• 제어로봇시스템학회:학술대회논문집
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• 1995.10a
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• pp.352-355
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• 1995

In this paper, estimation error bounds of the optimal FIR (Finite Impulse Response) filter, which is proposed by Kwon et al.[1, 2], are presented in discrete-time systems with the model uncertainty. Performance bounds are here represented by the upper bounds on the difference of the estimation error covariances between the nominal and real values in case of the systems with the noise or model parameter uncertainty. The estimation error bounds of the discrete-time optimal FIR filter is compared with those of the Kalman filter via a numerical example applied to the simulation problem by Toda and Patel[3]. Simulation results show that the former has robuster performance than the latter.

• Nuclear Engineering and Technology
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• v.48 no.3
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• pp.684-701
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• 2016

The Gaussian process model (GPM) is a flexible surrogate model that can be used for nonparametric regression for multivariate problems. A unique feature of the GPM is that a prediction variance is automatically provided with the regression function. In this paper, we estimate the safety margin of a nuclear power plant by performing regression on the output of best-estimate simulations of a large-break loss-of-coolant accident with sampling of safety system configuration, sequence timing, technical specifications, and thermal hydraulic parameter uncertainties. The key aspect of our approach is that the GPM regression is only performed on the dominant input variables, the safety injection flow rate and the delay time for AC powered pumps to start representing sequence timing uncertainty, providing a predictive model for the peak clad temperature during a reflood phase. Other uncertainties are interpreted as contributors to the measurement noise of the code output and are implicitly treated in the GPM in the noise variance term, providing local uncertainty bounds for the peak clad temperature. We discuss the applicability of the foregoing method to reduce the use of conservative assumptions in best estimate plus uncertainty (BEPU) and Level 1 probabilistic safety assessment (PSA) success criteria definitions while dealing with a large number of uncertainties.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2004.05a
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• pp.101-104
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• 2004

This paper addresses uncertainty issues encountered recently in measuring head vibration using the conventional 6-axis or 9-axis bite-bar Those conventional bite-bars are shown to present insufficient information to measure a complete 6 degree-of-freedom motion of head vibration. In order to overcome such limit, a theoretical measurement model that consists of four 3-axis linear accelerometers is suggested (Theoretical backgrounds presented in this paper shall have been addressed in the international congress of ICA 2004 in this April). It is shown to enable the direct measurement of three angular acceleration components and six angular velocity-dependent nonlinear terms. In audition to the three linear acceleration terms, those nine angular motion-dependent ones are found to make it possible to evaluate the general head vibration for a given position. To examine the feasibility of the proposed method, a newly designed 12-axis bite-bar was developed. Detailed experimental results obtained using the developed 12-axis bite-bar are illustrated in the presentation of this paper, which illustrates what amount of measurement accuracy provides. But, this paper provides more detailed experimental data and extended uncertainty factors.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2003.10a
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• pp.379-384
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• 2003

Noise is a problem in many electronic circuits and active control system. Arising from spuriously coupled noise from other circuits, it corrupts the signal of interest and introduces an uncertainty into information it contains. In this paper, re have researched noise characteristics of the inductive displacement sensor which has been designed. n first present basic concept and characteristics of magnetic field-coupled noise in the sensor output signal. Then, n are present relation noise and sensor performances. Finally, we concentrate low noise design of a sensor driver and a signal detection circuit.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2006.05a
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• pp.1265-1267
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• 2006

현재 ISO 에서는 공구제조업체에서 해당 공구의 수전달 진동을 측정하여 제시하도록 하는 규격을 제정 중에 있으며, 이를 위해서는 공구에서 발생하는 진동과 영향을 평가하는 기술이 확보되어야 하며, 이 과정에서 측정 불확도 수준을 결정하는 것이 매우 중요하다고 하겠다. 본 연구에서는 동력 공구 작업시 작업자의 손으로 전달되는 진동을 측정하고 그 영향을 평가함에 있어서 발생할 수 있는 불확도를 분석하였다. 먼저, ISO 5349 규격을 적용함에 있어서 존재하는 불확도 인자들을 분류하고, 각 인자들이 어느 정도 수준의 불확도를 발생시키는지 시험을 통해서 확인하고 분석하였다.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2004.11a
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• pp.685-688
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• 2004

