Ada provides full capacities of supporting object orientation, but the diversified objects patterned in Ada are so intricate that Ada95's aim would be demolished. In order to complement the disfigurement that Ada...Ada provides full capacities of supporting object orientation, but the diversified objects patterned in Ada are so intricate that Ada95's aim would be demolished. In order to complement the disfigurement that Ada does lack for a pristine notion of class, this paper presents a remolded object pattern known as A object, an Ada based class description language A ObjAda aiming at support for A object pattern and the related approach for key algorithms and implementation. In consequent, A ObjAda hereby promotes Ada with highlighted object orientation, which not only effectively exploits the capacities in Ada95, but also rationally hides befuddling concepts from Ada95.展开更多
This report presented a method that uses deep computing and stochastic gradient descent algorithm to automatically detect building from satellite images. In this method, a convolutional neural network architecture cal...This report presented a method that uses deep computing and stochastic gradient descent algorithm to automatically detect building from satellite images. In this method, a convolutional neural network architecture called U-Net was trained to highlight the building pixels from the rest of the image. This method applied a binary cross-entropy loss function, used ADAM algorithm for gradient descent optimization, and adopted interaction-over-union for accuracy measurement. Continuous loss decreases and accuracy increases were observed during the training and validation. Finally, the visualization of the predicted masks from the trained model after 20 epochs proved that the U-Net model delivers over 60% Intersection over Union accuracy results for detecting buildings from satellite images.展开更多
基金Supported by National Natural Science Foundation of China(6 97730 41)
文摘Ada provides full capacities of supporting object orientation, but the diversified objects patterned in Ada are so intricate that Ada95's aim would be demolished. In order to complement the disfigurement that Ada does lack for a pristine notion of class, this paper presents a remolded object pattern known as A object, an Ada based class description language A ObjAda aiming at support for A object pattern and the related approach for key algorithms and implementation. In consequent, A ObjAda hereby promotes Ada with highlighted object orientation, which not only effectively exploits the capacities in Ada95, but also rationally hides befuddling concepts from Ada95.
文摘This report presented a method that uses deep computing and stochastic gradient descent algorithm to automatically detect building from satellite images. In this method, a convolutional neural network architecture called U-Net was trained to highlight the building pixels from the rest of the image. This method applied a binary cross-entropy loss function, used ADAM algorithm for gradient descent optimization, and adopted interaction-over-union for accuracy measurement. Continuous loss decreases and accuracy increases were observed during the training and validation. Finally, the visualization of the predicted masks from the trained model after 20 epochs proved that the U-Net model delivers over 60% Intersection over Union accuracy results for detecting buildings from satellite images.
