• The Journal of Korean Medicine Ophthalmology and Otolaryngology and Dermatology
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• v.21 no.3
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• pp.52-68
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• 2008

Objectives and Methods : Salviae miltiorrhizae Radix (SMR) promotes blood circulation to remove blood stasis, cools the blood to relieve carbuncle, clears away heat from the heart and tranquilizes the mind. This study was designed to investigate the effects of SMR on immuno-potentiative action in terms of changes in the genetic profile of dendritic cells (DC) using by microarray analysis. Results and Conclusion: In this experiment, treatments with more than 250 ${\mu}g/ml$ upto 1000 ${\mu}g/ml$ of SMR elevated the proliferation rates of DC. Microscopic observations confirmed the tendency on proliferation rates. Expression levels of genes related with cellular methabolic process, cell communication, and macromolecule metabolic process were elevated by treatment with SMR in comparison of functional distribution in a Biological Process. In molecular functions, expression levels of genes related with receptor activation, nucleotide binding and nucleic acid binding were elevated. In cellular components, expression levels of genes related to cellular membrane-bound organelles were elevated. In addition, expression levels of genes related to Wnt signalling pathways and the glycerophospholipid metabolism were elevated through analysis using pathway analysis between up-and down-regulated genes in cells treated with SMR. Finally, genes related to JAK2, GRB2, CDC42, SMAD4, B2M, FOS and ESRI located the center of Protein interaction network of genes through treatment with SMR.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.6
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• pp.2870-2888
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• 2017

Recent studies have shown that the flow table size of hardware SDN switch cannot match the number of concurrent flows. Combined SDN Forwarding Element (CFE), which comprises several software switches and a hardware switch, becomes an alternative approach to tackle this problem. Due to the limited capacity of software switch, the way to route concurrent flows in CFE can largely affect the maximum delay that a flow suffers at CFE. As delay-guarantee is a nontrivial task for network providers with the increasing number of delay-sensitive applications, we propose an analytical model of CFE to evaluate a rules placement solution first. Next, we formulate the problem of Rules Placement with delay guarantee in CFE (RPCFE), and present the genetic-based rules placement (GARP) algorithm to solve the RPCFE problem. Further, we validate the analytical model of CFE through simulations in NS-3 and compare the performance of GARP with three benchmark algorithms.

• Animal cells and systems
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• v.13 no.3
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• pp.305-314
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• 2009

The control region of mitochondrial DNA (13516-14619) is located between srRNA and $tRNA^{lle}$ gene in swimming crab, Portunus trituberculatus. The present study was investigated the genetic polymorph isms of the control region in samples of P. trituberculatus collected at coastal waters of the Yellow Sea in Korea. A total of 300 substitution and indel polymorphic sites were identified. In addition to SNPs and indel variation, a hypervariable microsatellite motif was also identified at position from 14358 to 14391, which exhibited 10 alleles including 53 different suballeles. When the hypervariable microsatellite motif was removed from the alignment, 95 haplotypes were identified (93 unique haplotypes). The nucleotide and haplotype diversities were ranged from 0.024 to 0.028 and from 0.952 to 1.000, respectively. The statistically significant evidence for geographical structure was not detected from the analyses of neighbor-joining tree and minimum-spanning network, neither. This result suggest that population of P. trituberculatus are capable of extensive gene flow among populations. We believed that the polymorph isms of the control region will be used for informative markers to study phylogenetic relationships of P. trituberculatus.

