44results about How to "Good training results" patented technology

The invention provides an electroencephalogram sleep staging method based on a deep convolutional neural network. The electroencephalogram sleep staging method comprises the following steps that S1, sleep signals of a subject are collected, and multi-lead electroencephalogram signals in the sleep signals are extracted; S2, performing data preprocessing on the electroencephalogram signals; S3, constructing and training an end-to-end deep convolutional neural network classifier; and S4, performing electroencephalogram sleep staging by using the deep convolutional neural network classifier. Compared with the conventional CNN electroencephalogram sleep staging method, the electroencephalogram sleep staging method provided by the invention has the advantages that under the condition of the sameiteration times and learning rate, each batch of the model adopts higher data, and the obtained output result is more stable. In terms of accuracy and F score, the method provided by the invention has better classification performance.

The invention discloses a general steganography method based on deep learning. The method comprises the following steps: S1, carrying out hiding processing on a sender; dividing secret information tobe hidden into n groups of information fragments, wherein each group of information fragments correspond to one category label, a deep learning model is adopted, the category label and random noise are used as drive, a pseudo-natural image of a specified category is generated, and the pseudo-natural image is used as a secret-containing image input channel after hiding processing; and S2, carryingout extraction processing at a receiver: inputting the secret-containing image into a discriminator by the receiver to carry out image authenticity identification and image category judgment, then sending the image category information into a function converter to be processed to obtain a secret information fragment, and decoding the secret information fragment to obtain original secret information. According to the invention, the security and confidentiality of information transmission can be greatly improved.

The invention discloses a multi-modal fusion saliency detection method based on a convolution attention module. The method comprises: in a training stage, constructing a convolutional neural network;inputting the left viewpoint image and the depth image of the original image into a convolutional neural network for training to obtain a corresponding saliency detection image; calculating a loss function between a set formed by saliency detection images generated by the model and a set formed by corresponding real human eye gaze images to obtain an optimal weight vector and an offset term of theconvolutional neural network classification training model; and in a test stage, inputting the three-dimensional image in the selected data set into the trained convolutional neural network model toobtain a saliency detection image. According to the visual saliency detection method, extraction of image features is optimized by applying a novel module, multi-scale and multi-mode feature fusion iscarried out, and finally the detection efficiency and the detection accuracy of visual saliency detection are improved.

The invention relates to the technical field of natural language processing, in particular to a question-answering method for entity relationship extraction based on transfer learning. The acquisitionof a relationship classification result comprises the steps: obtaining and preprocessing a source domain text data set and a target domain text data set; inputting the preprocessed data into a skip-gram model for training to obtain word vectors of the source domain text data and the target domain text data, obtaining position vectors of the source domain text data and the target domain text data,and cascading the position vectors with the word vectors to obtain joint feature vectors of the source domain text data and the target domain text data; inputting the joint feature vector of the source domain text data into a BiLSTM network for pre-training to obtain network parameters in the pre-training process and context information and semantic features of the source domain text data; and inputting the joint feature vector of the target domain text data into a BiLSTM _ CNN fusion model for retraining to obtain a high-dimensional feature vector of the target domain text data, sending thehigh-dimensional feature vector into a classifier, and outputting the relationship classification result. Question-answering accuracy can be improved.

The invention discloses a method for testing fault modes of an integrated switching current circuit. The method includes the following steps: S1, establishing a switching current test circuit, and applying an excitation signal to the test circuit, S2, defining the fault modes of the switching current circuit, S3, collecting testable node time-domain response signals of the switching current circuit, S4, preprocessing time-domain response data, calculating fault characteristic parameters of the signals, extracting information entropy and kurtosis of the signals, and calculating a fuzzy set of the information entropy, and S5, constructing a neural network classifier according to the information entropy and the kurtosis, and acquiring the fault modes of the switching current test circuit. The method for testing the fault modes of the integrated switching current circuit is suitable for tests on a large-scale and complicated integrated switching current circuit with a large number of fault categories and is high in accuracy rate of fault diagnosis.

