• Korean Journal of Remote Sensing
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• v.17 no.4
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• pp.345-357
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• 2001

This review provides a summary of data assimilation applied to the seas around Korea. Currently the worldwide efforts are devoted to applying advanced assimilation to realistic cases, thanks to improvements in mathematical foundations of assimilation methods and the computing capabilities, and also to the availability of extensive observational data such as from satellites. Over the seas around Korea, however, the latest developments in the advanced assimilation methods have yet to be applied. Thus it would be timely to review the progress in data assimilation over the seas. Firstly, the definition and necessity of data assimilation are described, continued by a brief summary of major assimilation methods. Then a review of past research on the ocean data assimilation in the regional seas around Korea is given and future trends are considered. Special consideration is given to the assimilation of remotely-sensed data.

• The Korean Journal of Applied Statistics
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• v.27 no.7
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• pp.1077-1095
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• 2014

Unlike randomized trial, statistical strategies for inferring the unbiased causal relationship are required in the observational studies. Recently, new methods for the causal inference in the observational studies have been proposed such as the matching with the propensity score or the inverse probability treatment weighting. They have focused on how to control the confounders and how to evaluate the effect of the treatment on the result variable. However, these conventional methods are valid only when the treatment variable is categorical and both of the treatment and the result variables are directly observable. Research on the causal inference can be challenging in part because it may not be possible to directly observe the treatment and/or the result variable. To address this difficulty, we propose a method for estimating the average causal effect when both of the treatment and the result variables are latent. The latent class analysis has been applied to calculate the propensity score for the latent treatment variable in order to estimate the causal effect on the latent result variable. In this work, we investigate the causal effect of adolescents delinquency on their substance use using data from the 'National Longitudinal Study of Adolescent Health'.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.31 no.5C
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• pp.155-162
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• 2011

Settlement prediction based on field monitored data, which is used to control subsequent surcharges, is very important in construction management for soft ground improvement with the preloading method. Observational settlement prediction methods, which are suggested for an instantaneous loading, have been widely used in fields. However, they have difficulties in the settlement prediction with subsequent surcharge change. In this paper, a simple method to predict the settlement with subsequent surcharge change is suggested. The suggested method adopts assumptions to simplify the complex field condition and utilizes observational methods. The suggested method is applied to a large consolidation test result, FDM analysis results, and field monitored settlement data to confirm its practicability. From the applications, the suggested method produces reasonable prediction results with various subsequent surcharge changes.

• The Korean Journal of Applied Statistics
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• v.30 no.5
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• pp.615-632
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• 2017

Unlike randomized trial, statistical strategies for inferring the unbiased causal relationship are required in the observational studies. The matching with the propensity score is one of the most popular methods to control the confounders in order to evaluate the effect of the treatment on the outcome variable. Recently, new methods for the causal inference in latent class analysis (LCA) have been proposed to estimate the average causal effect (ACE) of the treatment on the latent discrete variable. They have focused on the application study for the real dataset to estimate the ACE in LCA. In practice, however, the true values of the ACE are not known, and it is difficult to evaluate the performance of the estimated the ACE. In this study, we propose a method to generate a synthetic data using the propensity score in the framework of LCA, where treatment and outcome variables are latent. We then propose a new method for estimating the ACE in LCA and evaluate its performance via simulation studies. Furthermore we present an empirical analysis based on data form the 'National Longitudinal Study of Adolescents Health,' where puberty as a latent treatment and substance use as a latent outcome variable.

• Publications of The Korean Astronomical Society
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• v.30 no.2
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• pp.205-209
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• 2015

The Eclipsing Binaries Minima (BIMA) Monitoring Project is a CCD-based photometric observational program initiated by Bosscha Observatory - Lembang, Indonesia in June 2012. Since December 2012 the National Astronomical Research Institute of Thailand (NARIT) has joined the BIMA Project as the main partner. This project aims to build an open-database of eclipsing binary minima and to establish the orbital period of each system and its variations. The project is conducted on the basis of multisite monitoring observations of eclipsing binaries with magnitudes less than 19 mag. Differential photometry methods have been applied throughout the observations. Data reduction was performed using IRAF. The observations were carried out in BVRI bands using three different small telescopes situated in Indonesia, Thailand, and Chile. Computer programs have been developed for calculating the time of minima. To date, more than 140 eclipsing binaries have been observed. From them 71 minima have been determined. We present and discuss the O-C diagrams for some eclipsing binary systems.

