• Progress in Medical Physics
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• v.22 no.2
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• pp.99-105
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• 2011

The purpose of this study is to evaluate the accuracy of IMRT in our clinic from based on TG119 procedure and establish action level. Five IMRT test cases were described in TG119: multi-target, head&neck, prostate, and two C-shapes (easy&hard). There were used and delivered to water-equivalent solid phantom for IMRT. Absolute dose for points in target and OAR was measured by using an ion chamber (CC13, IBA). EBT2 film was utilized to compare the measured two-dimensional dose distribution with the calculated one by treatment planning system. All collected data were analyzed using the TG119 specifications to determine the confidence limit. The mean of relative error (%) between measured and calculated value was $1.2{\pm}1.1%$ and $1.2{\pm}0.7%$ for target and OAR, respectively. The resulting confidence limits were 3.4% and 2.6%. In EBT2 film dosimetry, the average percentage of points passing the gamma criteria (3%/3 mm) was $97.7{\pm}0.8%$. Confidence limit values determined by EBT2 film analysis was 3.9%. This study has focused on IMRT commissioning and quality assurance based on TG119 guideline. It is concluded that action level were ${\pm}4%$ and ${\pm}3%$ for target and OAR and 97% for film measurement, respectively. It is expected that TG119-based procedure can be used as reference to evaluate the accuracy of IMRT for each institution.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.55 no.4
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• pp.275-283
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• 2010

This experiment was conducted to construct process models to estimate grain weight (GW) and grain nitrogen content (GN) in rice. A model was developed to describe the dynamic pattern of GW and GN during grain-filling period considering their relationships with temperature, solar radiation and growth traits such as LAI, shoot dry-weight, shoot nitrogen content, grain number during grain filling. Firstly, maximum grain weight (GWmax) and maximum grain nitrogen content (GNmax) equation was formulated in relation to Accumulated effective temperature (AET) ${\times}$ Accumulated radiation (AR) using boundary line analysis. Secondly, GW and GN equation were created by relating the difference between GW and GWmax and the difference between GN and GNmax, respectively, with growth traits. Considering the statistics such as coefficient of determination and relative root mean square of error and number of predictor variables, appropriate models for GW and GN were selected. Model for GW includes GWmax determined by AET ${\times}$ AR, shoot dry weight and grain number per unit land area as predictor variables while model for GN includes GNmax determined by AET ${\times}$ AR, shoot N content and grain number per unit land area. These models could explain the variations of GW and GN caused not only by variations of temperature and solar radiation but also by variations of growth traits due to different sowing date, nitrogen fertilization amount and row spacing with relatively high accuracy.

• Journal of radiological science and technology
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• v.39 no.4
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• pp.577-585
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• 2016

The purpose of this study is to evaluate the accuracy of beam delivery QA software using the MLC dynalog file, about the VMAT plan with AAPM TG-119 protocol. The Clinac iX with a built-in 120 MLC was used to acquire the MLC dynalog file be imported in MobiusFx(MFX). To establish VMAT plan, Oncentra RTP system was used target and organ structures were contoured in Im'RT phantom. For evaluation of dose distribution was evaluated by using gamma index, and the point dose was evaluated by using the CC13 ion chamber in Im'RT phantom. For the evaluation of point dose, the mean of relative error between measured and calculated value was $1.41{\pm}0.92%$(Target) and $0.89{\pm}0.86%$(OAR), the confidence limit were 3.21(96.79%, Target) and 2.58(97.42%, OAR). For the evaluation of dose distribution, in case of $Delta^{4PT}$, the average percentage of passing rate were $99.78{\pm}0.2%$(3%/3 mm), $96.86{\pm}1.76%$(2%/2 mm). In case of MFX, the average percentage of passing rate were $99.90{\pm}0.14%$(3%/3 mm), $97.98{\pm}1.97%$(2%/2 mm), the confidence limits(CL) were in case of $Delta^{4PT}$ 0.62(99.38%, 3%/3 mm), 6.6(93.4%, 2%/2 mm), in case of MFX, 0.38(99.62%, 3%/3 mm), 5.88(94.12%, 2%/2 mm). In this study, we performed VMAT QA method using dynamic MLC log file compare to binary diode array chamber. All analyzed results were satisfied with acceptance criteria based on TG-119 protocol.

• Journal of the Korea Concrete Institute
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• v.24 no.3
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• pp.293-303
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• 2012

Recently, as construction technology improved, concrete structures not only became larger, taller and longer but were able to perform various functions. However, if extreme loads such as impact, blast, and fire are applied to those structures, it would cause severe property damages and human casualties. Especially, the structural responses from extreme loading are totally different than that from quasi-static loading, because large pressure is applied to structures from mass acceleration effect of impact and blast loads. Therefore, the strain rate effect and damage levels should be considered when concrete structure is designed. In this study, the low velocity impact loading test of steel fiber reinforced concrete (SFRC) slabs including 0%~1.5% (by volume) of steel fibers, and strengthened with two types of FRP sheets was performed to develop an impact resistant structural member. From the test results, the maximum impact load, dissipated energy and the number of drop to failure increased, whereas the maximum displacement and support rotation were reduced by strengthening SFRC slab with FRP sheets in tensile zone. The test results showed that the impact resistance of concrete slab can be substantially improved by externally strengthening using FRP sheets. This result can be used in designing of primary facilities exposed to such extreme loads. The dynamic responses of SFRC slab strengthened with FRP sheets under low velocity impact load were also analyzed using LS-DYNA, a finite element analysis program with an explicit time integration scheme. The comparison of test and analytical results showed that they were within 5% of error with respect to maximum displacements.