The invention discloses a medical image focus detection modeling method, device and system based on federated learning, and the method comprises the steps: enabling a global server S to transmit a generated global parameter [omega]<0> to local focus recognition clients C<k>; generating a global parameter [omega]<theta+1> by using detection head network parameters returned by the K local focus identification clients C<k>; and sending the global parameter [omega]<theta+1> to each local focus identification client C<k> to obtain a corresponding medical image focus detection model. The training information of the intermediate model is transmitted to a data holder through codes, respective data information does not need to be shared, and the model is integrated through a corresponding strategy, so that better training and prediction results are returned.

The invention relates to a training device, in particular to a medical leg rehabilitation training device. The to-be-solved technical problem is to provide a medical leg rehabilitation training devicewith a high safety factor and a significant training result. In order to solve the above technical problem, the provided medical leg rehabilitation training device comprises a mounting plate, a firsthollow rod, a first connecting rod, a height adjusting mechanism, a bearing plate, a first fixing plate, a second fixing plate, a rotating mechanism, a second hollow rod, a first connecting block, athird hollow rod, a U-shaped handrailing, a fixing mechanism, a resetting mechanism and a pedal; the first hollow rod and the second fixing plate are fixedly connected to the top of the mounting platein sequence in the length direction of the mounting plate, and the first connecting rod is slidably connected with the first hollow rod. The medical leg rehabilitation training device achieves the effects of the high safety factor and significant training result.

The invention discloses a rapid ship target detection method, a storage medium and computing equipment, and the method comprises the steps: establishing a feature pyramid full-convolution network anda double-branch module which sequentially comprise an input layer, a feature extraction layer, a feature fusion layer and an output layer, determining a ship data set, inputting a generated training set into the built feature pyramid full-convolution network, and generating a target detection result. According to the method, the ship in the image can be rapidly and accurately detected; and the detection result is accurate and rapid, the requirement on embedded equipment is low, and the method has very high practical application value in various aspects such as military affairs, civil use and the like.

The invention provides an air conditioner on-hook spatial distribution automatic identification method and system based on a streetscape picture, which realizes air conditioner on-hook spatial distribution identification by taking the streetscape picture provided by a network platform as a data source, and comprises the following steps: capturing the streetscape picture through a crawler; and pre-screening the crawled street scene pictures based on the air conditioner hanging scene characteristics, labeling a window entity and an air conditioner hanging target entity, inputting the window entity and the air conditioner hanging target entity into a YOLOX network for air conditioner hanging entity model training and window entity model training, and after training is completed, predicting each street scene picture by using a corresponding model. Outputting the number of air conditioner outdoor units and window entities in each picture; and calculating the ratio of the number of air conditioner outdoor units to the number of windows, performing space projection in combination with the coordinate information of each street view picture, and outputting the corresponding air conditioner coverage rate of the region. By mining the network streetscape picture information, identifying the ratio of the air conditioner hanging unit to the window and extracting the air conditioner space distribution information, the labor cost can be greatly saved.

The invention discloses a resolution-by-resolution improved image super-resolution restoration method based on an attention mechanism, which is mainly based on a resolution-by-resolution improved super-resolution restoration network of a double attention mechanism, and improves the feature extraction capability of a model by introducing a convolution module of a feature dimension attention mechanism and a spatial dimension attention mechanism. Furthermore, related ideas of a resolution-by-resolution improvement network in the field of adversarial neural networks are used for reference, the network learning difficulty is simplified, and super-resolution restoration of resolution-by-resolution improvement is realized. Finally, the algorithm performance is tested through a DIV2K data set, andexperimental results show that the method can improve the input low-resolution image resolution by resolution, the same network can improve the resolution by 2-4 times at the same time, and the PSNRvalue of the reconstructed image is significantly superior to that of a current mainstream algorithm.

The invention discloses a method and a system for improving seismic data resolution based on a weak supervision generative adversarial network. The method comprises the following steps of: performing normalization processing on two pieces of three-dimensional seismic data belonging to different work areas; dividing a training set and a test set; obtaining a training sample pair in the training area in a random extraction mode; sending the low-resolution seismic data into a forward generator; sending the output of the forward generator to a reverse generator; sending the output of the forward generator and the corresponding high-resolution label to a discriminator for discrimination; and alternately training the generator and the discriminator, continuously updating network parameters until the model converges, and after training is finished, sending the whole block of low-resolution seismic data into the forward generator for testing to obtain a final high-resolution result. According to the method, the distribution characteristics of the high-resolution seismic data can be learned on the premise that paired input and labels do not exist, and the high-frequency information of the original seismic data can be accurately and effectively recovered.

