• Journal of Korean Society of Industrial and Systems Engineering
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• v.47 no.2
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• pp.21-29
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• 2024

Fueled by international efforts towards AI standardization, including those by the European Commission, the United States, and international organizations, this study introduces a AI-driven framework for analyzing advancements in drone technology. Utilizing project data retrieved from the NTIS DB via the "drone" keyword, the framework employs a diverse toolkit of supervised learning methods (Keras MLP, XGboost, LightGBM, and CatBoost) enhanced by BERTopic (natural language analysis tool). This multifaceted approach ensures both comprehensive data quality evaluation and in-depth structural analysis of documents. Furthermore, a 6T-based classification method refines non-applicable data for year-on-year AI analysis, demonstrably improving accuracy as measured by accuracy metric. Utilizing AI's power, including GPT-4, this research unveils year-on-year trends in emerging keywords and employs them to generate detailed summaries, enabling efficient processing of large text datasets and offering an AI analysis system applicable to policy domains. Notably, this study not only advances methodologies aligned with AI Act standards but also lays the groundwork for responsible AI implementation through analysis of government research and development investments.

• KEPCO Journal on Electric Power and Energy
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• v.6 no.4
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• pp.399-404
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• 2020

There are many attempts to apply AI technology to diagnose facilities or improve the work efficiency of the power industry. The emergence of new machine learning technologies, such as deep learning, is accelerating the digital transformation of the power sector. The problem is that traditional power systems face security risks when adopting state-of-the-art AI systems. This adoption has convergence characteristics and reveals new cybersecurity threats and vulnerabilities to the power system. This paper deals with the security measures and implementations of the power system using machine learning. Through building a commercial facility operations forecasting system using machine learning technology utilizing power big data, this paper identifies and addresses security vulnerabilities that must compensated to protect customer information and power system safety. Furthermore, it provides security guidelines by generalizing security measures to be considered when applying AI.

• Electronics and Telecommunications Trends
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• v.35 no.7
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• pp.56-66
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• 2020

As the development of artificial intelligence (AI) technology spreads to various industrial sectors, diversity in AI utilization rapidly increases, creating rich user experience. In addition, AI is required to solve various social problems through the use of public data. The spread of AI utilization across all sectors will continue, covering such industrial and public demands. This article examines the domestic and international trends in AI utilization technologies and establishes the direction of research and development (R&D), which is highly consistent with Korea's AI policy. ETRI, which leads AI's national R&D, has used its experience to establish AI R&D implementation strategies as well as technology roadmaps for the utilization of AI to improve individual quality of life, continuous growth in society, industrial innovation, and the solutions to public societal problems. In addition, it has derived tasks and implementation strategies for developing AI utilization technologies in 10 major areas including medical services.

• International Journal of Advanced Culture Technology
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• v.11 no.4
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• pp.442-448
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• 2023

Artificial Intelligence (AI) technology has recently grown explosively and is being used in a variety of application fields. Accordingly, the number of AI models is rapidly increasing. AI models are adapted and developed to fit a variety of data types, tasks, and environments, and the variety and volume of models continues to grow. The need to share models and collaborate within the AI community is becoming increasingly important. Collaboration is essential for AI models to be shared and improved publicly and used in a variety of applications. Therefore, with the advancement of AI, the introduction of Model Hub has become more important, improving the sharing, reuse, and collaboration of AI models and increasing the utilization of AI technology. In this paper, we collect data on the model hub and analyze the characteristics of the model hub and the AI models provided. The results of this research can be of great help in developing various multimodal AI models in the future, utilizing AI models in various fields, and building services by fusing various AI models.

• Journal of KIBIM
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• v.14 no.2
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• pp.13-24
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• 2024

This research suggests a novel visualization approach utilizing Generative AI to render photorealistic architectural alternatives images in the early design phase. Photorealistic rendering intuitively describes alternatives and facilitates clear communication between stakeholders. Nevertheless, the conventional rendering process, utilizing 3D modelling and rendering engines, demands sophisticate model and processing time. In this context, the paper suggests a rendering approach employing the text-to-image method aimed at generating a broader range of intuitive and relevant reference images. Additionally, it employs an Text-to-Image method focused on producing a diverse array of alternatives reflecting architects' styles when visualizing the exteriors of residential buildings from the mass model images. To achieve this, fine-tuning for architects' styles was conducted using the Low-Rank Adaptation (LoRA) method. This approach, supported by fine-tuned models, allows not only single style-applied alternatives, but also the fusion of two or more styles to generate new alternatives. Using the proposed approach, we generated more than 15,000 meaningful images, with each image taking only about 5 seconds to produce. This demonstrates that the Generative AI-based visualization approach significantly reduces the labour and time required in conventional visualization processes, holding significant potential for transforming abstract ideas into tangible images, even in the early stages of design.

