• Journal of Biomedical Engineering Research
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• v.15 no.2
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• pp.189-194
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• 1994

Effects of ${AI_2O_3}/{P_2O_5}$ ratio on the crystallization of a series of glasses with the nominal composition of 41.4wt % $SiO_2$, 35.0wt % CaO, 20.6wt % (${P_2O_5}$+${AI_2O_3}$) and 3.0wt% MgO were investigated with DTA, XRD and SEM. The major crystalline phases are apatite and anorthite. The glass transition temperature ($T_g$) and the softening point ($T_s$) are shifted to the upper temperature by increasing $AI_2O_3$ content. The temperature of apatite crystallization ($T_{p1}$) is increased by $AI_2O_3$ content, but the tempera¬ture of anorthite crystallization ($T_{p2}$) is not affected significantly. With increased of $AI_2O_3$, the apatite crystallization is decreased, but anorthite crystallization is increased.

• 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.

• Electronics and Telecommunications Trends
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• v.36 no.3
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• pp.106-118
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• 2021

The field of brain science (or neuroscience in a broader sense) has inspired researchers in artificial intelligence (AI) for a long time. The outcomes of neuroscience such as Hebb's rule had profound effects on the early AI models, and the models have developed to become the current state-of-the-art artificial neural networks. However, the recent progress in AI led by deep learning architectures is mainly due to elaborate mathematical methods and the rapid growth of computing power rather than neuroscientific inspiration. Meanwhile, major limitations such as opacity, lack of common sense, narrowness, and brittleness have not been thoroughly resolved. To address those problems, many AI researchers turn their attention to neuroscience to get insights and inspirations again. Biologically plausible neural networks, spiking neural networks, and connectome-based networks exemplify such neuroscience-inspired approaches. In addition, the more recent field of brain network analysis is unveiling complex brain mechanisms by handling the brain as dynamic graph models. We argue that the progress toward the human-level AI, which is the goal of AI, can be accelerated by leveraging the novel findings of the human brain network.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2021.11a
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• pp.223-224
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• 2021

The process before the model learning stage in AI R&D can be subdivided into data collection/cleansing-data purification-data labeling. After that, according to the purpose of development, it goes through a stage of verifying the model by performing learning by using the algorithm of the artificial intelligence model. Several studies describe an important part of AI research as the learning stage, and try to increase the accuracy by changing the structure and layer of the AI model. However, if the refinement and labeling process of the learning data is tailored only to the model format and is not made for the purpose of development, the desired AI model cannot be obtained. The latest research reveals that most AI research failures are the failure of the learning data rather than the structure of the AI model. analyzed.

• Convergence Security Journal
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• v.24 no.3
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• pp.187-193
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• 2024

With the advancement of AI and IoT, cybersecurity threats have increased dramatically. As the methods and objectives of cyber-attacks evolve, universities, like all major industries, are facing serious cybersecurity issues. Universities hold vast amounts of sensitive information such as students' personal data, research data, and intellectual property, making them prime targets for cyber threats. Therefore, this paper aims to present cybersecurity strategies from both institutional and technical perspectives to help university leaders and policymakers enhance their cybersecurity posture. The study reviews current trends through the flow of cyber-attacks and proposes governance, policy development, risk management, and the establishment of FIDO and AI-based security systems to respond to the increase in sophisticated threats such as ransomware and AI-based malware.

• Environmental Engineering Research
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• v.14 no.2
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• pp.102-110
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• 2009

Application of artificial intelligence (AI) approaches in eco-environmental modeling has gradually increased for the last decade. Comprehensive understanding and evaluation on the applicability of this approach to eco-environmental modeling are needed. In this study, we reviewed the previous studies that used AI-techniques in eco-environmental modeling. Decision Tree (DT) and Artificial Neural Network (ANN) were found to be major AI algorithms preferred by researchers in ecological and environmental modeling areas. When the effect of the size of training data on model prediction accuracy was explored using the data from the previous studies, the prediction accuracy and the size of training data showed nonlinear correlation, which was best-described by hyperbolic saturation function among the tested nonlinear functions including power and logarithmic functions. The hyperbolic saturation equations were proposed to be used as a guideline for optimizing the size of training data set, which is critically important in designing the field experiments required for training AI-based eco-environmental modeling.

