• Industrial Engineering and Management Systems
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• v.2 no.1
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• pp.9-27
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• 2003

In a wafer fabrication factory, the completion time of an order is affected by many factors related to the specifics of the order and the status of the system, so is difficult to predict precisely. The level of influence of each factor on the order completion time may also depend on the production system characteristics, such as the rules for releasing and dispatching. This paper presents a method to identify those factors that significantly impact upon the order completion time under various combinations of scheduling rules. Computer simulations and statistical analyses were used to develop effective due date assignment models for improving the due date related performances. The first step of this research was to select the releasing and dispatching rules from those that were cited so frequently in related wafer fabrication factory researches. Simulation and statistical analyses were combined to identify the critical factors for predicting order completion time under various combinations of scheduling rules. In each combination of scheduling rules, two efficient due date assignment models were established by using the regression method for accurately predicting the order due date. Two due date assignment models, called the significant factor prediction model (SFM) and the key factor prediction model (KFM), are proposed to empirically compare the due date assignment rules widely used in practice. The simulation results indicate that SFM and KFM are superior to the other due date assignment rules. The releasing rule, dispatching rule and due date assignment rule have significant impacts on the due date related performances, with larger improvements coming from due date assignment and dispatching rules than from releasing rules.

• Journal of the Institute of Convergence Signal Processing
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• v.8 no.3
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• pp.222-226
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• 2007

In this paper we composed the dual fuzzy rules using each region of specific points and $e-{\Delta}e$ phase plane In order to make dual fuzzy rule base. We composed the fuzzy control rules which can decrease rise time, delay time, maximum overshoot than basic fuzzy control rules. proposed method is alternately use at specific points of $e-{\Delta}e$ phase plane with two fuzzy control rules that is one control rule occruing the steady state error in transient region and another fuzzy control rule use to decrease the steady state error and rapidly converge at the convergence region. Also, two fuzzy control rules in the $e-{\Delta}e$ phase plane decide the change time according to response characteristics of plants. In order to confirm thef proposed algorithm. As the results of experiments through the hydraulic servo motor control system with a DSP processor, We verified that proposed dual fuzzy control rules get the good response compare with the basic fuzzy control rule.

• Transactions of The Korea Fluid Power Systems Society
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• v.1 no.1
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• pp.1-9
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• 2004

This paper addresses an approximation and tracking control of recurrent neural networks(RNN) using two-dimensional iterative learning algorithm for an electro-hydraulic servo system. And two dimensional learning rule is driven in the discrete system which consists of nonlinear output function and linear input. In order to control the trajectory of position, two RNN's with the same network architecture were used. Simulation results show that two RNN's using 2-D learning algorithm are able to approximate the plant output and desired trajectory to a very high degree of a accuracy respectively and the control algorithm using two same RNN was very effective to control trajectory tracking of electro-hydraulic servo system.

• Journal of Mechanical Science and Technology
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• v.19 no.3
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• pp.743-755
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• 2005

Human Sensibility Engineering System (HSES) has been applied to product development for customer's satisfaction based on ergonomic technology. The system is composed of three parts such as human sensibility analysis, inference mechanism, and presentation technologies. Inference mechanism translating human sensibility into design elements plays an important role in the HSES. In this paper, we propose a rule-based inference model for HSES. The rule-based inference model is composed of five rules and two inference approaches. Each of these rules reasons the design elements for selected human sensibility words with the decision variables from regression analysis in terms of forward inference. These results are evaluated by means of backward inference. By comparing the evaluation results, the inference model decides on product design elements which are closer to the customer's feeling and emotion. Finally, simulation results are tested statistically in order to ascertain the validity of the model.

• Proceedings of the Korea Society for Simulation Conference
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• 2003.11a
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• pp.51-57
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• 2003

In operating distributed systems, proactive management is one of the major concerns for better quality of service and future capacity planning. In order to handle this management problem effectively, it is necessary to analyze performances of the distributed system and events generated by components in the system. This paper provides a rule-based event parsing engine for proactive management. Our event parsing engine uses object hooking-based and event-token approaches. The object hooking-based approach prepares new conditions and actions in Java classes and allows dynamically exchange them as hook objects in run time. The event-token approach allows the event parsing engine consider a proper sequence and relationship among events as an event token to trigger an action. We analyze the performance of our event parsing engine with two different implementations of rule structure; one is table-based and the other is tree-based.

