and big data have accelerated the smartification of the field of e-commerce, logistics and distribution areas. In particular, ports, which are the center of global transportation networks and modern intelligent logistics, are rapidly responding to changes in the global economy and port environment caused by the 4th industrial revolution. Port traffic forecasting will have an important impact in various fields such as new port construction, port expansion, and terminal operation. Therefore, the purpose of this study is to compare the time series analysis and deep learning analysis, which are often used for port traffic prediction, and to derive a prediction model suitable for the future container prediction of Busan Port. In addition, external variables related to trade volume changes were selected as correlations and applied to the multivariate deep learning prediction model. As a result, it was found that the LSTM error was low in the single-variable prediction model using only Busan Port container freight volume, and the LSTM error was also low in the multivariate prediction model using external variables.

• Journal of the Korea Society of Computer and Information
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• v.25 no.3
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• pp.243-250
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• 2020

In this paper, we propose an effective software education scheme for universities. The key idea of this software education scheme is to analyze software curriculum of QS world university rankings Top 10, SW-oriented university, and regional main national university. And based on the results, we propose five improvements for the effective SW education method of universities. The first is to enhance the adaptability of the industry by developing courses based on the SW developer's job analysis in the curriculum development process. Second, it is necessary to strengthen the curriculum of the 4th industrial revolution core technologies(cloud computing, big data, virtual/augmented reality, Internet of things, etc.) and integrate them with various fields such as medical, bio, sensor, human, and cognitive science. Third, programming language education should be included in software convergence course after basic syntax education to implement projects in various fields. In addition, the curriculum for developing system programming developers and back-end developers should be strengthened rather than application program developers. Fourth, it offers opportunities to participate in industrial projects by reinforcing courses such as capstone design and comprehensive design, which enables product-based self-directed learning. Fifth, it is necessary to develop university-specific curriculum based on local industry by reinforcing internship or industry-academic program that can acquire skills in local industry field.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.8
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• pp.359-369
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• 2020

This study was an exploratory study for the development of a competency-based social welfare curriculum. This study was conducted through a literature review focused on guidelines for social welfare published by the KCSWE (Korean Council for Social Welfare Education). The purpose of this study was to explore social welfare professional competencies, subject goals, teaching methods, and the participatory learning environment for the development of a competency-based social welfare curriculum. The findings are as follows. First, each university had been researching professional competencies individually. Second, the social welfare subjects tended to include all elements of knowledge, skills, and attitudes, and these goals were matched with professional competencies set by each university. Third, the guidelines for social welfare subjects provided various teaching methods to achieve the goals of the subject as well as a participatory learning environment based on discussion. However, it was difficult to determine whether the various teaching methods were effective in achieving core and professional competencies. Therefore, it is necessary for KCSWE to set up a standardized competency-based curriculum and research professional competencies based on social welfare, a curriculum and subjects according to their competencies, effective teaching methods, and a method for evaluating educational outcomes.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.12
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• pp.689-698
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• 2019

The purpose of this study was to develop methods to practice and revitalize entrepreneurship education at vocational high schools. To achieve this goal, we analyzed the vocational high school program as well as effective entrepreneurship education programs at vocational high schools. In addition, FGI (Focus Group Interview) was conducted to determine strategies for developing entrepreneurship education at vocational high schools. The results were as follows. First, curriculum formation was found to important for vitalizing entrepreneurship education at vocational high schools. It is necessary to develop vocational high schools to account for the 4th Industrial Revolution as well as develop students' competence in entrepreneurship as the basis for the curriculum. Second, the operational aspect of the entrepreneurship education curriculum must be considered. Entrepreneurship education linked to regular curriculum is needed. Third, the competence of school members is an important factor for the efficient operation of vocational high school entrepreneurship education. Fourth, entrepreneurship education can consist of various educational activities through connection with the school and community. Based on these results, operating vocational high school entrepreneurship education will enable practical and dynamic entrepreneurship education at vocational high schools.

• Journal of the Korea Society of Computer and Information
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• v.26 no.12
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• pp.151-158
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• 2021

Internet of Things (IoT) technology, which provides services by connecting various objects in the real world and objects in the virtual world based on the Internet, is emerging as a technology that enables a hyper-connected society in the era of the 4th industrial revolution. Since IoT technology is a convergence technology that encompasses devices, networks, platforms, and services, various studies are being conducted. Among these studies, studies on measures that can measure service quality provided by IoT software are still insufficient. IoT software has hardware parts of the Internet of Things, technologies based on them, features of embedded software, and network features. These features are used as elements defining IoT software quality measurement metrics. However, these features are considered in the metrics related to IoT software quality measurement so far. Therefore, this paper presents a metric for reusability measurement among various quality factors of IoT software in consideration of these factors. In particular, since IoT software is used through IoT devices, services in IoT software must be designed to be changed, replaced, or expanded, and metrics that can measure this are very necessary. In this paper, we propose three metrics: changeability, replaceability, and scalability that can measure and evaluate the reusability of IoT software services were presented, and the metrics presented through case studies were verified. It is expected that the service quality verification of IoT software will be carried out through the metrics presented in this paper, thereby contributing to the improvement of users' service satisfaction.

• The Journal of Korean Association of Computer Education
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• v.22 no.5
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• pp.51-65
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• 2019

This study aims to provide educational implications for more strategic online software education by the types of online learning according to learners' self-regulated learning characteristics in the online software education environment and examining the characteristics of each type. For this, variables related to self-regulated learning characteristic were extracted from the log data of 809 students participating in the online software learning program of K University, and then analyzed using hierarchical cluster analysis. Based on hierarchical cluster analysis learner clusters according to the characteristics of self-regulated learning were derived and the differences between learners' learning characteristics and learning results according to cluster types were examined. As a result, the types of self-regulated learning of online software learners were classified as 'high level self-regulated learning type (group 1)', 'medium level self-regulated learning type (group 2)', and 'low level self-regulated learning type (group 3)'. The achievement level was found to be highest in 'high-level self-regulated learning type (group 1)' and 'low-level self-regulated learning type (group 3)' was the lowest. Based on these results, the implications for effective online software education were suggested.

• Journal of Digital Convergence
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• v.19 no.2
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• pp.1-12
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• 2021

With the recent 4th Industrial Revolution, the growth and value of big data are continuously increasing, and the government is also actively making efforts to open and utilize public data. However, the situation still does not reach the level of demand for public data use by citizens, At this point, it is necessary to identify research trends in the public data field and seek directions for development. In this study, in order to understand the research trends related to public data, the analysis was performed using topic modeling, which is mainly used in text mining techniques. To this end, we collected papers containing keywords of 'Public data' among domestic and foreign research papers (1,437 domestically, 9,607 overseas) and performed topic modeling based on the LDA algorithm, and compared domestic and foreign public data research trends. After analysis, policy implications were presented. Looking at the time series by topic, research in the fields of 'personal information protection', 'public data management', and 'urban environment' has increased in Korea. Overseas, it was confirmed that research in the fields of 'urban policy', 'cell biology', 'deep learning', and 'cloud·security' is active.