• Journal of Fashion Business
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• v.28 no.3
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• pp.122-139
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• 2024

Image-generated AI is rapidly emerging as a powerful tool to augment human creativity and transform the art and design process through deep learning capabilities. The purpose of this study was to propose and demonstrate the feasibility of a new design development method that combined traditional design methods and technology by constructing image-generated AI prompts based on artists' formative elements. The study methodology consisted of analyzing Kazmir Malevich's theoretical considerations and applying them to AI prompts for design, print pattern development, and 3D digital design. This study found that the suprematist works of Kazmir Malevich were suitable as design and print pattern prompts due to their clear geometric shapes, colors, and spatial arrangement. The AI-prompted designs and print patterns produced diverse results quickly and enabled an efficient design process compared to traditional methods, although additional refinement was required to perfect the details. The AI-generated designs were successfully produced as 3D garments, thereby demonstrating that AI technology could significantly contribute to fashion design through its integration with artistic principles. This study has academic significance in that it proposes a prompt composition method applicable to fashion design by combining AI and artistic elements. It also has industrial significance in that it contributes to design innovation and the implementation of creative ideas by presenting an AI-based design process that can be practically applied.

• International journal of advanced smart convergence
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• v.12 no.2
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• pp.84-89
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• 2023

In recent years, with the breakthrough of Artificial Intelligence (AI) technology in deep learning algorithms such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAE), AI generation technology has rapidly expanded in various sub-sectors in the art field. 2022 as the explosive year of AI-generated art, especially in the creation of AI-generated art creative design, many excellent works have been born, which has improved the work efficiency of art design. This study analyzed the application design characteristics of AI generation technology in two sub fields of artistic creative design of AI painting and AI animation production , and compares the differences between traditional painting and AI painting in the field of painting. Through the research of this paper, the advantages and problems in the process of AI creative design are summarized. Although AI art designs are affected by technical limitations, there are still flaws in artworks and practical problems such as copyright and income, but it provides a strong technical guarantee in the expansion of subdivisions of artistic innovation and technology integration, and has extremely high research value.

• Proceedings of the Korea Information Processing Society Conference
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• 2024.05a
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• pp.627-630
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• 2024

본 연구는 인공지능(AI)과 인간이 만든 예술작품 사이의 나타나는 기술적 요소에서 나타나는 차이점 탐구를 통해, 인공지능 예술의 특성, 가능성, 한계를 파악하고, 예술가의 역할에 대한 심층적 이해를 도모하는 것을 목적으로 한다. 연구 결과는 AI 생성 예술이 인간 예술과 경쟁할 수 있으며, 일반 대중 사이에서 높은 미학적 가치를 인정받을 수 있음을 나타냈다. 또한 AI 가 예술창작에서 중요한 역할을 할 수 있음을 나타냈다. 본 연구는 예술계 내에서 AI 예술의 위치와 사회적 수용에 대한 더 깊은 이해를 제공할 것으로 기대된다.

• The Bulletin of The Korean Astronomical Society
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• v.44 no.1
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• pp.52.2-52.2
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• 2019

Global map of solar surface magnetic field, such as the synoptic map or daily synchronic frame, does not tell us real-time information about the far side of the Sun. A deep-learning technique based on Conditional Generative Adversarial Network (cGAN) is used to generate farside magnetograms from EUVI $304{\AA}$ of STEREO spacecrafts by training SDO spacecraft's data pairs of HMI and AIA $304{\AA}$. Farside(or backside) data of daily synchronic frames are replaced by the Ai-generated magnetograms. The new type of data is used to calculate the Potential Field Source Surface (PFSS) model. We compare the results of the global field with observations as well as those of the conventional method. We will discuss advantage and disadvantage of the new method and future works.

