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175results about How to "Enhance expressive ability" patented technology

Non-negative matrix factorization-based face super-resolution processing method

The invention relates to the technical field of image super-resolution processing, in particular to a non-negative matrix factorization-based face super-resolution processing method. The method comprises the following steps: performing face alignment on high-resolution face images in a sample library, reading the aligned sample image library, utilizing a non-negative matrix factorization algorithm to perform a factorization operation to obtain a basic image W, performing alignment on input low-resolution face images to obtain the non-negative matrix factorization expression coefficient e of a target high-resolution face image, obtaining the target high-resolution image Z1=We in combination with the basic image W and the expression coefficient e and dividing the important areas of the face images in the sample library; performing factorization synthesis on the divided local areas; and weighting and combining the synthesized local area and the image Z1 to obtain a super-resolution image Z2. The method has the advantages of increasing semantic constraint like that the grayscale of the image is non-negative, improving the expression capacity of the characteristic basic image and finally improving the quality of the super-resolution image.
Owner:WUHAN UNIV

An image super-resolution method based on a channel attention mechanism and multilayer feature fusion

The invention relates to an image super-resolution method based on a channel attention mechanism and multilayer feature fusion, and the method comprises the steps of directly extracting the original features of a low-resolution image at the beginning of a residual branch by using a single-layer convolutional layer based on deep learning; using six cascaded convolutional circulation units based ona channel attention mechanism and multi-layer feature fusion to extract accurate depth features; carrying out upsampling on the depth features through a deconvolution layer, and carrying out dimensionality reduction on the upsampled features through a single-layer convolution layer to obtain a residual error of the high-resolution image; carrying out up-sampling on the low-resolution image by using a bicubic interpolation method in a mapping branch to obtain mapping of the high-resolution image; and adding the mapping and the residual of the high-resolution image pixel by pixel to obtain a final high-resolution image. The method is reasonable in design, fully considers the difference between the feature channels, efficiently utilizes the hierarchical features, and maintains a higher operation speed while obtaining higher accuracy.
Owner:ACADEMY OF BROADCASTING SCI STATE ADMINISTATION OF PRESS PUBLICATION RADIO FILM & TELEVISION +1

Method and device for training graph neural network model for characterizing knowledge graph

The embodiment of the invention provides a method and a device for training a graph neural network model used for characterizing a knowledge graph, and the method comprises the steps: obtaining a triad from the knowledge graph, and the triad comprises a first node, a second node, and a first connection edge pointing to the second node from the first node; then, in an edge embedding layer, determining a corresponding first edge vector according to the relationship type corresponding to the first connecting edge and the edge attribute characteristics; in the node embedding layer, the first nodeand the second node serving as target nodes respectively, conducting multi-level vector embedding according to the node attribute characteristics of the target nodes and a neighbor node set of the target nodes, and therefore a first high-order vector and a second high-order vector corresponding to the first node and the second node are obtained respectively; then, according to the first high-ordervector, the second high-order vector and the first edge vector, determining the probability that the first node is connected to the second node through the first connecting edge, and updating the edge embedding layer and the node embedding layer with the maximization probability as the target;
Owner:ALIPAY (HANGZHOU) INFORMATION TECH CO LTD

Multi-modal pre-training model training method, application method and device thereof

The invention provides a multi-modal pre-training model training method, and an application method and a device thereof. The multi-modal pre-training model training method comprises the following steps: constructing a multi-modal pre-training model of a double-tower structure; obtaining a positive sample data set comprising positive sample image-text pairs and a negative sample data set comprising negative sample image-text pairs; and training a multi-modal pre-training model according to the positive sample data set and the negative sample data set, wherein the multi-modal pre-training model comprises a cross-modal comparison learning module used for carrying out image-text similarity comparison learning on the positive sample image-text pair and the negative sample image-text pair. The multi-modal pre-training model adopts a double-tower structure and a cross-modal contrast learning algorithm, a large number of negative samples are constructed for image and text modals, the model expression ability is high, and the processing precision of image-text pairs is improved. According to the model, the overall similarity between images and texts is calculated, whether the images and texts correspond or not is judged according to the similarity, and on the basis of the image-text weak correlation hypothesis, the actual situation of semantic weak correlation between the images and texts in image-text pairs in actual application is better fit.
Owner:北京智源人工智能研究院 +1

Drawing image retrieval method and device

The invention discloses a drawing image retrieval method. The method comprises the following steps of constructing a drawing image training sample set, wherein the sample set comprises a plurality of drawing image training samples corresponding to the same drawing works, and the image type of each drawing image training sample in the sample set is marked according to fine classification and rough classification; based on a depth convolution neural network, constructing a drawing image feature extractor, automatically learning fine classification features and coarse classification features from the training sample set, obtaining drawing image feature vectors and storing the drawing image feature vectors into a drawing image feature database; by utilizing the constructed drawing image feature extractor, extracting the feature vector of a to-be-retrieved drawing image; calculating the distance between the feature vector of the to-be-retrieved drawing image and each drawing image feature vector in the drawing image feature database; and obtaining an image retrieval result based on the distance. According to the invention, the accuracy, the accuracy, the robustness and the usability of the drawing image retrieval system are improved.
Owner:盛世贞观(北京)科技有限公司

