182 results about "Feature transform" patented technology

The invention relates to the technical field of augmented reality, in particular to an AR real-time scene reconstruction and illumination matching system and method based on neural rendering, and the system comprises a real-time scene understanding module, a real-time neural radiation field module and a real-time reflection inference and enhancement module. Comprising a forward network, a feature transformation layer and a backward network, the forward network maps a high-dimensional rendering space to a feature manifold, the feature transformation layer executes mapping from the feature manifold to a rendering manifold, and the backward network performs rendering calculation based on a Riemannian metric driven adaptive ray tracing mechanism; the real-time reflection inference and enhancement module performs material prediction and illumination information calculation based on an illumination dynamic adaptation system on a differential manifold, real-time application of a neural radiation field technology in an AR scene is realized through an innovative differential geometry framework, and a real-time rendering frame rate of 30-60 fps is achieved while a high-quality rendering effect is kept.

The invention belongs to the technical field of image processing, and particularly relates to a lightweight unmanned aerial vehicle target tracking method based on separable convolution. The method comprises the following steps: step 1, preparing two remote sensing image data sets which are respectively used for training and testing; 2, inputting a frame into a separable convolution block for feature extraction to obtain a feature sequence; 3, inputting the feature sequence into an inverted bottleneck block and a forward feedback network, carrying out feature transformation and information mixing, and enhancing the feature expression capability; 4, the extracted category number is input into a fusion module to be processed, and a fusion sequence is obtained; and step 5, obtaining a classification regression vector through loss calculation, and then outputting a result graph. According to the method, a lightweight remote sensing target tracking network architecture is innovated, an improved feature extraction module UIB-P and a fusion module ICA are added, and an innovative loss function is adopted, so that the model greatly improves the global representation capability and precision of target tracking while reducing the calculation amount.

The present invention relates to the field of pipeline leakage monitoring. Disclosed is an oil and gas pipeline leakage wave identification and monitoring system. In the present invention, an mCNN is combined with LFLBs for performing feature extraction on an acoustic wave signal collected by a DFB, and the collected data improves information completeness; a three-way parallel one-dimensional CNN used in the present invention exhibits good temporal resolution and sensitivity to high-frequency feature transformations in signals; and the present invention integrates advantages of different scales, enabling the algorithm to learn more features, and incorporating the LFLBs to further extract high-level local features. An mCNN-LFLBs network model of the present invention exhibits significant innovation and advancement on the technical level, and also demonstrates extremely high value in actual application. The network model not only provides a novel and efficient technical means for critical fields such as natural gas pipeline inspection, but also introduces new ideas and methods to research fields related to deep learning and signal processing.

The invention discloses a meteorological prediction method and system for multi-resolution mixed data, and the method comprises the steps: carrying out the preprocessing of the obtained multi-resolution mixed data, obtaining standard data, carrying out the pyramid grading of the standard data according to the resolution, and obtaining first pyramid data, second pyramid data and third pyramid data, the method comprises the following steps: obtaining a plurality of feature values, respectively inputting the feature values into a corresponding first feature transformation network, a second feature transformation network and a third feature transformation network to obtain a first feature value, a second feature value and a third feature value, inputting all the feature values into a structure aggregation network to obtain a fourth feature value, and inputting the feature values into a multi-scale convolutional neural network to obtain a prediction result. According to the method, a hierarchical pyramid is constructed, the feature extraction precision and the prediction accuracy are improved, deep data fusion and potential association mining are performed through feature transformation and a structure aggregation network, and accurate meteorological prediction is realized through a multi-scale convolutional network and an adaptive fusion mechanism.

