• Journal of the Korea Convergence Society
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• v.7 no.4
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• pp.123-131
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• 2016

In this paper, we discussed learner's capability enhancement education suitable for the age of artificial intelligence (AI) using game analysis and archival research based on the 2016 Google Deepmind Challenge match between AI that possessed the finest deep neural networks and the master Baduk player that represented the best of the human minds. AlphaGo was a brilliant move that transcended the conventional wisdom of Baduk and introduced a new paradigm of Baduk. Lee Sedol defeated AlphaGo via the 'divine move and Great idea' that even AlphaGo could not have calculated. This was the triumph of human intuition and insights, which are deeply embedded in human nature as well as human courage and strength. Convergence science education that cultivates student abilities that can help them control machines in the age of AI must be in the direction of developing diverse human insights and positive spirits embedded in human nature not possessed by AI via implementing hearts-on experience and topophilia education obtained from the nature.

• Journal of the Korean Mathematical Society
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• v.54 no.4
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• pp.1121-1148
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• 2017

Go is an ancient game of great complexity and has a huge following in East Asia. It is also very rich mathematically, and can be played on any graph, although it is usually played on a square lattice. As with any game, one of the most fundamental problems is to determine the number of legal positions, or the probability that a random position is legal. A random Go position is generated using a model previously studied by the author, with each vertex being independently Black, White or Uncoloured with probabilities q, q, 1 - 2q respectively. In this paper we consider the probability of legality for two scenarios. Firstly, for an $N{\times}N$ square lattice graph, we show that, with $q=cN^{-{\alpha}}$ and c and ${\alpha}$ constant, as $N{\rightarrow}{\infty}$ the limiting probability of legality is 0, exp($-2c^5$), and 1 according as ${\alpha}$ < 2/5, ${\alpha}=2/5$ and ${\alpha}$ > 2/5 respectively. On the way, we investigate the behaviour of the number of captured chains (or chromons). Secondly, for a random graph on n vertices with edge probability p generated according to the classical $Gilbert-Erd{\ddot{o}}s-R{\acute{e}}nyi$ model ${\mathcal{G}}$(n; p), we classify the main situations according to their asymptotic almost sure legality or illegality. Our results draw on a variety of probabilistic and enumerative methods including linearity of expectation, second moment method, factorial moments, polyomino enumeration, giant components in random graphs, and typicality of random structures. We conclude with suggestions for further work.

• Journal of the Korean Society of Food Science and Nutrition
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• v.37 no.6
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• pp.761-766
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• 2008

The effects of various enzymes and emulsifiers on the loaf volume and crumb hardness of rice breads were studied. Four different enzymes [fungal ${\alpha}$-amylase (AMYL), maltogenic bacterial ${\alpha}$-amylase (NMYL), glucose oxidases (GO), and xylanase+hemicellulases (PTP)] and four emulsifiers [sorbitan monostearate (SMS), glycerol monostearate (GMS), sodium stearoyl lactylate (SSL), and glycerol ester+propylene glycol ester+sucrose ester+sorbitan ester (SP)] were supplemented to rice dough. The addition of AMYL, GO, and GO+AMYL increased loaf volume of rice breads. The highest loaf volume was observed in rice bread supplemented with AMYL. Rice breads supplemented with enzymes firmed at lower rates during storage, and AMYL, NMYL, and GO considerably decreased crumb hardness of rice breads, exhibiting a significant antistaling effect. The addition of emulsifiers produced rice breads with better specific loaf volume and crumb texture, and continuously retarded crumb hardness of rice breads during storage. Especially, rice bread supplemented with SSL demonstrated the highest loaf volume and the lowest crumb hardness during storage.

