• The Korean Journal of Applied Statistics
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• v.22 no.5
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• pp.943-960
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• 2009

This study suggests an algorithm for generating TMY(typical meteorological year) for the Korean peninsula, and generates the TMY based on the algorithm using 11 years(1998~2008) wind data observed at 77 sites of Regional Meteorological Offices(RMO). The algorithm consists of computing TMM scores based on the various statistics defined by the Fikenstein-Shafer statistical model and, in turn, generating TMY based on the TMM scores. Also the algorithm has two stages designed to yield the best representation of the regional wind characteristics appeared during the 11 years(1998~2008). The first stage is designed for the representation of each of 77 regions of RMO and the second is for the Korean peninsula. Various comparison studies are provided to demonstrate the properties of the TMY like its utility and typicality.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.1 no.1
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• pp.77-90
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• 1981

This study involves 1) air quality disperson predictions and 2) a comparison of the predicted data with the actually measured ones in terms of annual sulfur dioxide concentration in the Ulsan Industial Complex. The prediction was made by utilizing the CDMQC air quality simulation computer model. The higher concentrations were observed at the Bugok Dong (Sampling Site) and the Yeochun Dong Sampling Site with the values of 44 and 46 ppb, respectively whereas the predicted values for both sites were 52 and 47 ppb, respectively. A statistical examination has revealed that the level of confidence was 90.02% from the Chi-squared test and the corelation coefficient was 0.827. It thus demonstrates that the model used for the study appears to be applicable to yield reliable predictions in terms of annual sulfur dioxide concentrations in the study area.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.19 no.1
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• pp.159-169
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• 1994

A multichannel optical slotted ring network is designed using a wavelength division multiplexing(WDM) technique and photonic packet switching devices. The electronics speed bottleneck is removed out of the ring, which allows utilization of the full bandwidth for the optical fiber transmission medium. The ring channel adopts a slotted ring concept with a destination cell remove strategy for the eing access mechanism. The slot size in the ring is selected as the same as that of ATM based cell in order to be used as B-ISDN Access Networks. In this paper, we devised a mathematical method to measure the average transfer delay characteristics of the network. The analytical method turned out to yield accurate results over a broad range of parameters in comparison to simulation results. From the study, we observed the average transfer delay of the network as the network parameters vary.

• Korean Journal of Remote Sensing
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• v.20 no.3
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• pp.175-188
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• 2004

The objectives of this study were to estimate leaf area index (LAI) as a function of image-derived vegetation indices, and to compare measured and estimated LAI to the results of crop model simulation. Soil moisture, crop phenology, and LAI data were obtained several times during the 2001 growing season at monitoring sites established in two central Missouri experimental fields, one planted to com (Zea mays L.) and the other planted to soybean (Glycine max L.). Hyper- and multi-spectral images at varying spatial. and spectral resolutions were acquired from both airborne and satellite platforms, and data were extracted to calculate standard vegetative indices (normalized difference vegetative index, NDVI; ratio vegetative index, RVI; and soil-adjusted vegetative index, SAVI). When comparing these three indices, regressions for measured LAI were of similar quality $(r^2$ =0.59 to 0.61 for com; $r^2$ =0.66 to 0.68 for soybean) in this single-year dataset. CERES(Crop Environment Resource Synthesis)-Maize and CROPGRO-Soybean models were calibrated to measured soil moisture and yield data and used to simulate LAI over the growing season. The CERES-Maize model over-predicted LAI at all corn monitoring sites. Simulated LAI from CROPGRO-Soybean was similar to observed and image-estimated LA! for most soybean monitoring sites. These results suggest crop growth model predictions might be improved by incorporating image-estimated LAI. Greater improvements might be expected with com than with soybean.