The invention relates to the technical field of computer vision and deep learning, and discloses an image classification system and method fusing a space attention mechanism and long and short-term memory network sequence modeling, which combines the feature extraction capability of a convolutional neural network and space attention and time sequence attention mechanisms. And processing the spatial position sequence by using a long-short-term memory network. Firstly, advanced spatial features of an image are extracted through a feature extraction module by adopting a pre-trained convolutional neural network with a frozen weight, then a spatial attention module is introduced, a spatial attention graph is generated through channel dimension statistics, and important region features are enhanced. After the spatial attention is weighted, a dual-path feature is utilized, one path enters a feature transformation module, and a convolutional layer is used for reducing dimensionality and enhancing feature expression ability. Then, a sequence modeling module is carried out, the spatial features are flattened into a position sequence, a spatial position dependency relationship is modeled by adopting a bidirectional long-short-term memory network, and learnable attention vector dynamic aggregation key position features are introduced; the other path retains spatial global features. And outputting the dual-path features to a feature fusion module, extracting spatial global features and sequence aggregation features in parallel, and designing a gating mechanism to adaptively fuse the dual-path features. And finally, entering a classification module, and realizing end-to-end image classification based on fusion features.

The invention relates to an embankment slope stability assessment method based on artificial intelligence, and belongs to the technical field of embankment slope monitoring and assessment. The method comprises the following steps: acquiring and marking embankment slope stress sensing data; dividing the data into a plurality of spatio-temporal data blocks; constructing a stability evaluation model; extracting multi-scale convolution features by adopting multi-scale cavity convolution of a high-frequency channel and pooling-deconvolution operation of a low-frequency channel to obtain a fused multi-scale feature matrix; a hidden state sequence is obtained through a space attention mechanism and a double-door-setting mechanism; calculating time interval saliency based on the hidden state vector, then calculating a weighted feature vector, further obtaining a weighted feature matrix, and processing through deep convolution and point-by-point convolution to obtain a pooling feature vector; carrying out stability evaluation grade classification through learnable category prototype and gating feature transformation; and dynamically adjusting sample weight and constraint attention distribution by adopting a total loss function. According to the method, the progressive instability identification capability can be improved.

The invention discloses an electromagnetic field intelligent calculation method based on deep learning, and the method specifically comprises the steps: inputting a space-time input vector into an MFF-PINN neural network, the MFF-PINN neural network comprises parallel sub-networks and a linear superposition module, the sub-network comprises a scale transformation module, a Fourier feature transformation module and an MLP processing module, and the MFF-PINN neural network comprises a linear superposition module; firstly, scale transformation is carried out on a space-time input vector, then Fourier feature transformation is carried out on the vector after scale transformation, and the Fourier feature transformation module carries out Fourier transformation on the vector after scale transformation based on an effective frequency matrix; all the sub-networks share the Fourier feature transformation, and the output obtained by the Fourier feature transformation is input to the MLP processing module in the first sub-network; and carrying out linear superposition on the output of the sub-networks. According to the invention, the expression capability of the network on the high-frequency component and multi-scale characteristics of the electromagnetic field is obviously enhanced.

The invention relates to a construction method of an adaptive temperature guided mixed attention network for hyperspectral image classification, and provides a novel mixed attention network for hyperspectral image classification. A deterministic-probabilistic mixed attention mechanism is designed, and a traditional single-mode attention mechanism is expanded into a double-branch structure. The deterministic branch generates stable feature weights through explicit feature transformation and normalization, and the probabilistic branch introduces a soft attention mechanism of temperature parameter adjustment to realize flexible feature selection. The robustness and adaptability of feature selection are remarkably improved through the complementary effect of the two mechanisms. Meanwhile, a multi-level feature fusion strategy based on a learnable fusion coefficient is innovatively designed, and adaptive adjustment of feature importance is realized by dynamically balancing contributions of two types of attention.

The invention relates to the technical field of intelligent control and reinforcement learning technologies, in particular to a trajectory tracking control method fusing multi-objective optimization and a physical sensing network, which comprises the following steps: acquiring a real-time state vector of a to-be-controlled object, decomposing the acquired state vector into a sphere dynamic flow and a platform attitude flow, coding features of different attitude flows are extracted, a fusion feature vector is constructed, and at the same time, an attention mechanism is used to carry out feature transformation to determine control decision features; and for the determined control decision features, utilizing a multi-objective optimization function to carry out cooperative constraint on the generated actions, carrying out feature training in combination with an experience playback mechanism and a self-adaptive stable learning mechanism, and after training is completed, determining a trajectory tracking control instruction of the object to be controlled through dynamic adjustment of learning parameters. According to the invention, by establishing an adaptive stable learning mechanism, the learning rate and exploration noise are dynamically adjusted based on performance stagnation detection, and the training stability and convergence speed are improved.

