• Proceedings of the Safety Management and Science Conference
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• 2008.11a
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• pp.35-39
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• 2008

This paper considers the sequencing of products in mixed model assembly lines. The sequence which minimizes overall utility work in car assembly lines reduce the cycle time, the number of utility workers, and the risk of conveyor stopping. The sequencing problem is solved using Tabu Search. Tabu Search is a heuristic method which can provide a near optimal solution in real time. Various examples are presented and experimental results are reported to demonstrate the efficiency of the technique.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38C no.12
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• pp.1114-1125
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• 2013

License Plate Detection (LPD) is a key component in automatic license plate recognition system. Despite the success of License Plate Recognition (LPR) methods in the past decades, the problem is quite a challenge due to the diversity of plate formats and multiform outdoor illumination conditions during image acquisition. This paper aims at automatical detection of car license plates via image processing techniques. In this paper, we proposed a real-time and robust method for license plate detection using Heuristic Energy Map(HEM). In the vehicle image, the region of license plate contains many components or edges. We obtain the edge energy values of an image by using the box filter and search for the license plate region with high energy values. Using this energy value information or Heuristic Energy Map(HEM), we can easily detect the license plate region from vehicle image with a very high possibilities. The proposed method consists two main steps: Region of Interest (ROI) Detection and License Plate Detection. This method has better performance in speed and accuracy than the most of existing methods used for license plate detection. The proposed method can detect a license plate within 130 milliseconds and its detection rate is 99.2% on a 3.10-GHz Intel Core i3-2100(with 4.00 GB of RAM) personal computer.

• Journal of Distribution Science
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• v.15 no.9
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• pp.53-62
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• 2017

Purpose - This study aims to analyze the effects of preference for Korean Wave and heuristic on shopping satisfaction and switching barriers of Chinese people visiting Korea, and also their positive effects on consumers' behavioral intention with these variables as mediating variables by using the structural equation model. Research design, data, and Methodology - For this study, we collected 264 questionnaires of Chinese consumers' who have experienced tourism and shopping in Korea after excluding questionnaires that were not proper to be analyzed. The residence of Chinese visiter was Shandong Province, Beijing, Shanghai, Shenyang. The data of this study was analyzed using SPSS 23 statistics to verify the reliability and discriminant validity. Structural equation method was used to test the hypotheses in this study. Results - The effects of heuristic variable on shopping satisfaction and the switching barriers variable were statistically significant. The effects variables of Korean Wave effect variable were statistically significant only on the switching barriers variable. However, the Korean Wave effect variable did not have a statistically significant effect on the shopping satisfaction. Conclusions - The shopping satisfaction and Korean Wave effect variables had statistically significant effects on the behavioral intention variable. In the result of indirect analysis, only the heuristic effect had indirect effects on consumers' behavior intention.

• The KIPS Transactions:PartB
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• v.10B no.6
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• pp.719-724
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• 2003

This paper proposed a heuristic to enhance the speed of kNN without loss of its accuracy. The proposed heuristic minimizes the computation of the similarity between two documents which is the dominant factor in kNN. To do this, the paper proposes a method to calculate the upper limit of the similarity and to sort the training documents. The proposed heuristic was implemented on the existing framework of the text categorization, so called, AI :: Categorizer and it was compared with the conventional kNN with the well-known data, Router-21578. The comparisons show that the proposed heuristic outperforms kNN about 30∼40% with respect to the execution time.

• Journal of the Korean Society of Hazard Mitigation
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• v.10 no.3
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• pp.31-38
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• 2010

In this study, it is suggested that it has to reliability-based design methodology with respect to bridge structure-rail longitudinal interaction and bridge structure-vehicle interaction. For the structural analysis, commercial package, ABAQUS, are used for a three-dimensional finite element analysis. The optimization process utilizes a well-known optimizer, ADS(Automated Design Synthesis). Optimization technique is utilized the ALM-BFGS method for global area search and Golden Section Method for 1-D search. In general, ALM-BFGS method don't need the 1-D search, and that algorithm converge a 0.1~0.2 of Push-Off factor. But in this study, value of Push-Off factor is used 90, therefore 1-D search should be needed for effective convergency. That algorithm contains the "heuristic decision method". As a result of optimum design of 2-main steel girder birdge with 5${\times}$(1@50m), design methodology suggested in this study was demonstrated more economic and efficient than existing design and LCC optimization not considering bridge-rail longitudinal interaction and bridge-vehicle interaction.

