• Journal of Korean Society of Water and Wastewater
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• v.20 no.1
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• pp.35-43
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• 2006

Concerns related to protecting, identifying, and isolating of subsystems of a water distribution network have led to the realization of the increased importance of valves in the system. The most important purpose of valves in water distribution systems is to isolate a subsystem due to breakage, maintenance activities, or contamination. A subsystem called segment is isolated by the closure of adjacent valves. Minimizing the pipe failure impact, an efficient algorithm is required to identify adjacent valves quickly. In this paper, an algorithm to identify adjacent valves to be closed to isolate a subsystem from the remainder of a network when a pipe failure is presented. The algorithm is operated on a matrix called the valve location matrix containing the information of valve locations. An application to an existing water distribution system demonstrates the developed algorithm efficiently locates the adjacent valves for the isolation of a broken pipe.

• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.729-731
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• 2004

This paper deals with the design of a low order $H_{\infty}$ controller by using an iterative linear matrix inequality (LMI) method. The low order $H_{\infty}$ controller is represented in terms of LMIs with a rank condition. To solve the non-convex rank-constrained LMI problem, a linear penalty function is incorporated into the objective function so that minimizing the penalized objective function subject to LMIs amounts to a convex optimization problem. With an increasing sequence of the penalty parameter, the solution of the penalized optimization problem moves towards the feasible region of the original non-convex problem. The proposed algorithm is, therefore, convergent. Numerical experiments show the effectiveness of the proposed algorithm.

• Journal of Electrical Engineering and Technology
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• v.11 no.1
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• pp.241-246
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• 2016

This paper presents a method that can improve the performance of permanent magnet synchronous motor current control by minimizing the measured current phase delay caused by the Low Pass Filter(LPF) used to cut off the noises that flowed in when feedback currents are measured. Although existing methods that change the Cutoff Frequency of the LPF can minimize phase delays during high speed rotations, their noise cutoff effects are much lower and this may lead to the decline of control performance. Therefore, in this study, an algorithm that can compensate current phase delays through relatively simple calculations from the synchronous motor d-q axis coordinate transformation matrix and the inverse transformation matrix is proposed and the validity of the proposed method is verified by comparing the waveform of the calculated current with the waveform of actual currents through simulations and experiments.

• Proceedings of the KSME Conference
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• 2007.05a
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• pp.915-920
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• 2007

A new optimal state feedback and disturbance feedforward control design in the sense of minimizing $L_{2}-gain$ from disturbance to control output is proposed for disturbance attenuation of systems with bounded control input and measurable disturbance. The controller is derived in the framework of linear matrix inequality(LMI) optimization. A gain scheduled state feedback and disturbance feedforward control design is also suggested to improve disturbance attenuation performance. The control gains are scheduled according to the proximity to the origin of the state of the plant and the magnitude of disturbance. This procedure yields a stable linear time varying control structure that allows higher gain and hence higher performance controller as the state and the disturbance move closer to the origin. The main results give sufficient conditions for the satisfaction of a parameter-dependent performance measure, without violating the bounded control input condition.

• Journal of the Korean Society for Precision Engineering
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• v.24 no.11
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• pp.59-65
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• 2007

A new optimal state feedback and disturbance feedforward control design in the sense of minimizing $L_2$-gain from disturbance to control output is proposed for disturbance attenuation of systems with bounded control input and measurable disturbance. The controller is derived in the framework of linear matrix inequality(LMI) optimization. A gain scheduled state feedback and disturbance feedforward control design is also suggested to improve disturbance attenuation performance. The control gains are scheduled according to the proximity to the origin of the state of the plant and the magnitude of disturbance. This procedure yields a stable linear time varying control structure that allows higher gain and hence higher performance controller as the state and the disturbance move closer to the origin. The main results give sufficient conditions for the satisfaction of a parameter-dependent performance measure, without violating the bounded control input condition.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.11
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• pp.747-752
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• 2004