• Proceedings of the Korean Society of Plant Pathology Conference
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• 2003.10a
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• pp.29-29
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• 2003

Incompatible plant-pathogen interactions result in the rapid cell death response known as hypersensitive response (HR) and activation of host defense-related genes. To understand the molecular and cellular mechanism controlling defense response better, several approaches including isolation and characterization of novel genes, promoter analysis of those genes, protein-protein interaction analysis and reverse genetic approach etc. By using the yeast two-hybrid system a clone named Tsipl, Tsil -interacting protein 1, was isolated whose translation product apparently interacted with Tsil, an EREBP/AP2 type DNA binding protein. RNA gel blot analysis showed that the expression of Tsipl was increased by treatment with NaCl, ethylene, salicylic acid, or gibberellic acid. Transient expression analysis using a Tsipl::smGFP fusion gene in Arabidopsis protoplasts indicated that the Tsipl protein was targeted to the outer surface of chloroplasts. The targeted Tsipl::smGFP proteins were diffused to the cytoplasm of protoplasts in the presence of salicylic acid (SA) The PEG-mediated co-transfection analysis showed that Tsipl could interact with Tsil in the nucleus. These results suggest that Tsipl-Tsil interaction might serve to regulate defense-related gene expression. Basically the useful promoters are valuable tools for effective control of gene expression related to various developmental and environmental condition.(중략)

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.288-295
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• 1998

This paper proposes a new data classification method based on the tolerant rough set that extends the existing equivalent rough set. Similarity measure between two data is described by a distance function of all constituent attributes and they are defined to be tolerant when their similarity measure exceeds a similarity threshold value. The determination of optimal similarity theshold value is very important for the accurate classification. So, we determine it optimally by using the genetic algorithm (GA), where the goal of evolution is to balance two requirements such that (1) some tolerant objects are required to be included in the same class as many as possible. After finding the optimal similarity threshold value, a tolerant set of each object is obtained and the data set is grounded into the lower and upper approximation set depending on the coincidence of their classes. We propose a two-stage classification method that all data are classified by using the lower approxi ation at the first stage and then the non-classified data at the first stage are classified again by using the rough membership functions obtained from the upper approximation set. We apply the proposed classification method to the handwritten numeral character classification. problem and compare its classification performance and learning time with those of the feed forward neural network's back propagation algorithm.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2005.11a
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• pp.191-194
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• 2005

본 논문은 최적 탐색 알고리즘인 유전자 알고리즘을 이용하여 다항식 뉴럴네트워크(Polynomial Neural Networks : PNN)의 최적 설계가 그 목적이다. 기존의 다항식 뉴럴네트워크는 확장된 GMDH(Group Method of Data Handling) 방법에 기반을 두며, 네트워크의 성장과정을 통하여 각 층의 다항식뉴런(혹은 노드)에서 고정된 (설계자에 의해 미리 선택된) 노드 입력들의 수뿐만 아니라 다항식 차수(1차, 2차, 그리고 수정된 2차식)를 이용하였다. 더구나, 그 방법은 학습을 통해 생성된 PNN이 최적 네트워크 구조를 가진다는 것을 보증하지 못한다. 그러나, 제안된 GA-based PW 모델은 다음의 파라미터들- 즉 입력변수의 수, 입력변수, 및 다항식 차수-을 유전자 알고리즘을 이용하여 선택 동조함으로써 그 구조를 구조적으로 더 최적화된 네트워크가 되도록 하고, 기존의 PNN보다 훨씬 더 유연하고, 선호된 뉴럴 네트워크가 되도록 한다. 하중계수를 가진 합성성능지수가 그 모델의 근사화 및 일반화(예측) 능력 사이의 상호 균형을 얻기 위해 제안된다. GA-based PNN의 성능을 평가하기 위해 그 모델은 가스 터빈발전소의 NOx 배출 공정 데이터로 실험된다. 비교해석은 제안된 GA-based PNN이 앞서 나타난 다른 지능모델보다 더 우수한 예측능력뿐만 아니라 높은 정확성을 가진 모델임을 보인다.

