• Korean Journal of Remote Sensing
• /
• v.33 no.4
• /
• pp.445-454
• /
• 2017

The performance of spatial downscaling models depends on the quality of input coarse scale products. Thus, the impact of intrinsic errors contained in coarse scale satellite products on predictive performance should be properly assessed in parallel with the development of advanced downscaling models. Such an assessment is the main objective of this paper. Based on a synthetic satellite precipitation product at a coarse scale generated from rain gauge data, two synthetic precipitation products with different amounts of error were generated and used as inputs for spatial downscaling. Geographically weighted regression, which typically has very high explanatory power, was selected as the trend component estimation model, and area-to-point kriging was applied for residual correction in the spatial downscaling experiment. When errors in the coarse scale product were greater, the trend component estimates were much more susceptible to errors. But residual correction could reduce the impact of the erroneous trend component estimates, which improved the predictive performance. However, residual correction could not improve predictive performance significantly when substantial errors were contained in the input coarse scale data. Therefore, the development of advanced spatial downscaling models should be focused on correction of intrinsic errors in the coarse scale satellite product if a priori error information could be available, rather than on the application of advanced regression models with high explanatory power.

• The Transactions of the Korean Institute of Electrical Engineers A
• /
• v.51 no.8
• /
• pp.371-378
• /
• 2002

This paper presents the rapid and accurate algorithm for fault detection and location estimation in the transmission line. This algorithm uses wavelet transform for fault detection and harmonics elimination and utilizes least square error method for fault impedance estimation. Wavelet transform decomposes fault signals into high frequence component Dl and low frequence component A3. The former is used for fault phase detection and fault types classification and the latter is used for harmonics elimination. After fault detection, an adaptive data window technique using LSE estimates fault impedance. It can find a optimal data window length and estimate fault impedance rapidly, because it changes the length according to the fault disturbance. To prove the performance of the algorithm, the authors test relaying signals obtained from EMTP simulation. Test results show that the proposed algorithm estimates fault location within a half cycle after fault irrelevant to fault types and various fault conditions.

• International Journal of Precision Engineering and Manufacturing
• /
• v.8 no.2
• /
• pp.64-69
• /
• 2007

Aerostatic planar XY stages are frequently used as the main frames of precision positioning systems. The machining and assembly process of the rails and bed of the stage is one of first processes performed when the system is built. When the system is complete, the 2D position, motion, and stage flatness errors are measured in tests. If the stage errors exceed the application requirements, the stage must be remachined and the assembly process must be repeated. This is difficult and time-consuming work. In this paper, a method for estimating the errors of a planar XY stage is proposed that can be applied when the rails and bed of the stage are evaluated. Profile measurements, estimates of the motion error, and 2D position estimation models were considered. A comparison of experimental results and our estimates indicated that the estimated errors were within $1{\mu}m$ of their true values. Thus, the proposed estimation method for 2D position and flatness errors of an aerostatic planar XY stage is expected to be a useful tool during the assembly process of guideways.

• Fisheries and Aquatic Sciences
• /
• v.24 no.4
• /
• pp.139-152
• /
• 2021

The main purpose of this study is to fit catch-per-unit-effort (CPUE) data about Korea chub mackerel (Scomber japonicus) stock with a state-space production (SSP) model, and to provide stock assessment results. We chose a surplus production model for the chub mackerel data, namely annual yield and CPUE. Then we employed a state-space layer for a production model to consider two sources of variability arising from unmodelled factors (process error) and noise in the data (observation error). We implemented the model via script software ADMB-RE because it reduces the computational cost of high-dimensional integration and provides Markov Chain Monte Carlo sampling, which is required for Bayesian approaches. To stabilize the numerical optimization, we considered prior distributions for model parameters. Applying the SSP model to data collected from commercial fisheries from 1999 to 2017, we estimated model parameters and management references, as well as uncertainties for the estimates. We also applied various production models and showed parameter estimates and goodness of fit statistics to compare the model performance. This study presents two significant findings. First, we concluded that the stock has been overexploited in terms of harvest rate from 1999 to 2017. Second, we suggest a SSP model for the smallest goodness of fit statistics among several production models, especially for fitting CPUE data with fluctuations.

• Journal of the Ergonomics Society of Korea
• /
• v.13 no.1
• /
• pp.15-25
• /
• 1994

