• Journal of KIISE:Software and Applications
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• v.31 no.6
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• pp.829-839
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• 2004

Methods based on integer programming have been shown to be very effective in solving various crew pairing optimization problems. However, their applicability is limited to problems with linear constraints and objective functions. Also, those methods often require an unacceptable amount of time and/or memory resources given problems of larger scale. Heuristic methods such as neighborhood search, on the other hand, can handle large-scaled problems without too much difficulty and can be applied to problems having any form of objective functions and constraints. However, neighborhood search often gets stuck at local optima when faced with complex search spaces. This paper presents ,i hybrid algorithm of neighborhood search and integer programming, which nicely combines the advantages of both methods. The hybrid algorithm has been successfully tested on a large-scaled crew pairing optimization problem for a real subway line.

• Journal of the Korea Society for Simulation
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• v.3 no.2
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• pp.57-67
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• 1994

For many practical and industrial optimization problems where some or all of the system components are stochastic, the objective functions cannot be represented analytically. Therefore, modeling by computer simulation is one of the most effective means of studying such complex systems. In this paper, with discussion of simulation optimization techniques, a case study in machining process for application of simulation optimization is presented. Most of optimization techniques can be classified as single-or multiple-response techniques. The optimization of single-response category, these strategies are gradient based search methods, stochastic approximate method, response surface method, and heuristic search methods. In the multiple-response category, there are basically five distinct strategies for treating the responses and finding the optimum solution. These strategies are graphical method, direct search method, constrained optimization, unconstrained optimization, and goal programming methods. The choice of the procedure to employ in simulation optimization depends on the analyst and the problem to be solved.

• Journal of the Korean Institute of Telematics and Electronics
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• v.27 no.3
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• pp.131-136
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• 1990

In this paper, new methods are proposed which speed up the logical redundancy identification for the gate-level logic optimization. Redundancy indentification, as well as deterministic test pattern generation, can be viewed as a finite space search problem, of which execution time depends on the size of the search space. For the purpose of efficient search, we propose dynamic head line and mandatory assignment. Dynamic head lines are changed dynamically in the process of the redundancy identification. Mandatory assignement can avoid unnecessary assignment. They can reduce the search size efficiently. Especially they can be used even though the circuit is modified in the optimization procedure, that is different from the test pattern generation methods. Some experimental results are presented indicating that the proposed methods are faster than existing methods.

• Journal of KIISE:Software and Applications
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• v.33 no.7
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• pp.646-654
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• 2006

Directory searching and index searching methods are two main methods in web search engines. Both of the methods are applied to most of the well-known Internet search engines, which enable users to choose the other method if they are not satisfied with results shown by one method. That is, Index searching tends to come up with too many search results, while directory searching has a difficulty in selecting proper categories, frequently mislead to false ones. In this paper, we propose a novel method in which a category hierarchy is dynamically constructed. To do this, a category is regarded as a fuzzy set which includes keywords. Similarly extensible subcategories of a category can be found using fuzzy relational products. The merit of this method is to enhance the recall rate of directory search by expanding subcategories on the basis of similarity.

• Journal of Information Processing Systems
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• v.9 no.2
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• pp.315-332
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• 2013

This paper presents an approach for improving the use of VP-tree in video indexing and searching. A vantage-point tree or VP-tree is one of the metric space-based indexing methods used in multimedia database searches and data retrieval. Instead of relying on the Euclidean distance as a measure of search space, the proposed approach focuses on the trigonometric inequality for compressing the search range, which thus, improves the search performance. A test result of using 10,000 video files shows that this method reduced the search time by 5-12%, as compared to the existing method that uses the AESA algorithm.

• Journal of the Korea Society of Computer and Information
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• v.22 no.7
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• pp.47-54
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• 2017

Recently, correlation filter based trackers have shown excellent tracking performance and computational efficiency. In order to enhance tracking performance in the correlation filter based tracker, search area which is image patch for finding target must include target. In this paper, two methods to discriminatively represent target in the search area are proposed. Firstly, search area location is estimated using pyramidal Lucas-Kanade algorithm. By estimating search area location before filtering, fast motion target can be included in the search area. Secondly, we investigate multi-channel Local Edge Pattern(LEP) which is insensitive to illumination and noise variation. Qualitative and quantitative experiments are performed with eight dataset, which includes ground truth. In comparison with method without search area estimation, our approach retain tracking for the fast motion target. Additionally, the proposed multi-channel LEP improves discriminative performance compare to existing features.