• Proceedings of the Korea Society of Information Technology Applications Conference
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• 2005.11a
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• pp.259-262
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• 2005

This paper addresses the problem of protecting security policies in security mechanisms, such as the detection policy of an Intrusion Detection System. Unauthorized disclosure of such information might reveal the fundamental principles and methods for the protection of the whole network. In order to avoid this risk, we suggest two schemes for protecting security policies in Snort using the symmetric cryptosystem, Triple-DES.

• Structural Engineering and Mechanics
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• v.23 no.1
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• pp.29-42
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• 2006

This paper describes the design scheme of the three-dimensional structures based on the concept of the cellular automata simulation. The cellular automata simulation is performed according to the local rule. In this paper, the local rule is derived in the mathematical formulation from the optimization problem. The cell density is taken as the design variable. Two objective functions are defined for reducing the total weight of the structure and obtaining the fully stressed structure. The constraint condition is defined for defining the local rule. The penalty function is defined from the objective functions and the constraint condition. Minimization of the penalty function with respect to the design parameter leads to the local rule. The derived rule is applied to the design of the three-dimensional structure first. The final structure can be obtained successfully. However, the computational cost is expensive. So, in order to reduce the computational cost, the material parameters $c_1$ and $c_2$ and the value of the cell rejection criterion (CRC) are changed. The results show that the computational cost depends on the parameters and the CRC value.

• Journal of Korean Institute of Industrial Engineers
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• v.23 no.4
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• pp.699-708
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• 1997

Production control is usually composed of due-dote assignment, production input control, and priority dispatching rule. A production input control(PIC) is mainly to control the WIP level on the shop floor. On the other hand, a priority dispatching rule(PDR) is mainly to control the tardiness/earliness of on order and number of tardy jobs. Therefore, if we select a particular PIC which can control only a particular performance measure(i.e., tardiness), it may cause worsening other performance measure(i.e., WIP level, shopfloor time, etc.) This newly developed production input control, DRD(Dual Release-Dates), is mainly designed to control the WIP level on the shop floor by employing two different release-dates of an order(earliest release. date and latest release-date and the release condition (relationship between the current WIP level and the pre-defined maximum WIP level) while trying to meet the due-date of the order.

• Industrial Engineering and Management Systems
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• v.14 no.1
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• pp.22-31
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• 2015

In this paper, a two-stage hybrid flow shop problem is considered. Specifically, there exist identical parallel machines at stage 1 and two dedicated machines at stage 2, and the objective of the problem is to minimize makespan. After being processed by any machine at stage 1, a job must be processed by a specific machine at stage 2 depending on the job type, and one type of jobs can have different processing times on each machine. First, we introduce the problem and establish complexity of several variations of the problem. For some special cases, we develop optimal polynomial time solution procedures. Then, we establish some simple lower bounds for the problem. In order to solve this NP-hard problem, three heuristics based on simple rules such as the Johnson's rule and the LPT (Longest Processing Time first) rule are developed. For each of the heuristics, we provide some theoretical analysis and find some worst case bound on relative error. Finally, we empirically evaluate the heuristics.

• Journal of the Korean Society for Precision Engineering
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• v.20 no.6
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• pp.62-70
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• 2003

This paper deals with a approximation and tracking control of hydraulic servo system using a real time recurrent neural networks (RTRN) with 2-dimensional iterative learning rule. And it was driven that 2-dimensional iterative learning rule in discrete time. In order to control the trajectory of position, two RTRN with same network architecture were used. Simulation results show that two RTRN using 2-D learning algorithm is able to approximate the plant output and desired trajectory to a very high degree of a accuracy respectively and the control algorithm using two same RTRN was very effective to control trajectory tracking of electro-hydraulic servo system.