• Korean Journal of Remote Sensing
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• v.36 no.5_3
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• pp.1037-1051
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• 2020

The accurate monitoring and forecasting of the intensity of tropical cyclones (TCs) are able to effectively reduce the overall costs of disaster management. In this study, we proposed a multi-task learning (MTL) based deep learning model for real-time TC intensity estimation and forecasting with the lead time of 6-12 hours following the event, based on the fusion of geostationary satellite images and numerical forecast model output. A total of 142 TCs which developed in the Northwest Pacific from 2011 to 2016 were used in this study. The Communications system, the Ocean and Meteorological Satellite (COMS) Meteorological Imager (MI) data were used to extract the images of typhoons, and the Climate Forecast System version 2 (CFSv2) provided by the National Center of Environmental Prediction (NCEP) was employed to extract air and ocean forecasting data. This study suggested two schemes with different input variables to the MTL models. Scheme 1 used only satellite-based input data while scheme 2 used both satellite images and numerical forecast modeling. As a result of real-time TC intensity estimation, Both schemes exhibited similar performance. For TC intensity forecasting with the lead time of 6 and 12 hours, scheme 2 improved the performance by 13% and 16%, respectively, in terms of the root mean squared error (RMSE) when compared to scheme 1. Relative root mean squared errors(rRMSE) for most intensity levels were lessthan 30%. The lower mean absolute error (MAE) and RMSE were found for the lower intensity levels of TCs. In the test results of the typhoon HALONG in 2014, scheme 1 tended to overestimate the intensity by about 20 kts at the early development stage. Scheme 2 slightly reduced the error, resulting in an overestimation by about 5 kts. The MTL models reduced the computational cost about 300% when compared to the single-tasking model, which suggested the feasibility of the rapid production of TC intensity forecasts.

• Radiation Oncology Journal
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• v.22 no.2
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• pp.155-163
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• 2004

Purpose : To confirm the reproducibility of in vivo transmission dosimetry system and the accuracy of the a1gorithms for the estimation of transmission dose in head and neck radiation therapy patients. Materials and Methods : From September 5 to 18, 2001, transmission dose measurements were peformed when radiotherapy was given to brain or head and neck cancer patients. The data of 35 patients who were treated more than three times and whose central axis of the beam was not blocked were analyzed in this study. To confirm the reproducibility of this system, transmission dose was measured before dally treatment and then repetitively every hour during the treatment time, with a field size of 10$\times$10 cm$^{2}$ and a delivery of 100 MU. The accuracy of the transmission dose calculation algorithms was confirmed by comparing estimated dose with measured dose. To accurately estimate transmission dose, tissue inhomogeneity correction was done. Results : The measurement variations during a day were within $\pm$0.5$\%$ and the dally variations in the checked period were within $\pm$ 1.0$\%$, which were acceptable for system reproducibility. The mean errors between estimated and measured doses were within $\pm$5.0$\%$ in Patients treated to the brain, $\pm$2.5$\%$ in head, and $\pm$ 5.0%$\%$in neck. Conclusion : The results of this study confirmed the reproducibility of our system and its usefulness and accuracy for dally treatment. We also found that tissue inhomogeneity correction was necessary for the accurate estimation of transmission dose in patients treated to the head and neck.

• Management & Information Systems Review
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• v.36 no.3
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• pp.151-165
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• 2017

This study investigates the effect of revenue recognition by percentage of completion method on financial analysts' earnings forecasting information in order industry. Specifically, we examines how the analysts' earnings forecast errors and biases differ according to whether or not to report the unbilled revenue account balance and the level of unbilled revenue account balance. The sample consists of 453 firm-years listed in Korea Stock Exchange during the period from 2010 to 2014 since the information on unbilled revenue accounts can be obtained after the adoption of K-IFRS. The results are as follows. First, we find that the firms with unbilled revenue account balances have lower analysts' earnings forecast accuracy than the firms who do not report unbilled revue account balances. In addition, we find that the accuracy of analysts' earnings forecasts decreases as the amount of unbilled revenue increases. Unbilled revenue account balances occur when the revenue recognition of the contractor is faster than the client. There is a possibility that managerial discretionary judgment and estimation may intervene when the contractor calculates the progress rate. The difference between the actual progress of the construction and the progress recognized by the company lowers the predictive value of financial statements. Our results suggest that the analysts' earnings forecasts may be more difficult for the firms that report unbilled revenue balances as applying the revenue recognition method based on the progress criteria. Second, we find that the firms reporting unbilled revenue account balances tend to have higher the optimistic biases in analysts' earnings forecast than the firms who do not report unbilled revenue account balances. And we find that the analysts' earnings forecast biases are increases as the amount of unbilled revenue increases. This study suggests an effort to reduce the arbitrary adjustment and estimation in the measurement of the progress as well as the introduction of the progress measurement method which can reflect the actual progress. Investors are encouraged to invest and analyze the characteristics of the order-based industry accounting standards. In addition, the results of this study empower the accounting transparency enhancement plan for order industry proposed by the policy authorities.