• Journal of information and communication convergence engineering
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• v.5 no.3
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• pp.239-244
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• 2007

In the telematics system, a reasonable path search time should be guaranteed from a great number of user's queries, even though the optimal path with minimized travel time might be continuously changed by the traffic flows. Thus, the path search method should consider traffic flows of the roads and the search time as well. However, the existing path search methods are not able to cope efficiently with the change of the traffic flows and to search rapidly paths simultaneously. This paper proposes a new path search method for fast computation. It also reflects the traffic flows efficiently. Especially, in order to simplify the computation of variable heuristic values, it employs a simplification method for estimating values of traffic-adaptable heuristics. The experiments are carried out with the $A^*$ algorithm and the proposed method in terms of the execution time, the number of node accesses and the accuracy. The results obtained from the experiments show that the method achieves very fast execution time and the reasonable accuracy as well.

• Journal of Information Technology Applications and Management
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• v.13 no.4
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• pp.121-140
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• 2006

UDDI (Universal Discovery Description and Integration) Registry is used for Web Services registration and search. UDDI offers the search result to the keyword-based query. UDDI supports WSDL registration but it does not supports WSDL search. So it is required that contents based search and ranking using name and description in UDDI registration information and WSDL. This paper proposes a retrieval engine considering contents of services registered in the UDDI and WSDL. It uses Vector Space Model for similarity comparison between contents of those. UDDI registry information hierarchy and WSDL hierarchy are considered during searching process. This engine suppports two discovery methods. One is Keyword-based search and the other is template-based search supporting ranking for user's query. Template-based search offers how service interfaces correspond to the query for WSDL documents. Proposed retrieval engine can offer search result more accurately than one which UDDI offers and it can retrieve WSDL which is registered in UDDI in detail.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2005.04a
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• pp.351-358
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• 2005

Many methods have been developed and are in use for structural size optimization problems, In which the cross-sectional areas or sizing variables are usually assumed to be continuous. In most practical structural engineering design problems, however, the design variables are discrete. This paper proposes an efficient optimization method for structures with discrete-sized variables based on the harmony search (HS) meta-heuristic algorithm. The recently developed HS algorithm was conceptualized using the musical process of searching for a perfect state of harmony. It uses a stochastic random search instead of a gradient search so that derivative information is unnecessary In this paper, a discrete search strategy using the HS algorithm is presented in detail and its effectiveness and robustness, as compared to current discrete optimization methods, are demonstrated through a standard truss example. The numerical results reveal that the proposed method is a powerful search and design optimization tool for structures with discrete-sized members, and may yield better solutions than those obtained using current method.

• Proceedings of the ESK Conference
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• 1993.10a
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• pp.124-132
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• 1993

This study compared two prediction methods-regression and artificial neural network (ANN) on the visual search performance when monitoring a multi-parameter screen with different occurrence frequencies. Under the highlighting condition for the highest occurrence frequency parameter as a search cue, it was found from the requression analysis that variations of mean search time (MST) could be expained almost by three factors such as the number of parameters, the target occurrence frequency of a highlighted parameter, and the highlighted parameter size. In this study, prediction performance of ANN was evaluated as an alternative to regression method. Backpropagation method which was commonly used as a pattern associator was employed to learn a search behavior of subjects. For the case of increased number of parameters and incresed target occurrence frequency of a highlighted parameter, ANN predicted MST's moreaccurately than the regression method (p<0.000). Only the MST's predicted by ANN did not statistically differ from the true MST's. For the case of increased highlighted parameter size. both methods failed to predict MST's accurately, but the differences from the true MST were smaller when predicted by ANN than by regression model (p=0.0005). This study shows that ANN is a good predictor of a visual search performance and can substitute the regression method under certain circumstances.