• Progress in Medical Physics
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• v.20 no.4
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• pp.269-276
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• 2009

Radiation treatment techniques using photon beam such as three-dimensional conformal radiation therapy (3D-CRT) as well as intensity modulated radiotherapy treatment (IMRT) demand accurate dose calculation in order to increase target coverage and spare healthy tissue. Both jaw collimator and multi-leaf collimators (MLCs) for photon beams have been used to achieve such goals. In the Pinnacle3 treatment planning system (TPS), which we are using in our clinics, a set of model parameters like jaw collimator transmission factor (JTF) and MLC transmission factor (MLCTF) are determined from the measured data because it is using a model-based photon dose algorithm. However, model parameters obtained by this auto-modeling process can be different from those by direct measurement, which can have a dosimetric effect on the dose distribution. In this paper we estimated JTF and MLCTF obtained by the auto-modeling process in the Pinnacle3 TPS. At first, we obtained JTF and MLCTF by direct measurement, which were the ratio of the output at the reference depth under the closed jaw collimator (MLCs for MLCTF) to that at the same depth with the field size $10{\times}10\;cm^2$ in the water phantom. And then JTF and MLCTF were also obtained by auto-modeling process. And we evaluated the dose difference through phantom and patient study in the 3D-CRT plan. For direct measurement, JTF was 0.001966 for 6 MV and 0.002971 for 10 MV, and MLCTF was 0.01657 for 6 MV and 0.01925 for 10 MV. On the other hand, for auto-modeling process, JTF was 0.001983 for 6 MV and 0.010431 for 10 MV, and MLCTF was 0.00188 for 6 MV and 0.00453 for 10 MV. JTF and MLCTF by direct measurement were very different from those by auto-modeling process and even more reasonable considering each beam quality of 6 MV and 10 MV. These different parameters affect the dose in the low-dose region. Since the wrong estimation of JTF and MLCTF can lead some dosimetric error, comparison of direct measurement and auto-modeling of JTF and MLCTF would be helpful during the beam commissioning.

• The Korean Journal of Nuclear Medicine
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• v.37 no.5
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• pp.288-300
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• 2003

Purpose: To obtain regional blood flow and tissue-blood partition coefficient with time-activity curves from ${H_2}^{15}O$ PET, fitting of some parameters in the Kety model is conventionally accomplished by nonlinear least squares (NLS) analysis. However, NLS requires considerable compuation time then is impractical for pixel-by-pixel analysis to generate parametric images of these parameters. In this study, we investigated several fast parameter estimation methods for the parametric image generation and compared their statistical reliability and computational efficiency. Materials and Methods: These methods included linear least squres (LLS), linear weighted least squares (LWLS), linear generalized least squares (GLS), linear generalized weighted least squares (GWLS), weighted Integration (WI), and model-based clustering method (CAKS). ${H_2}^{15}O$ dynamic brain PET with Poisson noise component was simulated using numerical Zubal brain phantom. Error and bias in the estimation of rCBF and partition coefficient, and computation time in various noise environments was estimated and compared. In audition, parametric images from ${H_2}^{15}O$ dynamic brain PET data peformed on 16 healthy volunteers under various physiological conditions was compared to examine the utility of these methods for real human data. Results: These fast algorithms produced parametric images with similar image qualify and statistical reliability. When CAKS and LLS methods were used combinedly, computation time was significantly reduced and less than 30 seconds for $128{\times}128{\times}46$ images on Pentium III processor. Conclusion: Parametric images of rCBF and partition coefficient with good statistical properties can be generated with short computation time which is acceptable in clinical situation.