• Korean Journal of Agricultural and Forest Meteorology
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• v.11 no.4
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• pp.174-184
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• 2009

Evapotranspiration (ET) is one of the major hydrologic processes in terrestrial ecosystems. A reliable estimation of spatially representavtive ET is necessary for deriving regional water budget, primary productivity of vegetation, and feedbacks of land surface to regional climate. Moderate resolution imaging spectroradiometer (MODIS) provides an opportunity to monitor ET for wide area at daily time scale. In this study, we applied a MODIS-based ET algorithm and tested its reliability for nine flux tower sites in East Asia. This is a stand-alone MODIS algorithm based on the Penman-Monteith equation and uses input data derived from MODIS. Instantaneous ET was estimated and scaled up to daily ET. For six flux sites, the MODIS-derived instantaneous ET showed a good agreement with the measured data ($r^2=0.38$ to 0.73, ME = -44 to $+31W\;m^{-2}$, RMSE =48 to $111W\;m^{-2}$). However, for the other three sites, a poor agreement was observed. The predictability of MODIS ET was improved when the up-scaled daily ET was used ($r^2\;=\;0.48$ to 0.89, ME = -0.7 to $-0.6\;mm\;day^{-1}$, $RMSE=\;0.5{\sim}1.1\;mm\;day^{-1}$). Errors in the canopy conductance were identified as a primary factor of uncertainty in MODIS-derived ET and hence, a more reliable estimation of canopy conductance is necessary to increase the accuracy of MODIS ET.

• Journal of Korea Water Resources Association
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• v.55 no.6
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• pp.393-404
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• 2022

Reliable terrestrial rainfall observations from satellites at finer spatial resolution are essential for urban hydrological and microscale agricultural demands. Although various traditional "top-down" approach-based satellite rainfall products were widely used, they are limited in spatial resolution. This study aims to assess the potential of a novel "bottom-up" approach for rainfall estimation, the parameterized SM2RAIN model, applied to the C-band SAR Sentinel-1 satellite data (SM2RAIN-S1), to generate high-spatial-resolution terrestrial rainfall estimates (0.01° grid/6-day) over Central South Korea. Its performance was evaluated for both spatial and temporal variability using the respective rainfall data from a conventional reanalysis product and rain gauge network for a 1-year period over two different sub-regions in Central South Korea-the mixed forest-dominated, middle sub-region and cropland-dominated, west coast sub-region. Evaluation results indicated that the SM2RAIN-S1 product can capture general rainfall patterns in Central South Korea, and hold potential for high-spatial-resolution rainfall measurement over the local scale with different land covers, while less biased rainfall estimates against rain gauge observations were provided. Moreover, the SM2RAIN-S1 rainfall product was better in mixed forests considering the Pearson's correlation coefficient (R = 0.69), implying the suitability of 6-day SM2RAIN-S1 data in capturing the temporal dynamics of soil moisture and rainfall in mixed forests. However, in terms of RMSE and Bias, better performance was obtained with the SM2RAIN-S1 rainfall product over croplands rather than mixed forests, indicating that larger errors induced by high evapotranspiration losses (especially in mixed forests) need to be included in further improvement of the SM2RAIN.

• Journal of Sericultural and Entomological Science
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• v.8
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• pp.11-25
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• 1968

