• 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.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.597-599
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• 2022

When IoT device performs on-device AI, the device is required to use various AI models selectively according to target service and surrounding environment. Also, AI model can be updated by additional training such as federated learning or adapting the improved technique. Hence, for successful on-device AI, IoT device should acquire various AI models selectively or update previous AI model to new one. In this paper, we propose AI model repository to tackle this issue. The repository supports AI model registration, searching, management, and deployment along with dashboard for practical usage. We implemented it using Node.js and Vue.js to verify it works well.

• Journal of the Society of Disaster Information
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• v.19 no.2
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• pp.432-438
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• 2023

Purpose: The purpose of this study is to enable users to conveniently report risks by automatically classifying risk categories in real time using AI for images reported in the life safety prevention service app. Method: Through a system consisting of a life safety prevention service platform, life safety prevention service app, AI model serving server and sftp server interconnected through the Internet, the reported life safety images are automatically classified in real time, and the AI model used at this time An AI learning algorithm for generation was also developed. Result: Images can be automatically classified by AI processing in real time, making it easier for reporters to report matters related to life safety.Conclusion: The AI image automatic classification system presented in this paper automatically classifies reported images in real time with a classification accuracy of over 90%, enabling reporters to easily report images related to life safety. It is necessary to develop faster and more accurate AI models and improve system processing capacity.

• Journal of The Korean Association of Information Education
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• v.25 no.2
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• pp.423-436
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• 2021

In the future, artificial intelligence (AI) technology is expected to become a general-purpose technology (GPT), and it is predicted that AI competency will become an essential competency. Several nations around the world are fostering experts in the field of AI to achieve technological proficiency while working to develop the necessary infrastructure and educational environment. In this study, we investigated the status of software education at the liberal arts level at 31 universities in Seoul, along with precedents from domestic and foreign AI education research. Based on this, we concluded that an AI literacy education model is needed to link software education at the liberal arts level with professional AI education. And we classified 20 AI-related lectures released in the KOCW according to the AI literacy competencies required; based on the results of this classification, we propose a model for AI literacy education in the liberal arts for undergraduate students. The proposed AI literacy education model may be considered as AI·SW convergence to experience AI along with literacy in the humanities, deviating from the existing theoretical and computer-science-based approach. We expect that our proposed AI literacy education model can contribute to the proliferation of AI.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2022.06a
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• pp.84-86
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• 2022

최근 미디어, 금융 등 다양한 분야의 기업들이 AI를 활용해 제공하는 서비스가 늘어남에 따라 학습된 모델을 엣지 자원에 배포하여 기능을 제공하는 서비스형태 또한 늘어나고 있다. AI-Application이 동작하기 위해서는 AI-Model 파일뿐 아니라 동작을 위한 설정 파일들이 필요하여 AI-Application이 사용 중인 AI-Model의 정보를 수집, 관리하는 것은 중요한 이슈라고 할 수 있다. 하지만 단일 서비스서버에서 동작하는 형태가 아닌 각 자원이 산재되어 다양한 형태로 서비스를 제공하는 엣지컴퓨팅의 구조적인 특성상 AI-Application의 기존 서비스구조, 기능을 수정하지 않고 정보를 수집하는 과정은 다양한 문제에 부딪치게 된다. 이에 따라 본 논문에서는 기존 서비스구조를 변경하지 않고 독립적으로 AI-Application에서 사용중인 AI-Model의 정보를 파악하고, 사용자 요청에 대응할 수 있는 관리구조를 제안한다.

• Annual Conference on Human and Language Technology
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• 2023.10a
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• pp.722-725
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• 2023

대규모 언어 모델(LLM)은 대규모의 데이터를 학습하여 얻은 지식을 기반으로 텍스트와 다양한 콘텐츠를 인식하고 요약, 번역, 예측, 생성할 수 있는 딥러닝 알고리즘이다. 초기 공개된 LLM은 영어 기반 모델로 비영어권에서는 높은 성능을 기대할 수 없었으며, 이에 한국, 중국 등 자체적 LLM 연구개발이 활성화되고 있다. 본 논문에서는 언어가 LLM의 성능에 영향을 미치는가에 대하여 한국어 기반 LLM과 영어 기반 LLM으로 KoBEST의 4가지 Task에 대하여 성능비교를 하였다. 그 결과 한국어에 대한 사전 지식을 추가하는 것이 LLM의 성능에 영향을 미치는 것을 확인할 수 있었다.

• Journal of the Korean Society of Systems Engineering
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• v.19 no.1
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• pp.91-106
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• 2023

Multidimensional efforts such as budgeting, organizing, and institutionalizing are being carried out for the adoption of defense AI. However, there is little interest in eliminating duplication of defense resources that may occur during the AI adoption. In this study, we propose a theoretical conceptual model to optimize duplication of AI technology that may occur during the AI adoption in the vast defense field. For a systematic approach, the JCA of the US DoD and system abstraction method are applied, and the IMO logical structure is used to decompose AI requirements and identify duplication. As a result of analyzing the effectiveness of our conceptual model through six example defense AI requirements, it was found that the amount of requirements of data and AI technologies could be reduced by up to 41.7% and 70%, respectively, and estimated costs could be reduced by up to 35.5%.

• International journal of advanced smart convergence
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• v.10 no.2
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• pp.201-208
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• 2021

Recently, artificial intelligence(AI) has been used throughout society, and social interest in it is increasing. Accordingly, the necessity of AI education is becoming a big topic in the education field. As a response to this trend, the Korean education authorities have also announced plans for AI education, and various studies have been performed in academic field to revitalize AI education in the future. However, the curriculum research on what differentiates AI education from existing SW education and what and how to train AI is still in its infancy. In this paper, Therefore, we focused on the experiences of elementary school students in solving problems in their own lives, and developed a teaching-learning model based on design-based research so that students can design a problem-solving process and experience the process of feedback. We applied the developed teaching-learning model to the problem-solving process and confirmed that it increased students' understanding and satisfaction with AI education.

• 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.

• Journal of Industrial Convergence
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• v.21 no.1
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• pp.147-157
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• 2023

In this study, we approached from the perspective of AI convergence education in elementary, middle and high schools to understand AI convergence education. We examined what capabilities AI convergence education ultimately seeks to pursue, and analyzed various examples of AI convergence education in three dimensions: core curriculum, convergence model, AI learning elements and learning activities. In addition, factors to be considered in order for AI convergence education to be actively carried out include the cultivation of AI convergence education capabilities of teachers, the development and dissemination of AI teaching and learning methods and teaching and learning models, and evaluation methods for AI convergence education.