• 제어로봇시스템학회:학술대회논문집
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• 1991.10a
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• pp.1219-1219
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• 1991

This paper proposes the model order reduction with pass band error reduction in the frequency domain of discrete time linear systems. The algorithm is the new method of reduced order model which reduces passband error by changing controllability and observability gramian used with weighted functions. A numerical example shows that this algorithm has lower passband error than balanced w&l and weighted function characteristics in frequency domain.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.333-333
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• 2000

When the generalized singular perturbation method is used for model reduction, the state variables of the original system is reconstructed from the reduced order model. The state reduction error is defined, which shows how well the reconstructed state variables approximate the state variables of the original system equation.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.366-370
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• 2003

This paper is on weighted model reduction using structurally balanced truncation. For a given weighted(single or double-sided) transfer function, a state space realization with the linear fractional transformation form is obtained. Then we prove that two block diagonal LMI(linear matrix inequality) solutions always exist, and it is possible to get a reduced order model with guaranteed stability and a priori error bound. Finally, two examples are used to show the validity of proposed weighted reduction method, and the method is compared with other existing methods.

• Journal of Korea Multimedia Society
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• v.25 no.10
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• pp.1448-1456
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• 2022

This paper studies a model to diagnose efficiency reduction of inverter using Multilayer Perceptron(MLP). In this study, two inverter data which started operation at different day was used. A Multilayer Perceptron model was made to predict photovoltaic power data of the latest inverter. As a result of the model's performance test, the Mean Absolute Percentage Error(MAPE) was 4.1034. The verified model was applied to one-year-old and two-year-old data after old inverter starting operation. The predictive power of one-year-old inverter was larger than the observed power by 724.9243 on average. And two-year-old inverter's predictive value was larger than the observed power by 836.4616 on average. The prediction error of two-year-old inverter rose 111.5572 on a year. This error is 0.4% of the total capacity. It was proved that the error is meaningful difference by t-test. The error is predicted value minus actual value. Which means that PV system actually generated less than prediction. Therefore, increasing error is decreasing conversion efficiency of inverter. Finally, conversion efficiency of the inverter decreased by 0.4% over a year using this model.

• Journal of Korean Society for Quality Management
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• v.38 no.4
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• pp.491-503
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• 2010

This paper presents a research model, which identifies a relationship between medical error reduction, efficiency of organizational systems, and employee satisfaction with organizational support. The proposed model was tested through hypotheses, based on data collected from 210 respondents from the medical staff of large -sized (i.e., more than 500 beds) residential hospitals in cities of South Korea. The results of the study showed that medical error reduction is associated with corrective system and employee satisfaction with organizational support. Therefore, it is very important that organizations improve their employees' satisfaction by providing sufficient support (e.g., information support and sharing, work guide book, etc.) for their work. In addition, in organizational systems, the corrective system has positive relationship with medical error reduction. In terms of corrective procedures, leaders or managers can make improvements by providing and supporting a friendly work environment where errors may be reported without blame and discussed in order to be corrected.

• 제어로봇시스템학회:학술대회논문집
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• 1987.10b
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• pp.155-160
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• 1987

In this paper, the model reduction method of the linear time invariant continuous systems is proposed. The denominator of reduced order model is determined by the eigenvalue selected considering the error of the power series that exists between original system and reduced order system at each time moments. And the numerator of model is founded by the time moment matching method. The method suggested is compared with other various methods in examples.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.12-12
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• 2000

This paper consider a new weighted model reduction using block diagonal solutions of Lyapunov inequalities. With the input and/or output weighting function, the stability of reduced order system is quaranteed and a priori error bound is proposed. to achieve this, after finding the solutions of two Lyapunov inequalities and balancing the full order system, we find the reduced order systems using the direct truncation and the singular perturbation approximation. The proposed method is compared with other existing methods using numerical example.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.1205-1208
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• 1996

This paper proposes balanced model reduction of non-minimum phase plant. The algorithm presented in this paper is to convert high-order non-minimum phase plant into low-oder minimum phase plant using balanced model reduction. Balanced model reduction requires the error bound that Hankel singular value produces. This algorithm shows the tolerance that admits the method of this paper.

• Journal of Institute of Control, Robotics and Systems
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• v.8 no.5
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• pp.351-356
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• 2002

This papaer deals with a model reduction problem for linear systems with time varying parameters. For this problem, a controllability Grammian and an observability Grammian are introduced and computed by solving linear matrix inequalities. Using the controllability/observability Grammian, a balanced state space realization for linear parameter varying systems is obtained. From the balanced state space realization, a reduced model can be obtained by truncating not only states but also time varying parameters and an upper bound of the model reduction error is derived as well.

• Journal of the Korean Society for Precision Engineering
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• v.16 no.3 s.96
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• pp.215-221
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• 1999

The object of the thermal error compensation system in machine tools is improving the accuracy of a machine tool through real time error compensation. The accuracy of the machine tool totally depends on the accuracy of thermal error model. A thermal error model can be obtained by appropriate combination of temperature variables. The proposed method for optimal variable selection in the thermal error model is based on correlation grouping and successive regression analysis. Collinearity matter is improved with the correlation grouping and the judgment function which minimizes residual mean square is used. The linear model is more robust against measurement noises than an engineering judgement model that includes the higher order terms of variables. The proposed method is more effective for the applications in real time error compensation because of the reduction in computational time, sufficient model accuracy, and the robustness.