314results about How to "Improve classification performance" patented technology

The invention discloses an equipment state sensing system based on peak shifting and danger situation sensing, which relates to the technical field of electric digital data processing and comprises a data acquisition unit, a model training and optimizing unit, a situation sensing unit, a peak shifting regulation and control unit and a system interface and user interaction unit. According to the method, the device state data feature similarity is dynamically calculated by using the Gaussian kernel function, the similarity matrix is adjusted in combination with the gradient update item, dynamic weighting is performed in feature extraction, and redundant features are reduced by adopting the adaptive discarding probability, so that the feature extraction efficiency of the model in a high-dimensional feature space is improved; the problem that the dimension reduction effect is affected by too many redundant features of the equipment state data is solved. Equipment state features are extracted layer by layer through a deep forest algorithm of a multi-layer random forest structure, a decision tree is optimized in combination with a dynamic adjustment weight mechanism and a reinforcement learning strategy, and the classification capability of the model for equipment state changes and abnormal states is enhanced.

The invention provides an RSVP type unbalanced electroencephalogram signal classification method based on a self-adaptive channel mixed attention mechanism and decoupling learning, and aims to improve the feature extraction and classification performance of RSVP electroencephalogram signals. Firstly, collected RSVP electroencephalogram signals are preprocessed, including common average reference, band-pass filtering and standardization processing, and a training set and a test set are divided; then, multi-scale time and space features are extracted through a parallel convolution layer and a space convolution layer, and the features of different levels are fused through an interpolation method; the training process is decoupled into representation learning and classifier learning, the representation learning uses a triple loss function based on difficult sample sampling, and the classifier learning uses the optimized Focal Loss to perform feature learning and classifier optimization. A self-adaptive channel mixed attention mechanism is introduced, and the flexibility and robustness of the model are enhanced through dynamic weighting and multi-level feature fusion. According to the method, the problem of class imbalance in the RSVP task is solved.

The invention discloses an intrusion detection method and device based on a graph neural network and a medium, and relates to the field of computer networks, and the method comprises the steps: constructing a graph structure according to network flow data in a current time window; the graph structure comprises a node set, an edge set, a node feature matrix, an edge feature matrix and an adjacent matrix; the adjacent matrix is used for representing a communication state between the nodes; inputting the graph structure corresponding to the current time window into a network node traffic type identification model to obtain the traffic type of each node; the network node traffic type identification model is obtained by training a preset graph neural network by adopting a training sample set; in a preset graph neural network, for an input graph structure, a Bi-LSTM layer is adopted to extract time feature information, and a graph convolution layer and a graph attention mechanism are combined to extract space feature information in a time dimension, so that a traffic type of each node is obtained. According to the invention, more accurate network intrusion detection is realized.

The invention discloses a satellite remote sensing image cloud classification method based on space-time coding guidance, and particularly relates to the technical field of satellite image processing and deep learning, and the method comprises the following steps: carrying out the preprocessing of the original data of a geostationary meteorological satellite and a Cloud Sat satellite; preprocessing operation such as cutting, projection conversion and feature selection is carried out on the geostationary meteorological satellite data, a top cloud type is selected for the Cloud Sat data to serve as a label, and space-time matching is carried out on the processed geostationary meteorological satellite data and the Cloud Sat data to obtain a training and testing data set; and establishing a GTCCT model, performing time and geocoding on the input data, inputting the coded feature data into a network for training, and finally obtaining the GTCCT model for cloud classification.

The invention relates to integrated airborne communication antenna equipment and a communication method. The method comprises the following steps: carrying out CSI data acquisition and space-time focus modulation on a communication channel to obtain CSI space-time characteristic data; performing residual vector quantization operation to generate a quantized data stream, and performing recursive coding processing to obtain a target data stream; mapping the target data stream to a three-dimensional tensor space, constructing a real value tensor model, and performing iterative operation to obtain a CSI feature vector; performing fractional order wavelet transform decomposition on the CSI feature vector to obtain a radio frequency feature set, and inputting the radio frequency feature set into a federal fractional scattering network for feature processing to obtain an identity authentication result; and calculating an amplitude-phase weight coefficient of the antenna array, generating a beam parameter, and performing compensation matrix operation on the beam parameter according to the CSI feature vector to obtain a pre-coded signal. According to the invention, the dynamic adjustment of the antenna beam parameters is realized, and the accuracy and anti-interference capability of beam forming are improved.

