• Asian Journal of Innovation and Policy
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• 제9권1호
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• pp.56-78
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• 2020

Changes in our lives due to Artificial Intelligence (AI) are currently ongoing, and there is little refutation of the effectiveness of AI. However, there have been active discussions to minimize the side effects of AI and use it responsibly, and publishing the AI Charter of Ethics (AICE) is one result of it. This study examines how our society is responding to threats from AI that may emerge in the future by examining various AIECs in the Republic of Korea. First, we summarize seven AI threats and classify these into three categories: AI's value judgment, malicious use of AI, and human alienation. Second, from Korea's seven AICEs, we draw fourteen topics based on three categories: protection of social values, AI control, and fostering digital citizenship. Finally, we review them based on the seven AI threats to evaluate any gaps between the threats and our responses. The analysis indicates that Korea has not yet been able to properly respond to the threat of AI's usurpation of human occupations (jobs). In addition, although Korea's AICEs present appropriate responses to lethal AI weapons, these provisions will be difficult to realize because the competition for AI weapons among military powers is intensifying.

• 펄프종이기술
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• 제40권2호
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• pp.43-56
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• 2008

The purpose of the experiment was to evaluate the material stability of the paper of the annals of Joseon Dynasty for the conservation purpose and the categories were as listed below : 1. Procuring traditional Hanji and semi-Hanji from different regional hanji paper mill. 2. Artificial accelerated aging treatment to examination its stability (UV irradiation, dry heat) 3. Analysis of optical, physical characteristic. The suitable Hanji for categories would be used as a reconstruction material for the waxel paper in the papers of the annals of Joseon Dynasty.

• International Journal of Internet, Broadcasting and Communication
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• 제15권3호
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• pp.191-202
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• 2023

This study aims to propose a framework for evaluating the impact of artificial intelligence (AI) services, based on the concept of AI service impact. It also suggests a model for evaluating this impact and identifies relevant factors and measurement approaches for each item of the model. The study classifies the impact of AI services into five categories: ethics, safety and reliability, compliance, user rights, and environmental friendliness. It discusses these five categories from a broad perspective and provides 21 detailed factors for evaluating each category. In terms of ethics, the study introduces three additional factors-accessibility, openness, and fairness-to the ten items initially developed by KISDI. In the safety and reliability category, the study excludes factors such as dependability, policy, compliance, and awareness improvement as they can be better addressed from a technical perspective. The compliance category includes factors such as human rights protection, privacy protection, non-infringement, publicness, accountability, safety, transparency, policy compliance, and explainability.For the user rights category, the study excludes factors such as publicness, data management, policy compliance, awareness improvement, recoverability, openness, and accuracy. The environmental friendliness category encompasses diversity, publicness, dependability, transparency, awareness improvement, recoverability, and openness.This study lays the foundation for further related research and contributes to the establishment of relevant policies by establishing a model for evaluating the impact of AI services. Future research is required to assess the validity of the developed indicators and provide specific evaluation items for practical use, based on expert evaluations.

• Journal of Information Processing Systems
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• 제20권2호
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• pp.149-158
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• 2024

Traditional manual identification of crop leaf diseases is challenging. Owing to the limitations in manpower and resources, it is challenging to explore crop diseases on a large scale. The emergence of artificial intelligence technologies, particularly the extensive application of deep learning technologies, is expected to overcome these challenges and greatly improve the accuracy and efficiency of crop disease identification. Crop leaf disease identification models have been designed and trained using large-scale training data, enabling them to predict different categories of diseases from unlabeled crop leaves. However, these models, which possess strong feature representation capabilities, require substantial training data, and there is often a shortage of such datasets in practical farming scenarios. To address this issue and improve the feature learning abilities of models, this study proposes a deep transfer learning adaptation strategy. The novel proposed method aims to transfer the weights and parameters from pre-trained models in similar large-scale training datasets, such as ImageNet. ImageNet pre-trained weights are adopted and fine-tuned with the features of crop leaf diseases to improve prediction ability. In this study, we collected 16,060 crop leaf disease images, spanning 12 categories, for training. The experimental results demonstrate that an impressive accuracy of 98% is achieved using the proposed method on the transferred ResNet-50 model, thereby confirming the effectiveness of our transfer learning approach.

• Journal of Biosystems Engineering
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• 제23권6호
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• pp.607-614
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• 1998

An automatic grading algorithm was developed to replace the manual trading of white ginseng. The algorithm consists of three consecutive stages, (a) image acquisition and preprocessing, (b) mathematical feature extraction, and (c) grade decision using artificial neural network. Mathematical features such as area ratio, mean and standard deviation of graylevel, skewness of graylevel histogram, and the number of run segment are extracted from five equally divided parts of ginseng. An artificial neural network model was used to classify white ginsengs into three categories. The performance of the algorithm was evaluated using 120 ginseng samples and the rate of successful classification was 74%.

