• Journal of Biosystems Engineering
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• v.35 no.5
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• pp.287-293
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• 2010

In order to optimize an agricultural tractor clutch mechanism system, its structural static and kinematic mechanism were analyzed. The operating force of the mechanical tractor clutch system is currently not appropriate to drive comfortably. So it is needed to reduce the clutch operating force by applying advanced engineering design techniques. In the present study, an optimization technology is applied to the design of tractor clutch systems to reduce the operating force. As a result of the optimization using 2 link-angles and 1 link-length which are the main design variables of the clutch linkage system, the maximum pushing force of the maximum clutch pedal was found 182.8N, 14% decreased compared to the existing clutch system. The effectiveness of the optimum design is certified by menas of an experiment.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.16 no.3 s.108
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• pp.298-306
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• 2006

In our previous paper, we developed a robust saturation controller for the linear time-invariant (LTI) system involving both actuator's saturation and structured real parameter uncertainties. This controller can only guarantee the closed-loop robust stability of the system in the presence of actuator's saturation. But we cannot analytically make any comment on control performance of this controller. In this paper, we suggest a method to use linear matrix inequality (LMI) optimization problem which can analytically explain control performance of this robust saturation controller only in nominal system. The availability of design method using LMI optimization problem for this robust saturation controller is verified through a numerical example for the building with an active mass damper (AMD) system.

• Journal of the Korean Society for Precision Engineering
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• v.6 no.4
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• pp.71-83
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• 1989

The optimization in the machining process has been a long-standing goal of the manufacturing community. The optimization is composed of two main subjects;one is to select an optimum cutting condition, and the other is to detect the emergency situation and take necessary actions in real-time base. This paper proposes a reliable and practical guide system whose purpose is the optimization of cutting conditions, and the detection of tool failure in the machining process. The optimal cutting conditions are determined through the estimation of tool wear rate and the establishment of access- ible field from the measured cutting temperature and force. Tool breakage is detected by the normal force component acting on minor flank face extracted from on-line sensed feed force and radial force. In experiments, the proposed guide system has proved availability for the decision of reliable cutting conditions for the given tool-work system and the detection of tool breakage in ordinary cutting environments.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2002.05a
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• pp.399-404
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• 2002

Analyzing acoustic-structural systems such as automobiles and aircraft the FRF-based substructuring method is one of the most powerful tools. In this paper, an optimization procedure far the engine mount system of passenger car has been presented using the design sensitivity analysis based on the multi-domain FRF-based substructuring formulation. The proposed method is applied to an optimization problem of the engine mount system, of which objective is to minimize the interior sound over the concerned rpm range. The design variables selected are the stiffnesses of the engine mounts and bushes. Plugging the gradient information calculated by the proposed method into nonlinear optimization software, we can obtain the optimal stiffnesses of the engine mounts and bushings through design iterations. The optimized interior noise in the passenger car shows that the proposed method is very useful in the realistic situation.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2001.06a
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• pp.399-408
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• 2001

This paper presents a simultaneous optimization approach of case-based reasoning (CBR) using a genetic algorithm(GA) for the prediction of stock price index. Prior research suggested many hybrid models of CBR and the GA for selecting a relevant feature subset or optimizing feature weights. Most studies, however, used the GA for improving only a part of architectural factors for the CBR system. However, the performance of CBR may be enhanced when these factors are simultaneously considered. In this study, the GA simultaneously optimizes multiple factors of the CBR system. Experimental results show that a GA approach to simultaneous optimization of CBR outperforms other conventional approaches for the prediction of stock price index.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.26 no.8
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• pp.357-365
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• 2014

This paper proposed a multi-objective optimization method for building energy system design using primary energy consumption and initial cost. The designing of building energy systems is a complex task, because life cycle cost and efficiency of building are determined by decisions of engineer during the early stage of design. Therefore, methods such as pareto analysis that can generate various alternatives for decision making are necessary. In this study, the optimization is performed using the NSGAII and case study was carried out for feasibility of the proposed method. As a result, alternative solutions can be obtained for the optimal building energy system design.

• Nuclear Engineering and Technology
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• v.42 no.4
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• pp.414-425
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• 2010

In this paper, a procedure is developed for identifying a number of representative solutions manageable for decision-making in a multiobjective optimization problem concerning the test intervals of the components of a safety system of a nuclear power plant. Pareto Front solutions are identified by a genetic algorithm and then clustered by subtractive clustering into "families". On the basis of the decision maker's preferences, each family is then synthetically represented by a "head of the family" solution. This is done by introducing a scoring system that ranks the solutions with respect to the different objectives: a fuzzy preference assignment is employed to this purpose. Level Diagrams are then used to represent, analyze and interpret the Pareto Fronts reduced to the head-of-the-family solutions.

• 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.

• 한국전산유체공학회:학술대회논문집
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• 2006.10a
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• pp.28-29
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• 2006

Computational approaches for the aerodynamic design and optimization are introduced. In this paper the aerodynamic design methods and applications, which have been applied to various aerospace vehicles at Konkuk University, are introduced. It is shown that system approximation technique reduces computational cost for CFD analysis and improves efficiency for the design optimization process.

• Journal of Korean Institute of Industrial Engineers
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• v.39 no.2
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• pp.90-98
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• 2013

The k-out-of-n system with mixed redundancy is defined as k-out-of-n system which both includes warm-standby and cold-standby components. In case that operating components in the system fail and the system needs quick transition of standby components to operation state, the k-out-of-n system with mixed redundancy is useful for decreasing system failure rate and operational cost. Reliability-Redundancy Optimization Problem (RROP) involves selection of components with multiple choices and redundancy levels for maximizing system reliability with constraints such as cost, weight, etc. A solution methodology by using harmony search algorithm for RROP of the k-out-of-n system with mixed redundancy to maximize system reliability was suggested in this study.