Korean Journal of Agricultural and Forest Meteorology
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v.7
no.1
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pp.115-123
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2005
Quantitative understanding of spatial characteristics of the study site is a prerequisite to investigate water and carbon cycles in agricultural and forest ecosystems, particularly with complex, heterogeneous landscapes. The spatial characteristics of variables related with topography, vegetation and soil in Gwangneung forest watershed are quantified in this study. To characterize topography, information on elevation, slope and aspect extracted from DEM is analyzed. For vegetation and soil, a land-cover map classified from LANDSAT TM images is used. Four satellite images are selected to represent different seasons (30 June 1999, 4 September 2000, 23 September 2001 and 14 February 2002). As a flux index for CO₂ and water vapor, normalized difference vegetation index (NDVI) is calculated from satellite images for three different grid sizes: MODIS grid (7km x 7km), intensive observation grid (3km x 3km), and unit grid (1km x 1km). Then, these data are analyzed to quantify the spatial scale of heterogeneity based on semivariogram analysis. As expected, the scale of heterogeneity decreases as the grid size decreases and are sensitive to seasonal changes in vegetation. For the two unit grids where the two 40 m flux towers are located, the spatial scale of heterogeneity ranges from 200 to 1,000m, which correspond well to the climatology of the computed tower flux footprint.
Journal of the Korean Association of Geographic Information Studies
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v.17
no.4
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pp.40-51
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2014
This study aims to investigate urban inundation considering building footprints based on dual-drainage scheme. For this purpose, LiDAR data is cultivated to generate two original data set in terms of DEM with $1{\times}1$ meter and building layer of the study drainage area in Seoul and then the building layer is overlapped as vector polygon with the mesh data with the same size as DEM. Then, terrain data for modeling were re-sampled to reduce resolution as $10{\times}10$ meters. As results, the simulated depth without considering building footprints has a tendency to underestimate the inundation depth compared to observed data analized by CCTV imagery. Otherwise, the simulation result considering building footprints revealed definitely higher fitness. The difference of inundation depth came from the variation of inundation volume which was relevant to inundation extent. If the building footprints are enlarged, the possible inundation depth is increased, which results in being inundation depth higher because hydrological conditions such as rainfall depth are conservational. Otherwise, according to comparison of inundation extents, there were no significant difference but the case of considering building footprint was revealed slightly higher fitness. Thus, it is concluded that the considering building footprint for inundation analysis of urban watershed should be required to improve simulation accuracy synthetically.
Journal of Korean Society of Coastal and Ocean Engineers
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v.26
no.4
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pp.207-216
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2014
Total organic carbon (TOC) is an important indicator used as an direct biological index in the research field of the marine carbon cycle. It is possible to produce the sufficient TOC estimation data by using the Chemical Oxygen Demand(COD) data because the available TOC data is relatively poor than the COD data. The outlier detection and treatment (removal) should be carried out reasonably and objectively because the equation for a COD-TOC conversion is directly affected the TOC estimation. In this study, it aims to suggest the optimal regression model using the available salinity, COD, and TOC data observed in the Korean coastal zone. The optimal regression model is selected by the comparison and analysis on the changes of data numbers before and after removal, variation coefficients and root mean square (RMS) error of the diverse detection methods of the outlier and influential observations. According to research result, it is shown that a diagnostic case combining SIQR (Semi - Inter-Quartile Range) boxplot and Cook's distance method is most suitable for the outlier detection. The optimal regression function is estimated as the TOC(mg/L) = $0.44{\cdot}COD(mg/L)+1.53$, then determination coefficient is showed a value of 0.47 and RMS error is 0.85 mg/L. The RMS error and the variation coefficients of the leverage values are greatly reduced to the 31% and 80% of the value before the outlier removal condition. The method suggested in this study can provide more appropriate regression curve because the excessive impacts of the outlier frequently included in the COD and TOC monitoring data is removed.
This study is a sensitivity analysis of the parameters which affect the simulation results under various design rainfall conditions, using the SWMM model, for three selected basins in urban areas. The sensitivity of the peak flow rate is defined by $S_Q$ (=1.0 - (min. ratio of peak flow rate/max. ratio of peak flow rate)), and the rainfall conditions are classified in terms of design rainfall frequency, duration, and distribution. The simulation results show that in most conditions the parameters - the impermeable area ratio, the sewer slope, and the initial infiltration capacity - have more significant effects on the results than other parameters. As the design rainfall frequency increases, the sensitivity of the sewer slope and sewer roughness increases, while the parameters related with the surface runoff decrease. When the rainfall duration increases, the sensitivities of most parameters of surface runoff and sewer flow decrease. Also, at the 1st quarterly Huff rainfall distribution condition, the impermeable area ratio has high sensitivity, but at the 4th quarterly condition the parameters related with sewer flow show higher sensitivities. These tendencies can be explained by considering the procedure for computing the effective rainfall and kinematic wave on the surface and sewer flow.
