• 정보교육학회논문지
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• 제26권1호
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• pp.65-73
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• 2022

본 연구에서는 엔트리 텍스트 모델 학습을 활용해서 초등학교 인공지능 교육 내용을 개발하고 이를 실제 수업에 적용한다. 초·중등 인공지능 내용 체계표를 바탕으로 실과 소프트웨어 교육과 인공지능 교육의 성취 기준을 재구성한다. 기계학습이 가능한 텍스트, 이미지, 소리 중에서 다양한 플랫폼에서 지원하고 초등학생의 데이터 준비 시간을 줄일 수 있으면서 손쉽게 이해가 가능한 '텍스트 모델 학습을 활용한 감정 인식 프로그램 제작'을 교육 내용으로 선정한다. 엔트리 인공지능을 교육 플랫폼으로 선정해서 텍스트 모델 학습을 활용한 감정인식 프로그램을 만드는 인공지능 교육 내용을 개발하고 실제 초등학교 수업에 적용한다. 수업 적용 결과 엔트리 인공지능 수업에 긍정적인 반응과 흥미를 보였다. 본 연구 내용을 기반으로 초등학생을 대상으로 한 수업의 효과성에 대한 양적 연구가 후속 연구로 필요함을 제언한다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권9호
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• pp.3870-3884
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• 2020

The proposed framework of Four Layer Robust Storage in Cloud (FLRSC) architecture involves host server, local host and edge devices in addition to Virtual Machine Monitoring (VMM). The goal is to protect the privacy of stored data at edge devices. The computational intelligence (CI) part of our algorithm distributes blocks of data to three different layers by partially encoded and forwarded for decoding to the next layer using hash and greed Solomon algorithms. VMM monitoring uses snapshot algorithm to detect intrusion. The proposed system is compared with Tiang Wang method to validate efficiency of data transfer with security. Hence, security is proven against the indexed efficiency. It is an important study to integrate communication between local host software and nearer edge devices through different channels by verifying snapshot using lamport mechanism to ensure integrity and security at software level thereby reducing the latency. It also provides thorough knowledge and understanding about data communication at software level with VMM. The performance evaluation and feasibility study of security in FLRSC against three-layered approach is proven over 2³² blocks of data with 98% accuracy. Practical implications and contributions to the growing knowledge base are highlighted along with directions for further research.

• 한국실내디자인학회논문집
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• 제7호
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• pp.70-78
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• 1996

Artificial Intelligence is generally concerned with tasks whose execution appears to involve some intelligence if done by humans, and knowledge-based system ( in other word, expert system) is the research about the specific domain. This concept also can be applied to interior design field. So the purpose of this study is in reconstructing the accomplishment of artificial Intelligence and knowledge engineering, searching basic theories and cased to knowledge engineering , searching basic theories and cases to formulate knowledge -based design system, and testing the posibilities how the design information can be dealt in computer system. Given that recognition , two major problems must be solved before knowledge-based CAD systems could be come practical : Firstly , identification of the interior of designers use .Secondly , representing this knowledge in a computationally effective manner. I had discussed the basic concepts on which to base a knowledge- based design model, knowledge representation schemes, and problem solving, I could find the possibility which the knowledge-based system can be applied to the interior design according to this study. But there are non-deductive, often irrational and now easily computerized design process in interior design. Those are problems which are relevant to the machine learning and the creativity in design. So there should be a lot of research about the machine learning and the creatively in design in order to construct successfully intelligent knowledge-based design system.

• Coupled systems mechanics
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• 제8권6호
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• pp.523-535
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• 2019

Artificial Intelligence (AI) is anticipated to be the future of technology. Hence, AI has been applied in various fields over the years and its applications are expected to grow in number with the passage of time. There has been a growing need for accurate, direct, and quick prediction of geotechnical and foundation engineering models especially since the success of each project relies on numerous amounts of data. In this study, two applications of AI in the field of geotechnical and foundation engineering are presented - spatial interpolation of standard penetration test (SPT) data and prediction of consolidation of clay. SPT and soil profile data may be predicted and estimated at any location and depth at a site that has no available borehole test data using artificial intelligence techniques such as artificial neural networks (ANN) based on available geospatial information from nearby boreholes. ANN can also be used to accelerate the calculation of various theoretical methods such as the one-dimensional consolidation theory of clay with high efficiency by using lesser computation resources. The results of the study showed that ANN can be a valuable, powerful, and practical tool in providing various information that is needed in geotechnical and foundation design.

• 정보화정책
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• 제23권1호
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• pp.74-93
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• 2016

본 연구의 목적은 인공지능 분야 국가 과학기술 ICT 정책을 제언하는 것이다. 인공지능 산업의 글로벌 시장 확대에 따른 주요국의 동향을 분석하고 국가적 측면에서 한국의 현황을 진단 한 후 신(新)넛크래커 현상 극복 등 경쟁력 및 사업화 강화를 위한 정책제언을 한다. 우리의 현황은 거버넌스, 연구개발(R&D), 법 제도, 사업화, 인력양성 등을 진단하였다. 그리고 거버넌스 체계 개선, 미래 시장선도형 기초 원천 장기적 R&D 추진, 인공지능 사업화 플랫폼 구축 지원, 연구개발 촉진 법제도 및 이용환경 조정, 실무 융합형 시스템통합 인력양성 등 국가적 측면의 정책을 제언하였다.

