• Journal of the Korean Society for Railway
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• v.13 no.5
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• pp.535-540
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• 2010

We present an optimization model for how to assign the freight trains to transshipment tracks and allocate the containers to the freight trains in a rail container terminal. We formulate this problem as a multi-criteria integer programming to minimize the makespan of job schedule and simultaneously to maximize the loading throughput, i.e. the number of containers to be disposed per day. We also apply our model to the instance obtained from the real-world data of the Uiwang Inner Container Depot. From the experiments, we can see an improvement of approximately 6% in makespan, which means that our model can contribute to the improvement of the disposal capacity of containers without additional expansion of facilities.

• Structural Engineering and Mechanics
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• v.63 no.5
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• pp.639-646
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• 2017

According to the multi-index system of dam safety assessment and the standard of safety, a comprehensive evaluation model for dam safety based on a cloud model is established to determine the basic probability assignment of the Dempster-Shafer theory. The Dempster-Shafer theory is improved to solve the high conflict problems via fusion calculation. Compared with the traditional Dempster-Shafer theory, the application is more extensive and the result is more reasonable. The uncertainty model of dam safety multi-index comprehensive evaluation is applied according to the two theories above. The rationality and feasibility of the model are verified through application to the safety evaluation of a practical arch dam.

• Journal of Korean Society of Transportation
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• v.17 no.1
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• pp.141-157
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• 1999

This paper first reviews current practices of traveler information providing and provides some perspectives regarding the possible near term milestones in traveler information providing. Then, reviews of four types of prediction models: 1) dynamic traffic assignment (DTA) model; 2) statistical model; 3) simulation model; and 4) heuristic model are described in the sense that various prediction models are needed to support providing predictive traveler information in the near future. Next, the functional requirements and capabilities of the four types of prediction models are discussed and summarized along with some advantages and disadvantages of these models with reference to short-term travel time prediction. Furthermore, a comprehensive prediction procedure, which combines the four types of prediction models, is presented, together with the data requirements for each type of prediction model.

• Journal of the military operations research society of Korea
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• v.8 no.2
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• pp.17-30
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• 1982

This paper offers an optimal artillery deployment scheme for the defending unit when two forces are confronted at a military front line. When proposed gun sites, types and number of guns as well as targets are given, the solutions of the two models in this paper direct each (unit of) guns to a certain location. The aim of the models is to maximize the number of guns which can hit important targets. Unlike widely used target assignment models, these models are formulated using the set covering problem concept. These models do not contain probabilities and time. Thus they are simple as models, easy in implementation, and yield tractable solutions. The dynamic and probabilistic feature of battle situations is implicitly reflected on the models. The first model is for the case that enemies' approaching route is clearly predictable, while the second model is for the unpredictable approaching route case.

• Proceedings of the Korean Society of Tribologists and Lubrication Engineers Conference
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• 2000.06a
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• pp.54-61
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• 2000

A method and results of classification of 4 types metallic wear debris were presented by using their color features. The color image of wear debris was used (or the initial data, and the color properties of the debris were specified by HSI color model. Particle was characterized by a set of statistical features derived from the distribution of HSI color model components. The initial feature set was optimized by a principal component analysis, and multidimensional scaling procedure was used for the definition of classification plane. It was found that five features, which include mean values of H and S, median S, skewness of distribution of S and I, allow to distinguish copper based alloys, red and dark iron oxides and steel particles. In this work, a method of probabilistic decision-making of class label assignment was proposed, which was based on the analysis of debris-coordinates distribution in the classification plane. The obtained results demonstrated a good availability for the automated wear particle analysis.

• ETRI Journal
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• v.35 no.3
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• pp.550-553
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• 2013

Due to limited spectrum resources and differences in link loads, network congestion is one of the key issues in cognitive radio wireless mesh networks. In this letter, a congestion avoidance model with power control, channel allocation, and routing under the signal-to-interference-and-noise ratio is presented. As a contribution, a nested optimization scheme combined with a genetic algorithm and linear programming solver is proposed. Extensive simulation results are presented to demonstrate the effectiveness of our algorithm.

• IE interfaces
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• v.15 no.2
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• pp.209-217
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• 2002

Quality of Service(QoS) in the telecommunications can be evaluated by analyzing the opinion data which result from the surveyed opinions of respondents and quantify subjective satisfaction on the QoS from the customers' viewpoints. For analyzing the opinion data, MOS(mean opinion score) method and Cumulative Probability Curve method are often used. The methods are based on the scoring method, and therefore, have the intrinsic deficiency due to the assignment of arbitrary scores. In this paper, we propose an analysis method of the opinion data using logit models which can be used to analyze the ordinal categorical data without assigning arbitrary scores to customers' opinion, and develop an analysis procedure considering the usage of procedures provided by SAS(Statistical Analysis System) statistical package. By the proposed method, we can estimate the relationship between customer satisfaction and network performance parameters, and provide guidelines for network planning. In addition, the proposed method is compared with Cumulative Probability Curve method with respect to prediction errors.

• Journal of the Korean Society for Precision Engineering
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• v.19 no.11
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• pp.192-199
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• 2002

This paper demonstrates that hybrid neural-genetic multimodel parameter estimation algorithm can be applied to structured system Identification of electro-hydraulic servo system. This algorithm are consist of a recurrent incremental credit assignment (ICRA) neural network and a genetic algorithm. The ICRA neural network evaluates each member of a generation of model and genetic algorithm produces new generation of model. We manufactured electro-hydraulic servo system and the hybrid neural-genetic multimodel parameter estimation algorithm is applied to the task to find the parameter values(mass, damping coefficient, bulk modulus, spring coefficient) which minimize total square error.

• Journal of the Korean Operations Research and Management Science Society
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• v.13 no.2
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• pp.9-17
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• 1988

This vehicle scheduling problem with multiple depots and multiple vehicle types (VMM) is to determine the optimal vehicle routes to minimize the total travel costs. The object of this paper is to develope an exact algorithm for the VMM. In this paper the VMM is transformed into a mathematical model of the vehicle problem with multiple depots. Then an efficient branch and bound algorithm is developed to obtain an exact solution for this model. In order to enhance the efficiency, this algorithm emphasizes the follows; First, a heuristic algorithm is developed to get a good initial upper bound. Second, an primal-dual approach is used to solve subproblems which are called the quasi-assignment problem, formed by branching strategy is presented to reduce the number of the candidate subproblems.

• Journal of Institute of Control, Robotics and Systems
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• v.9 no.6
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• pp.442-447
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• 2003

This paper demonstrates that a modified hybrid neural-genetic multimodel parameter estimation algorithm can be applied to structured system identification of an electro-hydraulic servo system. This algorithm is consists of a recurrent incremental credit assignment(ICRA) neural network and a genetic algorithm. The ICRA neural network evaluates each member of a generation of model and genetic algorithm produces new generation of model. The modified hybrid neural-genetic multimodel parameter estimation algorithm is applied to an electro-hydraulic servo system the task to find the parameter values such as mass, damping coefficient, bulk modulus, spring coefficient and disturbance, which minimizes the total square error.