The invention discloses a personnel state detection system and method based on a wifi router, and the method comprises the following steps: S1, deploying the wifi router, collecting subcarrier CSI which changes due to the disturbance of the movement of personnel, and forming a CSI signal sequence; s2, configuring a neural network classifier and loading parameters; s3, preprocessing the CSI signal sequence to obtain a stable CSI time sequence signal; s4, constructing a graph structure taking the subcarriers as graph nodes, and generating a spectrogram; s5, performing Laplace feature transformation on the spectrogram, and extracting a frequency domain feature vector of the spectrogram; s6, constructing a multi-scale Sheng differential equation model, and generating a state trajectory under each time scale; and S7, splicing the state tracks to form a state evolution sequence, inputting the state evolution sequence into a neural network classifier, and generating a state recognition result. According to the method, CSI is used for modeling personnel disturbance, multi-scale dynamic representation and accurate recognition are achieved, and the method is suitable for a non-contact personnel state sensing scene.

Provided in the present disclosure are a digital-human processing method and apparatus, and a device, a medium and a product. The method comprises: performing semantic analysis processing and / or representation generation processing on 3D data which is represented on the basis of a neural radiance field; and performing feature transformation, quantization processing and encoding processing on processed data.

The invention provides a machine vision coding method based on feature distillation, and relates to the technical field of image processing.The method comprises the steps that an image to be processed is input into a machine vision coding model, and the model extracts first potential feature representation of multiple channels through an analysis encoder; the method comprises the following steps: quantitatively dividing into basic layer quantitative features containing semantic and spatial structure features and enhancement layer quantitative features containing detail and texture features; the hyper-priori correlation module encodes hyper-priori information and generates enhanced auxiliary features and basic auxiliary features, and the conditional entropy coding network realizes encoding and decoding of the basic layer quantization features and the enhanced layer quantization features based on the enhanced auxiliary features and the basic auxiliary features. A machine vision task result is obtained through a feature transformation and task processing module, and after splicing is conducted through a splicer, a reconstructed image is output through a synthesis decoder. According to the invention, both machine vision and image reconstruction can be considered.

The invention discloses a road snow-blown visibility recognition method and system based on image recognition, and the method comprises the steps: carrying out the sequential processing of an original snow-blown image through initial feature extraction, multi-level down-sampling and feature enhancement, and multi-scale context fusion, and directly outputting a visibility interval and confidence, according to the method, downsampling, feature transformation and feature enhancement processing processes are repeatedly executed for multiple times, deep feature maps with gradually reduced scales are sequentially obtained, automatic and objective recognition of the visibility of the whole road line blown snow is achieved, the adaptability to the scene of the blown snow which is high in burstiness and non-uniform in space is improved, and the visibility of the whole road line blown snow is improved. Therefore, the model can more accurately capture the key depth of field and texture degradation characteristics which influence the visibility, and the recognition result is more accurate.

The invention provides a video moment retrieval method and device, electronic equipment and a storage medium, relates to the technical field of artificial intelligence, and is suitable for the fields of financial science and technology and medical health. The method comprises the following steps: performing space-time coding on a target video to obtain an initial space-time feature map; performing feature transformation on the initial spatial-temporal feature map through a first hierarchical adaptive granularity converter to obtain a first hierarchical spatial-temporal feature map; performing feature transformation on the first-level spatial-temporal feature map through a second-level adaptive granularity converter to obtain a second-level spatial-temporal feature map; performing feature fusion on the first-level spatio-temporal feature map and the second-level spatio-temporal feature map to obtain multi-scale spatio-temporal features; retrieval demand information is obtained, and then text coding is carried out to obtain retrieval demand information features; and carrying out feature decoding on the retrieval demand information features and the multi-scale spatial-temporal features, and then carrying out moment prediction to obtain starting and ending moments of the target fragment. The video time retrieval efficiency can be improved.

