• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.3
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• pp.718-723
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• 2017

When structural analysis modelling methods of practical fields are investigated, a slab is generally modeled by a finite element mesh using plate elements and a shear wall is modeled using a shell element or wall element for 3-D structural analysis. The point worthy of notice in this practice is that a shear wall is modelled using only one wall or shell element divided by floors and column lines to produce structural models. The modeling method like this can cause analysis errors according to the type of computer programs in use, and these errors reduce the reliability of the analysis results. Therefore, to secure the reliability of structural analysis, studies of the causes of errors and finding reasonable modeling methods are necessary. In this study, the causes of analysis errors according to the modelling methods of a shear wall, which are used in practical fields, were investigated and some considering matters for modelling a shear wall are presented to reduce the analysis errors on these analysis results.

• Journal of Navigation and Port Research
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• v.28 no.3
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• pp.161-167
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• 2004

Maritime safety and marine environmental protection are the most important topic in marine society. But, so many marine accidents have been occurred with the development of marine transportation industry. On the other side, ship is being operated under a highly dynamic environment and many factors are related with ship's collision Nowadays, the increasing tendency to the human errors of ship's collision is remarkable, and the investigation of the human errors has been heavily concentrated. This study analysed on the human errors of ship's collision related to the negligence of lookout and classified basic error type using GEMS(Generic Error Modeling System) dynamic model.

• Proceedings of the Korea Committee for Ocean Resources and Engineering Conference
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• 2003.05a
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• pp.149-156
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• 2003

This paper presents an underwater hybrid navigation system for a semi-autonomous underwater vehicle (SAUV). The navigation system consists of an inertial measurement unit (IMU), an ultra-short baseline (USBL) acoustic navigation sensor and a doppler velocity log (DVL) accompanying a magnetic compass. The errors of inertial measurement units increase with time due to the bias errors of gyros and accelerometers. A navigational system model is derived to include the error model of the USBL acoustic navigation sensor and the scale effect and bias errors of the DVL, of which the state equation composed of the navigation states and sensor parameters is 25 in the order. The conventional extended Kalman filter was used to propagate the error covariance, update the measurement errors and correct the state equation when the measurements are available. Simulation was performed with the 6-d.o.f. equations of motion of SAUV in a lawn-mowing survey mode. The hybrid underwater navigation system shows good tracking performance by updating the error covariance and correcting the system's states with the measurement errors from a DVL, a magnetic compass and a depth senor. The error of the estimated position still slowly drifts in horizontal plane about 3.5m for 500 seconds, which could be eliminated with the help of additional USBL information.

• Journal of the Korean Data and Information Science Society
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• v.28 no.5
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• pp.971-980
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• 2017

The varying coefficient regression model has gained lots of attention since it is capable to model dynamic changes of regression coefficients in many regression problems of science. In this paper we propose a varying coefficient regression model that effectively considers the errors on both input and response variables, which utilizes the kernel method in estimating the varying coefficient which is the unknown nonlinear function of smoothing variables. We provide a generalized cross validation method for choosing the hyper-parameters which affect the performance of the proposed model. The proposed method is evaluated through numerical studies.

• Journal of Korean Institute of Industrial Engineers
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• v.28 no.1
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• pp.63-75
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• 2002

To improve the efficiency of the electric power generation, monthly maximum electric power consumptions for a next one year should be forecasted in advance and used as the fundamental input to the yearly electric power-generating master plan, which has a greatly influence upon relevant sub-plans successively. In this paper, we analyze the past 22-year hourly maximum electric load data available from KEPCO(Korea Electric Power Corporation) and select necessary data from the raw data for our model in order to reflect more recent trends and seasonal components, which hopefully result in a better forecasting model in terms of forecasted errors. After analyzing the selected data, we recommend to KEPCO the Winters' multiplicative model with decomposition and exponential smoothing technique among many candidate forecasting models and provide forecasts for the electric power consumptions and their 95% confidence intervals up to December of 1999. It turns out that the relative errors of our forecasts over the twelve actual load data are ranged between 0.1% and 6.6% and that the average relative error is only 3.3%. These results indicate that our model, which was accepted as the first statistical forecasting model for monthly maximum power consumption, is very suitable to KEPCO.