The invention discloses a micro-expression recognition method and system based on a state space model and double-flow fusion, and relates to the technical field of expression recognition, and the method comprises the steps: extracting a starting frame image and a top frame image of a micro-expression continuous video frame sequence; inputting the initial frame image and the top frame image into a micro-expression recognition model based on a double-flow fusion network, extracting an optical flow image, and performing face key point detection and clipping operation on the top frame image and the optical flow image respectively; carrying out image block embedding operation to obtain texture features of the top frame image after cutting and motion features of the optical flow image; the two features learn the global dependency relationship and the local correlation between the image blocks from the positive direction and the negative direction through a parallel multi-layer bidirectional Mama fusion module, and deep image features and motion features are extracted after feature fusion; and the deep features of the two modes are interacted through a double-flow cross attention classification module, comprehensive features are learned, and a plurality of classifiers are combined to identify and output the micro-expression type more accurately.

The invention relates to an ICU patient dysphoria video recognition method based on space-time motion detection. Compared with the prior art, the defect that dysphoria video recognition is difficult to carry out on an ICU patient is overcome. The method comprises the following steps: acquiring and preprocessing a data set; constructing an ICU patient dysphoria identification model; training an ICU patient dysphoria identification model; obtaining video stream data to be identified; iCU patient dysphoria atom action recognition; and performing dysphoria quantitative classification based on an RASS score table. According to the method, the Slowfast basic model is improved, the detection precision and speed of the model are remarkably improved, the dysphoria behavior of the patient is automatically and accurately detected and understood from the perspective of space-time motion detection, the dysphoria condition of the patient is automatically scored according to the RASS score table, and accurate recognition of the dysphoria video of the ICU patient is achieved.

The invention provides a relation extraction model construction method and system based on semantic enhancement and knowledge distillation, relates to the technical field of relation extraction, and aims to solve the problem that in the prior art, when continuous learning is processed, a model is often difficult to keep memorizing an old relation when a new relation is learned, and forgetting is caused. Model overfitting is easily caused by excessive dependence on a small amount of data, so that a new relationship cannot be effectively generalized. The method comprises the steps of obtaining a relationship prototype of a training sample and a typical sample, performing semantic enhancement on the relationship prototype to obtain an expanded enhanced data set and an enhanced memory set, and performing feature extraction, relationship classification prediction and relationship extraction based on a student model and a multi-teacher model. According to the method, the overfitting problem caused by scarcity of small sample data is effectively relieved through a semantic enhancement technology, old relation forgetting caused by new relation learning in the continuous learning process is reduced through a multi-teacher knowledge distillation method, and the classification accuracy and generalization ability of the model facing different relations are improved.

The invention discloses an unmanned aerial vehicle target detection method based on feature fusion and detection head collaborative optimization, and the method comprises the steps: obtaining a multi-scale feature map through employing a backbone network formed by stacking a convolution module CBS and a residual module C2f, sequentially transmitting the feature maps of various scales to a multi-level feature fusion module (ML-FFM), carrying out the feature fusion from a deep layer to a shallow layer, and carrying out the detection of a target target. Semantic information of shallow layer features is enhanced; inputting the fused feature map into a refined feature enhancement module (RFEM), eliminating position conflicts and redundant information based on a space-time attention mechanism, and optimizing details and positioning capability of deep features; and the enhanced feature map is sent to a prediction head based on Transform for detection, and target classification and positioning of the aerial image of the unmanned aerial vehicle are completed. According to the method, the problems that the unmanned aerial vehicle aerial image is large in scale change, and small targets are high in proportion and dense in distribution are solved, so that the precision of target detection in the unmanned aerial vehicle aerial image is improved. The modules proposed in the method can significantly reduce the calculation amount, and are suitable for the real-time detection scene of the unmanned aerial vehicle.