• Journal of Radiation Protection and Research
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• v.47 no.1
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• pp.50-57
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• 2022

Background: There are several proton therapy facilities in operation or planned in Taiwan, and these facilities are anticipated to not only treat cancer but also provide beam services to the industry or academia. The simplified approach based on the Monte Carlo-based data sets (source terms and attenuation lengths) with the point-source line-of-sight approximation is friendly in the design stage of the proton therapy facilities because it is intuitive and easy to use. The purpose of this study is to expand the Monte Carlo-based data sets to allow the simplified approach to cover the application of proton beams more widely. Materials and Methods: In this work, the MCNP6 Monte Carlo code was used in three simulations to achieve the purpose, including the neutron yield calculation, Monte Carlo-based data sets generation, and dose assessment in simple cases to demonstrate the effectiveness of the generated data sets. Results and Discussion: The consistent comparison of the simplified approach and Monte Carlo simulation results show the effectiveness and advantage of applying the data set to a quick shielding design and conservative dose assessment for proton therapy facilities. Conclusion: This study has expanded the existing Monte Carlo-based data set to allow the simplified approach method to be used for dose assessment or shielding design for beam services in proton therapy facilities. It should be noted that the default model of the MCNP6 is no longer the Bertini model but the CEM (cascade-exciton model), therefore, the results of the simplified approach will be more conservative when it was used to do the double confirmation of the final shielding design.

• Journal of The Korean Society of Agricultural Engineers
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• v.49 no.2
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• pp.49-60
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• 2007

The alpine agricultural activities are usually performed at higher and steep areas in nature. Thus, significant amounts of soil erosion are occurring compared with those from other areas. Thus, the soil erosion induced environmental impacts in these areas are getting greater. The Doam watershed is located at alpine areas and it has been well known that the agricultural activities in the watershed are causing accelerated soil erosion and water quality degradations. Many modeling approaches were employed to solve soil erosion and water quality issues. In this study, the Soil and Water Assessment Tool (SWAT) model was utilized to simulate the hydrologic and sediment behaviors in the Doam watershed. In many previous modeling studies, the digital soil map and its corresponding soil properties were used without modification to reflect soil conditioning at many agricultural fields of the Doam watershed. Thus, the soil sample was taken at the agricultural field within the Doam watershed and analyzed for its physical properties. In this study, the digital topsoil properties in the agricultural fields within the Doam watershed were replaced with the soil properties for reconditioned soil analyzed in this study to simulate the impacts of using soil properties for reconditioned soil in hydrologic and sediment modeling at the Doam watershed using the SWAT model. The hydrologic component of the SWAT model was calibrated and validated for measured flow data from 2002 to 2003. The $R^2$ value was 0.79 and the EI value was 0.53 for weekly simulated data. The calibrated model parameters were used for hydrologic component validation and the $R^2$ value was 0.86 and the EI value was 0.74 for weekly data. For sediment comparison, the $R^2$ value was 0.67 and the EI value was 0.59. These statistics improved with the use of soil properties of the reconditioned soil in the field compared with the results obtained without considering soil reconditioning. The simulated sediment amounts with and without considering the soil properties of the reconditioned soil were 284,813 ton and 158,369 ton, respectively. This result indicates that there could be approximately 79% of errors in estimated sediment yield at the Doam watershed, although the model comparison with the measured data gave similar satisfactory statistics with and without considering soil properties from the reconditioned soil.

• Tunnel and Underground Space
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• v.14 no.1
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• pp.54-68
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• 2004

In this study, scaled model tests were performed on blasting demolition of reinforced concrete structures and the experimental results were analyzed in comparison with the results of numerical analysis. The tests were designed to induce a progressive collapse, and physical properties of the scaled model were determined using scale factors obtained ken dimension analysis. The scaled model structure was made of a mixture of plaster, sand and water at the ratio determined to yield the best scaled-down strength. Lead wire was used as a substitute for reinforcing bars. The scaled length was at the ratio of 1/10. Selecting the material and scaled factors was aimed at obtaining appropriately scaled-down strength. PFC2D (Particle Flow Code 2-Dimension) employing DEM (Distinct Element Method) was used for the numerical analysis. Blasting demolition of scaled 3-D plain concrete laymen structure was filmed and compared to results of numerical simulation. Despite the limits of 2-D simulation the resulting demolition behaviors were similar to each other. Based on the above experimental results in combination with bending test results of RC beam, numerical analysis was carried out to determine the blasting sequence and delay times. Scaled model test of RC structure resulted in remarkably similar collapse with the numerical results up to 900㎳ (mili-second).

