• Journal of Positioning, Navigation, and Timing
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• v.12 no.1
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• pp.11-22
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• 2023

In this paper, we propose an efficient satellite selection method for multi-constellation GNSS. The number of visible satellites has increased dramatically recently due to multi-constellation GNSS. By the increased availability, the overall GNSS performance can be improved. Whereas, due to the increase of the number of visible satellites, the computational burden in implementing advanced processing such as integer ambiguity resolution and fault detection can be increased considerably. As widely known, the optimal satellite selection method requires very large computational burden and its real-time implementation is practically impossible. To reduce computational burden, several sub-optimal but efficient satellite selection methods have been proposed recently. However, these methods are prone to the local optimum problem and do not fully utilize the information redundancy between different constellation systems. To solve this problem, the proposed method utilizes the inter-system biases and geometric assignments. As a result, the proposed method can be implemented in real-time, avoids the local optimum problem, and does not exclude any single-satellite constellation. The performance of the proposed method is compared with the optimal method and two popular sub-optimal methods by a simulation and an experiment.

• Journal of Korea Technology Innovation Society
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• v.14 no.1
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• pp.22-39
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• 2011

This study examines the wage level from a viewpoint of major-job match as part of an analysis on the skill mismatch problem in 4-year college graduates. The empirical analysis explicitly incorporate the sample selection bias as an econometric problem not only suggested but merely introduced in the earlier studies. This study also set up a major-job match variable, which was usually handled as a binary variable for analytical convenience, as a polychotomous choice variable in selection equation as provided by the survey. In particular, it considered multi-cohort survey on graduates of the years 1982, 1992, and 2002 for the empirical analysis. As a result of empirical analysis, the wage premium of a major-job match was identified. This result was consistent after the consideration of a sample selection bias and also after modeling the major-job match variable as polychotomously selective. Through an analysis classified by the major, this study identified a relatively high wage premium among Social Science, Engineering, and Science majors. However, there was a difference in the effect of selection among these majors. Also, by assessing cohort effects this study found that the skill mismatch had rapidly progressed in 1992, while difference between 1992 and 2002 cohorts are insignificant. The analysis suggests that wage level is better understood within the context of both sample selection and major-job match, and regardless of model specification the major-job match affects wage strongly.

• Journal of Internet Computing and Services
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• v.11 no.2
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• pp.61-72
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• 2010

This paper addresses the problem of moving object localization using Ultra-Wide-Band(UWB) range measurement and the method of location accuracy improvement of the indoor moving object. Unlike outdoor environment, it is difficult to track moving object position due to various noises in indoor. UWB is a radio technology that has attention for localization applications recently. UWB's ranging technique offer the cm accuracy. Its capabilities for data transmission, range accurate estimation and material penetration are suitable technology for indoor positioning application. This paper propose a positioning algorithm of an moving object using UWB ranging technique and particle filter. Existing positioning algorithms eliminate estimation errors and bias after location estimation of mobile object. But in this paper, the proposed algorithm is that eliminate predictable UWB range distance error first and then estimate the moving object's position. This paper shows that the proposed positioning algorithm is more accurate than existing location algorithms through experiments. In this study, the position of moving object is estimated after the triangulation and eliminating the bias and the ranging error from estimation range between three fixed known anchors and a mobile object using UWB. Finally, a particle filter is used to improve on accuracy of mobile object positioning. The results of experiment show that the proposed localization scheme is more precise under the indoor.

• Journal of the Korea Convergence Society
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• v.11 no.9
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• pp.1-6
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• 2020

In this study, we propose an association inference model based on data bias optimization for road hazard detection. This is a mining model based on association analysis to collect user's personal characteristics and surrounding environment data and provide traffic accident prevention services. This creates transaction data composed of various context variables. Based on the generated information, a meaningful correlation of variables in each transaction is derived through correlation pattern analysis. Considering the bias of classified categorical data, pruning is performed with optimized support and reliability values. Based on the extracted high-level association rules, a risk detection model for personal characteristics and driving road conditions is provided to users. This enables traffic services that overcome the data bias problem and prevent potential road accidents by considering the association between data. In the performance evaluation, the proposed method is excellently evaluated as 0.778 in accuracy and 0.743 in the Kappa coefficient.

