• Journal of the Korean Operations Research and Management Science Society
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• v.26 no.2
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• pp.13-46
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• 2001

SDP (Semidefinite Programming), as a sort of “cone-LP”, optimizes a linear function over the intersection of an affine space and a cone that has the origin as its apex. SDP, however, has been developed in the process of searching for better solution methods for NP-hard combinatorial optimization problems. We surveyed the basic theories necessary to understand SDP researches. First, We examined SDP duality, comparing it to LP duality, which is essential for the interior point method, Second, we showed that SDP can be optimized from an interior solution in polynomial time with a desired error limit. finally, we summarized several research papers that showed SDP can improve solution methods for some combinatorial optimization problems, and explained why SDP has become one of the most important research topics in optimization. We tried to integrate SDP theories. relatively diverse and complicated. to survey research papers with our own perspective, and thus to help researcher to pursue their SDP researches in depth.

• Journal of the Korean Operations Research and Management Science Society
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• v.34 no.4
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• pp.73-89
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• 2009

Circular shareholdings among three companies are formed when company A owns stock in company B, company B owns stock in company C, and company C owns stock in company A. Since circular shareholdings among large family-controlled firms are used to give the controlling shareholder greater control or more opportunities to expropriate minority investors, the government has encouraged large business groups to gradually remove their circular shareholdings. In this paper, we propose a combinatorial optimization model that can answer the question, which equity investments among complicated investment relationships of one large business group should be removed to resolve its circular shareholdings. To the best knowledge of the authors, our research is the first one that has approached the circular shareholding problem in respect of management science. The proposed combinatorial optimization model are formulated into integer programming problem and applied to some Korean major business groups.

• Journal of the Korean Operations Research and Management Science Society
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• v.29 no.4
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• pp.117-134
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• 2004

This study suggests a data driven optimization approach, which simulates the models of human learning processes from cognitive sciences. It shows how the human learning processes can be simulated and applied to solving combinatorial optimization problems. The main advantage of using this method is in applying it into problems, which are very difficult to simulate. 'Undecidable' problems are considered as best possible application areas for this suggested approach. The concept of an 'undecidable' problem is redefined. The learning models in human learning and decision-making related to combinatorial optimization in cognitive and neural sciences are designed, simulated, and implemented to solve an optimization problem. We call this approach 'SLO : simulated learning for optimization.' Two different versions of SLO have been designed: SLO with position & link matrix, and SLO with decomposition algorithm. The methods are tested for traveling salespersons problems to show how these approaches derive new solution empirically. The tests show that simulated learning for optimization produces new solutions with better performance empirically. Its performance, compared to other hill-climbing type methods, is relatively good.

• 한국정보디스플레이학회:학술대회논문집
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• 2006.08a
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• pp.33-38
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• 2006

An evolutionary optimization process involving genetic algorithm and combinatorial chemistry was employed in an attempt to develop titanate-based red phosphors suitable for tri-color white light emitting diodes We screened a eight-cation oxide system including $(K,Li,Na)_x(Y,Gd,La,Eu)_yTi_zO_{\delta}$ in terms of luminescent efficiency. The combination of genetic algorithm and combinatorial chemistry was proven to enhance the searching efficiency when applied for phosphor screening. As a result, the composition was optimized to be $(Na_{0.92}Li_{0.08})(Y_{0.8}Gd_{0.2})TiO_4:Eu^{3+}$, The luminance of this phosphor was 110 % of that of well-known scheelite variant phosphor at an excitation of 400 nm.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.34C no.5
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• pp.66-70
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• 1997

This paper presents a parallel combinatorial variant of evolution strategy (PCES) to solve well-known combinatorial optimization problems, Quadratic assignment problems (QAPs). The PCES reduces the possibility of getting stuck in local minima due to maintenance of subpopulation and thus it is more effective than the CES. Experiment results on two benchmark problems show that the PCES is better than the cES and the genetic algorithm(GA).

