70results about How to "Effective modeling" patented technology

The invention discloses a human body behavior recognition method and system based on a graph convolution network, and the method comprises the steps: extracting human body skeleton information from animage containing human body behaviors, obtaining a human body joint point position information sequence, and constructing a topological graph sequence with any length of a human body skeleton; performing feature extraction and topological structure adaptive evolution on the topological graph sequence through a topological learnable graph convolution-based space-time graph convolution network to obtain new node features fused with local space-time features and a topological graph sequence with a new topological structure; performing feature extraction through a graph convolution long-term andshort-term memory neural network; global spatio-temporal features are obtained through global pooling operation; and performing human body behavior recognition based on the global spatial-temporal features through a classifier. The features of a whole graph are directly learned, the weight matrix in graph convolution is expanded to the whole topological graph structure, the relation between any two nodes in the graph is learned, limitation of the topological structure is avoided, and the recognition accuracy is high.

The invention discloses a text interaction matching method and device for financial knowledge questions and answers, belongs to the field of natural language processing, and provides a method for mapping user questions to standard questions in order to accurately judge the matching degree of the financial knowledge questions of a user and the standard financial knowledge questions, and the methodcomprises the following steps: 1, constructing a question pair knowledge base; s2, constructing a question pair matching model training data set; s3, constructing a question pair matching model, comprising the following steps: S301, constructing a character mapping conversion table; s302, constructing an input layer; s303, constructing a character vector mapping layer; s304, constructing a text coding model of an attention mechanism; s305, constructing a text interaction matching layer; and S4, training a question pair matching model and question selections with the intention. (2) The devicecomprises a question pair knowledge base construction unit, a question pair matching model training data set generation unit, a question pair matching model construction unit and a question pair matching model training unit.

The invention relates to a cross-media sequencing method based on a multi-depth network structure. The method comprises the following steps of 1, building a cross-media data set including a plurality of media types, and extracting feature vectors of all media data; 2, training the multi-depth network structure by using the cross-media data set, and using the trained multi-depth network structure for unified expression of study of different media data; 3, using the trained multi-depth network structure to obtain the unified expression of different media data so as to calculate the similarity of different media type data; and 4, taking each datum of each media type to be used as an inquiry sample, retrieving data in another media, calculating the similarity of the inquiry sample and the inquiry sample, performing sequencing according to the sequence from high similarity to low similarity, and obtaining a result sequencing table of target media data. The method provided by the invention has the advantages that various network structures are used in a combined way; associated information between the media and inside the media can realize modeling at the same time; further, the unified expression study is performed by using two stages of networks; and the accuracy rate of the cross-media sequencing is improved.

The invention discloses a question pair matching method and device based on a deep feature fusion neural network, belongs to the field of natural language processing, and aims to solve the technical problem of how to accurately judge the matching degree of a user question and a standard question and sort out a complete question pair matching model. The technical scheme is that the method comprisesthe following steps: S1, constructing a question pair knowledge base; S2, constructing a question pair matching model training data set; S3, constructing a question pair matching model, comprising the following steps: S301, constructing a character mapping conversion table; S302, constructing an input layer; S303, constructing a character vector mapping layer; S304, constructing a neural networkcoding layer based on depth feature fusion; S305, constructing a text similarity matching layer; and S4, training the question pair matching model and selecting a standard problem. The device comprises a question pair knowledge base construction unit, a question pair matching model training data set generation unit, a question pair matching model construction unit and a question pair matching model training unit.

The invention discloses a cross view angle face recognition method based on structuralized dictionary domain transfer. The method comprises the steps that S1, trained sub-dictionaries having discrimination performance on sample categories are connected in series to form a structuralized source domain dictionary; S2, a target domain and a plurality of intermediate domain dictionaries are learned; S3, image face codes of the source domain and the target domain, the source domain dictionary, the target domain dictionary and the intermediate domain dictionaries are calculated, source domain reconstruction images of face images of the source domain and the target domain, target domain reconstruction images and intermediate domain reconstruction images are obtained and connected in series to form the domain sharing characteristic of the face images of the source domain and the domain sharing characteristic of the face images of the target domain; S4, according to the domain sharing characteristic of the face images of the source domain, a support vector machine model is trained for each sample category in a face set of the source domain, the domain sharing characteristic of the face images of the target domain is input into the support vector machine models of all categories, and the category corresponding to the support vector machine model with the highest score is defined as the category of the face images of the target domain.

