• Journal of the Korean Institute of Navigation
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• v.23 no.3
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• pp.35-44
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• 1999

This research aims to seek active control of ball-beam position stability by resorting to neural networks whose layers are given bias weights. The controller consists of an LQR (linear quadratic regulator) controller and a neural networks controller in parallel. The latter is used to improve the responses of the established LQR control system, especially when controlling the system with nonlinear factors or modelling errors. For the learning of this control system, the feedback-error learning algorithm is utilized here. While the neural networks controller learns repetitive trajectories on line, feedback errors are back-propagated through neural networks. Convergence is made when the neural networks controller reversely learns and controls the plant. The goals of teaming are to expand the working range of the adaptive control system and to bridge errors owing to nonlinearity by adjusting parameters against the external disturbances and change of the nonlinear plant. The motion equation of the ball-beam system is derived from Newton's law. As the system is strongly nonlinear, lots of researchers have depended on classical systems to control it. Its applications of position control are seen in planes, ships, automobiles and so on. However, the research based on artificial control is quite recent. The current paper compares and analyzes simulation results by way of the LQR controller and the neural network controller in order to prove the efficiency of the neural networks control algorithm against any nonlinear system.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.41 no.5
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• pp.335-341
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• 2013

The variation of systematic bias errors in the wind tunnel testing has been studied. A Design of Experiments(DOE) approach to an experimental study of fuselage drag and stability characteristics of a helicopter configuration was applied. When forces and moments measured in one time block differ significantly from measurements made in another time block under assumption that sample observations can be expected to yield same results within permissible measuring errors. The practical implication of this paper is that the systematic error can not be assumed not to exist. The those error reduction could be achieved through the process of randomization, blocking, and replication of the data points.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.6
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• pp.1045-1050
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• 2008

A GNSS augmentation system provides precision information using corrected GNSS pseudorange measurements. Common bias errors are corrected by PRC (Pseudorange Correction) between reference stations and a rover. However non-common errors (ionospheric and tropospheric noise error) are not corrected. Using position error variance this paper analyzes non-common error (noise errors) of ionosphere and troposphere wet vapor.

• The Korean Journal of Applied Statistics
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• v.21 no.3
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• pp.361-375
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• 2008

We propose proper methods to impute the item nonresponse in 4-8-4 rotation sample survey. We consider nonignorable nonresponse mechanism that can happen when survey deals with sensitive question (e.g. income, labor force). We utilize modeling imputation method based on Bayesian approach to avoid a boundary solution problem. We also estimate a interview time bias using imputed data and calculate cell expectation and marginal probability on fixed time after removing estimated bias. We compare the mean squared errors and bias between maximum likelihood method and Bayesian methods using simulation studies.

• The Journal of Korea Robotics Society
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• v.7 no.3
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• pp.205-215
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• 2012

A GPS sensor is widely used in many areas such as navigation, or air traffic control. Particularly, the car navigation system is equipped with GPS sensor for locational information. However, when a car goes through a tunnel, forest, or built-up area, GPS receiver cannot get the enough number of satellite signals. In these situations, a GPS receiver does not reliably work. A GPS error can be formulated by sum of bias error and sensor noise. The bias error is generated by the geometric arrangement of satellites and sensor noise error is generated by the corrupted signal noise of receiver. To enhance GPS sensor accuracy, these two kinds of errors have to be removed. In this research, we make the road database which includes Road Database File (RDF). RDF includes road information such as road connection, road condition, coordinates of roads, lanes, and stop lines. Among the information, we use the stop line coordinates as a feature point to correct the GPS bias error. If the relative distance and angle of a stop line from a car are detected and the detected stop line can be associated with one of the stop lines in the database, we can measure the bias error and correct the car's location. To remove the other GPS error, sensor noise, the Kalman filter algorithm is used. Additionally, using the RDF, we can get the information of the road where the car belongs. It can be used to help the GPS correction algorithm or to give useful information to users.