• Journal of the Korean Ceramic Society
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• v.37 no.2
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• pp.134-139
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• 2000

The main objective of the present investigation is to show the feasibility of combinatorial chemistry by applying this method to phosphor syntehses. In this respect barium hexaaluminate phosphor was prepared by the split-pool combinatorial method, which enabled much more rapid search of optimum compositions of target phosphors than conventional synthetic methods. Barium hexaaluminate phosphors doped with Eu2+ exhibit blue emission while those co-doped with Mn2+ and Eu2+ exhibit green emission. Basically, the phosphor doped with 1.3 mole of Ba and 0.06~0.15 mole of Eu2+ exhibit the maximum value of emission intensity at 435${\mu}{\textrm}{m}$. Under the UV and VUV extitations, the barium hexaaluminate phosphor co-doped with Mn2+ and Eu2+ shows strong green emission.

• Journal of the Korean Operations Research and Management Science Society
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• v.21 no.2
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• pp.125-140
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• 1996

Simulated annealing (SA) is a very promising general purpose algorithm which can be conveniently utilized for various complicated combinatorial optimization problems. But its slowness has been pointed as a major drawback. In this paper, we propose an accelerated SA and test its performance experimentally by applying it for two standard combinatorial optimization problems (TSP(Travelling Salesman Problem) and GPP(Graph Partitioning Problem) of various sizes. It turns out that performance of the proposed method is consistently better both in convergenge speed and the quality of solution than the conventional SA or SE (Stochastic Evolution). In the second part of the paper we apply the accelerated SA to solve the virtual path management problem encountered in ATM netowrks. The problem is modeled as a combinatorial optimization problem to optimize the utilizy of links and an efficient SA implementation scheme is proposed. Two application examples are given to demonstrate the validity of the proposed algorithm.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2004.11a
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• pp.171-178
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• 2004

This study suggests a general paradigm enhancing genetic mutability. Mutability among heterogeneous members in a genetic population has been a major problem in application of genetic programming to diverse business problems. This suggested paradigm is implemented to developing new methods from existing methods. Within the evolutionary approach taken to designing new methods, a general representation scheme of the genetic programming framework, called a kernel, is introduced. The kernel is derived from the literature of algorithms and heuristics for combinatorial optimization problems. The commonality and differences among these methods have been identified and again combined by following the genetic inheritance merging them. The kernel was tested for selected methods in combinatorial optimization. It not only duplicates the methods in the literature, it also confirms that each of the possible solutions from the genetic mutation is in a valid form, a running program. This evolutionary method suggests diverse hybrid methods in the form of complete programs through evolutionary processes. It finally summarizes its findings from genetic simulation with insight.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.7
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• pp.699-708
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• 2015

In the testing of a software component-based robot system, generating test cases for the system is a time-consuming and difficult task that requires the combining of test data. This paper proposes an adaptive combinatorial testing method which is based on the input/output relationship among components and which automatically generates the test cases for the system. The proposed algorithm first generates an input/output relationship graph in order to analyze the input/output relationship of the system. It then generates the reduced set of test cases according to the analyzed type of input/output relationship. To validate the proposed algorithm some comparisons are given in terms of the time complexity and the number of test cases.

• IMMUNE NETWORK
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• v.23 no.4
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• pp.35.1-35.16
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• 2023

Defining the molecular dynamics associated with T cell differentiation enhances our understanding of T cell biology and opens up new possibilities for clinical implications. In this study, we investigated the dynamics of CD5 expression in CD8⁺ T cell differentiation and explored its potential clinical uses. Using PBMCs from 29 healthy donors, we observed a stepwise decrease in CD5 expression as CD8⁺ T cells progressed through the differentiation stages. Interestingly, we found that CD5 expression was initially upregulated in response to T cell receptor stimulation, but diminished as the cells underwent proliferation, potentially explaining the differentiation-associated CD5 downregulation. Based on the proliferation-dependent downregulation of CD5, we hypothesized that relative CD5 expression could serve as a marker to distinguish the heterogeneous CD8⁺ T cell population based on their proliferation history. In support of this, we demonstrated that effector memory CD8⁺ T cells with higher CD5 expression exhibited phenotypic and functional characteristics resembling less differentiated cells compared to those with lower CD5 expression. Furthermore, in the retrospective analysis of PBMCs from 30 non-small cell lung cancer patients, we found that patients with higher CD5 expression in effector memory T cells displayed CD8⁺ T cells with a phenotype closer to the less differentiated cells, leading to favorable clinical outcomes in response to immune checkpoint inhibitor (ICI) therapy. These findings highlight the dynamics of CD5 expression as an indicator of CD8⁺ T cell differentiation status, and have implications for the development of predictive biomarker for ICI therapy.