• Journal of The Korean Association of Information Education
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• v.24 no.1
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• pp.21-28
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• 2020

Technological advancements such as artificial intelligence, robots, and big data are revolutionizing the entire society. In this paper, we analyzed preliminary teachers' daily internet/gaming/smartphone habit and the difference between preference factors in gender and diagnosis group in the situation of ethical dilemma in driverless cars. The result shows most of the male students are in high risk group of daily internet/gaming usage, and male students tend to be more immersed in games compared to female students, which negatively affects their daily lives. Students who have at least one of the daily internet/gaming/smartphone habits are more likely to be classified as high-risk group in all three of daily internet/gaming/smartphone habit. Fortunately, the students perceived themselves addicted and wanted change their habits. An analysis by a moral machine of these students tells that there is no significant difference in preference between male and female students and among diagnosis groups. However, specifically in the ethical dilemma of driverless cars, all the groups of male, female, normal, high-risk showed they have priority in pedestrians over drivers, a large number of people over small, and people who obey traffic rules over who do not. The tendency was pronounced in female group and high-risk students prioritized people who are older and in lower social status.

• Korean Journal of Radiology
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• v.25 no.7
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• pp.613-622
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• 2024

Objective: In Korea, radiology has been positioned towards the early adoption of artificial intelligence-based software as medical devices (AI-SaMDs); however, little is known about the current usage, implementation, and future needs of AI-SaMDs. We surveyed the current trends and expectations for AI-SaMDs among members of the Korean Society of Radiology (KSR). Materials and Methods: An anonymous and voluntary online survey was open to all KSR members between April 17 and May 15, 2023. The survey was focused on the experiences of using AI-SaMDs, patterns of usage, levels of satisfaction, and expectations regarding the use of AI-SaMDs, including the roles of the industry, government, and KSR regarding the clinical use of AI-SaMDs. Results: Among the 370 respondents (response rate: 7.7% [370/4792]; 340 board-certified radiologists; 210 from academic institutions), 60.3% (223/370) had experience using AI-SaMDs. The two most common use-case of AI-SaMDs among the respondents were lesion detection (82.1%, 183/223), lesion diagnosis/classification (55.2%, 123/223), with the target imaging modalities being plain radiography (62.3%, 139/223), CT (42.6%, 95/223), mammography (29.1%, 65/223), and MRI (28.7%, 64/223). Most users were satisfied with AI-SaMDs (67.6% [115/170, for improvement of patient management] to 85.1% [189/222, for performance]). Regarding the expansion of clinical applications, most respondents expressed a preference for AI-SaMDs to assist in detection/diagnosis (77.0%, 285/370) and to perform automated measurement/quantification (63.5%, 235/370). Most respondents indicated that future development of AI-SaMDs should focus on improving practice efficiency (81.9%, 303/370) and quality (71.4%, 264/370). Overall, 91.9% of the respondents (340/370) agreed that there is a need for education or guidelines driven by the KSR regarding the use of AI-SaMDs. Conclusion: The penetration rate of AI-SaMDs in clinical practice and the corresponding satisfaction levels were high among members of the KSR. Most AI-SaMDs have been used for lesion detection, diagnosis, and classification. Most respondents requested KSR-driven education or guidelines on the use of AI-SaMDs.

• Journal of Korea Society of Digital Industry and Information Management
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• v.18 no.4
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• pp.85-96
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• 2022

Personalized recommendation technology is one of the most important technologies in electronic commerce environment. It helps users overcome information overload by suggesting information that match user's interests. In e-commerce environment, both mobile device users and smart device users have risen dramatically. It creates new challenges. Our method suggests product information that match user's device interests beyond only user's interests. We propose a device-centered personalized recommendation method. Our method uses both purchase and share behavior for user's devices interests. Moreover, it considers data type preference for each device. This paper presents a new recommendation method and algorithm. Then, an e-commerce scenario with a computer, a smartphone and an AI-speaker are described. The scenario shows our work is better than previous researches.