• Biotechnology and Bioprocess Engineering:BBE
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• v.10 no.5
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• pp.385-399
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• 2005

The phenotypic response of a cell results from a well orchestrated web of complex interactions which propagate from the genetic architecture through the metabolic flux network. To rationally design cell factories which carry out specific functional objectives by controlling this hierarchical system is a challenge. Transcriptome analysis, the most mature high-throughput measurement technology, has been readily applied In strain improvement programs in an attempt to Identify genes involved in expressing a given phenotype. Unfortunately, while differentially expressed genes may provide targets for metabolic engineering, phenotypic responses are often not directly linked to transcriptional patterns, This limits the application of genome-wide transcriptional analysis for the design of cell factories. However, improved tools for integrating transcriptional data with other high-throughput measurements and known biological interactions are emerging. These tools hold significant promise for providing the framework to comprehensively dissect the regulatory mechanisms that identify the cellular control mechanisms and lead to more effective strategies to rewire the cellular control elements for metabolic engineering.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2000.10a
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• pp.113-116
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• 2000

The purpose of this study is to present a method for determining the k most vital arcs in shortest path problem using an evolutionary algorithm. The problem of finding the k most vital arcs in shortest path problem is to find a set of k arcs whose simultaneous removal from the network causes the greatest increase in the total length of shortest path. The problem determining the k most vital arcs in shortest path problem has known as NP-hard. Therefore, in order to deal with the problem of real world the heuristic algorithm is needed. In this study we propose to the method of finding the k-MVA in shortest path problem using an evolutionary algorithm which known as the most efficient algorithm among heuristics. For this, the expression method of individuals compatible with the characteristics of shortest path problem, the parameter values of constitution gene, size of the initial population, crossover rate and mutation rate etc. are specified and then the effective genetic algorithm will be proposed. The method presented in this study is developed using the library of the evolutionary algorithm framework (EAF) and then the performance of algorithm is analyzed through the computer experiment.

• Journal of Electrical Engineering and Technology
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• v.2 no.4
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• pp.413-419
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• 2007

The optimal power flow (OPF) problem was introduced by Carpentier in 1962 as a network constrained economic dispatch problem. Since then, it has been intensively studied and widely used in power system operation and planning. In the past few decades, many stochastic optimization methods such as Genetic Algorithm (GA), Evolutionary Programming (EP), and Particle Swarm Optimization (PSO) have been applied to solve the OPF problem. In particular, PSO is a newly proposed population based stochastic optimization algorithm. The main idea behind it is based on the food-searching behavior of birds and fish. Compared with other stochastic optimization methods, PSO has comparable or even superior search performance for some hard optimization problems in real power systems. Nowadays, some modifications such as breeding and selection operators are considered to make the PSO superior and robust. In this paper, we propose the Modified PSO (MPSO), in which the mutation operator of GA is incorporated into the conventional PSO to improve the search performance. To verify the optimal solution searching ability, the proposed approach has been evaluated on an IEEE 3D-bus test system. The results showed that performance of the proposed approach is better than that of the standard PSO.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.10
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• pp.1699-1708
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• 2007

The optimal power flow(OPF) problem was introduced by Carpentier in 1962 as a network constrained economic dispatch problem. Since then, the OPF problem has been intensively studied and widely used in power system operation and planning. In these days, OPF is becoming more and more important in the deregulation environment of power pool and there is an urgent need of faster solution technique for on-line application. To solve OPF problem, many heuristic optimization methods have been developed, such as Genetic Algorithm(GA), Evolutionary Programming(EP), Evolution Strategies(ES), and Particle Swarm Optimization(PSO). Especially, PSO algorithm is a newly proposed population based heuristic optimization algorithm which was inspired by the social behaviors of animals. However, population based heuristic optimization methods require higher computing time to find optimal point. This shortcoming is overcome by a straightforward parallel processing of PSO algorithm. The developed parallel PSO algorithm is implemented on a PC cluster system with 6 Intel Pentium IV 2GHz processors. The proposed approach has been tested on the IEEE 30-bus system. The results showed that computing time of parallelized PSO algorithm can be reduced by parallel processing without losing the quality of solution.