• Journal of the Korean Mathematical Society
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• v.39 no.4
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• pp.509-524
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• 2002

We Study the Problems Of identification for the damped sine-Gordon equations with constant parameters. That is, we establish the existence and necessary conditions for the optimal constant parameters based on the fundamental optimal control theory and the transposition method studied in Lions and Magenes [5].

• East Asian mathematical journal
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• v.1
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• pp.101-112
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• 1985

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.49 no.7
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• pp.354-362
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• 2000

This paper presents the new adaptive optimal scheme for the nonlinear systems, which is based on the Picard's iterative approximation and fast Walsh transform. It is well known that the Walsh function approach method is very difficult to apply for the analysis and optimal control of nonlinear systems. However, these problems can be easily solved by the improvement of the previous adaptive optimal scheme. The proposed method is easily applicable to the analysis and optimal control of nonlinear systems.

• International Journal of Automotive Technology
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• v.8 no.5
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• pp.563-573
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• 2007

Optimal design of an automotive catalytic converter for minimization of cold-start emissions is numerically performed using a micro genetic algorithm for two optimization problems: optimal geometry design of the monolith for various operating conditions and optimal axial catalyst distribution. The optimal design process considered in this study consists of three modules: analysis, optimization, and control. The analysis module is used to evaluate the objective functions with a one-dimensional single channel model and the Romberg integration method. It obtains new design variables from the control module, produces the CO cumulative emissions and the integral value of a catalyst distribution function over the monolith volume, and provides objective function values to the control module. The optimal design variables for minimizing the objective functions are determined by the optimization module using a micro genetic algorithm. The control module manages the optimal design process that mainly takes place in both the analysis and optimization modules.

• Management Science and Financial Engineering
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• v.3 no.1
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• pp.75-88
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• 1997

Tabu search (TS) has been successfully applied for solving many complex combinatorial optimization problems in the areas of operations research and production control. However, TS is for single-objective problems in its present form. In this article, a TS-based heuristic is developed to determine Pareto-efficient solutions to a multi-objective combinatorial optimization problem. The developed algorithm is then applied to the discrete optimal design problem in statistics to demonstrate its usefulness.

• 제어로봇시스템학회:학술대회논문집
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• 1987.10b
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• pp.464-469
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• 1987

The Kalman filter is an optimal linear estimator that has been an active research topic for the past three decades. The scheme has become the milestone of modern filtering, and it is applied to many areas including navigations and controls of free vehicle. The Kalman filter technique is matured. But some problems are still remained to be resolved. The prevention of divergence induced by digital implementation, nonoptimal application for nonlinear system, and application to non-Gaussian processes are some of the problems. This paper surveys the problems. The square root filtering is suggested to prevent the divergence. The extended Kalman filter is used for nonlinear systems. And, many other approaches to Kalman-like optimal estimators are also investigated.

• Bulletin of the Korean Mathematical Society
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• v.51 no.1
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• pp.139-156
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• 2014

In this paper, we investigate the error estimates of a quadratic elliptic control problem with pointwise control constraints. The state and the co-state variables are approximated by the order k = 1 Raviart-Thomas mixed finite element and the control variable is discretized by piecewise linear but discontinuous functions. Approximations of order $h^{\frac{3}{2}}$ in the $L^2$-norm and order h in the $L^{\infty}$-norm for the control variable are proved.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.14 no.2
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• pp.73-83
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• 2014

Many recent theoretical developments in the field of machine learning and control have rapidly expanded its relevance to a wide variety of applications. In particular, a variety of portfolio optimization problems have recently been considered as a promising application domain for machine learning and control methods. In highly uncertain and stochastic environments, portfolio optimization can be formulated as optimal decision-making problems, and for these types of problems, approaches based on probabilistic machine learning and control methods are particularly pertinent. In this paper, we consider probabilistic machine learning and control based solutions to a couple of portfolio optimization problems. Simulation results show that these solutions work well when applied to real financial market data.

• Journal of Institute of Control, Robotics and Systems
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• v.3 no.1
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• pp.46-51
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• 1997

The optimal control of a continuous process has been performed using genetic algorithms(GAs). GAs are robust and easily applicable for complex and highly nonlinear problems. We introduce the heuristics 'dynamic range' which reduces the search space dramaticaly keeping the robust search of GAs. GAs with dynamic range show the better performance than SQP(Successive Quadratic Programing) method which converges to a local minimum. The proposed methology has been applied to the optimal control of the continuous MMA-VA copolymerization reactor for the production of the desired molecular wieght and the composition of VA in dead copolymer.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.50 no.10
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• pp.464-472
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• 2001

This study uses distributed parameter systems as the spatial discretization technique, modelling in lumped parameter systems, and applies fast WALSH transform and the Picard's iteration method to high order partial differential equations and matrix partial differential equations. This thesis presents a new algorithm which usefully exercises the optimal control in the distributed parameter systems. In exercising optimal control of distributed parameter systems, excellent consequences are found without using the existing decentralized control or hierarchical control method. This study will help apply to linear time-varying systems and non-linear systems. Further research on algorithm will be required to solve the problems of convergence in case of numerous applicable intervals.