• Title/Summary/Keyword: AI technology

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Predicting User Acceptance of Strong AI using Extension of Theory of Planned Behavior: Focused on the Age Group of 20s (확장된 계획적 행동이론을 통해 본 강한 인공지능 제품에 대한 이용자의 수용의도: 20대 연령층을 중심으로)

  • Rhee, Chang Seop;Rhee, Hyunjung
    • The Journal of the Korea Contents Association
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    • v.20 no.10
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    • pp.284-293
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    • 2020
  • The rapid progress of AI technology gives us the expectation to solutions to various problems in our society, and at the same time, it gives us anxiety about the side effects that can occur if AI develops beyond human control. This study was conducted in the early 20s with less objection to advanced devices. We attempted to provide clues to understand thoughts and attitudes of the targets about the future environment that will be brought by AI through the process of finding intent the acceptance of strong AI technology. For this, we applied the Theory of Planned Behavior, and further expanded this research model to identify factors affecting the attitude toward AI. As a result, the attitude toward AI and perceived behavioral control had a significant effect on the intention to use to strong AI. In addition, we found that the expectation of the benefit of improving task performance and the anxiety on the threat of relationship disturbance had a significant effect on the attitude toward AI. This study suggests implications for AI-related companies establishing the direction of technology development and for government setting a policy direction for AI adoption.

The development of Improved AI PigMoS System for AI Traceability (AI 이력관리를 지원하는 개선된 AI PigMoS 시스템의 개발)

  • Son, Yong-sook;Kim, Hyun-ju;Chung, Ki-Hwa;Lee, Gwang-seok
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • 2012.10a
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    • pp.701-703
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    • 2012
  • 양돈산업에서의 인공수정(Artificial Insemination, AI) 기술은 1994년 이후 본격적으로 국내 양돈농가에 보급되어 양돈 산업 발전에 기초가 되었다. 현재 우리나라에서 양돈분야에서의 AI 공급은 크게 3단계 그룹으로 분류되어 있다. 각 단계에서의 수많은 변수들로 인하여 현재까지 체계적이고 종합적인 관리의 시도는 이루어지지 않았다. 이에 웹을 기반으로 전국 AI센터의 통합정보시스템을 설계 구축하고 AI센터와 소비자 단계의 이력 관리를 지원하는 개선된 AI PigMoS 시스템을 제안하고 구현하였다. 본 논문에서 제안한 AI PigMoS 시스템은 웹을 기반으로 전국 AI센터의 정보를 통합관리 운영할 수 있으며, 또한 웅돈, 정액생산 및 판매관리 등에 대해서 이력추적을 할 수 있도록 설계 하였다. 이는 전국 AI센터의 효율적인 관리운영 뿐만 아니라 통합된 AI센터 관련정보의 분석 및 미래 예측자료 등으로 활용되어 효율적인 돼지 개량 체계를 구축할 것으로 기대한다.

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AI and Public Services: Focusing on Analytics on Citizens' Perceptions of AI Speaker and Non-Contact Smart City Services in the Era of Post-Corona (AI와 공공서비스: 포스트 코로나 시대 AI 스피커 및 비대면 스마트시티 서비스 시민 인식 분석을 중심으로)

  • Kim, Byoung Joon
    • Journal of Information Technology Services
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    • v.20 no.5
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    • pp.43-54
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    • 2021
  • Currently, citizens' expectations and concerns on utilizing artificial intelligence (AI) technologies in the public sector are widening with the rapid digital transformation. Furthermore the level of global acceptance on the AI and other intelligent digital technologies is augmenting with the needs of non-face-to-face types of public services more than ever due to the unforeseen and unpredictable pandemic, COVID-19. Thus, this study intended to empirically examine what policy directions for the public should be considered to provide well-designed services as well as to promote the evidence-based public policies in terms of Al speaker technology as a non-contact smart city service. Based on the survey of senior citizens' perceptions on AI (AI Speaker technology), this study conducted structure equation modeling analyses to identify whether technology acceptance models on to the varied dependent variables such as actual use, perception, attitude, and brand royalty. The Results of the empirical analyses showed that AI increased the positive level of citizens' perception, attitude and brand royalty on non-contact public services (smart city services) which are becoming more crucial for developing AI oriented government and providing intelligent public services effectively. In addition, theoretical and practical implications are discussed for understanding the changes of public service in the post-corona.