• Journal of Korea Water Resources Association
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• v.34 no.6
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• pp.659-672
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• 2001

Generally, design flood for a hydraulic structure is estimated using statistical analysis of runoff data. However, due to the lack of runoff data, it is difficult that the statistical method is applied for estimation of design flood. In this case, the synthetic unit hydrograph method is used generally and the models such as NYMO method, Snyder method, SCS method, and HYMO method have been widely used in Korea. In this study, these methods and KICT method, which is developed in year 2000, are compared and analyzed in 10 study areas. Firstly, peak flow and peak time of representative unit hydrograph and synthetic unit hydrograph in study area are compared, and secondly, the shape of unit hydrograph is compared using a root mean square error(RMSE). In Nakayasu method developed in Japan, synthetic unit hydrograph is very different from peak flow, peak time, and the shape of representative unit hydrograph, and KICT method(2000) is superior to others. Also, KICT method(2000) is superior to others in the aspects of using hydrologic and topographical data. Therefore, Nakayasu method is not a proper in hydrological practice. Moreover, it is considered that KICT model is a better method for the estimation of design flood. However, if other model, i.e. SCS method, Nakayasu method, and HYMO method, is used, parameters or regression equations must be adjusted by analysis of real data in Korea.

• Journal of the Korean earth science society
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• v.39 no.3
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• pp.228-240
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• 2018

Solar energy is calculated using meteorological (14 station), ceilometer (2 station) and microwave radiometer (MWR, 7 station)) data observed from the Weather Information Service Engine (WISE) on the Seoul metropolitan area. The cloud optical thickness and the cloud fraction are calculated using the back-scattering coefficient (BSC) of the ceilometer and liquid water path of the MWR. The solar energy on the surface is calculated using solar radiation model with cloud fraction from the ceilometer and the MWR. The estimated solar energy is underestimated compared to observations both at Jungnang and Gwanghwamun stations. In linear regression analysis, the slope is less than 0.8 and the bias is negative which is less than $-20W/m^2$. The estimated solar energy using MWR is more improved (i.e., deterministic coefficient (average $R^2=0.8$) and Root Mean Square Error (average $RMSE=110W/m^2$)) than when using ceilometer. The monthly cloud fraction and solar energy calculated by ceilometer is greater than 0.09 and lower than $50W/m^2$ compared to MWR. While there is a difference depending on the locations, RMSE of estimated solar radiation is large over $50W/m^2$ in July and September compared to other months. As a result, the estimation of a daily accumulated solar radiation shows the highest correlation at Gwanghwamun ($R^2=0.80$, RMSE=2.87 MJ/day) station and the lowest correlation at Gooro ($R^2=0.63$, RMSE=4.77 MJ/day) station.

• Journal of Korean Society of Forest Science
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• v.61 no.1
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• pp.37-44
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• 1983

This study was to estimate total annual volume growth by the measurement of mean tree growth during the last 10 years. Surveyed Forest stand was the second block (20.80 ha.)of Kyung Hee University Forests located at San 58 and 64, Gaegok-Ri, Gapyung-Yeup, Gapyung-Goon, Kyunggi province in Korea. The stand was mainly composed of uneven-aged Pinus densiflora and the estimation of tree volume was conducted by taking the cores at the D.B.H. of the sample tree which was selected by sub-sampling. The results obtained were as follows; 1) The regression between the diameter (D) and diameter growth ($\hat{I}$) was $\hat{I}=0.5499+0.0101D$. 2) The estimated equation of confidence interval for the diameter growth was $S^2{\hat{I}}=0.00817(0.09538-0.00952D+0.00027D^2$) 3) The equation for estimating tree height (H) from diameter was $H=1.32376D^{0.77958}$ 4) The equation for estimating tree volume from diameter and height $V=0.0000622D^{1.6918}H^{1.1397}$ 5) Total annual tree volume growth was $5.4041m^3/ha$, and ranged from 5.6131 to $5.1984m^3/ha$. 6) Annual growth rate of total tree volume and its error were 8.8% and 3.9%, respectively. The annual volume growth per tree for any districts can be estimated by this method, and the annual volume growth will be successfully predicted. Because of poor forest growing stock in Korea, annual amount of allowable cut should not exceed annual tree volume growth for better forest management. Accordingly, annual amount of allowable cut should be either equal to or less than annual tree volume growth for the balanced establishment between timber supply and demand in Korea. Demand shortage will be substituted with imported timber. Such plans enable Korean Government to develop a better policy of forest resources management.

