• Transactions of the Korean Society of Automotive Engineers
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• v.10 no.6
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• pp.234-240
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• 2002

This paper treats the problem of estimating the longitudinal velocity of a braking vehicle using measurements from an accelerometer and wheel speed data from standard anti-lock braking wheel speed sensors. We develop and experimentally test three velocity estimation algorithms of increasing complexity. The algorithm that works the best gives peak errors of less than 3 percent even when the accelerometer signal is significantly biased.

• Survey Research
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• v.10 no.1
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• pp.169-186
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• 2009

The propensity score method is used to minimize the bias level in social survey, which comes from nonresponse. The theoretical concept and the background of the propensity score method is discussed first. The propensity score method was first applied in the epidemiology observational study. I have summarized the process of the three propensity score methods that were used to reduce estimation bias in this study. Matching by propensity score is applied to the relatively large control group. Subclassification has the advantage of using whole control group data and regression adjustment is applied to multiple covariates as well as propensity score of each unit is computable and usable. Lastly, the application procedures of propensity score method to reduce the nonresponse bias is suggested and its applicability to real situation is reviewed with the existing data.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.2
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• pp.415-421
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• 2009

TOA(time-of-arrival) and TDOA(time-difference-of-arrival) positioning techniques are commonly used in many radio-navigation systems. From the literature, it is known that the position estimate and error covariance matrix of TDOA obtained by GN(Gauss-Newton) method is exactly the same as that of TOA when the error source of the range measurement is only an IID white Gaussian noise. In case of geo-location and indoor positioning, however, multi-path or NLOS(non-line-of-sight) error is frequently appeared in range measurements. Though its occurrence is random, the multipath acts like a bias for a stationary user if it occurs. This paper presents the comparisons of error characteristics between TOA and TDOA positioning in presence of multi-path or NLOS error. It is analytically shown that the position estimate of TDOA is exactly the same as that of TOA even when bias errors are included in range measurements with different magnitudes. By computer simulation, position estimation error and error distribution are analyzed in presence of range bias errors.

• ETRI Journal
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• v.34 no.3
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• pp.379-387
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• 2012

In this paper, an error compensation technique for a dead reckoning (DR) system using a magnetic compass module is proposed. The magnetic compass-based azimuth may include a bias that varies with location due to the surrounding magnetic sources. In this paper, the DR system is integrated with a Global Positioning System (GPS) receiver using a finite impulse response (FIR) filter to reduce errors. This filter can estimate the varying bias more effectively than the conventional Kalman filter, which has an infinite impulse response structure. Moreover, the conventional receding horizon Kalman FIR (RHKF) filter is modified for application in nonlinear systems and to compensate the drawbacks of the RHKF filter. The modified RHKF filter is a novel RHKF filter scheme for nonlinear dynamics. The inverse covariance form of the linearized Kalman filter is combined with a receding horizon FIR strategy. This filter is then combined with an extended Kalman filter to enhance the convergence characteristics of the FIR filter. Also, the receding interval is extended to reduce the computational burden. The performance of the proposed DR/GPS integrated system using the modified RHKF filter is evaluated through simulation.

• The Korean Journal of Applied Statistics
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• v.30 no.1
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• pp.69-82
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• 2017

R-indicator (representativeness indicator) is used to check the representativeness of samples when non-responses occur. The representativeness is related with the accuracy of parameter estimator and the accuracy is related with bias of the estimator. Hence, unbiased estimator generates high accuracy. Therefore, high value of R-indicator guarantees the accuracy of parameter estimation with a small bias. R-indicator is calculated through propensity scores obtained by logit or probit modeling. In this paper we investigate the degree of relation between R-indicator and different non-response rates in strata using simulation studies. We also analyze a modified Korea Economic Census data for real data analysis.

• Safety and Health at Work
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• v.12 no.2
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• pp.174-183
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• 2021

Background: Toluene diisocyanate (TDI) is a highly reactive chemical that causes sensitization and has also been associated with increased lung cancer. A risk assessment was conducted based on occupational epidemiologic estimates for several health outcomes. Methods: Exposure and outcome details were extracted from published studies and a NIOSH Health Hazard Evaluation for new onset asthma, pulmonary function measurements, symptom prevalence, and mortality from lung cancer and respiratory disease. Summary exposure-response estimates were calculated taking into account relative precision and possible survivor selection effects. Attributable incidence of sensitization was estimated as were annual proportional losses of pulmonary function. Excess lifetime risks and benchmark doses were calculated. Results: Respiratory outcomes exhibited strong survivor bias. Asthma/sensitization exposure response decreased with increasing facility-average TDI air concentration as did TDI-associated pulmonary impairment. In a mortality cohort where mean employment duration was less than 1 year, survivor bias pre-empted estimation of lung cancer and respiratory disease exposure response. Conclusion: Controlling for survivor bias and assuming a linear dose-response with facility-average TDI concentrations, excess lifetime risks exceeding one per thousand occurred at about 2 ppt TDI for sensitization and respiratory impairment. Under alternate assumptions regarding stationary and cumulative effects, one per thousand excess risks were estimated at TDI concentrations of 10 - 30 ppt. The unexplained reported excess mortality from lung cancer and other lung diseases, if attributable to TDI or associated emissions, could represent a lifetime risk comparable to that of sensitization.