• Journal of the Korean Society of Food Science and Nutrition
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• v.31 no.5
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• pp.871-876
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• 2002

This study was conducted to verify the relation between relation between vegetarian diet and the serum lipid levels, blood sugar and blood pressure from October 1996 to February 1997. The vegetarians subjects were 245 Buddhist nuns (age:23～79 yrs) and control subjects consisted of 235 healthy female adults (age: 23～70 yrs) selected from the teachers, the nurses and the housekeepers living in Chinju Gyeongsang Nam-do. The contents included anthropometric measurement, questionnaires about eating behavior score and preference for taste and biochemical characteristics of the blood. The results were summarized as follows. The average duration of vegetarian diet of the vegetarians was 13.1 years. Vegetarians prefer to a pepper, a sweet and a acidic in the right order but that non-vegetarians prefer to a sweet, a acidic and pepper in the right order. Both groups of less than a decade and more than two decade of vegetarian diet prefer to a pepper, sweet, a acidic, a bitter, a salty and a lily, and a 10～20 yr group with vegetarian diet was fond of a pepper, a bitter, a acidic, a sweet, and a oily, in the right order. This seems to be ascribable to a difference in the health knowledge and interest. Vegetarians and non-vegetarians who said that they were fond of salty were 38.8% and 52.8%, medium was 33.9% and 33.6%, and not salty was 27.3% and 13.6%. Eating behavior score of vegetarians and non-vegetarians were 25.1 and 23.1 respectively. Eating behavior scores of vegetarians were significantly higher than those of non-vegetarians (p<0.05). Eating behavior scores of the group with more than a decade of vegetarian diet were significantly higher than those of the group with less than a decade of vegetarian diet. Levels of serum total-cholesterol, LDL-cholesterol, and AI of the salty group were significantly higher (p<0.05) than those of not salty group. Levels of serum triglyceride, HDL-cholesterol, blood sugar had no significant relation with preference of salty. Blood pressure was not related with preference of salty, but that of those who prefer a salty tended to be high. This study also reveals that the preference of a salty was significant influence on serum total-cholesterol, LDL-cholesterol, and AI, but the vegetarians did not prefer salty and have a good eating behavior. Consequently, vegetarian diet can be considerably effective in reducing the level of the risk factors of cardiovascular disease.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.7
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• pp.869-876
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• 2021

In the field of music, various AI composition methods using machine learning have recently been attempted. However, most of this research has been centered on Western music, and little research has been done on Korean traditional music. Therefore, in this paper, we will create a data set of Korean traditional music, create a melody using three algorithms based on the data set, and compare the results. Three models were selected based on the similarity between language and music, LSTM, Music Transformer and Self Attention. Using each of the three models, a melody generator was modeled and trained to generate melodies. As a result of user evaluation, the Self Attention method showed higher preference than the other methods. Data set is very important in AI composition. For this, a Korean traditional music data set was created, and AI composition was attempted with various algorithms, and this is expected to be helpful in future research on AI composition for Korean traditional music.

• Journal of the Korea Computer Graphics Society
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• v.30 no.3
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• pp.149-158
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• 2024

Photo booths have traditionally provided a fun and easy way to capture and print photos to cherish memories. These booths allow individuals to capture their desired poses and props, sharing memories with friends and family. To enable diverse expressions, generative AI-powered photo booths have emerged. However, existing AI photo booths face challenges such as difficulty in taking group photos, inability to accurately reflect user's poses, and the challenge of applying different concepts to individual subjects. To tackle these issues, we present CINEMAPIC, a photo booth system that allows users to freely choose poses, positions, and concepts for their photos. The system workflow includes three main steps: pre-processing, generation, and post-processing to apply individualized concepts. To produce high-quality group photos, the system generates a transparent image for each character and enhances the backdrop-composited image through a small number of denoising steps. The workflow is accelerated by applying an optimized diffusion model and GPU parallelization. The system was implemented as a prototype, and its effectiveness was validated through a user study and a large-scale pilot operation involving approximately 400 users. The results showed a significant preference for the proposed system over existing methods, confirming its potential for real-world photo booth applications. The proposed CINEMAPIC photo booth is expected to lead the way in a more creative and differentiated market, with potential for widespread application in various fields.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2000.11a
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• pp.38-43
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• 2000