Based on Fatal Accident Reporting System (FARS) data, safety belt effect- tiveness in preventing fatalities is investigated for the following five types of crashes: frontal, left, rear, right, and rollover. Passenger cars containing two occupants, a driver and a right front passenger, are included in this analysis. For each crash type, these cars containing the two occupants are classified into four categories according to the safety belt usage categories for the two front seat occupants, namely, both belted, both unbelted, and either one was belted but not both. Relative risks of driver and right front passenger fatalities are compared among these four cases. For each crash type, two independent estimates of safety belt effectiveness are obtained for drivers and for right front passengers. The weighted average of the two estimates is calculated for drivers and for right front passengers for the five crash types. Using FARS data starting 1978 throught 1983, safety belts are more effective in rollover accidents than in frontal collisions. In rollover accidents, safety belt effectiveness estimate for drivers is $68%{\pm} 6% $ and that for right front passengers is $71%{\pm}6% $ , in which the error limits indicate one standard error. Sfety belt effectiveness estimates for drivers and right front passengers involved in frontal collisions are $41%{\pm} 9% $ and $37%{\pm} 10% $ , respectively. For left and right sided collisions and for both drivers and right-front-passengers, none of the four estimates are significantly different from 0%, statistically : however, when left and right sided collisions are combined with far sided occupants(drivers involved in right sided collisions and right front passengers involved in left sided collisions) safety belt effectiveness is significant, $38%{\pm} 12% $ . For rear collisions, the estimate for drivers shows statistically significant positive effect, $60%{\pm}23% $ . while for right-front-passengers the estimate is not significantly different from 0%. These results show that a safety belt is an effective restraint system not only in frontal crashes but also in a variety of crashes.

• Communications for Statistical Applications and Methods
• /
• v.26 no.1
• /
• pp.47-55
• /
• 2019

It is important to identify informative variables in high dimensional data analysis; however, it becomes a challenging task when covariates are contaminated by measurement error due to the bias induced by measurement error. In this article, we present a two-step approach for variable selection in the presence of measurement error. In the first step, we directly select important variables from the contaminated covariates as if there is no measurement error. We then apply, in the following step, orthogonal regression to obtain the unbiased estimates of regression coefficients identified in the previous step. In addition, we propose a modification of the two-step approach to further enhance the variable selection performance. Various simulation studies demonstrate the promising performance of the proposed method.

• The Transactions of the Korean Institute of Electrical Engineers
• /
• v.41 no.9
• /
• pp.1021-1030
• /
• 1992

When using a least squares estimator with exponential forgetting factor to identify continuous-time deterministic system, the problem of determining minimum eigenvalue is described in this paper. It is well known fact that the convergence rate of parameter estimates relies on various factors consisting of the estimator and especially, theirproperties can be directly affected by all eigenvalues in the parameter error differential equation. Fortunately, there exists only one adjusting eigenvalue in the given estimator and then, the parameter convergence rates depend on this minimum eigenvalue. In this note, a new result to determine the minimum eigenvalue is proposed. Under the assumption that the input has as many spectral lines as the number of parameter estimates, it can be proven that the minimum eigenvalue converges to a constant value, which is a function of the forgetting factor and the parameter estimates number.

• Proceedings of the IEEK Conference
• /
• 2001.06a
• /
• pp.89-92
• /
• 2001

In this paper, adaptive multi-stage parallel interference cancellation (PIC) receiver is considered for a multi-rate DS-CDMA system. In each stage of the adaptive multi-stage PIC receiver, multiple access interference (MAI) estimates are obtained using the sub-bit estimates from the Previous stage and the adaptive weights for the sub-bit estimates. The adaptive weights are obtained by minimizing the mean squared error between the received signal and its estimate through a least mean square (LMS) algorithm. It is shown that the adaptive multi- stage PIC receiver achieves smaller BER than the matched filter receiver, multi-stage PIC receiver, and multi-stage partial PIC receiver for the multi-rate DS-CDMA system in a Rayleigh fading channel.

• The Korean Journal of Applied Statistics
• /
• v.25 no.6
• /
• pp.925-932
• /
• 2012

The article introduces a method to estimate a cointegrated vector autoregressive model, using mixed-frequency data, in terms of a state-space representation of the vector error correction(VECM) of the model. The method directly estimates the parameters of the model, in a state-space form of its VECM representation, using the available data in its mixed-frequency form. Then it allows one to compute in-sample smoothed estimates and out-of-sample forecasts at their high-frequency intervals using the estimated model. The method is applied to a mixed-frequency data set that consists of the quarterly real gross domestic product and three monthly coincident indicators. The result shows that the method produces accurate smoothed and forecasted estimates in comparison to a method based on single-frequency data.

• Journal of the Institute of Electronics Engineers of Korea SC
• /
• v.48 no.3
• /
• pp.21-29
• /
• 2011

This paper presents a method that estimates distance and velocity of an object with reliability regardless of maneuver status of the target in stereo vision system. A stereo vision system can calculate a distance with disparity from left and right images. However, the distance estimation error may occur due to quantization error of image pixel. A sub-pixel interpolation method minimizes the quantization error and estimates accurate disparity with real value. Extended Kalman filter (EKF) was used to minimize the error covariance and estimate the object's velocity. However, divergence problem occurs due to model uncertainty when a target maneuvers highly, which makes the estimation error increase. In this paper, we propose a virtual model extended Kalman filter (VMEKF) method that minimizes the processing time and provides reliable estimation ability regardless of maneuver status. Computer simulations and experimental results in real road environments demonstrate that the proposed method gives a reliable estimation performance and reduces processing time under various maneuver status while comparing other estimation filters.