• 융합경영연구
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• 제11권6호
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• pp.1-8
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• 2023

Purpose: In the era of the Fourth Industrial Revolution, individuals equipped with fundamental understanding and practical skills in artificial intelligence (AI) are essential. This study aimed to validate the effectiveness of AI education for enhancing AI literacy among business major student. Research design, data and methodology: Data for analyzing the effectiveness of the AI Fundamental Education Program for business major students were collected through surveys conducted at the beginning and end of the semester. Structural equation modeling was employed to perform basic statistical analyses regarding gender, grade, and prior software (SW) education duration. To validate the effectiveness of AI education, seven variables - AI interest, AI perception, data analysis/utilization, AI projects, AI literacy, AI self-efficacy, and AI learning persistence - were defined and derived. Results: All seven operationally defined variables showed statistically significant positive changes. The average differences were observed as follows: 0.47 for AI interest, 0.32 for AI perception, 0.37 for data analysis/utilization, 0.27 for AI projects, 0.25 for AI literacy, 0.39 for AI self-efficacy, and 0.41 for AI learning persistence. Statistically, AI interest exhibited the most substantial average difference. Conclusions: Through this study, the applied AI education was confirmed to enhance learners' overall competencies in AI, proving its utility and effectiveness in AI literacy education for business major students. Future research endeavors should build upon these results, focusing on ongoing studies related to AI education programs tailored to learners from diverse academic backgrounds and conducting continuous efficacy evaluations.

• 한국전자통신학회논문지
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• 제18권3호
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• pp.421-426
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• 2023

인공지능 융합기술 중 Chatbot은 인공지능 기반의 대화형 시스템이며, 인간과의 상호작용을 제공할 수 있는 시스템을 말한다. 챗봇의 발전으로 NLP, NLU 그리고 NLG로 발전하게 되면서, 챗봇이 재조명되고 있다. 하지만 인공지능 챗봇은 학습한 데이터에 따라 편향된 정보를 제공할 수 있고, 프라이버시 침해, 사이버 보안의 우려를 비롯한 심각한 피해를 줄 수 있으며, 이에 인공지능 기술의 이해와 효과적이고 책임감 있게 사용할 수 있는 능력인 AI 리터러시 함양이 필수적임을 제시했다. 인공지능의 지속적인 진화와 보편화에 따라, AI 리터리시 역시 범위를 확장하며 새로운 영역을 포함하게 될 것이다. 본 연구는 인공지능 기술에 대한 경각심을 일깨우고, 인간의 AI 리터러시 역량 함양을 통해 기술에 매몰되지 않는 인간 존중의 기술 사용을 제안하는데 그 의의가 있다고 하겠다.

• 디지털융복합연구
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• 제18권5호
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• pp.17-23
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• 2020

AI 기술이 고도화됨에 따라 국내외에서 AI 로봇에 대한 법적 지위 및 규제 문제, 로봇등록제의 필요성이 대두되고 있다. AI 로봇의 형태 및 활동범위는 더 이상 한 국가내에 놓여진 하드웨어에 머물지 않을 것이므로, AI 로봇에 대한 정의 및 규제는 소프트웨어를 포함시킨 포괄적 개념으로 확장되어야하며, 이들에 대한 정보도 국제적으로 각국 정부가 안전하게 관리하고 공유할 수 있는 형태로 정의되어야한다. 본 연구는 이러한 관점에서 'AI 로봇'을 하드웨어와 소프트웨어를 포괄하는 AI 법인이라는 개념으로 확장시키고, (가칭) Juridical AI Chain이라는 허가형 블록체인을 이용해 AI 법인 등록제를 운영하는 방안을 제시한다. 블록체인은 각국 정부기관들의 관리 및 공유가 가능한 분산형 공유 장부이므로, 블록체인 기반 등록제의 운영은 AI 로봇의 상용화가 초래할 범세계적 문제들에 효과적으로 대처할 수 있는 방안이 될 것이다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권8호
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• pp.2292-2313
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• 2023

