• Proceedings of the IEEK Conference
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• 2002.07c
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• pp.1535-1538
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• 2002

The flexibility of content addressable mem-ory (CAM) can greatly be extended through the use of trits (ternary digits) Trits consist of binary logical values “0” and “1” with addition of “x” (“dont’t care”). The “dont’t care“is extremely useful for providing com- pact representation of sets of bit strings. In this paper, we propose a new ternary CAM with Hamming distance search functions. Each memory cell in the CAM consists of a pair of lambda diodes which can store trits, namely, a logical “0”, “1” and “x” (“dont’t care“). The CAM can compare stored data and an input data in parallel, and find stored data with Hamming distance within a certain range (“near match“). Also, the interrogation characteristics of the ternary CAM are analyzed in detail. Furthermore, the results obtained these analyses are fully confirmed by simulation using the circuit analysis program HSPICE.

• The KIPS Transactions:PartD
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• v.11D no.3
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• pp.563-570
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• 2004

As the use of the Internet becomes more popular, a huge amount of information is published on the Web, and users can access the information effectively with Web search services. Since Web search services retrieve relevant documents from those collected by Web robots we need to improve the crawling quality of Web robots. In this paper, we suggest evaluation criteria for Web robots such as efficiency, continuity, freshness, coverage, silence, uniqueness and safety, and present various functions to improve the performance of Web robots. We also investigate the functions implemented in the conventional Web robots of NAVER, Google, AltaVista etc. It is expected that this study could contribute the development of more effective Web robots.

• Nuclear Engineering and Technology
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• v.49 no.6
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• pp.1250-1258
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• 2017

The multigroup transport theory is the basis for many neutronics modules. A significant point of the cross-section (XS) generation procedure is the choice of the energy groups' boundaries in the XS libraries, which must be carefully selected as an unsuitable energy meshing can easily lead to inaccurate results. This decision can require considerable effort and is particularly difficult for the common user, especially if not well-versed in reactor physics. This work investigates a genetic algorithm-based tool which selects an appropriate XS energy structure (ES) specific for the considered problem, to be used for the condensation of a fine multigroup library. The procedure is accelerated by results storage and fitness calculation speedup and can be easily parallelized. The extension is applied to the coupled code SIMMER and tested on the European Sustainable Nuclear Industrial Initiative (ESNII+) Advanced Sodium Technological Reactor for Industrial Demonstration (ASTRID)-like reactor system with different fitness functions. The results show that, when the libraries are condensed based on the ESs suggested by the algorithm, the code actually returns the correct multiplication factor, in both reference and voided conditions. The computational effort reduction obtained by using the condensed library rather than the fine one is assessed and is much higher than the time required for the ES search.

• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.161-161
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• 2019

Water security issues, stimulated by increasing population and changing climate, are growing and pausing major challenges for water resources managers around the world. Proper utilization, management and distribution of all available water resources is key to sustainable development for achieving water security To alleviate the water shortage, most of the current research on multi-sources combined water supplies depends on an overall generalization of regional water supply systems, which are seldom broken down into the detail required to address specific research objectives. This paper proposes the concept of optimization framework on multi water sources selection. A multi-objective water allocation model with four objective functions is introduced in this paper. Harmony search algorithm is employed to solve the applied model. The objective functions addresses the economic, environmental, and social factors that must be considered for achieving a sustainable water allocation to solve the issue of water security.

• Journal of Ship and Ocean Technology
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• v.9 no.4
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• pp.35-46
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• 2005

The performance of optimisation methods, based on penalty functions, is highly problem- dependent and many methods require additional tuning of some variables. This additional tuning is the influences of penalty coefficient, which depend strongly on the degree of constraint violation. Moreover, Binary-coded Genetic Algorithm (BGA) meets certain difficulties when dealing with continuous and/or discrete search spaces with large dimensions. With the above reasons, Real-coded Micro-Genetic Algorithm (R$\mu$GA) is proposed to find the global optimum of continuous and/or discrete nonlinear constrained engineering problems without handling any of penalty functions. R$\mu$GA can help in avoiding the premature convergence and search for global solution-spaces, because of its wide spread applicability, global perspective and inherent parallelism. The proposed R$\mu$GA approach has been demonstrated by solving three different engineering design problems. From the simulation results, it has been concluded that R$\mu$GA is an effective global optimisation tool for solving continuous and/or discrete nonlinear constrained real­world optimisation problems.