The invention discloses a multi-level track disease recognition system based on vehicle body vibration data, and particularly relates to the field of blasting auxiliary devices. Comprising the steps that a data acquisition module acquires heterogeneity data of a track environment; the environment adaptive module analyzes the correlation between the vehicle body vibration data and the heterogeneity data, constructs a dual-view similarity matrix, and realizes adaptive classification of the track environment through a spectral clustering method; the label enhancement module generates high-quality pseudo labels and forms a completely labeled sample set; the disease recognition module adopts an attention-guided multi-task cascade CNN architecture, and outputs a multi-level recognition result containing disease positions and severity. By the adoption of the technical scheme, the problem that an existing track state evaluation method cannot evaluate the real state of track diseases in a complex environment is solved, and good performance is achieved in the aspect of geometric and structural disease recognition.

The invention provides a hyperspectral image classification method based on a state space model. The problem that an existing method is poor in classification precision is mainly solved. According to the scheme, the method comprises the following steps: 1) inputting a hyperspectral image, and preprocessing the hyperspectral image; 2) eliminating spectrum variability by using a neighborhood spectrum fusion method, sensing spectrum detail differences by using a sub-spectrum scanning method, and obtaining image spectrum classification information; 3) constructing a spatial feature extraction network by using an SS2D layer, an improved spatial attention mechanism and a plurality of residual blocks, and acquiring spatial classification information of the hyperspectral image by using the network; and 4) fusing space and spectrum classification information at a decision layer to obtain a final classification result. According to the method, the influence caused by spectrum variability can be reduced in a relatively small range, local spectrum information is not ignored on the premise that comprehensive spectrum information can be perceived, and spatial information is combined, so that the classification effect of the hyperspectral image is effectively improved.

The invention relates to the technical field of big data processing, in particular to an online state grid user classification method, system and device based on a graph convolutional neural network and a storage medium. The invention discloses an online state grid user classification method based on a graph convolutional neural network. The method comprises the following steps: extracting user explicit and implicit features; the method specifically comprises the following steps: firstly, constructing a user graph model by taking online state grid user data as input, extracting feature information in graph data by using a stack network formed by a convolutional neural network, and propagating vector representations of aggregated users and neighbor users thereof by using a cyclic convolution operation; classifying users; according to the method, the graph convolutional neural network model is used for constructing the portrait of the state grid user on the Internet, dominant and implicit features of the user are deeply mined, and an accurate user feature vector is formed. Meanwhile, user vector subdivision is achieved according to probability representation output by the full connection layer, and potential user groups are effectively recognized.

The invention discloses a driver distraction detection method based on ResPrompt and double cross attention, and the method comprises the steps: inputting a front / side visual angle image of a driver distraction behavior into a linear embedding module and a ResPrompt module, carrying out the partitioning of the image through the linear embedding module, generating a feature vector, and generating a prompt vector of the image through the ResPrompt module; performing multi-scale feature extraction on the feature vector of the front / side view angle image and the most matched prompt vector by using a Swindow-Transfomer module to generate each view angle feature; and fusing the double-view-angle features by using a double-cross attention module, inputting the double-view-angle features into a classifier fusing center loss, and performing behavior detection on the features to generate a final detection result. According to the method, the accuracy and robustness of detection can be effectively improved, and reliable technical support is provided for safe operation of semi-automatic and automatic driving vehicles.

The invention belongs to the field of medical ICD automatic coding, and particularly relates to a medical ICD coding classification method based on a graph neural network, and the method comprises the steps: obtaining a data set, and carrying out the preprocessing of the data set, and obtaining a text vector matrix; constructing a graph structure according to the text vector matrix, wherein the graph structure comprises a node feature matrix and an adjacent matrix; inputting the graph structure into a graph neural network model, outputting a prediction ICD coding result, and training the model by adopting a multi-label loss function based on cross entropy; inputting to-be-coded medical record data into the trained graph neural network model to obtain a predicted ICD code; according to the method, the semantic similarity between the medical terms, the level distance between the ICD codes and the co-occurrence frequency relation are included into a graph structure, the complex relation between words and labels can be comprehensively captured, the potential relevance between the ICD codes is better utilized, and the context understanding ability and accuracy of a classification model are enhanced.