• Journal of Korean Society of Forest Science
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• v.111 no.1
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• pp.136-149
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• 2022

In this study, the performance of a nonlinear mixed-effects (NLME) model used to estimate the stem taper of Pinus densiflora in Gangwon Province was compared with that of a nonlinear fixed-effects (NLFE) model using several performance measures. For the diameters of whole tree stems, the NLME model improved on the performance of the NLFE model by 26.4%, 42.9%, 43.1%, and 0.9% in terms of BIAS, MAB, RMSE, and FI, respectively. For the cross-section areas of whole tree stems, the NLME model improved on the performance of the NLFE model by 67.7%, 44.7%, 45.8%, and 1.0% in terms of BIAS, MAB, RMSE, and FI, respectively. Based on the analysis of 12 relative height classes of tree stems, stem taper estimation performance was also reasonably improved by the NLME model, which showed better MAB, RMSE, and FI at every relative height class compared with those of the NLFE model. In some classes, the NLFE model had better BIAS than the NLME model (stem diameter: 0.05, 0.2, 0.3, and 0.8; stem cross-section area: 0.05, 0.3, 0.5, 0.6, and 1.0). However, the NLME model enhanced the performance of stem diameter and cross-section area estimations at the lowest stem part (0.2 m from the ground). Improvements for stem diameter in terms of BIAS, MAB, RMSE, and FI were 84.2%, 69.8%, 68.7%, and 3.1%, respectively. For stem cross-section areas, the improvements in BIAS, MAB, RMSE, and FI were 98.5%, 70.1%, 68.7%, and 3.1%, respectively. The cross-section area at 0.2 m from the ground occupied 22.7% of total cross-section area. Improvements in estimation of cross-section area at the lowest stem part indicate that stem volume estimation performance could also be enhanced. Although NLME models are more difficult to fit than NLFE models, the use of NLME models as a standard method for the estimating the parameters of stem taper equations should be considered.

• Korean Journal of School Psychology
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• v.17 no.3
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• pp.263-286
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• 2020

The purpose of this study was to explore how overly positive self-estimations in peer relationships relate to subjective well-being and to the occurrence of interpersonal behaviors supporting basic psychological needs among elementary school students. This study tested the optimal margin hypothesis of positive illusion by examining the curvilinear relationship between these variables. The sample consisted of 346 fifth and sixth grade students. The self-criterion residual method was used to derive self-estimation bias scores by regressing the real peer relations index (i.e., In-degree) on their perceived peer relationship qualities. The results showed that girls more strongly overestimated the quality of their peer relationships than boys. Self-estimation biases had a positive curvilinear relationship with negative affects and a negative curvilinear relationship with relatedness needs supporting interpersonal behaviors. These results supported the existence of the optimal margin of positive illusion because overestimations of the quality of peer relationships were associated with lower levels of negative affects and relatedness needs-supporting interpersonal behaviors, though these benefits flattened out and no further benefit was observed after an optimal level of overestimation. However, self-estimation bias was linearly associated with positive affect, autonomy needs-supporting interpersonal behaviors, and competence needs-supporting interpersonal behaviors. These results indicated that optimal margin hypothesis was not supported for all outcome variables.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.160-160
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• 2023

Generally, Global Climate Models (GCM) cannot be used directly due to their inherent error arising from over or under-estimation of climate variables compared to the observed data. Several bias correction methods have been devised to solve this problem. Most of the traditional bias correction methods are one dimensional as they bias correct the climate variables separately. One such method is the Quantile Mapping method which builds a transfer function based on the statistical differences between the GCM and observed variables. Laux et al. introduced a copula-based method that bias corrects simulated climate data by employing not one but two different climate variables simultaneously and essentially extends the traditional one dimensional method into two dimensions. but it has some limitations. This study uses objective functions to address specifically, the limitations of Laux's methods on the Quantile Mapping method. The objective functions used were the observed rank correlation function, the observed moment function and the observed likelihood function. To illustrate the performance of this method, it is applied to ten GCMs for 20 stations in South Korea. The marginal distributions used were the Weibull, Gamma, Lognormal, Logistic and the Gumbel distributions. The tested copula family include most Archimedean copula families. Five performance metrics are used to evaluate the efficiency of this method, the Mean Square Error, Root Mean Square Error, Kolmogorov-Smirnov test, Percent Bias, Nash-Sutcliffe Efficiency and the Kullback Leibler Divergence. The results showed a significant improvement of Laux's method especially when maximizing the observed rank correlation function and when maximizing a combination of the observed rank correlation and observed moments functions for all GCMs in the validation period.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.23 no.6
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• pp.563-573
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• 2013

The ship radiated noise level fluctuates by the interference between direct and reflected paths. The effect of sea surface reflection path on interference depends strongly on sea surface roughness. This paper describes error characteristics of ship acoustic signature estimation by sea surface scattering effect. The coherent reflection coefficient which explains a magnitude of sea surface scattering and its resultant interference acoustic field is analyzed quantitatively as a function of a grazing angle, effective surface height, frequency, source-receiver range and depths of source and receiver. Theoretical interference acoustic field is compared with experimental result for two different sea surfaces and five different frequencies by changing source-receiver range. It is found that both matches well each other and a magnitude of interference acoustic field is decreasing by increasing a grazing angle, effective surface height, frequency, and depths of source and receiver and decreasing source-receiver range. For given experimental conditions, the transmission anomaly which is a bias error of ship acoustic signature estimation, is about a range of 1~3 dB. The bias error of an existing ship radiated noise measurement system is also analyzed considering wind speed, source depth and frequency.