• The Journal of the Convergence on Culture Technology
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• v.9 no.1
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• pp.777-786
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• 2023

Generative AI systems are expected to be more widely utilized. However, relatively little attention has been paid to understanding how users perceive and accept generative AI results. To identify strategies for increasing the future use of generative AI and prepare for potential issues, we organized design workshop for the general user group and the designer group. They created artwork utilizing Novel AI and semi-structured interview was followed to evaluate their attitudes toward generative AI and its copyright. Results indicate that the general public views generative AI positively, while the design-related group views it quite negatively. The participants expressed concerns as to the misuse the system, specifically related to copyright issues. People who are likely to utilize generative AI outcomes have insisted more strongly that copyrights should be their own. Those working in the design field highly evaluated the possibility of using generative AI in their work. Copyright perceptions were not significantly influenced by users' satisfaction or their level of involvement in the creation process. We discuss design implications for interfaces using generative AI based on the findings.

• Korean Journal of Artificial Intelligence
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• v.10 no.2
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• pp.7-11
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• 2022

OLED displays cannot be used permanently due to burn-in or generation of dark spots due to degradation. Therefore, the time when the display can operate normally is very important. It is close to impossible to physically measure the time when the display operates normally. Therefore, the time that works normally should be predicted in a way other than a physical way. Therefore, if you do computer simulations based on artificial intelligence, you can increase the accuracy of prediction by saving time and continuous learning. Therefore, if we do computer simulations based on artificial intelligence, we can increase the accuracy of prediction by saving time and continuous learning. In this paper, a dataset in the form of development from generation to diffusion of dark spots, which is one of the causes related to the life of OLED, was generated by applying the finite element method. The dark spots were generated in nine conditions, such as 0.1 to 2.0 ㎛ with the size of pinholes, the number was 10 to 100, and 50% with water content. The learning data created in this way may be a criterion for generating an artificial intelligence-based dataset.

• International Journal of Internet, Broadcasting and Communication
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• v.16 no.1
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• pp.99-105
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• 2024

Runway released an updated version, Gen-2, in March 2023, which introduced new features that are different from Gen-1: it can convert text and images into videos, or convert text and images together into video images based on text instructions. This update will be officially open to the public in June 2023, so more people can enjoy and use their creativity. With this new feature, users can easily transform text and images into impressive video creations. However, as with all new technologies, comes the instability of AI, which also affects the results generated by Runway. This article verifies the feasibility of using Runway to generate the desired video from several aspects through personal practice. In practice, I discovered Runway generation problems and propose improvement methods to find ways to improve the accuracy of Runway generation. And found that although the instability of AI is a factor that needs attention, through careful adjustment and testing, users can still make full use of this feature and create stunning video works. This update marks the beginning of a more innovative and diverse future for the digital creative field.

• The Journal of the Convergence on Culture Technology
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• v.9 no.3
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• pp.425-433
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• 2023

In this study, we developed a virtual artist play lesson model using gamification concepts and AI-generated art programs to foster creative thinking in freshman art majors. Targeting first-year students in the Digital Media Art Department at Sichuan Film & Television University in China, this course aims to alleviate fear of artistic creation and enhance problem-solving abilities. The educational model consists of four stages: persona creation, creative writing, text visualization, and virtual exhibitions. Through persona creation, students established their artist identities, and by introducing game-like elements into writing experiences, they discovered their latent creativity. Using AI-generated art programs for text visualization, students gained confidence in their creations, and in the virtual exhibitions, they were able to enhance their self-esteem as artists by appreciating and evaluating each other's works. This educational model offers a new approach to promoting creative thinking and problem-solving skills while increasing learner engagement and interest. Based on these research findings, we expect that by developing and implementing educational strategies that cultivate creative thinking, more students will grow their artistic capacities and creativity, benefiting not only art majors but also students from various fields.

• Korean Journal of Cognitive Science
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• v.14 no.2
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• pp.15-25
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• 2003

The Monkey and Banana Problem is an example commonly used for illustrating simple problem solving. It can be solved by conventional approaches, but this requires a procedural aspect when inferences are processed, and this fact works as a limitation condition in solving complex problems. However, if we use DNA computing methods which are naturally able to realize massive parallel processing. the Monkey and Banana Problem can be solved effectively without weakening the fundamental aims above. In this paper, we design a method of representing the problem using DNA molecules, and show that various solutions are generated through computer-simulations based on the design. The simulation results are obviously interesting in that these are contrary to the fact that the Prolog program for the Monkey and Banana Problem, which was implemented from the conventional point of view, gives us only one optimal solution. That is, DNA computing overcomes the limitations of conventional approaches.