• The Transactions of the Korean Institute of Electrical Engineers P
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• v.58 no.2
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• pp.164-171
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• 2009

This paper presents new learning algorithm of dynamic Bayesian networks (DBN) by means of constrained least square (LS) estimation algorithm and gradient descent method. First, we propose constrained LS based parameter estimation for a Markov chain (MC) model given observation data sets. Next, a gradient descent optimization is utilized for online estimation of a hidden Markov model (HMM), which is bi-linearly constructed by adding an observation variable to a MC model. We achieve numerical simulations to prove its reliability and superiority in which a series of non stationary random signal is applied for the DBN models respectively.

• International Journal of Reliability and Applications
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• v.3 no.1
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• pp.51-60
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• 2002

This paper presents the methodology for obtaining point and interval estimating of the parameters of a new two-parameter distribution with multiple-censored and singly censored data (Type-I censoring or Type-II censoring) as well as complete data, using the maximum likelihood method. The basis is the likelihood expression for multiple-censored data. Furthermore, this model can be extended to a three-parameter distribution that is added a scale parameter. Then, the parameter estimation can be obtained by the graphical estimation on probability plot.

• Nuclear Engineering and Technology
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• v.48 no.5
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• pp.1280-1290
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• 2016

This paper presents an advanced estimation method for obtaining the probability density functions of a damage parameter for valve leakage detection in a reciprocating pump. The estimation method is based on a comparison of model data which are simulated by using a mathematical model, and experimental data which are measured on the inside and outside of the reciprocating pump in operation. The mathematical model, which is simplified and extended on the basis of previous models, describes not only the normal state of the pump, but also its abnormal state caused by valve leakage. The pressure in the cylinder is expressed as a function of the crankshaft angle, and an additional volume flow rate due to the valve leakage is quantified by a damage parameter in the mathematical model. The change in the cylinder pressure profiles due to the suction valve leakage is noticeable in the compression and expansion modes of the pump. The damage parameter value over 300 cycles is calculated in two ways, considering advance or delay in the opening and closing angles of the discharge valves. The probability density functions of the damage parameter are compared for diagnosis and prognosis on the basis of the probabilistic features of valve leakage.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.47 no.10
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• pp.747-752
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• 2019

By using the failure information and the cumulative test execution time obtained by performing the reliability growth test, it is possible to estimate the parameter of the reliability growth model, and the Mean Time Between Failure (MTBF) of the product can be predicted through the parameter estimation. However the failure information could be acquired periodically or the number of sample data of the obtained failure information could be small. Because there are various constraints such as the cost and time of test or the characteristics of the product. This may cause the error of the parameter estimation of the reliability growth model to increase. In this study, the Bayesian method is applied to estimating the parameters of the reliability growth model when the number of sample data for the fault information is small. Simulation results show that the estimation accuracy of Bayesian method is more accurate than that of Maximum Likelihood Estimation (MLE) respectively in estimation the parameters of the reliability growth model.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.35S no.2
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• pp.146-156
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• 1998

Thispapr proposes hybrid navigation parameter estimation using sequential aerial images. The proposed navigation parameter estimation system is composed of two parts: relative position estimation and absolute position estimation. the relative position estimation recursively computes the current velocity and absolute position estimation. The relative position estimation recursively computes the current velocity and position of an aircraft by accumulating navigation parameters extracted from two succesive aerial images. Simple accumulation of parameter values decreases reliability of the extracted parameters as an aircraft goes on navigating. therefore absolute position estimation is required to compensate for position error generated in the relative position step. The absolute position estimation algorithm combining image matching and digital elevation model(DEM) matching is presented. Computer simulation with real aerial image sequences shows the efficiency of the proposed hybrial algorithm.

