• Smart Structures and Systems
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• v.11 no.1
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• pp.103-122
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• 2013

It is essential to update the model with reflecting observation or inspection data for reliability estimation of existing structures. Authors proposed updated reliability analysis by using Particle Filter. We discuss how to apply the proposed method through numerical examples on reinforced concrete structures after verification of the method with hypothetical linear Gaussian problem. Reinforced concrete structures in a marine environment deteriorate with time due to chloride-induced corrosion of reinforcing bars. In the case of existing structures, it is essential to monitor the current condition such as chloride-induced corrosion and to reflect it to rational maintenance with consideration of the uncertainty. In this context, updated reliability estimation of a structure provides useful information for the rational decision. Accuracy estimation is also one of the important issues when Monte Carlo approach such as Particle Filter is adopted. Especially Particle Filter approach has a problem known as degeneracy. Effective sample size is introduced to predict the covariance of variance of limit state exceeding probabilities calculated by Particle Filter. Its validity is shown by the numerical experiments.

• Journal of KSNVE
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• v.7 no.2
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• pp.301-310
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• 1997

This paper presents active noise control method to form the near-field quiet zone for passengers in an automobile. The actuator model including interior acoustic plant, speaker and amplifier is experimentally identified in forms of auto-regressive and moving average by means of least mean square algorithm, The digital controller is composed of the regulator and Kalman filter to be designed based on LQG (linear quadratic gaussian). If the actuator model is prefiltered with digital filter to be properly designed for concentrating control performance index on the frequency band of primary noise source, LQG design approach can be effectively applied for the design of headset controller. Experimental results demonstrate that near-field quiet zone showing about 10dB noise reduction at microphone position can be formed using the headset located at passenger seat.

• Proceedings of the Korea Information Processing Society Conference
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• 2016.04a
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• pp.743-744
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• 2016

Object tracking in video sequences is a challenging task and has various applications. Particle filtering has been proven very successful for non-Gaussian and non-linear estimation problems. In this study, we first try to develop a color-based particle filter. In this approach, the color distributions of video frames are integrated into particle filtering. Color distributions are applied because of their robustness and computational efficiency. The model of the particle filter is defined by the color information of the tracked object. The model is compared with the current hypotheses of the particle filter using the Bhattacharyya coefficient. The proposed tracking method directly incorporates the scale and motion changes of the objects. Experimental results have been presented to show the effectiveness of our proposed system.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.652-654
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• 1996

A study of the coordinated control of a TCSC and an existing PSS is presented when both are used to damp the low frequency oscillations. TCSC is modeled by the first order delay model. Linear quadratic Gaussian controller is used for designing PSS and TCSC supplementary controller. The performance of the proposed controllers is simulated in a one machine infinite bus model. As a result, it is shown that to damp the low frequency oscillations efficiently, it is necessary to control TCSC and PSS simultaneously.

• Proceedings of the KIEE Conference
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• 1994.11a
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• pp.72-74
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• 1994

This paper presents a linear quadratic gaussian(LQG) based power system stabilizer(PSS) to control subsynchronous resonance(SSR) that occurs in a series capacitor compensated power systems. The complete SSR system based on the IEEE first benchmark model is employed in this study. Eigenvalue analysis and time domain simulations using a nonlinear system model show that the proposed PSS controls SSR efficiently.

• Proceedings of the KSPS conference
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• 2005.11a
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• pp.159-162
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• 2005

Identification is the process automatically identify who is speaking on the basis of information obtained from speech waves. In training phase, each speaker models are trained using each speaker's speech data. GMMs (Gaussian Mixture Models), which have been successfully applied to speaker modeling in text-independent speaker identification, are not efficient in insufficient training data environment. This paper proposes speaker modeling method using MLLR (Maximum Likelihood Linear Regression) method which is used for speaker adaptation in speech recognition. We make SD-like model using MLLR adaptation method instead of speaker dependent model (SD). Proposed system outperforms the GMMs in small training data environment.

