• 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.

• Journal of Broadcast Engineering
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• v.1 no.2
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• pp.176-187
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• 1996

This paper deals with the accurate estimation of 3- D pose (position and orientation) of a moving object with reference to the world frame (or robot base frame), based on a sequence of stereo images taken by cameras mounted on the end - effector of a robot manipulator. This work is an extension of the previous work[1]. Emphasis is given to the 3-D pose estimation relative to the world (or robot base) frame under the presence of not only the measurement noise in 2 - D images[ 1] but also the camera position errors due to the random noise involved in joint angles of a robot manipulator. To this end, a new set of discrete linear Kalman filter equations is derived, based on the following: 1) the orientation error of the object frame due to measurement noise in 2 - D images is modeled with reference to the camera frame by analyzing the noise propagation through 3- D reconstruction; 2) an extended Jacobian matrix is formulated by combining the result of 1) and the orientation error of the end-effector frame due to joint angle errors through robot differential kinematics; and 3) the rotational motion of an object, which is nonlinear in nature, is linearized based on quaternions. Motion parameters are computed from the estimated quaternions based on the iterated least-squares method. Simulation results show the significant reduction of estimation errors and also demonstrate an accurate convergence of the actual motion parameters to the true values.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.26 no.2
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• pp.157-164
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• 2008

GPS baseline analysis is a mathematical procedure which estimates a baseline vector from carrier-phase double-differenced observations. Least squares technique is generally applied for the processing and integer ambiguities in the observations should be resolved to obtain maximum accuracy of the solution. In GPS control surveying, after assembling the baseline solutions into a network, adjustment is performed to derive final coordinate sets of unknown points. This paper deals with details of GPS baseline analysis for the $2^{nd}$ order national geodetic network adjustment. After reviewing GPS campaigns carried out by National Geographic Information Institute (NGII) and their observations. technical issues and considerations for the GPS baseline analysis are presented with emphasis of selecting the processing strategies and software. Finally, the analyzed results will be evaluated by examining the close of figures formed by joining the processed baseline vectors.

• Journal of Korea Water Resources Association
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• v.36 no.6
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• pp.1025-1033
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• 2003

In common with workers in hydrologic fields, scientists and engineers relate one variable to two or more other variables for purposes of predication, optimization, and control. Statistics methods have improved to establish such relationships. Regression, as it is called, is indeed the most commonly used statistics technique in hydrologic fields; relationship between the monitored variable stage and the corresponding discharges(rating curve). Regression methods expressed in the form of mathematical equations which has parameters, so called parametric methods. some times, the establishment of parameters is complicated and uncertain. Many non-parametric regression methods which have not parameters, have been proposed and studied. The most popular of these are kernel regression method. Kernel regression offer a way of estimation the regression function without the specification of a parametric model. This paper conducted comparisons of some bandwidth selection methods which are using the least squares and cross-validation.

• Journal of Digital Contents Society
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• v.11 no.2
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• pp.217-224
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• 2010

The purpose of this study was to examine the usefulness of IDEAL technique in breast MRI by performing a quantitative comparative analysis in patients diagnosed with DCIS. On a 3.0T MR scanner, fat-suppressed T2-weighted images and T1-weighted images before and after contrast enhancement were obtained from 20 patients histologically diagnosed with ductal carcinoma in situ (DCIS). The findings from the quantitative image analysis are the following: 1) On T2-weighted images, SNR were not significantly different in the lesion area itself between the CHESS and IDEAL groups, while the IDEAL group showed higher SNR at the ductal area and fat area than the CHESS group. In addition, the CNR were higher for the IDEAL group in those regions. 2) On T1-weighted images before enhancement, SNR were not significantly different in the lesion area itself between the CHESS and IDEAL groups, while the IDEAL group showed higher SNR at the ductal area and fat area than the CHESS group. In addition, the CNR were higher for the IDEAL group in those regions. 3) On T1-weighted images after enhancement, SNR were not significantly different in the lesion area itself between the CHESS and IDEAL groups, while the IDEAL group showed higher SNR at the ductal area and fat area than the CHESS group.

• Nuclear Engineering and Technology
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• v.38 no.1
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• pp.81-92
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• 2006

In this study, the core dynamics of a PWR reactor is identified online by a recursive least-squares method. Based on the identified reactor model consisting of the control rod position and the core average coolant temperature, the future average coolant temperature is predicted. A model predictive control method is applied to designing an automatic controller for the thermal power control of PWR reactors. The basic concept of the model predictive control is to solve an optimization problem for a finite future at current time and to implement as the current control input only the first optimal control input among the solutions of the finite time steps. At the next time step, this procedure for solving the optimization problem is repeated. The objectives of the proposed model predictive controller are to minimize both the difference between the predicted core coolant temperature and the desired temperature, as well as minimizing the variation of the control rod positions. In addition, the objectives are subject to the maximum and minimum control rod positions as well as the maximum control rod speed. Therefore, a genetic algorithm that is appropriate for the accomplishment of multiple objectives is utilized in order to optimize the model predictive controller. A three-dimensional nuclear reactor analysis code, MASTER that was developed by the Korea Atomic Energy Research Institute (KAERI) , is used to verify the proposed controller for a nuclear reactor. From the results of a numerical simulation that was carried out in order to verify the performance of the proposed controller with a $5\%/min$ ramp increase or decrease of a desired load and a $10\%$ step increase or decrease (which were design requirements), it was found that the nuclear power level controlled by the proposed controller could track the desired power level very well.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.32 no.4
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• pp.43-52
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• 2004

