• 반도체디스플레이기술학회지
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• 제22권3호
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• pp.136-141
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• 2023

Artificial intelligence is becoming a global hot topic and is being actively applied in various industrial fields. Not only is artificial intelligence being applied to industrial sites in an on-premises method, but cloud-based artificial intelligence platforms are expanding into "as a service" type. The purpose of this study is to develop and verify a measurement tool for an evaluation framework for the adoption of a cloud-based artificial intelligence platform and test the interrelationships of evaluation variables. To achieve this purpose, empirical testing was conducted to verify the hypothesis using an expanded technology acceptance model, and factors affecting the intention to adopt a cloud-based artificial intelligence platform were analyzed. The results of this study are intended to increase user awareness of cloud-based artificial intelligence platforms and help various industries adopt them through the evaluation framework.

• 한국건축시공학회:학술대회논문집
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• 한국건축시공학회 2022년도 봄 학술논문 발표대회
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• pp.225-226
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• 2022

The construction industry, which has a labour-intensive and conservative nature, is exclusive to adopt new technologies. However, the construction industry is viably introducing the 4th Industrial Revolution technologies represented by artificial intelligence, Internet of Things, robotics and unmanned transportation to promote change into a smart industry. An image-based artificial intelligence technology is a field of computer vision technology that refers to machines mimicking human visual recognition of objects from pictures or videos. The purpose of this article is to explore image-based artificial intelligence technologies which would be able to apply to the construction sites. In this study, we show two examples which is one for a construction waste classification model and another for cast in-situ anchor bolts defection detection model. Image-based intelligence technologies would be used for various measurement, classification, and detection works that occur in the construction projects.

• 지식경영연구
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• 제19권1호
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• pp.97-118
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• 2018

The finance-investment industry is currently focusing on research related to artificial intelligence and big data, moving beyond conventional theories of financial engineering. However, the case of equity optimization portfolio by using an artificial intelligence, big data, and its performance is rarely realized in practice. Thus, the purpose of this study is to propose process improvements in equity selection, information analysis, and portfolio composition, and lastly an improvement in portfolio returns, with the case of an equity optimization model based on quantitative research by an artificial intelligence. This paper is an empirical study of the portfolio based on an artificial intelligence technology of "D" asset management, which is the largest domestic active-quant-fiduciary management in accordance with the purpose of this paper. This study will apply artificial intelligence to finance, analyzing financial and demand-supply information and automating factor-selection and weight of equity through machine learning based on the artificial neural network. Also, the learning the process for the composition of portfolio optimization and its performance by applying genetic algorithms to models will be documented. This study posits a model that the asset management industry can achieve, with continuous and stable excess performance, low costs and high efficiency in the process of investment.

• Journal of Information Processing Systems
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• 제14권6호
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• pp.1301-1306
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• 2018

Artificial intelligence is one of the key technologies of the Fourth Industrial Revolution. This paper introduces the diverse kinds of approaches to subjects that tackle diverse kinds of research fields such as model-based MS approach, deep neural network model, image edge detection approach, cross-layer optimization model, LSSVM approach, screen design approach, CPU-GPU hybrid approach and so on. The research on Superintelligence and superconnection for IoT and big data is also described such as 'superintelligence-based systems and infrastructures', 'superconnection-based IoT and big data systems', 'analysis of IoT-based data and big data', 'infrastructure design for IoT and big data', 'artificial intelligence applications', and 'superconnection-based IoT devices'.

• 한국컴퓨터정보학회논문지
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• 제28권4호
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• pp.187-195
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• 2023

본 연구는 엔트리 인공지능 모델을 활용하여 인문사회계열 대학생을 위한 인공지능 교양 교과목을 개발하는 데 목적이 있다. 컴퓨터, 인공지능, 교육학 전문가 집단을 구성하고 선행연구 분석, 델파이 기법을 활용하여 최종 인공지능 교양 교과목을 개발하였다. 연구결과 교육 주제는 크게 이미지 분류, 영상인식, 텍스트 분류, 소리 분류 총 4가지로 구성하였다. 교육 내용은 주제별로 1) 인공지능 원리 이해, 2) 엔트리 인공지능 모델 활용 실습, 3) 윤리적 영향성 확인, 4) 배운 내용을 기반으로 실생활 문제 해결을 위한 팀별 아이디어 회의 단계로 구성하였다. 본 교과목을 통해 인문사회계열 대학생은 인공지능 핵심기술의 원리 이해를 바탕으로 엔트리 인공지능 모델을 통해 직접 구현할 수 있고 더 나아가 실생활의 다양한 문제를 인공지능으로 해결해보는 경험을 기저로 기술을 이해하고 인공지능 시대 필요한 윤리를 모색해보며 책임감 있게 사용하는데 긍적적인 기여를 기대해볼 수 있을 것이다.