This paper presents an iterative linear matrix inequality (LMI) approach to the design of a static output feedback (SOF) stabilization controller. A linear penalty function is incorporated into the objective function for the non-convex rank constraint so that minimizing the penalized objective function subject to LMIs amounts to a convex optimization problem. Hence, the overall procedure results in solving a series of semidefinite programs (SDPs). With an increasing sequence of the penalty parameter, the solution of the penalized optimization problem moves towards the feasible region of the original non-convex problem. The proposed algorithm is, therefore, convergent. Extensive numerical experiments are Deformed to illustrate the proposed algorithm.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.53 no.12
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• pp.655-660
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• 2004

This paper deals with the design of a fixed-structure $H_\infty$ power system stabilizer (PSS) by using an iterative linear matrix inequality (LMI) method. The fixed-structure $H_\infty$ controller is represented in terms of LMIs with a rank condition. To solve the non-convex rank-constrained LMI problem, a linear penalty function is incorporated into the objective function so that minimizing the penalized objective function subject to LMIs amounts to a convex optimization problem. With an increasing sequence of the penalty parameter, the solution of the penalized optimization problem moves towards the feasible region of the original non-convex problem. The proposed algorithm is, therefore, convergent. Numerical experiments show the practical applicability of the proposed algorithm.

• Korean Journal of Computational Design and Engineering
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• v.6 no.4
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• pp.244-253
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• 2001

For more complete process planning, machining sequence determination is critical to attain machining economics. Although many studies have been conducted in recent years, most of them suggests the non-unique machining sequences. When the tool approach directions(TAD) are considered fur a feature, both machining time and number of setups can be reduced. Then, the unique machining sequence can be extracted from alternate(non-unique) sequences by minimizing the idle time between operations within a sequence. This study develops an algorithm to generate the best machining sequence for composite prismatic features in a vertical milling operation. The algorithm contains five steps to produce an unique sequence: a precedence relation matrix(PRM) development, tool approach direction determination, machining time calculation, alternate machining sequence generation, and finally, best machining sequence generation with idle times. As a result, the study shows that the algorithm is effective for a given composite feature and can be applicable fur other prismatic parts.

• Smart Structures and Systems
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• v.30 no.4
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• pp.339-351
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• 2022

Evaluating the current condition of existing structures is of primary importance for economic and safety reasons. This can be addressed by Structural System Identification (SSI). A reliable static SSI depends on well-designed sensor configuration and loading cases, as well as efficient parameter estimation algorithms. Static SSI by the Measurement Error-Minimizing Observability Method (MEMOM) is a model-based deterministic static SSI method that could estimate structural parameters from static responses. In the current state of the art, this method is only applicable when structures are subjected to one loading case. This might lead to lack of information in some local regions of the structure (such as the null curvatures zones). To address this issue, the SSI by MEMOM using multiple loading cases is proposed in this work. Observability equations obtained from different loading cases are concatenated simultaneously and an optimization procedure is introduced to obtain the estimations by minimizing the discrepancy between the predicted response and the measured one. In addition, a Genetic-Algorithm (GA)-based Optimal Sensor Placement (OSP) method is proposed to tackle the OSP problem under multiple static loading cases for the very first time. In this approach, the Fisher Information Matrix (FIM)'s determinant is used as the metric of the goodness of sensor configurations. The numerical examples of a 3-span continuous bridge and a 13-story frame, are analyzed to validate the applicability of the extended SSI by MEMOM and the GA-based OSP method.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.9C
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• pp.860-870
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• 2005

In this paper we propose a new method to derive GRM(Generalized Reed-Muller) coefacients for each $2^{n}$ polarities using cell of karnaugh map(k-map). Generally, there are the serial and parallel method to derive GRM coefficients. As a serial method, Green method generates GRM coefncients using transform matrix. And as a parallel method, Besslich algorithm produces GRM coefficients of each polarity using the generated anteriorly. Green's method generates GRM coefficients for n-variable by calculating transform matrix for one-variable and n-times kronecker product this matrix. And Besslich's method generates GRM coefficients of each polarity in order of Grey-code. But those methods have disadvantages that the number of variable exceeding four makes transform matrix large and there are so many operation steps. In this paper, GRM coefficients is generated by producing cell [$f_{i}$] minimizing variable on k-map and operating this cell [$f_{i}$] and transform matrix for one-variable. So, we can generate GRM coefficients of all polarities easily by using the proposed method.