• Journal of Korea Multimedia Society
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• v.9 no.12
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• pp.1681-1688
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• 2006

GIS systems have gradually been utilized in life information as well as special businesses such as traffic, sight-seeing, tracking, and disaster services. Most GIS services focus on showing stored information on maps, not providing a service to register and modify their preferred information. In this paper, we present a new method which reduces DXF map data into Simple Geographic Information File format using format conversion algorithms. We also present the prototype implementation of a GIS search system based on ontologies to support associated information. Our contribution is to propose a new digital map format to provide a fast map loading service and individual customized information on the map service.

• Structural Monitoring and Maintenance
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• v.1 no.4
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• pp.427-449
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• 2014

A hybrid optimization method for the identification of state-space models is presented in this study. Hybridization is succeeded by combining the advantages of deterministic and stochastic algorithms in a superior scheme that promises faster convergence rate and reliability in the search for the global optimum. The proposed hybrid algorithm is developed by replacing the original stochastic mutation operator of Evolution Strategies (ES) by the Levenberg-Marquardt (LM) quasi-Newton algorithm. This substitution results in a scheme where the entire population cloud is involved in the search for the global optimum, while single individuals are involved in the local search, undertaken by the LM method. The novel hybrid identification framework is assessed through the Monte Carlo analysis of a simulated system and an experimental case study on a shear frame structure. Comparisons to subspace identification, as well as to conventional, self-adaptive ES provide significant indication of superior performance.

• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.803-804
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• 2008

In this paper, we propose an edge dependent interpolation (EDI) method based on adaptive search range. The proposed EDI method uses the vector matching to determine the edge direction, and the vector matching process is terminated when the previous sum of absolute difference (SAD) is smaller than the next one. The adaptive search range method enables the EDI algorithm to estimate edge direction more accurately and to reduce the computational complexity. The experimental results show that the proposed method produces better performance than conventional algorithms.

• The Journal of Korean Association of Computer Education
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• v.6 no.1
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• pp.1-8
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• 2003

We introduce a efficient vertical mining algorithm that reduces searching complexity for frequent k-itemsets significantly. This method includes sorting items by their LSI(Least Support Itemsets) similarity and then searching frequent itemsets in tree-based manner. The search tree structure provides several useful heuristics and therefore, reduces search space significantly at early stages. Experimental results on various data sets shows that the proposed algorithm improves searching performance compared to other algorithms, especially for a database having long pattern.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.28 no.8
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• pp.47-53
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• 2014

This paper presents an Differential Search(DS) Algorithm for solving the economic load dispatch(ELD) problems with Valve-Point loading constraints. DS algorithm simulates the Brownian-like random-walk movement used by an organism to migrate. Numerical results on a test system consisting of 13 units show that the proposed approach is faster, more robust and powerful than conventional algorithms. Case studies show the simulation results are better than Lagrange method, the Hopfield neural networks and GA.

• Journal of Advanced Marine Engineering and Technology
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• v.38 no.10
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• pp.1340-1346
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• 2014

Previous studies of reliable server assignment considered only to assign the same cost of server, that is, homogeneous servers. In this paper, we generally deal with reliable server assignment with different server costs, i.e., heterogeneous servers. We formulate this problem as a nonlinear integer programming mathematically. Our problem is defined as determining a deployment of heterogeneous servers to maximize a measure of service availability. We propose two metaheuristic algorithms (tabu search and particle swarm optimization) for solving the problem of reliable server assignment. From the computational results, we notice that our tabu search outstandingly outperforms particle swarm optimization for all test problems. In terms of solution quality and computing time, the proposed method is recommended as a promising metaheuristic for a kind of reliability optimization problems including reliable sever assignment and e-Navigation system.

• Journal of Information Processing Systems
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• v.16 no.2
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• pp.478-493
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• 2020

We designed a new meta-heuristic algorithm named Photon Search Algorithm (PSA) in this paper, which is motivated by photon properties in the field of physics. The physical knowledge involved in this paper includes three main concepts: Principle of Constancy of Light Velocity, Uncertainty Principle and Pauli Exclusion Principle. Based on these physical knowledges, we developed mathematical formulations and models of the proposed algorithm. Moreover, in order to confirm the convergence capability of the algorithm proposed, we compared it with 7 unimodal benchmark functions and 23 multimodal benchmark functions. Experimental results indicate that PSA has better global convergence and higher searching efficiency. Although the performance of the algorithm in solving the optimal solution of certain functions is slightly inferior to that of the existing heuristic algorithm, it is better than the existing algorithm in solving most functions. On balance, PSA has relatively better convergence performance than the existing metaheuristic algorithms.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.35 no.4
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• pp.98-109
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• 2012

Recently, the optimisation of end-of-life (EOL) product remanufacturing processes has been highlighted. In particular, computer remanufacturing becomes important as the amount of disposed of computers is rapidly increasing. At the computer remanufacturing, depending on the selections of used computer parts, the value of remanufactured computers will be different. Hence, it is important to select appropriate computer parts at the reassembly. To this end, this study deals with a decision making problem to select the best combination of computer parts for minimising the total remanufacturing computer cost. This problem is formulated with an integer nonlinear programming model and heuristic search algorithms are proposed to resolve it.

• Journal of the Korea Society of Computer and Information
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• v.20 no.9
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• pp.21-29
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• 2015

The set partitioning problem is a well-known NP-hard combinatorial optimization problem, and it is formulated as an integer programming model. This paper proposes an Integer Programming-based Local Search for solving the set partitioning problem. The key point is to solve the set partitioning problem as the set covering problem. First, an initial solution is generated by a simple heuristic for the set covering problem, and then the solution is set as the current solution. Next, the following process is repeated. The original set covering problem is reduced based on the current solution, and the reduced problem is solved by Integer Programming which includes a specific element in the objective function to derive the solution for the set partitioning problem. Experimental results on a set of OR-Library instances show that the proposed algorithm outperforms pure integer programming as well as the existing heuristic algorithms both in solution quality and time.

• Proceedings of the KIEE Conference
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• 2003.07a
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• pp.403-405
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• 2003

In this paper, we applied a chaos search method for feeder reconfiguration problem in unbalanced distribution system. Chaos method, in optimization problem, searches the global optimal solution on the regularity of chaotic motions and more easily escapes from local or near optimal solution than stochastic optimization algorithms. The chaos search method applied to the IEEE 13 unbalanced test feeder systems, and the test results indicate that it is able to determine appropriate switching options for global optimum configuration.