The invention discloses a city appearance environment early warning method and system based on AI intelligent garbage identification, and relates to the technical field of artificial intelligence and garbage classification technologies. The method comprises the steps that firstly, garbage classification throwing stations are set; step 2, acquiring and processing image data; step 3, carrying out image frame selection and tracking; 4, setting a threshold value and generating prompt information; 5, scheduling the garbage trucks, connecting the garbage trucks in series according to the uploaded alarm stations, and designing a route; by monitoring the state of the garbage throwing station in real time, the system can timely find the situation that the number of the garbage cans reaches the standard, and automatically generate early warning information, so that an environmental sanitation management department can timely dispatch garbage trucks to carry out clearing and transportation, and environmental pollution and resource waste caused by long-time garbage accumulation are avoided; the dynamic adjustment function of the system ensures that the driving route of the garbage truck can be rapidly updated when the actual situation changes, for example, early warning is newly added, and the garbage treatment efficiency and the response speed are further improved.

The invention relates to the technical field of image processing, and discloses an image classification method and system based on machine vision, and the method comprises the steps: obtaining an initial image set collected by a visual device, carrying out the preprocessing of image data in the initial image set, and obtaining a first image set; performing feature extraction on the image data in the first image set to obtain a second image set, and performing dimension reduction processing on the second image set to obtain an input image set; splitting the input image set into a training data set and a test data set, and inputting the training data set to train the pre-training model; taking the classification accuracy of the test data set as an evaluation index of a training result, updating the weight, determining a model parameter value, and outputting an image classification model; an image classification model is adopted to classify images in the platform, category marking is carried out, a sand dune cat algorithm is adopted to carry out image recommendation according to search information of a user, and an image classification result is output based on the search information; according to the invention, the classification accuracy and efficiency are improved.

The invention belongs to the field of software security and vulnerability detection, and particularly discloses a method and system for automatically generating and labeling a vulnerability program, and the method comprises the steps: determining a vulnerability repairing program of obtained vulnerability data, extracting a code mode containing a specific type from the vulnerability repairing program, and obtaining a screened vulnerability mode; the step of extracting the code mode of the specific type comprises the substeps of extracting a repair statement of a single modification block, generating a symbolized repair statement, removing repeated repair statements, identifying a key variable and describing a vulnerability triggering statement; according to the vulnerability mode, accurately injecting the vulnerability mode into a normal program in combination with a code analysis tool, and generating a vulnerability program containing a specific vulnerability type; and performing vulnerability type labeling on the vulnerability program in combination with static analysis and a large language model. According to the invention, the detection accuracy of the vulnerability detection model can be improved.

The invention provides an image classification method and device based on a student model, equipment and a medium, and relates to the technical field of knowledge distillation, and the method comprises the steps: obtaining a to-be-classified image; inputting the to-be-classified image into a target student model to obtain a category corresponding to the to-be-classified image; inputting the initial image set into a trained teacher model and a preset initial student model, and obtaining a teacher model imbalance category probability and a student model imbalance category probability; inputting the mixed image set into a trained teacher model and a preset initial student model, and obtaining a teacher model imbalance category probability and a student model imbalance category probability; determining a corresponding loss; according to the loss, performing back propagation to update student model parameters so as to obtain a target student model; when the image is classified, the resource computing power required by computer hardware can be reduced, the response speed of a computer is improved, and the phenomenon of computer jamming is avoided.

The invention discloses a social network public opinion monitoring method and system based on artificial intelligence. The method comprises the following steps of obtaining public opinion data from a social network platform and performing data preprocessing; hot events are automatically extracted from the public opinion data, and a density peak clustering algorithm is utilized to identify a high-density area and determine the popularity of the hot events. A BERT model is adopted to carry out sentiment classification on a text, context features are extracted in combination with bidirectional LSTM, and sentiment tendency is output through a Softmax classifier. For a specific target event, a multi-head attention mechanism and a convolutional neural network are used for specific target emotion classification. And performing noise reduction and decomposition on the time sequence data, constructing a plurality of basic prediction models, generating a combined prediction model in combination with weighted average, and monitoring and predicting the public opinion development trend in real time. Accurate public opinion analysis and decision support are provided for governments and enterprises, public opinion risks are reduced, social stability is maintained, and social construction is promoted.

