• Journal of the Korean Society of Systems Engineering
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• v.18 no.1
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• pp.90-98
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• 2022

On February 12, 2019, the U.S. Department of Defense newly established and announced the "Defense AI Strategy" to accelerate the use of artificial intelligence (AI) technology for military purposes. As China and Russia invested heavily in AI for military purposes, the U.S. was concerned that it could eventually lose its advantage in AI technology to China and Russia. In response, China and Russia, which are hostile countries, and especially China, are speeding up the development of new military theories related to the overall construction and operation of the Chinese military based on AI. With the rapid development of AI technology, major advanced countries such as the U.S. and China are actively researching the application of AI technology, but most existing studies do not address the special topic of defense. Fortunately, the "Future Defense 2030 Technology Strategy" classified AI technology fields from a defense perspective and analyzed advanced overseas cases to present a roadmap in detail, but it has limitations in comparing private technology-oriented benchmarking and AI technology's maturity level. Therefore, this study tried to overcome the limitations of the "Future Defense 2030 Technology Strategy" by comparing and analyzing Chinese and U.S. military research cases and evaluating the maturity level of military use of AI technology, not AI technology itself.

• Annual Conference of KIPS
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• 2023.05a
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• pp.377-379
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• 2023

Concept drift detection on data stream is the major issue to maintain the performance of the machine learning model. Since the online stream is to be a function of time, the classical statistic methods are hard to apply. In particular case of seasonal time series, a novel window strategy with Fourier analysis however, gives a chance to adapt the classical methods on the series. We explore the KS-test for an adaptation of the periodic time series and show that this strategy handles a complicate time series as an ordinary tabular dataset. We verify that the detection with the strategy takes the second place in time delay and shows the best performance in false alarm rate and detection accuracy comparing to that of arbitrary window sizes.

• Journal of Digital Convergence
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• v.19 no.7
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• pp.227-236
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• 2021

This study aims to develop an AI education program for secondary school students to help understand AI and to provide an experience of solving real-life problems by using AI, and to analyze the effectiveness of education. The education program based on the AI education system for K-12 developed in the previous study was composed of a total of 12 lessons by selecting the self-driving cars, which is emerging as a recent issue among real life problems, as the main topic. Classes were conducted for secondary school students who had experience in software education, and the effectiveness of education and class satisfaction were analyzed. As a result of the analysis, it was confirmed that the understanding of AI and the sense of AI efficacy were improved, and the class satisfaction was high in all items such as educational content, fun in class, difficulty of class, and interest in AI. Based on these results, implications for AI education for secondary students were proposed.

• The Korean Fashion and Textile Research Journal
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• v.26 no.4
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• pp.326-338
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• 2024

This study investigates the intersection of generative AI and fashion design, with emphasis on the recognition of artificial-intelligence (AI)-generated works as creative outputs and the associated ethical and legal challenges. Generative AI technologies, such as GANs and diffusion models, have revolutionized the fashion industry by enabling the rapid creation of innovative designs. Despite these advancements, significant issues persist regarding the attribution of authorship and copyright protection. Current intellectual property frameworks in the U.S., the EU, and South Korea predominantly recognize human creators, which implies that AI-generated works are not copyright protected unless significant human creative input is demonstrated. This study reviews the relevant policies and guidelines from major organizations such as WIPO and the U.S. Copyright Office and examines various case studies to illustrate these points. Additionally, the ethical implications of AI in fashion design, particularly concerning data bias and transparency, are critically analyzed. The findings underscore the necessity for transparent and fair data usage, clear documentation of the creative process, and human-AI collaboration. This collaboration should enhance creativity without overshadowing the unique artistic contributions of human designers. The study concludes by recommending the development of robust legal and ethical guidelines to ensure responsible and innovative use of AI in fashion design. These guidelines aim to protect the rights of human designers while fostering a collaborative environment in which AI serves as an enabler of creativity and innovation.

• Journal of Digital Convergence
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• v.19 no.1
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• pp.141-149
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• 2021

Due to the rapid rise of artificial intelligence technology around the world, SW education conducted in elementary and secondary schools is expanding including AI education. Therefore, this study aims to present an AI education system based on 4P(Play, Problem Solving, Product Making, Project) that can be applied from kindergarten to high school. The AI education system presented in this study is designed to be applied in 4P-based Play, Problem Solving, Product Making, and Project 4 stages so that it can be applied by school age and step by step. The level was presented by dividing it into two areas: AI literacy and AI development. In order to verify the validity of the developed AI education system, the Delphi method was applied to 15 experts who had experience in SW education or AI education. The AI education system derived as a result of the verification will be able to contribute to the development of a content system for AI education at each school level in the future.