The application discloses an aerial view generation method based on multi-scale feature transformation and time sequence context, and relates to the technical field of map generation. The method fully utilizes the complementarity of image and laser radar data by fusing the image and the laser radar data, improves the accuracy and robustness of aerial view generation, and can still remain stable under bad weather; a multi-scale space conversion module extracts different scale features, enhances the feature expression capability, and makes the aerial view clearer and more accurate; time sequence information is introduced, past time features are used to enhance current features, and dynamic perception capability is improved; an advanced backbone network and a feature alignment and fusion module are adopted, so that high efficiency and flexibility are ensured; and specific network structures such as Swin-T, PointPillars and random inactivation layers are applied, so that the accuracy and generalization capability are further improved.

The invention discloses an intelligent substation equipment fault diagnosis method, device and equipment, and relates to the technical field of intelligent substation fault diagnosis, and the method comprises the following steps: carrying out the space alignment of a power sampling signal based on an equipment connection relation, and obtaining a structured graph signal; performing feature transformation on the structured graph signal to obtain a statistical incidence matrix and mapping the statistical incidence matrix into real-time state feature points in a Riemannian manifold space; according to curvature characteristics of the Riemannian manifold space, obtaining geometric deviation between the real-time state feature point and a preset ideal state point by adopting a logarithm mapping operator; and calculating the fault contribution degree of each device by using the geometric deviation, determining a fault device, and outputting a device diagnosis result. The method is used for solving the problems that weak fault sensing sensitivity is insufficient and faults are difficult to trace accurately under complex working conditions in the prior art.

A method for point cloud segmentation is disclosed. The method may comprise performing a down-sampling and feature transformation step on an embedding feature extracted from point cloud data to produce a representation of the point cloud data, wherein performing the down-sampling and feature transformation step comprises performing a down-sampling sub-step for reducing a number of points by a pooling layer and performing one or more times of a feature transformation sub-step; and performing an up-sampling and feature transformation step on the representation of the point cloud data to produce the point cloud segmentation, wherein performing the up-sampling and feature transformation step comprises performing an up-sampling sub-step for restoring the number of points by an un-pooling layer and performing one or more times of the feature transformation sub-step. The feature transformation sub-step comprises at least one of capturing a local feature of the point cloud data by a local perceiver; capturing a global feature of the point cloud data by a selective state space model (SSM) block; and capturing a cross-channel dependency of the point cloud data by a channel modulator.

A multimodal entity linking method based on dual encoders and a hybrid expert mechanism is proposed. This invention relates to multimodal entity linking technology at the intersection of natural language processing and computer vision. Addressing the problems of low inference efficiency, insufficient cross-modal interaction, and shallow modal fusion in existing methods, this invention proposes a multimodal entity linking method based on dual encoders and a hybrid expert mechanism. A dual-tower architecture is used to independently encode mentions and entities. Entity embeddings can be pre-computed offline and indexed, and linking is completed during inference through fast vector retrieval. A hybrid expert mechanism is introduced to achieve adaptive feature transformation of samples, and a gating network dynamically selects expert combinations. Bidirectional cross-modal attention is used to establish fine-grained alignment at the word-image block granularity. A channel attention mechanism dynamically balances the contributions of textual and visual modalities. The model is jointly optimized by multiple constraints, including load balancing loss. This invention achieves efficient retrieval while maintaining deep inference capabilities, simplifies inference time complexity, and is suitable for scenarios such as knowledge graph construction and intelligent question answering systems.

The present invention relates to a machine vision encoding method and system based on self-supervised learning. The method includes the following steps: randomly sampling image information into sub-blocks, inputting the sub-blocks into a backbone network head to extract and transform feature channels to obtain a first feature; transforming the first feature to obtain a feature in a low-dimensional space, adding uniform noise to the feature in the low-dimensional space through a quantizer to obtain a quantized feature, and reconstructing a compressed feature to obtain a second feature; transforming the second feature to a low-dimensional space, adding uniform noise to the feature in the low-dimensional space through a quantizer to reduce redundancy, extracting and encoding side information, decoding the side information, using a mixed Gaussian entropy model to predict the probability distribution parameters and bit rate of the second feature, and reconstructing the dimension of the encoded feature as a third feature; extracting and transforming the dimension of the third feature, extracting and weighting the convolution feature to form a heat map, obtaining valid positive samples through the heat map, and obtaining an encoding result. Compared with the existing technology, the present invention has the advantages of low encoding complexity and high semantic reliability.