• Korean Journal of Oriental Medicine
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• v.16 no.3
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• pp.141-147
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• 2010

In this paper, we designed and implemented an integrated search and collaboration system. Users can search the traditional korean medicine knowledge in our system, which consists of medicinal materials, formulas, diseases, terminology, and clinical information. In general, the existing information systems providing the korean medicine knowledge do not provide the update function. Thus, it can be a problem if there are incomplete information. In order to solve this problem, our system implements the functions that users can work together to improve the knowledge. Therefore, wrong information can be updated easily so that flexible management about the korean medicine information is possible.

• Journal of Ocean Engineering and Technology
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• v.17 no.6
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• pp.38-46
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• 2003

Aim of this article is to propose Micro-Genetic Simulated Annealing (${\mu}GSA$) as a hybrid metaheuristics approach to find the global optimum of nonlinear optimisation problems. This approach combines the features of modern metaheuristics such as micro-genetic algorithm (${\mu}GAs$) and simulated annealing (SA) with the general robustness of parallel exploration and asymptotic convergence, respectively. Therefore, ${\mu}GSA$ approach can help in avoiding the premature convergence and can search for better global solution, because of its wide spread applicability, global perspective and inherent parallelism. For the superior performance of the ${\mu}GSA$, the five well-know benchmark test functions that were tested and compared with the two global optimisation approaches: scatter search (SS) and hybrid scatter genetic tabu (HSGT) approach. A practical application to structural sandwich panel is also examined by optimism the weight function. From the simulation results, it has been concluded that the proposed ${\mu}GSA$ approach is an effective optimisation tool for soloing continuous nonlinear global optimisation problems in suitable computational time frame.

• Journal of the Korean Institute of Gas
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• v.1 no.1
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• pp.33-40
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• 1997

An optimization system, APROGA II using genetic algorithm, was developed to solve multi-modal and multiobjective problems. To begin with, Multi-Niche Crowding(MNC) algorithm was used for multi-modal optimization problem. Secondly, a new algorithm was suggested for multiobjective optimization problem. Pareto dominance tournaments and Sharing on the non-dominated frontier was applied to it to obtain multiple objectives. APROGA II uses these two algorithms and the system has three search engines(previous APROGA search engine, multi-modal search engine and multiobjective search engine). Besides, this system can handle binary and discrete variables. And the validity of APROGA II was proved by solving several test functions and case study problems successfully.

• Journal of computational fluids engineering
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• v.17 no.4
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• pp.32-40
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• 2012

Efficient Global Optimization (EGO) method is a global optimization technique which can select the next sample point automatically by infill sampling criteria (ISC) and search for the global minimum with less samples than what the conventional global optimization method needs. ISC function consists of the predictor and mean square error (MSE) provided from the kriging model which is a stochastic metamodel. Also the constrained EGO method can minimize the objective function dealing with the constraints under EGO concept. In this study the constrained EGO method applied to the RAE2822 airfoil shape design formulated with the constraint. But the noisy CFD data caused the kriging model to fail to depict the true function. The distorted kriging model would make the EGO deviate from the correct search. This distortion of kriging model can be handled with the interpolation(p=free) kriging model. With the interpolation(p=free) kriging model, however, the search of EGO solution was stalled in the narrow feasible region without the chance to update the objective and constraint functions. Then the accuracy of EGO solution was not good enough. So the three-step search method was proposed to obtain the accurate global minimum as well as prevent from the distortion of kriging model for the noisy constrained CFD problem.

• KIEE International Transactions on Power Engineering
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• v.5A no.4
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• pp.331-338
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• 2005

In this paper, an improved maintenance scheduling approach suitable for the competitive environment is proposed by taking account of profits and costs of generation companies and the formulated combinatorial optimization problem is solved by using Reactive Tabu search (RTS). In competitive power markets, electricity prices are determined by the balance between demand and supply through electric power exchanges or by bilateral contracts. Therefore, in decision makings, it is essential for system operation planners and market participants to take the volatility of electricity price into consideration. In the proposed maintenance scheduling approach, firstly, electricity prices over the targeted period are forecasted based on Artificial Neural Network (ANN) and also a newly proposed aggregated bidding curve. Secondary, the maintenance scheduling is formulated as a combinatorial optimization problem with a novel objective function by which the most profitable maintenance schedule would be attained. As an objective function, Opportunity Loss by Maintenance (OLM) is adopted to maximize the profit of generation companies (GENCOS). Thirdly, the combinatorial optimization maintenance scheduling problem is solved by using Reactive Tabu Search in the light of the objective functions and forecasted electricity prices. Finally, the proposed maintenance scheduling is applied to a practical test power system to verify the advantages and practicability of the proposed method.