77 results about "Transform coding" patented technology

The invention discloses a landslide risk assessment method based on an extreme rainfall and geology coupling model, and relates to the technical field of geological disasters. Comprising the following steps: S1, constructing a three-dimensional probability density field of a fracture network and a non-Gaussian random field model of a permeability coefficient tensor; s2, setting a physical kernel layer according to the non-Gaussian random field model, setting a data driving layer through space-time Transform coding, and constructing a graph attention network model; s3, generating an adversarial network through physical information, constructing extreme rainfall coupling data, and updating the non-Gaussian permeability coefficient random field model according to the graph attention network model; and S4, acquiring an entropy generation rate according to the mechanical field data, the seepage field data and the temperature field data, and determining a risk level. Physical interpretability grading early warning of landslide risks is realized, and meanwhile, risk space distribution can be visually displayed through a sliding surface probability cloud picture, so that accurate decision support is provided for disaster prevention and control.

A system and method for adaptive data processing combining compression and encryption. The system analyzes input data characteristics, compares probability distributions, and creates a transformation matrix to convert data into a dyadic distribution. It generates a main data stream of transformed data and a secondary stream of transformation information. The system dynamically selects and applies processing techniques, including transformation, encoding, compression, and encryption algorithms, based on analyzed characteristics and real-time performance metrics. It compresses the main data stream using Huffman coding and implements security measures to protect the output. A feedback loop monitors technique effectiveness, updates a knowledge base, and influences future selections. The system can operate in lossless, lossy, or modified lossless modes, adapting to different application requirements. This approach offers an efficient solution for scenarios where both data reduction and security are critical concerns.

The invention discloses a hydropower station water level detection method and system. The method comprises the steps that an original water flow characteristic parameter set is obtained through multiple sets of Doppler effect radar flow measuring devices; performing segmentation extraction to obtain a water flow feature sub-data set; inputting a Transform network coding layer to generate a water flow feature coding vector; outputting a preliminary water level predicted value sequence through a decoding layer; a radar flow measurement model is called for correction to obtain a correction sequence; time sequence integration is carried out to obtain a water level change curve. The system comprises a multi-source Doppler radar signal acquisition unit, a spatial-temporal feature segmentation extraction unit, a Transform coding processing unit, a water level preliminary prediction unit, a radar model correction unit and a time sequence integration output unit. According to the method and system, multi-source parameters are deeply fused, the environmental adaptability is improved, the continuity and reliability of a detection result are enhanced, the water level of the hydropower station can be accurately monitored, and support is provided for scheduling decision and safety prevention and control.

The invention discloses a visual defect detection method based on multilevel Transform. The method comprises the following steps: firstly, extracting multi-scale features of an input image through a pre-trained convolutional neural network, and performing hierarchical fusion; then, a multi-level Transform coding and decoding structure is used for carrying out deep reconstruction on the fusion features, and long-distance dependency relationships of different granularities are captured through hierarchical patch segmentation and a self-attention mechanism; meanwhile, introducing a standard deviation to estimate branch learning reconstruction uncertainty; and finally, a pixel-level anomaly score graph is generated by calculating a normalized residual error and adopting a multi-scale anomaly score aggregation strategy, so that accurate anomaly positioning and discrimination are realized. According to the method, multi-scale feature representation and global semantic modeling capability are fused, the accuracy, robustness and generalization capability of anomaly detection are effectively improved, only normal sample training is needed, and the method is suitable for complex scenes such as industrial visual detection and has a good application prospect.

The invention relates to the technical field of multi-modal data prediction, and discloses a fashion preference prediction method and device, and the method comprises the steps: obtaining multi-modal data comprising an image, a text and a user behavior time sequence, carrying out the feature extraction, and obtaining an image feature, a text feature and a time sequence feature; constructing spatial features based on the city embedded table and the corresponding regional culture label codes; aligning the dimensions of the spatial features and the time sequence features, and fusing the aligned spatial features and time sequence features by using a gating attention mechanism to obtain space-time fusion features; splicing the space-time fusion feature with the image feature and the text feature to obtain a multi-modal fusion feature; decoupling the multi-modal fusion feature to obtain a material decoupling feature, a style decoupling feature and a scene decoupling feature; and after feature splicing is carried out on the multiple decoupling features, the fashion preference prediction probability is obtained through Transform coding and MLP classification.

