• The Journal of Korean Medicine
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• v.44 no.4
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• pp.72-86
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• 2023

Objectives: The medical field is rapidly evolving with AI and digital technologies like AI-based X-ray analysis and digital therapeutics gaining approval. Telemedicine is becoming prominent, and medical schools are adapting by integrating AI education. Pusan National University leads a talent training project for AI in health. Korean Medicine is incorporating AI with diagnostic systems and chatbots. However, there's a lack of research on education awareness in Korean Medicine Colleges. The study aims to assess opinions on integrating AI, digital therapeutics, and DNA test into the Korean medicine college curriculum for improved education. Methods: We selected appropriate four specific areas: artificial intelligence in medicine, digital therapeutics, DNA test, and telemedicine. The questionnaire developed for this study underwent expert evaluation and was subsequently administered to registered KMDs of the Association of Korean Medicine, as well as students from 12 Korean Medicine universities. The survey was designed to analyze the awareness and perceived importance of the 4 areas. Results: Both KMDs and Korean medicine students exhibited comparable awareness levels across the four objectives. Notably, both groups identified a high educational necessity and importance of artificial intelligence in medicine for clinical settings. Statistically significant differences were observed between KMDs and students in their perspectives on the importance of telemedicine and DNA test in the Korean medicine field, the educational necessity of DNA test within Korean medicine universities, and the need for comprehension of regulations related to digital therapeutics. Conclusion: The survey of Korean medicine professionals and students underscores a strong understanding of key areas such as Telemedicine, medical AI, DNA test, and digital therapeutics. Medical AI is identified as crucial for future education. There's a consensus on the need for curriculum changes in Korean medicine schools, particularly in adapting to evolving healthcare trends. The focus should be on practical clinical application, with a call for additional research to better integrate student and practitioner perspectives in future curriculum reform discussions.

• Nutrition Research and Practice
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• v.16 no.6
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• pp.801-812
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• 2022

BACKGROUND/OBJECTIVES: Diet planning in childcare centers is difficult because of the required knowledge of nutrition and development as well as the high design complexity associated with large numbers of food items. Artificial intelligence (AI) is expected to provide diet-planning solutions via automatic and effective application of professional knowledge, addressing the complexity of optimal diet design. This study presents the results of the evaluation of the utility of AI-generated diets for children and provides related implications. MATERIALS/METHODS: We developed 2 AI solutions for children aged 3-5 yrs using a generative adversarial network (GAN) model and a reinforcement learning (RL) framework. After training these solutions to produce daily diet plans, experts evaluated the human- and AI-generated diets in 2 steps. RESULTS: In the evaluation of adequacy of nutrition, where experts were provided only with nutrient information and no food names, the proportion of strong positive responses to RL-generated diets was higher than that of the human- and GAN-generated diets (P < 0.001). In contrast, in terms of diet composition, the experts' responses to human-designed diets were more positive when experts were provided with food name information (i.e., composition information). CONCLUSIONS: To the best of our knowledge, this is the first study to demonstrate the development and evaluation of AI to support dietary planning for children. This study demonstrates the possibility of developing AI-assisted diet planning methods for children and highlights the importance of composition compliance in diet planning. Further integrative cooperation in the fields of nutrition, engineering, and medicine is needed to improve the suitability of our proposed AI solutions and benefit children's well-being by providing high-quality diet planning in terms of both compositional and nutritional criteria.