• Journal of Intelligence and Information Systems
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• v.15 no.3
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• pp.137-150
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• 2009

This paper proposes the hybrid heuristic method to apply the opportunity time to solve the vehicle routing and scheduling problem with time constraints(VRSPTW). The opportunity time indicates the idle time which remains after the vehicle performs the unloading service required by each customer's node. In this proposed heuristic, we add the constraints to VRSPTW model for the opportunity time. We also obtain the initial solution by applying the cost evaluation function to the insertion strategy considering the opportunity time. In addition, we improve the former result by applying the opportunity time to the tabu search strategy by swapping the customer's node. Finally, we suggest the construction strategies of initial routing which can efficiently acquire the nearest optimal solution from various types of data in terms of geographical condition, scheduling horizon and vehicle capacity. Our experiment show that our heuristic can get the nearest optimal solution more efficiently than the Solomon's I1 heuristic.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.9
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• pp.3396-3411
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• 2015

Sensors used in Wireless Sensor Networks (WSN) have mostly limited capacity which affects the performance of their applications. One of the data-gathering methods is to use mobile sinks to visit these sensors so that they can save their limited battery energies from forwarding data packages to static sinks. The main disadvantage of employing mobile sinks is the delay of data collection due to relative low speed of mobile sinks. Since sensors have very limited memory capacities, whenever a mobile sink is too late to visit a sensor, that sensor's memory would be full, which is called a 'memory overflow', and thus, needs to be purged, which causes loss of collected data. In this work, a method is proposed to generate mobile sink tours, such that the number of overflows and the amount of lost data are minimized. Moreover, the proposed method does not need either the sensor locations or sensor memory status in advance. Hence, the overhead stemmed from the information exchange of these requirements are avoided. The proposed method is compared with a previously published heuristic. The simulation experiment results show the success of the proposed method over the rival heuristic with respect to the considered metrics under various parameters.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.1
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• pp.1-8
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• 2018

Software testing is important to determine the reliability of the system, a task that requires a lot of effort and cost. Model-based testing has been proposed as a way to reduce these costs by automating test designs from models that regularly represent system requirements. For each path of model to generate an input value to perform a test, meta-heuristic technique is used to find the test data. In this paper, we propose an automatic test data generation method using a slicing method and a priority policy, and suppress unnecessary computation by excluding variables not related to target path. And then, experimental results show that the proposed method generates test data more effectively than conventional method.

• Journal of the Korean Operations Research and Management Science Society
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• v.21 no.3
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• pp.63-74
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• 1996

Ordering plays an important role in solving an LP problem with sparse matrix by the interior point method. Since ordering is NP-complete, we try to find an efficient method. The objective of this paper is to present an efficient heuristic ordering method for implementation of the minimum deficiency method. Both the ordering method and the data structure play important roles in implementation. First we define a new heuristic pseudo-deficiency ordering method and a data structure for the method-quotient graph and cliqued storage. Next we show an experimental result in terms of time and nonzero numbers by NETLIB problems.

• Journal of KIISE:Software and Applications
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• v.30 no.9
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• pp.862-866
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• 2003

Traveling Salesman Problem(TSP) is a combinational optimization problem, Genetic Algorithm(GA) and Lin-Kernighan(LK) Heuristic［1］that is Local Search Heuristic are one of the most commonly used methods to resolve TSP. In this paper, we introduce ACS(Ant Colony System) Algorithm as another approach to solve TSP and propose a new pheromone updating method. ACS uses pheromone information between cities in the Process where many ants make a tour, and is a method to find a optimal solution through recursive tour creation process. At the stage of Global Updating of ACS method, it updates pheromone of edges belonging to global best tour of created all edge. But we perform once more pheromone update about created all edges before global updating rule of original ACS is applied. At this process, we use the frequency of occurrence of each edges to update pheromone. We could offer stochastic value by pheromone about each edges, giving all edges' occurrence frequency as weight about Pheromone. This finds an optimal solution faster than existing ACS algorithm and prevent a local optima using more edges in next time search.