• Journal of Creative Information Culture
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• v.7 no.3
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• pp.177-185
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• 2021

In future education, the paradigm of education is changing due to changes in learner-led and assessment methods. In addition, AI-based learning infrastructure and software education are increasingly needed. Thus, this study aims to examine the effectiveness of AI-based evaluation in future education by combining it with learner-led assessment. Using AI education and evaluation literature and Step 7 of the Learner-Driven Software Assessment Method, we sought to extract evaluation elements tailored to elementary school level in conjunction with the 2015 revised elementary practical course content elements, software understanding, procedural problem solving, and structural evaluation elements. In the future, we will develop a grading system that applies AI-based learner-led evaluation elements in software education and continuously demonstrate its effectiveness, and help the school site prepare for future education independently through AI-based learner-led assessment in software education.

• Journal of Korean Society for Quality Management
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• v.50 no.4
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• pp.647-664
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• 2022

Purpose: The purpose of this study is to derive major policies that domestic small and medium-sized manufacturing companies should consider to maximize productivity and quality improvement by utilizing manufacturing data and AI, and to find priorities and implications. Methods: In this study, domestic and international issues and literature review by country were conducted to derive major considerations such as manufacturing AI technology, manufacturing AI talent, manufacturing AI data and manufacturing AI ecosystem. Additionally, the questionnaire survey targeting 46 experts of manufacturing data and AI industry were conducted. Finally, the major considerations and detailed factors importance were derived by applying the Analytic Hierarchy Process (AHP). Results: As a result of the study, it was found that 'manufacturing AI technology', 'manufacturing AI talent', 'manufacturing AI data', and 'manufacturing AI ecosystem' exist as key considerations for domestic manufacturing AI. After empirical analysis, the importance of the four key considerations was found to be 'manufacturing AI ecosystem (0.272)', 'manufacturing AI data (0.265)', 'manufacturing AI technology (0.233)', and 'manufacturing AI talent (0.230)'. The importance of the derived four viewpoints is maintained at a similar level. In addition, looking at the detailed variables with the highest importance for each of the four perspectives, 'Best Practice', 'manufacturing data quality management regime, 'manufacturing data collection infrastructure', and 'manufacturing AI manpower level of solution providers' were found. Conclusion: For the sustainable growth of the domestic manufacturing AI ecosystem, it should be possible to develop and promote manufacturing AI policies in a balanced way by considering all four derived viewpoints. This paper is expected to be used as an effective guideline when developing policies for upgrading manufacturing through domestic manufacturing data and AI in the future.

• East Asian mathematical journal
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• v.35 no.4
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• pp.509-528
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• 2019

As various changes in education in general and learning environment in particular have promoted different needs and expectations for learning at both personal and social levels, the roles that schools and school teachers typically have with respect to their students are being challenged. Especially with the recent, rapid progress of the artificial intelligence(AI) field, AI could serve beyond the way in which it has been used. Based on a review of some of the related literature and the current development of AI, a view on utilizing AI to be a collaborative, complementary partner with an human mathematics teacher in the classroom in order to support both students and teachers will be discussed.

• Journal of Digestive Cancer Research
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• v.12 no.1
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• pp.31-37
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• 2024

Pancreatic cancer is a highly fatal malignancy with a 5-year survival rate of < 10%. Endoscopic ultrasound (EUS) is a useful noninvasive tool for differential diagnosis of pancreatic malignancy and treatment decision-making. However, the performance of EUS is suboptimal, and its accuracy for differentiating pancreatic malignancy has increased interest in the application of artificial intelligence (AI). Recent studies have reported that EUS-based AI models can facilitate early and more accurate diagnosis than other preexisting methods. This article provides a review of the literature on EUS-based AI studies of pancreatic malignancies.