• Korean Journal of Clinical Pharmacy
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• v.23 no.2
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• pp.137-141
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• 2013

Purpose: This study compared the performance of new NONMEM estimation methods using a population analysis dataset collected from a clinical study that consisted of 40 individuals and 567 observations after a single oral dose of glimepiride. Method: The NONMEM 7.2 estimation methods tested were first-order conditional estimation with interaction (FOCEI), importance sampling (IMP), importance sampling assisted by mode a posteriori (IMPMAP), iterative two stage (ITS), stochastic approximation expectation-maximization (SAEM), and Markov chain Monte Carlo Bayesian (BAYES) using a two-compartment open model. Results: The parameters estimated by IMP, IMPMAP, ITS, SAEM, and BAYES were similar to those estimated using FOCEI, and the objective function value (OFV) for diagnosing the model criteria was significantly decreased in FOCEI, IMPMAP, SAEM, and BAYES in comparison with IMP. Parameter precision in terms of the estimated standard error was estimated precisely with FOCEI, IMP, IMPMAP, and BAYES. The run time for the model analysis was shortest with BAYES. Conclusion: In conclusion, the new estimation methods in NONMEM 7.2 performed similarly in terms of parameter estimation, but the results in terms of parameter precision and model run times using BAYES were most suitable for analyzing this dataset.

• International Journal of Aeronautical and Space Sciences
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• v.16 no.4
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• pp.614-623
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• 2015

When the speed of a coaxial rotor helicopter in forward flight increases, the wake skew angle of the rotor increases and consequently the position of the vena contracta of the upper rotor with respect to the lower rotor changes. Considering ambient air and the effect of the upper rotor, this study proposes a nonuniform inflow model for the lower rotor of a coaxial rotor helicopter in forward flight. The total required power of the coaxial rotor system was compared against Dingeldein's experimental data, and the results of the proposed model were well matched. A plant model was also developed from first principles for flight simulation, unknown parameter estimation and control analysis. The coaxial rotor helicopter used for this study was manufactured for surveillance and reconnaissance and does not have any stabilizer bar. Therefore, a feedback controller was included during flight test and parameter estimation to overcome unstable situations. Predicted responses of parameter estimation and validation show good agreement with experimental data. Therefore, the methodology described in this paper can be used to develop numerical plant model, study non-uniform inflow model, conduct performance analysis and parameter estimation of coaxial rotor as well as other rotorcrafts in forward flight.

• Proceedings of the KSME Conference
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• 2001.06b
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• pp.565-570
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• 2001

Vehicle dynamics control (VDC) system requires more information on driving conditions compared with ABS and/or TCS. In order to develop the VDC system, tire slip angles, vehicle side-slip angle, and vehicle lateral velocity as well as road friction coefficient are needed. Since there are not any cheap and reliable sensors, recent researches on parameter estimation have given rise to a number of parameter estimation techniques. This paper presents new vehicle model to estimate vehicle's yaw rate. This model is improved from the conventional 2 degrees of freedom vehicle model, so-called bicycle model, taking nonlinear effects into account. These nonlinear effects are: (i) tyre nonlinearity; (ii) lateral load transfer during cornering; (iii) variable gear ratio with respect to vehicle velocity. Estimation results are validated with the experimental results.

• Journal of Biomedical Engineering Research
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• v.22 no.3
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• pp.293-301
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• 2001

A patient-specific musculoskeletal model, whose parameters can be identified noninvasively, was developed for the automatic generation of patient-specific stimulation pattern in FES. The musculotendon system was modeled as a torque-generator and all the passive systems of the musculotendon working at the same joint were included in the skeletal model. Through this, it became possible that the whole model to be identified by using the experimental joint torque or the joint angle trajectories. The model parameters were grouped as recruitment of muscle fibers, passive skeletal system, static and dynamic musculotendon systems, which were identified later in sequence. The parameters in each group were successfully estimated and the maximum normalized RMS errors in all the estimation process was 8%. The model predictions with estimated parameter values were in a good agreement with the experimental results for the sinusoidal, triangular and sawlike stimulation, where the normalized RMS error was less than 17%, Above results show that the suggested musculoskeletal model and its parameter estimation method is reliable.

• Journal of Korean Society of Transportation
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• v.14 no.3
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• pp.7-26
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• 1996

The estimation of real-time Origin-Destination(O-D) parameters, which gives travel demand between combinations of origin and destination points on a urban freeway network, from on-line surveillance traffic data is essential in developing an efficient ATMS strategy. On this need a real-time O-D parameter estimation model is formulated as a parameter adaptive filtering model based on the extended Kalman Filter. A Monte Carlo test have shown that the estimation of time-varying O-D parameter is possible using only traffic counts. Tests with field data produced the interesting finding that off-ramp volume predictions generated using a constant freeway O-D matrix was replaced by real-time estimates generated using the parameter adaptive filter.