• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.9
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• pp.2976-2990
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• 2022

The adaptive adjustment of reference or weight vectors in decomposition-based methods has been a hot research topic in the evolutionary community over the past few years. Although various methods have been proposed regarding this issue, most of them aim to diversify solutions in the objective space to cover the true Pareto fronts as much as possible. Different from them, this paper proposes a knee point-based reference vector adaptive adjustment strategy to concurrently balance the convergence and diversity. To be specific, the knee point-based reference vector adaptive adjustment strategy firstly utilizes knee points to construct the adaptive reference vectors. After that, a new fitness function is defined mathematically. Then, this paper further designs a many-objective evolutionary algorithm with knee point-based reference vector adaptive adjustment strategy, where the mating operation and environmental selection are designed accordingly. The proposed method is extensively tested on the WFG test suite with 8, 10 and 12 objectives and MPDMP with state-of-the-art optimizers. Extensive experimental results demonstrate the superiority of the proposed method over state-of-the-art optimizers and the practicability of the proposed method in tackling practical many-objective optimization problems.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.361-364
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• 2022

This study was carried out for the purpose of Exploring the types of AI platforms for creative activities and how to use them. In order to learn AI in the fields of art creation and music creation, which are representative areas of creative activity, types of AI platforms that can experience AI and perform simple programming were investigated. In addition, the utilization plan was presented so that each AI platform can be used to express students' ideas abundantly and to enhance their creativity. Through this, it is meaningful to suggest that the AI platform can be used as a teaching aid to enhance students' expressive power and creativity in creative activities.

• Journal of the Korea Society of Computer and Information
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• v.28 no.12
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• pp.281-288
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• 2023

This study aimed to develop an integrated arts education program for improving AI literacy among elementary school students. First, we developed two thematic programs that are research on the goals of the art, music, physical curriculum in the 2022 revised elementary school curriculum, and a matrix of goals and elements of integrated art education. The developed program was revised and supplemented through the first expert validity test, and the second revision was made based on the results of students' AI literacy pre/post-test and satisfaction survey with the program. Finally, the final program was developed through the third expert validity test. We hope that the developed program will be used as a convergence education program to cultivate AI literacy in elementary school students.

• Journal of the Korea Computer Graphics Society
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• v.29 no.4
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• pp.1-6
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• 2023

To enhance immersive experiences for metaverse environements, background music is often used. However, the background music is mostly pre-matched and repeated which might occur a distractive experience to users as it does not align well with rapidly changing user-interactive contents. Thus, we implemented a system to provide a more immersive metaverse conversation experience by 1) developing a regression neural network that extracts emotions from an utterance using KEMDy20, the Korean multimodal emotion dataset 2) selecting music corresponding to the extracted emotions from an utterance by the DEAM dataset where music is tagged with arousal-valence levels 3) combining it with a virtual space where users can have a real-time conversation with avatars.

• The Journal of the Convergence on Culture Technology
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• v.10 no.1
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• pp.517-524
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• 2024

Generative AI, exemplified by models like ChatGPT, has revolutionized human-machine interactions in the 21st century. As these advancements permeate various sectors, their intersection with the arts is both promising and challenging. Despite the arts' historical resistance to AI replacement, recent developments have sparked active research in AI's role in artistry. This study delves into the potential of AI in visual arts education, highlighting the necessity of swift adaptation amidst the Fourth Industrial Revolution. This research, conducted at a 4-year global higher education institution located in Gyeongbuk, involved 70 participants who took part in a creative convergence module course project. The study aimed to examine the influence of AI collaboration in visual arts, analyzing distinctions across majors, grades, and genders. The results indicate that creative activities with AI positively influence students' creativity and digital media literacy. Based on these findings, there is a need to further develop effective educational strategies and directions that incorporate AI.

• International journal of advanced smart convergence
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• v.9 no.1
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• pp.215-221
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• 2020

The paradigm of agriculture is also changing to address the problem of food shortages due to the increase of the world population, climate conditions that are increasingly subtropical, and labor shortages in rural areas due to aging population. With the development of Information Communication Technology (ICT), our daily lives are changing rapidly and heralds a major change in agricultural management. In a hyper-connected society, the introduction of high-tech into traditional Agriculture of the past is absolutely necessary. In the development process of Agriculture, the first generation produced by hand, the second generation applied mechanization, and the third generation introduced automation. The fourth generation is the current ICT operation and the fifth generation is artificial intelligence. This paper investigated Smart Farm that increases productivity through convergence of Agriculture and ICT, such as smart greenhouse, smart orchard and smart Livestock. With the development of sustainable food production methods in full swing to meet growing food demand, Smart Farming is emerging as the solution. In overseas cases, the Netherlands Smart Farm, the world's second-largest exporter of agricultural products, was surveyed. Agricultural automation using Smart Farms allows producers to harvest agricultural products in an accurate and predictable manner. It is time for the development of technology in Agriculture, which benchmarked cases of excellence abroad. Because ICT requires an understanding of Internet of Things (IoT), big data and artificial intelligence as predicting the future, we want to address the status of theory and actual Agriculture and propose future development measures. We hope that the study of the paper will solve the growing food problem of the world population and help the high productivity of Agriculture and smart strategies of sustainable Agriculture.

