• The Mathematical Education
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• v.63 no.3
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• pp.467-484
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• 2024

The rapid advancement of generative AI has ushered in an era where anyone can create and freely utilize personalized chatbots without the need for programming expertise. This study aimed to develop a customized chatbot based on OpenAI's GPTs for the purpose of pre-service teacher education and to analyze its educational performance in mathematics as assessed by educators guiding pre-service teachers. Responses to identical questions from a general-purpose chatbot (ChatGPT), a customized GPTs-based chatbot, and an elementary mathematics education expert were compared. The expert's responses received an average score of 4.52, while the customized GPTs-based chatbot received an average score of 3.73, indicating that the latter's performance did not reach the expert level. However, the customized GPTs-based chatbot's score, which was close to "adequate" on a 5-point scale, suggests its potential educational utility. On the other hand, the general-purpose chatbot, ChatGPT, received a lower average score of 2.86, with feedback indicating that its responses were not systematic and remained at a general level, making it less suitable for use in mathematics education. Despite the proven educational effectiveness of conventional customized chatbots, the time and cost associated with their development have been significant barriers. However, with the advent of GPTs services, anyone can now easily create chatbots tailored to both educators and learners, with responses that achieve a certain level of mathematics educational validity, thereby offering effective utilization across various aspects of mathematics education.

• Journal of The Korean Association of Information Education
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• v.24 no.1
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• pp.39-48
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• 2020

This study analyzes PISA's ICT background survey on OECD countries published in December 2019 to analyze ICT accessibility and utilization of each subject. In Korea, ICT accessibility at home is 40.40%, ranking 17th, slightly higher than the OECD average (43.01%). Access to schools is 40.40%, ranking 21st below the OECD average(43.01%), which has improved from the lowest group to three in the middle three years ago. In nine subjects, the proportion of students using digital device in the classroom is 2.96%, well below the OECD average(8.22%), and ranked 31st in the OECD country. This shows that the state needs a change in education policy in order to cultivate the talent needed for the AI era.

• Korean Journal of Materials Research
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• v.10 no.3
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• pp.191-198
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• 2000

A serpentine mineral was treated in hydrochloric acid solution to obtain amorphous silica residue with high surface area. The highly porous silica with aluminum hydroxide and sodium hydroxide was hydrothermally reacted in an autoclave. A numerous experiments were performed in terms of reaction time, temperature, alkalinity, and calcinations temperature. As a result, a ZSM-5 zeolite of the highest crystallinity was produced under such conditions as $^170{\circ}C$ of the reaction temperature, 24 hours of the reaction time, and $11.7Na_2O{\cdot}Al_2O_3{\cdot}90SiO_2{\cdot}3510H_2O{\cdot}10.8(TPA)_2O$ the composition along with 3 hours of the calcinations at $600^{\circ}C$.

• Resources Recycling
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• v.6 no.1
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• pp.29-34
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• 1997

The feasibility of using the industrial inorganic waste materials such as l~mestone sludge. Soundly sand. coal fly 'ash, and chemical glasses as a raw material for cement clinker by melting treatmeut was iovestigated. The slag wh~ch is obtained from thc melts of the mixtnres of waste materials is composed of P-C,S(ZCaO - SIOJ and C,AS(ZCaO . AI,O, . SiO,) phases. The effect of melting tempcrabre, coaling condition and CIS ratio on the fo~mation of P-C,S phasc was examed. In order to obtain thc P-CiS phase which is useful in thc utilhtion as a clinkcr malcrid, it B found that sudl considerations as low melting temperature as possible of the wastc mixhire, quenching the melts and law CIS ratio of the mlxhlre are necessary.

• Journal of Internet Computing and Services
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• v.21 no.6
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• pp.133-139
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• 2020

Big data, artificial intelligence (AI), and machine learning are keywords that represent the Fourth industrial Revolution. In addition, as the development of science and technology, the Korean government, public institutions and industries want professionals who can collect, analyze, utilize and predict data. This means that data analysis and utilization education become more important. Education on data analysis and utilization is increasing with trends in other academy. However, it is true that not many academy run long-term and systematic education. Korea Institute of Science and Technology Information (KISTI) is a data ecosystem hub and one of its performance missions has been providing data utilization and analysis education to meet the needs of industries, institutions and governments since 1966. In this study, KISTI's data education was analyzed using the number of curriculum trainees per year from 2001 to 2019. With this data, the change of interest in education in information and data field was analyzed by reflecting social and historical situations. And we identified the characteristics of KISTI and trainees. It means that the identity, characteristics, infrastructure, and resources of the institution have a greater impact on the trainees' interest of data-use education.In particular, KISTI, as a research institute, conducts research in various fields, including bio, weather, traffic, disaster and so on. And it has various research data in science and technology field. The purpose of this study can provide direction forthe establishment of new curriculum using data that can represent KISTI's strengths and identity. One of the conclusions of this paper would be KISTI's greatest advantages if it could be used in education to analyze and visualize many research data. Finally, through this study, it can expect that KISTI will be able to present a new direction for designing data curricula with quality education that can fulfill its role and responsibilities and highlight its strengths.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2022.06a
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• pp.304-305
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• 2022

