• The Journal of Korean Association of Computer Education
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• v.8 no.1
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• pp.73-80
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• 2005

To achieve a natural and plausible interaction with deformable objects and to setup the desirable initial conditions of simulation, user should be able to define and control the geometric constraints intuitively. In addition, user should be able to utilize the simulation as a problem solving platform by experimenting various simulation situations without major modification of the simulator. The proposed physically based geometric constraint simulation system solves the problem using a non-linear finite element method approach to represent deformable objects and constraint forces are generated by defining geometric constraints on the nodes of the object to maintain the restriction. It allows user to define and modify geometric constraints and an algorithm converts these geometric constraints into constraint forces which seamlessly integrate controllability to the simulation system. Simulator can handle linear, angular, inequality based geometric constraints on the objects. Our experimental results show that constraints are maintained in the tight error bound and preserve desired shape of deformable object during the entire simulation.

• International Journal of Control, Automation, and Systems
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• v.6 no.2
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• pp.160-170
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• 2008

Temperature control of an enclosed thermal system which has many applications including Rapid Thermal Processing (RTP) of semiconductor wafers showed an input-constraint violation for nonlinear controllers due to inherent strong coupling between the elements [1]. In this paper, a constrained nonlinear optimal control design is developed, which accommodates input constraints using the linear algebraic equivalence of the nonlinear controllers, for the temperature control of an enclosed thermal process. First, it will be shown that design of nonlinear controllers is equivalent to solving a set of linear algebraic equations-the linear algebraic equivalence of nonlinear controllers (LAENC). Then an input-constrained nonlinear optimal controller is designed based on that LAENC using the constrained linear least squares method. Through numerical simulations, it is demonstrated that the proposed controller achieves the equivalent performances to the classical nonlinear controllers with less total energy consumption. Moreover, it generates the practical control solution, in other words, control solutions do not violate the input-constraints.

• Journal of Institute of Control, Robotics and Systems
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• v.5 no.1
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• pp.11-15
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• 1999

In this paper, a robust pole placement controller for time invariant linear systems with polytopic uncertainties is presented. The proposed controller is a fixed order output feedback controller which stabilizes the uncertain systems and satisfies the constraints on the closed-loop pole location. The proposed controller can be obtained by minimizing a certain nonlinear object function subject to linear matrix inequality constraints. An algorithm for solving the nonlinear optimization problem is also proposed.

• Journal of Korean Society for Quality Management
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• v.20 no.1
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• pp.101-106
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• 1992

In this paper fuzzy goal programming problems with fuzzy constraints and fuzzy coefficients in both matrix and right hand side of the constraints set are considered. Because of fuzzy coefficients in both members of each constraint ranking methods for fuzzy numbers are considered. An additive model to solve fuzzy goal programming problems is formulated. The diversity of each methods provides a lot of different models of auxiliary linear programming problems from which fuzzy solutions to the fuzzy goal programming problem can be obtained.

• Proceedings of the KIEE Conference
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• 2001.07b
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• pp.590-592
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• 2001

We introduce a technique for designing homogeneous magnets using linear programming. The method has several advantages over existing techniques including: it allows complete flexibility in arbitrary geometric constraints on both the coil locations and the shape of the homogeneous volume; it guarantees a globally optimal solution, and it automatically choose the minimum number of coils necessary for the constraints.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.48 no.3
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• pp.1-5
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• 2011

In this thesis, we have developed novel geometrical analysis method for the double constraints linear constraint minimum variance (LCMV) beamforming algorithm. The proposed analysis method was matched well with the computer simulation result.

• Korean Management Science Review
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• v.25 no.1
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• pp.43-53
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• 2008

This paper considers a profit-based unit commitment problem with fuel consumption constraint and maintenance cost, which is one of the key decision problems in electricity industry. The nature of non-linearity inherent in the constraints and objective functions makes the problem intractable which have led many researches to focus on Lagrangian based heuristics. To solve the problem more effectively, we propose mixed integer programming based solution algorithm linearizing the complex non-linear constraints and objectives functions. The computational experiments using the real-world operation data taken from a domestic electricity power generator show that the proposed algorithm solves the given problem effectively.

• Journal of Korean Institute of Industrial Engineers
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• v.23 no.4
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• pp.661-667
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• 1997

We consider a generalized problem of the continuous multiple choice knapsack problem and study on the LP relaxation of the candidate problems which are generated in the branch and bound algorithm for solving the generalized problem. The LP relaxed candidate problem is called the generalized continuous multiple choice linear knapsack problem and characterized by some variables which are partitioned into continuous multiple choice constraints and the others which only belong to simple upper bound constraints. An efficient algorithm of order O($n^2logn$) is developed by exploiting some structural properties and applying binary search to ordered solution sets, where n is the total number of variables. A numerical example is presented.

• Journal of the Korean Statistical Society
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• v.29 no.3
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• pp.361-373
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• 2000

A simple and efficient algorithm is introduced for generalized least squares estimation under nonnegativity constraints in the components of the parameter vector. This algorithm gives the exact solution to the estimation problem within a finite number of pivot operations. Besides an illustrative example, an empirical study is conducted for investigating the performance of the proposed algorithm. This study indicates that most of problems are solved in a few iterations, and the number of iterations required for optimal solution increases linearly to the size of the problem. Finally, we will discuss the applicability of the proposed algorithm extensively to the estimation problem having a more general set of linear inequality constraints.

• Journal of Institute of Control, Robotics and Systems
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• v.4 no.5
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• pp.592-599
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• 1998

In this paper, we design a tracking controller which satisfies transient response specifications and maintains tracking error within a tolerable limit for the uncertain track-following system of an optical disk drive. To this end, a robust $H_{\infty}$ control problem with regional stability constraints and sinusoidal disturbance rejection is considered. The internal model principle is used for rejecting the sinusoidal disturbance caused by eccentric rotation of the disk. We show that a condition satisfying the regional stability constraints can be expressed in terms of a linear matrix inequality (LMI) using the Lyapunov theory and S-procedure. Finally, a tracking controller is obtained by solving an LMI optimization problem involving two linear matrix inequalities. The proposed controller design method is evaluated through an experiment.