• Asian-Australasian Journal of Animal Sciences
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• v.19 no.2
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• pp.160-164
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• 2006

Currently, boars selected for commercial use as AI sires are evaluated on grow-finish performance and carcass characteristics. If AI sires were also evaluated and selected on semen production, it may be possible to reduce the number of boars required to service sows, thereby improving the productivity and profitability of the boar stud. The objective of this study was to estimate genetic correlations between production and semen traits in the boar: average daily gain (ADG), backfat thickness (BF) and muscle depth (MD) as production traits, and total sperm cells (TSC), total concentration (TC), volume collected (SV), number of extended doses (ND), and acceptance rate of ejaculates (AR) as semen traits. Semen collection records and performance data for 843 boars and two generations of pedigree data were provided by Smithfield Premium Genetics. Backfat thickness and MD were measured by real-time ultrasound. Genetic parameters were estimated from five four-trait and one five-trait animal models using MTDFREML. Average heritability estimates were 0.39 for ADG, 0.32 for BF, 0.15 for MD, and repeatability estimates were 0.38 for SV, 0.37 for TSC, 0.09 for TC, 0.39 for ND, and 0.16 for AR. Semen traits showed a strong negative genetic correlation with MD and positive genetic correlation with BF. Genetic correlations between semen traits and ADG were low. Therefore, current AI boar selection practices may be having a detrimental effect on semen production.

• Journal of Korean Philosophical Society
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• v.147
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• pp.261-281
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• 2018

This paper will cover recent discussions on the risks of human being due to the development of artificial intelligence(AI). We will consider AI research as artificial narrow intelligence(ANI), artificial general intelligence(AGI), and artificial super intelligence(ASI). First, we examine the risks of ANI, or weak AI systems. To maximize efficiency, humans will use autonomous AI extensively. At this time, we can predict the risks that can arise by transferring a great deal of authority to autonomous AI and AI's judging and acting without human intervention. Even a sophisticated system, human-made artificial intelligence systems are incomplete, and virus infections or bugs can cause errors. So I think there should be a limit to what I entrust to artificial intelligence. Typically, we do not believe that lethal autonomous weapons systems should be allowed. Strong AI researchers are optimistic about the emergence of artificial general intelligence(AGI) and artificial superintelligence(ASI). Superintelligence is an AI system that surpasses human ability in all respects, so it may act against human interests or harm human beings. So the problem of controlling superintelligence, i.e. control problem is being seriously considered. In this paper, we have outlined how to control superintelligence based on the proposed control schemes. If superintelligence emerges, it is judged that there is no way for humans to completely control superintelligence at this time. But the emergence of superintelligence may be a fictitious assumption. Even in this case, research on control problems is of practical value in setting the direction of future AI research.

• The Korean Journal of Applied Statistics
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• v.33 no.6
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• pp.753-762
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• 2020

With an increasing interest in integrating artificial intelligence (AI) into interview processes, the Republic of Korea (ROK) army is trying to lead and analyze AI-powered interview platform. This study is to analyze the AI interview data using a unified non-crossing multiple quantile tree (UNQRT) model. Compared to the UNQRT, the existing models, such as quantile regression and quantile regression tree model (QRT), are inadequate for the analysis of AI interview data. Specially, the linearity assumption of the quantile regression is overly strong for the aforementioned application. While the QRT model seems to be applicable by relaxing the linearity assumption, it suffers from crossing problems among estimated quantile functions and leads to an uninterpretable model. The UNQRT circumvents the crossing problem of quantile functions by simultaneously estimating multiple quantile functions with a non-crossing constraint and is robust from extreme quantiles. Furthermore, the single tree construction from the UNQRT leads to an interpretable model compared to the QRT model. In this study, by using the UNQRT, we explored the relationship between the results of the Army AI interview system and the existing personnel data to derive meaningful results.

• International Journal of Computer Science & Network Security
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• v.24 no.5
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• pp.64-72
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• 2024

In recent times cyber attackers can use Artificial Intelligence (AI) to boost the sophistication and scope of attacks. On the defense side, AI is used to enhance defense plans, to boost the robustness, flexibility, and efficiency of defense systems, which means adapting to environmental changes to reduce impacts. With increased developments in the field of information and communication technologies, various exploits occur as a danger sign to cyber security and these exploitations are changing rapidly. Cyber criminals use new, sophisticated tactics to boost their attack speed and size. Consequently, there is a need for more flexible, adaptable and strong cyber defense systems that can identify a wide range of threats in real-time. In recent years, the adoption of AI approaches has increased and maintained a vital role in the detection and prevention of cyber threats. In this paper, an Ensemble Deep Restricted Boltzmann Machine (EDRBM) is developed for the classification of cybersecurity threats in case of a large-scale network environment. The EDRBM acts as a classification model that enables the classification of malicious flowsets from the largescale network. The simulation is conducted to test the efficacy of the proposed EDRBM under various malware attacks. The simulation results show that the proposed method achieves higher classification rate in classifying the malware in the flowsets i.e., malicious flowsets than other methods.

