• Journal of Korean Institute of Industrial Engineers
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• v.33 no.2
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• pp.282-289
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• 2007

While demands for express couriers service are rapidly increasing due to recent progress of electronic commerce, express courier service companies are struggling to take a larger market share through ongoing improvement in their service processes. Cut-off time is the time limit that all orders delivered before the limit are guaranteed for the delivery within the very next day. Extending cut-off time for express service centers can provide the express company with increase of total sales, but it may also cause increasing the possibility not to satisfy customer needs due to work delay in the consolidation terminal. We develop a design model for express courier service network based on a recursive optimization/simulation procedure. With the optimization model, we seek key design parameters such as the cut-off time for express service centers and the capacity of the consolidation terminal maximizing total sales profit while satisfying the desired level of performances. With the simulation model, we consider the dynamic nature of the network and obtain relationships between the design parameters and the performance measures with the multiple linear regression. The validity of the model is examined with an example.

• The KIPS Transactions:PartD
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• v.12D no.1 s.97
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• pp.43-50
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• 2005

The Processing domains of spatio-temporal databases are divided into time-series databases for moving objects and sequence databases for discrete historical objects. Recently the selectivity estimation techniques for query optimization in spatio-temporal databases have been studied, but focused on query optimization in time-series databases. There wat no previous work on the selectivity estimation techniques for sequence databates as well. Therefore, we construct T-Minskew histogram for query optimization In sequence databases and propose a selectivity estimation method using the T-Minskew histogram. Furthermore we propose an effective histogram maintenance technique for food performance of the histogram.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.18 no.2
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• pp.169-176
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• 2008

This paper presents optimal design of controllable magnetorheological(MR) shock absorbers for passenger vehicle. In order to achieve this goal, two MR shock absorbers (one for front suspension; one for rear suspension) are designed using an optimization methodology based on design specifications for a commercial passenger vehicle. The optimization problem is to find optimal geometric dimensions of the magnetic circuits for the front and rear MR shock absorbers in order to improve the performance such as damping force as an objective function. The first order optimization method using commercial finite element method(FEM) software is adopted for the constrained optimization algorithm. After manufacturing the MR shock absorbers with optimally obtained design parameters, their field-dependent damping forces are experimentally evaluated and compared with those of conventional shock absorbers. In addition, vibration control performances of the full-vehicle installed with the proposed MR shock absorbers are evaluated under bump road condition and obstacle avoidance test.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.34 no.1
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• pp.74-80
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• 2006

This paper addresses the trajectory optimization of Unmanned Aerial Vehicles(UAVs) under multiple ground threats like enemy's anti-air radar sites. The power of radar signal reflected by the vehicle and the flight time are considered in the performance cost to be minimized. The bank angle is regarded as control input for a 1st-order lag vehicle, and input parameter optimization method based on Sequential Quadratic Programming (SQP) is used for trajectory optimization. The proposed path planning method provides more practical trajectories with enhanced survivability than those of Voronoi diagram method.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2003.05a
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• pp.1205-1205
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• 2003

Rotary compressors are one of the most important parts of air-conditioners in the industry This device usually has noise problems during the circulation process of the refrigerant and muffler is used for the noise reduction. The acoustic performance of the muffler depends on its shape and its hole locations on the upper surface. Therefore finding the optimum location of the muffler holes is a topic of increasing importance in the compressor industry. In this research the optimization of the muffler hole locations and the importance of the resonator cavity on the lower surface of the muffler in acoustic point of view is studied. At first, the topology optimization for the 2 hole muffler is performed based on a model without resonator cavity by using genetic algorithm. The 2 hole muffler's acoustic analysis and experiment results are matching, however, the optimized model's results are not. By adding the resonator cavity and also by changing the cavity shape, the acoustic analysis and experiment result comparison is Performed for different cavity shapes. The topology optimization of the revised model with cavity is carried out for noise reduction. Finally, the optimized design is produced and tested for validation.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.5 no.3
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• pp.183-188
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• 2005

Although it is widely used to find an optimum setting of manufacturing process parameters in a variety of engineering fields, the Taguchi method has a difficulty in dealing with multi-response situations in which several response variables should be considered at the same time. For example, electrode wear, surface roughness, and material removal rate are important process response variables in an electrical discharge machining (EDM) process. A simultaneous optimization should be accomplished. Many researches from various disciplines have been conducted for such multi-response optimizations. One of them is a fuzzy logic approach presented by Lin et al. [1]. They showed that two response characteristics are converted into a single performance index based upon fuzzy logic. However, it is pointed out that information regarding relative importance of response variables is not considered in that method. In order to overcome this problem, a desirability function can be adopted, which frequently appears in the statistical literature. In this paper, we propose a novel approach for the multi-response optimization by incorporating fuzzy logic into desirability function. The present method is illustrated by an EDM data of Lin and Lin [2].

