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Section 3 introduces grid labeling concept for efficient training set definition.
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Section 2 introduces grid-based CS and outlines the procedures of AMP-CTLS.
Inspired by the idea of learning from label proportions, we firstly introduce grid labeling to get the truth of training data more efficiently by giving the proportions of the labels in each group.
The main contributions of our work are (1) introducing grid labeling for SAR image classification, to acquire the truth of the training data rapidly and at low cost; (2) building an SVM-based learning model by taking in consideration the constraints of label proportions; and (3) proposing an efficient inference method to reweight samples according to sample reliability and label proportions.
The increasing number of distributed energy resources connected to power systems raises operational challenges for the network operator, such as introducing grid congestion and voltage deviations in the distribution network level, as well as increasing balancing needs at the whole system level.
We discretise the ranges of the parameters by introducing grid points.
In this article, we introduce grid ordinal and multinomial logistic models to handle multi-center modeling of multi-category response, including model assumption checking.
Therefore, we introduced grid-patterned PEDOT as an ion storage layer.
The radiation validation tool introduces no grid in itself.
This method introduces the grid's previous scheduling function to build a dynamic optimization model to calculate the cost and economic aspect [3, 4].
This paper introduces the Grid Diagram Table (GDT) as an alternative tool for determining pinch points and utility targets as well as for the retrofitting of HEN.
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