The invention discloses a dynamic space-time CNN-Transform emotion brain-computer interface decoding method, and relates to the technical field of brain-computer interfaces, a dynamic time feature extraction module is designed according to the sensitivity of multi-scale convolution to an electroencephalogram sequence along a time dimension, and electroencephalogram sequence time feature information is mined by using convolution kernels of different sizes; constructing a local-global spatial feature extraction module by referring to close correlation between asymmetry of left and right brain regions of the brain and an emotional state, and sequentially extracting spatial feature information of the left brain, the right brain and the whole brain of the electroencephalogram sequence; a spatial-temporal feature fusion module is designed, and spatial-temporal feature relations among the left brain, the right brain and the whole brain are mined; the long-time dependency relationship in the electroencephalogram sequence is captured through an attention mechanism by referring to the advantage of Transform on long-time sequence processing, so that the emotion electroencephalogram decoding precision is effectively improved; and finally, performing emotion recognition on the feature sequence subjected to Transform coding by using a multi-layer perceptron to realize end-to-end emotion electroencephalogram decoding.

The invention discloses a high-precision remote sensing image cultivated land boundary extraction method, which comprises the following steps of: acquiring high-resolution remote sensing image data, preprocessing the data, and obtaining cultivated land parcel vector data through manual annotation; constructing a multi-task depth semantic segmentation model based on a coding and decoding structure; constructing a mixed loss function; acquiring a high-resolution remote sensing image of a cultivated land area to be detected and performing data preprocessing; inputting a high-resolution remote sensing image of a cultivated land area to be detected into the trained high-resolution remote sensing image cultivated land extraction model based on multi-task learning; and converting the cultivated land semantic segmentation result into cultivated land parcel vector data containing latitude and longitude coordinates. According to the method, through staged Transform coding based on overlapping patches and efficient sequence reduction, efficient multi-scale coding is realized, the problem of insufficient remote dependence capture is solved, and the computing power bottleneck of high-resolution self-attention is relieved.

The invention discloses a multi-modal data fusion and intelligent analysis method and system for advanced early warning of wind and light storage equipment, and the method comprises the steps: deploying data collection equipment, synchronizing the data collection equipment to a virtual equipment model through a digital twin interface, and generating a physical enhancement feature; recombining the physical enhancement features into a three-dimensional feature tensor; orthogonal constraint tensor decomposition is carried out on the three-dimensional tensor, and cross-scale fault features are extracted; constructing a fault propagation graph network FPGN based on the core tensor, and outputting an early warning decision through graph convolution and Transform coding; according to the early warning deviation, a gradient descent method is adopted to adjust the physical constraint weight, and the model is retrained; the three-dimensional feature tensor recombination realizes unified characterization of multi-modal data, orthogonal constraint tensor decomposition forces different dimension features to be independent, a cross-scale coupling mode of a fault is extracted, and the effects of dimension reduction and efficiency improvement are achieved; according to the FPGA network, graph convolution and Transform time sequence coding are fused, and the propagation intensity of modeling faults in an equipment topology network is realized, so that early warning from local anomalies to system-level risks is realized.

There is provided a computer-implemented method for verifying the robustness of a neural network classifier with respect to one or more parameterised transformations applied to an input, the classifier comprising one or more convolutional layers, the method comprising: encoding each layer of the classifier as one or more algebraic classifier constraints; encoding each transformation as one or more algebraic transformation constraints; encoding a change in an output classifier label from the classifier as an algebraic output constraint; determining whether a solution exists which satisfies the classifier constraints, transformation constraints and output constraints, and determining the classifier as robust to the local transformations if no such solution exists. A perception system and a computer readable medium are also provided.

The invention discloses a knowledge graph multi-modal document analysis and image table semantization knowledge recall method, and belongs to the technical field of knowledge engineering and information retrieval. The invention provides an innovative scheme for fusing a visual language model, semantic abstract generation and knowledge graph modeling. The method comprises the following steps: constructing a vertical domain knowledge graph by adopting a BERT-BiLSTM-CRF model; according to the method, multi-modal document analysis is realized through models such as DocLayout-YOLO, TableMaster, UniMERNet and the like; the method comprises the following steps of: segmenting an image into 16 * 16 block sequences by adopting a vit-gpt2-image-adaptation model, and realizing image semantization through 768-dimensional vector space mapping and Transform coding; constructing a document summary tree based on DBSCAN clustering and LLM recursive summary; and designing a hybrid retrieval space fusing semantic vectors and structured vectors, and reordering by adopting a double-attention mechanism. According to the method, the knowledge base document retrieval recall rate is increased to 99%, the question and answer accuracy rate reaches 90% or above, the index construction time is shortened by 60%, and the problem that semantic understanding and recall of non-text elements in complex documents are difficult is effectively solved.