With improving computing performance, various digital platforms are being developed to enable easily utilization of high-performance computing environments. EDISON 1.0 is an online simulation platform widely used in computational science and engineering education. As the research paradigm changes, the demand for developing the EDISON 1.0 platform centered on simulation into the EDISON 2.0 platform centered on data and artificial intelligence is growing. Herein, a data framework, a core module for data-centric research on EDISON 2.0 digital platform, is proposed. The proposed data framework provides the following three functions. First, it provides a data repository suitable for the data lifecycle to increase research reproducibility. Second, it provides a new data model that can integrate, manage, search, and utilize heterogeneous data to support a data-driven interdisciplinary convergence research environment. Finally, it provides an exploratory data analysis (EDA) service and data enrichment using an AI model, both developed to strengthen data reliability and maximize the efficiency and effectiveness of research endeavors. Using the EDISON 2.0 data framework, researchers can conduct interdisciplinary convergence research using heterogeneous data and easily perform data pre-processing through the web-based UI. Further, it presents the opportunity to leverage the derived data obtained through AI technology to gain insights and create new research topics.

• Journal of information and communication convergence engineering
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• 제20권3호
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• pp.166-173
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• 2022

Recently, studies have been conducted on intelligent transportation systems (ITS) that provide safety and convenience to humans. Systems that compose the ITS adopt architectures that applied the cloud computing which consists of a high-performance general-purpose processor or graphics processing unit. However, an architecture that only used the cloud computing requires a high network bandwidth and consumes much power. Therefore, applying edge computing to ITS is essential for solving these problems. In this paper, we propose an edge artificial intelligence (AI) device based ITS. Edge AI which is applicable to various systems in ITS has been applied to license plate recognition. We implemented edge AI on a field-programmable gate array (FPGA). The accuracy of the edge AI for license plate recognition was 0.94. Finally, we synthesized the edge AI logic with Magnachip/Hynix 180nm CMOS technology and the power consumption measured using the Synopsys's design compiler tool was 482.583mW.

• 문화기술의 융합
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• 제8권2호
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• pp.1-6
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• 2022

라이프로그는 다양한 디지털 센서로부터 수집되는 개인의 디지털 기록으로, 활동량, 수면 정보, 체중 변화, 체질량, 근육량, 지방량 등이 포함된다. 최근, 웨어러블 디바이스가 보편화되면서 양질의 라이프로그 데이터가 많이 생산되고 있다. 라이프로그 데이터는 개인의 신체의 상태를 보여주는 데이터로, 개개인의 건강관리 뿐만 아니라, 질병의 원인 및 치료에도 활용될 수 있다. 그러나, 현재는, AI/ML 기반의 상관관계 분석 및 개인화를 반영하지 못하고 있다. 단순 기록이나 단편적인 통계치를 제시하는 수준에 그치고 있다. 이에 본 논문에서는, 라이프로그 데이터와 질병과의 연관성 및 AI/ML 기술의 라이프로그 데이터의 적용 사례를 살펴보고, 더 나아가, AI/ML을 활용한 라이프로그 데이터 분석 프로세스를 제안하고, 실제 갤럭시워치에서 수집된 데이터를 사용하여, 분석 프로세스를 실증한다. 더불어, 미래의 헬스케어 서비스인, 라이프로그 데이터와 식단, 건강정보, 질병정보와의 융복합 서비스 로드맵을 제안한다.

• Korean Journal of Radiology
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• 제24권11호
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• pp.1061-1080
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• 2023

Artificial intelligence (AI) in radiology is a rapidly developing field with several prospective clinical studies demonstrating its benefits in clinical practice. In 2022, the Korean Society of Radiology held a forum to discuss the challenges and drawbacks in AI development and implementation. Various barriers hinder the successful application and widespread adoption of AI in radiology, such as limited annotated data, data privacy and security, data heterogeneity, imbalanced data, model interpretability, overfitting, and integration with clinical workflows. In this review, some of the various possible solutions to these challenges are presented and discussed; these include training with longitudinal and multimodal datasets, dense training with multitask learning and multimodal learning, self-supervised contrastive learning, various image modifications and syntheses using generative models, explainable AI, causal learning, federated learning with large data models, and digital twins.