• Journal of Korean Society of Transportation
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• v.31 no.6
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• pp.67-89
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• 2013

This study is to perform empirical analysis on observational before-after studies in Naive Method, Comparison Group(CG) Method and Empirical Bayes(EB) Method, and to compare with their results and to propose ways to apply to evaluation researches. For this purpose, the evaluation of road safety audit executed on Y$\breve{o}$ng-dong freeway in 2005 and 2006 was performed. As a result, all three methods have showed improved effects due to safety treatments. The safety effectiveness of Naive method is the largest, CG Method is the second and EB method is the last. The results of Naive method are overestimated due to the trend of reducing traffic accidents and those of CG method are affected by the external casual effects of comparison group. In the EB method, as "regression to the mean" phenomenon are controlled by reference group's accident model, it's result is relatively more accurate than that of other methods. In the conduct of evaluation studies, the analysts have to understand the pros and cons of each evaluation method. And after leading the survey on accident trends of related all sites, evaluation analysis is performed to be able to minimize bias.

• Journal of Astronomy and Space Sciences
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• v.34 no.1
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• pp.31-35
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• 2017

Optical observation is one of the most common techniques used for characterizing the physical properties of unknown objects and debris in space. This research presents measurements and properties of the new object 96019 from ground-based optical methods. Optical observations of this small object were performed using a charge-coupled device (CCD) camera and the Santel-500 telescope at the Zvenigorod Observatory. The orbital elements and physical properties of this object, such as area-to-mass ratio, have been determined. The results show that this small object has a low area-to-mass ratio, between 0.009 and $0.12m^2/kg$. The light curve of object 96019 is given: Over the time intervals, variations in brightness are analyzed and the maximum brightness was found to be 12.4 magnitudes. The observational results show that, this object brightens by about three magnitudes over a time span of three minutes. Based on these observations, the characteristics and physical properties of this object are discussed.

• International Journal of Advanced Culture Technology
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• v.4 no.2
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• pp.1-6
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• 2016

Impervious surfaces are important indicators for urban development monitoring. Accurate mapping of urban impervious surfaces with observational satellites, such as VNREDSat-1, remains challenging due to the spectral diversity not captured by an individual PAN image. In this article, five multi-resolution image fusion techniques were compared for the task of classifting urban impervious surfaces. The result shows that for VNREDSat-1 dataset, UNB and Wavelet tranformation methods are the best techniques in reserving spatial and spectral information of original MS image, respectively. However, the UNB technique gives the best results when it comes to impervious surface classification, especially in the case of shadow areas included in non-impervious surface group.

• Journal of The Korean Astronomical Society
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• v.34 no.4
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• pp.297-299
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• 2001

We have constructed the foundations to a series of theoretical diagnostic methods to probe the jet phenomenon in young stars as observed at various optical forbidden lines. We calculate and model in a self-consistent manner the physical and radiative processes which arise within an inner disk-wind driven magneto centrifugally from the circumstellar accretion disk of a young sun-like star. Comparing with real data taken at high angular resolution, our approach will provide the basis of systematic diagnostics for jets and their related young stellar objects, to attest the emission mechanisms of such phenomena. This work can help bring first-principle theoretical predictions to confront actual multi-wavelength observations, and will bridge the link between many very sophiscated numerical simulations and observational data. Analysis methods discussed here are immediately applicable to new high-resolution data obtained with HST and Adaptic Optics.

• The Korean Journal of Applied Statistics
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• v.34 no.3
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• pp.309-327
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• 2021

Dynamic treatment regimes (DTRs) are decision-making rules designed to provide personalized treatment to individuals in multi-stage randomized trials. Unlike classical methods, in which all individuals are prescribed the same type of treatment, DTRs prescribe patient-tailored treatments which take into account individual characteristics that may change over time. The Q-learning method, one of regression-based algorithms to figure out optimal treatment rules, becomes more popular as it can be easily implemented. However, the performance of the Q-learning algorithm heavily relies on the correct specification of the Q-function for response, especially in observational studies. In this article, we examine a number of double-robust weighted least-squares estimating methods for Q-learning in high-dimensional settings, where treatment models for propensity score and penalization for sparse estimation are also investigated. We further consider flexible ensemble machine learning methods for the treatment model to achieve double-robustness, so that optimal decision rule can be correctly estimated as long as at least one of the outcome model or treatment model is correct. Extensive simulation studies show that the proposed methods work well with practical sample sizes. The practical utility of the proposed methods is proven with real data example.