• Investigative Magnetic Resonance Imaging
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• v.18 no.2
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• pp.120-132
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• 2014

Purpose : To compare dynamic susceptibility contrast imaging, diffusion-weighted imaging, and susceptibility-weighted imaging (SWI) for the differentiation of tumor recurrence and delayed radiation therapy (RT)-related changes in patients treated with RT for primary brain tumors. Materials and Methods: We enrolled 24 patients treated with RT for various primary brain tumors, who showed newly appearing enhancing lesions more than one year after completion of RT on follow-up MRI. The enhancing-lesions were confirmed as recurrences (n=14) or RT-changes (n=10). We calculated the mean values of normalized cerebral blood volume (nCBV), apparent diffusion coefficient (ADC), and proportion of dark signal intensity on SWI (proSWI) for the enhancing-lesions. All the values between the two groups were compared using t-test. A multivariable logistic regression model was used to determine the best predictor of differential diagnosis. The cutoff value of the best predictor obtained from receiver-operating characteristic curve analysis was applied to calculate the sensitivity, specificity, and accuracy for the diagnosis. Results: The mean nCBV value was significantly higher in the recurrence group than in the RT-change group (P=.004), and the mean proSWI was significantly lower in the recurrence group (P<.001). However, no significant difference was observed in the mean ADC values between the two groups. A multivariable logistic regression analysis showed that proSWI was the only independent variable for the differentiation; the sensitivity, specificity, and accuracy were 78.6% (11 of 14), 100% (10 of 10), and 87.5% (21 of 24), respectively. Conclusion: The proSWI was the most promising parameter for the differentiation of newly developed enhancing-lesions more than one year after RT completion in brain tumor patients.

• Journal of Environmental Impact Assessment
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• v.23 no.6
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• pp.432-444
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• 2014

When the water quality modelling is done with a manual calibration, it is possible that the researcher's opinion may affect the objectivity of the research. Hence, the role of the automatic calibration is highly important. This research applies a technique to automatically calibrate the water quality parameters by implementing an optimization method. This involves estimating the optimum water quality parameters targeting influential parameters towards the lake's BOD, DO, Phosphorus, Nitrogen and Phytoplankton. To accurately calculate the water temperature and hydraulic characteristics of a deep, stratifying lake, EFDC, a 3-dimensional hydraulic model which can be linked to the WASP7 was applied. With EFDC, the segment of the lake is formed and utilized as an input data of the WASP7. For the calibration of the water quality parameters of the WASP7, an influence coefficient algorithm and a genetic algorithm was applied. Of the five water quality variables for calibration, the normalized residuals of the observed and calculated values of DO, TN, CBOD were relatively small and the three water quality variables were calibrated properly. Yet the accuracy of the calibration of TP and Chl-a was relatively low.

• Korean Journal of Soil Science and Fertilizer
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• v.36 no.4
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• pp.200-209
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• 2003

Distribution coefficient ($K_d$) is an universal parameter estimating cadmium partition for a soil-water-crop system in agricultural lands. This study was performed to find some factors affecting soil-water partition coefficients for cadmium in some Korean soils. The distribution coefficients ($K_d$) of cadmium for the 15 series of agricultural soils were measured at quasi-steady state in the pH ranges from 2 to 11. The adsorption data of the selected soils showed a linear relationship between log $K_d$ and pH, which was well agreed with theoretically expected results ; $log\;K_d=0.6339pH+0.5532(r^2=0.70^{**})$. Normalization of the partition coefficients were performed in a range of pH 3.5 ~ 8.5 to minimize adverse effects of Al dissolution, cationic competition, and organic matter dissolution. The $K_d$-om, partition coefficients normalized for organic matter, improved this linearity to the pH of soils. The values of $K_d$-om measured from the field samples were significantly correlated with those of $K_d$ predicted from the sorption-edge experimental data ($r^2=0.68^{**}$).

• Journal of Biosystems Engineering
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• v.43 no.2
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• pp.148-159
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• 2018

