37results about How to "Increase the amount of training data" patented technology

The invention discloses a face data enhancement method based on key point disturbance technology, comprising steps of utilizing a face calibration method based on the human face key point technology, performing disturbance on the key point position, and obtaining a new human face image through linear transformation, affine transformation, and partitioning affine transformation . The human face calibration method based on the human face key point technology performs the human face calibration by the linear transformation, the affine transformation, and the partitioning affine transformation, performs disturbance on the position of the key point, and enabling one human face image to generate a plurality of human face images. The face data enhancement method based on the key point disturbance technology can solve the problem that the data is insufficient in the process of human face recognition training, generates the plurality of images through one human face image, increases the training quantity and improves the accuracy of the human face recognition.

The invention discloses a transaction object prediction method and device, and relates to the technical field of computers. One specific embodiment of the method comprises the steps of obtaining a plurality of prediction reference texts of a target transaction object in each period of a plurality of historical transaction periods, and determining a fluctuation tendency factor of each prediction reference text; determining a text tendency index of the target transaction object in the period by utilizing the rising and falling tendency factor; and inputting the price data and the text tendency index of the target transaction object in each period of the plurality of historical transaction periods into a pre-trained prediction model to obtain information of at least one preset dimension of the target transaction object in a future transaction period. According to the embodiment of the invention, the method can improve the prediction accuracy of the transaction object through introducing the features, affecting the change trend of the transaction object, in the prediction reference text into the model for training and prediction and analyzing a large amount of historical data containing the features.

The invention discloses an optic nerve simulation method and an optic nerve simulation system based on deep learning, wherein the method comprises the steps of transmitting a visible light signal; acquiring a visual neural electrical signal for obtaining a first visual neural simulated electrical signal, performing two-dimensional imaging on a brain visual processing cortex for obtaining a first near-infrared brain functional area simulated two-dimensional image; performing filtering, prior modification and sampling on the received visible light signal, and furthermore obtaining a second visual neural simulated electrical signal and a second near-infrared brain functional area simulated two-dimensional image; and performing analysis and evaluation, using data after evaluation as parameters for correcting a deep leaning module, and transmitting the data to the deep leaning module in a multi-dimensional vector form, thereby reconstructing a deep learning network by means of the multidirectional vector. According to the optic nerve simulation method, medical simulation and rehabilitation training can be performed on a human brain and an optic nerve system by means of a deep learning method. Simple operation and high reliability are realized. Furthermore along with increase of training data volume, system gain can be remarkably improved.

The embodiment of the present application provides a user identification method, device, electronic equipment, storage medium, and computer program product containing instructions, which relate to the technical field of computer communication networks. By filling the abnormal data in the sample user data with the median, Splitting the data of the string type in the sample user data by One-Hot encoding and based on the cardinality of the data of the enumeration type in the sample user data The method of obtaining enumerated data of multiple feature dimensions preprocesses the sample user data to obtain the preprocessed sample user data, which increases the amount of training data for the deep learning network model and reduces the risk of overfitting In case of occurrence, the user data to be processed is input into the deep learning network model to identify illegal users, thereby improving the accuracy and recognition efficiency of illegal user identification.

The invention discloses a face data enhancement method based on the key point perturbation technology. The method utilizes the face alignment method based on the key point technology of the face first, then disturbs the position of the key point, and then respectively uses linear transformation and affine Transformation, block affine transformation to get a new face picture. The face alignment method based on face key point technology in the present invention performs face alignment with linear transformation, affine transformation, and block affine transformation, and disturbs the position of key points, so that one face picture can generate multiple Pictures of faces. The invention is a method capable of solving the lack of data in the face recognition training process. A plurality of pictures are generated from one face picture, thereby increasing the amount of training and improving the accuracy of face recognition.

The invention relates to a power system transient stability evaluation method, which belongs to the technical field of power system stability analysis. Firstly, the transient stability simulation is carried out for different operating conditions and preset faults of the power system, and the data of the power system before the fault is collected. Then, the similarity evaluation index of different preset faults is constructed based on the Jaccard distance and the Hausdorff distance, and the data sets of different preset faults are clustered based on the similarity evaluation index; the different preset faults in each cluster are clustered. The multi-task Siamese neural network with shared weights is sequentially trained to obtain multiple multi-task Siamese neural networks for transient stability evaluation. This method considers the similarity of different preset faults in the transient stability assessment, and uses different data sets of similar preset faults to train the twin network, which is beneficial to improve the generalization ability of the transient stability assessment model, thereby improving the transient stability assessment results. accuracy.