In this present study, the correlation between subjective preference of essential odors and EEG response were investigated quantitatively. EEG signals were measured from 19 electrodes according to the International 10-20 system (Fpl, Fp2, F2/4, F7/8, Fz, C3/4, Cz, P3/4, Pz, T3/4, T5/6, Ol/2) from 8 healthy males subjects in four odor conditions. Four odor conditions (Rose oil bulgarian, Lemon oil misitano, Jasmin abs, Laverder iol france (KIMEX co. Ltd)) were applied for each subject in the experiment. Through the subjective evaluation, the most pleasant odor for each subject was determined. The power spectrum of ${\alpha}$/${\beta}$ of EEG signals from the most pleasant odor was compared with those from the control condition, which has no odor at all. It was observed that the power spectrum of ${\alpha}$/${\beta}$ of EEG from the most pleasant odor was increased significantly on F3, F3, F4,74 comparing to the control condition. This result indicates that the power spectrum of ai${\beta}$ could be a new index for measuring the levels of pleasantness of odors.

• Journal of Korea Entertainment Industry Association
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• v.15 no.3
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• pp.41-50
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• 2021

While artificial intelligence is rapidly replacing human jobs, the art field where human creativity is important is considered an exception. There are currently several AI mastering services in the field of mastering music, a profession at the border between art and technology. In general, the quality of AI mastering is considered to be inferior to the work of a human professional mastering engineer. In this paper, acoustic analysis, listening experiments, and expert interviews were conducted to compare AI and human experts. In the acoustic analysis, In the analysis of audio, there was no significant difference between the results of professional mastering engineers and the results of artificial intelligence. In the listening experiment, the non-musicians could not distinguish between the sound quality of the professional mastering engineer's work and the artificial intelligence work. The group of musicians showed a preference for a specific sound source, but the preference for a specific mastering did not appear significantly. In an expert interview, In expert interviews, respondents answered that there was no significant difference in quality between the two mastering services, and the biggest difference was the communication method between the mastering service provider and the user. In addition, as data increases, it is expected that artificial intelligence mastering will achieve rapid quality improvement and further improvement in communication.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.3
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• pp.366-370
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• 2003

In this research, we suggest a hybrid intelligent Web recommendation systems based on Web data mining and case-based reasoning (CBR). One of the important research topics in the field of Internet business is blending artificial intelligence (AI) techniques with knowledge discovering in database (KDD) or data mining (DM). Data mining is used as an efficient mechanism in reasoning for association knowledge between goods and customers＇ preference. In the field of data mining, the features, called attributes, are often selected primary for mining the association knowledge between related products. Therefore, most of researches, in the arena of Web data mining, used association rules extraction mechanism. However, association rules extraction mechanism has a potential limitation in flexibility of reasoning. If there are some goods, which were not retrieved by association rules-based reasoning, we can＇t present more information to customer. To overcome this limitation case, we combined CBR with Web data mining. CBR is one of the AI techniques and used in problems for which it is difficult to solve with logical (association) rules. A Web-log data gathered in real-world Web shopping mall was given to illustrate the quality of the proposed hybrid recommendation mechanism. This Web shopping mall deals with remote-controlled plastic models such as remote-controlled car, yacht, airplane, and helicopter. The experimental results showed that our hybrid recommendation mechanism could reflect both association knowledge and implicit human knowledge extracted from cases in Web databases.

• Korean Journal of Poultry Science
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• v.50 no.3
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• pp.187-191
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• 2023

Korean native ducks (KNDs) continue to have a high preference from consumers due to their excellent meat quality and taste characteristics. However, due to low productivity and fixed plumage color phenotype, it could not secure a large share in the domestic market compared to imported species. In order to improve the market share of KNDs, the genetic characteristics of the breed should be identified and used for improvement and selection. Therefore, this study was conducted to identify the genetic information of colored and white KNDs using next-generation resequencing data and screening for differences between the two groups. As a result of the analysis, the genetic variants that showed significant differences between the colored and white KND groups were mainly identified as mutations related to tyrosine activity. The variants were located in the genes that affect melanin synthesis and regulation, such as EGFR, PDGFRA, and DDR2, and these were reported as the candidate genes related to plumage pigmentation in poultry. Therefore, the results of this study are expected to be useful as a basis for understanding and utilizing the genetic characteristics of KNDs for genetic improvement and selection of white broiler KNDs.