• 한국인공지능학회지
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• 제7권1호
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• pp.17-22
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• 2019

The rapid development of artificial intelligence technology has exerted a great influence on all fields of the world, which of course also affects the world economy. This has also aroused a large number of economists' interest in this proposition. Since the definition of artificial intelligence is not unified yet, the results from previous researches are not reliable enough. At present, most scholars use the neoclassical growth model or task-based model to explore the path of artificial intelligence on economic variables. There into, most of them use the degree of automation to represent the artificial intelligence. They find that the degree of automation can change the proportion of industries. This only verifies that artificial intelligence can affect the economic variables. But the magnitude of artificial intelligence on economic variables can not be correctly estimated. Therefore, in order to have a better understanding on the impact of artificial intelligence on economic growth, this paper systematically reviews and collates previous literature on this topic. The results of this paper indicate that both in theoretical and empirical studies, artificial intelligence has a positive effect on economic growth. Then, some suggestions and limitations have also been put forward accordingly.

• Journal of information and communication convergence engineering
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• 제19권3호
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• pp.142-147
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• 2021

In this paper, we propose an intelligent research fund management system that applies artificial intelligence technology to an integrated research fund management system. By defining research fund management rules as work rules, a detection model learned using deep learning is designed, through which the disapproval status is presented for each research fund usage history. The disapproval detection system of the RCMS implemented in this study predicts whether the newly registered usage details are recognized or disapproved using an artificial intelligence model designed based on the use of an 8.87 million research fund registered in the RCMS. In addition, the item-detail recommendation system described herein presents the usage details according to the usage history item newly registered by the artificial intelligence model through a correlation between the research cost usage details and the item itself. The accuracy of the recommendation was shown to be 97.21%.

• International Journal of Computer Science & Network Security
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• 제24권2호
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• pp.1-14
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• 2024

While text-to-image models have made remarkable progress in image synthesis, certain models, particularly generative diffusion models, have exhibited a noticeable bias to- wards generating images related to the culture of some developing countries. This paper introduces an empirical investigation aimed at mitigating the bias of image generative model. We achieve this by incorporating symbols representing Saudi culture into a stable diffusion model using the Dreambooth technique. CLIP score metric is used to assess the outcomes in this study. This paper also explores the impact of varying parameters for instance the quantity of training images and the learning rate. The findings reveal a substantial reduction in bias-related concerns and propose an innovative metric for evaluating cultural relevance.

• 한국정보교육학회:학술대회논문집
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• 한국정보교육학회 2021년도 학술논문집
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• pp.349-354
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• 2021

본 연구에서는 2022년 개정 교육과정에서 소프트웨어와 인공지능 교육이 필수화되어 각 교과에서 인공지능과 융합할 수 있는 교육 모형을 제안한다. 제안한 인공지능 융합 교육 모형은 교과 내용(성취기준+주제)을 한 축으로 한다. 두 번째 축은 인공지능 도구이고, 세 번째 축은 인공지능 기술이고, 네 번째 축은 생활 속 적용 데이터이다. 인공지능을 각 교과에 적용하기 위해서 각 교과의 성취기준과 교과 내용에 인공지능 도구, 인공지능 기술, 생활 속 데이터 적용을 하여야 한다. 이렇게 성취기준과 교과 내용을 구성하면 각 교과와의 융합이 잘 된다고 볼 수 있다. 따라서 성취기준과 주제별로 교과서를 구성할 때에 인공지능 도구, 인공지능 기술, 생활속 데이터를 추가하는 것이 필요하다.

• 전자통신동향분석
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• 제38권6호
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• pp.22-30
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• 2023

Recently, artificial intelligence has been in the spotlight across various fields. Artificial intelligence uses massive amounts of data to train machine learning models and performs various tasks using the trained models. For model training, large, high-quality data sets are essential, and database systems have provided such data. Driven by advances in artificial intelligence, attempts are being made to improve various components of database systems using artificial intelligence. Replacing traditional complex algorithm-based database components with their artificial-intelligence-based counterparts can lead to substantial savings of resources and computation time, thereby improving the system performance and efficiency. We analyze trends in the application of artificial intelligence to database systems.