• Journal of the Korean Operations Research and Management Science Society
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• v.33 no.2
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• pp.17-26
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• 2008

The traveling salesman problem is to find tours through all cities at minimum cost ; simply visiting the cities only once that a salesman wants to visit. As such, the traveling salesman problem is a NP-complete problem ; an heuristic algorithm is preferred to an exact algorithm. In this paper, we suggest an effective cost relaxation using a candidate arc set which is obtained from a regression function for the traveling salesman problem. The proposed method sufficiently consider the characteristics of cost of arcs compared to existing methods that randomly choose the arcs for relaxation. For test beds, we used 31 instances over 100 cities existing from TSPLIB and randomly generated 100 instances from well-known instance generators. For almost every instances, the proposed method has found efficiently better solutions than the existing method.

• Proceedings of the IEEK Conference
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• 1998.06a
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• pp.23-25
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• 1998

Many of the currently used PN code acquisition algorithms detect the phase of the incoming PN signal on the basis of ML estimation principle and utilize statistics grounded in taking inner products. By showing that any set of 2n-1 PN sequences arising in SSRG or MSRG (those typically used in IS'95 implementations) configuration constitutes a linearly independent set and that the number of candidate PN sequences has to equal the dimension of the span of the candidate PN sequences, we prove that the lowerbounding computational complexity involved in any PN code acquistion, utilizing (only) inner product computations at each stage of acquisition, corresponds precisely to those, such as double dwell acquistion circuitries, currently used.

• Journal of the Korean Institute of Telematics and Electronics
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• v.26 no.9
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• pp.1395-1401
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• 1989

An efficient file allocation algorithm and a new method which calculate appraisal value of candidate computer site for distributed computer systems are proposed. The file allocation problem size is reduced by using the preassignment condition. The appraisal value of candidate node is calcualted as the user state array and node state array are varied according to control variables. As the selection criteria is applied to the candidates, the reasonable node is selected and assign state is determined. The proposed algorithm is heuriatic polynomial time algorithm. By performing algorithm for sample problems. It is shown that the proposed algorithm is superior to conventional method in terms of deviation from optimal solution.

• Proceedings of the KSPS conference
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• 2006.11a
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• pp.142-145
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• 2006

Discrimination between speech and music is important in many multimedia applications. Previously we proposed a new parameter for speech/music discrimination, the mean of minimum cepstral distances (MMCD), and it outperformed the conventional parameters. One weakness of it is that its performance depends on range of candidate frames to compute the minimum cepstral distance, which requires the optimal selection of the range experimentally. In this paper, to alleviate the problem, we propose a multi-dimensional MMCD parameter which consists of multiple MMCDs with different ranges of candidate frames. Experimental results show that the multi-dimensional MMCD parameter yields an error rate reduction of 22.5% compared with the optimally chosen one-dimensional MMCD parameter.

• The Transactions of the Korea Information Processing Society
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• v.6 no.3
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• pp.633-640
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• 1999

Reinforcement learning performs learning through interacting with trial-and-error in dynamic environment. Therefore, in dynamic environment, reinforcement learning method like Q-learning and TD(Temporal Difference)-learning are faster in learning than the conventional stochastic learning method. However, because many of the proposed reinforcement learning algorithms are given the reinforcement value only when the learning agent has reached its goal state, most of the reinforcement algorithms converge to the optimal solution too slowly. In this paper, we present COMREL(COMpressed REinforcement Learning) algorithm for finding the shortest path fast in a maze environment, select the candidate states that can guide the shortest path in compressed maze environment, and learn only the candidate states to find the shortest path. After comparing COMREL algorithm with the already existing Q-learning and Priortized Sweeping algorithm, we could see that the learning time shortened very much.