The invention discloses a push-up assisting device suitable for beginners, which comprises a trunk supporting plate, wherein the trunk supporting plate is provided with a head end close to the head ofa user and a tail end close to the foot of the user; a foot supporting plate rotatably connected to the tail end of the trunk supporting plate and arranged in a vertical direction; an auxiliary rod wherein one end of the auxiliary rod is rotatably connected to the lower surface of the trunk supporting plate, and the other end of the auxiliary rod slidably braces against the ground; an elastic piece is also arranged between the auxiliary rod and the trunk supporting plate, and the elastic piece keeps the trunk supporting plate and the auxiliary rod at a certain angle. The push-up assisting device suitable for beginners can relieve upper limb pressure, is conducive to standard actions completion, reduces injury risks, and is simple in structure and convenient for use.

The present invention relates to a craniofacial contour surgery simulation teaching aid. The craniofacial contour surgery simulation teaching aid comprises a head mold for simulating a human head and an elastic head hood; the elastic head hood covers the head mold; and a first opening corresponding to the dentition of the head mold is formed in the elastic head hood. With the craniofacial contour surgery simulation teaching aid of the invention adopted, an intraoral approach craniofacial contour surgery can be reproduced vividly and realistically; and the first opening is formed in the elastic head hood, so that a professional doctor can carry out the training of the intraoral approach craniofacial contour surgery, time for a plastic surgery doctor to master the craniofacial contour surgery can be shortened, and a surgery training effect can be ensured.

The invention belongs to the technical field of face recognition, and discloses an artificial intelligence recognition device based on big data, and the device comprises: a three-dimensional laser scanning module which obtains face information data through a three-dimensional low-light scanner, and meanwhile, preprocesses the three-dimensional face point cloud; the three-dimensional image preprocessing module that is used for extracting three-dimensional multimedia visual target image features by analyzing two aspects of visual two-dimensional multimedia target image feature extraction and three-dimensional multimedia visual target image change feature generation; the face model training module that is used for calling an OpenCV internal function to obtain a trained model based on an OpenCV technology; the interaction recognition module that is used for inputting face information data and carrying out recognition; and the remote face recognition module that performs data processing and face recognition in the LabVIEW development environment, and finally feeds back the recognized identity information to the slave computer. According to the invention, the computer technology can be developed to intelligence and automation.

The invention discloses an SVM-based credit default prediction method under differential privacy in the technical field of credit default. The SVM-based credit default prediction method comprises thefollowing steps of S1, preprocessing data; S2, carrying out variable selection; and S3, designing a weighted SVM optimization model under differential privacy according to a differential privacy serial combinatorial property. An effective solution is provided for a differential privacy SVM learning problem under data imbalance, and the solution can specifically solve the data imbalance problem faced when differential privacy SVM learning is used to predict customer default. The method is suitable for application scenes with data imbalance including credit card default prediction, such as disaster prediction, medical diagnosis and other fields, and is also suitable for the technical scheme of the invention.

The invention discloses a model training method and device, electronic equipment and a storage medium, and relates to the technical field of deep learning. According to the specific implementation scheme, derivative models are generated according to an obtained meta-model set, the multiple derivative models are added into the meta-model set as meta-models, and the number of models in the meta-model set is increased for subsequent meta-training on the meta-model set; and then, the meta-models in the meta-model set are screened according to the performance parameters of the trained meta-models so as to obtain the meta-models with good performance for adaptive training of a target task. According to the model training method and device, the electronic equipment and the storage medium, the manner of carrying out screening after enriching the meta-models is adopted, so that the performance of the screened and reserved meta-models is improved, an adaptive training result does not need to bereversely transmitted to a meta-training process, a better training result can also be achieved, and the training efficiency is effectively improved.