The present application relates to the technical field of data processing, and more particularly to a dimension reduction and feature extraction method based on mutual information and genetic algorithm, comprising using an improved mutual information formula to calculate mutual information to measure the role of each dimension for each class; using the mutual information value as the fitness value of the feature dimension, first using the roulette method in the genetic algorithm to generate multiple information-carrying feature subsets; then using mutual information to optimize the generated feature subsets in dimension; optimizing the feature subsets, controlling the mutation and difference degree of the optimized feature subsets, and generating new feature subsets; and fusing the evaluation results of the multi-source feature subsets. The present application considers the fixity of the dimensionality after transformation, the high efficiency and small influence of feature extraction, and the neglect of small features; the feature subset is mutated, the dimensionality of the reduced feature subset is changed, and the effective difference degree between the feature subsets is effectively controlled.

The invention relates to the technical field of edge intelligence and federated learning, and provides an edge heterogeneous data enhancement method based on reinforcement learning and meta learning, and the method comprises the steps that a client constructs a strategy network based on a Monte Carlo algorithm; mapping local data into client features, performing spatial mapping on the client features based on a policy network, and outputting a feature transformation matrix; the feature transformation matrix is linearly mapped into alignment features, the Euclidean distance between the alignment features and global model parameters is calculated to serve as a feature alignment metric value, and a reward value is defined; and updating parameters of the strategy network by adopting a strategy gradient algorithm, and calculating an accumulated long-term discount reward based on a reward value to guide a network optimization direction. According to the method provided by the invention, through multi-module collaborative optimization, the generalization performance, the convergence speed and the reasoning precision of federal learning in a cross-domain task are remarkably improved, and the method has important technical value and application potential.

The invention belongs to the technical field of open set recognition, and discloses a hyperspectral open set recognition system and method based on class semantic reconstruction. The hyperspectral open set recognition system based on class semantic reconstruction comprises a grouping spectral space reservation transformer module which comprises three stages of hierarchical structures, and each stage of hierarchical structure comprises a grouping pixel embedding module and a space enhancement feature transformation module. The spectrum-spatial feature fusion module is used for fusing the stage spectrum-spatial features into discriminative spectrum-spatial features; the class semantic reconstruction module comprises a plurality of class auto-encoders and is used for performing class semantic reconstruction on the discriminative spectral-spatial features to obtain reconstruction errors, constructing and training a classification model through the reconstruction errors and finally realizing open set recognition in combination with a multi-dimensional scoring function; according to the method, inter-class confusion can be reduced, and interference of background noise information is reduced by reconstructing semantic features instead of original pixels.

This application provides an image compression system, an image processing method, an encoding / decoding method, and an electronic device. The image compression system includes a first selection module, an entropy encoding module, an entropy decoding module, a quantization module, N encoding networks, and one decoding network. The N encoding networks have different encoding losses. The first selection module is used to select a target encoding network from the N encoding networks based on the number of times the image to be encoded has been encoded. The target encoding network is used to perform feature transformation on the image to be encoded to obtain a first feature map. The quantization module is used to quantize the first feature map to obtain a second feature map. The entropy encoding module is used to entropy encode the second feature map to obtain a bitstream. The entropy decoding module is used to entropy decode the bitstream to obtain a third feature map. The decoding network is used to perform feature transformation based on the third feature map to obtain a reconstructed image. This effectively reduces the loss ratio of the image after multiple encoding and decoding operations.

The present application relates to the technical field of speech recognition, and provides a speech recognition method and device, electronic equipment and storage medium, wherein the method comprises: performing feature extraction on a speech signal to be recognized, inputting an extracted acoustic feature sequence into a speech recognition model to obtain target recognition text that has been optimized by text; wherein the speech recognition model comprises an encoder and a decoder, and a hybrid expert module for a text optimization task is embedded in the encoder; the encoder is used for performing layer-by-layer encoding processing on the acoustic feature sequence, and performing feature transformation corresponding to the text optimization task on intermediate level features through the hybrid expert module in the encoding process to obtain encoding features containing text optimization semantics; and the decoder is used for decoding the encoding features to obtain the target recognition text, effectively solving the problems of bloated architecture and error accumulation caused by separation of recognition and text optimization in a traditional speech recognition system, and achieving dual improvement of inference efficiency and recognition quality.