Purpose: Unmanned air vehicle (UAV) remote sensing was applied to test various vegetation indices and make prediction models of protein content of rice for monitoring grain quality and proper management practice. Methods: Image acquisition was carried out by using NIR (Green, Red, NIR), RGB and RE (Blue, Green, Red-edge) camera mounted on UAV. Sampling was done synchronously at the geo-referenced points and GPS locations were recorded. Paddy samples were air-dried to 15% moisture content, and then dehulled and milled to 92% milling yield and measured the protein content by near-infrared spectroscopy. Results: Artificial neural network showed the better performance with $R^2$ (coefficient of determination) of 0.740, NSE (Nash-Sutcliffe model efficiency coefficient) of 0.733 and RMSE (root mean square error) of 0.187% considering all 54 samples than the models developed by PR (polynomial regression), SLR (simple linear regression), and PLSR (partial least square regression). PLSR calibration models showed almost similar result with PR as 0.663 ($R^2$) and 0.169% (RMSE) for cloud-free samples and 0.491 ($R^2$) and 0.217% (RMSE) for cloud-shadowed samples. However, the validation models performed poorly. This study revealed that there is a highly significant correlation between NDVI (normalized difference vegetation index) and protein content in rice. For the cloud-free samples, the SLR models showed $R^2=0.553$ and RMSE = 0.210%, and for cloud-shadowed samples showed 0.479 as $R^2$ and 0.225% as RMSE respectively. Conclusion: There is a significant correlation between spectral bands and grain protein content. Artificial neural networks have the strong advantages to fit the nonlinear problem when a sigmoid activation function is used in the hidden layer. Quantitatively, the neural network model obtained a higher precision result with a mean absolute relative error (MARE) of 2.18% and root mean square error (RMSE) of 0.187%.

• Journal of the Korean Geotechnical Society
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• v.18 no.2
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• pp.161-170
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• 2002

This paper describes a systematic way of simultaneously identifying the ambient pore pressure and the rigidity index $(=G/s_u)$ of soil by applying an optimization technique to the early part of piezocone dissipation test result. An analytical solution developed by Randolph & Wroth(1979) was implemented in normalized from to express the build-up and dissipation of excess pore pressures around a piezocone as a function of the rigidity index. An ambient pore pressure and optimal rigidity index were determined by minimizing the differences between theoretical and measured excess pore pressure curves using optimization technique. The effectiveness of the proposed back-analysis method was examined against the well-documented performance of piezocone dissipation tests(Tanaka & Sakagami, 1989), from the viewpoints of proper determination of selected target parameters and saving of test duration. It is shown that the proposed back-analysis method can evaluate properly the ambient pore pressure and the rigidity index by using only the early phase of the dissipation test data. Also, it is shown that the proposed back-analysis method permits the horizontal coefficient of consolidation to be identified rationally. Consideration for strain level of back-analyzed rigidity index shows that it corresponds to at least intermediate to large strain level.

• Journal of the Korean Geotechnical Society
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• v.19 no.5
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• pp.133-144
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• 2003

Horizontal consolidation characteristics of Busan marine clay were investigated by computing coefficient of horizontal consolidation from Piezocone data and comparing their results with those of standard consolidation test. It is well known that current prediction models of $c_h$ for high plastic soils have large uncertainties, and show a great difference between the predicted and the measured values. However, the spherical models and expanding cavity theory of Torstensson(1977), and Burns & Mayne(1998) based on modified Cam-Clay model with critical limit state concepts have relative reliability in estimating $c_h$ and good applicability in highly plasticity soils. In this paper, a normalization technique was used to evaluate $c_h$ using the Burns and Mayne's method based on the dissipation test, and their normalized consolidation curves give 0.015 of time factor($T_{50}$) when 50% degree of consolidation is completed. Comparison study using Piezocone data obtained at other similar ground site shows 1.5 times less systematicality than that of standard consolidation test, which indicates considerable approximation with the measured values because standard consolidation test gives the difference of three to few times compared with the measured values. In addition, design chart for estimating $c_h$ based on the chart from Robertson et al.(1992) and using the other method of the direct prediction from the of dissipation test was newly proposed. It is judged that new proposed chart is very applicable to Korean marine soils, especially in very high plastic soils.

• Korean Journal of Agricultural and Forest Meteorology
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• v.16 no.2
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• pp.114-124
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• 2014

For classifying fire damaged areas and analyzing burn severity of two large-fire areas damaged over 100 ha in 2011, three methods were employed utilized supervised classification, unsupervised classification and Normalized Difference Vegetation Index (NDVI). In this paper, the post-fire imageries of SPOT were used to compute the Maximum Likelihood (MLC), Minimum Distance (MIN), ISODATA, K-means, NDVI and to evaluate large-scale patterns of burn severity from 1 m to 5 m spatial resolutions. The result of the accuracy verification on burn severity from satellite images showed that average overall accuracy was 88.38 % and the Kappa coefficient was 0.8147. To compare the accuracy between burn severity and field survey at Uljin and Youngduk, two large fire sites were selected as study areas, and forty-four sampling plots were assigned in each study area for field survey. The burn severities of the study areas were estimated by analyzing burn severity (BS) classes from SPOT images taken one month after the occurrence of the fire. The applicability of composite burn index (CBI) was validated with a correlation analysis between field survey data and burn severity classified by SPOT5, and by their confusion matrix. The result showed that correlation between field survey data and BS by SPOT5 were closely correlated in both Uljin (r = -0.544 and p<0.01) and Youngduk (r = -0.616 and p<0.01). Thus, this result supported that the proposed burn severity analysis is an adequate method to measure burn severity of large fire areas in Korea.