• International Journal of Internet, Broadcasting and Communication
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• 제12권4호
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• pp.232-237
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• 2020

Given the recent pace of development and expansion of Artificial Intelligence (AI) technology, the influence and ripple effects of AI technology on the whole of our lives will be very large and spread rapidly. The National Artificial Intelligence R&D Strategy, published in 2019, emphasizes the importance of artificial intelligence education for K-12 students. It also mentions STEM education, AI convergence curriculum, and budget for supporting the development of teaching materials and tools. However, it is necessary to create a new type of curriculum at a time when artificial intelligence curriculum has never existed before. With many attempts and discussions going very fast in all countries on almost the same starting line. Also, there is no suitable professor for K-12 students, and it is difficult to make K-12 students understand the concept of AI. In particular, it is difficult to teach elementary school students through professional programming in AI education. It is also difficult to learn tools that can teach AI concepts. In this paper, we propose an educational model for elementary school students to improve their understanding of AI through play or experience. This an experiential education model that combineds exploratory learning and discovery learning using multi-intelligence and the PLAY teaching-learning model to undertand the importance of data training or data required for AI education. This educational model is designed to learn how a computer that knows only binary numbers through UA recognizes images. Through code.org, students were trained to learn AI robots and configured to understand data bias like play. In addition, by learning images directly on a computer through TeachableMachine, a tool capable of supervised learning, to understand the concept of dataset, learning process, and accuracy, and proposed the process of AI inference.

• 농업과학연구
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• 제50권3호
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• pp.497-505
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• 2023

This study evaluated the impact of rain shelters made of plastic film on spring frost damage and fruit quality in Asian pears ('Niitaka') over two years. In 2021, during the coldest spring days (between 0:00 am and 7:00 am), temperatures dropped to -1.20 - 0.43℃ at 120 cm and -1.33 - 0.57℃ at 200 cm above ground level in the control. Conversely, the rain shelter treatment maintained higher temperatures, -0.40 - 0.87℃ at 120 cm and -0.43 - 0.77℃ at 200 cm. Flower damage was significantly lower in the rain-sheltered group, with incidences of 1.3 and 6.9% at 120 and 200 cm, respectively, compared with 18.1 and 22.6% in the control group. Visual observations verified the prevention of frost adhesion on flower organs in the sheltered group, compared with noticeable pistil death and petal browning in the control group. In 2022, when temperatures remained above 0℃, fruitlet stalk length was 5 - 6 mm longer in the sheltered group. The cumulative impact of rain shelters was evident in the improved fruit quality over the two years. This study suggests resilient cultivation strategies in the face of climate change to reduce frost damage, increase productivity, improve fruit quality, and potentially increase incomes of the farmers.

• 한국컴퓨터정보학회:학술대회논문집
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• 한국컴퓨터정보학회 2023년도 제67차 동계학술대회논문집 31권1호
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• pp.447-448
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• 2023

본 연구에서는 비전공자들을 위한 디지털 교육을 위하여 노 코드 프로그램을 활용한 정보, 수학, 과학 융합교육 교양 강좌를 개발하였다. 노 코드 프로그램으로는 오렌지3 데이터 마이닝을 선정하였는데, 이는 데이터 분석, 시각화, 머신러닝 모델의 활용이 용이하다는 강점을 가지고 있다. 또한, 산업환경 변화에 대비하는 핵심 교과인 과학, 수학, 정보의 중요성과 데이터 분석과의 밀접성을 고려하여 교육 내용을 융합할 수 있도록 선정하였다. 개발된 교육 프로그램은 8인이 전문가 검토 결과 내용 타당도가 확보되었음을 확인할 수 있었다. 추후 연구에서는 이 강좌를 대학의 학부생에게 적용하여 그 효과성을 확인해 보고자 한다.