• Journal of the Korean Society for Library and Information Science
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• v.57 no.4
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• pp.333-351
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• 2023

This study investigates user behavior in library spaces through the lens of AI camera analytics. By leveraging the face recognition and tracking capabilities of AI cameras, we accurately identified the gender and age of visitors and meticulously collected video data to track their movements. Our findings revealed that female users slightly outnumbered male users and the dominant age group was individuals in their 30s. User visits peaked between Tuesday to Friday, with the highest footfall recorded between 14:00 and 15:00 pm, while visits decreased over the weekend. Most visitors utilized one or two specific spaces, frequently consulting the information desk for inquiries, checking out/returning items, or using the rest area for relaxation. The library stacks were used approximately twice as much as they were avoided. The most frequented subject areas were Philosophy(100), Religion(200), Social Sciences(300), Science(400), Technology(500), and Literature(800), with Literature(800) and Religion(200) displaying the most intersections with other areas. By categorizing users into five clusters based on space utilization patterns, we discerned varying objectives and subject interests, providing insights for future library service enhancements. Moreover, the study underscores the need to address the associated costs and privacy concerns when considering the broader application of AI camera analytics in library settings.

• International Journal of Advanced Culture Technology
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• v.7 no.2
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• pp.188-194
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• 2019

Always, emotion is mutant. This is principle of literary treatment. In the literature, sadness is not sadness, and 'loving emotion' is not 'loving emotion.' Despite loving of our, loving is sadness. Also loving is to cry. This crying becomes love. This study is to show the mutant emotion which is to be able to code Deep Learning AI. We explored the Sijo "Streams that cried last night", because this Sijo was useful to study mutant emotion. The result was that this Sijo was coding the mutant emotion. Almost continuously, the sadness codes were spawning and concentrating. So this Sijo was making the emotion of love with the sadness. If this study is continued, It is believed that our lives will be much happier. And the method of literary therapy will be able to more upgrade.

• International conference on construction engineering and project management
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• 2022.06a
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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.

• Journal of the Korea Convergence Society
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• v.10 no.8
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• pp.195-203
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• 2019

The aim of this study is to explore the paradigm shifts in culture and system related to life in terms of AI and the present point of view in which creating human values together are important. An approach that focuses on how AI-related phenomena work in modern society forms the basis of this research. Therefore, to clarify the meaning of "AI phenomenon" converging it as a part of social culture, this study was intended to find out the value incorporated in the social system such as ethics and equality together with the literature review. Inferring the technical culture that are combined with the AI that the members of society can do together is as important as technical understanding in the functional aspect. Therefore, this study was intended to suggest new culture that the cultural imagination and the social system create harmonizing each other, that is, the possibility of "AI culture". So, this article has a characteristic of a preliminary study, too.

• Trans-
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• v.13
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• pp.117-140
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• 2022

Recently, artificial intelligence technology is being used throughout the industry. Currently, Currently, AI art generators are used in the NFT industry, and works using them have been exhibited and sold. AI art generators in the art field include Gated Photos, Google Deep Dream, Sketch-RNN, and Auto Draw. AI art generators in the music field are Beat Blender, Google Doodle Bach, AIVA, Duet, and Neural Synth. The characteristics of AI art generators are as follows. First, AI art generator in the art field are being used to create new works based on existing work data. Second, it is possible to quickly and quickly derive creative results to provide ideas to creators, or to implement various creative materials. In the future, AI art generators are expected to have a great influence on content planning and production such as visual art, music composition, literature, and movie.

• Journal of the Korea Safety Management & Science
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• v.22 no.1
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• pp.9-14
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• 2020

Artificial intelligence (AI) has been applied to most industries by enhancing automation and contributing greatly to efficient processes and high-quality production. This research analyzes the applications of AI-based automobile accident prevention systems. It deals with AI-based collision prevention systems that learn information from various sensors attached to cars and AI-based accident detection systems that automatically report accidents to the control center in the event of a collision. Based on the literature review, technological and institutional changes are taking place at the national levels, which recognize the effectiveness of the systems. In addition, start-ups at home and abroad as well as major car manufacturers are in the process of commercializing auto parts equipped with AI-based collision prevention technology.