• Honam Mathematical Journal
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• v.25 no.1
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• pp.3-15
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• 2003

We prove an analogue of the Robinson-Schensted correspondence between generalized biwords and oscillating semi-standard tableaux. We give a geometric construction of the correspondence and examine combinatorial properties of the correspondence.

• Proceedings of the Korean Ceranic Society Conference
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• 2003.10a
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• pp.234.1-234
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• 2003

• The Korean Journal of Pesticide Science
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• v.8 no.1
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• pp.8-15
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• 2004

A synthesis of new $\beta$-ketoacetoanilide chloride derivatives and anti fungal activity of these compounds library against 6 typical plant pathogens were described. Reaction of ketene dimer with chlorine followed by treatment of aniline derivatives gave 89 kinds of the corresponding $\beta$-ketoacetoanilide chlorides through combinatorial synthetic technology using Carousel Reaction Stations. Evaluation of antifungal activity (in vivo) of this chemical library against rice blast, rice sheath blight, tomato aray mold, tomato late blight, wheat leaf rust and barley powdery mildew was carried out. In general, $\beta$-ketoacetoanilide chlorides which present a substituent at 4 in phenyl group(para) of the compounds showed selective control activity against tomato late blight caused by Phytophthora infestans.

• The KIPS Transactions:PartC
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• v.17C no.5
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• pp.399-406
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• 2010

Many problems are arisen due to the weakness in the security and invasion to privacy by malicious attacker or internal users while various data services are available in ubiquitous network environment. The matter of controlling security for various contents and large capacity of data has appeared as an important issue to solve this problem. The allocation methods of Ito, Saito and Nishizeki based on traditional polynomial require all shares to restore the secret information shared. On the contrary, the secret information can be restored if the shares beyond the threshold value is collected. In addition, it has the effect of distributed DBMS operation which distributes and restores the data, especially the flexibility in realization by using parameters t,v,k in combinatorial design which has regularity in DB server and share selection. This paper discuss the construction of new share allocation method and data distribution/storage management with the application of matrix structure of t-(v,k,1) design for allocating share when using secret sharing in management scheme to solve the matter of allocating share.

• Journal of KIISE:Computing Practices and Letters
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• v.15 no.12
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• pp.928-932
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• 2009

Most diseases are caused by complex processes of various factors. Although previous researches have tried to identify the causes of the disease, there are still lots of limitations to clarify the complex factors. Here, we present a disease classification model based on an evolutionary learning approach of combinatorial features using the data sets from the genetics and cohort studies. We implemented a system for finding the combinatorial risk factors and visualizing the results. Our results show that the proposed method not only improves classification accuracy but also identifies biologically meaningful sets of risk factors.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2004.07b
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• pp.998-1001
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• 2004

ZnO, $SnO_2$ 타겟 각각의 RF 파워를 50 W, 38 W로 고정시킨 후 combinatorial RF magnetron sputtering법을 사용하여 기판 위치에 따라서 조성 구배를 주어 여러 가지 조성의 Zn-Sn-O(ZTO) 박막을 제작하였다. 시편의 열처리에 따른 물성 변화를 분석하기 위해 Rapid Thermal Annealer(RTA)을 이용하여 450, $650{^\circ}C$의 온도 및 $10^{-2}$ Ton의 진공 분위기에서 각각 1 시간 동안 열처리하였다. XRD 분석 결과 상온에서 제작된 ZTO 박막은 Sn 18 at%의 조성을 갖는 시편을 제외하고 모두 비정질상으로 나타났다. $450^{\circ}C$에서 열처리 후 구조적인 변화는 보이지 않았으나, 캐리어 농도와 이동도는 증가하였으며 Sn 54 at%의 조성에서 최고 $25.4cm^2/Vsec$의 전자 이동도를 나타내었다. $26{\leq}Sn$ $at%{\leq}65$의 조성 범위를 갖는 박막은 가시광 영역에서 80 % 이상의 투과도를 가졌으며 $650^{\circ}C$에서 결정화가 되면서 투과도가 증가하였다.

