• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2004.04a
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• pp.563-566
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• 2004

An intelligent Kalman filter (IKF) is proposed for tracking an incoming anti-ship missile. In the proposed IKF, the unknown target acceleration is regarded as an additive process noise. When the target maneuver is occurred, the residual of the Kalman filter increases in proportion to its magnitude. From this fact, the overall process noise variance can be approximated from the filter residual and its variation at every sampling time. A fuzzy system is utilized to approximate this valiance, and the genetic algorithm (GA) is applied to optimize the fuzzy system. In computer simulations, the tracking performance of the proposed IKF is compared with those of conventional maneuvering target tracking methods.

• Proceedings of the KSME Conference
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• 2003.04a
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• pp.1124-1129
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• 2003

In mechanical design, design process is mainly composed of design, explanation and evaluation. In this paper, Using Genetic Algorithms (GA), Evolutionary computation is introduced as new design process. This method promote the efficiency and power of design. Due to the known characteristics of the stage, the approach basically involves a synthetic design method with the composition of building blocks representing the elements of mechanical objects. In order for the building blocks to be more suitable for representation and evolution of mechanical structures, Elementary Cell Blocks (ECBs) are introduced as new building blocks. In this paper, we have demonstrated the implementation of the approach with the design of gear systems.

• Journal of Advanced Marine Engineering and Technology
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• v.30 no.6
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• pp.753-759
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• 2006

Integrating processes are frequently encountered in process industries. In this paper, new tuning formulae of the PID controllers for set-point tracking and load disturbance rejection are presented for integrating processes involving time delay. First, the controller parameter sets are tuned using a real-coded genetic algorithm (RCGA) such that performance criterion(IAE, ISE or ITSE) is minimized. Then, tuning rules are addressed using tuned PID parameter sets. tuning model and another RCGA. The performances of the proposed rules are tested on two processes.

• Journal of Mechanical Science and Technology
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• v.15 no.8
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• pp.1132-1142
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• 2001

The paper describes the construction of global function approximation models for use in design optimization via global search techniques such as genetic algorithms. Two different approximation methods referred to as evolutionary fuzzy modeling (EFM) and neuro-fuzzy modeling (NFM) are implemented in the context of global approximate optimization. EFM and NFM are based on soft computing paradigms utilizing fuzzy systems, neural networks and evolutionary computing techniques. Such approximation methods may have their promising characteristics in a case where the training data is not sufficiently provided or uncertain information may be included in design process. Fuzzy inference system is the central system for of identifying the input/output relationship in both methods. The paper introduces the general procedures including fuzzy rule generation, membership function selection and inference process for EFM and NFM, and presents their generalization capabilities in terms of a number of fuzzy rules and training data with application to a three-bar truss optimization.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.32B no.8
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• pp.1119-1126
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• 1995

The construction of rule-base for a nonlinear time-varying system, becomes much more complicated because of model uncertainty and parameter variations. Furthemore, FLC does not have an ability of adjusting rule- base in responding to some sudden changes of control environments. To cope with these problems, an auto-tuning method of the fuzzy rule-base is required. In this paper, the GA-based Fuzzy-Neural control system combining Fuzzy-Neural control theory with the genetic algorithm(GA), which is known to be very effective in the optimization problem, will be proposed. The tuning of the proposed system is performed by two tuning processes(the course tuning process and the fine tuning/adaptive learning process). The effectiveness of the proposed control system will be demonstrated by computer simulations using a two degree of freedom robot manipulator.

• Journal of Korean Institute of Industrial Engineers
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• v.37 no.4
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• pp.342-349
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• 2011

This paper considers the inbound and outbound truck scheduling problem in a cross docking terminal. The unloading process from inbound trucks and loading process to outbound trucks are assumed to be performed within a time window. If some items are not able to be loaded to their scheduled outbound trucks within the time window, they are stored in the terminal and shipped using the truck visiting the next time window. The objective of this paper is to schedule inbound and outbound trucks to minimize the number of items unable to ship within the time window. A mathematical model for an optimal solution is derived, and a rule-based local search heuristic algorithm and genetic algorithm (GA) are proposed. The performance of the algorithms are evaluated using randomly generated several examples.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2006.05a
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• pp.563-564
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• 2006

In silicon wafer manufacturing process, the grinding process has been adopted to improve the flatness of water. The grinding of wafer is usually used by the infeed grinding machine. Grinding conditions are spindle speed, feed speed, rotation speed, grinding stone etc. But grinding condition selection and analysis is so difficult in grinding machine. In the intelligent grinding system based on knowledge many researchers have studied expert system, neural network, fuzzy etc. In this paper we deal grinding condition selection method, Taguchi method and Genetic Analysis.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1996.10a
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• pp.302-305
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• 1996

In this paper, an optimal identification method using fuzzy-neural networks is proposed for modeling of nonlinear complex systems. The proposed fuzzy-neural modeling implements system structure and parameter identification using the intelligent schemes together with optimization theory, linguistic fuzzy implication rules, and neural networks(NNs) from input and output data of processes. Inference type for this fuzzy-neural modeling is presented as simplified inference. To obtain optimal model, the learning rates and momentum coefficients of fuzz-neural networks(FNNs) and parameters of membership function are tuned using genetic algorithm(GAs). For the purpose of its application to nonlinear processes, data for route choice of traffic problems and those for activated sludge process of sewage treatment system are used for the purpose of evaluating the performance of the proposed fuzzy-neural network modeling. The show that the proposed method can produce the intelligence model w th higher accuracy than other works achieved previously.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.5 no.3
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• pp.80-85
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• 2006

In an effort to reduce the evaluation time of the precision of manufactured disk cams, an effective measuring method with an exclusively built profile-measuring machine and subsequent data analysis procedure is proposed. The design and measuring data are interpolated by cubic spline curves to compute the precision error which is defined by the maximum and minimum distances between two curves. The minimum zone criterion of ISO is employed to evaluate the form error, and genetic algorithm is used to search the orientation and location of design data for the measured data which minimizes the form error. The proposed system was applied to marine engine cams, and it shows that the form error is reduced to 30% down compared with the method which minimizes the form error with the assumption that the centers of measured data design cam curve are identical.

• Journal of Magnetics
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• v.16 no.3
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• pp.268-270
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• 2011

This study introduces a new topology optimization scheme combing the genetic algorithm (GA) with the on/off sensitivity method for the magnetic actuator core and the armature design. The design process intended to maximize the first eigen-frequency of the armature part and the magnetic actuating force acting on the armature simultaneously. GA based optimal design was carried out to obtain the initial structure and the modified on/off sensitivity method was succeeded to accelerate the design process. Final results show tens of percent improvement in actuating force as well as the first eigen-frequency of the armature.