The invention relates to a pathological image morphological subtype classification method based on an optimal transmission prototype network, and belongs to the technical field of medical image processing. The method comprises the steps of constructing a pathological image data set, performing preliminary grouping by using an unsupervised clustering method, and performing cooperation with a pathologist to optimize the data set; preprocessing the image through a data enhancement strategy; performing feature extraction on the enhanced image through a pre-training model; the method comprises the following steps of: constructing and training an OT-ProtoNet model; setting a prototype, determining dimensions and quantity, realizing a category specific prototype based on a soft distribution mechanism, calculating similarity based on an OT distance, setting training parameters and selecting an optimization algorithm to train a model, and ensuring that the model effectively learns key features in data; and carrying out image morphological subtype classification on the basis of an OT-ProtoNet model. According to the pathological image morphological subtype classification method, the pre-training features can be fully utilized, the classification accuracy can be improved, and interpretability can be kept.

The invention discloses a feature extraction and recognition method for skin lesion hyperspectral image classification, and belongs to the technical field of image processing, and the method comprises the following steps: S1, carrying out the preprocessing of a hyperspectral image; s2, extracting diversity features of the preprocessed hyperspectral image; s3, the extracted features enter a feature fusion module, and feature fusion is realized through a three-dimensional full convolutional neural network of multi-granularity feature fusion; s4, after feature fusion, lesion region segmentation is carried out in a segmentation module; s5, classifying the segmented features in a classification module; s6, enabling the classified lesion areas to enter a detection module, and improving the detection precision and real-time performance of lesion cells in the microscopic hyperspectral image; and S7, verifying a detection result of the detection module by a clinician according to a pathological result of a pathological expert. The feature extraction and recognition method for skin lesion hyperspectral image classification has higher diagnosis and classification capabilities, and is suitable for various medical image analysis tasks.

The invention relates to a hyperspectral image classification method based on a meta-pseudo-label multi-scale image convolution network, and the method comprises the steps: building a meta-pseudo-label multi-scale feature search image convolution network (MPL-MFSGCN), dividing the HSI into superpixel images of different scales through employing an SLIC superpixel segmentation technology, and converting the superpixel images into image structures; secondly, constructing a multi-scale feature search graph convolutional network MFSGCN architecture, optimizing a superpixel feature weight combination by using a neural network search algorithm, and enhancing the discrimination of feature expression; a semi-supervised teacher-student model sharing the same graph convolutional network is adopted to generate meta-pseudo tags, and a student network is finely adjusted to improve the robustness of the network; the key features can be automatically captured under the small sample condition, the classification precision higher than that of an advanced method is obtained compared with a common HSI data set, and the classification accuracy is effectively improved.

The invention discloses a petroleum pipe microscopic fracture morphology identification method, system and device and a medium, and belongs to the technical field of failure analysis image process.The method comprises the steps that a deep learning classification network with a residual module is constructed and trained, and a deep learning classification network loaded with a network connection weight matrix is obtained; and finally, the deep learning classification network is utilized to efficiently and accurately identify the to-be-tested object region image. According to the method, massive data sets can be quickly processed and analyzed by using the high-speed processing capability of a modern computer, so that the efficiency is greatly improved. Compared with a traditional method, the automatic fracture classification method can reduce subjective interference of people and enhance reliability and consistency of results. And meanwhile, the identification method is simple, engineering application integration is facilitated, and good practicability and expansibility are achieved.

The invention discloses a potential landslide identification method and system based on a graph neural network, and the method comprises the steps: constructing a multi-level feature fusion graph attention network model which comprises a node layer, a subnet layer and a global layer, and employs a dual-branch encoder-decoder structure; the method comprises the following steps of: pre-training a dual-branch encoder-decoder structure model until convergence by adopting a combined data set of a data set 1 of a large sample and a data set 2 of a small sample to form a pre-training model; based on auxiliary set node features obtained by inputting the data set 1 and the data set 2 into the pre-training model, training and verifying the graph attention network model until model convergence training is completed; and inputting the pictures needing to be identified in the data set 2 into the trained picture attention network model to obtain a prediction result of the potential landslide. Through the technical scheme of the invention, effective extraction of the target features in the small data set 2 is realized, and the classification performance and generalization ability of the model are improved.

