• Journal of Advanced Technology Convergence
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• v.1 no.2
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• pp.01-09
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• 2022

Recently, safety issues in companies and public institutions are no longer a task that can be postponed, and when a major safety accident occurs, not only direct financial loss, but also indirect loss of social trust in the company and public institution is greatly increased. In particular, in the case of a fatal accident, the damage is even more serious. Accordingly, as companies and public institutions expand their investments in industrial safety education and prevention, open AI learning model creation technology that enables safety management services without being affected by user behavior in industrial sites where high-risk situations exist, edge terminals System development using inter-AI collaboration technology, cloud-edge terminal linkage technology, multi-modal risk situation determination technology, and AI model learning support technology is underway. In particular, with the development and spread of artificial intelligence technology, research to apply the technology to safety issues is becoming active. Therefore, in this paper, an open cloud platform design method that can support AI model learning for high-risk site safety management is presented.

• Journal of Fashion Business
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• v.26 no.2
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• pp.28-47
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• 2022

Rapid development of information and communication technology is leading the digital transformation (hereinafter, DT) of various industries. At this point in rapid online transition, fashion manufacturers operating offline-oriented businesses have become highly interested in DT and artificial intelligence (hereinafter AI), which leads DT. The purpose of this study is to examine the development status and application case of AI-based digital technology developed for the fashion industry, and to examine the DT stage and AI application status of domestic fashion manufacturers. Hence, in-depth interviews were conducted with five domestic IT companies developing AI technology for the fashion industry and six domestic fashion manufacturers applying AI technology. After analyzing interviews, study results were as follows: The seven major AI technologies leading the DT of the fashion industry were fashion image recognition, trend analysis, prediction & visualization, automated fashion design generation, demand forecast & optimizing inventory, optimizing logistics, curation, and ad-tech. It was found that domestic fashion manufacturers were striving for innovative changes through DT although the DT stage varied from company to company. This study is of academic significance as it organized technologies specialized in fashion business by analyzing AI-based digitization element technologies that lead DT in the fashion industry. It is also expected to serve as basic study when DT and AI technology development are applied to the fashion field so that traditional domestic fashion manufacturers showing low growth can rise again.

• Journal of Practical Engineering Education
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• v.16 no.3_spc
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• pp.275-282
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• 2024

This study studied a teaching model for software education using generative AI. The purpose of the study is to use ChatGPT as an instructor's assistant in programming classes for non-major students by using ChatGPT in software education. In addition, we designed ChatGPT to enable individual learning for learners and provide immediate feedback when students need it. The research method was conducted using ChatGPT as an assistant for non-computer majors taking a liberal arts Python class. In addition, we confirmed whether ChatGPT has the potential as an assistant in programming education for non-major students. Students actively used ChatGPT for writing assignments, correcting errors, writing coding, and acquiring knowledge, and confirmed various advantages, such as being able to focus on understanding the program rather than spending a lot of time resolving errors. We were able to see the potential for ChatGPT to increase students' learning efficiency, and we were able to see that more research is needed on its use in education. In the future, research will be conducted on the development, supplementation, and evaluation methods of educational models using ChatGPT.

• The Journal of the Convergence on Culture Technology
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• v.10 no.5
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• pp.743-749
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• 2024

Recently, the use of generative AI has become more active in university education, but jewelry design education and research using generative AI are still insufficient. Accordingly, we would like to discuss the possibilities and limitations of visualization of jewelry design ideation and expression using generative AI in the jewelry design development education and ideation stage, as well as the experience and application of generative AI by college students. To analyze the impact of generative AI on the learning experience, it was analyzed from the perspectives of 'usability', 'usefulness', 'reliability', and 'satisfaction'. As a result, generative AI confirmed positive results in terms of usability and usefulness to trainees, and confirmed the potential for effects using personalized education and collective intelligence. The combination of jewelry design education and generative AI is part of convergence education, and the significance of this study is to lay the foundation for effective use in jewelry design education by analyzing learners' experiences and perceptions of using generative AI. This kind of education reflects the trends of the times for nurturing future society's talents and will contribute to the promotion of learners' broad creative thinking.

• The Journal of the Korea Contents Association
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• v.20 no.11
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• pp.38-48
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• 2020

The domestic AI speaker market is growing into a full-fledged early audience market beyond the innovative consumer market with 3 million domestic supply units at the end of 2018, but the reality is that for various reasons, we are not satisfied with the use. There are many previous papers on AI Speaker, but the majority of research so far tends to be biased towards the acceptance of the device's own performance. Many changes are being made, such as OTT providers trying to secure the market through collaboration with AI speaker providers. This study tried to identify the priorities for content services, which can be another major selection factor for AI speakers, excluding the factors of unsatisfactory technology. First, this study identified the priorities among AI speaker selection factors using AHP (Analytic Hierarchy Process), based on the AI speaker selection factors derived through literature research. The most important hierarchical factor are Concierge Service, Education Service, and Entertainment Service order in AI speaker selection, and the primary content among the individual factors was the one that ranked weather/temperature/fine dust (11.6%) and child caring content was in the second place (10.8%), and then music service was in the third place (9.8%). The three top priorities were derived from the items in the top tier 1, 2 and 3 priorities. Of the total 15 individual services, 6 sub-layers of Concierge Service (weather/temperature/fine dust, news, voice schedule notification) and Education Service (foreign language, toddler, reading books) were in the top 8, and two of the Entertainment Service Music service and movie service ranked third and sixth.

