• KSCE Journal of Civil and Environmental Engineering Research
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• v.42 no.6
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• pp.843-852
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• 2022

Despite continuous safety management, the death rate of construction workers is not decreasing every year. Accordingly, various studies are in progress to prevent construction site accidents. In this paper, we developed an AI-based priority inspection target selection model that preferentially selects sites are expected to cause construction accidents among construction sites with construction costs of less than 5 billion won (KRW). In particular, Random Forest (90.48 % of accident prediction AUC-ROC) showed the best performance among applied AI algorithms (Classification analysis). The main factors causing construction accidents were construction costs, total number of construction days and the number of construction performance evaluations. In this study an ROI (return of investment) of about 917.7 % can be predicted over 8 years as a result of better efficiency of manual inspections human resource and a preemptive response to construction accidents.

• Journal of Internet Computing and Services
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• v.22 no.1
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• pp.109-135
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• 2021

his study classified the development process of artificial intelligence (AI) speakers through analysis of the news text of artificial intelligence (AI) speakers shown in traditional news reports, and identified the characteristics of each product by period. The theoretical background used in the analysis are news frames and topic frames. As analysis methods, topic modeling and semantic network analysis using the LDA method were used. The research method was a content analysis method. From 2014 to 2019, 2710 news related to AI speakers were first collected, and secondly, topic frames were analyzed using Nodexl algorithm. The result of this study is that, first, the trend of topic frames by AI speaker provider type was different according to the characteristics of the four operators (communication service provider, online platform, OS provider, and IT device manufacturer). Specifically, online platform operators (Google, Naver, Amazon, Kakao) appeared as a frame that uses AI speakers as'search or input devices'. On the other hand, telecommunications operators (SKT, KT) showed prominent frames for IPTV, which is the parent company's flagship business, and 'auxiliary device' of the telecommunication business. Furthermore, the frame of "personalization of products and voice service" was remarkable for OS operators (MS, Apple), and the frame for IT device manufacturers (Samsung) was "Internet of Things (IoT) Integrated Intelligence System". The econd, result id that the trend of the topic frame by AI speaker development period (by year) showed a tendency to develop around AI technology in the first phase (2014-2016), and in the second phase (2017-2018), the social relationship between AI technology and users It was related to interaction, and in the third phase (2019), there was a trend of shifting from AI technology-centered to user-centered. As a result of QAP analysis, it was found that news frames by business operator and development period in AI speaker development are socially constituted by determinants of media discourse. The implication of this study was that the evolution of AI speakers was found by the characteristics of the parent company and the process of co-evolution due to interactions between users by business operator and development period. The implications of this study are that the results of this study are important indicators for predicting the future prospects of AI speakers and presenting directions accordingly.

• Journal of Chest Surgery
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• v.26 no.11
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• pp.821-826
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• 1993

Between January 1983 and December 1992, we had experienced 79 patients of ventricular septal defect [ VSD ] associated with aortic insufficiency [AI] which constitute 4.6 % of total numbers of VSD. The mean age of the patients was 10.2 years with a range of 1 to 35 years and the average degree of aortic insufficiency classified by Sellers was 2.1. The type of VSD was subpulmonic in 57 patients and perimembranous in 22. Most common pathologic finding causing AI was prolapse of right coronary cusp [ 54 cases ; 71.4% ] ,followed by prolapse of both right and non-coronary cusp[ 12 cases ; 7.9% ]. VSD closure alone was performed in 51 patients and their mean age was 7.7 years [ ranged 1 to 13 years ]. VSD closure and aortic valve reconstruction was performed in 22 patients, VSD closure and aortic valve replacement in 6 patients, and the mean age of the patients was 14.5 years [ ranged 2 to 28 years ], 20.4 years [ ranged 18 to 35 years ] respectively. There was no hospital mortality. All patients were followed up from 1 month to 9 year 4 months [average; 21.4 months ] and there was one late death. Our data suggests that, early closure of VSD without any manipulation on the valve may be sufficient procedure to improve or at least withhold progression of AI in children and furthermore patients with VSD associated AI should be corrected promptly after diagnosis.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.1
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• pp.83-92
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• 2023