• Journal of the Korean Ceramic Society
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• v.40 no.12
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• pp.1170-1176
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• 2003

We developed an evolutionary optimization process involving a genetic algorithm and combinatorial chemistry (combi-chem), which was tailored exclusively for tile development of LED phosphors with a high luminescent efficiency, when excited by soft ultra violet irradiation. The ultimate goal of our study was to develop oxide red phosphors, which are suitable for three-band white Light Emitting Diodes (LED). To accomplish this, a computational evolutionary optimization process was adopted to screen a Eu$^{3+}$-doped alkali earth borosilicate system. The genetic algorithm is a well-known, very efficient heuristic optimization method and combi-chem is also a powerful tool for use in an actual experimental optimization process. Therefore the combination of a genetic algorithm and combi-chem would enhance the searching efficiency when applied to phosphor screening. Vertical simulations and an actual synthesis were carried out and promising red phosphors for three-band white LED applications, such as Eu$_{0.14}$Mg$_{0.18}$Ca$_{0.07}$Ba$_{0.12}$B$_{0.17}$Si$_{0.32}$O$_{\delta}$, were obtained.

• Journal of Korea Society of Industrial Information Systems
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• v.27 no.4
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• pp.37-45
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• 2022

In this paper, a multi agent-multi task assignment problem, which is a representative problem of combinatorial optimization, is presented. The objective of the problem is to determine the coordinated agent-task assignment that maximizes the sum of the achievement rates of each task. The achievement rate is represented as a concave down increasing function according to the number of agents assigned to the task. The problem is expressed as an NP-hard problem with a non-linear objective function. In this paper, to solve the assignment problem, we propose a hybrid cross-entropy algorithm as an effective and efficient solution methodology. In fact, the general cross-entropy algorithm might have drawbacks (e.g., slow update of parameters and premature convergence) according to problem situations. Compared to the general cross-entropy algorithm, the proposed method is designed to be less likely to have the two drawbacks. We show that the performances of the proposed methods are better than those of the general cross-entropy algorithm through numerical experiments.

• Journal of Korea Society of Industrial Information Systems
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• v.28 no.1
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• pp.27-34
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• 2023

The optimal assignment problem between agents and tasks is known as one of the representative problems of combinatorial optimization and an NP-hard problem. This paper covers multi agent-multi task assignment problems with uncertain completion probability. The completion probabilities are generally uncertain due to endogenous (agent or task) or exogenous factors in the system. Assignment decisions without considering uncertainty can be ineffective in a real situation that has volatility. To consider uncertain completion probability mathematically, a mathematical formulation with stochastic programming is illustrated. We also present an algorithm by using the sample average approximation method to solve the problem efficiently. The algorithm can obtain an assignment decision and the upper and lower bounds of the assignment problem. Through numerical experiments, we present the optimality gap and the variance of the gap to confirm the performances of the results. This shows the excellence and robustness of the assignment decisions obtained by the algorithm in the problem with uncertainty.

• Smart Structures and Systems
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• v.31 no.3
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• pp.247-257
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• 2023

In structural health monitoring of large-scale structures, optimal sensor placement plays an important role because of the high cost of sensors and their supporting instruments, as well as the burden of data transmission and storage. In this study, a vibration sensor placement algorithm based on deep reinforcement learning (DRL) is proposed, which can effectively solve non-convex, high-dimensional, and discrete combinatorial sensor placement optimization problems. An objective function is constructed to estimate the quality of a specific vibration sensor placement scheme according to the modal assurance criterion (MAC). Using this objective function, a DRL-based algorithm is presented to determine the optimal vibration sensor placement scheme. Subsequently, we transform the sensor optimal placement process into a Markov decision process and employ a DRL-based optimization algorithm to maximize the objective function for optimal sensor placement. To illustrate the applicability of the proposed method, two examples are presented: a 10-story braced frame and a sea-crossing bridge model. A comparison study is also performed with a genetic algorithm and particle swarm algorithm. The proposed DRL-based algorithm can effectively solve the discrete combinatorial optimization problem for vibration sensor placements and can produce superior performance compared with the other two existing methods.