• Journal of the Korean Geotechnical Society
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• v.24 no.11
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• pp.71-77
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• 2008

In this study, CPT-based methodology for estimating the ultimate lateral resistance, $p_u$, is proposed and verified for lateral loaded piles in sandy soil. Preexistent methods estimating the ultimate lateral resistance, $p_u$, and the ultimate lateral capacity, $H_u$, of pile have been based on the vertical effective stress, relative density, and the coefficient of lateral earth pressure. Similarly, cone resistance $q_c$ in pure sandy soil is expressed by those essential factors. As correlation between $p_u$ and $q_c$ are normalized with average effective stress ${\sigma}_m$, estimation methodology for the lateral loaded pile of $p_u$ in sandy soil is proposed. The method is verified by calibration chamber test results in pure sand. The standard derivation of estimated $p_u$ is 0.279, and COV (Coefficient Of Variation) of estimated $p_u$ is 0.272. These results showed that the estimated pus by the method are analogous with the measured $p_us$ in calibration chamber test.

• Korean Journal of Radiology
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• v.22 no.2
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• pp.233-242
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• 2021

Objective: To evaluate the association of MRI features with the major genomic profiles and prognosis of World Health Organization grade III (G3) gliomas compared with those of glioblastomas (GBMs). Materials and Methods: We enrolled 76 G3 glioma and 155 GBM patients with pathologically confirmed disease who had pretreatment brain MRI and major genetic information of tumors. Qualitative and quantitative imaging features, including volumetrics and histogram parameters, such as normalized cerebral blood volume (nCBV), cerebral blood flow (nCBF), and apparent diffusion coefficient (nADC) were evaluated. The G3 gliomas were divided into three groups for the analysis: with this isocitrate dehydrogenase (IDH)-mutation, IDH mutation and a chromosome arm 1p/19q-codeleted (IDHmut1p/19qdel), IDH mutation, 1p/19q-nondeleted (IDHmut1p/19qnondel), and IDH wildtype (IDHwt). A prediction model for the genetic profiles of G3 gliomas was developed and validated on a separate cohort. Both the quantitative and qualitative imaging parameters and progression-free survival (PFS) of G3 gliomas were compared and survival analysis was performed. Moreover, the imaging parameters and PFS between IDHwt G3 gliomas and GBMs were compared. Results: IDHmut G3 gliomas showed a larger volume (p = 0.017), lower nCBF (p = 0.048), and higher nADC (p = 0.007) than IDHwt. Between the IDHmut tumors, IDHmut1p/19qdel G3 gliomas had higher nCBV (p = 0.024) and lower nADC (p = 0.002) than IDHmut1p/19qnondel G3 gliomas. Moreover, IDHmut1p/19qdel tumors had the best prognosis and IDHwt tumors had the worst prognosis among G3 gliomas (p < 0.001). PFS was significantly associated with the 95th percentile values of nCBV and nCBF in G3 gliomas. There was no significant difference in neither PFS nor imaging features between IDHwt G3 gliomas and IDHwt GBMs. Conclusion: We found significant differences in MRI features, including volumetrics, CBV, and ADC, in G3 gliomas, according to IDH mutation and 1p/19q codeletion status, which can be utilized for the prediction of genomic profiles and the prognosis of G3 glioma patients. The MRI signatures and prognosis of IDHwt G3 gliomas tend to follow those of IDHwt GBMs.

• Korean Journal of Remote Sensing
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• v.37 no.6_1
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• pp.1631-1645
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• 2021

This research aims to provide the characteristics of the world's first active lidar sensor Atmospheric Laser Doppler Instrument (ALADIN) wind data and Geostationary Korea Multi Purpose Satellite 2A (GK2A) Atmospheric Motion Vector (AMV) data by comparing two wind data. As a result of comparing the data from September 2019 to August 1, 2020, The total number of collocated data for the AMV (using IR channel) and Mie channel ALADIN data is 177,681 which gives the Root Mean Square Error (RMSE) of 3.73 m/s and the correlation coefficient is 0.98. For a more detailed analysis, Comparison result considering altitude and latitude, the Normalized Root Mean Squared Error (NRMSE) is 0.2-0.3 at most latitude bands. However, the upper and middle layers in the lower latitudes and the lower layer in the southern hemispheric are larger than 0.4 at specific latitudes. These results are the same for the water vapor channel and the visible channel regardless of the season, and the channel-specific and seasonal characteristics do not appear prominently. Furthermore, as a result of analyzing the distribution of clouds in the latitude band with a large difference between the two wind data, Cirrus or cumulus clouds, which can lower the accuracy of height assignment of AMV, are distributed more than at other latitude bands. Accordingly, it is suggested that ALADIN wind data in the southern hemisphere and low latitude band, where the error of the AMV is large, can have a positive effect on the numerical forecast model.