Travel time estimation under given traffic conditions is important for providing drivers with travel time prediction information. But the present expressway travel time estimation process cannot calculate a reliable travel time. The objective of this study is to estimate the path travel time spent in a through lane between origin tollgates and destination tollgates on an expressway as a prerequisite result to offer reliable prediction information. Useful and abundant toll collection system (TCS) data were used. When estimating the path travel time, the path travel time is estimated combining the link travel time obtained through a preprocessing process. In the case of a lack of TCS data, the TCS travel time for previous intervals is referenced using the linear interpolation method after analyzing the increase pattern for the travel time. When the TCS data are absent over a long-term period, the dynamic travel time using the VDS time space diagram is estimated. The travel time estimated by the model proposed can be validated statistically when compared to the travel time obtained from vehicles traveling the path directly. The results show that the proposed model can be utilized for estimating a reliable travel time for a long-distance path in which there are a variaty of travel times from the same departure time, the intervals are large and the change in the representative travel time is irregular for a short period.
Journal of the Korea Academia-Industrial cooperation Society
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v.17
no.10
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pp.39-45
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2016
This study analyses the macroeconomic effects of the direct payment system (DPS) for environment-friendly agriculture in Korea. We utilized the applied general equilibrium model (AGE model) for the general agricultural sector as well as the environmentally-friendly agricultural sector. We considered several scenarios based on various direct payment amounts to measure and analyze economic impacts. Scenario 1 considers the current direct payment system. Scenario 2 examines an additional 5% increase from the direct payment amount in scenario 1. Scenario 3 reviews an increase of 10% in direct payment amount while Scenario 4 considers an additional increase of 15% compared with Scenario 1. Lastly, scenario 5 examines a 20% increase in direct payment amounts compared with scenario 1. In addition, the baseline considers conditions prior to the introduction of the direct payment system. The simulation analysis results show that capital formation, production volume, and labor productivity increased in the environment-friendly agricultural sector. In contrast, employment in the environment-friendly agricultural sector decreased. The price of environment-friendly agricultural products following the introduction of the DPS remain consistent with the price of environment-friendly agricultural product before introducing the DPS. This results from price elasticity of supply and demand are inelastic, and there is no change in the income of consumers during the analysis period. However, additional research is necessary for improvement of the model using complementary statistical data for the environmental-friendly agriculture sector.
The use of climatic information is essential in the industial society. More specialized weather servies are required to perform better industrial acivities including agriculture. Especially, crop models require daily weather data of crop growing area or cropping zones, where routine weather observations are rare. Estimates of the spatial distribution of daily climates might complement the low density of standard weather observation stations. This study was conducted to estimate the spatial distribution of daily minimum and maximum temperatures in Korean Peninsula. A topoclimatological technique was first applied to produce reasonable estimates of monthly climatic normals based on 1km $\times$ 1km grid cell over study area. Harmonic analysis method was then adopted to convert the monthly climatic normals into daily climatic normals. The daily temperatures for each grid cell were derived from a spatial interpolation procedure based on inverse-distance weighting of the observed deviation from the climatic normals at the nearest 4 standard weather stations. Data collected from more than 300 automatic weather systems were then used to validate the final estimates on several dates in 1997. Final step to confirm accuracy of the estimated temperature fields was comparing the distribution pattern with the brightness temperature fields derived from NOAA/AVHRR. Results show that differences between the estimated and the observed temperatures at 20 randomly selected automatic weather systems(AWS) range from -3.$0^{\circ}C$ to + 2.5$^{\circ}C$ in daily maximum, and from -1.8$^{\circ}C$ to + 2.2$^{\circ}C$ in daily minimum temperature. The estimation errors, RMSE, calculated from the data collected at about 300 AWS range from $1.5^{\circ}C$ to 2.5$^{\circ}C$ for daily maximum/minimum temperatures.
Korean Journal of Agricultural and Forest Meteorology
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v.24
no.4
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pp.201-217
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2022
The United States is one of the largest producers of major crops such as wheat, maize, and soybeans, and is a major exporter of these crops. Therefore, it is important to estimate the crop production of the country in advance based on reliable long- term weather forecast information for stable crops supply and demand in Korea. The purpose of this study is to improve the seasonal predictability of the agro-climatic indices over the United States by using regional-scale daily temperature. For long-term numerical weather prediction, a dynamical downscaling is performed using Weather Research and Forecasting (WRF) model, a regional climate model. As the initial and lateral boundary conditions of WRF, the global hourly prediction data obtained from the Pusan National University Coupled General Circulation Model (PNU CGCM) are used. The integration of WRF is performed for 22 years (2000-2021) for period from June to December of each year. The empirical quantile mapping, one of the bias correction methods, is applied to the timeseries of downscaled daily mean, minimum, and maximum temperature to correct the model biases. The uncorrected and corrected datasets are referred WRF_UC and WRF_C, respectively in this study. The daily minimum (maximum) temperature obtained from WRF_UC presents warm (cold) biases over most of the United States, which can be attributed to the underestimated the low (high) temperature range. The results show that WRF_C simulates closer to the observed temperature than WRF_UC, which lead to improve the long- term predictability of the temperature- based agro-climatic indices.
