• Information Systems Review
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• v.12 no.2
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• pp.189-203
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• 2010

Is presenting guidance of information performance measurement as government PRM version 2.0 these common reference models in public sector. Government PRM is consisted of assessment classification system and standard line of sight and performance management standard form. Through this, is sorting performance element and define cause-and effect. Government PRM is supplying measurement categories at assessment classification system, but relative importance for application standard by measurement categories is not presenting. In this study, importance for government PRM's measurement categories been applying by commonness Test of information performance measurement of public sector wishes to deduce estimation and priority. Research model used Fuzzy QFD, and designed so that can reflect well PRM's development purpose. I applied Fuzzy AHP and FPP method that graft together fuzzy theory to minimize uncertainty and ambiguity in that expert opinion. Is drawn to element that "Standard model offer for information department and management" is the most important in government PRM's development purpose. "Quality of service" is showing the highest priority in customer results in measurement category. Importance for government PRM's measurement categories can offer common valuation basis in government and public institution. Hereafter if examine closely quantitative cause-and effect for structure model of measurement classification system when study government PRM more objective and efficient reference model become.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.1
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• pp.12-17
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• 2010

The oil tanker ship over 150GRT must equip oil content meter which satisfy requirements of revised MARPOL 73/78. Online measurement of oil content in complex samples is required to have fast response, continuous measurement, and satisfaction of ${\pm}10ppm$ or ${\pm}10%$ error in this field. The research of this paper is to develop oil content measurement system using analysis of light transmission and scattering among turbidity measurement methods. Light transmission and scattering are analytical methods commonly used in instrumentation for online turbidity measurement of oil in water. Gasoline is experimented as a sample and the oil content approximately ranged from 14ppm to 600ppm. TSK Fuzzy Model may be suitable to associate variously derived spectral signals with specific content of oil having various interfering factors. Proposed Parallel TSK Fuzzy Model is reasonably used to classify oil content in comparison with other models. Those measurement methods would be effectively applied and commercialized to oil content meter that is key components of oil discharge monitoring control equipment.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.05a
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• pp.303-306
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• 2003

In radar tracking, since the sensor measures range, azimuth and elevation angle of a target, the measurement equation is nonlinear and the extended Kalman filter (EKF) is applied to nonlinear estimation. The conventional EKF has been widely used as a nonlinear filter for radar tracking, but the considerably large measurement error due to the linearization of nonlinear function in highly nonlinear situations may deteriorate the performance of the EKF To solve this problem, a fuzzy-model-based Kalman filter (FMBKF) is proposed for radar tracking. The FMBKF uses a local model approximation based on a TS fuzzy model instead of a Jacobian matrix to linearize nonlinear measurement equation. The hybrid GA and RLS method is used to identify the premise and the consequent parameters and the rule numbers of this TS fuzzy model. In two-dimensional radar tracking problem, the proposed method is compared with the conventional EKF.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.6
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• pp.863-870
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• 2008

In this paper, a new filtering scheme for vehicle tracking with distorted measurement is presented. This filtering scheme is essential for the implementation of the adaptive cruise control (ACC) system. The proposed method combines the IMM and the probabilistic fuzzy model and is named as the Fuzzy IMM (FIMM). The IMM is employed to recognize the intention of the preceding vehicle. The probabilistic furry model is introduced to compensate the distortion of the range sensor. Finally, a computer simulation is performed to illustrate the validity of the suggested algorithms.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.311-314
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• 2003

In radar tracking, since the sensor measures range, azimuth and elevation angle of a target, the measurement equation is nonlinear and the extended Kalman filter (EKF) is applied to nonlinear estimation. The conventional EKF has been widely used as a nonlinear filter for radar tracking, but the considerably large measurement error due to the linearization of nonlinear function in highly nonlinear situations may deteriorate the performance of the EKF. To solve this problem, a fuzzy-model-based Kalman filter (FMBKF) is proposed for radar tracking. The FMBKP uses a local model approximation based on a TS fuzzy model instead of a Jacobian matrix to linearize nonlinear measurement equation. The hybrid GA and RLS method is used to identify the premise and the consequent parameters and the rule numbers of this TS fuzzy model. In two-dimensional radar tracking problem, the proposed method is compared with the conventional EKF.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1995.10b
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• pp.303-306
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• 1995

In this study we present the inverse correlation method to select the exploratory variables, while Sugeno used RC method in his paper[6] We assume linear model with measurement error variables as in Fuller's Book[9]. we provide possibilistic linear model and predict the fuzzy response variable in case of fuzzy exploratory variables. By plotting data we can divide them for piecewise plane and provide the piecwise possibilistic linear model. If the exploratory variable is fuzzy trapezoidal variable or interval variable, then we estimate fuzzy trapezoidal variable or interval variable, then we estimate fuzzy trapezoidal response variable respondent to it. We will illustrate using Nonlinear System data in Sugeno's paper

• Proceedings of the KIEE Conference
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• 1997.07b
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• pp.510-512
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• 1997

Measurement errors in a temperature measurement system are mainly due to the consisting elements' accuracies and the circuit parameters' changes following the environment variations such as temperature. Further, system's non-linearity makes the measurement accuracy worse, and accordingly a linearization method should be considered to avoid this worsening. In this study, an error-correction method and a linearization method are proposed and a system utilizing these methods is realized.

• Journal of the Korean Society of Industry Convergence
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• v.27 no.2_1
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• pp.249-256
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• 2024

Traditional methods for monitoring targets rely heavily on probabilistic data association (PDA) or Kalman filtering. However, achieving optimal performance in a densely congested tracking environment proves challenging due to factors such as the complexities of measurement, mathematical simplification, and combined target detection for the tracking association problem. This article analyzes a target tracking problem through the lens of fuzzy logic theory, identifies the fuzzy rules that a fuzzy tracker employs, and designs the tracker utilizing fuzzy rules and Kalman filtering.

• Transactions on Control, Automation and Systems Engineering
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• v.1 no.1
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• pp.1-7
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• 1999

This paper gives a simulation study of a new fuzzy logic control(FLC) approach for the mold level control in continuous casting processes. The proposed FLC is PID type hybridizing the conventional fuzzy PI control and Fuzzy PD control with a simplified design scheme. It is shown that, compared with the conventional control, this new control strategy can achieve superior performance for steady-state response and is more robust against process parameter variations and disturbances.

• Geomechanics and Engineering
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• v.12 no.6
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• pp.871-882
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• 2017

Indirect measure of the tensile strength of laboratory samples is an important topic in rock engineering. One of the most important tests, the Brazilian strength test is performed to obtain the tensile strength of rock, concrete and other quasi brittle materials. Because the measurements are provided indirectly and the inspected rock materials may have heterogeneous properties, uncertainty quantification is required for a reliable test evaluation. In addition to the conventional measurement evaluation uncertainty methods recommended by the Guide to the Expression of Uncertainty in Measurement (GUM), such as Taylor's and Monte Carlo Methods, a fuzzy set-based approach is also proposed and resulting uncertainties are discussed. The results showed that when a tensile strength measurement is measured by a laboratory test, its uncertainty can also be expressed by one of the methods presented.