• Journal of the Korea Society for Simulation
• /
• v.22 no.2
• /
• pp.53-62
• /
• 2013

Cloud environment is one in the field of distributed computing and it consists of physical nodes and virtual nodes. In distributed cloud environment, an optimal path search is that each node to perform a search for an optimal path. Synchronization of each node is required for the optimal path search via fast data transmission because of real-time environment. Therefore, a quantization technique is required in order to guarantee QoS(Quality of Service) and search an optimal path. The quantization technique speeds search data transmission of each node. So a main server can transfer data of real-time environment to each node quickly and the nodes can perform to search optimal paths smoothly. In this paper, we propose the quantization technique to solve the search problem. The quantization technique can reduce the total data transmission. In order to experiment the optimal path search system which applied the quantized data transmission, we construct a simulation of cloud environment. Quantization applied cloud environment reduces the amount of data that transferred, and then QoS of an application for the optimal path search problem is guaranteed.

• Journal of Korea Multimedia Society
• /
• v.5 no.6
• /
• pp.745-752
• /
• 2002

A* algorithm is widely used in optimal car route search which is a kind of informed search, since the locations of starting and ending points are known a priori. Unidirectional A* algorithm requires considerable search time but guarantees a optimal path, bidirectional A* algorithm does not guarantee a optimal path and takes even longer search time than unidirectional search to guarantee a optimal path. In this paper, a new bidirectional A* algorithm which requites less search time and guarantees a optimal path is proposed. To evaluate the efficiency of the proposed algorithm, several experiments are conducted in real road map and the results show that the algorithm is very effective in terms of finding a optimal path and search time.

• Journal of the Korean Operations Research and Management Science Society
• /
• v.39 no.3
• /
• pp.133-144
• /
• 2014

Naver, a leading search engine in South Korea, may show modified and reorganized search results for some trendy and popular keywords; when popular words such as the titles of soap operas and films are searched for,all the detailed and well-organized information regarding them can be presented. By recognizing that search engines may modify and reorganize search results for some popular keywords, we mathematically model the impact of the degree of modification of search results on the search engine's profit to derive its optimal modification degree. We show how the optimal degree of search result modification may change according to the different shapes of the search engine's advertising revenue function.

• Proceedings of the Korean Operations and Management Science Society Conference
• /
• 1996.10a
• /
• pp.135-138
• /
• 1996

In solving the Travel Salesman Problem(TSP), we easily reach local optimal solutions with the existing methods such as TWO-OPT, THREE-OPT, and Lin-Kernighen. Tabu search, as a meta heuristic, is a good mechanism to get an optimal or a near optimal solution escaping from the local optimal. By utilizing AI concepts, tabu search continues to search for improved solutions. In this study, we focus on developing a new neighborhood structure that maintains the feasibility of the tours created by exchange operations in TSP. Intelligent methods are discussed, which keeps feasible tour routes even after exchanging several edges continuously. An extended tabu search model, performing cycle detection and diversification with memory structure, is applied to TSP. The model uses effectively the information gathered during the search process. Finally, the results of tabu search and simulated annealing are compared based on the TSP problems in the prior literatures.

• Journal of Korean Institute of Industrial Engineers
• /
• v.29 no.2
• /
• pp.165-171
• /
• 2003

Visual search in an unstructured search field is a fruitful research area for computational modeling. Search models that describe relationship between search time and probability of target detection have been used for prediction of human search performance and provision of ideal goals for search training. Until recently, however, most of models were focused on detecting a single target in a search field, although, in practice, a search field includes multiple targets and search models for multiple targets may differ from search models for a single target. This study proposed a random search model for multiple targets, generalizing a random search model for a single target which is the most typical search model. To test this model, human search data were collected and compared with the model. This model well predicted human performance in visual search for multiple targets. This paper also proposed how to determine optimal stopping time in multiple-target search.

• Journal of the Society of Naval Architects of Korea
• /
• v.42 no.2 s.140
• /
• pp.175-182
• /
• 2005

It is necessary to develop an efficient optimization technique to optimize the engineering structures which have given design spaces, discrete design values and several design goals. As optimization techniques, direct search method and stochastic search method are widely used in designing of engineering structures. The merit of the direct search method is to search the optimum points rapidly by considering the search direction, step size and convergence limit. And the merit of the stochastic search method is to obtain the global optimum points by spreading point randomly entire the design spaces. In this paper, a Pareto optimal based multi-objective function method (PMOFM) is developed by considering the search direction based on Pareto optimal points, step size, convergence limit and random search generation . The PMOFM can also apply to the single objective function problems, and can consider the discrete design variables such as discrete plate thickness and discrete stiffener spaces. The design results are compared with existing Evolutionary Strategies (ES) method by performing the design of double bottom structures which have discrete plate thickness and discrete stiffener spaces.

• Proceedings of the IEEK Conference
• /
• 2000.06d
• /
• pp.39-42
• /
• 2000

Motion estimation plays an important role for video coding. In this paper, we derive optimal search patterns for fast block matching motion estimation. By analyzing the block matching algorithm as a function of block shape and size, we can find an optimal search pattern for initial motion estimation. The proposed idea, which has been verified experimentally by computer simulations, can provide an analytical basis for the current MPEG-2 proposals. In order to choose a more compact search pattern for BMA, we exploit the statistical relationship between the motion and the frame difference of each block.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 2002.10a
• /
• pp.451-454
• /
• 2002

The fiber optic communication technology is considered as a key solution for the future communication. However the assembly of the fiber optic components highly depends on manual or semi-automated alignment process. And the light search algorithm is recognized an important factor to reduce the manufacturing process time. Therefore this paper investigates optimal search algorithm for the automatic alignment of fiber optic components. The experiments show the effectiveness of Hill Climbing Search, Adaptive Hill Climbing Search and Steepest Search algorithms, in a view of process time.

• The Transactions of the Korean Institute of Electrical Engineers D
• /
• v.53 no.5
• /
• pp.323-330
• /
• 2004

In this paper, we propose a design method of PID controller using an improved Tabu Search. Tabu Search is improved by neighbor solution creation using Gaussian random distribution and generalized Hermite Biehler Theorem for stable bounds. The range of admissible proportional gains are determined first in closed form. Next the optimal PID gains are selected by improved Tabu Search. The results of Computer simulations represent that the proposed Tabu Search algorithm shows a fast convergence speed and a good control performance.

• Journal of Institute of Control, Robotics and Systems
• /
• v.3 no.2
• /
• pp.156-161
• /
• 1997

In this thesis, based on Genetic Algorithm, a new route search algorithm is presented to search an optimal route between the origin and the destination in intelligent route guidance systems in order to minimize the route traveling time. The proposed algorithm is effectively employed to complex road networks which have diverse turn constrains, time-delay constraints due to cross signals, and stochastic traffic volume. The algorithm is also shown to significantly promote search efficiency by changing the population size of path individuals that exist in each generation through the concept of age and lifetime to each path individual. A virtual road-traffic network with various turn constraints and traffic volume is simulated, where the suggested algorithm promptly produces not only an optimal route to minimize the route cost but also the estimated travel time for any pair of the origin and the destination, while effectively avoiding turn constraints and traffic jam.