• Korean Journal of Agricultural and Forest Meteorology
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• v.24 no.3
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• pp.155-163
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• 2022

The optimization of long-range ensemble climate prediction for rice phenology model with advanced bias correction method is conducted. The daily long-range forecast(6-month) of mean/ minimum/maximum temperature and observation of January to October during 1991-2021 is collected for rice phenology prediction. In this study, the concept of "buffer period" is newly introduced to reduce the problem after bias correction by quantile mapping with constructing the transfer function by month, which evokes the discontinuity at the borders of each month. The four experiments with different lengths of buffer periods(5, 10, 15, 20 days) are implemented, and the best combinations of buffer periods are selected per month and variable. As a result, it is found that root mean square error(RMSE) of temperatures decreases in the range of 4.51 to 15.37%. Furthermore, this improvement of climatic variables quality is linked to the performance of the rice phenology model, thereby reducing RMSE in every rice phenology step at more than 75~100% of Automated Synoptic Observing System stations. Our results indicate the possibility and added values of interdisciplinary study between atmospheric and agriculture sciences.

• 한국노년학
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• v.37 no.4
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• pp.871-891
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• 2017

The purpose of this paper is to reduce the reverse-causality and overestimate bias of analysis on how health affected middle-aged and elderly worker's early retirement. From the Korean Longitudinal Study of Ageing(KLoSA) panel data, I researched 1,049 people who were 45-52 years old in 2006. To eliminate the reverse-causality problem, I used the health data which is surveyed before retirement. To reduce bias, I controlled the health status when retirees worked. The main results are as follows. First, the worsened health still affects the hazard of early retirement, with reducing the endogeneity problem. Second, chronic illness is one of the strong predictors of early retirement to self-employed, and self-reported bad health is the main health predictor of wage workers. These results give two implications; first, the impact magnitude of the health indicator depends on employment type. Each employment type has different flexibility of working hours. It seems that the flexibility can reduce early retirement hazard with health problems. Self-employed, who has more flexibility of working hours can work until they have to stop working due to the serious health problem or doctor's advice. Second, to promote middle-aged and elderly workers to keep working, the long-term health policy which decreases chronic illness is needed.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.45 no.4
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• pp.137-145
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• 2008

In this paper, novel CMOS charge pump having NMOS and PMOS transfer switches and a bulk-pumping circuit has been proposed. The NMOS and PMOS transfer switches allow the charge pump to improve the current-driving capability at the output. The bulk-pumping circuit effectively solves the bulk forward problem of the charge pump. To verify the effectiveness, the proposed charge pump was designed using a 80-nm CMOS process. The comparison results indicate that the proposed charge pump enhances the current-driving capability by more than 47% with pumping speed improved by 9%, as compared to conventional charge pumps having either NMOS or PMOS transfer switch. They also indicate that the charge pump reduces the worst-case forward bias of p-type bulk by more than 24%, effectively solving the forward current problem.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.2
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• pp.200-209
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• 2019

When forecasting future values, a model estimated after minimizing training errors can yield test errors higher than the training errors. This result is the over-fitting problem caused by an increase in model complexity when the model is focused only on a given dataset. Some regularization and resampling methods have been introduced to reduce test errors by alleviating this problem but have been designed for use with only a given dataset. In this paper, we propose a new optimization approach to reduce test errors by transforming a test error minimization problem into a training error minimization problem. To carry out this transformation, we needed additional data for the given dataset, termed pseudo data. To make proper use of pseudo data, we used three types of missing data imputation techniques. As an optimization tool, we chose the least squares method and combined it with an extra pseudo data instance. Furthermore, we present the numerical results supporting our proposed approach, which resulted in less test errors than the ordinary least squares method.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.2594-2599
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• 2003

We consider some problems of the Modified SPRT(Sequential Probability Ratio Test) method for fault detection and isolation of inertial redundant sensor systems and propose an Advanced SPRT method to solve the problems of the Modified SPRT method. One problem of the Modified SPRT method to apply to inertial sensor system comes from the effect of inertial sensor errors such as misalignment, scale factor error and sensor bias in the parity vector, which make the Modified SPRT method hard to be applicable. The other problem is due to the correlation of parity vector components which may induce false alarm. We use a two-stage Kalman filter to remove effects of the inertial sensor errors and propose the modified parity vector and the controlled parity vector which removes the effect of correlation of parity vector components. The Advanced SPRT method is derived form the modified parity vector and the controlled parity vector. Some simulation results are presented to show the usefulness of the Advanced SPRT method to redundant inertial sensor systems.

• Journal of Labour Economics
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• v.35 no.2
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• pp.79-115
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• 2012

In the study, we try to get reliable estimates of intergenerational income mobility in Korea. At first, we show that the low estimates of previous studies are mainly due to sample selection problem. The direct estimations using OLS after correcting this problem show higher values than previous estimates. We also compute the attenuation bias by decomposing the variances of earnings into the variances of permanent and transitory components of earnings by the results of the regression. Additionally, we try to estimate the range of intergenerational mobility by comparing the OLS results with the results of the two samples instrumental variable estimation and the three samples instrumental variable estimation. The results of these estimations are a little higher than or similar to OLS results.