• Journal of Bio-Environment Control
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• v.28 no.4
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• pp.366-375
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• 2019

This experiment aims to determine the proper irrigation scheduling based on a whole-substrate capacitance using a newly developed device (SCMD) by comparing with the integrated solar radiation automated irrigation system (ISR) and sap flow sensor automated irrigation system (SF) for the cultivation of tomato (Solanum lycopersicum L. 'Hoyong' 'Super Doterang') during spring to winter season. For the SCMD system, irrigation was conducted every 10 minutes after the first irrigation was started until the first run-off was occurred, of which the substrate capacitance was considered to be 100%. When the capacitance threshold (CT) was reached to the target point, irrigation was re-conducted. After that, when the target drain volume (TDV) was occurred, the irrigation stopped. The irrigation volume per event for the SCMD was set to 50, 75, or 100 mL at CT 0.9 and TDV 100 mL during the spring to summer cultivation, and the CT was set to 0.65, 0.75, 0.80, or 0.90 in the winter cultivation. When the irrigation volume per event was set to 50, 75, or 100 mL, the irrigation frequency in a day was 39, 29, and 19, respectively, and the drain rate was 3.04, 9.25, and 20.18%, respectively. When the CT was set to 0.65, 0.75, or 0.90 in winter, the irrigation frequency was about 6, 7, 15 times, respectively and the drain rate was 9.9, 10.8, 35.3% respectively. The signal of stem sap flow at the beginning of irrigation starting time did not correspond to that of solar irradiance when the irrigation volume per event was set to 50 or 75 mL, compared to that of 100 mL. In winter cultivation, the stem sap flow rate and substrate volumetric water content at the CT 0.65 treatment were very low, while they were very high at CT 0.90 was high. All the integrated data suggest that the proper range of irrigation volume per event is from 75 to 100 mL under at CT 0.9 and TDV 100 mL during the spring to summer cultivation, and the proper CT seems to be higher than 0.75 and lower than 0.90 under at 75 mL of the irrigation volume per event and TDV 70 mL during the winter cultivation. It is going to be necessary to investigate the relationship between capacitance value and substrate volumetric water content by determining the correction coefficient.

• Korean journal of applied entomology
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• v.42 no.1
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• pp.43-51
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• 2003

This study was carried out to develop the forecasting model of Pseudococcus comtocki Kuwana for timing spray. Field phonology and temperature-dependent development of p. comstocki were studied, and its stage transition models were developed. p comstocki occurred three generations a year in Suwon. The 1 st adults occurred during mid to late June, and the 2nd adults were abundant during mid to late August. The 3rd adults were observed after late October. The development times of each instar of p. comstocki decreased with increasing temperature up to 25$^{\circ}C$, and thereafter the development times increased. The estimated low-threshold temperatures were 14.5, 8.4, 10.2, 11.8, and 10.1$^{\circ}C$ for eggs, 1st+2nd nymphs, 3rd nymphs, preoviposition, and 1st nymphs to preoviposition, respectively. The degree-days (thermal constants) for completion of each instar development were 105 DD for egg,315 DD for 1st+2nd nymph, 143 DD for 3rd nymph, 143 DD for preoviposition, and 599 DD for 1 st nymph to preoviposition. The stage transition models of p. comstocki, which simulate the proportion of individuals shifted from a stage to the next stage, were constructed using the modified Sharpe and DeMichele model and the Weibull function. In field validation, degree-day models using mean-minus-base, sine wave, and rectangle method showed 2-3d, 1-7d, and 0-6 d deviation with actual data in predicting the peak oviposition time of the 1st and 2nd generation adults, respectively. The rate summation model, in which daily development rates estimated by biophysical model of Sharpe and DeMichele were accumulated, showed 1-2 d deviation with actual data at the same phonology predictions.

• Journal of Korean Society of Transportation
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• v.23 no.3 s.81
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• pp.95-108
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• 2005

The purpose of this study is to develop the ITS evaluation model using the Probabilistic Risk Analysis (PRA) methodology and to demonstrate the goodness-of-fit of the large ITS projects through the comparative analysis between DEA and PRA model. The results of this study are summarized below. First, the evaluation mode] using PRA with Monte-Carlo Simulation(MCS) and Latin-Hypercube Sampling(LHS) is developed and applied to one of ITS projects initiated by local government. The risk factors are categorized with cost, benefit and social-economic factors. Then, PDF(Probability Density Function) parameters of these factors are estimated. The log-normal distribution, beta distribution and triangular distribution are well fitted with the market and delivered price. The triangular and uniform distributions are valid in benefit data from the simulation analysis based on the several deployment scenarios. Second, the decision making rules for the risk analysis of projects for cost and economic feasibility study are suggested. The developed PRA model is applied for the Daejeon metropolitan ITS model deployment project to validate the model. The results of cost analysis shows that Deterministic Project Cost(DPC), Deterministic Total Project Cost(DTPC) is the biased percentile values of CDF produced by PRA model and this project need Contingency Budget(CB) because these values are turned out to be less than Target Value(TV;85% value), Also, this project has high risk of DTPC and DPC because the coefficient of variation(C.V) of DTPC and DPC are 4 and 15 which are less than that of DTPC(19-28) and DPC(22-107) in construction and transportation projects. The results of economic analysis shows that total system and subsystem of this project is in type II, which means the project is economically feasible with high risk. Third, the goodness-of-fit of PRA model is verified by comparing the differences of the results between PRA and DEA model. The difference of evaluation indices is up to 68% in maximum. Because of this, the deployment priority of ITS subsystems are changed in each mode1. In results. ITS evaluation model using PRA considering the project risk with the probability distribution is superior to DEA. It makes proper decision making and the risk factors estimated by PRA model can be controlled by risk management program suggested in this paper. Further research not only to build the database of deployment data but also to develop the methodologies estimating the ITS effects with PRA model is needed to broaden the usage of PRA model for the evaluation of ITS projects.