• Journal of Integrative Natural Science
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• v.14 no.4
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• pp.176-182
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• 2021

We live in the age of artificial intelligence, and big data and artificial intelligence education are no longer just for majors, but are required to be able to handle non-majors as well. Software and artificial intelligence education for non-majors is not just a general education, it creates talents who can understand and utilize them, and the quality of education is increasingly important. Through such education, we can nurture creative talents who can create and use new values by fusion with various fields of computing technology. Since 2015, many universities have been implementing software-oriented colleges and AI-oriented colleges to foster software-oriented human resources. However, it is not easy to provide AI basic statistics education of big data analysis deception to non-majors. Therefore, we would like to present a big data education model for non-majors in big data analysis so that big data analysis can be directly applied.

• Journal of Korea Society of Digital Industry and Information Management
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• v.19 no.4
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• pp.53-71
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• 2023

This study explores the relationship between various user factors and the level of trust in ChatGPT, a sophisticated language model exhibiting human-like capabilities. Specifically, we considered demographic characteristics such as age, education, gender, and major, along with factors related to previous AI experience, including duration, frequency, proficiency, perception, and familiarity. Through a survey of 140 participants, comprising 71 females and 69 males, we collected and analyzed the data to see how these user factors have a relationship with trust in ChatGPT. Both descriptive and inferential statistical methods, encompassing multiple linear regression models, were employed in our analysis. Our findings reveal significant relationships between user factors such as gender, the perception of prior AI interactions, self-evaluated proficiency, and Trust in ChatGPT. This research not only enhances our understanding of trust in artificial intelligence but also offers valuable insights for AI developers and practitioners in the field.

• Electronics and Telecommunications Trends
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• v.39 no.4
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• pp.21-31
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• 2024

Major global powers are investing heavily in artificial intelligence (AI) and hyper-connected networks, demonstrating their crucial role in future warfare. To advance and utilize AI in national defense, it is essential to have policy support at the governmental or national level. This includes establishing a research and development infrastructure, creating a common development environment, and fostering AI expertise through education and training programs. To achieve advancements in hyper-connected networks, it is essential to establish a foundation for a robust and resilient infrastructure by comprehensively building integrated satellite, aerial, and ground networks, along with developing 5G & edge computing and low-orbit satellite communication technologies. This multi-faceted approach will ensure the successful integration of AI and hyper-connected networks, strengthening national defense and positioning nations at the forefront of technological advancements in warfare.

• Journal of Digital Convergence
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• v.19 no.9
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• pp.201-207
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• 2021

As artificial intelligence gradually expands into jobs, iIt is necessary to nurture talents with AI literacy capabilities required for non-majors. Therefore, in this study, based on the necessity and current status of AI education, AI literacy competency improvement education was conducted for non-majors so that AI learning could be sustainable in relation to future majors. For non-majors at University D, problem-solving solutions through project-based data analysis and visualization were applied over 15 weeks, and the AI ability improvement and effectiveness of learners before and after education were analyzed and verified. As a result, it was possible to confirm a statistically significant level of positive change in the learners' data analysis and utilization ability, AI literacy ability, and AI self-efficacy. In particular, it not only improved the learners' ability to directly utilize public data to analyze and visualize it, but also improved their self-efficacy to solve problems by linking this with the use of AI.

• The Journal of the Convergence on Culture Technology
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• v.10 no.4
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• pp.305-310
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• 2024

With the emergence of generative AI, a new era of coexistence with humanity has begun. The vast data-driven learning capabilities of AI are being utilized in various industries to achieve a level of productivity distinct from human learning. However, AI also manifests societal phenomena such as technophobia. This study aims to analyze collaborative AI models based on an understanding of AI and identify areas within the jewelry industry where these models can be applied. The utilization of collaborative AI models can lead to the acceleration of idea development, enhancement of design capabilities, increased productivity, and the internalization of multimodal functions. Ultimately, AI should be used as a collaborative tool from a utilitarian perspective, which requires a proactive, human-centric mindset. This research proposes collaborative AI strategies for the jewelry business, hoping to enhance the industry's competitiveness.