• Magazine of the Korean Society of Agricultural Engineers
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• v.37 no.1
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• pp.65-72
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• 1995

This paper describes the applications of the SCS TR-20 hydrologic model for simula- tion of hourly inflow rates from sixty-six ungaged tributaries and subareas between the Naju station and the estuarin dam at the Yongsan River Basin. The model was tested for the ungaged conditions with fifteen storm events at Naju station. Hourly simulated run- off data were compared with the observed, and the results showed less correlationships between the two data than those from TANK model. The coefficients of correlation ranged from 0.74 to 0.87. The curve numbers and time of concentration were defined from topographic dta for each of sixty-six tributaries for the estuarine dam and used for TR-20 applications. The results were within an acceptable range of errors in simulating the inflow fluctuations for the flood forecasting at the estuarine dam.

• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 1998.10a
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• pp.370-375
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• 1998

The objectives of this study are to evaluate the applicability of SCS TR-20 model for small ungauged watershed, to show the behavior of the model with variation of topography in watershed, and to evaluate the storage effect of paddy field for flood flow. For this purpose, simulated data from the model were compared with the observed flood data at two sites (HS#3, HS#4) in Balan watershed. From the comparison between simulated and observed data, it was found that the model is applicable to this watershed.

• Investigative Magnetic Resonance Imaging
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• v.12 no.1
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• pp.20-26
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• 2008

Purpose : To present the T1 and T2 relaxation times of the major cerebral metabolites at 1.5T and 3.0T and compare those between 1.5T and 3.0T. Materials and Methods : Using the phantom containing N-acetyl aspartate (NAA), Choline (Cho), and Creatine (Cr) at both 1.5T and 3.0T MRI, the T1 relaxation times were calculated from the spectral data obtained with 5000 ms repetition time (TR), 20 ms echo time (TE), and 11 different mixing time (TM)s using STEAM (STimulated Echo-Acquisition Mode) method. The T2 relaxation times were obtained from the spectral data obtained with 3000 ms TR and 5 different TEs using PRESS (Point-RESolved Spectroscopy) method. The T1 and T2 relaxation times obtained at 1.5T were compared with those of 3.0T. Results : The T1 relaxation times of NAA were $2293\;{\pm}\;48\;ms$ at 1.5T and $2559\;{\pm}\;124\;ms$ at 3.0T (11.6% increase at 3.0T). The T1 relaxation times of Cho were $2540\;{\pm}\;57\;ms$ at 1.5T and $2644\;{\pm}\;76\;ms$ at 3.0T (4.1% increase at 3.0T). The T1 relaxation times of Cr were $2543\;{\pm}\;75\;ms$ at 1.5T and $2665\;{\pm}\;94\;ms$ at 3.0T (4.8% increase). The T2 relaxation times of NAA were $526\;{\pm}\;81\;ms$ at 1.5T and $468\;{\pm}\;74\;ms$ at 3.0T (11.0% decrease at 3.0T). The T2 relaxation times of Cho were $220\;{\pm}\;44ms$ at 1.5T and $182\;{\pm}\;35\;ms$ at 3.0T (17.3% decrease at 3.0T). The T2 relaxation times of Cr were $289\;{\pm}\;47\;ms$ at 1.5T and $275\;{\pm}\;57\;ms$ at 3.0T (4.8% decrease at 3.0T). Conclusion : The T1 relaxation times of the major cerebral metabolites (NAA, Cr, Cho), which were measured at the phantom, were 4.1%-11.6% longer at 3.0T than at 1.5T. The T2 relaxation times of them were 4.8%-17.3% shorter at 3.0T than at 1.5T. To optimize MR spectroscopy at 3.0T, TR should be lengthened and TE should be shortened.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.46 no.3
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• pp.241-247
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• 2001

Boundary line method was adopted to analyze the relationships between rice yield and meteorological conditions during rice growing period. Boundary lines of yield responses to mean temperature($T_a$) and sunshine hour( $S_{h}$) and diurnal temperature range($T_r$) were well-fitted to hyperbolic functions of f($T_a$) =$$\beta$_{0t}$(1-EXP(-$$\beta$_{1t}$ $\times$ ($T_a$) ) and f( $S_{h}$)=$$\beta$_{0t}$((1-EXP($$\beta$_{1t}$$\times$ $S_{h}$)), to quadratic function of f($T_r$) =$\beta$$_{0r}$(1-($T_r$ 1r)$^2$), respectively. to take into account to, the sterility caused by low temperature during reproductive stage, cooling degree days [$T_c$ =$\Sigma$(20-$T_a$] for 30 days before heading were calculated. Boundary lines of yield responses to $T_c$ were fitted well to exponential function of f($T_c$) )=$\beta$$_{0c}$exp(-$$\beta$_{1c}$$\times$$T_c$ ). Excluding the constants of $\beta$$_{0s}$ from the boundary line functions, formed are the relative function values in the range of 0 to 1. And these were used as yield indices of the meteorological elements which indicate the degree of influence on rice yield. Assuming that the meteorological elements act multiplicatively and independently from each other, meteorological yield index (MIY) was calculated by the geometric mean of indices for each meteorological elements. MIY in each growth period showed good linear relationship with rice yield. The MIY's during 31 to 45 days after transplanting(DAT) in vegetative stage, during 30 to 16 days before heading (DBH) in reproductive stage and during 20 days after heading (DAH) in ripening stage showed greater explainablity for yield variation in each growth stage. MIY for the whole growth period was calculated by the following three methods of geometric mean of the indices for vegetative stage (MIVG), reproductive stage (HIRG) and ripening stage (HIRS). MI $Y_{I}$ was calculated by the geometric mean of meteorological indices showing the highest determination coefficient n each growth stage of rice. That is, (equation omitted) was calculated by the geometric mean of all the MIY's for all the growth periods devided into 15 to 20 days intervals from transplanting to 40 DAH. MI $Y_{III}$ was calculated by the geometric mean of MIY's for 45 days of vegetative stage (MIV $G_{0-45}$ ), 30 days of reproductive stage (MIR $G_{30-0}$) and 40 days of ripening stage (MIR $S_{0-40}$). MI $Y_{I}$, MI $Y_{II}$ and MI $Y_{III}$ showed good linear relationships with grain yield, the coefficients of determination being 0.651, 0.670 and 0.613, respectively.and 0.613, respectively.