The invention discloses an acoustic scene identification method based on data enhancement. The method comprises the following steps: firstly, collecting and marking audio samples of different sound scenes; then preprocessing is carried out, and pre-emphasis, framing and windowing processing are carried out on the audio samples; data enhancement is then performed, extracting a harmonic source and an impact source of each audio sample to obtain more sufficient audio samples, extracting logarithmic Mel filter bank characteristics from the audio samples and the harmonic sources and the impact sources of the audio samples, stacking the three characteristics into a three-channel high-dimensional characteristic, and constructing more abundant training samples by adopting a hybrid enhancement technology; and finally, inputting the three-channel high-dimensional features into an Xception network for judgment, and identifying the sound scene corresponding to each audio sample. According to the data enhancement method, the generalization capability of the Xception network classifier can be effectively improved, and the training process of the network is stabilized. When the acoustic scene isidentified, the method can obtain a better identification effect.

The invention discloses a layered structure-based dynamic plasma sheath electron density modeling method. The method comprises the steps of dividing a plasma sheath into multiple layers, wherein each layer can be approximately considered to be uniform; performing modeling on time-varying electron density of each layer; and finally synthesizing a space-time electron density matrix. According to the method, a dynamic random process of electron density change is established by utilizing a classical theory, so that the difficulty in the condition that data cannot be obtained from a flight experiment is overcome; plasmas are equivalent to a layered structure, and the electron density of each layer is modeled, so that high difficulty and high complexity in direct modeling of space-time two-dimensional electron density are avoided; for dynamic plasmas in different conditions, the space-time electronic densities of the dynamic plasmas can be modeled by using a unified modeling method; and theoretical and data support is provided for research on electromagnetic wave propagation in the dynamic plasmas, influence of the dynamic plasmas on signals of different systems and the like.

The invention provides a gas supply and demand dynamic prediction system for steel enterprises and a method thereof, belonging to the technical field of gas prediction in steel industry. The system comprises a data acquisition subsystem, a data processing subsystem, a data modeling subsystem, a model verification subsystem, a model application subsystem and a computer network connecting each subsystem. The gas supply and demand dynamic prediction method comprises the following steps: abstracting generation and consumption characteristics of gas; qualitatively analyzing the generation and consumption characteristics of the gas; dynamically predicting and modeling in a sectional short-term method; and dynamically predicting the supply and demand of the gas. The system and the method of the invention have the advantages of proposing the sectional short-term dynamic prediction and modeling method which is based on historic statistic data, combines the production state information and comprehensively applies real-time data, the production state data and production process technical data. The invention provides decision-making support for distributing staff in short-term gas distribution, and simultaneously provides basic data for the implementation of various intelligent distribution schemes.

The invention provides a loose point cloud data segmentation method and device, discloses a point cloud data processing method and device, relates to radar point cloud data segmentation and especially relates to loose point cloud data segmentation. The segmentation method comprises the following steps: S101, inputting point cloud data A and an initial subclass B of inner points, wherein the subclass B of the inner points is also a subclass of the point cloud data A, and the point cloud data A is a set of points with space correlation, formed by radar scanning; S102, calculating a model b most conforming to the subclass B through a random sample consistency algorithm; and S103, taking all points, which do not belong to the subclass B, in the point cloud data A as test points a, testing the model b by use of the test pints a, and dividing the test points into the inner points, outer points and unknown points according to a test result. The segment method provided by the invention can be applied to processing point cloud data established on standard Cartesian coordinates, data compression operation can be selected, and the operation efficiency is improved.