• Journal of Bio-Environment Control
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• v.33 no.2
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• pp.120-128
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• 2024

Since ChatGPT was released in 2022, the generative artificial intelligence (AI) industry has seen massive growth and is expected to bring significant innovations to cognitive tasks. AI-based image generation, in particular, is leading major changes in the digital world. This study investigates the technical foundations of Midjourney, Stable Diffusion, and Firefly-three notable AI image generation tools-and compares their effectiveness by examining the images they produce. The results show that these AI tools can generate realistic images of tomatoes, strawberries, paprikas, and cucumbers, typical crops grown in greenhouse. Especially, Firefly stood out for its ability to produce very realistic images of greenhouse-grown crops. However, all tools struggled to fully capture the environmental context of greenhouses where these crops grow. The process of refining prompts and using reference images has proven effective in accurately generating images of strawberry fruits and their cultivation systems. In the case of generating cucumber images, the AI tools produced images very close to real ones, with no significant differences found in their evaluation scores. This study demonstrates how AI-based image generation technology can be applied in agriculture, suggesting a bright future for its use in this field.

• Journal of Platform Technology
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• v.8 no.3
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• pp.22-29
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• 2020

The 4th industrial revolution is progressing by country as a mega trend that leads various technological convergence directions in the social and economic fields from the initial simple manufacturing innovation. The epidemic of infectious diseases such as COVID-19 is shifting digital-centered non-face-to-face business from economic operation, and the use of AI and big data technology for personalized services is essential to spread online. In this paper, we analyze cases focusing on the application of artificial intelligence technology, which is a key technology for the effective implementation of the digital new deal promoted by the government, as well as the major technological characteristics of the 4th industrial revolution and describe the use cases in the field of disaster response. As a disaster response use case, AI assistants suggest appropriate countermeasures according to the status of the reporter in an emergency call. To this end, AI assistants provide speech recognition data-based analysis and disaster classification of converted text for adaptive response.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.168-168
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• 2023

The frequent occurrence of cyanobacterial harmful algal blooms (CHABs) in inland waters under climate change seriously damages the ecosystem and human health and is becoming a big problem in South Korea. Satellite remote sensing is suggested for effective monitoring CHABs at a larger scale of water bodies since the traditional method based on sparse in-situ networks is limited in space. However, utilizing a standalone variable of satellite reflectances in common CHABs dual-models, which relies on both chlorophyll-a (Chl-a) and phycocyanin or cyanobacteria cells (Cyano-cell), is not fully beneficial because their seasonal variation is highly impacted by surrounding meteorological and bio-environmental factors. Along with the development of Artificial Intelligence (AI), monitoring CHABs from space with analyzing the effects of environmental factors is accessible. This study aimed to investigate the potential application of AI in the dual-model strategy (Chl-a and Cyano-cell are output parameters) for monitoring seasonal dynamics of CHABs from satellites over Korean inland waters. The Sentinel-3 satellite was selected in this study due to the variety of spectral bands and its unique band (620 nm), which is sensitive to cyanobacteria. Via the AI-based feature selection, we analyzed the relationships between two output parameters and major parameters (satellite water-leaving reflectances at different spectral bands), together with auxiliary (meteorological and bio-environmental) parameters, to select the most important ones. Several AI models were then employed for modelling Chl-a and Cyano-cell concentration from those selected important parameters. Performance evaluation of the AI models and their comparison to traditional semi-analytical models were conducted to demonstrate whether AI models (using water-leaving reflectances and environmental variables) outperform traditional models (using water-leaving reflectances only) and which AI models are superior for monitoring CHABs from Sentinel-3 satellite over a Korean inland water body.

• Journal of Convergence for Information Technology
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• v.12 no.2
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• pp.157-162
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• 2022

In a society full of knowledge and information, digital literacy and artificial intelligence (AI) education that can utilize AI technology is needed to solve numerous everyday problems based on computational thinking. In this study, data-centered AI education was conducted while teaching computer programming to non-computer programming students at universities, and the correlation between major factors related to academic performance was analyzed in addition to student satisfaction surveys. The results indicated that there was a strong correlation between grades and problem-solving ability-based tasks, and learning satisfaction. Multiple regression analysis also showed a significant effect on grades (F=225.859, p<0.001), and student satisfaction was high. The non-computer programming students were also able to understand the importance of data and the concept of AI models, focusing on specific examples of project types, and confirmed that they could use AI smoothly in their fields of interest. If further cases of AI education are explored and students' AI education is activated, it will be possible to suggest its direction that can collaborate with experts through interest in AI technology.

• Journal of Digital Convergence
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• v.14 no.4
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• pp.139-147
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• 2016

The purpose of this study is to identify the relationships between AI education characteristics and psychological capital, psychological capital and flow, AI characteristics and flow through meditating effect of psychological capital in the digital convergence society. There are three AI characteristics: correctness, usefulness, easy of use. This empirical study was examined by 282 questionnaires to the three universities that teach accounting information system. It was performed by three-step method of the hierarchical regression analysis for the multiple regression analysis and parameter using the SPSS 22.0. The results and implications by analysis are as follows. First, AI characteristics and psychological capital have statistically significant positive influence. From AI attribute, correctness was established as the most important element. Second, psychological capital positively(+) influences flow. It allowed for the developed in flow. Third, psychological capital was shown as the major meditative variable between AI characteristics and flow. Through these, this paper suggests to reinforce self-efficacy, hope, resilience, optimism.