• Journal of Practical Engineering Education
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• v.15 no.2
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• pp.357-364
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• 2023

Due to the rapid advancement of AI technology, generative artificial intelligence platforms are experiencing innovative applications in various fields. In this paper, it examines research cases involving the utilization of AI in education, explore instances where generative AI platforms are applied in the realm of creative endeavors, and discuss the direction of utilizing generative AI in educational contexts. In the field of computer graphics, this study introduced generative AI platforms that are applicable for image creation, editing, and video editing. It also proposed platforms that can be utilized in the video editing production process. These generative AI platforms not only offer advantages in terms of efficiency, by reducing the efforts of creators and saving time in the production process, but they also present positive aspects in enhancing individual capabilities. It is advocated that their swift integration into education is necessary, considering these benefits. This study aims to provide direction for the expansion of creative education utilizing generative AI platforms.

• International Journal of Advanced Culture Technology
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• v.11 no.3
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• pp.225-230
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• 2023

With the development of AI technology, there is a growing discussion about Text-to-Image Generative AI. We presented a Generative AI video production method and delineated a methodology for the production of personalized AI-generated videos with the objective of broadening the landscape of the video domain. And we meticulously examined the procedural steps involved in AI-driven video production and directly implemented a video creation approach utilizing the VQGAN-CLIP model. The outcomes produced by the VQGAN-CLIP model exhibited a relatively moderate resolution and frame rate, and predominantly manifested as abstract images. Such characteristics indicated potential applicability in OTT-based video content or the realm of visual arts. It is anticipated that AI-driven video production techniques will see heightened utilization in forthcoming endeavors.

• Knowledge Management Research
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• v.25 no.3
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• pp.99-119
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• 2024

This research aims to develop high-quality generative AI services by overcoming the limitations of existing Retrieval-Augmented Generation (RAG) models and implementing an enhanced graph-based RAG system to improve knowledge-based question answering (QA) systems. While traditional RAG models demonstrate high accuracy and fluency by utilizing retrieved information, their accuracy can be compromised due to the use of pre-loaded knowledge without rework. Additionally, the inability to incorporate real-time data after the RAG configuration leads to a lack of contextual understanding and potential biased information. To address these limitations, this study implements an enhanced RAG system utilizing graph technology. This system is designed to efficiently search and utilize information. In particular, LangGraph is employed to evaluate the reliability of retrieved information and to generate more accurate and improved answers by integrating various information. Furthermore, the specific operation method, key implementation steps, and case studies are presented with implementation code and verification results to enhance understanding of Advanced RAG technology. This research provides practical guidelines for actively implementing enterprise services utilizing Advanced RAG, making it significant.

• Journal of The Korean Association of Information Education
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• v.25 no.3
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• pp.437-448
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• 2021

As the core technology of the Fourth Industrial Revolution, AI is applied to various fields of society(e.g. politics, culture, industry, economy, etc.) and causes revolutionary changes. Students who will lead the age of AI need the ability to recognize social changes due to AI, acquire AI related knowledge and utilize AI in various situations. However, it is difficult for elementary school students to understand the concept and principles of AI. Therefore, this study developed an AI education program by selecting educational contents and methods appropriate to the level of elementary school students, and investigated the educational effects of the program by applying it to an actual educational setting. The content selected in this study is 'Social Awareness on AI', 'Understanding AI' and 'Utilizing AI', and eight content elements were selected. To help students learn AI easily and pleasantly at their level, activity-centered education, convergence of subjects and project-based learning were selected as instructional methods, and 20 sessions of education program were developed and implemented. In addition, the effects of the program were analyzed concerning 'perception on AI', 'convergent thinking', 'creative problem-solving' and 'collaboration capability', and positive changes were verified for all four aspects.

• Proceedings of the Korea Information Processing Society Conference
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• 2024.05a
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• pp.673-674
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• 2024

본 연구에서는 게임에서 자연스런 대화를 통해 스토리 몰입을 제공하는 AI 캐릭터를 위한 LLM 활용을 소개한다. 사용자는 게임 속 AI 캐릭터와 대화하며 스토리를 이어간다. 게임 속에서 사용자는 정해진 대사를 선택할 수도 있고, AI 캐릭터와 대화할 때 직접 대사를 입력할 수도 있다. 대사를 입력하면 그에 맞는 AI 캐릭터의 답변이 제공되고, 앞으로의 스토리에도 영향을 미친다. 결론적으로 LLM 기반 AI 캐릭터와의 자연스러운 대화를 통해 게임의 몰입도와 접근성을 높이는 것이 본 연구의 목표이다.