Various formulae for estimation of leaf production in mulberry trees were investigated and obtained. Four varieties of mulberry trees were used as the materials, and seven characters namely branch length. branch diameter, node number per branch, total branch weight, branch weight except leaves, leaf weight and leaf area, were studied. The formulae to estimate the leaf yield of mulberry trees are as follows: 1. Varietal differences were appeared in means, variances, standard devitations and standard errors of seven characters studied as shown in table 1. 2. Y$_1$=a$_1$X$_1$${\times}$P$_1$......(l) where Y$_1$ means yield per l0a by branch number and leaf weight determination. a$_1$.........leaf weight per branch. X$_1$.......branch number per plant. P$_1$........plant number per l0a. 3. Y$_2$=(a$_2$${\pm}$S. E.${\times}$X$_2$)+P$_1$.......(2) where Y$_2$ means leaf yield per l0a by branch length and leaf weight determination. a$_2$......leaf weight per meter of branch length. S. E. ......standard error. X$_2$....total branch length per plant. P$_1$........plant number per l0a as written above. 4. Y$_3$=(a$_3$${\pm}$S. E${\times}$X$_3$)${\times}$P$_1$.....(3) where Y$_3$ means of yield per l0a by branch diameter measurement. a$_3$.......leaf weight per 1cm of branch diameter. X$_3$......total branch diameter per plant. 5. Y$_4$=(a$_4$${\pm}$S. E.${\times}$X$_4$)P$_1$......(4) where Y$_4$ means leaf yield per 10a by node number determination. a$_4$.......leaf weight per node X$_4$.....total node number per plant. 6. Y$\sub$5/= {(a$\sub$5/${\pm}$S. E.${\times}$X$_2$)Kv}${\times}$P$_1$.......(5) where Y$\sub$5/ means leaf yield per l0a by branch length and leaf area measurement. a$\sub$5/......leaf area per 1 meter of branch length. K$\sub$v/......leaf weight per 100$\textrm{cm}^2$ of leaf area. 7. Y$\sub$6/={(X$_2$$\div$a$\sub$6/${\pm}$S. E.)}${\times}$K$\sub$v/${\times}$P$_1$......(6) where Y$\sub$6/ means leaf yield estimated by leaf area and branch length measurement. a$\sub$6/......branch length per l00$\textrm{cm}^2$ of leaf area. X$_2$, K$\sub$v/ and P$_1$ are written above. 8. Y$\sub$7/= {(a$\sub$7/${\pm}$S. E. ${\times}$X$_3$)}${\times}$K$\sub$v/${\times}$P$_1$.......(7) where Y$\sub$7/ means leaf yield estimates by branch diameter and leaf area measurement. a$\sub$7/......leaf area per lcm of branch diameter. X$_3$, K$\sub$v/ and P$_1$ are written above. 9. Y$\sub$8/= {(X$_3$$\div$a$\sub$8/${\pm}$S. E.)}${\times}$K$\sub$v/${\times}$P$_1$.......(8) where Y$\sub$8/ means leaf yield estimates by leaf area branch diameter. a$\sub$8/......branch diameter per l00$\textrm{cm}^2$ of leaf area. X$_3$, K$\sub$v/, P$_1$ are written above. 10. Y$\sub$9/= {(a$\sub$9/${\pm}$S. E.${\times}$X$_4$)${\times}$K$\sub$v/}${\times}$P$_1$......(9) where Y$\sub$7/ means leaf yield estimates by node number and leaf measurement. a$\sub$9/......leaf area per node of branch. X$_4$, K$\sub$v/, P$_1$ are written above. 11. Y$\sub$10/= {(X$_4$$\div$a$\sub$10/$\div$S. E.)${\times}$K$\sub$v/}${\times}$P$_1$.......(10) where Y$\sub$10/ means leaf yield estimates by leaf area and node number determination. a$\sub$10/.....node number per l00$\textrm{cm}^2$ of leaf area. X$_4$, K$\sub$v/, P$_1$ are written above. Among many estimation methods. estimation method by the branch is the better than the methods by the measurement of node number and branch diameter. Estimation method, by branch length and leaf area determination, by formulae (6), could be the best method to determine the leaf yield of mulberry trees without destroying the leaves and without weighting the leaves of mulberry trees.

• Korean Journal of Remote Sensing
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• v.33 no.5_2
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• pp.721-732
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• 2017

With the increasing socio-economic importance of rice as a global staple food, several models have been developed for rice yield estimation by combining remote sensing data with carbon cycle modelling. In this study, we aimed to estimate rice yield in Korea using such an integrative model using satellite remote sensing data in combination with a biophysical crop growth model. Specifically, daily meteorological inputs derived from MODIS (Moderate Resolution imaging Spectroradiometer) and radar satellite products were used to run a light use efficiency based crop growth model, which is based on the MODIS gross primary production (GPP) algorithm. The modelled biomass was converted to rice yield using a harvest index model. We estimated rice yield from 2003 to 2014 at the county level and evaluated the modelled yield using the official rice yield and rice straw biomass statistics of Statistics Korea (KOSTAT). The estimated rice biomass, yield, and harvest index and their spatial distributions were investigated. Annual mean rice yield at the national level showed a good agreement with the yield statistics with the yield statistics, a mean error (ME) of +0.56% and a mean absolute error (MAE) of 5.73%. The estimated county level yield resulted in small ME (+0.10~+2.00%) and MAE (2.10~11.62%),respectively. Compared to the county-level yield statistics, the rice yield was over estimated in the counties in Gangwon province and under estimated in the urban and coastal counties in the south of Chungcheong province. Compared to the rice straw statistics, the estimated rice biomass showed similar error patterns with the yield estimates. The subpixel heterogeneity of the 1 km MODIS FPAR(Fraction of absorbed Photosynthetically Active Radiation) may have attributed to these errors. In addition, the growth and harvest index models can be further developed to take account of annually varying growth conditions and growth timings.