Domestic Research Trends of Learning with AI (국내 AI활용교육 연구동향)

  • Huh, Miseon;Bae, Yoonju;Seok, Huijin;Lee, Jeongmin
    • Journal of The Korean Association of Information Education
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    • v.25 no.6
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    • pp.973-985
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    • 2021
  • The purpose of this study is to suggest the direction and implications of learning with AI in the future by analyzing the trends of research learning with AI in the field of education. For doing this, the final 78 papers published in domestic journals over the past three years from 2019 to July 2021 were selected for analysis through review. The analysis results are as follows. First of all, papers in 2020 among the three years were most published, and the most utilized research method was the qualitative research. In addition, according to the analysis by study subject, studies on elementary school students were the most common, followed by studies on college and graduate students. In the analysis by subject, research related to foreign language education was most utilized and chatbot was most used in the AI technology type. Finally, the research learning with AI accounted for the majority, and student support accounted for the majority as the type of education system learning with AI at the implementation stage among the areas of teaching and learning and evaluation. Based on these results, the direction and implications of learning with AI in the future were presented. This study is meaningful in that it grasped research trends of learning with AI in domestic from an overall perspective, and examined learning with AI focusing on the instructor-learner and the teaching and learning design process.

Trends in and Forecasting of AI-Based Radio Wave Technology (전파기술의 AI 적용 동향 및 전망)

  • Jeon, S.I.;Kim, Y.;Kim, B.C.;You, S.J.;Lee, J.;Byun, W.J.
    • Electronics and Telecommunications Trends
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    • v.35 no.5
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    • pp.69-82
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    • 2020
  • In many technologies, artificial intelligence (AI) is becoming an important topic for areas based on the field of big data. However, applied AI cases and the research status of radio wave technology are not widely known to the public. The spread of AI to other areas is being followed by radio wave technologies, and much effort is being taken to evolve it into intelligent radio wave technologies in the future. This paper presents the recent areas of interest in radio wave technology, such as spectral sharing, illegal spectrum monitoring, radar detection, radio wave medical imaging, and channel modeling; examines the requirements for applying AI; and describes the applied cases, research trends, and standardization efforts that apply AI technology to them. On this basis, we will discuss the prospects of AI application to the expected radio wave technology of the future.

A Research on AI Generated 2D Image to 3D Modeling Technology

  • Ke Ma;Jeanhun Chung
    • International Journal of Internet, Broadcasting and Communication
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    • v.16 no.2
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    • pp.81-86
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    • 2024
  • Advancements in generative AI are reshaping graphic and 3D content design landscapes, where AI not only enriches graphic design but extends its reach to 3D content creation. Though 3D texture mapping through AI is advancing, AI-generated 3D modeling technology in this realm remains nascent. This paper presents AI 2D image-driven 3D modeling techniques, assessing their viability in 3D content design by scrutinizing various algorithms. Initially, four OBJ model-exporting AI algorithms are screened, and two are further evaluated. Results indicate that while AI-generated 3D models may not be directly usable, they effectively capture reference object structures, offering substantial time savings and enhanced design efficiency through manual refinements. This endeavor pioneers new avenues for 3D content creators, anticipating a dynamic fusion of AI and 3D design.

The Application of Delphi-AHP Method in the Priority of Policies for Expanding the Use of Artificial Intelligence