The purpose of this research is to develop a creative education program that utilizes AI education program based on big data for general education high schools, and to investigate its effectiveness. In order to achieve the purpose of the research, we developed a creative education program using artificial intelligence based on big data for first-year general high school students, and carried out on-site classes at schools and a validation process by experts. In order to measure the creative problem-solving ability and class satisfaction of high school students, a creative problem-solving ability test was conducted before and after the program application, and a class satisfaction test was conducted after the program. The results of this study are as follows. First, AI education program based on big data were statistically effective to improve the creative problem solving ability according to independent sample t test about 'problem discovery and analysis', 'idea generation', 'execution plan', 'conviction and communication', and 'innovation tendency' except 'execution', 'the difference between pre- and post-scores of male student and female student' on first year high school students. Secondly, in satisfaction conducted after classes of AI education program based on big data, the average of 'Satisfaction', 'Interest', 'Participation', 'Persistence' were 3.56 to 3.92, and the overall average was 3.78. Therefore, it was investigated that there was a lesson effect of the AI education program based on big data developed in this research.

• Korean Journal of Childcare and Education
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• v.19 no.2
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• pp.61-85
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• 2023

Objective: This study aims to investigate whether science teaching attitude of early childhood teachers mediates the relationship between the professional learning environment of institutions and their intention to accept artificial intelligence (AI) technology, and whether the experience of using smart and digital devices moderates the effect of science teaching attitude. Methods: An online survey was conducted targeting 118 teachers with more than 1 year of experience in kindergarten and day care center settings. Descriptive statistical analysis, correlation analysis, and The Process macro model 4, 14 were performed using SPSS 27.0 and The Process macro 3.5. Results: First, the science teaching attitude of early childhood teachers served as a mediator between the professional learning environment of institutions and teachers' intention to accept AI technology. Second, the experience of using smart and digital devices was found to moderate the effect of teachers' science teaching attitude on their intention to accept AI technology. Conclusion/Implications: This results showed that an institutional environment that supports teachers' professionalism development and provides rich experience is crucial for promoting teachers' active acceptance of AI technology. The findings highlight the importance of creating a supportive institutional envionment for teacher's professional growth, enhancing science teaching attitudes, and facilitating the use of various devices.

• Journal of Engineering Education Research
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• v.27 no.3
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• pp.3-13
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• 2024

Since the launch of ChatGPT, many college students used it extensively in various ways in their curricular learning activities. This study investigates the utilization of ChatGPT in the curriculum of first and second-year engineering students, aiming to examine its influence from a learner perspective. We explored how ChatGPT is used in each subject and learning activity to understand how learners perceive the use of ChatGPT. From the survey data on engineering college students at E university, we examined students' perception on 'shortening time to perform tasks' through ChatGPT, 'dependence on ChatGPT', 'their contribution to individual capacity building', and 'their influence on academic grade'. The majority of students reported extensive use of ChatGPT for learning activities, particularly showing high dependency in liberal arts subjects and coding-related activities. While the use of ChatGPT in liberal arts was seen as not contributing to the enhancement of individual capacity, its use in coding was positively evaluated. Furthermore, the contribution of ChatGPT to the creativity in report writing tasks was highly rated. These findings offer several important implications for the use of AI tools like ChatGPT in engineering education. Firstly, the positive impact of ChatGPT's high usability and individual-capacity enhancement in coding should be expanded to other areas of learning. Secondly, as AI technology progresses, the contribution of AI tools compared to learners is expected to increase, suggesting that students should be encouraged to effectively use AI tools to achieve their learning objectives while maintaining a balanced approach to avoid overreliance on AI.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.17 no.1
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• pp.1-9
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• 2024

This paper analyzes the trends in generative AI technology based on patent application documents. To achieve this, we selected 5,433 generative AI-related patents filed in South Korea, the United States, and Europe from 2003 to 2023, and analyzed the data by country, technology category, year, and applicant, presenting it visually to find insights and understand the flow of technology. The analysis shows that patents in the image category account for 36.9%, the largest share, with a continuous increase in filings, while filings in the text/document and music/speech categories have either decreased or remained stable since 2019. Although the company with the highest number of filings is a South Korean company, four out of the top five filers are U.S. companies, and all companies have filed the majority of their patents in the U.S., indicating that generative AI is growing and competing centered around the U.S. market. The findings of this paper are expected to be useful for future research and development in generative AI, as well as for formulating strategies for acquiring intellectual property.

