Abstract: Disclosed are a photovoltaic shutdown circuit and a photovoltaic system. The photovoltaic shutdown circuit comprises a shutdown power line, a signal receiving module, a signal processing module, and a controllable switch module. The signal processing module comprises a signal modulation unit and a threshold comparison unit. A switch end of the controllable switch module is connected in series to the shutdown power line. The signal receiving module is connected to the shutdown power line for receiving a first signal with a controlling signal; the signal modulation unit is for reverting the controlling signal; the threshold comparison unit is connected to the signal modulation unit to receive the reverted controlling signal and compare the reverted controlling signal with a set threshold to output a driving signal. The controllable switch module is connected to the threshold comparison unit to receive the driving signal and control an on/off of the controllable switch module.

Abstract: A method for generating a human-computer interactive abstract image includes: S1: obtaining and preprocessing the original abstract images used as a training dataset B to obtain edge shape feature maps used as a training dataset A; S2: using the training dataset A and the training dataset B as cycle generative objects of a Cycle-GAN model, and training the Cycle-GAN model to capture a mapping relationship between the edge shape feature maps and the original abstract images; S3: obtaining a line shape image drawn by a user; and S4: according to the mapping relationship, intercepting a generative part in the Cycle-GAN model that the dataset B is generated from the dataset A, discarding a cycle generative part and a discrimination part in the Cycle-GAN model, and generating a complete abstract image based on the line shape image to generate the human-computer interactive abstract image.

Abstract: A method for positioning key features of a lens based on ocular B-mode ultrasound images includes: acquiring and preprocessing the ocular B-mode ultrasound images to obtain a preprocessed B-mode ultrasound image, eyeball coordinates and lens coordinates; sending the preprocessed B-mode ultrasound image, the eyeball coordinates and the lens coordinates into a trained target detection network YOLOv3 to obtain eyeball position images and lens position images; substituting the eyeball position images and the lens position images into a trained feature extraction network group to obtain image features and feature coordinates corresponding to the eyeball position images and the lens position images, respectively; substituting the image features into a trained collaborative learning network to screen key image features; and marking a feature coordinate corresponding to the key image features on the ocular B-mode ultrasound images to complete positioning the key features of the lens.

Abstract: A method for generating a human-computer interactive abstract image includes: S1: obtaining and preprocessing the original abstract images used as a training dataset B to obtain edge shape feature maps used as a training dataset A; S2: using the training dataset A and the training dataset B as cycle generative objects of a Cycle-GAN model, and training the Cycle-GAN model to capture a mapping relationship between the edge shape feature maps and the original abstract images; S3: obtaining a line shape image drawn by a user; and S4: according to the mapping relationship, intercepting a generative part in the Cycle-GAN model that the dataset B is generated from the dataset A, discarding a cycle generative part and a discrimination part in the Cycle-GAN model, and generating a complete abstract image based on the line shape image to generate the human-computer interactive abstract image.

Abstract: A method for positioning key features of a lens based on ocular B-mode ultrasound images includes: acquiring and preprocessing the ocular B-mode ultrasound images to obtain a preprocessed B-mode ultrasound image, eyeball coordinates and lens coordinates; sending the preprocessed B-mode ultrasound image, the eyeball coordinates and the lens coordinates into a trained target detection network YOLOv3 to obtain eyeball position images and lens position images; substituting the eyeball position images and the lens position images into a trained feature extraction network group to obtain image features and feature coordinates corresponding to the eyeball position images and the lens position images, respectively; substituting the image features into a trained collaborative learning network to screen key image features; and marking a feature coordinate corresponding to the key image features on the ocular B-mode ultrasound images to complete positioning the key features of the lens.

Abstract: A method for differentiable architecture search based on a hierarchical grouping mechanism includes: obtaining a target dataset; selecting a set number of normal cells and two reduction cells; enabling the reduction cells to be located at ? and ? of all cells, respectively, and then concatenating all cells to form an initial search network; training the initial search network to optimize a cost function to complete a one-level search or a two-level search, wherein in the one-level search, control weight parameters are shared by the inter-group operations and are shared by the intra-group operations among the cells; and in the two-level search, the control weight parameters are shared by the inter-group operations and are not shared by the intra-group operations, obtaining normal cells and reduction cells based on the one-level search or the two-level search; and constructing a target network using the normal cells and the reduction cells.