Recently, as digital communication technology is widely applied to the maritime field, large amounts of maritime data are being accumulated. Accordingly, attempts to create new value by applying data science and Artificial Intelligence(AI) technologies are emerging. Typically, Ministry of Oceans and Fisheries has been providing korean e-Navigation service since 2021 based on LTE-Maritime communication network, as well as R&D for creating value-added service through analyzing huge-sized maritime traffic data is underway. By the way, to do any data-based research, legal system, as a research infra, that researchers can get the data whenever they need is essential. This paper looked at types of data in maritime fields, checked related legal system about scientific analysis and utilization. It is confirmed that there are some legal factors which restrict its scientific analysis and utilization, and suggested ways of improvement to boost R&D using maritime data as a conclusion.

• Journal of Intelligence and Information Systems
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• v.29 no.3
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• pp.167-183
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• 2023

In the recent field of recommendation systems, various studies have been conducted to model sparse data effectively. Among these, GLocal-K(Global and Local Kernels for Recommender Systems) is a research endeavor combining global and local kernels to provide personalized recommendations by considering global data patterns and individual user characteristics. However, due to its utilization of kernel tricks, GLocal-K exhibits diminished performance on highly sparse data and struggles to offer recommendations for new users or items due to the absence of side information. In this paper, to address these limitations of GLocal-K, we propose the GEase-K (Global and EASE kernels for Recommender Systems) model, incorporating the EASE(Embarrassingly Shallow Autoencoders for Sparse Data) model and leveraging side information. Initially, we substitute EASE for the local kernel in GLocal-K to enhance recommendation performance on highly sparse data. EASE, functioning as a simple linear operational structure, is an autoencoder that performs highly on extremely sparse data through regularization and learning item similarity. Additionally, we utilize side information to alleviate the cold-start problem. We enhance the understanding of user-item similarities by employing a conditional autoencoder structure during the training process to incorporate side information. In conclusion, GEase-K demonstrates resilience in highly sparse data and cold-start situations by combining linear and nonlinear structures and utilizing side information. Experimental results show that GEase-K outperforms GLocal-K based on the RMSE and MAE metrics on the highly sparse GoodReads and ModCloth datasets. Furthermore, in cold-start experiments divided into four groups using the GoodReads and ModCloth datasets, GEase-K denotes superior performance compared to GLocal-K.

• The Transactions of the Korean Institute of Electrical Engineers C
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• v.52 no.1
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• pp.12-18
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• 2003

Recently the research for utilization of waste heat produced from electric power plants, casting factories, heat treating factories or commercial building are being afforded by the need for energy saving. The objective of this study is to develop a thermoelectric generation system which converts unused energy from close-at-hand sources such as garbage incineration heat and industrial exhaust etc. into electricity. This paper presents a thermoelectric technology on a optimum system design method and efficiency and cost effective thermoelectric element on order to extract the maximum power output from energy conversion of waste energy. It is shown that the longitudinal stresses of module contacted with two point constrained AI tubes could be released more than those with a one-point constrained.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.10a
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• pp.351-354
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• 2016

The competition of Go between AlphaGo and Lee Sedol attracted global interest leading AlphaGo to victory. The core function of AlphaGo is deep-learning system, studying by computer itself. Afterwards, the utilization of deep-learning system using artificial intelligence is said to be verified. Recently, the government passed the loT Act and developing its business model to promote loT. This study is on analyzing IoT business environment using deep-learning AI and constructing specialized business models.

• Journal of Information Technology Applications and Management
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• v.29 no.3
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• pp.75-93
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• 2022

The purpose of this study is to compare and analyze each government's ICT SME support policies to cope with changes in the ICT ecosystem paradigm. In particular, the core policies and policy trends of the Moon's government are presented through keyword network analysis and cognitive map analysis. As a result, core technologies such as ICT(Information Communication Technology), AI(Artificial Intelligence), Big Data, and 5G, which have high values of betweenness centrality and closeness centrality, are major keywords with high propagation power. The cognitive map analysis shows that the opportunity factors for the 4th industrial revolution are being activated through the ICT infrastructure circulation process, the domestic market circulation process, and the global market circulation process. This study is meaningful in terms of cognitive map analysis and utilization based on scientific analysis.