This specification discloses a method for constructing a scene-to-image cross-domain mapping sample set driven by attribute features, which belongs to the field of deep model compression technology. The method includes augmenting a basic sample set with an image feature transformation method to obtain a material library for sample synthesis; the basic sample set includes simulated target materials and real-shot environment materials; spatially aligning the actual target and the actual environment in the actual application scene to obtain the position information of the actual target in the environment coordinate system; selecting corresponding simulated target materials and real-shot environment materials from the material library based on the actual application scene; based on the position information of the actual target in the environment coordinate system, performing threshold segmentation on the selected simulated target materials and fusing them with the real-shot environment materials to obtain high-confidence synthetic samples, so as to solve the problem of obvious pixel block fragmentation and sample distortion in the synthetic sample area of the existing sample synthesis method, resulting in the inability to effectively synthesize the high-confidence sample set required for intelligent algorithm training.

The invention discloses a multivariate time sequence prediction method and system based on a graph diffusion space-time convolution network, and the method comprises the steps: inputting multivariate time sequence data, and obtaining an initial node feature; constructing a dynamic adjacency matrix, generating a global diffusion matrix through the dynamic adjacency matrix, and performing sparsification on the global diffusion matrix to obtain an adjacency matrix; sending the adjacent matrix and the initial node features into a graph convolution module to obtain spatial convolution features; sending the spatial convolution features into a time convolution module to obtain space-time convolution features; the graph convolution module and the time convolution module form a layer of the model, time dimension compression is carried out on output of each layer, jump connection is carried out along feature dimension splicing, and finally feature transformation and dimension remodeling are carried out to generate a prediction result; training the model; the method has the advantages that the model can effectively mine the hidden relation and long-distance dependence between the nodes, and prediction deviation in a dynamic scene is reduced.

A model training method includes performing sampling on the target model to obtain a submodel of the target model, and a quantity of feature transformation layers of the submodel is less than a quantity of feature transformation layers of the target model, and / or a size of a weight matrix of at least one of feature transformation layers of the submodel is less than a size of a weight matrix of a corresponding feature transformation layer of the target model; augmenting the submodel to obtain an augmented model; and training the augmented model to obtain a trained augmented model.

The present application relates to the technical field of expression recognition, and in particular to an expression recognition method and system based on multi-scale features and spatial attention. The method comprises: using an HNFER neural network model to perform feature extraction on acquired facial image data to obtain an original input feature map; performing pooling and concatenation on the extracted features on the basis of a CoordAtt attention mechanism to obtain a feature map; performing deep convolution processing on the feature map to obtain an attention map, and then obtaining a final feature map by means of element multiplication; and performing feature transformation and normalization on the final feature map to obtain expression category probabilities and outputting the expression category probabilities . In the present application, by integrating scale-aware technology and spatial attention technology, a model can more accurately recognize and categorize different emotional states, and can maintain high performance even under complex environmental conditions.

The application discloses a micro bearing production and manufacturing detection data analysis method and system based on big data, relates to the technical field of data processing, and pre-trains a recurrent time sequence network through collection of vibration signals of bearings in a known state, obtains an evolution feature vector of bearings in the same batch through the network combined with a transient noise suppression operator, extracts a static feature vector combined with a machining time sequence and an end face image, corrects a visual missing sample after splicing, constructs a reconstructed kernel feature matrix, obtains a feature transformation and a mapping matrix based on two-way alternate optimization of the matrix and a real label, establishes a bearing state database, collects multi-modal data of a bearing to be detected to generate an initial to-be-detected feature vector, calculates the similarity of the to-be-detected feature vector with database samples after mapping, and finally outputs a detection result. The application deeply fuses multi-modal features, effectively overcomes interference such as workshop visual pollution, thermal expansion and group tolerance drift, and greatly improves detection accuracy and robustness.