• 제어로봇시스템학회:학술대회논문집
• /
• 1997.10a
• /
• pp.524-527
• /
• 1997

Conventional target tracking algorithms based on the linear estimation techniques perform quite efficiently when the target motion does not involve maneuvers. Target maneuvers involving short term accelerations, however, cause a bias in the measurement sequence. Accurate compensation for the bias requires processing more samples of which adds to the computational complexity. The primary motivation for employing a neural network for this task comes from the efficiency with which more features can be as inputs for bias compensation. A system architecture that efficiently integrates the fusion capabilities of a trained multilayer neural net with the tracking performance of a Kalman filter is described. The parallel processing capability of a properly trained neural network can permit fast processing of features to yield correct acceleration estimates and hence can take the burden off the primary Kalman filter which still provides the target position and velocity estimates.

• Proceedings of the Korean Association for Survey Research Conference
• /
• 2003.06a
• /
• pp.19-28
• /
• 2003

Weighting adjustment is a method of improving the efficiency of the estimator by incorporating auxiliary variables at the estimation stage. One commonly used method of weighting adjustment is the poststratification, which is a special case of regression estimation but is relatively feasible in terms of actual implementation. If too many auxiliary variables are used in the poststratification, the bias of the resulting point estimator is no longer negligible and the final weights may have extreme weights. In this study, we propose a method of weight ing adjustment that compromises the efficiency and the bias of the point estimator. A limited simulation study is also presented.

• Korean Medical Education Review
• /
• v.12 no.1
• /
• pp.3-8
• /
• 2010

This study is designed to evaluate the effectiveness of teaching or studying programs, and thus to overcome the selectionbias in studies. Selection-bias derived from unobservable characteristics in the course of participants selection of the teaching or studying programs, in the case of cross-section data instrumental variable(IV) method and two stage least square estimation were suggested as an analysis tool. Panel data were analyzed by using both fixed effect in which individual effects are captured by intercept terms and random effect estimation where an unobserved effect can be characterized as being randomly drawn from a given distribution.

• Journal of Multimedia Information System
• /
• v.5 no.4
• /
• pp.245-256
• /
• 2018

This study estimated rainfall information more effectively by image signals through the information system of weather radar. Based on this, we suggest the way to estimate quantitative precipitation utilizing overlapped observation area of radars. We used the overlapped observation range of ground hyetometer observation network and radar observation network which are dense in our country. We chose the southern coast where precipitation entered from seaside is quite frequent and used Sungsan radar installed in Jeju island and Gudoksan radar installed in the southern coast area. We used the rainy season data generated in 2010 as the precipitation data. As a result, we found a reflectivity bias between two radar located in different area and developed the new quantitative precipitation estimation method using the bias. Estimated radar rainfall from this method showed the apt radar rainfall estimate than the other results from conventional method at overall rainfall field.

• Communications for Statistical Applications and Methods
• /
• v.2 no.2
• /
• pp.227-230
• /
• 1995

In this paper, we discuss simple ways of implementing non-basic kernel density estimators which typically ceed extra pilot estimation. The methods utilize order statistics at the pilot estimation stages. We focus mainly on bariable lacation and scale kernel density estimator (Jones, Hu and McKay, 1994), but the same idea can be applied to other methods too.

• Communications for Statistical Applications and Methods
• /
• v.6 no.2
• /
• pp.501-510
• /
• 1999

In this paper, we consider a comparable study on three Bayes procedures for the multivariate normal mean estimation problem. In specific we consider hierarchical Bayes empirical Bayes and robust Bayes estimators for the normal means. Then three procedures are compared in terms of the four comparison criteria(i.e. Average Relative Bias (ARB) Average Squared Relative Bias (ASRB) Average Absolute Bias(AAB) Average Squared Deviation (ASD) using the real data set.

• 제어로봇시스템학회:학술대회논문집
• /
• 1994.10a
• /
• pp.238-242
• /
• 1994

Shuster's algorithm for spin-axis determination is extended to include sensor bias and mounting angle as its solve-for parameters. The relation between direct and derived measurements bias is obtained by linearizing their kinematic equations. A one-step least-square estimation technique referred to as the 'closed form' solution is used, and the solution provides a more refined and decent initial guess for the subsequent filtering process contained within the differential correction module. The modified algorithm is applied for attitude determination of a GEO communication satellite in transfer orbit, and its results are presented.

• Quantitative Bio-Science
• /
• v.37 no.2
• /
• pp.133-141
• /
• 2018

Graphical lasso is one of the most popular methods to estimate a sparse precision matrix, which is an inverse of a covariance matrix. The objective function of graphical lasso imposes an ${\ell}_1$-penalty on the (vectorized) precision matrix, where a tuning parameter controls the strength of the penalization. The selection of the tuning parameter is practically and theoretically important since the performance of the estimation depends on an appropriate choice of tuning parameter. While information criteria (e.g. AIC, BIC, or extended BIC) have been widely used, they require an asymptotically unbiased estimator to select optimal tuning parameter. Thus, the biasedness of the ${\ell}_1$-regularized estimate in the graphical lasso may lead to a suboptimal tuning. In this paper, we propose a two-staged bias-correction procedure for the graphical lasso, where the first stage runs the usual graphical lasso and the second stage reruns the procedure with an additional constraint that zero estimates at the first stage remain zero. Our simulation and real data example show that the proposed bias correction improved on both edge recovery and estimation error compared to the single-staged graphical lasso.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2020.06a
• /
• pp.120-120
• /
• 2020

Spatial precipitation data is one of the essential components in modeling hydrological problems. The estimation of these data has achieved significant achievements own to the recent advances in remote sensing technology. However, there are still gaps between the satellite-derived rainfall data and observed data due to the significant dependence of rainfall on spatial and temporal characteristics. An effective approach based on the Convolutional Neural Network (CNN) model to correct the satellite-derived rainfall data is proposed in this study. The Mekong River basin, one of the largest river system in the world, was selected as a case study. The two gridded precipitation data sets with a spatial resolution of 0.25 degrees used in the CNN model are APHRODITE (Asian Precipitation - Highly-Resolved Observational Data Integration Towards Evaluation) and PERSIANN-CDR (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks). In particular, PERSIANN-CDR data is exploited as satellite-based precipitation data and APHRODITE data is considered as observed rainfall data. In addition to developing a CNN model to correct the satellite-based rain data, another statistical method based on standard deviations for precipitation bias correction was also mentioned in this study. Estimated results indicate that the CNN model illustrates better performance both in spatial and temporal correlation when compared to the standard deviation method. The finding of this study indicated that the CNN model could produce reliable estimates for the gridded precipitation bias correction problem.

• International Journal of Internet, Broadcasting and Communication
• /
• v.12 no.3
• /
• pp.95-101
• /
• 2020

We studied the Implementation and design of inertial sensor that enables to improve performance by reduce the noise of rotor which Angle of inclination. Analyze model equation including motion equation and error, signal processing filter algorithm on high frequency bandwidth with eliminates error using estimation of error coefficient method is was designed and the prototype inertial sensor showed the pick off noise up to 0.2 mV and bias error performance of about 0.06 deg/hr by the experiments. Accordingly, we confirmed that the design of inertial sensor was valid for high rotation.