• Journal of Korean Society of Forest Science
• /
• v.99 no.3
• /
• pp.285-291
• /
• 2010

Since 2006, the $5^{th}$ National Forest Inventory (NFI) has been implemented to provide forest resources statistics at the national level and at the county level as well. However, it needs a small-area estimator for estimating forest statistics at the county-level due to a small number of samples collected within a county. This study was conducted to evaluate the applicability of a geographical-based synthetic estimator for estimating forest growing stock volumes at the county level. The NFI-field plots surveyed were post-stratified into three forest cover types. In the synthetic estimator, field plots within a geographical-based super-county for each county were used to estimate stratum weights and stratum mean volumes. It was resulted that estimated stratum weights using the synthetic estimation were significantly differ from forest cover maps. The standard errors of estimated mean by the synthetic estimation that ranged from ${\pm}3.5\;m^3$/ha to ${\pm}7.7\;m^3$/ha were more smaller than those (${\pm}7.8\;m^3/ha{\sim}{\pm}24.7\;m^3/ha$) by the direct estimation. This means that the synthetic estimation is possible to provide more precise estimates of mean volumes.

• Journal of Korean Society of Water and Wastewater
• /
• v.30 no.2
• /
• pp.179-185
• /
• 2016

In this study, the RDII predictions were compared using two methodologies, i.e., the RTK-based and regression methods. Long-term (1/1/2011~12/31/2011) monitoring data, which consists of 10-min interval streamflow and the amount of precipitation, were collected at the domestic study area (1.36 km2 located in H county), and used for the construction of the RDII prediction models. The RTK method employs super position of tri-triangles, and each triangle (called, unit hydrograph) is defined by three parameters (i.e., R, T and K) determined/optimized using Genetic Algorithm (GA). In regression method, the MovingAverage (MA) filtering was used for data processing. Accuracies of RDII predictions from these two approaches were evaluated by comparing the root mean square error (RMSE) values from each model, in which the values were calculated to 320.613 (RTK method) and 420.653 (regression method), respectively. As a results, the RTK method was found to be more suitable for RDII prediction during extreme rainfall event, than the regression method.

• Korean Journal of Medicinal Crop Science
• /
• v.20 no.3
• /
• pp.165-170
• /
• 2012

This study was conducted to evaluate the effect of light spectrum using red (660 nm), blue (460 nm), red and blue mixed (Red : Blue = 6 : 4) LED (light-emitting diode), and fluorescent lamp on antioxidant activities and functional components of dandelion (Taraxacum officinale). Total polyphenol contents in dandelion irradiated with the red and blue mixed or the red LED were 121.77 mg/100 g or 115.36 mg/100 g, respectively, which were greater than those in dandelion treated with blue LED and fluorescent lamp. Asparagine showed the highest content among amino acids in leaves and roots regardless of treatments. Total amino acid was the highest when illuminated with the red LED. DPPH (1,1-diphenyl-2-picrylhydrazyl) radical-scavenging activity and SOD (super oxide dismutase) activity of ethanol extracts were increased under all of the LED treatments compared with fluorescent lamp, and the antioxidant activities were increased by the red and the mixed LED illumination. The results indicate that application of the red and the mixed LED illumination promote antioxidant activity and increase functional components during cultivation of dandelion.

• The Korean Journal of Applied Statistics
• /
• v.30 no.2
• /
• pp.215-231
• /
• 2017

The US presidential primaries take place sequentially in different places with a time lag. However, they have not attracted as much attention in terms of modelling as the US presidential election has. This study applied several autologistic models to find the relation between the outcome of the primary election for a Democrat candidate with socioeconomic attributes in consideration of spatial and temporal dependence. According to the result applied to the 2016 election data at the county level, Hillary Clinton was supported by people in counties with high population rates of old age, Black, female and Hispanic. In addition, spatial dependence was observed, representing that people were likely to support the same candidate who was supported from neighboring counties. Positive auto-correlation was also observed in the time-series of the election outcome. Among several autologistic models of this study, the model specifying the effect of Super Tuesday had the best fit.