• The Korean Journal of Nuclear Medicine Technology
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• v.20 no.2
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• pp.21-26
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• 2016

Purpose Because of many advantages, PET-CT Scanners generally use CT Data for attenuation correction. By using CT based attenuation correction, we can get anatomical information, reduce scan time and make more accurate correction of attenuation. However in case metal artifact occurred during CT scan, CT-based attenuation correction can induce artifacts and quantitative errors that can affect the PET images. Therefore this study infers true SUV of metal artifact region from attenuation corrected image count -to- non attenuation corrected image count ratio. Materials and Methods Micro phantom inserted $^{18}F-FDG$ 4mCi was used for phantom test and Biograph mCT S(40) is used for medical test equipment. We generated metal artifact in micro phantom by using metal. Then we acquired both metal artifact region of correction factor and non metal artifact region of correction factor by using attenuation correction image count -to- non attenuation correction image count ratio. In case of clinical image, we reconstructed both attenuation corrected images and non attenuation corrected images of 10 normal patient($66{\pm}15age$) who examined PET-CT scan in SNUH. After that, we standardize several organs of correction factor by using attenuation corrected image count -to- non attenuation corrected count ratio. Then we figured out metal artifact region of correction factor by using metal artifact region of attenuation corrected image count -to- non attenuation corrected count ratio And we compared standard organs correction factor with metal artifact region correction factor. Results according to phantom test results, metal artifact induce overestimation of correction factor so metal artifact region of correction factors are 12% bigger than the non metal artifact region of correction factors. in case of clinical test, correction factor of organs with high CT number(>1000) is $8{\pm}0.5%$, correction factor of organs with CT number similar to soft tissue is $6{\pm}2%$ and correction factor of organs with low CT number(-100>) is $3{\pm}1%$. Also metal artifact correction factors are 20% bigger than soft tissue correction factors which didn't happened metal artifact. Conclusion metal artifact lead to overestimation of attenuation coefficient. because of that, SUV of metal artifact region is overestimated. Thus for more accurate quantitative evaluation, using attenuation correction image count -to-non attenuation correction image count ratio is one of the methods to reduce metal artifact affect.

• Progress in Medical Physics
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• v.26 no.3
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• pp.143-152
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• 2015

In this study, we aim to design the architecture of the kV imaging system for tumor tracking in the dual-head gantry system and analyze its accuracy by simulations. We established mathematical formulas and algorithms to track the tumor position with the two-pair kV imaging systems when they are in the non-orthogonal positions. The algorithms have been designed in the homogeneous coordinate framework and the position of the source and the detector coordinates are used to estimate the tumor position. 4D XCAT (4D extended cardiac-torso) software was used in the simulation to identify the influence of the angle between the two-pair kV imaging systems and the resolution of the detectors to the accuracy in the position estimation. A metal marker fiducial has been inserted in a numerical human phantom of XCAT and the kV projections were acquired at various angles and resolutions using CT projection software of the XCAT. As a result, a positional accuracy of less than about 1mm was achieved when the resolution of the detector is higher than 1.5 mm/pixel and the angle between the kV imaging systems is approximately between $90^{\circ}$ and $50^{\circ}$. When the resolution is lower than 1.5 mm/pixel, the positional errors were higher than 1mm and the error fluctuation by the angles was greater. The resolution of the detector was critical in the positional accuracy for the tumor tracking and determines the range for the acceptable angle range between the kV imaging systems. Also, we found that the positional accuracy analysis method using XCAT developed in this study is highly useful and will be a invaluable tool for further refined design of the kV imaging systems for tumor tracking systems.

• The Korean Journal of Financial Management
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• v.24 no.2
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• pp.1-40
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• 2007

We test a model of investment-cashflow-growth opportunities relationship in order to estimate the sensitivities to investments. In this study, we use a new proxy variable for the value of growth opportunities(hereafter "VGO"), which is based on the seminal papers of M&M(1958:1961:1963) and Lee(2006;2007). The empirical findings on the sensitivities of cashflow and growth opportunities are as follows. First, when the traditional proxy variables for the growth opportunities such as Tobin's Q, MBR and sales growth are included with the new proxy VGO in the estimation, their coefficients are turned out to be insignificant. Second, only the new proxy variable VGO shows a statistically significant positive sensitibity to investment, which can be regarded that the growth opportunities hold the positive influences to investments. Third, the Tobin's Q can be decomposed into three factors such as the value of growth opportunities(VGO), the value of asset-in-place and valuation errors. It turns out that only the VGO shows a statistically significant positive relationship with investment among others. This means that the new variable VGO is a good proxy variable for the growth opportunities in the investment-cashflow sensitivity analysis. In sum, thanks to the above findings in this study, we can say that it will not be proper to choose a proxy variable for the growth opportunities from the traditional set of proxies such as Tobin's Q, MBR, or sales growth rate.