• Korean Journal of Remote Sensing
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• v.37 no.6_1
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• pp.1739-1756
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• 2021

Atmospheric nitrogen dioxide (NO₂) is mainly caused by anthropogenic emissions. It contributes to the formation of secondary pollutants and ozone through chemical reactions, and adversely affects human health. Although ground stations to monitor NO₂ concentrations in real time are operated in Korea, they have a limitation that it is difficult to analyze the spatial distribution of NO₂ concentrations, especially over the areas with no stations. Therefore, this study conducted a comparative experiment of spatial interpolation of NO₂ concentrations based on two linear-regression methods(i.e., multi linear regression (MLR), and regression kriging (RK)), and two machine learning approaches (i.e., random forest (RF), and support vector regression (SVR)) for the year of 2020. Four approaches were compared using leave-one-out-cross validation (LOOCV). The daily LOOCV results showed that MLR, RK, and SVR produced the average daily index of agreement (IOA) of 0.57, which was higher than that of RF (0.50). The average daily normalized root mean square error of RK was 0.9483%, which was slightly lower than those of the other models. MLR, RK and SVR showed similar seasonal distribution patterns, and the dynamic range of the resultant NO₂ concentrations from these three models was similar while that from RF was relatively small. The multivariate linear regression approaches are expected to be a promising method for spatial interpolation of ground-level NO₂ concentrations and other parameters in urban areas.

• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.195-211
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• 2014

Cloud computing services provide IT resources as services on demand. This is considered a key concept, which will lead a shift from an ownership-based paradigm to a new pay-for-use paradigm, which can reduce the fixed cost for IT resources, and improve flexibility and scalability. As IT services, cloud services have evolved from early similar computing concepts such as network computing, utility computing, server-based computing, and grid computing. So research into cloud computing is highly related to and combined with various relevant computing research areas. To seek promising research issues and topics in cloud computing, it is necessary to understand the research trends in cloud computing more comprehensively. In this study, we collect bibliographic information and citation information for cloud computing related research papers published in major international journals from 1994 to 2012, and analyzes macroscopic trends and network changes to citation relationships among papers and the co-occurrence relationships of key words by utilizing social network analysis measures. Through the analysis, we can identify the relationships and connections among research topics in cloud computing related areas, and highlight new potential research topics. In addition, we visualize dynamic changes of research topics relating to cloud computing using a proposed cloud computing "research trend map." A research trend map visualizes positions of research topics in two-dimensional space. Frequencies of key words (X-axis) and the rates of increase in the degree centrality of key words (Y-axis) are used as the two dimensions of the research trend map. Based on the values of the two dimensions, the two dimensional space of a research map is divided into four areas: maturation, growth, promising, and decline. An area with high keyword frequency, but low rates of increase of degree centrality is defined as a mature technology area; the area where both keyword frequency and the increase rate of degree centrality are high is defined as a growth technology area; the area where the keyword frequency is low, but the rate of increase in the degree centrality is high is defined as a promising technology area; and the area where both keyword frequency and the rate of degree centrality are low is defined as a declining technology area. Based on this method, cloud computing research trend maps make it possible to easily grasp the main research trends in cloud computing, and to explain the evolution of research topics. According to the results of an analysis of citation relationships, research papers on security, distributed processing, and optical networking for cloud computing are on the top based on the page-rank measure. From the analysis of key words in research papers, cloud computing and grid computing showed high centrality in 2009, and key words dealing with main elemental technologies such as data outsourcing, error detection methods, and infrastructure construction showed high centrality in 2010~2011. In 2012, security, virtualization, and resource management showed high centrality. Moreover, it was found that the interest in the technical issues of cloud computing increases gradually. From annual cloud computing research trend maps, it was verified that security is located in the promising area, virtualization has moved from the promising area to the growth area, and grid computing and distributed system has moved to the declining area. The study results indicate that distributed systems and grid computing received a lot of attention as similar computing paradigms in the early stage of cloud computing research. The early stage of cloud computing was a period focused on understanding and investigating cloud computing as an emergent technology, linking to relevant established computing concepts. After the early stage, security and virtualization technologies became main issues in cloud computing, which is reflected in the movement of security and virtualization technologies from the promising area to the growth area in the cloud computing research trend maps. Moreover, this study revealed that current research in cloud computing has rapidly transferred from a focus on technical issues to for a focus on application issues, such as SLAs (Service Level Agreements).