Video target tracking method based on twin network fusion multi-template features

The invention relates to a video target tracking method based on twin network fusion multi-template features, and provides a semi-supervised template online updating strategy, when a to-be-tracked target in a video sequence has complex conditions such as occlusion, deformation and illumination change, the target change and the occluded condition are evaluated by calculating an APCE value and template similarity, when the appearance of the target is greatly changed, the features extracted from the previous frame of picture are fused with the original template features to obtain a new template with higher expression capability, so that the method is favorable for adapting to various complex conditions; in order to improve the generalization ability of the model and adapt to multiple types oftargets, a regularization technology is adopted in the training process to prevent model overfitting; in order to further improve the speed of the algorithm, only an original template is adopted fortracking in a non-complex situation, so that the calculated amount is greatly reduced, and the method provided by the invention achieves a higher running speed than other methods under the condition of obtaining better tracking performance.
Owner:HENAN UNIV OF SCI & TECH

Safety device threat intelligence sharing method based on lightweight field body

The invention relates to the technical field of threat intelligence and discloses a safety device threat intelligence sharing method based on a lightweight field body. The safety device threat intelligence sharing method comprises the following steps that 1, the lightweight body is adopted as an inter-device information communication medium, and a threat intelligence general-field body is established; 2, an intelligence producer localizes the threat intelligence general-field body, obtains original threat intelligence information from network space and converts and maps the original threat intelligence information into lightweight body knowledges; 3, an intelligence forwarding person forwards the lightweight body knowledges to an intelligence user based on body communication service; 4, the intelligence user converts and adapts the received lightweight body knowledges into locally dedicated strategy descriptions acting on operation of network space. The safety device threat intelligence sharing accuracy is improved, the expansibility, content correlation, interface opening and concept consistency capability of inter-device threat intelligence interaction is improved, and the threat intelligence sharing efficiency is improved.
Owner:NO 30 INST OF CHINA ELECTRONIC TECH GRP CORP

Heart disease risk diagnosis method based on deep convolutional neural network model

PendingCN111000551AReal-time risk probabilityReduce the risk of sudden cardiac emergenciesDiagnostic recording/measuringSensorsData miningIntracardiac Electrogram
The invention relates to a heart disease risk diagnosis method based on a deep convolutional neural network model. The method comprises the following steps: building a heart disease risk prediction model based on a deep convolutional neural network, and carrying out training, learning and testing; acquiring an electrocardiogram and a cardiogram of a user by using a wearable device; comparing the electrocardiogram and the cardiogram of the user, and identifying electrocardiogram feature points and cardiogram feature points of the specific event occurrence time of the cardiac impulse cycle; calculating a time interval value of any two feature points to obtain a plurality of feature values, and calculating a plurality of physiological indexes according to the plurality of feature values; outputting the heart disease risk probability corresponding to the physiological indexes through a DCNN model according to each input physiological index; calculating a heart disease risk comprehensive probability value of the user by adopting a formula; and judging the heart disease risk level of the user according to the heart disease risk comprehensive probability value. According to the invention,the risk probability of cardiac acute diseases of the user can be predicted in real time.
Owner:THE FIRST PEOPLES HOSPITAL OF CHANGZHOU

Zero sample image classification method and system based on a convolutional neural network and a factor space

The invention provides a zero sample image classification method and system based on a convolutional neural network and a factor space, and the method comprises the steps: building a unified zero-sample classification neural network: firstly, extracting image features in a data set through a classical convolutional neural network, and enabling the image features to serve as the input of the neuralnetwork; the dimensionality of known factors is reduced by using a factor pressure reduction technology, and the known factors and potential factors are embedded into a network to serve as an intermediate layer to jointly determine a final classification result; the network enables image input to final category output. And training a zero sample classification network, and iteratively determiningnetwork model parameters. And identifying the image by using the zero sample classification neural network to finish classification of the zero sample image. According to the method, a convolutionalneural network model is used for uniformly processing the relationship among the visual space, the factor space and the category space, the problem that the generalization ability of specific linear or nonlinear function expression is not high is solved, and the factors serving as auxiliary knowledge are embedded into the network and are easy to understand, train and use.
Owner:CHINA ACAD OF LAUNCH VEHICLE TECH

Construction method and fermenting method of antibiotic-resistance-free recombinant bacillus subtilis for expressing glutamate decarboxylase