• Korean Journal of Agricultural and Forest Meteorology
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• v.7 no.2
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• pp.132-140
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• 2005

This study was conducted to develop regional site index equations for main tree species in Gangwon, Gyunggi-Chungcheong, Gyungsang, and Jeolla area of Korea, using environmental and soil factors obtained from a digital forest site map. Using the large data set obtained from the digital forest map, a total of 28 environmental and soil factors were regressed on site index by tree species for developing the best site index equations for each of the regions. The selected main tree species were Larix 1eptolepis, Pinus koraiensis, Pinus densiflora, Pinus thunbergii, and Quercus acutissima. Finally, four to five environmental and soil factors by species were chosen as independent variables in defining the best regional site index equations with the highest coefficients of determination $(R^2)$. For those site index equations, three evaluation statistics such as mean difference, standard deviation of difference and standard error of difference were applied to the data sets independently collected from fields within the region. According to the evaluation statistics, it was found that the regional site index equations by species developed in this study conformed well to the independent data set, having relatively low bias and variation. It was concluded that the regional site index equations by species had sufficient capability for the estimation of site productivity.

• KDI Journal of Economic Policy
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• v.31 no.1
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• pp.169-206
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• 2009

This study stems from a question, "How should we understand the pattern of the Korean economy after the 1990s?" Among various analytic methods applicable, this study chooses a Structural Vector Autoregression (SVAR) with long-run restrictions, identifies diverse impacts that gave rise to the current status of the Korean economy, and differentiates relative contributions of those impacts. To that end, SVAR is applied to four economic models; Blanchard and Quah (1989)'s 2-variable model, its 3-variable extensions, and the two other New Keynesian type linear models modified from Stock and Watson (2002). Especially, the latter two models are devised to reflect the recent transitions in the determination of foreign exchange rate (from a fixed rate regime to a flexible rate one) as well as the monetary policy rule (from aggregate targeting to inflation targeting). When organizing the assumed results in the form of impulse response and forecasting error variance decomposition, two common denominators are found as follows. First, changes in the rate of economic growth are mainly attributable to the impact on productivity, and such trend has grown strong since the 2000s, which indicates that Korea's economic growth since the 2000s has been closely associated with its potential growth rate. Second, the magnitude or consistency of impact responses tends to have subsided since the 2000s. Given Korea's high dependence on trade, it is possible that low interest rates, low inflation, steady growth, and the economic emergence of China as a world player have helped secure capital and demand for export and import, which therefore might reduced the impact of each sector on overall economic status. Despite the fact that a diverse mixture of models and impacts has been used for analysis, always two common findings are observed in the result. Therefore, it can be concluded that the decreased rate of economic growth of Korea since 2000 appears to be on the same track as the decrease in Korea's potential growth rate. The contents of this paper are constructed as follows: The second section observes the recent trend of the economic development of Korea and related Korean articles, which might help in clearly defining the scope and analytic methodology of this study. The third section provides an analysis model to be used in this study, which is Structural VAR as mentioned above. Variables used, estimation equations, and identification conditions of impacts are explained. The fourth section reports estimation results derived by the previously introduced model, and the fifth section concludes.

• Journal of Bio-Environment Control
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• v.29 no.2
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• pp.161-170
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• 2020