• Journal of Korean Library and Information Science Society
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• v.53 no.1
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• pp.231-263
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• 2022

The understanding and capability to utilize artificial intelligence (AI) incorporated technology has become a required basic skillset for the people living in today's information age, and various members of the university have also increasingly become aware of the need for AI education. Amidst such shifting societal demands, both domestic and international university libraries have recognized the users' need for educational content centered on AI, but a user-centered service that aims to provide personalized recommendations of digital AI educational content is yet to become available. It is critical while the demand for AI education amongst university students is progressively growing that university libraries acquire a clear understanding of user intention towards an AI educational content recommender system and the potential factors contributing to its success. This study intended to ascertain the factors affecting acceptance of such system, using the Extended Technology Acceptance Model with added variables - innovativeness, self-efficacy, social influence, system quality and task-technology fit - in addition to perceived usefulness, perceived ease of use, and intention to use. Quantitative research was conducted via online research surveys for university students, and quantitative research was conducted through written interviews of university librarians. Results show that all groups, regardless of gender, year, or major, have the intention to use the AI-related Educational Content Recommendation System, with the task suitability factor being the most dominant variant to affect use intention. University librarians have also expressed agreement about the necessity of the recommendation system, and presented budget and content quality issues as realistic restrictions of the aforementioned system.

• Asian-Australasian Journal of Animal Sciences
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• v.32 no.8_spc
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• pp.1284-1295
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• 2019

Considerable progress in reproduction of dairy goats has been made, with advances in reproductive technology accelerating dairy goat production since the 1980s. Reproduction in goats is described as seasonal. The onset and length of the breeding season is dependent on various factors such as breed, climate, physiological stage, male effect, breeding system, and photoperiod. The reproductive physiology of goats was investigated extensively, including hypothalamic and pituitary control of the ovary related to estrus behavior and cyclicity etc. Photoperiodic treatments coupled with the male effect allow hormone-free synchronization of ovulation, but the kidding rate is still less than for hormonal treatments. Different protocols have been developed to meet the needs and expectations of producers; dairy industries are subject to growing demands for year round production. Hormonal treatments for synchronization of estrus and ovulation in combination with artificial insemination (AI) or natural mating facilitate out-of-season breeding and the grouping of the kidding period. The AI with fresh or frozen semen has been increasingly adopted in the intensive production system, this is perhaps the most powerful tool that reproductive physiologists and geneticists have provided the dairy goat industry with for improving reproductive efficiency, genetic progress and genetic materials transportation. One of the most exciting developments in the reproduction of dairy animals is embryo transfer (ET), the so-called second generation reproductive biotechnology following AI. Multiple ovulation and ET (MOET) program in dairy goats combining with estrus synchronization (ES) and AI significantly increase annual genetic improvement by decreasing the generation interval. Based on the advances in reproduction technologies that have been utilized through experiments and investigation, this review will focus on the application of these technologies and how they can be used to promote the dairy goat research and industry development in the future.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.5
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• pp.279-291
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• 2023

In recent years, language model-based generative AI technologies have made remarkable progress. In particular, it has attracted a lot of attention due to its increasing potential in various fields such as summarization and code writing. As a reflection of this interest, the number of patent applications related to generative AI has been increasing rapidly. In order to understand these trends and develop strategies accordingly, future forecasting is key. Predictions can be used to better understand the future trends in the field of technology and develop more effective strategies. In this paper, we analyzed patents filed to date to identify the direction of development of language model-based generative AI. In particular, we took an in-depth look at research and invention activities in each country, focusing on application trends by year and detailed technology. Through this analysis, we tried to understand the detailed technologies contained in the core patents and predict the future development trends of generative AI.