• Journal of the Korean Statistical Society
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• 제27권3호
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• pp.319-329
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• 1998

In the linear regression model with an autocorrelated disturbances, the Cochrane-Orcutt estimator (COE) is a well known alternative to the Generalized Least Squares estimator (GLSE). The efficiency of COE has been studied empirically in a Monte Carlo study when the unknown parameters are estimated by maximum likelihood method. In this paper, it is theoretically proved that the COE is shown to be inferior to the GLSE. The comparisons are based on the difference of corresponding information matrices or the ratio of their determinants.

• Communications for Statistical Applications and Methods
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• 제28권6호
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• pp.627-641
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• 2021

An asymmetric least squares estimation method has been employed to estimate linear models for percentile regression. An asymmetric maximum likelihood estimation (AMLE) has been developed for the estimation of Poisson percentile linear models. In this study, we propose generalized nonlinear percentile regression using the AMLE, and the use of the parametric bootstrap method to obtain confidence intervals for the estimates of parameters of interest and smoothing functions of estimates. We consider three conditional distributions of response variables given covariates such as normal, exponential, and Poisson for three mean functions with one linear and two nonlinear models in the simulation studies. The proposed method provides reasonable estimates and confidence interval estimates of parameters, and comparable Monte Carlo asymptotic performance along with the sample size and quantiles. We illustrate applications of the proposed method using real-life data from chemical and radiation epidemiological studies.

• 설비공학논문집
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• 제15권6호
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• pp.533-542
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• 2003

This study aims to propose a simplified mathematical model for calculating vertical air temperature distribution in a four-sided atrium. In the first stage of the mathematical modeling, the computer model combined zonal model and solar radiation model using Monte Carlo method and Ray tracing technique went through a computer simulation with architectural variables applied to a four-sided atrium in summer. In the next stage, Curve Expert, a computer program that gets the most suitable solution ac-cording to the least squares method, is used to analyze the results of the computer simulation and to derive the mathematical model. The accuracy of the mathematical model was evaluated through a comparison of calculation results from a mathematical model and computer simulation. In this validation step using the least square method, the R2 value of the Zones 1, 2 and 3 showed higher than 0.945. Zone 4 has an R2 value of 0.911, lower than the previous three zones. However the relative error was below 0.5%, which is considered very small.

• 응용통계연구
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• 제21권6호
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• pp.901-910
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• 2008

국내 삼일열 말라리아는 1990년대 중반부터 급격히 증가하여 2000년대에는 연평균 1600여명이 발병하고 있다. 본 연구에서는 이미 알려진 잠복기 분포와 2001년부터 2006년까지의 발병자수 자료에 바탕하여 후향연산식을 활용하여 국내 삼일열 말라리아의 감염분포를 최소제곱법으로 추정하였다. 추정된 감염분포는 평균이 207일이고 표준편차가 30.7일인 정규분포를 이루었다. 이를 이용하여 연간 유병자수 분포를 산출한 결과, 말라리아의 하루 평균 유병자수는 628.8명 이었다.

• ETRI Journal
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• 제35권6호
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• pp.1084-1093
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• 2013

problem of pedestrian localization using mobile nodes containing impulse radio ultra wideband (IR-UWB) is considered. IEEE 802.15.4a-based IR-UWB can achieve accurate ranging. However, the coverage is as short as 30 m, owing to the restricted transmit power. This factor may cause a poor geometric relationship among the mobile nodes and anchor nodes in certain environments. To localize a group of pedestrians accurately, an enhanced cooperative localization method is proposed. We describe a sequential algorithm and define problems that may occur in the implementation of the algorithm. To solve these problems, a batch algorithm is proposed. The batch algorithm can be carried out after performing the sequential algorithm to linearize the nonlinear range equation. When a sequential algorithm cannot be performed due to a poor geometric relationship among nodes, a batch algorithm can be carried out directly. Herein, Monte Carlo simulations are presented to illustrate the proposed method and verify its performance.

• 제어로봇시스템학회논문지
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• 제19권7호
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• pp.633-639
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• 2013

Several estimation methods used in the range measurement based wireless localization area have individual problems. These problems may not occur according to certain application areas. However, these problems may give rise to serious problems in particular applications. In this paper, three methods, ILS (Iterative Least Squares), DS (Direct Solution), and DSRM (Difference of Squared Range Measurements) methods are considered. Problems that can occur in these methods are defined and a simple hybrid solution is presented to solve them. The ILS method is the most frequently used method in wireless localization and has local minimum problems and a large computational burden compared with closed-form solutions. The DS method requires less processing time than the ILS method. However, a solution for this method may include a complex number caused by the relations between the location of reference nodes and range measurement errors. In the near-field region of the complex solution, large estimation errors occur. In the DSRM method, large measurement errors occur when the mobile node is far from the reference nodes due to the combination of range measurement error and range data. This creates the problem of large localization errors. In this paper, these problems are defined and a hybrid localization method is presented to avoid them by integrating the DS and DSRM methods. The defined problems are confirmed and the performance of the presented method is verified by a Monte-Carlo simulation.