• MALSORI
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• no.60
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• pp.191-201
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• 2006

Discrimination between speech and music is important in many multimedia applications. Previously we proposed a new parameter for speech/music discrimination, the mean of minimum cepstral distances (MMCD), and it outperformed the conventional parameters. One weakness of MMCD is that its performance depends on range of candidate frames to compute the minimum cepstral distance, which requires the optimal selection of the range experimentally. In this paper, to alleviate the problem, we propose a multi-dimensional MMCD parameter which consists of multiple MMCDS with combination of different candidate frame ranges. Experimental results show that the multi-dimensional MMCD parameter yields an error rate reduction of 22.5% compared with the optimally chosen one-dimensional MMCD parameter.

• ETRI Journal
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• v.31 no.5
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• pp.534-544
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• 2009

Finding link-disjoint or node-disjoint paths under multiple constraints is an effective way to improve network QoS ability, reliability, and so on. However, existing algorithms for such scheme cannot ensure a feasible solution for arbitrary networks. We propose design principles of an algorithm to fill this gap, which we arrive at by analyzing the properties of optimal solutions for the multi-constrained link-disjoint path pair problem. Based on this, we propose the link-disjoint optimal multi-constrained paths algorithm (LIDOMPA), to find the shortest link-disjoint path pair for any network. Three concepts, namely, the candidate optimal solution, the contractive constraint vector, and structure-aware non-dominance, are introduced to reduce its search space without loss of exactness. Extensive simulations show that LIDOMPA outperforms existing schemes and achieves acceptable complexity. Moreover, LIDOMPA is extended to the node-disjoint optimal multi-constrained paths algorithm (NODOMPA) for the multi-constrained node-disjoint path pair problem.

• 한국지구물리탐사학회:학술대회논문집
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• 2003.11a
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• pp.684-689
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• 2003

One field identified by an inverse method is one of multiple candidate solutions those are independently obtained through a specific estimation technique. While averaging of optimized fields can provide a better description of the spatial feature of an unknown field, it deteriorates the flow and transport characteristics of the optimized fields. As a result, the averaged field is not suited for modeling aquifer performances. Based on genetic algorithm, an optimal field synthesis technique is developed, which combines diversely optimized fields into a refined group of fields. Each field in the population is paired, and a sub-region of each field is exchanged by crossover operation to create a group of synthesized fields of enhanced modeling capability. The population of the fields is evolved till the synthesized fields become sufficiently similar. Applications of the optimal field synthesis to synthetic cases indicate that the objective functions of the fields assessing the modeling capabilities are further reduced after the optimal field synthesis. The identified fields from various inverse techniques may yield a range of modeling results under varied flow situations. The uncertainty is narrowed down through the optimal field synthesis and the associated modeling results converge on that of the reference field. The developed inverse modeling facilitates the construction of a reliable simulation model and hence trustworthy predictions of the future performances.

• Journal of Korean Library and Information Science Society
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• v.45 no.4
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• pp.409-428
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• 2014

The purpose of this study is to analyze the economic feasibility of Busan Regional Central Library. The four candidate sites were included for the analysis. To achieve the purpose of this study, we applied three types of indices such as Benefit-Cost analysis, Net Present Value, and Internal Rate of Return. We used CVM(Contingent Valuation Method) to calculate the benefit; we conducted two investigations to calculate WTP, which are for pretest and for main survey. From results of pretest, we designed five optimal prices. On the basis of them, main survey was accomplished to figure out WTPs for four candidate sites. The results show that four candidate sites were all feasible from an economic point of view. Of these, the Busan Citizen Park site got the highest point of B/C ratio.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.37 no.5
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• pp.609-617
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• 2013

This paper presents a near-minimum time path planning algorithm for autonomous driving. The problem of near-minimum time path planning is an optimization problem in which it is necessary to take into account not only the geometry of the circuit but also the dynamics of the vehicle. The path planning algorithm consists of a candidate path generation and a velocity optimization algorithm. The candidate path generation algorithm calculates the compromises between the shortest path and the path that allows the highest speeds to be achieved. The velocity optimization algorithm calculates the lap time of each candidate considering the vehicle driving performance and tire friction limit. By using the calculated path and velocity of each candidate, we calculate the lap times and search for a near-minimum time path. The proposed algorithm was evaluated via computer simulation using CarSim and Matlab/Simulink.