• 한국지반신소재학회논문집
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• 제17권4호
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• pp.277-292
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• 2018

This paper presents the development and implementation of an artificial intelligence (AI)-based deep excavation induced wall and ground displacements and wall support member forces prediction program (ANN-EXCAV). The program has been developed in a C# environment by using the well-known AI technique artificial neural network (ANN). Program used ANN to predict the induced displacement, groundwater drawdown and wall and support member forces parameters for deep excavation project and run the stability check by comparing predict values to the calculated allowable values. Generalised ANNs were trained to predict the said parameters through databases generated by numerical analysis for cases that represented real field conditions. A practical example to run the ANN-EXCAV is illustrated in this paper. Results indicate that the program efficiently performed the calculations with a considerable accuracy, so it can be handy and robust tool for preliminary design of wall and support members for deep excavation project.

• ETRI Journal
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• 제43권4호
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• pp.630-639
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• 2021

Autonomous driving requires a computerized perception of the environment for safety and machine-learning evaluation. Recognizing semantic information is difficult, as the objective is to instantly recognize and distinguish items in the environment. Training a model with real-time semantic capability and high reliability requires extensive and specialized datasets. However, generalized datasets are unavailable and are typically difficult to construct for specific tasks. Hence, a light detection and ranging semantic dataset suitable for semantic simultaneous localization and mapping and specialized for autonomous driving is proposed. This dataset is provided in a form that can be easily used by users familiar with existing two-dimensional image datasets, and it contains various weather and light conditions collected from a complex and diverse practical setting. An incremental and suggestive annotation routine is proposed to improve annotation efficiency. A model is trained to simultaneously predict segmentation labels and suggest class-representative frames. Experimental results demonstrate that the proposed algorithm yields a more efficient dataset than uniformly sampled datasets.

• 한국IT서비스학회지
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• 제22권4호
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• pp.1-19
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• 2023

This study is a study on the effect of technical readiness factors on counselors' intention to use when applying AICC. AICC counselors experience improved customer service and emotional stability by receiving various monitor notification window services based on artificial intelligence algorithms such as customer counseling history, prohibited word control system, and customized counseling system. Accordingly, this study tried to verify using factors derived from technology readiness theory and technology acceptance theory among the factors affecting the intention to continue using AICC provided to counselors. To verify the research hypothesis, the causal relationship between variables such as Optimism, Innovativeness, Discomfort, Insecurity, and Technology Acceptance Theory, such as Team Support, Ease of Usage, and Innovation Resistance, was verified. As a result of empirical analysis, first, it was verified that Optimism has a positive (+) effect on Team Support and Ease of Usage, and Discomfort and Insecurity have a negative (-) effect on Ease of Usage and Team Support. Second, it was confirmed that Team Support and Ease of Usage had a positive effect on the Intention to use AICC. Based on the above empirical analysis results, the concepts of Technical Readiness were clearly proved, and in practical terms, AICC helped inquiry, quality evaluation, recording, and management of counseling history, ultimately increased corporate work efficiency.

• 공학교육연구
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• 제26권3호
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• pp.12-19
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• 2023

The purpose of this study is to develop the Artificial Intelligence thinking-based education program for improving AI literacy and verify its effectiveness for beginner. This program consists of 17 sessions, was designed according to the "ABCDE" model and is a project-based program. This program was conducted on 51 first-year middle school students and 36 respondents excluding missing values were analyzed in R language. The effect of this program on ethics, understanding, social competency, execution plan, data literacy, and problem solving of AI literacy is statistically significant and has very large practical significance. According to the result of this study, this program provided learners experiencing Artificial Intelligence education for the first time with Artificial Intelligence concepts and principles, collection and analysis of information, and problem-solving processes through application in real life, and served as an opportunity to enhance AI literacy. In addition, education program to enhance AI literacy should be designed based on AI thinking.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2022년도 추계학술발표대회
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• pp.481-483
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• 2022

With the advent of the era of artificial intelligence, more and more fields have begun to use artificial intelligence technology, especially the medical field. Cancer is one of the biggest problems in the medical field. [1] If it can be detected early and treated early, the possibility of cure will be greatly increased. Malignant skin cancer, as one of the types of cancer with the highest fatality rate in recent years has problems such as relying on the experience of doctors and being unable to be detected and detected in time. Therefore, if artificial intelligence technology can be used to help doctors in early detection of skin cancer, or to allow everyone to detect skin lesions or spots anytime, anywhere, it will have great practical significance. In this paper we used attention residual learning convolutional neural network (ARL-CNN) model [2] to classify skin cancer pictures.