• International Journal of Aeronautical and Space Sciences
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• 제11권4호
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• pp.326-337
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• 2010

This work addresses problems regarding trajectory planning for unmanned aerial vehicle sensors. Such sensors are used for taking measurements of large nonlinear systems. The sensor investigations presented here entails methods for improving estimations and predictions of large nonlinear systems. Thoroughly understanding the global system state typically requires probabilistic state estimation. Thus, in order to meet this requirement, the goal is to find trajectories such that the measurements along each trajectory minimize the expected error of the predicted state of the system. The considerable nonlinearity of the dynamics governing these systems necessitates the use of computationally costly Monte-Carlo estimation techniques, which are needed to update the state distribution over time. This computational burden renders planning to be infeasible since the search process must calculate the covariance of the posterior state estimate for each candidate path. To resolve this challenge, this work proposes to replace the computationally intensive numerical prediction process with an approximate covariance dynamics model learned using a nonlinear time-series regression. The use of autoregressive time-series featuring a regularized least squares algorithm facilitates the learning of accurate and efficient parametric models. The learned covariance dynamics are demonstrated to outperform other approximation strategies, such as linearization and partial ensemble propagation, when used for trajectory optimization, in terms of accuracy and speed, with examples of simplified weather forecasting.

• 방사성폐기물학회지
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• 제19권1호
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• pp.87-111
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• 2021

Nuclear power plants (NPPs) produce radioactive waste and decommissioning this waste entails additional cost; determining these costs for various types and specifications of radioactive waste can be challenging. The purpose of this study is to identify major determinants of the decommissioning cost and their impact on NPPs. To this end, data from defunct NPPs were gathered and 2SLS (Two Stage Least Squares) regression models were developed to investigate the major contributors depending on the reactor types, viz. PWR (Pressurized Water Reactors) and BWR (Boiling Water Reactors). Additionally, cost estimations and the Monte Carlo simulation were performed as part of performance validation. Our study established that the decommissioning costs are primarily influenced by the level of radioactivity in the decommissioned waste, which can be realized from operational factors like operation period, overall efficiency, and plant capacity, as well as from duration of decommissioning and labour cost. While our study provides an improved statistical approach to recognize these factors, we acknowledge that our models have limitations in forecasting accurately which we envisage to bolster in future studies by identifying more substantive factors.

• 응용통계연구
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• 제23권5호
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• pp.967-975
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• 2010

데이터마이닝 분야에서의 회귀모형에는 연관성이 높은 설명변수들이 포함되어 다중공선성을 유발하는 경우가 많은데, 다중공선성이 야기하는 문제를 해결하기 위하여 주성분회귀분석을 적용할 수 있다. 이 분석에서는 적절한 주성분을 선정하는 과정이 핵심인데, 기존의 선정방법들은 다중공선성을 잘 해결하지 못하거나 모형의 적합성을 저하시킨다는 지적을 받고 있다. 따라서 본 논문에서는 다중공선성 문제와 적합성 저하 현상을 동시에 해결할 수 있는 새로운 선정방법을 제안하였다. 다중공선성에 의해 최소제곱추정량의 분산이 팽창되는 문제를 주성분회귀에 의해 해결할 수 있지만, 주성분의 일부를 선정함에 따라 발생하는 편의도 동시에 통제해야 한다. 따라서 주성분회귀추정량의 평균제곱오차를 최소가 되게 하는 상태지수를 측정하고, 이 값에 영향을 미치는 주요 요인들을 컨조인트분석에 의해 파악하여 주성분 선정기준 모형을 구축하였다. 선정기준의 상한과 하한을 설정하고, 상태지수가 상한을 초과하면 해당 주성분을 제외시키고, 하한에 미달하면 해당 주성분을 포함시킨다. 그리고 상한과 하한 사이의 상태지수에 대응하는 주성분들에 대해서는 일반화선형검정을 순차적으로 적용하여 주성분을 선정하는 방법이다.

• Smart Structures and Systems
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• 제31권2호
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• pp.101-111
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• 2023

Structural health monitoring (SHM) covers a range of damage detection strategies for buildings. In real-time, SHM provides a basis for rapid decision making to optimise the speed and economic efficiency of post-event response. Previous work introduced an SHM method based on identifying structural nonlinear hysteretic parameters and their evolution from structural force-deformation hysteresis loops in real-time. This research extends and generalises this method to investigate the impact of a wide range of flag-shaped or pinching shape nonlinear hysteretic response and its impact on the SHM accuracy. A particular focus is plastic stiffness (K_p), where accurate identification of this parameter enables accurate identification of net and total plastic deformation and plastic energy dissipated, all of which are directly related to damage and infrequently assessed in SHM. A sensitivity study using a realistic seismic case study with known ground truth values investigates the impact of hysteresis loop shape, as well as added noise, on SHM accuracy using a suite of 20 ground motions from the PEER database. Monte Carlo analysis over 22,000 simulations with different hysteresis loops and added noise resulted in absolute percentage identification error (median, (IQR)) in K_p of 1.88% (0.79, 4.94)%. Errors were larger where five events (Earthquakes #1, 6, 9, 14) have very large errors over 100% for resulted K_p as an almost entirely linear response yielded only negligible plastic response, increasing identification error. The sensitivity analysis shows accuracy is reduces to within 3% when plastic drift is induced. This method shows clear potential to provide accurate, real-time metrics of non-linear stiffness and deformation to assist rapid damage assessment and decision making, utilising algorithms significantly simpler than previous non-linear structural model-based parameter identification SHM methods.