33results about How to "Enrich training data" patented technology

This disclosure provides a training method and apparatus for an adversarial defense model for coordinated optimization of distribution network security and economy, which can be applied to the field of distribution system optimization and scheduling technology. The method includes: obtaining a trained meta-defense model and a trained meta-attack model; performing the following operations based on an objective function until the combined performance fluctuation of the obtained defense model and attack model meets preset conditions. The objective function is constructed based on the security operation indicators and economic operation indicators of the sample distribution network: training the meta-defense model in the first state of the sample distribution network to obtain an intermediate defense model; training the meta-attack model based on attack samples in the attack sample pool to obtain an attack model; updating the attack sample pool to obtain an updated attack sample pool; obtaining the defense model; and when the combined performance fluctuation of the defense model and attack model does not meet the preset conditions, using the defense model as the meta-defense model and the attack model as the meta-attack model.

The invention discloses a left ventricle segmentation method based on artificial intelligence and a CTA image. According to the technical scheme, the left ventricle segmentation method is characterized by comprising the steps of obtaining a CT image; inputting the CT image into the 8-layer U-Net convolutional neural network to obtain a left ventricle segmentation image; the convolutional neural network comprises a contraction path and an expansion path; the contraction path comprises nine encoder blocks, each encoder block comprises two convolutions, the kernel size is 3 * 3 pixels, the stride is 1 pixel, the zero filling number is 1 pixel, and batch normalization and rectification linear units are used as activation functions, so that the number of feature channels is doubled; the extension path comprises eight decoder blocks, each decoder comprises two convolutions, the kernel size is 3 * 3 pixels, the stride is one pixel, the zero padding number is one pixel, then batch normalization and rectification linear units are used as activation functions, the number of feature channels is halved, and the left ventricle segmentation accuracy of CT can be further improved.