• Korean Journal of Agricultural and Forest Meteorology
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• v.16 no.4
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• pp.359-367
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• 2014

Drought index can be used to implement an early warning system for drought and to operate a drought monitoring service. In this study, an approach was examined to determine agricultural drought index (ADI) at high spatial resolution, e.g., 270 m. The value of ADI was calculated based on soil water balance between supply and demand of water. Water supply is calculated by the cumulative effective precipitation with the application of the weight to the precipitation from two months ago. Water demand is derived from the actual evapotranspiration, which was calculated applying a crop coefficient to the reference evapotranspiration. The amount of surface runoff on a given soil type was also used to calculate soil residual moisture. Presence of drought was determined based on the probability distribution in the given area. In order to assess the reliability of this index, the amount of residual moisture, which represents severity of drought, was compared with measurements of soil moisture at three experimental between July 2012 and December 2013. As a result, the ADI had greater correlation with measured soil moisture compared with the standardized precipitation index, which suggested that the ADI would be useful for drought warning services.

• Journal of Platform Technology
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• v.8 no.3
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• pp.10-21
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• 2020

Partial discharge occurs a lot in high-voltage power equipment such as switchgear, transformers, and switch gears. Partial discharge shortens the life of the insulator and causes insulation breakdown, resulting in large-scale damage such as a power outage. There are several types of partial discharge that occur inside the product and the surface. In this paper, we design a predictive model that can predict the pattern and probability of occurrence of partial discharge. In order to analyze the designed model, learning data for each type of partial discharge was collected through the UHF sensor by using a simulator that generates partial discharge. The predictive model designed in this paper was designed based on CNN during deep learning, and the model was verified through learning. To learn about the designed model, 5000 training data were created, and the form of training data was used as input data for the model by pre-processing the 3D raw data input from the UHF sensor as 2D data. As a result of the experiment, it was found that the accuracy of the model designed through learning has an accuracy of 0.9972. It was found that the accuracy of the proposed model was higher in the case of learning by making the data into a two-dimensional image and learning it in the form of a grayscale image.