• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.23 no.11
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• pp.951-956
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• 2013

This paper describes techniques used to analyze the sweet spot of sound field reproduced by ear-level linear arrays of loudspeakers by geometrical approach method. Previous researches have introduced various sweet spot definitions in their own way. In general, sweet spot is defined as an area whose stereophonic sound effect is valid. Its size is affected by the geometrical arrangement of the system. In this paper, a case when plane waves are generated by linear arrays of loudspeakers in the horizontal plane is considered. So the sweet spot is defined as an area in which the listener can perceive the desired azimuth angle. Because there are many loudspeakers, impulse responses at listener's ears are in the form of pulse-train and the time-duration of the pulse-train affects the localization performance of the listener. So we calculated the maximum time duration of pulse-train by geometrical approach method and identified with the results of impulse response simulation. This paper also includes parameter analysis with respect to aperture size, so it suggests a tool for sound engineers to expect the sweet spot size and listener's sound perception.

• Transactions on Control, Automation and Systems Engineering
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• v.2 no.1
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• pp.31-39
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• 2000

We present a robust and reliable H$\infty$ state-feedback controller design for linear uncertain systems, which have norm-bounded time-varying uncertainty in the state matrix, and their prespecified sets of actuators are susceptible to failure. These controllers should guarantee robust stability of the systems and H$\infty$ norm bound against parameter uncertainty and/or actuator failures. Based on the linear matrix inequality (LMI) approach, two state-feedback controller design methods are constructed by formulating to a set of LMIs corresponding to all failure cases or a single LMI that covers all failure cases, with an additional costraint. Effectiveness and geometrical property of these controllers are validated via several numerical examples. Furthermore, the proposed LMI frameworks can be applied to multiobjective problems with additional constraints.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.11
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• pp.1048-1052
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• 2007

This paper presents a rank-constrained linear matrix inequality(LMI) approach to simultaneous linear-quadratic(LQ) optimal control by static output feedback. Simultaneous LQ optimal control is formulated as an LMI optimization problem with a nonconvex rank condition. An iterative penalty method recently developed is applied to solve this rank-constrained LMI optimization problem. Numerical experiments are performed to illustrate the proposed method, and the results are compared with those of previous work.

• Journal of the Korean Operations Research and Management Science Society
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• v.19 no.3
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• pp.111-129
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• 1994

In this study, we develope a revised model as well as application of decision problem under ambiguity based on the subjectively weighted linear utility medel. Bayes'rule is used when there are ambiguous probabilities on a decision problem and test information is available. A procedure for assessing the ambiguity aversion function is also presented. Decision problem of chemical corporation is used for an illustration of the application of the subjectively weighted linear utility model using Bayesian approach. We present the optimal decisiond using newly developed model. We also perform the sensitivity analysis to assure ourselves about the conclusion we obtianed on degree of ambiguity aversion due to characterize parameter of subjectively weighted linear utility model.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1993.10a
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• pp.118-118
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• 1993

We consider a discrete event dynamic system called periodic job shop, where an identical mixture of items called minimal part set(MPS) is repetitively produced in the same processing order and the primary performance measure is the cycle time. The precedence relationships among events(starts of operations) are represented by a directed graph with rocurront otructure. When each operation starts as soon as all its preceding operations complete(called earliest starting), the occurrences of events are modeled in a linear system using a special algebra called minimax algebra. By investigating the eigenvalues and the eigenvectors, we develop conditions on the directed graph for which a stable steady state or a finite eigenvector exists. We demonstrate that each finite eigenvector, characterized as a finite linear combination of a class of eigenvalue, is the minimum among all the feasible schedules and an identical schedule pattern repeats every MPS. We develop an efficient algorithm to find a schedule among such schedules that minimizes a secondary performance measure related to work-in-process inventory. As a by-product of the linear system approach, we also propose a way of characterizing stable steady states of a class of discrete event dynamic systems.

• Journal of Electrical Engineering and Technology
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• v.8 no.5
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• pp.1202-1211
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• 2013

This paper deals with a robust controller for an induction motor (IM) which is represented as a linear parameter varying systems. To do so linear matrix inequality (LMI) based approach and robust Lyapunov feedback are associated. This approach is related to the fact that the synthesis of a linear parameter varying (LPV) feedback controller for the inner loop take into account rotor resistance and mechanical speed as varying parameter. An LPV flux observer is also synthesized to estimate rotor flux providing reference to cited above regulator. The induction motor is described as a polytopic LPV system because of speed and rotor resistance affine dependence. Their values can be estimated on line during systems operations. The simulation and experimental results largely confirm the effectiveness of the proposed control.

• Journal of the Korean Statistical Society
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• v.27 no.4
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• pp.407-420
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• 1998

This paper is concerned with selecting covariates to be included in building linear random effects models designed to analyze clustered response normal data. It is based on a Bayesian approach, intended to propose and develop a procedure that uses probabilistic considerations for selecting premising subsets of covariates. The approach reformulates the linear random effects model in a hierarchical normal and point mass mixture model by introducing a set of latent variables that will be used to identify subset choices. The hierarchical model is flexible to easily accommodate sign constraints in the number of regression coefficients. Utilizing Gibbs sampler, the appropriate posterior probability of each subset of covariates is obtained. Thus, In this procedure, the most promising subset of covariates can be identified as that with highest posterior probability. The procedure is illustrated through a simulation study.

• Journal of applied mathematics & informatics
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• v.15 no.1_2
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• pp.333-341
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• 2004

In the real world there are many linear programming problems where all decision parameters are fuzzy numbers. Several approaches exist which use different ranking functions for solving these problems. Unfortunately when there exist alternative optimal solutions, usually with different fuzzy value of the objective function for these solutions, these methods can not specify a clear approach for choosing a solution. In this paper we propose a method to remove the above shortcoming in solving fuzzy number linear programming problems using the concept of expectation and variance as ranking functions

• Journal of the Korean Institute of Telematics and Electronics
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• v.25 no.10
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• pp.1166-1172
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• 1988

This paper presents an efficient method of analyzing linear time-varying systems via Taylor polynomials. While the approach suggested by Sparis and Mouroutsos gives an implicit form for unknown state vector and requires to solve a linear algebraic equation with large dimension when the number of terms increases, the method proposed in this paper shows an explicit form and has no need to solve any linear algebraic equation.

• Journal of applied mathematics & informatics
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• v.14 no.1_2
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• pp.97-113
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• 2004

In this paper we shall study moving boundary problems, and we introduce an approach for solving a wide range of them by using calculus of variations and optimization. First, we transform the problem equivalently into an optimal control problem by defining an objective function and artificial control functions. By using measure theory, the new problem is modified into one consisting of the minimization of a linear functional over a set of Radon measures; then we obtain an optimal measure which is then approximated by a finite combination of atomic measures and the problem converted to an infinite-dimensional linear programming. We approximate the infinite linear programming to a finite-dimensional linear programming. Then by using the solution of the latter problem we obtain an approximate solution for moving boundary function on specific time. Furthermore, we show the path of moving boundary from initial state to final state.