• Journal of The Korean Association of Information Education
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• v.24 no.3
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• pp.243-254
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• 2020

In order to cultivate AI(artificial intelligence) manpower, major countries are making efforts to apply AI education from elementary school. In order to introduce AI education in elementary school, it is necessary to have a curriculum and educational content for elementary school level. This study developed educational contents to experience the principle of AI learning at the unplugged level for the purpose of AI education for elementary school students. The educational content developed was selected as an AI that evaluates the emotion of sentences. In addition, to solve the problem, data attributes were derived and collected, and the process of AI learning was simulated to solve the problem. As a result of the study, the attitude of elementary school students to AI increased post than before. In addition, the task performance rate was averaged at 85%, showing that the proposed AI education content has educational significance.

• Proceedings of the Korean Society of Applied Pharmacology
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• 1995.10a
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• pp.107-113
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• 1995

Basement membrane laminin is a multidomain glycoprotein that interacts with itself, heparin and cells. The distal long arm plays major cell and heparin interactive roles. The long arm consists of three subunits (A, B1, B2) joined in a coiled-coil rod attached to a terminal A chain globule (G). The globule is in turn subdivided into five subdomains (Gl-5). In order to analyze the functions of this region, recombinant G domains (rG, rAiG, rG5, rGΔ2980-3028) were expressed in Sf9 insect cells using a baculovirus expression vector. A hybrid molecule (B-rAiG), consisting of recombinant A chain(rAiG) and the authentic B chains (E8-B)was assembled in vitro. The intercalation of rAiG into E8-B chains suppressed a heparin binding activity identified in subdomain Gl-2. By the peptide napping and ligand blotting, the relative affinity of each subeomain to heparin was assigned as Gl> G2= G4> G5> G3, such that G1 bound strongly and G3 not at all. The active heparin binding site of G domain in intact laminin appears to be located in G4 and proximal G5. Cell binding was examined using fibrosarcoma Cells. Cells adhered to E8, B-rAiG, rAiG and rG, did not bind on denatured substrates, poorly bound to the mixture of E8-B and rG. Anti-${\alpha}$6 and anti-${\beta}$1 integrin subunit separately blocked cell adhesion on E8 and B-rAiG, but not on rAiG. Heparin inhibited cell adhesion on rAiG, partially on B-rAiG, and not on E8. In conclusion, 1) There are active and cryptic cell and heparin binding activities in G domain. 2) Triple-helix assembly inactivates cell and heparin binding activities and restores u6131 dependent cell binding activities.

• Journal of the Korea Convergence Society
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• v.9 no.9
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• pp.97-104
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• 2018

The purpose of this study was to investigate the expectation toward medical artificial intelligence(AI) of students in majoring health, and to utilize it as a basic data for widespread use of medical AI for 500 students majoring in health science at Cheonan city. The awareness of AI was 18.6%, the reliability of AI was 24.8%, and agreement to use of medical AI was 38%. Also, the higher the awareness and reliability of AI were, the higher the expectation of AI was. As a result, education on medical AI in the major field should be a cornerstone for the development of an effective healthcare environment utilizing medical AI by raising awareness, reliability and expectation of AI.

• Journal of Digital Convergence
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• v.17 no.1
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• pp.149-158
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• 2019

In this study, the concept of emotional labor and AI(appreciative inquiry) are connected to examine how acceptance of AI, which is related to changes in hospital organizational culture, affects nurses' emotional labor, organizational commitment, and job satisfaction. For this purpose, the data collected from 156 nurses working at a major hospital were analyzed using structural equation modeling. The findings of this study are as follows: First, nurses' acceptance of AI had a significant positive effect on deep acting but not on surface acting. Second, deep acting had a significant positive effect on organizational commitment, but surface acting did not. Third, nurses' organizational commitment had a significant positive effect on job satisfaction. Fourth, deep acting significantly mediated the relationship between nurses' acceptance of AI and organizational commitment, but surface acting did not. Fifth, deep acting and organizational commitment significantly mediated the relationship between nurses' acceptance of AI and job satisfaction, but surface acting and organizational commitment did not. Theoretical and practical implications are provided based on the relationships between the variables found in this study.