The invention discloses a triple non-stationary wireless communication channel modeling method under space-time consistency, which organically integrates a low-complexity parametric method and a high-precision geometric method by capturing smooth and consistent evolution of clusters in an environment in a space-time domain and path gains related to modeling frequency. Smooth and consistent cluster evolution, soft switching of cluster power and effective modeling of frequency-related path gains are realized, the problem that an existing channel model cannot model space-time-frequency non-stationary characteristics of a channel under space-time consistency is solved, and the accuracy and universality of constructing the channel model are improved. The statistical channel model under the millimeter-wave large-scale MIMO high-dynamic scene, which is constructed by adopting the method disclosed by the invention, has the characteristics of high accuracy, simplicity and feasibility, can support reasonable design and performance analysis of a communication and inductance integrated system, and meanwhile, provides a real and reliable simulation verification platform for a wireless communication network system-level algorithm.

The invention discloses a breast cancer sentinel lymph node metastasis prediction method and system and a storage medium, and the method comprises the steps: obtaining a WSI with a label as a training data set, and carrying out the preprocessing, and obtaining an image block set; constructing a WSI classification model; pre-training a feature extractor by using the image block set to obtain a feature vector set; inputting the feature vector set into a prototype clustering module, and extracting a plurality of prototypes through clustering; the breast cancer sentinel lymph node WSI is divided into image blocks, and then the image blocks are input into a feature extractor to extract image block features; matching the image block features with a prototype input feature fusion module, generating a soft distribution histogram, and constructing a feature vector of breast cancer sentinel node WSI; and sending the feature vector of the breast cancer sentinel lymph node WSI into a full connection layer to obtain a WSI classification score, and performing transfer judgment. The method can better solve the problem of micro-metastasis identification while maintaining accurate identification of macro-metastasis, so that breast cancer sentinel lymph node metastasis can be accurately diagnosed.

The invention discloses a speech enhancement method and system fusing ultrasonic signal features. The method comprises the steps: firstly predefining an ultrasonic signal, and then actively transmitting and receiving the ultrasonic signal through a loudspeaker and a microphone of a device; and performing channel estimation to obtain a channel impact response, and inputting the channel impact response to a neural network to realize speech enhancement, wherein the channel impact response reflects motion features of facial vocal organs when a user speaks, and the motion features serve as supplementary modal information of speech. According to the invention, the speech enhancement task is assisted by making full use of the user voice action characteristics, the speech enhancement effect is improved, and the method has wide application prospects.

The invention discloses a generation type-based auxiliary template enhanced garment matching scheme generation method and a system. The method comprises the steps of constructing a generation type-based auxiliary template enhanced garment matching model; constructing a training set; inputting the training set into the constructed auxiliary template enhanced garment matching model based on the generative mode for training to obtain a trained auxiliary template enhanced garment matching model based on the generative mode; inputting an upper garment to be matched into the trained auxiliary template enhanced garment matching model based on the generative mode, and outputting the most matched lower garment; and outputting the upper garment to be matched and the most matched lower garment as a final garment matching scheme.

The invention discloses a false news detection method based on a knowledge perception attention network, and belongs to the technical field of artificial intelligence. The method comprises the steps that related knowledge in a knowledge graph is extracted based on news texts, the news texts and the extracted related knowledge serve as input data, a false news detection model based on knowledge perception is constructed, and news samples are classified. Firstly, entity mentions in news are recognized through entity links and aligned with corresponding entities in a knowledge graph, and an entity sequence is obtained; secondly, for each entity in the entity sequence, obtaining a neighbor entity of the entity in the knowledge graph as an entity context of the entity; and finally, through a false news detection model, fusing the news text with the entity and the entity context features to complete false news detection. According to the method, the ambiguity problem caused by entity mentionin news texts can be solved, and meanwhile, news representations of supplementary information, learning semantic level and knowledge level can be provided for entities in news.

The invention discloses a video statement positioning method based on a multi-stage aggregation Transformer model, and in the video statement Transformer model, each video slice or word can adaptivelyaggregate and align information from all other video slices or words in two modes according to the semantics of the video slice or word. Through multi-layer superposition, finally obtained video statement joint representation has rich visual language clue capture capability, and finer matching can be realized. In the multi-stage aggregation module, the stage characteristics of the starting stage,the stage characteristics of the intermediate stage and the stage characteristics of the ending stage are connected in series to form the characteristic representation of the candidate segment. Because the obtained characteristics representation captures the specific information of different stages, the method is very suitable for accurately positioning the starting position and the ending position of the video clip. The two modules are integrated together to form an effective and efficient network, and the accuracy of video statement positioning is improved.