This paper addresses the study on developing the angular vibration calibration system which requires the highly accurate measurement technique of the amplitude and period of an oscillating angular motion. Two developed models for the low and high frequency ranges are introduced and their main features are also compared. In addition to the angular vibration exciters, a new measurement method, referred to the 'equi-angle sampling method', is proposed and its theoretical backgrounds are introduced. The proposed method is shown to provide much less measurement uncertainty, compared the fringe counting method. Experimental results demonstrate what amount of angular vibration amplitude measurement uncertainty is improved by suing the proposed equi-angle sampling method.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10a
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• pp.834-838
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• 1992

Tracking performance depends on the quantity of the measurement data. In the Kalman-Bucy filter and other trackers, this dependence is well understood in terms of the measurement noise covariance matrix, which specifies the uncertainty in the value of measurement inputs. In this paper, we derived approximated error covariance matrix to evaluate the dependence of target detection probability and false alarm probability in the presence of uncertainty of measurement origin.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.11A
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• pp.1105-1116
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• 2008

Cognitive radio is one of the most effective techniques to improve the spectrum utilization efficiency. To implement the cognitive radio, spectrum sensing is considered as the key functionality because only counting on it, can the secondary users identify the spectrum holes and utilize them efficiently without causing interference to primary users. Generally, there are several spectrum sensing methods; the most common and simplest method is energy detection. However, the conventional energy detection has some disadvantages, which are caused by noise, especially, uncertain noise power leads to degradation of energy detector. In this paper, to solve this problem, we proposed sliding window-based energy detection method which can devide the frequency band of primary signal and noise using sliding window to estimate the power of primary user without the noise effect and achieve the better performance. It can calculate the energy of primary user only and can detect the primary signal. The simulation result shows that our proposed method outperforms conventional one.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.6
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• pp.3-8
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• 2014

Recently, a spectrum sensing technique employing the maximum value of a received power spectrum as a test statistic has been presented in the literature for the purpose of detecting a wireless microphone signal in TV bands This detects the presence of a primary user by comparing the test statistic with some threshold, which depends on the background noise power level as well as a target false alarm rate. Therefore its performance may deteriorate when the noise power uncertainty occurs. As a means to mitigate this difficulty, we present a spectrum sensing strategy adopting the ratio of the maximum and the minimum value of the power spectrum as a test statistic and analyze its performance of spectrum sensing.

• Structural Engineering and Mechanics
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• v.47 no.3
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• pp.361-381
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• 2013

A Bayesian probabilistic method is proposed for online estimation of the process noise and measurement noise parameters for Kalman filter. Kalman filter is a well-known recursive algorithm for state estimation of dynamical systems. In this algorithm, it is required to prescribe the covariance matrices of the process noise and measurement noise. However, inappropriate choice of these covariance matrices substantially deteriorates the performance of the Kalman filter. In this paper, a probabilistic method is proposed for online estimation of the noise parameters which govern the noise covariance matrices. The proposed Bayesian method not only estimates the optimal noise parameters but also quantifies the associated estimation uncertainty in an online manner. By utilizing the estimated noise parameters, reliable state estimation can be accomplished. Moreover, the proposed method does not assume any stationarity condition of the process noise and/or measurement noise. By removing the stationarity constraint, the proposed method enhances the applicability of the state estimation algorithm for nonstationary circumstances generally encountered in practice. To illustrate the efficacy and efficiency of the proposed method, examples using a fifty-story building with different stationarity scenarios of the process noise and measurement noise are presented.