• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.295-298
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• 1996

In this paper, a self-tuning optimal control algorithm is proposed to retain the optimal performance of an active suspension system, when the vehicle has some time varying parameters and parameter uncertainties. We consider a 2 DOF time-varying quarter car model which has the parameter variation of sprung mass, suspension spring constant and suspension damping constant. Instead of solving algebraic riccati equation on line, we propose a neural network approach as an alternative. The optimal feedback gains obtained from the off line computation, according to parameter variations, are used as the neural network training data. When the active suspension system is on, the parameters are identified by the recursive least square method and the trained neural network controller designer finds the proper optimal feedback gains. The simulation results are represented and discussed.

• Journal of Electrical Engineering and Technology
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• v.1 no.1
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• pp.8-15
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• 2006

Since the power swing depends on the operating time of the relay, the swing's magnitude can be reduced by an autoreclosure relaying system with an optimal reclosing algorithm. This paper proposes a method for stability improvement using optimal reclosure relaying. An optimal reclosure algorithm is applied to identify both temporary and permanent faults, and to predict system stability by applying WAM and high speed communication technology. It provides optimal control by predicting and determining the degree of stability, considering the real time transient stability using EEEAC. For temporary faults, the algorithm determines the system's stability and either recloses optimally for stable systems, or inserts series capacitance before optimal reclosure for unstable systems. It also applies an optimal reclosure algorithm to minimize shock and damage to the power system when reclosure fails due to permanent faults.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1999.10a
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• pp.345-350
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• 1999

The effectiveness of software error compensation for thermally induced machine tool errors relies on the prediction accuracy of the pre-established thermal error models. The selection of optimal sensor locations is the most important in establishing these empirical models. In this paper, a methodology for the selection of optimal sensor locations is proposed to establish a robust linear model which is not subjected to collinearity. Correlation coefficient and time delay are used as thermal parameters for optimal sensor location. Firstly, thermal deformation and temperatures are measured with machine tools being excited by sinusoidal heat input. And then, after correlation coefficient and time delays are calculated from the measured data, the optimal sensor location is selected through hard c-means clustering and sequential selection method. The validity of the proposed methodology is verified through the estimation of thermal expansion along Z-axis by spindle rotation.

• Journal of the Korean Operations Research and Management Science Society
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• v.41 no.1
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• pp.87-98
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• 2016

A single machine scheduling problem for jobs with stochastic processing time is considered in this study. Shortest processing time (SPT) sequencing according to the expected processing times of jobs is optimal for schedules with minimal expected mean flow time when all the jobs arrive to be scheduled and their expected processing times are known. However, SPT sequencing according to the expected processing time may not be optimal for the minimization of the mean flow time when the actual processing times of jobs are known. This study evaluates the complexity of SPT sequencing through a comparison of the mean flow times of schedules based on the expected processing times and actual processing times of randomly generated jobs. Evaluation results show that SPT sequencing according to the expected flow time exhibits chaotic variation to the optimal mean flow time. The relative deviation from the optimal mean flow time increases as the number of jobs, processing time, or coefficient of variation increases.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.6
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• pp.1122-1129
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• 2007

For a system with multiple real-time tasks of different deadlines, it is very difficult to find the optimal checkpoint interval because of the complexity in considering the scheduling of tasks. In this paper, we determine the optimal checkpoint interval for multiple real-time tasks that are scheduled by RM(Rate Monotonic) algorithm. Faults are assumed to occur with Poisson distribution. Checkpoints are inserted in the execution of task with equal distance in the same task, but different distances in other tasks. When faults occur, rollback to the latest checkpoint and re-execute task after the checkpoint. We derive the equation of maximum slack time for each task, and determine the number of re-executable checkpoint intervals for fault recovery. The equation to check the schedulibility of tasks is also derived. Based on these equations, we find the probability of all tasks executed within their deadlines successfully. Checkpoint intervals which make the probability maximum is the optimal.

• Journal of the Korean Operations Research and Management Science Society
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• v.20 no.1
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• pp.115-130
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• 1995

This paper considers a single machine nonpreemptive scheduling problem with a given common due date. In the problem, the optimal job sequence is sought to minimize the sum of earliness/tardiness and flow time measures in the situation where all jobs are available at time zero, and weights per unit length of earliness/tardiness and flow time are V and W, respectively. Some dominant solution properties are characterized to deriva both an optimal starting time for an arbitrary sequence and sequence improvement rules. The optimal schedule is found to the case W .geq. V/. By the way, it is difficult to find the optimal schedule for the case W < V. Therefore, the derived properties are put on together to construct a heuristic solution algorithm for the case W < V, and its effectiveness is rated at the mean relative error of about 3% on randomly generated numerical problems.

• Journal of the Korea Institute of Military Science and Technology
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• v.3 no.1
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• pp.26-37
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• 2000

This research paper deals with the problem of determining the optimal life time in terms of economical sense for a self-propelled artillery. Equivalent Annual Cost Method(EACM) is used to evaluate the optimal life time, based on the acquisition cost, and the operation and maintenance cost. It is assumed that the operation and maintenance cost includes the costs for spare parts, petroleum and ammunition for training. From the result of this study, the optimal life time for a self-propelled artillery is between 13.9 years and 16.1 years with 95% confidence interval.

• Journal of the Korean Society for Precision Engineering
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• v.14 no.6
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• pp.90-98
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• 1997

A time-optimal control law for quick, strongly nonlinear systems has been developed and demonstrated. This procedure involves the utilzation of neural networks as state feedback controllers that learn the time-optimal control actions by means of an iterative minimization of both the final time and the final state error for the systems with constrained inputs and/or states. A neural identifier or a genetic algorithm identifier could be utilized for modeling the partially known systems and the unknown systems. The nature of neural networks as a parallel processor would circumvent the problem of "curwe of dimensionality". The control law has been demonstrated for both a torque input motor and a velocity input motor identified by a genetic algorithm called GENOCOPed GENOCOP.

• Kyungpook Mathematical Journal
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• v.58 no.2
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• pp.333-346
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• 2018

An approximation scheme utilizing Bezier curves is considered for solving time-delayed optimal control problems with terminal inequality constraints. First, the problem is transformed, using a $P{\acute{a}}de$ approximation, to one without a time-delayed argument. Terminal inequality constraints, if they exist, are converted to equality constraints. A computational method based on Bezier curves in the time domain is then proposed for solving the obtained non-delay optimal control problem. Numerical examples are introduced to verify the efficiency and accuracy of the proposed technique. The findings demonstrate that the proposed method is accurate and easy to implement.

• Journal of the military operations research society of Korea
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• v.24 no.1
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• pp.176-188
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• 1998

In this paper, it is identified that the optimal level of each performance measures (service and inventory level) is not sensitive to an uncertain environment under JIT Kanban system designed by Moeeni. Moreover, it is proposed that the optimal design method considering multiple performance characteristics is the optimal level decision method according to the relative importance differences of each performance when there exist multiple performance characteristics. The result from the simulation analysis shows that the number of Kanban for stage 3 (final process) and stage 2 is increased at the service level. It is found that the expected loss is minimal when the cycle time decreases and the container size increases. However, the stage 1 is not affected by the number and cycle time of Kanban. It is thus important to consider carefully the cycle time and the container size of the Kanban to satisfy the demand in right time. In case of inventory level, the working inventory level decreases when the container size is decreased and the working inventory level also decreases slightly when the cycle time of the Kankban is increased in stage 1 and 2.