In this paper, the performance of the KOMPSAT-1 orbit determination (OD) accuracy at the ground station was analyzed by using the flight data. The Bayesian least squares estimation was used for the orbit determination and the assessment of the orbit accuracy was evaluated based on orbit overlap comparisons. We also compared the result from OD using GPS navigation solutions with NORAD TLE and the result from OD using range data. Furthermore, the effect of observation type and OBT drift on the accuracy was investigated. As a consequence, It is shown that the OD accuracy using only GPS position data is on the order of 5m RMS (Root Mean Square) with 4 hrs arc overlap for the 30hr arc and the GPS velocity data is not proper as a observation for the OD due to its inferior quality. The significant deterioration of the accuracy due to the critical clock bias was not founded by means of the comparison of OD result from other observations.

• The Journal of Industrial Distribution & Business
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• v.10 no.1
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• pp.69-76
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• 2019

Purpose - The purpose of this paper is to empirically investigate the effectiveness of Offcial Development Assistance (ODA) in recipient countries' economy. ODA is designed to mitigate poverty and stimulate economic growth in the developing countries. We classify total ODA into bilateral ODA and multilateral ODA depending on the number of donor countries. If the ODA flows from one donor country to one recipient country, it is classified as bilateral ODA. If the multiple countries simultaneously become donor countries through the international organizations such as United Nations and World Bank, it is classified as multilateral ODA. This paper compares the effect of bilateral ODA and multilateral ODA in determining recipient countries' economic development, and tries to provide policy implications to Korean ODA. Research design, data, and methodology - Our primary explanatory variables are bilateral and multilateral ODA. Private credit in recipient countries is adopted as additional explanatory variables to capture the level of financial development in recipient countries. We measure the ODA effectiveness using economic growth and quality of life of the recipient countries as the dependent variable. We collect 142 recipient countries' data from OECD statistics, during the period from 1970-2014. Panel least squares estimation with country fixed effect is employed as the empirical model. Results - Our results support that ODA variable has a negatively significant impact on recipient countries' economic growth, while it is positively correlated with human development index. Recipient countries' private credit is positively correlated with economic growth and human development index. The interaction variable of ODA and financial development turns out to be significant in general. We find that the positive effect of ODA depends on recipient countries' financial market development and this effect is stronger in multilateral aid than bilateral one. Conclusions - From the analysis, we have confirmed that the recipient countries financial development is the necessity condition to achieve positive effect of ODA. Based on these results, we suggest that Korean government should increase the share of multilateral funding and pay attention to recipient countries' financial market development to maximize the effectiveness of ODA.

• Journal of Magnetics
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• v.17 no.2
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• pp.145-152
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• 2012

Patients who underwent hip arthroplasty using the conventional fat suppression technique (CHESS) and a new technique (IDEAL) were compared quantitatively to assess the effectiveness and usefulness of the IDEAL technique. In 20 patients who underwent hip arthroplasty from March 2009 to December 2010, fat suppression T2 and T1 weighted images were obtained on a 3.0T MR scanner using the CHESS and IDEAL techniques. The level of distortion in the area of interest, the level of the development of susceptibility artifacts, and homogeneous fat suppression were analyzed from the acquired images. Quantitative analysis revealed the IDEAL technique to produce a lower level of image distortion caused by the development of susceptibility artifacts due to metal on the acquired images compared to the CHESS technique. Qualitative analysis of the anterior area revealed the IDEAL technique to generate fewer susceptibility artifacts than the CHESS technique but with homogeneous fat suppression. In the middle area, the IDEAL technique generated fewer susceptibility artifacts than the CHESS technique but with homogeneous fat suppression. In the posterior area, the IDEAL technique generated fewer susceptibility artifacts than the CHESS technique. Fat suppression was not statistically different, and the two techniques achieved homogeneous fat suppression. In conclusion, the IDEAL technique generated fewer susceptibility artifacts caused by metals and less image distortion than the CHESS technique. In addition, homogeneous fat suppression was feasible. In conclusion, the IDEAL technique generates high quality images, and can provide good information for diagnosis.

• Journal of the Korean Association of Geographic Information Studies
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• v.20 no.1
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• pp.42-55
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• 2017

Since the phases and patterns of the climate adaptability of vegetation can greatly differ from region to region, an intensive pixel scale approach is required. In this study, Partial Least Squares (PLS) regression on satellite image-based vegetation index is conducted for to assess the effect of climate factors on vegetation productivity and to predict future productivity of forests vegetation in South Korea. The results indicate that the mean temperature of wettest quarter (Bio8), mean temperature of driest quarter (Bio9), and precipitation of driest month (Bio14) showed higher influence on vegetation productivity. The predicted 2050 EVI in future climate change scenario have declined on average, especially in high elevation zone. The results of this study can be used in productivity monitoring of climate-sensitive vegetation and estimation of changes in forest carbon storage under climate change.