• Korean Journal of Food Science and Technology
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• v.34 no.5
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• pp.893-897
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• 2002

The chloroform layer from 75% ethanol extract of Dalbergia odorifera T.CHEN showed strong antioxidative activity on lard and palm oil as tested by Rancimat method. Antioxidative active compound isolated and identified by silica gel column chromatography, thin layer chromatography, mass spectrophotometer, $^1H-NMR$ and $^{13}C-NMR$ was identified as mucronulatol (3(R&S)-3,7-Dihydroxy-2',4-dimethoxyisoflavan). Results of Rancimat method revealed the induction period of Mucronulatol increased longer than those of synthetic antioxidant, BHA and BHT, at the same concentration. Mucronulatol combined with ${\delta}-tocopherol(200ppm)$, and with ascorbic acid (200 ppm) and citric acid (200 ppm) on lard and palm oil, respectively, showed strong synergistic effects.

• International journal of advanced smart convergence
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• v.9 no.3
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• pp.49-58
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• 2020

Non-orthogonal multiple access (NOMA) has been recognized as a significant technology in the fifth generation (5G) and beyond mobile communication, which encompasses the advanced smart convergence of the artificial intelligence (AI) and the internet of things (IoT). In NOMA, since the channel resources are shared by many users, it is essential to establish the user fairness. Such fairness is achieved by the power allocation among the users, and in turn, the less power is allocated to the stronger channel users. Especially, the first and second strong channel users have to share the extremely small amount of power. In this paper, we consider the power optimization for the two users with the small power. First, the closed-form expression for the power allocation is derived and then the results are validated by the numerical results. Furthermore, with the derived analytical expression, for the various channel environments, the optimal power allocation is investigated and the impact of the channel gain difference on the power allocation is analyzed.

• Kyungpook Mathematical Journal
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• v.50 no.2
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• pp.195-211
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• 2010

Let A be a locally finite total algebra of finite type such that $k^A(a_1,\cdots,a_n)\;{\neq}\;a_i$ ai for every operation $k^A$, elements $a_1,\cdots,a_n$ an and $1\;\leq\;i\;\leq\;n$. We show that the weak subalgebra lattice of A uniquely determines its (strong) subalgebra lattice. More precisely, for any algebra B of the same finite type, if the weak subalgebra lattices of A and B are isomorphic, then their subalgebra lattices are also isomorphic. Moreover, B is also total and locally finite.

• The Journal of the Korea institute of electronic communication sciences
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• v.12 no.1
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• pp.139-146
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• 2017

The interest about Artificial Intelligence through the AlphaGo Match in Korea has been increasing rapidly. So far, very little has been done in Artificial Intelligence. The aim of this paper is to reveal technology convergence and to assess the country profile in the field of artificial intelligence(AI). Technology convergence map was created after extracting USPTO patent grants and Web of Science data and generating matrics in the field of AI. Several Indicators were obtained by extracting and calculating SCOPUS Data that KISTI has. According to USPTO patent grants, it shows that AI technology has a strong relationship with several sectors such as cost/price determination, image analysis, and surgery, etc. Also, AI has a active convergence with some fields of Electrical and Electronic Engineering, BioTechnologies, and Medicine etc. According to country profile analysis, Korea reaches a global average growth index. However, in terms of specialization index (SI) and average of relative citations (ARC), there is a large gap between Korea and research leading countries.