• Convergence Security Journal
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• v.20 no.2
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• pp.123-135
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• 2020

Since AlphaGo's Match in 2016, there has been a growing calls for artificial intelligence applications in various industries, and research related to it has been actively conducted. The same is true in the military field, and since there has been no weapon system with artificial intelligence so far, effort to implement it are posing a challenge. Meanwhile, AlphaGo Zero, which beat AlphaGo, showed that artificial intelligence's self-training data-based approach can lead to better results than the knowledge-based approach by humans. Taking this point into consideration, this paper proposes to apply Reinforcement Learning, which is the basis of AlphaGo Zero, to the Shipboard Combat System or Combat Management System. This is how an artificial intelligence application to the Shipboard Combat System or Combat Management System that allows the optimal tactical assist with a constant win rate to be recommended to the user, that is, the commanding officer and operation personnel. To this end, the definition of the combat performance of the system, the design plan for the Shipboard Combat System, the mapping with the real system, and the training system are presented to smoothly apply the current operations.

• Proceedings of the Korean Vacuum Society Conference
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• 2011.02a
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• pp.431-431
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• 2011

Graphene, two dimensional sheet of sp2-hybridized carbon, has attracted an enormous amount of interest due to excellent electrical, chemical and mechanical properties for the application of transparent conducting films, clean energy devices, field-effect transistors, optoelectronic devices and chemical sensors. Especially, graphene is promising candidate to detect the gas molecules and biomolecules due to the large specific surface area and signal-to-noise ratios. Despite of importance to the disease diagnosis, there are a few reports to demonstrate the graphene- and rGO-FET for biological sensors and the sensing mechanism are not fully understood. Here we describe scalable and facile fabrication of rGO-FET with the capability of label-free, ultrasensitive electrical detection of a cancer biomarker, prostate specific antigen/${\alpha}1$-antichymotrypsin (PSA-ACT) complex, in which the ultrathin rGO sensing channel was simply formed by a uniform self-assembly of two-dimensional rGO nanosheets on aminated pattern generated by inkjet printing. Sensing characteristics of rGO-FET immunosensor showed the highly precise, reliable, and linear shift in the Dirac point with the analyte concentration of PSA-ACT complex and extremely low detection limit as low as 1 fg/ml. We further analyzed the charge doping mechanism, which is the change in the charge carrier in the rGO channel varying by the concentration of biomolecules. Amenability of solution-based scalable fabrication and extremely high performance may enable rGO-FET device as a versatile multiplexed diagnostic biosensor for disease biomarkers.

• The Transactions of the Korea Information Processing Society
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• v.3 no.3
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• pp.460-470
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• 1996

In this paper, we describe the programming methodology which can produce computer Go.After computer Go program with the rules of Go determines a territory for itself, it must evaluate the exact next move. The common design principle of computer Go is to combine such heuristic elements as pattern match, alpha-beta pruning and influence function. In this study, we introduce many other approaches and their results on computer Go, and then show data structures and algorithms to implement computer Go project.

• Magazine of the Korea Concrete Institute
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• v.28 no.3
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• pp.14-16
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• 2016

• Tuberculosis and Respiratory Diseases
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• v.45 no.6
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• pp.1265-1276
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• 1998