The invention discloses a federated undersampling learning method for unbalanced network intrusion detection, which comprises a federated learning server and a client, and communication connection is established between the federated learning server and the client; all models for network intrusion detection are configured in the server; the server is used for maintaining a global model and deploying the global model to the client for training and learning, and the client adopts a local data set to train a local model downloaded from the server and uploads the local model after training and learning to the server; the server aggregates the local models uploaded by the client to obtain a global model with updated parameters; and when the client adopts the local data set to train the server, undersampling processing is performed on the local data set to obtain a balanced data set to perform learning training on the client. When federated learning is carried out on an intrusion detection network model, the influence of training data imbalance in a client on the model detection performance is solved through an undersampling method.

The present invention discloses a method and system for replicating digital human images based on NeRF, and relates to the technical field of digital human image replicating. The method comprises: collecting digital human images to obtain training data sets; expanding and generating training samples; reconstructing a model using the expanded digital human image data training data; extracting features from the results of digital human image replicating output by the autoencoder, and inputting the feature vector after feature extraction into the classifier model to perform digital human image data screening. The present invention improves the details and authenticity of the generated samples, makes the expanded data closer to the real collected images in terms of fidelity, enhances the adaptability and stability when screening digital human image data, makes the screening model more adaptable when facing diversified replica data, improves the classification effect, and finally ensures the high quality and consistency of the replica data.

The invention relates to a noise feature vector classification model construction method, a speech enhancement method and device, computer equipment, a storage medium and a computer program product. The method comprises the following steps: coding penalty parameters and kernel function parameters of a support vector machine; taking the penalty parameter and the kernel function parameter as optimization variables of a genetic algorithm, setting an initial population, and constructing a fitness evaluation function based on classification accuracy; performing selection operation, crossover operation and mutation operation on the initial population, and updating an iterative population according to a fitness evaluation function to obtain an optimal penalty parameter and an optimal kernel function parameter; and training a support vector machine through the optimal penalty parameter and the optimal kernel function parameter, and constructing a noise feature vector classification model. In a noisy environment, the classification effect of the noise feature vector is improved under the condition that nonlinear and non-stationary noise exists, and the noise feature classification requirement in a complex sound signal processing scene is met.

The invention discloses a generalizable deep forgery detection method based on multi-classification guidance, and belongs to the technical field of image processing. According to the technical scheme, data enhancement is carried out on an image, and then the image is input into a binary classification network and a multi-classification network of a double-flow neural network; combining the features of the two branches through a fusion module; the fused features are input into a binary classifier to determine whether the input is authentic or counterfeited. The method has the beneficial effects that the generalizable deep counterfeiting detection method based on multi-classification guidance shows remarkable beneficial effects in the aspects of improving the model generalization ability, enhancing the feature expression ability, optimizing the loss function design, preventing over-fitting, improving the calculation efficiency, enhancing the label consistency and the like; the method is beneficial to suppressing the propagation of false information, maintaining the right to know and security of the public, enhancing the vigilance of people to the authenticity of information, laying a solid foundation for the applications of identity authentication, digital forensics and the like in multiple fields, and making an important contribution to the construction of a safer and more credible information society.

The invention discloses an image classification method and system based on similarity uncertainty optimization, and belongs to the technical field of computer vision. Comprises: acquiring a to-be-processed image; processing the to-be-processed image through a trained image classification model to obtain an image classification result; and when the image classification model is trained, quantifying the similarity uncertainty between a difficult sample in a training set containing noise and a dynamic class prototype, and performing back propagation optimization on a constructed network in combination with classification loss and divergence evaluation loss calculated through a clean sample in the training set. According to the method, more discriminative information can be learned from samples with relatively high uncertainty, the robustness of the model is improved, and the problem that noise data affects model prediction stability during existing model training is solved.

The invention relates to the field of image recognition or understanding, in particular to a face and tongue coat recognition and tea drink recommendation method and system based on artificial intelligence. The method comprises the following steps: identifying a plurality of image annotation labels, training a feature extraction model and a classification model by using the annotated images, obtaining a new image, performing feature extraction by using the trained feature extraction model, and inputting the feature into the trained classification model to obtain a label of the new image. The device is used for applying the method. The existing method and system for identifying the face and the tongue coating of the tea making machine and recommending the tea making have the problem that the identification of a face image and a tongue coating image is not accurate enough. The method for identifying the face and the tongue coating of the tea making machine and recommending the tea making based on the artificial intelligence provided by the invention is accurate in identification of the face image and the tongue coating image.