• Journal of Korean Society of Steel Construction
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• v.13 no.5
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• pp.525-533
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• 2001

The wind resistant design of long-span bridges has urged a special attention to the prevention of the flutter occurrence Therefore calculation of flutter derivatives is indispensable to this prediction. A used system identification method must identify all the flutter derivatives from noisy experimental data In this paper MITD(Modified Ibrahim Tim Domain) method and AKF (Adaptive Kalman Filter) method are applied to extract flutter derivatives from section-model tests. The robustness and reliability of proposal SI methods under a high signal-to-noise ratio is demonstrated through numerical simulation for windtunnel test.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.5
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• pp.560-570
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• 1999

This paper formulates the suboptimal robust Kalman filtering problem into two coupled Linear Matrix Inequality (LMI) problems by applying Lyapunov theory to the augmented system which is composed of the state equation in the uncertain linear system and the estimation error dynamics. This formulations not only provide the sufficient conditions for the existence of the desired filter, but also construct the suboptimal robust Kalman filter. The proposed filter can guarantee the optimized upper bound of the estimation error variance for uncertain systems with parametric uncertainties in both the state and measurement matrices. In addition, this paper shows how the problem of finding the minimizing solution subject to Quadratic Matrix Inequality (QMI), which cannot be easily transformed into LMI using the usual Schur complement formula, can be successfully modified into a generic LMI problem.

• Journal of Institute of Control, Robotics and Systems
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• v.7 no.5
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• pp.445-453
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• 2001

The multiple targets tracking problem has been one of the main issues in the radar applications area in the last decade. Besides the standard Kalman filtering, various methods including the variable dimen-sion filter, input estimation filter, interacting multiple model(IMM) filter, dederated variable dimension filter with input estimation, etc., have proposed to address the tracking and sensor fusion issues. In this pa- per, two existing tracking algorithm, i.e, the IMM filter and the variable dimension filter with input estima-tion(VDIE), are combined for the purpose of improving the tracking performance for maneuvering targets. To evaluate the tracking performance of the proposed algorithm, three typical maneuvering patterns, i.e., waver, pop-up, and high-diver motions, are defined and are applied to the modified IMM filter as well as the standard IMM filter. The smaller RMS tracking errors, in position and velocity, of the modified IMM filter than the standard IMM filter are demonstrated though computer simulations.

• Journal of Aerospace System Engineering
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• v.10 no.1
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• pp.8-14
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• 2016

The optimal estimation of a bearing only target tracking problem be achieved through the solution of the Fokker-Planck equation and the Bayesian update. Recently, a nonlinear filtering algorithm using a direct quadrature method of moments in which the associated Fokker-Planck equation can be propagated efficiently and accurately was proposed. Although this approach has demonstrated its promising in the field of nonlinear filtering in several examples, the "degeneracy" phenomenon, similar to that which exists in a typical particle filter, occasionally appears because only the weights are updated in the modified Bayesian rule in this algorithm. Therefore, in this paper to enhance the performance, a more stable measurement update process based upon the update equation in the Extended Kalman filters and a more accurate initialization and re-sampling strategy for weight and abscissas are proposed. Simulations are used to show the effectiveness of the proposed filter and the obtained results are promising.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.9
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• pp.881-885
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• 2008

To calculate the attitude in ARS(Attitude Reference System) using 3 gyros and 3 accelerometers, gyro drift must be compensated with accelerometer to avoid divergence of attitude error. Kalman filter is most popular method to integrate those two sensor outputs. In this paper, new Kalman filtering method is proposed for roll and pitch attitude estimation. New states are defined to make linear equation and algorithm for changing Kalman filter parameters is proposed to ignore disturbances of acceleration. This algorithm can be easily applied to low cost ARS.

• ETRI Journal
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• v.35 no.5
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• pp.849-858
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• 2013

In this paper, we propose a new adaptive single model to track a maneuvering target with abrupt accelerations. We utilize the stochastic differential equation to model acceleration of a maneuvering target with stochastic volatility (SV). We assume the generalized autoregressive conditional heteroscedasticity (GARCH) process as the model for the tracking procedure of the SV. In the proposed scheme, to track a high maneuvering target, we modify the Kalman filtering by introducing a new GARCH model for estimating SV. The proposed tracking algorithm operates in both the non-maneuvering and maneuvering modes, and, unlike the traditional decision-based model, the maneuver detection procedure is eliminated. Furthermore, we stress that the improved performance using the GARCH acceleration model is due to properties inherent in GARCH modeling itself that comply with maneuvering target trajectory. Moreover, the computational complexity of this model is more efficient than that of traditional methods. Finally, the effectiveness and capabilities of our proposed strategy are demonstrated and validated through Monte Carlo simulation studies.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.22 no.2
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• pp.287-291
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• 2013

Fault Diagnosis is a process of detecting and isolating faults in a system. On demanding for safety and high reliability systems make it important for some reasons such as economical and environmental incentives. Especially embedded technology and IT technology combined with precise sensing techniques has been doing well developed and applied to fault diagnosis and prognosis in industrial systems like as automotive, ship, heavy industry and aerospace as well. This paper, as an empirical application of diesel engine, presents a method how to get raw data from physical systems, what to consider for successful implementation and which theoretic mathematical models should be applied. In a sense of system level Adaptive Filtering (we call Modified Kalman Filter) and a unit of part level Hidden Markov Process was developed and applied.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.211-211
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• 2000

The multiple targets tracking problem has been one of main issues in the radar applications area in the last decade. Besides the standard Kalman filtering, various methods including the variable dimension filter, input estimation filter, interacting multiple model (IMM) filter, federated variable dimension filter with input estimation, probable data association (PDA) filter etc. have been proposed to address the tracking and sensor fusion issues. In this paper, two existing tracking algorithms, i.e. the IMMPDA filter and the variable dimension filter with input estimation (VDIE), are combined for the purpose of improving the tracking performance of maneuvering targets in clutter. To evaluate the tracking performance of the proposed algorithm, three typical maneuvering patterns i.e. Waver, Pop-Up, and High-Diver motions, are defined and are applied to the modified IMMPDA filter considered as well as the standard IMM filter. The smaller RMS tracking errors, in position and velocity, of the modified IMMPDA filter than the standard IMM filter are demonstrated through computer simulations.

• Geotechnical Engineering
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• v.9 no.3
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• pp.67-76
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• 1993

Korea being composed of a number of mountains has been damaged and destroyed in lives and properties by the occurrence of many landslides during the wet seasons. Therefore, it is necessary to study the forecast system and risk analysis for the occurrence of landslides : the rise of groundwater levels due to rainfall is the main cause of landslides. In this paper, the autoregressive models are used to predict the grondwater levls using cases of both time invariant and time -varing autoregressive coefficients. In the former case, AR(1), AR(2), and AR(3) models are selected and their single-valued parameters are estimated to fit them to the observed groundwater level series. In the latter case, modified AR(1) and typical AR(2) models are used as process model and a discrete Kalman Filtering technique is utilized to estimate the parameters which are themselves a function of time. The results show that the real time forecast system using the time-varying autoregressive coefficinets as well as time -invariant AR model is good to predict the groundwater level in hillside slopes and we might get better result if we use the time-hourly rainfall intensity as well as the observed groundwater level.