• International journal of advanced smart convergence
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• v.2 no.1
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• pp.21-23
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• 2013

The antenna pattern in this case has a main beam pointed in the desired signal direction, and has a null in the direction of the interference.The conventional antenna pattern concepts of beam width, side lobes, and main beams are not used, as the antenna weights are designed to achieve a set performance criterion such as maximization of the output SNR.A new direction of arrival estimation method using effects of model errors and sensitivity analysis is proposed. Two subspaces are used to form a signal space whose phase shift between the reference signal and its effects of model error signal. Through simulation, the performance showed that the proposed method leads to increased resolution and improved accuracy of DOA estimation relative to those achieved with existing method. Since a desired signal is obtained after interference rejection through correction effects of model error, the effect of channel interference on the estimation is significantly reduced.

• Structural Engineering and Mechanics
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• v.81 no.1
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• pp.103-115
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• 2022

The prediction error variances for frequencies are usually considered as unknown in the Bayesian system identification process. However, the error variances for mode shapes are taken as known to reduce the dimension of an identification problem. The present study attempts to explore the effectiveness of Bayesian approach of model parameters updating using Markov Chain Monte Carlo (MCMC) technique considering the prediction error variances for both the frequencies and mode shapes. To remove the ergodicity of Markov Chain, the posterior distribution is obtained by Gaussian Random walk over the proposal distribution. The prior distributions of prediction error variances of modal evidences are implemented through inverse gamma distribution to assess the effectiveness of estimation of posterior values of model parameters. The issue of incomplete data that makes the problem ill-conditioned and the associated singularity problem is prudently dealt in by adopting a regularization technique. The proposed approach is demonstrated numerically by considering an eight-storey frame model with both complete and incomplete modal data sets. Further, to study the effectiveness of the proposed approach, a comparative study with regard to accuracy and computational efficacy of the proposed approach is made with the Sequential Monte Carlo approach of model parameter updating.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.53 no.6
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• pp.925-933
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• 2020

This study is aimed to compare stock assessment models which are effective in assessing red snow crab Chinonoecetes japonicus resources and to select and apply an effective stock assessment model in the future. In order to select an effective stock assessment model, a process-error model, observation-error model, and a Bayesian state-space model were estimated. Analytical results show that the least error is observed between the estimated CPUE (catch per unit effort) and the observed CPUE when using the Bayesian state-space model. For the Bayesian state-space model, the 95% credible interval(CI) ranges for the maximum sustainable yield (MSY), carrying capacity (K), catchability coefficient (q), and intrinsic growth (r) are estimated to be 10,420-47,200 tons, 185,200-444,800 tons, 3.81E-06-9.02E-06, and 0.14-0.66, respectively. The results show that the Bayesian state-space model was most reliable among models.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.32 no.3
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• pp.225-231
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• 2014

When a spatial regression model that uses kernel density values as a dependent variable is applied to retail business data, a unique model cannot be selected because kernel density values change following kernel bandwidths. To overcome this problem, this paper suggests how to use the point pattern analysis, especially the L-index to select a unique spatial regression model. In this study, kernel density values of retail business are computed by the bandwidth, the distance of the maximum L-index and used as the dependent variable of spatial regression model. To test this procedure, we apply it to meeting room business data in Seoul, Korea. As a result, a spatial error model (SEM) is selected between two popular spatial regression models, a spatial lag model and a spatial error model. Also, a unique SEM based on the real distribution of retail business is selected. We confirm that there is a trade-off between the goodness of fit of the SEM and the real distribution of meeting room business over the bandwidth of maximum L-index.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.29 no.2 s.233
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• pp.277-283
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• 2005

Kriging model is widely used as design analysis and computer experiment (DACE) model in the field of engineering design to accomplish computationally feasible design optimization. In general, kriging model has been applied to many engineering applications as an interpolation model because it is usually constructed from deterministic simulation responses. However, when the responses include not only global nonlinearity but also numerical error, it is not suitable to use Kriging model that can distort global behavior. In this research, generalized kriging model that can represent both interpolation and regression is proposed. The performances of generalized kriging model are compared with those of interpolating kriging model for numerical function with error of normal distribution type and trigonometric function type. As an application of the proposed approach, the response of a simple dynamic model with numerical integration error is predicted based on sampling data. It is verified that the generalized kriging model can predict a noisy response without distortion of its global behavior. In addition, the influences of maximum likelihood estimation to prediction performance are discussed for the dynamic model.

