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 a multi-party collaboration-based engineering performance guarantee insurance risk identification method and platform. The method includes: first, all parties participating in the collaborative training risk assessment model perform preprocessing operations and value mapping on locally stored engineering project information data Operate the privacy-protected engineering business data; then, each participant and the cloud server complete the training of the risk assessment model through distributed collaboration to obtain the global risk assessment model; finally, process the engineering project data to be assessed and input them into the global risk assessment model. Evaluate models for risk identification predictions. The present invention can carry out multi-party collaborative risk identification across data sources under the premise of protecting data privacy, and improves the risk identification capability of the risk assessment model while ensuring the security of engineering information data.

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 invention discloses a telephone appeal text classification algorithm based on DenseNet facing the electric power field, belonging to the technical field of text classification algorithms, through preprocessing the text to be classified, data augmentation, establishing a vocabulary dictionary, word vector id matching, and word vector reduction Dimensions, concatenated eigenvalues, and random permutations and combinations of concatenated eigenvalues ​​are used to obtain a text classifier, and the text classifier is used to classify text. The DenseNet-based phone appeal text classification algorithm for the electric power field provided by the present invention can effectively make up for the shortcomings of traditional algorithms, and can well adapt to the characteristics of strong professionalism, large length differences, and mixed characters and numbers in electric power appeal texts. On the premise of ensuring the classification accuracy, the complexity of the model is reduced, and the rapid and accurate classification of the telephone appeal text in the electric power field is realized, which satisfies the classification requirements well.

The invention discloses a method for automatic analysis of lung pathological sounds based on multi-task classification, relates to the technical field of lung pathology analysis, and comprises the following steps: inputting the extracted audio features into a multi-task classification model of a convolutional neural network MobileNetV2, The multi-task classification model of the convolutional neural network MobileNetV2 includes an output for a lung pathological sound recognition task and an output for a lung disease prediction task. The present invention uses the multi-task learning method to implicitly increase the amount of training data, and improves the generalization performance of the model through the domain knowledge of multiple label information of the same data, thereby improving the prediction accuracy of the multi-task classification model of the convolutional neural network MobileNetV2 In addition, the multi-task classification model using the lightweight convolutional neural network MobileNetV2 has fewer parameters and requires less computing power and memory size for training equipment, so that the prediction and classification tasks can be completed on mobile or embedded devices.

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.