There is systematic spatial variations in environmental properties due to sensitive reaction to external conditions at plastic greenhouse occupied 99.2% of domestic agricultural facilities. In order to construct 3 dimensional distribution of temperature, relative humidity, CO₂ and illuminance, measurement matrix as 3 by 3 by 5 in direction of width, height and length, respectively, dividing indoor space of greenhouse was designed and tested at experimental site. Linear regression analysis was conducted to evaluate optimal estimation method in terms with horizontal and vertical variations. Even though sole measurement point for temperature and relative humidity could be feasible to assess indoor condition, multiple measurement matrix is inevitably required to improve spatial precision at certain time domain such as period of sunrise and sunset. In case with CO₂, multiple measurement matrix could not successfully improve the spatial predictability during a whole experimental period. In case with illuminance, prediction performance was getting smaller after a time period of sunrise due to systematic interference such as indoor structure. Thus, multiple sensing methodology was proposed in direction of length at higher height than growing bed, which could compensate estimation error in spatial domain. Appropriate measurement matrix could be constructed considering the transition of stability in indoor environmental properties due to external variations. As a result, optimal measurement matrix should be carefully designed considering flexibility of construction relevant with the type of property, indoor structure, the purpose of crop and the period of growth. For an instance, partial cooling and heating system to save a consumption of energy supplement could be successfully accomplished by the deployment of multiple measurement matrix.

• Journal of Korean Society of Transportation
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• v.30 no.2
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• pp.101-116
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• 2012

Accident prediction models have been utilized to predict accident possibilities in existing or projected freeways and to evaluate programs or policies for improving safety. In this study, a traffic accident prediction model for freeways was developed for the above purposes. When selecting variables for the model, the highest priority was on the ease of both collecting data and applying them into the model. The dependent variable was set as the number of total accidents and the number of accidents including casualties in the unit of IC(or JCT). As a result, two models were developed; the overall accident model and the casualty-related accident model. The error structure adjusted to each model was the negative binomial distribution and the Poisson distribution, respectively. Among the two models, a more appropriate model was selected by statistical estimation. Major nine national freeways were selected and five-year dada of 2003~2007 were utilized. Explanatory variables should take on either a predictable value such as traffic volumes or a fixed value with respect to geometric conditions. As a result of the Maximum Likelihood estimation, significant variables of the overall accident model were found to be the link length between ICs(or JCTs), the daily volumes(AADT), and the ratio of bus volume to the number of curved segments between ICs(or JCTs). For the casualty-related accident model, the link length between ICs(or JCTs), the daily volumes(AADT), and the ratio of bus volumes had a significant impact on the accident. The likelihood ratio test was conducted to verify the spatial and temporal transferability for estimated parameters of each model. It was found that the overall accident model could be transferred only to the road with four or more than six lanes. On the other hand, the casualty-related accident model was transferrable to every road and every time period. In conclusion, the model developed in this study was able to be extended to various applications to establish future plans and evaluate policies.

• Journal of The Korean Society of Agricultural Engineers
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• v.61 no.6
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• pp.111-121
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• 2019

Evapotranspiration (ET) of vegetation is one of the major components of the hydrologic cycle, and its accurate estimation is important for hydrologic water balance, irrigation management, crop yield simulation, and water resources planning and management. For agricultural crops, ET is often calculated in terms of a short or tall crop reference, such as well-watered, clipped grass (reference crop evapotranspiration, $ET_o$). The Penman-Monteith equation recommended by FAO (FAO 56-PM) has been accepted by researchers and practitioners, as the sole $ET_o$ method. However, its accuracy is contingent on high quality measurements of four meteorological variables, and its use has been limited by incomplete and/or inaccurate input data. Therefore, this study evaluated the applicability of Backpropagation Neural Network (BPNN) model for estimating $ET_o$ from less meteorological data than required by the FAO 56-PM. A total of six meteorological inputs, minimum temperature, average temperature, maximum temperature, relative humidity, wind speed and solar radiation, were divided into a series of input groups (a combination of one, two, three, four, five and six variables) and each combination of different meteorological dataset was evaluated for its level of accuracy in estimating $ET_o$. The overall findings of this study indicated that $ET_o$ could be reasonably estimated using less than all six meteorological data using BPNN. In addition, it was shown that the proper choice of neural network architecture could not only minimize the computational error, but also maximize the relationship between dependent and independent variables. The findings of this study would be of use in instances where data availability and/or accuracy are limited.