Background : Nitric oxide(NO) is an endogenously produced free radical that plays an important role in regulating vascular tone, inhibition of platelet aggregation and white blood cell adhesion to endothelial cells, and host defense against infection. The highly reactive nature of NO with oxygen radicals suggests that it may either promote or reduce oxidant-induced cell injury in several biological pathways. Oxidant injury and interactions between pulmonary vascular endothelium and leukocytes are important in the pathogenesis of acute lung injury, including acute respiratory distress syndrome(ARDS). In ARDS, therapeutic administration of NO is a clinical condition providing exogenous NO in oxidant-induced endothelial injury. The role of exogenous NO from NO donor or the suppression of endogenous NO production was evaluated in oxidant-induced endothelial injury. Method : The oxidant injury in cultured rat lung microvascular endothelial cells(RLMVC) was induced by hydrogen peroxide generated from glucose oxidase(GO). Cell injury was evaluated by $^{51}$chromium($^{51}Cr$) release technique. NO donor, such as S-nitroso-N-acetylpenicillamine(SNAP) or sodium nitroprusside(SNP), was added to the endothelial cells as a source of exogenous NO. Endogenous production of NO was suppressed with N-monomethyl-L-arginine(L-NMMA) which is an NO synthase inhibitor. L-NMMA was also used in increased endogenous NO production induced by combined stimulation with interferon-$\gamma$(INF-$\gamma$), tumor necrosis factor-$\alpha$(TNF-$\alpha$), and lipopolysaccharide(LPS). NO generation from NO donor or from the endothelial cells was evaluated by measuring nitrite concentration. Result : $^{51}Cr$ release was $8.7{\pm}0.5%$ in GO 5 mU/ml, $14.4{\pm}2.9%$ in GO 10 mU/ml, $32.3{\pm}2.9%$ in GO 15 mU/ml, $55.5{\pm}0.3%$ in GO 20 mU/ml and $67.8{\pm}0.9%$ in GO 30 mU/ml ; it was significantly increased in GO 15 mU/ml or higher concentrations when compared with $9.6{\pm}0.7%$ in control(p < 0.05; n=6). L-NMMA(0.5 mM) did not affect the $^{51}Cr$ release by GO. Nitrite concentration was increased to $3.9{\pm}0.3\;{\mu}M$ in culture media of RLMVC treated with INF-$\gamma$ (500 U/ml), TNF-$\alpha$(150 U/ml) and LPS($1\;{\mu}g/ml$) for 24 hours ; it was significantly suppressed by the addition of L-NMMA. The presence of L-NMMA did not affect $^{51}Cr$ release induced by GO in RLMVC pretreated with INF-$\gamma$, TNF-$\alpha$ and LPS. The increase of $^{51}Cr$ release with GO(20 mU/ml) was prevented completely by adding 100 ${\mu}M$ SNAP. But the add of SNP, potassium ferrocyanate or potassium ferricyanate did not protect the oxidant injury. Nitrite accumulation was $23{\pm}1.0\;{\mu}M$ from 100 ${\mu}M$ SNAP at 4 hours in phenol red free Hanks' balanced salt solution. But nitrite was not detectable from SNP upto 1 mM The presence of SNAP did not affect the time dependent generation of hydrogen peroxide by GO in phenol red free Hanks' balanced salt solution. Conclusion : Hydrogen peroxide generated by GO causes oxidant injury in RLMVC. Exogenous NO from NO donor prevents oxidant injury, and the protective effect may be related to the ability to release NO. These results suggest that the exogenous NO may be protective on oxidant injury to the endothelium.

• Genomics & Informatics
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• v.17 no.4
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• pp.44.1-44.3
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• 2019

Artificial intelligence (AI), big data, and ubiquitous robotic companions -the three most notable technologies of the 4th Industrial Revolution-are receiving renewed attention each day. Technologies that can be experienced in daily life, such as autonomous navigation, real-time translators, and voice recognition services, are already being commercialized in the field of information technology. In the biosciences field in Korea, such technologies have become known to the local public with the introduction of the AI doctor Watson in large number of hospitals. Additionally, AlphaFold, a technology resembling the AI AlphaGo for the game Go, has surpassed the limit on protein folding predictions-the most challenging problems in the field of protein biology. This report discusses the significance of AI technology and big data on the bioscience field. The introduction of automated robots in this field is not just only for the purpose of convenience but a prerequisite for the real sense of AI and the consequent accumulation of basic scientific knowledge.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.1096-1097
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• 2023

본 연구에서는 항만 교통 혼잡 문제를 해결하기 위해 최적화와 관련된 요소와 트럭 운전기사와 터미널 사이의 협상과 관련된 요소를 새로운 방식으로 고려한 중장기 및 실시간 스케줄링 모델을 제시한다. 중장기 스케줄링 모델은 몬테카를로 시뮬레이션, 실시간 스케줄링 모델은 알파고 제로의 원리와 베이즈 정리를 이용하여 구현했다. 실험 결과 제시된 알파고 제로를 이용한 실시간 스케줄링 시스템이 화물차 평균 지연시간을 30분에서 4분으로 대폭 줄여 지연 시간을 최소화하는 것을 입증했다. 실험 관련 코드는 다음 주소에서 확인할 수 있다 : https://github.com/yulleta/Application_of_AlphaGo-Zero_to_port_arrival_scheduling