• Journal of the Korea Convergence Society
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• v.9 no.9
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• pp.23-29
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• 2018

Computational creativity is a field of artificial intelligence research to replicate creativity of human beings, creating works in various fields or helping human authors. The copyright of works produced by computational creativity has not been established in most countries yet, however, there will be the need for systems to protect the copyrights with the development of the technology in the future. In this paper, we propose a copyright protection system based on blockchain technology that protects the copyright of various contributors contributing to the creation of computer creative creativity, and transparently and safely records the contribution of copyrighted works. The proposed system records the contribution of all related works from the machine learning of computer creativity to the creation of the final work on the blockchain so that it is possible to establish quantitative evaluation criteria for the copyright when the future copyright law system is revised.

• Journal of KIISE
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• v.45 no.1
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• pp.30-35
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• 2018

GuessWhat?! is a game in which two machine players, composed of questioner and answerer, ask and answer yes-no-N/A questions about the object hidden for the answerer in the image, and the questioner chooses the correct object. GuessWhat?! has received much attention in the field of deep learning and artificial intelligence as a testbed for cutting-edge research on the interplay of computer vision and dialogue systems. In this study, we discuss the objective function and characteristics of the GuessWhat?! game. In addition, we propose a simple solver for GuessWhat?! using a simple rule-based algorithm. Although a human needs four or five questions on average to solve this problem, the proposed method outperforms state-of-the-art deep learning methods using only two questions, and exceeds human performance using five questions.

• Progress in Medical Physics
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• v.31 no.3
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• pp.111-123
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• 2020

Deep learning (DL) is a subset of machine learning and artificial intelligence that has a deep neural network with a structure similar to the human neural system and has been trained using big data. DL narrows the gap between data acquisition and meaningful interpretation without explicit programming. It has so far outperformed most classification and regression methods and can automatically learn data representations for specific tasks. The application areas of DL in radiation oncology include classification, semantic segmentation, object detection, image translation and generation, and image captioning. This article tries to understand what is the potential role of DL and what can be more achieved by utilizing it in radiation oncology. With the advances in DL, various studies contributing to the development of radiation oncology were investigated comprehensively. In this article, the radiation treatment process was divided into six consecutive stages as follows: patient assessment, simulation, target and organs-at-risk segmentation, treatment planning, quality assurance, and beam delivery in terms of workflow. Studies using DL were classified and organized according to each radiation treatment process. State-of-the-art studies were identified, and the clinical utilities of those researches were examined. The DL model could provide faster and more accurate solutions to problems faced by oncologists. While the effect of a data-driven approach on improving the quality of care for cancer patients is evidently clear, implementing these methods will require cultural changes at both the professional and institutional levels. We believe this paper will serve as a guide for both clinicians and medical physicists on issues that need to be addressed in time.

• Journal of Information Science Theory and Practice
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• v.10 no.spc
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• pp.135-142
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• 2022

Recently, with the development of data processing technology and the increase of computational power, methods to solving social problems using Artificial Intelligence (AI) are in the spotlight, and AI technologies are replacing and supplementing existing traditional methods in various fields. Meanwhile in Korea, heavy rain is one of the representative factors of natural disasters that cause enormous economic damage and casualties every year. Accurate prediction of heavy rainfall over the Korean peninsula is very difficult due to its geographical features, located between the Eurasian continent and the Pacific Ocean at mid-latitude, and the influence of the summer monsoon. In order to deal with such problems, the Korea Meteorological Administration operates various state-of-the-art observation equipment and a newly developed global atmospheric model system. Nevertheless, for precipitation nowcasting, the use of a separate system based on the extrapolation method is required due to the intrinsic characteristics associated with the operation of numerical weather prediction models. The predictability of existing precipitation nowcasting is reliable in the early stage of forecasting but decreases sharply as forecast lead time increases. At this point, AI technologies to deal with spatio-temporal features of data are expected to greatly contribute to overcoming the limitations of existing precipitation nowcasting systems. Thus, in this project the dataset required to develop, train, and verify deep learning-based precipitation nowcasting models has been constructed in a regularized form. The dataset not only provides various variables obtained from multiple sources, but also coincides with each other in spatio-temporal specifications.