• Korean Journal of Remote Sensing
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• v.34 no.2_1
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• pp.249-265
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• 2018

The Korea Meteorological Administration has been operating wind profiler at 9 stations since the year of 2007. Among these stations, Bukgangneung is the only one that produces regularly both rawinsonde and wind profiler wind measurements at the same time. In this study, wind profiler measurements were compared with rawinsonde wind at Bukgangneung. Unlike most other studies which have used the temporal measurements for several days in summer season, in this study the routine rawinsonde measurments during almost one year (2016) were employed for the accuracy test of the wind. The monthly mean maximum observation height in Bukgangneung shows a large seasonal variation; it was relatively high in summer (4,310 m in July) and low in winter (2,130 m in December). The vertical observation rates at the altitude above these heights were less than 50%. The monthly and vertical wind comparison between rawinsonde and wind profiler shows that absolute bias and RMSE of zonal and meridional wind velocity are mostly less than 1 m/s and less than 2 m/s, respectively. In winter season the RMSE of wind velocity increased to 2~3 m/s. However, at some high altitudes and certain months, large errors were found. It is shown that these errors were related with very weak wind (less than 1 m/s) of wind profiler at 3,500~4,000 m from January to May and dramatic changes of wind the height of 1,500~2,500 m for in April. For Snow events the errors were lower than those for the winter season and for the heavy rain events the errors increased to 3~4 m/s at the height of 4~5 km.

• Journal of the Korea Computer Industry Society
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• v.6 no.5
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• pp.689-696
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• 2005

Finite failure NHPP models presented in the literature exhibit either constant, monotonic increasing or monotonic decreasing failure occurrence rates per fault. In this paper, Goel-Okumoto and Yamada-Ohba-Osaki model was reviewed, proposes the Kappa(2) reliability model, which can capture the nomotonic decreasing nature of the failure occurrence rate per fault. Algorithm to estimate the parameters used to maximum likelihood estimator and bisection method, model selection based on sum of the squared errors and Kolmogorov distance, for the sake of efficient model, was employed. Analysis of failure using real data set, SYS2(Allen P.Nikora and Michael R.Lyu), for the sake of proposing two parameter of the Kappa distribution, was employed. This analysis of failure data compared with the Kappa model and the existing model using arithmetic and Laplace trend tests, bias tests is presented.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.10a
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• pp.569-572
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• 2014

This paper presents a CMOS switched-capacitor interface circuit for MEMS capacitive sensors. It consist of a capacitance to voltage converter(CVC), a second-order ${\Sigma}{\Delta}$ modulator, and a comparator. A bias circuit is also designed to supply constant bias voltages and currents. This circuit employes the correlated-double-sampling(CDS) and chopper-stabilization(CHS) techniques to reduce low-frequency noise and offset. The designed CVC has a sensitivity of 20.53mV/fF and linearity errors less than 0.036%. The duty cycle of the designed ${\Sigma}{\Delta}$ modulator output increases about 5% as the input voltage amplitude increases by 100mV. The designed interface circuit shows linearity errors less than 0.13%, and the current consumption is 0.73mA. The proposed circuit is designed in a 0.35um CMOS process with a supply voltage of 3.3V. The size of the designed chip including PADs is $1117um{\times}983um$.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.33 no.6
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• pp.333-344
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• 2021

Technique for the long-gap filling that occur frequently in ocean monitoring data is developed. The method estimates the unknown values of the long-gap by the summation of the estimated trend and selected residual components of the given missing intervals. The method was used to impute the data of the long-term missing interval of about 1 month, such as temperature and water temperature of the Ulleungdo ocean buoy data. The imputed data showed differences depending on the monitoring parameters, but it was found that the variation pattern was appropriately reproduced. Although this method causes bias and variance errors due to trend and residual components estimation, it was found that the bias error of statistical measure estimation due to long-term missing is greatly reduced. The mean, and the 90% confidence intervals of the gap-filling model's RMS errors are 0.93 and 0.35~1.95, respectively.

• Atmosphere
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• v.31 no.5
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• pp.473-488
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• 2021

A new physical/statistical diagnostic downscale model has been developed for use to improve near-surface air temperature forecasts. The model includes a series of physical and statistical correction methods that account for un-resolved topographic and land-use effects as well as statistical bias errors in a low-resolution atmospheric model. Operational temperature forecasts of the Local Data Assimilation and Prediction System (LDAPS) were downscaled at 100 m resolution for three months, which were used to validate the model's physical and statistical correction methods and to compare its performance with the forecasts of the Korea Meteorological Administration Post-processing (KMAP) system. The validation results showed positive impacts of the un-resolved topographic and urban effects (topographic height correction, valley cold air pool effect, mountain internal boundary layer formation effect, urban land-use effect) in complex terrain areas. In addition, the statistical bias correction of the LDAPS model were efficient in reducing forecast errors of the near-surface temperatures. The new high-resolution downscale model showed better agreement against Korean 584 meteorological monitoring stations than the KMAP, supporting the importance of the new physical and statistical correction methods. The new physical/statistical diagnostic downscale model can be a useful tool in improving near-surface temperature forecasts and diagnostics over complex terrain areas.