Abstract: A method for developing an epoxy resin impregnated glass fiber Direct Current (DC) bushing, comprising: according to length parameters of each layer of capacitive screen or resistive screen designed depending on insulation requirements, selecting bushing design parameters, determining a winding machine program according to the bushing design parameters, and winding a core body according to the winding machine program, wherein during the core body winding process, the core body begins to be initially cured; after the core body is wound, curing the core body by an oven according to a preset oven temperature and duration; machining the cured core body according to a preset core body design drawing; after the inner wall of a flange is polished and cleaned and is heated and pretreated by the oven, injecting glue at the position of a glue injection hole of the flange for gluing the core body and the flange; sequentially assembling a collector ring, a hollow composite insulator, and a voltage-equalizing sealing cove

Abstract: The present invention discloses a display panel and a display device. The display panel includes a gate driver circuit and a plurality of driving wires. By disposing gate driver circuits with different distribution density in non-bending frame regions and adjusting corresponding driving wires wired from an output end of the gate driver circuit to a bending displaying region, normal driving of the bending displaying region can be achieved and simultaneously damages to the gate driver circuit during folding can be mitigated or obviated.

Abstract: A method for identifying an individual of an oplegnathus punctatus based on a convolutional neural network is provided. Target initial positioning involves three continuous convolutional layers and an average pooling layer. A region of feature interest point is obtained, a hyperparameter candidate box is set to obtain a region, thereby obtaining an approximate position of a target object. II_Net backbone convolutional neural network includes six convolutional layers and four pooling layers, which includes a LeakyReLU activation function used as an activation function of the first convolutional layer, convolutional network layers of Alexnet and parameter data, and a maximum pooling layer of an overlapped pooling structure. Fully connected layers use a genetic algorithm to improve data transmission between layers. With the established model, identification of the individual of the oplegnathus punctatus is performed by using test data.

Abstract: Disclosed is a method for detecting densely occluded fish based on a YOLOv5 network, belonging to the technical field of fish images. The method includes a data set establishment and processing part, a model training part and a model testing part; the data set establishment and processing part includes data collection of fish pictures, data labelling and data division of the fish pictures; and the data division is to divide data into a training set, a verification set and a test set.

Abstract: Disclosed is a method for detecting Ophiocephalus argus cantor under intra-class occulusion based on cross-scale layered feature fusion, including image collecting, image processing and network model, where collected images are labeled, image sizes are adjusted to obtain input images, and the input images are input into an object detection network, integrated by convolution and inserted into cross-scale layered feature fusion modules, characterized by including dividing all features input into the cross-scale layered feature fusion modules into n layers, composed of s feature mapping subsets, and fusing features of each feature mapping subset with that of other feature mapping subsets, and connecting; carrying out convolution operation, outputting training result; adjusting network parameters by a loss function to obtain parameters for a network model; inputting final output candidate boxes into a non-maximum suppression module to screen correct prediction boxes, so that prediction result is obtained.

Abstract: A method for identifying an individual of an oplegnathus punctatus based on a convolutional neural network is provided. Target initial positioning involves three continuous convolutional layers and an average pooling layer. A region of feature interest point is obtained, a hyperparameter candidate box is set to obtain a region, thereby obtaining an approximate position of a target object. II_Net backbone convolutional neural network includes six convolutional layers and four pooling layers, which includes a LeakyReLU activation function used as an activation function of the first convolutional layer, convolutional network layers of Alexnet and parameter data, and a maximum pooling layer of an overlapped pooling structure. Fully connected layers use a genetic algorithm to improve data transmission between layers. With the established model, identification of the individual of the oplegnathus punctatus is performed by using test data.