• 공학교육연구
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• 제21권6호
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• pp.74-80
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• 2018

This paper proposes practical teaching methods for efficient progress of project-based learning in engineering design education. Engineering design courses consist of three categories; introductory, individual and capstone design courses. This study concentrates on the case of individual design courses. Individual design courses act as bridges between introductory and capstone design courses and deal with applicable projects based on theoretical frameworks. In this study, practical teaching methods are applied to Artificial Intelligence curriculum as an individual design course for Juniors in Computer Engineering Major. The results on application of practical teaching methods show relatively positive in all aspects.

• 국제학술발표논문집
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• The 9th International Conference on Construction Engineering and Project Management
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• pp.57-66
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• 2022

Claims and disputes are major causes of cost and schedule overruns in the construction business. In order to manage claims and disputes effectively, it is necessary to analyze various types of contract documents punctually and accurately. Since volume of such documents is so vast, analyzing them in a timely manner is practically very challenging. Recently developed approaches such as artificial intelligence (AI), machine learning algorithms, and natural language processing (NLP) have been applied to various topics in the field of construction contract and claim management. Based on the systematic literature review, this paper analyzed the goals, methodologies, and application results of such approaches. AI methods applied to construction contract management are classified into several categories. This study identified possibilities and limitations of the application of such approaches. This study contributes to providing the directions for how such approaches should be applied to contract management for future studies, which will eventually lead to more effective management of claims and disputes.

• 한국정보과학회논문지:시스템및이론
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• 제27권8호
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• pp.701-709
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• 2000

실세계에서의 바람은 자연 발생적인 것과 인위적으로 생성한 것으로 분류할 수 있다. 이제까지의 연구 결과들은 자연 현상으로서의 바람을 모델링하였다. 본 논문에서는 사람의 입이나 선풍기, 에어컨 등에서 발생하는 인공적인 바람을 모델링하기 위한 바람 모델을 제시한다. 본 논문의 바람 모델에서는 생성된 바람이 도달하는 물체를 찾아내고, 그 물체에 가해지는 힘을 계산하는 방법을 제공한다. 특히, 이 모델은 가상 현실과 같은 실시간 처리가 필요한 분야들에서 사용 가능하도록 최적화된 계산을 수행하도록 설계되었다. 본 논문에서 제시한 방법은 기존의 자연 발생적인 바람 모델들과는 보완적인 관계에 있다. 이들 모델들을 통합하여 종합적인 바람 생성 시스템을 구성할 수 있을 것으로 기대된다.

• 한국의류산업학회지
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• 제19권1호
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• pp.79-89
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• 2017

Sensory attributes and preferences that contribute to consumer satisfaction with artificial leather were measured by subjective evaluation, and subjective hand and preferences were analyzed in relation to its use. Using tactile and visual senses, 50 experts in fashion and textile industry evaluated leathers classified into two categories, suede and polyurethane coated, according to different manufacturing methods. They answered questions on subjective hand and preferences of different artificial leathers of various fashion items (jackets, purses, bags, shoes, boots, furniture, etc.), using specific adjectives to describe the hand properties. As a result, it was found that the subjective hand properties of artificial leathers were related to 'Thickness', 'Fullness/softness', 'Surface contour', 'Stickiness', and 'Elasticity'. The leather type from different manufacturing methods influenced their perceived hand and preferences relating to use. By use, different hands were preferred. The preferences for jackets and furniture of suede type leathers were related to their surface properties, whereas the preferences for items of the other type of leathers were associated with their resilience. On the other hand, in the case of polyurethane coated leathers, the preferences for jackets were significantly affected by their thickness, while those for the other items were influenced by their resilience and surface properties.

• 로봇학회논문지
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• 제11권4호
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• pp.205-216
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• 2016

The localization of the robot is one of the most important factors of navigating mobile robots. The use of featured information of landmarks is one approach to estimate the location of the robot. This approach can be classified into two categories: the natural-landmark-based and artificial-landmark-based approach. Natural landmarks are suitable for any environment, but they may not be sufficient for localization in the less featured or dynamic environment. On the other hand, artificial landmarks may generate shaded areas due to space constraints. In order to improve these disadvantages, this paper presents a novel development of the localization system by using artificial and natural-landmarks-based approach on a topological map. The proposed localization system can recognize far or near landmarks without any distortion by using landmark tracking system based on top-view image transform. The camera is rotated by distance of landmark. The experiment shows a result of performing position recognition without shading section by applying the proposed system with a small number of artificial landmarks in the mobile robot.