Recently, the high value added business is steadily growing in the culture and art area. To generated high value from a performance, the satisfaction of audience is necessary. The flow in a critical factor for satisfaction, and it should be induced from audience and measures. To evaluate interest and emotion of audience on contents, producers or investors need a kind of index for the measurement of the flow. But it is neither easy to define the flow quantitatively, nor to collect audience's reaction immediately. The previous studies of the group flow were evaluated by the sum of the average value of each person's reaction. The flow or "good feeling" from each audience was extracted from his face, especially, the change of his (or her) expression and body movement. But it was not easy to handle the large amount of real-time data from each sensor signals. And also it was difficult to set experimental devices, in terms of economic and environmental problems. Because, all participants should have their own personal sensor to check their physical signal. Also each camera should be located in front of their head to catch their looks. Therefore we need more simple system to analyze group flow. This study provides the method for measurement of audiences flow with group synchronization at same time and place. To measure the synchronization, we made real-time processing system using the Differential Image and Group Emotion Analysis (GEA) system. Differential Image was obtained from camera and by the previous frame was subtracted from present frame. So the movement variation on audience's reaction was obtained. And then we developed a program, GEX(Group Emotion Analysis), for flow judgment model. After the measurement of the audience's reaction, the synchronization is divided as Dynamic State Synchronization and Static State Synchronization. The Dynamic State Synchronization accompanies audience's active reaction, while the Static State Synchronization means to movement of audience. The Dynamic State Synchronization can be caused by the audience's surprise action such as scary, creepy or reversal scene. And the Static State Synchronization was triggered by impressed or sad scene. Therefore we showed them several short movies containing various scenes mentioned previously. And these kind of scenes made them sad, clap, and creepy, etc. To check the movement of audience, we defined the critical point, ${\alpha}$and ${\beta}$. Dynamic State Synchronization was meaningful when the movement value was over critical point ${\beta}$, while Static State Synchronization was effective under critical point ${\alpha}$. ${\beta}$ is made by audience' clapping movement of 10 teams in stead of using average number of movement. After checking the reactive movement of audience, the percentage(%) ratio was calculated from the division of "people having reaction" by "total people". Total 37 teams were made in "2012 Seoul DMC Culture Open" and they involved the experiments. First, they followed induction to clap by staff. Second, basic scene for neutralize emotion of audience. Third, flow scene was displayed to audience. Forth, the reversal scene was introduced. And then 24 teams of them were provided with amuse and creepy scenes. And the other 10 teams were exposed with the sad scene. There were clapping and laughing action of audience on the amuse scene with shaking their head or hid with closing eyes. And also the sad or touching scene made them silent. If the results were over about 80%, the group could be judged as the synchronization and the flow were achieved. As a result, the audience showed similar reactions about similar stimulation at same time and place. Once we get an additional normalization and experiment, we can obtain find the flow factor through the synchronization on a much bigger group and this should be useful for planning contents.
Three dimensional analysis of malocclusion and craniofacial deformation is essential for the successful orthodontic treatment. But the orthodontists are not familiar with diagnosis and treatment plane based on lateral cephalometric analysis. Since orthodontists do not posses a sufficient knowledge in standard value of posteroanterior cephalometric anaysis and of clinical importance for transverse jaw growth. In this study male(n=130) and female(n=171) aged from 6 to 16 and diagnosed as Class I malocclusion were selected to analysis width of cranium, maxilla and mandible on the posteroanterior cephalogram. The changes as a function of chronologic age and cervical vertebrae maturity index(CVXI) were examined. The Proper regression model was selected by sex with polynominal regression models and method of variable selection. Mean of each measurements and 70% confidence interval of individual measurement according to age was assesed and a graphs were made. Results are as follows :1. All the measurements for the width are gradually incresed as increase in chronologic age and CVMI. From the total amount of change between age 6 and 16, there is a tendency that mandibular width is broader than maxillary width and the width of male is broader than female. 2. There is no statistically significant sexual difference in Mx-Mn difference, Mx-Mn width differential, Mx/Mn ratio according to age and CVMI. 3. Mean of each measurement and 70% confidence interval of individual measurement according to age and sex were assessed and graphs were made for maxillary width, mandibular width, Mx-Mn difference, Mx/Mn ratio. 4. The width of maxilla and mandible in Korean children are broader than Western children during growth period.
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