• The Korean Journal of Food & Health Convergence
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• v.7 no.5
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• pp.1-7
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• 2021

The aging of the population has a fundamental impact on the national economy, including decline in productive population, atrophy of available funds, slowdown of technological innovation, slowdown of economic growth, and decrease in vitality of society as a whole. Increase of elderly population would lead to increase in elderly welfare consumers, which would also lead to increase the demand for elderly welfare services. However, due to the continuation of the low birth rate, there is a great shortage of human resources who can handle this. In such a situation, the main goal of the elderly welfare system in the future should aim to actively try to design effective policies, prepare systems, and implement services for the problems of the aged society, and to find ways to expand the finances, manpower, methods, and facilities necessary for the welfare of the elderly. Elderly welfare services in Korea have been changed and developed in accordance with socioeconomic changes such as industrialization and urbanization. This study examines the changes in elderly welfare services in Korea by the flow of times and presents a method which utilizes artificial intelligence and Internet of Things in services for the elderly welfare consumers to improve both quality and efficiency.

• Prospectives of Industrial Chemistry
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• v.23 no.6
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• pp.3-13
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• 2020

저출산 고령화 사회로의 진입은 대한민국뿐만 아니라 전 세계적으로 많은 사회 문제를 야기하고 있다. 그 중에서 고령 인구 증가로 인한 의료 수요 증가와 이를 뒷받침 할 의료인력 부족은 곧 다가올 사회문제이다. 4차 산업 혁명으로 인해 다양한 사회문제에 대한 혁신적인 해법들이 제시되고 있는데, 본 기고문에서는 다가올 고령화 사회에서 의료인력 부족 등에 의한 해결법으로 원격의료 지원을 위한 인공지능 활용을 다루고자 한다. 병 진단 및 예측을 위한 여러 가지 인공지능 알고리즘은 이미 많이 개발 되어 있으나, 일반적으로 딥러닝에 많이 쓰이는 인공신경망 구조인 합성곱 뉴럴네트워크(convolution neural network)나 기존 퍼셉트론(perceptron) 구조에서 벗어나 확률론적 인공신경망 중에 하나인 베이지안 뉴럴네트워크(Bayesian neural network)를 다루고자 한다. 그중에서 연산효율적이며 뉴로모픽 하드웨어로 구현 가능성이 높고 실제 진단 예측(diagnosis prediction) 문제 해결에 강점을 보이는 알고리즘으로써 naive Bayes classifer를 활용한 연구를 소개하고자 한다.

• Journal of Korea Technical Association of The Pulp and Paper Industry
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• v.44 no.6
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• pp.9-14
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• 2012

Currently a variety of efforts to conserve and restore the injured the Annal of the Joseon Dynasty are continued and many studies about the aging pathway of the beeswax-treated volume have being progressed. In this study, two chambers were set up to assess the influence of VOCs(Volatile Organic Compounds) generated in process of beeswax deterioration. One chamber was closed and the other was continuously ventilated in order to remove the VOCs. In results, the acid compounds of beeswax-treated Hanjis aged in closed chamber was more than that aged in ventilated chamber. The folding endurance of beeswax-treated Hanjis aged in closed chamber was lower than that aged in ventilated chamber. Accumulated acid compounds of beeswax-treated Hanji in closed chamber may cause accelerated aging and strength decrease. In conclusion, accumulated acid compounds can accelerate the aging of beeswax-treated Hanji and cause the severe deterioration of inside of beeswax-treated volume. Therefore, to preserve more safely beeswax-treated volumes of the Annals of the Joseon Dynasty, it is essential that VOCs emitted from beeswax-treated volume are ventilated.

• Journal of Korea Foundry Society
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• v.41 no.6
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• pp.528-534
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• 2021

To confirm effects of natural and artificial aging of precipitate on thermal diffusivity and hardness, the studied Al-Mg-Si alloy were manufactured by gravity casting method with 0.6 wt% and 1.0 wt% additional Cu element. The samples were used for measuring thermal diffusivity and hardness. The addition of Cu, promoted by intermediates such as Q'' and θ'' phases, contributing to the improvement of hardness and high-temperature thermal diffusivity. The natural aging decreased the hardness of the Al-Mg-Si-Cu alloys with increasing time, but did not affect the thermal diffusivity.

• Journal of Korean Association for Spatial Structures
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• v.24 no.2
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• pp.83-90
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• 2024

Machine learning is widely applied to various engineering fields. In structural engineering area, machine learning is generally used to predict structural responses of building structures. The aging deterioration of reinforced concrete structure affects its structural behavior. Therefore, the aging deterioration of R.C. structure should be consider to exactly predict seismic responses of the structure. In this study, the machine learning based seismic response prediction model was developed. To this end, four machine learning algorithms were employed and prediction performance of each algorithm was compared. A 3-story coupled shear wall structure was selected as an example structure for numerical simulation. Artificial ground motions were generated based on domestic site characteristics. Elastic modulus, damping ratio and density were changed to considering concrete degradation due to chloride penetration and carbonation, etc. Various intensity measures were used input parameters of the training database. Performance evaluation was performed using metrics like root mean square error, mean square error, mean absolute error, and coefficient of determination. The optimization of hyperparameters was achieved through k-fold cross-validation and grid search techniques. The analysis results show that neural networks and extreme gradient boosting algorithms present good prediction performance.

