• Journal of Biosystems Engineering
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• v.21 no.2
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• pp.191-197
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• 1996

Allowable stacking duration of the corrugated fiberboard boxes being widely used for packaging fruits and vegetables was analyzed by the creep behavior and the cumulative load correction factor for the boxes. The stacking boxes were assumed to be stored at a nearly constant temperature and relative humidity condition. When the stacking duration was short period, the stacking height determined by two methods showed a little difference between them, but almost no difference was shown as the stacking duration was longer. Allowable stacking duration was rapidly decreased with the increase of static load applied on the stacking boxes, and allowable stacking duration of Box A was estimated the longer than that of Box B. A model of allowable stacking duration for the corrugated fiberboard box was developed as a function of the stacking load and the ambient relative humidity.

• Journal of Korean Society on Water Environment
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• v.31 no.3
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• pp.241-252
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• 2015

Runoff behaviors by five bias correction methods were analyzed, which were Change Factor methods using past observed and estimated data by the estimation scenario with average annual calibration factor (CF_Y) or with average monthly calibration factor (CF_M), Quantile Mapping methods using past observed and estimated data considering cumulative distribution function for entire estimated data period (QM_E) or for dry and rainy season (QM_P), and Integrated method of CF_M+QM_E(CQ). The peak flow by CF_M and QM_P were twice as large as the measured peak flow, it was concluded that QM_P method has large uncertainty in monthly runoff estimation since the maximum precipitation by QM_P provided much difference to the other methods. The CQ method provided the precipitation amount, distribution, and frequency of the smallest differences to the observed data, compared to the other four methods. And the CQ method provided the rainfall-runoff behavior corresponding to the carbon dioxide emission scenario of SRES A1B. Climate change scenario with bias correction still contained uncertainty in accurate climate data generation. Therefore it is required to consider the trend of observed precipitation and the characteristics of bias correction methods so that the generated precipitation can be used properly in water resource management plan establishment.