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.10a
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• pp.361-367
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• 1998

The main two applications of the Genetic Algorithms(GA) are the optimization and the machine learning. Machine Learning has two objectives that make the complex system learn its environment and produce the proper output of a system. The machine learning using the Genetic Algorithms is called GA machine learning or genetic-based machine learning (GBML). The machine learning is different from the optimization problems in finding the rule set. In optimization problems, the population of GA should converge into the best individual because optimization problems, the population of GA should converge into the best individual because their objective is the production of the individual near the optimal solution. On the contrary, the machine learning systems need to find the set of cooperative rules. There are two methods in GBML, Michigan method and Pittsburgh method. The former is that each rule is expressed with a string, the latter is that the set of rules is coded into a string. Th classifier system of Holland is the representative model of the Michigan method. The classifier systems arrange the strength of classifiers of classifier list using the message list. In this method, the real time process and on-line learning is possible because a set of rule is adjusted on-line. A classifier system has three major components: Performance system, apportionment of credit system, rule discovery system. In this paper, we solve the food search problem with the learning and evolution of an artificial ant using the learning classifier system.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2006.05a
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• pp.321-326
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• 2006

This paper is proposed an efficiency optimization control algorithm for a synchronous reluctance motor which minimizes the copper and iron losses. The design of the speed controller based on adaptive fuzzy-neural networks(AFNN) controller that is implemented using fuzzy control and neural networks. There exists a variety of combinations of d and q-axis current which provide a specific motor torque. The objective of the efficiency optimization controller is to seek a combination of d and q-axis current components, which provides minimum losses at a certain operating point in steady state. It is shown that the current components which directly govern the torque production have been very well regulated by the efficiency optimization control scheme. The proposed algorithm allows the electromagnetic losses in variable speed and torque drives to be reduced while keeping good torque control dynamics. The control performance of the hybrid artificial intelligent controller is evaluated by analysis for various operating conditions. Analysis results are presented to show the validity of the proposed algorithm.

• The KIPS Transactions:PartB
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• v.15B no.1
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• pp.53-60
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• 2008

This paper proposes a new multiobjective optimization method for discriminative training of hidden Markov models (HMMs) used as the recognizer for automatic lipreading. While the conventional Baum-Welch algorithm for training HMMs aims at maximizing the probability of the data of a class from the corresponding HMM, we define a new training criterion composed of two minimization objectives and develop a global optimization method of the criterion based on simulated annealing. The result of a speaker-dependent recognition experiment shows that the proposed method improves performance by the relative error reduction rate of about 8% in comparison to the Baum-Welch algorithm.

• Earthquakes and Structures
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• v.17 no.1
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• pp.63-73
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• 2019

This paper studies damage detection as an optimization problem. A new objective function based on changes in natural frequencies, and Natural Frequency Vector Assurance Criterion (NFVAC) was developed. Due to their easy and fast acquisition, natural frequencies were utilized to detect structural damages. Moreover, they are sensitive to stiffness reduction. The method presented here consists of two stages. Firstly, Finite Element Model (FEM) is updated. Secondly, damage severities and locations are determined. To minimize the proposed objective function, a new bio-inspired optimization algorithm called salp swarm was employed. Efficiency of the method presented here is validated by three experimental examples. The first example relates to three-story shear frame with two single damage cases in the first story. The second relates to a five-story shear frame with single and multiple damage cases in the first and third stories. The last one relates to a large-scale eight-story shear frame with minor damage case in the first and third stories. Moreover, the performance of Salp Swarm Algorithm (SSA) was compared with Particle Swarm Optimization (PSO). The results show that better accuracy is obtained using SSA than using PSO. The obtained results clearly indicate that the proposed method can be used to determine accurately and efficiently both damage location and severity in multi-story shear frames.