• Electronics and Telecommunications Trends
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• v.38 no.3
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• pp.38-46
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• 2023

The value of living a long and healthy life without suffering has increased owing to aging populations, transition to welfare societies, and global interest in health deriving from the novel coronavirus disease pandemic. New drug development has gained attention as both a tool to improve the quality of life and high-value market, with blockbuster drugs potentially generating over 10 billion dollars in annual revenue. However, for newly discovered substances to be used as drugs, various properties must be verified over a long period in a time-consuming and costly process. Recently, the development of artificial intelligence technologies, such as deep and reinforcement learning, has led to significant changes in drug development by enabling the effective identification of drug candidates that satisfy desired properties. We explore and discuss trends in artificial intelligence for de novo drug design.

• Clinical and Experimental Reproductive Medicine
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• v.7 no.1_2
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• pp.27-34
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• 1980

The status of artificial insemination was surveyed by investigating 20796 heads of adult dairy cattle out of 30464 heads at 1649 dairy farms located Seoul and Gyung-Gi and Chung-Cheong province area, and the results are as follows: 1. There were 286 small farms feeding less than 5 heads occupying 17.3% of total farm number, and 1083 medium sized dairy farms feeding between $6{\sim}20$ heads occupying 65.64% and also 280 large dairy farms feeding more than 21 heads occupying 17.06% of total. 2. Population of cow aging from 2 to 6 years were 87.26% and among 20907 heads of dairy cattles which had calved previously 13196 heads had histories of one to four calving 90.40%. 3. The successful conception rate with first A. I. occupied only 53.43% and 92.4% after third A. I. service. The average A. I. frequency for successful conception per/head was 1.8 times. 4. The breeding status was pregnancy 61.72%, uncertain pregnancy 19.39%, and as physiological vacancy 12.48% and reproductive disorders 10.62%. 5. Among 1264 heads of reproductive disorders 856 heads were clue to ovarian diseases and occupied 68.98% of total disorders and uterus diseases 10.64% and repeat breeders occupied 17.64%.

• Journal of Korean Dental Science
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• v.10 no.1
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• pp.29-34
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• 2017

Purpose: The purpose of this study was to evaluate the microleakage of various types of resin-bonded fixed partial dentures (RBFPDs) after artificial aging. Materials and Methods: Forty models with missing first molar were fabricated using artificial resin teeth and were divided into four groups: Group A, conventional RBFPDs design; Group B, modified RBFPDs design; Group C, assembled 3-piece RBFPDs design; and Group D, assembled 3-piece RBFPDs with different occlusal rest positions. Half of the specimens underwent chewing simulation process (240,000 cycles, 50 N load, 1.7 Hz) and thermocycling (temperatures $5^{\circ}C{\sim}55^{\circ}C$, dwelling time 30 seconds) and the remaining 20 specimens didn't receive any treatment. All the specimens were immersed in 2% methylene blue solution for 24 hours to evaluate microleakage, and were sectioned at the middle part of abutment teeth. To evaluate the microleaskage, a dye penetration was calculated. Result: With artificial aging, cyclic loading and thermocycling, a 3-piece RBPFD and a 2-piece RBPFD using original tooth undercuts have significantly lower microleakge (P<0.05) compared to the conventional design of RBPFD and modified RBPFD. Conclusion: Within the limit of this experiment, the assembled RBFPDs exhibited a smaller microleakage than the conventional RBFPDs, implying that the assembled RBFPDs can be more effective for reducing the dislodgement of the RBFPDs.

• The Journal of Korean Academy of Prosthodontics
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• v.60 no.3
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• pp.231-238
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• 2022

Purpose. This study aimed to investigate the effect of different veneering methods on the tensile bond strength between polyetherketoneketone (PEKK) and denture base resins. Materials and methods. A total of 80 PEKK T-shaped specimens were fabricated and the primer (Visio.link) was applied after airborne-particle abrasion with 110 ㎛ alumina oxide powder. According to the veneering method, the specimens were divided into four groups (n = 20) to be veneered with the gingival colored packable photopolymerized composite resin (SR Adoro); flowable photopolymerized composite resin, (Crea.lign); heat-polymerized resin (Vertex); and self-polymerized resin (ProBase Cold). Each group was divided into two subgroups (n = 10) according to the artificial thermal aging. After the tensile bond strength measurement via universal testing machine, the fracture sections of all specimens were observed. Two-way ANOVA and Tukey's HSD post hoc test were used for the statistical analysis (α = .05). Results. The results of the two-way ANOVA showed statistically significant differences in the tensile bond strength according to the veneering method and artificial thermal aging of denture base resins (P<.001). The highest tensile bond strength showed in the packable photopolymerized resin group before and after the artificial thermal aging. The lowest tensile bond strength showed in the heat-polymerized resin group. The mixed and adhesive fracture showed in all groups. Conclusion. The veneering method and artificial thermal aging can influence in the tensile bond strength between the resin and PEKK. The artificial thermal aging can reduce the tensile bond strength.

• Membrane Journal
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• v.33 no.2
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• pp.61-69
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• 2023

The technical importance of membrane-based artificial lung technology has been re-emphasized after the recent breakout of COVID-19 to treat acute lung-failure patients. The world population, particularly in Korea, is aging at an unprecedented rate, which can increase the demand for better artificial organs (AO) in the near future. Membrane technology plays a key role in artificial organ markets. Among them, membrane-based artificial lung (AL) technology has improved significantly in the past 50 years, but the survival rate of lung-failure patients is still very low. Most AL works focus on the clinical application of the AL device, not on the development of the AL membrane itself. This review summarizes the challenges and recent progress of membrane-based AL technology.