• Journal of Astronomy and Space Sciences
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• v.22 no.3
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• pp.273-282
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• 2005

A star sensor daytime observation model is developed in order to test the performance of the star sensor useful for daylight application. The centroid errors of the star sensor in the day time application are computed by using the model. The standard atmospheric model (LOWTRAN7) is utilized to calculate the physical quantities of the daylight atmospheric environments where the star sensor is immersed. This observation model takes the separation angles between the sun and star, the centroid algorithm and the various system specifications of the star sensor into the account. The developed star sensor model will provide more realistic measurement errors in estimating the performance of the attitude determination from the vector observations.

• Proceedings of the Korea Water Resources Association Conference
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• 2007.05a
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• pp.1263-1267
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• 2007

A more complete two-dimensional vertical numerical model has been developed to describe the saltwater intrusion in an estuary. The model is based on the previous studies in order to obtain a better accuracy. The non-linear terms of the governing equations are analyzed and the $\sigma$-coordinate system is employed in the vertical direction with full transformation which is recently issued in several studies because numerical errors can be generated during the coordinate transformation of the diffusion term. The advection terms of the governing equations are discretized by an upwind scheme in second-order of accuracy. By employing an explicit scheme for the longitudinal direction and an implicit scheme for the vertical direction, the numerical model is free from the restriction of temporal step size caused by a relatively small grid ratio. In previous researches, some terms induced from the transformation have been intentionally excluded since they are asked the complicate discretization of the numerical model. However, the lack of these terms introduces significant errors during the numerical simulation of scalar transport problems, such as saltwater intrusion and sediment transport in an estuary. The numerical accuracy attributable to the full transformation is verified by comparing results with a previous model in a simply sloped topography. The numerical model is applied to the Han River estuary. Very reasonable agreements for salinity intrusion are observed.

• Journal of Drive and Control
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• v.16 no.2
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• pp.22-29
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• 2019

This study presented a model predictive tracking control algorithm of autonomous truck based on object state estimation using extended Kalman filter. To design the model, the 1-layer laser scanner was used to estimate position and velocity of the object using extended Kalman filter. Based on these estimations, the desired linear path for object tracking was computed. The lateral and yaw angle errors were computed using the computed linear path and relative positions of the truck. The computed errors were used in the model predictive control algorithm to compute the optimal steering angle for object tracking. The performance evaluation was conducted on Matlab/Simulink environments using planar truck model and actual point data obtained from laser scanner. The evaluation results showed that the tracking control algorithm developed in this study can track the object reasonably based on the model predictive control algorithm based on the estimated states.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.12
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• pp.2326-2332
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• 2011

eLoran is enhanced Loran-C and eLoran is researched for as GPS backup system because this system is resistant to signal interference and has high accuracy. TOA measurements of eLoran include errors proportional to the range such as PF, SF, ASF and EF. Therefore these error factors must be compensated for improved accuracy of position. Generally, error models or GPS aided compensation methods are used, but these methods are limited by lack of infrastructure or system performance. Therefore, this paper proposes new model of error factors included in eLoran TOA measurements and navigation algorithm using this model. Error factors in this model are sum of a certain size of error and error proportional to the range. And feasibility and performance of proposed navigation algorithm are verified by using raw measurements.

• Journal of Korean Academy of Nursing
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• v.29 no.5
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• pp.1030-1041
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• 1999

This study examined those problems noticed under the Asymptotically Generalized Least Squares estimator in evaluating a structural model of physical health. The problems were highly correlated parameter estimates and high standard errors of some parameter estimates. Separate analyses of the endogenous part of the model and of the metric of a latent factor revealed a highly skewed and kurtotic measurement indicator as the focal point of the manifested problems. Since the sample sizes are far below that needed to produce adequate AGLS estimates in the given modeling conditions, the adequacy of the Maximum Likelihood estimator is further examined with the robust statistics and the bootstrap method. These methods demonstrated that the ML methods were unbiased and statistical decisions based upon the ML standard errors remained almost the same. Suggestions are made for future studies adopting structural equation modeling technique in terms of selecting of a reference indicator and adopting those statistics corrected for nonormality.