The invention relates to a construction method and a fermenting method of antibiotic-resistance-free recombinant bacillus subtilis for expressing glutamate decarboxylase and belongs to the technical field of bioengineering. The construction method includes taking bacillus subtilis WB600 as an original strain, and knocking out a D-alanine racemase gene on a chromosome of the bacillus subtilis WB600 so as to obtain D-alanine deficient WB600 (dal); fusing an optimized gad gene with an antibiotic-resistance-free expression vector pUB110 (a antibiotics resistance gene is replaced by the D-alanine racemase gene) through an overlap extension PCR technology to obtain a polymer, transforming the polymer into competence of the bacillus subtilis WB600, and enabling the polymer to recombine in a host so as to obtain a recombinant plasmid Pub-HpaII-P43-gad-dal, which is named as bacillus subtilis SK44.001 with the preservation number being CCTCC NO:M 2016774. The fermentation liquor enzyme activity of the antibiotic-resistance-free recombinant bacillus subtilis can be up to 8.6 U/mL, and accordingly the antibiotic-resistance-free recombinant bacillus subtilis has significant industrial application value.
Owner:JIANGNAN UNIV

Knowledge graph information representation learning method, system, equipment and terminal

The invention belongs to the technical field of knowledge maps, and discloses a knowledge map information representation learning method, a system, equipment and terminal, and the knowledge map information representation learning method comprises the steps: carrying out the preprocessing according to a path constraint resource distribution method; calculating the reliability of all paths, and outputting the reliability to a training set and a test set; initializing the model and setting parameters; generating a triple according to an iterator, and randomly replacing head and tail entities; calculating a loss function of the triple according to the score function; calculating a loss function of an additional path according to the path reliability; performing parameter optimization by using an Adam method; and performing model verification by using entity prediction and relation prediction. According to the method, rich path information contained in the knowledge graph is considered, the modeling effect of entities and relationships is improved, the modeling of the relationships can be optimized by inputting vectors into a complex plane and using rotation to represent the vectors, and the method can be used for link prediction and recommendation systems.
Owner:XIDIAN UNIV

Device and method for controlling multiple function of communication apparatus by related parallel state machine

InactiveCN101309254AImprove clarityThe description is concise and clearData switching networksComputer hardwareIndependent function
The invention provides a control device which adopts the correlated and parallel state machine mode to control the multi-group function of the communication device in the communication device of the communication system; the control device controls the correlation operation of a plurality of sub state machines according to the correlations of the sub state machines; each sub state machine is corresponding to a group of independent function in the communication device. The control device includes a judging device which judges if the correlation matched with the current sub state machine exists before current sub state machine processes the corresponding processed event, a first event processing device used for processing the processed event corresponding to the correlation to realize the correlation operation of the sub state machines if the correlation exists and a second event processing device used for processing the processed event corresponding to the current sub state machine if no correlation is available; the invention also provides a method which adopts the correlated and parallel state machine mode to control the multi-group function of the communication device. The control device and the method which adopts the correlated and parallel state machine mode to control the multi-group function of the communication device has the advantages of reducing the complexity of the communication device system, uneasy malfunction of the communication device and easy maintenance.
Owner:上海宇梦通信科技有限公司

Unsupervised remote sensing image change detection method, storage medium and computing device

The invention discloses an unsupervised remote sensing image change detection method, a storage medium and computing equipment. The method comprises the following steps: constructing a multi-scale image convolutional neural network; respectively inputting the dual-temporal images into a multi-scale image convolutional neural network, extracting spatial features and inter-spectrum features, and jointly calculating to generate an initial pseudo label; cascading the two images of the dual-temporal image, inputting the two images into a multi-scale image convolutional neural network, and trainingthe multi-scale image convolutional neural network to generate a two-channel difference image; utilizing a metric learning module of the multi-scale graph convolutional neural network to update the initial pseudo label as a label of a two-channel difference graph, and training the generated two-channel difference graph; and comparing two channels of the trained two-channel difference image to obtain a binary change image with the same size as the original image, and completing image change detection. According to the method, the change detection graph of the pair of dual-temporal images can beefficiently and accurately obtained in an unsupervised manner.
Owner:XIDIAN UNIV

Polarized SAR image classification method based on ACGAN

The invention discloses a polarized SAR image classification method based on ACGAN, and the method comprises the steps: carrying out the Pauli decomposition of a polarized scattering matrix, and building a feature matrix based on pixel points; replacing each element in the feature matrix with an image block of a neighborhood of the element to obtain a feature matrix based on the image block; constructing a training data set by using the feature matrix based on the image blocks, and training the ACGAN network model by using the training data set to obtain a pixel-level classification result; and finally, converting the feature matrix into an RGB pseudo-color image, and dividing the image into K super-pixel areas by using an SLIC super-pixel algorithm. And a final classification result is optimized by combining the pixel-level classification result and the super-pixel block. According to the method, the polarization scattering information and the spatial neighborhood information of the polarization SAR data are fully utilized, and the generative adversarial network with the auxiliary classifier is used for competitive adversarial training, so that the classifier can extract classification features more effectively, and higher classification precision is obtained.
Owner:XIDIAN UNIV
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