• International Journal of Aeronautical and Space Sciences
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• v.13 no.4
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• pp.499-506
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• 2012

In this paper, a dynamic modeling for the velocity and position information of a single frequency stand-alone GPS(Global Positioning System) receiver is described. In static condition, the position error dynamic model is identified as a first/second order transfer function, and the velocity error model is identified as a band-limited Gaussian white noise via non-parametric method of a PSD(Power Spectrum Density) estimation in continuous time domain. A Kalman filter is proposed considering both correlated/white measurements noise based on identified GPS error model. The performance of the proposed Kalman filtering method is verified via numerical simulation.

• Asian-Australasian Journal of Animal Sciences
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• v.10 no.1
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• pp.70-74
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• 1997

Data on 2,571 lactation records of Nili-Ravi buffaloes from four institutional herds and four field recording centers were analyzed under an animal model to see the effect of season definition on the error variance of the fitted model. Herd-year-season(HYS) was the main fixed effect along with permanent environment, breeding value and residuals as the random effects. All known relationships among the animals were considered. The error variance differed for various HYS combinations. It was minimum when then months were not grouped into seasons. The four or Five season scenarios were better than the two season scenarios. The average number of lactations represented in a HYS combination varied widely from 6 to 28. Very few subclasses for a given HYS combination warrants the use of fewer seasons for animal model evaluation of buffaloes.

• Journal of Navigation and Port Research
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• v.37 no.4
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• pp.329-335
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• 2013

This paper is to develop the position error equations including the attitude errors, the errors of nadir and ship's heading, and the errors of ship's position in the free-gyro positioning and directional system. In doing so, the determination of ship's position by two free gyro vectors was discussed and the algorithmic design of the free-gyro positioning and directional system was introduced briefly. Next, the errors of transformation matrices of the gyro and body frames, i.e. attitude errors, were examined and the attitude equations were also derived. The perturbations of the errors of the nadir angle including ship's heading were investigated in each stage from the sensor of rate of motion of the spin axis to the nadir angle obtained. Finally, the perturbation error equations of ship's position used the nadir angles were derived in the form of a linear error model and the concept of FDOP was also suggested by using covariance of position error.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.6
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• pp.526-530
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• 2011

This paper presents a new fuzzy model-based design approach for waypoints-tracking control of nonlinear underwater vehicles (UUVs) on a horizontal plane. The waypoints-tracking control problem is converted into the stabilization one for the error model between the given nonlinear UUV and the waypoints. By using the sector nonlinearity, the error model is modeled in Takagi-Sugeno's form. We then derive stabilization conditions for the error model in the format of linear matrix inequality. A numerical simulation is provided to illustrate the effectiveness of the proposed methodology.

• Transactions of the Korean Society of Automotive Engineers
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• v.10 no.5
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• pp.29-34
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• 2002

This study presents the process of the continuous-time system identification for unknown nonlinear systems. The Radial Basis Function(RBF) error filtering identification model is introduced at first. This identification scheme includes RBF network to approximate unknown function of nonlinear system which is structured by affine form. The neural network is trained by the adaptive law based on Lyapunov synthesis method. The identification scheme is applied to engine and the performance of RBF error filtering Identification model is verified by the simulation with a three-state engine model. The simulation results have revealed that the values of the estimated function show favorable agreement with the real values of the engine model. The introduced identification scheme can be effectively applied to model-based nonlinear control.