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.26 no.3
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• pp.263-273
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• 2008

The precision of sensors' position is particularly important in the application of road extraction or digital map generation. In general, the various ranging solution systems such as GPS, Total Station, and Laser Ranger have been employed for the position of the sensor. Basically, the ranging solution system has problems that the signal may be blocked or degraded by various environmental circumstances and has low temporal resolution. To overcome those limitations a IMU/range integrated system could be introduced. In this paper, after pointing out the limitation of extended Kalman filter which has been used for workhorse in navigation and geodetic community, the two sampling based nonlinear filters which are sigma point Kalman filter using nonlinear transformation and carefully chosen sigma points and particle filter using the non-gaussian assumption are implemented and compared with extended Kalman filter in a simulation test. For the ranging solution system, the GPS and Total station was selected and the three levels of IMUs(IMU400C, HG1700, LN100) are chosen for the simulation. For all ranging solution system and IMUs the sampling based nonlinear filter yield improved position result and it is more noticeable that the superiority of nonlinear filter in low temporal resolution such as 5 sec. Therefore, it is recommended to apply non-linear filter to determine the sensor's position with low degree position sensors.

• Korean Journal of Agricultural and Forest Meteorology
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• v.4 no.4
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• pp.224-236
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• 2002

A long-term growth simulation was performed at 496 land units in the western coastal plains (WCP) of North Korea to test the potential adaptability of each land unit for growing South Korean rice cultivars. The land units for rice cultivation (CZU), each of them represented by a geographically referenced 5 by 5 km grid tell, were identified by analyzing satellite remote sensing data. Surfaces of monthly climatic normals for daily maximum and minimum temperature, precipitation number of rain days and solar radiation were generated at a 1 by 1 km interval by spatial statistical methods using observed data at 51 synoptic weather stations in North and South Korea during 1981-2000. Grid cells felling within a same CZU and, at the same time, corresponding to the satellite data- identified rice growing pixels were extracted and aggregated to make a spatially explicit climatic normals relevant to the rice growing area of the CZU. Daily weather dataset for 30 years was randomly generated from the monthly climatic normals of each CZU. Growth and development parameters of CERES-rice model suitable for 11 major South Korean cultivars were derived from long-term field observations. Eight treatments comprised of 2 transplanting dates $\times$ 2 cropping systems $\times$ 2 irrigation methods were assigned to each cultivar. Each treatment was simulated with the randomly generated 30 years' daily weather data (from planting to physiological maturity) for 496 land units in WCP to simulate the growth and yield responses to the interannual climate variation. The same model was run with the input data from the 3 major crop experiment stations in South Korea to obtain a 30 year normal performance of each cultivar, which was used as a "reference" for comparison. Results were analyzed with respect to spatial and temporal variation in yield and maturity, and used to evaluate the suitability of each land unit for growing a specific South Korean cultivar. The results may be utilized as decision aids for agrotechnology transfer to North Korea, for example, germplasm evaluation, resource allocation and crop calendar preparation.

• Korean Journal of Remote Sensing
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• v.34 no.4
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• pp.611-624
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• 2018

The percentage of moisture content in rice before harvest is crucial to reduce the economic loss in terms of yield, quality and drying cost. This paper discusses the application of artificial neural network (ANN) in developing a reliable prediction model using the low altitude fixed-wing unmanned air vehicle (UAV) based reflectance value of green, red, and NIR and statistical moisture content data. A comparison between the actual statistical data and the predicted data was performed to evaluate the performance of the model. The correlation coefficient (R) is 0.862 and the mean absolute percentage error (MAPE) is 0.914% indicate a very good accuracy of the model to predict the moisture content in rice before harvest. The model predicted values are matched well with the measured values($R^2=0.743$, and Nash-Sutcliffe Efficiency = 0.730). The model results are very promising and show the reliable potential to predict moisture content with the error of prediction less than 7%. This model might be potentially helpful for the rice production system in the field of precision agriculture (PA).