• International Journal of Naval Architecture and Ocean Engineering
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• v.7 no.5
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• pp.873-887
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• 2015

The springing response of a large blunt ship was considered to be influenced by a second order interaction between the incoming irregular wave and the blunt geometry of the forebody of the ship. Little efforts have been made to simulate this complicated fluid-structure interaction phenomenon under irregular waves considering the second order effect; hence, the above mentioned premise still remains unproven. In this paper, efforts were made to quantify the second order effect between the wave and vibrating flexible ship structure by analyzing the experimental data obtained through the model basin test of the scaled-segmented model of a large blunt ship. To achieve this goal, the measured vertical bending moment and the wave elevation time history were analyzed using a higher order spectral analysis technique, where the quadratic interaction between the excitation and response was captured by the cross bispectrum of two randomly oscillating variables. The nonlinear response of the vibrating hull was expressed in terms of a quadratic Volterra series assuming that the wave excitation is Gaussian. The Volterra series was then orthogonalized using Barrett's procedure to remove the interference between the kernels of different orders. Both the linear and quadratic transfer functions of the given system were then derived based on a Fourier transform of the orthogonalized Volterra series. Finally, the response was decomposed into a linear and quadratic part to determine the contribution of the second order effect using the obtained linear and quadratic transfer functions of the system, combined with the given wave spectrum used in the experiment. The contribution of the second order effect on the springing response of the analyzed ship was almost comparable to the linear one in terms of its peak power near the resonance frequency.

• Journal of the Society of Naval Architects of Korea
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• v.34 no.4
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• pp.91-98
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• 1997

Most dynamic systems have various random properties m excitation and system parameters. In this paper, a procedure for structural response and reliability analysis is proposed for the linear dynamic system with random properties in both excitation and system parameters. The system parameter and response with random properties are modeled by the perturbation technique, and then the response analysis is formulated by probabilistic and vibration theories. Probabilistic FEM is also used for the calculation of mean response which is difficult by the proposed response model. The first passage analysis by the integral equation method is used to analyze the probability of failure. The integral equation method results in the first passage probability in terms of crossing rates and first passage probability densities. In this study it is assumed that excitations, system parameters and responses are Gaussian. As an application example, the probabilities of failure at transient state are calculated for a sdof system with random mass and spring constant subjected to stationary white-noise excitation and the results are compared to those of numerical simulation.

• Journal of the Korea Institute of Military Science and Technology
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• v.26 no.1
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• pp.10-21
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• 2023

For various target tracking applications, it is well known that the Kalman filter is the optimal estimator(in the minimum mean-square sense) to predict and estimate the state(position and/or velocity) of linear dynamical systems driven by Gaussian stochastic noise. In the case of nonlinear systems, Extended Kalman filter(EKF) and/or Unscented Kalman filter(UKF) are widely used, which can be viewed as approximations of the(linear) Kalman filter in the sense of the conditional expectation. However, to implement EKF and UKF, the exact dynamical model information and the statistical information of noise are still required. In this paper, we propose the recurrent neural-network based Kalman filter, where its Kalman gain is obtained via the proposed GRU-LSTM based neural-network framework that does not need the precise model information as well as the noise covariance information. By the proposed neural-network based Kalman filter, the state estimation performance is enhanced in terms of the tracking error, which is verified through various linear and nonlinear tracking problems with incomplete model and statistical covariance information.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.2 no.3
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• pp.43-56
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• 1982

A recursive algorithms for prediction of streamflows by Kalman filtering theory and Self-tuning predictor based on the state space description of the dynamic systems have been studied and the applicabilities of the algorithms to the rainfall-runoff processes have been investigated. For the representation of the dynamics of the processes, a low-order ARMA process has been taken as the linear discrete time system with white Gaussian disturbances. The state vector in the prediction model formulated by a random walk process. The model structures have been determined by a statistical analysis for residuals of the observed and predicted streamflows. For the verification of the prediction algorithms developed here, the observed historical data of the hourly rainfall and streamflows were used. The numerical studies shows that Kalman filtering theory has better performance than the Self-tuning predictor for system identification and prediction in rainfall-runoff processes.