  • Han, Eunyoung
    • Journal of Internet Computing and Services
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    • v.22 no.4
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    • pp.99-110
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    • 2021
  • Governments around the world are actively establishing strategies and initiatives to spread the use of artificial intelligence (AI), for AI is not a mere new technology, but is an innovative technology that brings about extensive changes in industrial and social structures and is a core engine that will lead the 4th Industrial Revolution. The South Korean government has also been paying attention to AI as a technology and tool for innovative growth, but its application to the industries is still rather sluggish. The government has prepared multifarious AI-related policies with the aim of constructing South Korea as an AI powerhouse, but there is no clear strategy on which detailed policies to implement first and which industries to apply AI preferentially. With these limitations of South Korea's AI policies in mind, this paper analyzed the priorities of industries in AI adoption and the priorities of AI-related national policies, using Delphi-AHP method for 30 top-level AI experts in South Korea. The results of analysis show that AI application is urgent and necessary in the fields of medical/healthcare, public and safety, and manufacturing, which seems to reflect the peak of the COVID-19 crisis in the second half of 2020 at the time of the investigation. And it turns out that policies related to AI talent cultivation, data, and R&D investment are important and urgent above all in order for organizations to apply AI. This suggests that strategies are required to focus limited national resources on these industries and policies first.

A Study on the Effective Preprocessing Methods for Accelerating Point Cloud Registration

  • Chungsu, Jang;Yongmin, Kim;Taehyun, Kim;Sunyong, Choi;Jinwoo, Koh;Seungkeun, Lee
    • Korean Journal of Remote Sensing
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    • v.39 no.1
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    • pp.111-127
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    • 2023
  • In visual slam and 3D data modeling, the Iterative Closest Point method is a primary fundamental algorithm, and many technical fields have used this method. However, it relies on search methods that take a high search time. This paper solves this problem by applying an effective point cloud refinement method. And this paper also accelerates the point cloud registration process with an indexing scheme using the spatial decomposition method. Through some experiments, the results of this paper show that the proposed point cloud refinement method helped to produce better performance.

A Study on the Development Strategy of Artificial Intelligence Technology Using Multi-Attribute Weighted Average Method (다요소 가중 평균법을 이용한 인공지능 기술 개발전략 연구)

  • Chang, Hae Gak;Choi, Il Young;Kim, Jae Kyeong
    • Journal of Information Technology Services
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    • v.19 no.2
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    • pp.93-107
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    • 2020
  • Recently, artificial intelligence (AI) technologies has been widely used in various fields such as finance, and distribution. Accordingly, Korea has also announced its AI R&D strategy for the realization of i-Korea 4.0 in May 2018. However, Korea's AI technology is inferior to major competitors such as the US, Canada, and Japan Therefore, in order to cope with the 4th industrial revolution, it is necessary to allocate AI R&D budgets efficiently through selection and concentration so as to gain competitive advantage under a limited budget. In this study, the importance of each AI technology was evaluated in multi-dimensional way through the questionnaire of expert group using the evaluation index derived from the literature review From the results of this study, we draw the following implication. In order to successfully establish the AI technology development strategies, it is necessary to prioritize the cognitive computing technology that has great market growth potential, ripple effect of technology development, and the urgency of technology development according to the principle of selection and concentration. To this end, it is necessary to find creative ideas, manage assessments, converge multidisciplinary systems and strengthen core competencies. In addition, since AI technology has a large impact on socioeconomic development, it is necessary to comprehensively grasp and manage scientific and technological regulations in order to systematically promote AI technology development.

Media and AI Technology: Media Intelligence (미디어와 AI 기술: 미디어 지능화)

  • Cho, Y.S.;Lee, N.K.;Choi, D.J.;Seo, J.I.;Lee, T.J.;Park, J.K.;Lee, H.W.;Kim, H.M.
    • Electronics and Telecommunications Trends
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    • v.35 no.5
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    • pp.92-101
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    • 2020
  • Artificial intelligence (AI) has become the hottest topic in information and communications technology (ICT) in recent years. Along with the advancement of AI technology, technologies such as big data, cloud, and high-speed wired and wireless communication are being applied to existing media areas in earnest, affecting all parts of the media value chain from content production to consumption. AI technology is now spreading across the media industry faster than any other industry. In the future, the gap between those with and without AI technology will widen, further deepening the polarization of the media ecosystem. Media intelligence, which combines media and AI technologies, is now perceived as essential, not optional. In this paper, we examine the current status of technology development and standardization by major domestic and foreign institutions on how AI is being utilized in the media industry. In addition, we discuss what technology should be developed to lead media intelligence.