The invention discloses a text classification method and device and a processor. A normalized text vector is obtained by preprocessing and coding an input text. Then utilizing a multi-layer structure in a pre-trained bad text recognition model, firstly carrying out first-level category classification on the input vector through a first Transform coding layer and a classification layer, and meanwhile, carrying out related semantic clustering through a first clustering layer to generate a first clustering text vector; then, a second Transform coding layer and a classification layer complete finer-grained secondary category classification based on the first clustering text vector, and continuous aggregation is performed through a second clustering layer to generate a second clustering text vector; and finally, the third Transform coding layer and the classification layer are combined with the second clustering vector to output a global classification result. And finally, all classification results are screened by setting a confidence threshold, high-confidence categories are reserved, and accurate classification results with clear levels are obtained.

A method comprising: processing a video frame; determining a reference block for a current block of the video frame; predicting the current block with an intra block copy method; deriving intra prediction information for the current block based on the reference block; selecting a transform coding for the current block based on the intra prediction information; and applying the selected transform coding to the current block.

The application discloses a 360-degree image compression method and device based on adaptive latitude-aware transform coding, has obvious advantages in code rate saving, and can effectively solve the distortion redundancy problem of ERP images.The method comprises the following steps: (1) designing an adaptive latitude-aware module; (2) constructing a multi-scale gated convolutional neural network; (3) guiding spatial features by transform modulation importance feature activation maps; and (4) constructing a learned overall framework of 360-degree image compression.

The present application relates to the technical field of data processing, and especially relates to a data asset management method, device, storage medium and system, the method comprising: acquiring video data to be processed; determining key texture features and local chroma features of each video frame; cutting the video data to be processed into several video segments; determining a predicted compression interference characteristic value for each video segment based on the similarity of the key texture features of adjacent video frames in the video segment and the dispersion of the local chroma features of each video frame; configuring a label for each video segment according to the predicted compression interference characteristic value corresponding to the video segment; determining a division area for each video block in each region based on the spatial position relationship between the key texture features and each region in the video frame of the video data to be processed; performing transform coding on each residual value, or adjusting the number of predicted frames in each video segment based on the predicted compression interference characteristic value, and performing inter-frame prediction compression on each video frame, thereby improving the video compression efficiency and spatial utilization.

The present application belongs to the technical field of target real-time decoding positioning, and provides a kind of encoding target real-time decoding positioning method of slit measurement system, its steps include: S1 the layout of encoding target of slit detection point;S2 for the binary coding of encoding target image shot at large inclination angle, and the center position of encoding area and positioning area is obtained as the initial point of compression transformation;S3 adopts single direction wide edge detection to combine edge projection method, and is compressed to project transformation to encoding target point;S4 encoding target decoding;S5 subpixel positioning of encoding target edge pixel optimization, the present application provides edge projection benchmark using single direction wide edge detection, reduces the influence of compression projection transformation by large inclination angle shooting, realizes the compression target point projection transformation using edge projection, and the edge of positioning area is combined with the edge of encoding target, reduces the edge mis-extraction rate, improves the subpixel positioning precision, while having the characteristics of fast and real-time, suitable for long slit multi-point detection.

The invention provides an image coding method and device, equipment and a storage medium, and relates to the technical field of computers. The method comprises the following steps that: a CPU (Central Processing Unit) analyzes an image to be coded in a wavelet transform coding process to obtain original pixel data, and transmits the original pixel data to a GPU (Graphics Processing Unit); the GPU performs preprocessing operation on the original pixel data in parallel by using a plurality of threads to obtain standard pixel data meeting the wavelet transform requirement; wherein one thread processes one pixel column in the original pixel data; the GPU performs forward wavelet transform on the standard pixel data to obtain a sub-band coefficient; and the GPU performs entropy coding processing on the sub-band coefficients to obtain coded data. According to the scheme, image analysis is completed in the CPU, and preprocessing, wavelet transform and entropy coding processing are executed in parallel in the GPU, so that the coding throughput rate and the resource utilization rate in the image coding process can be improved.

The invention discloses a cerebrovascular segmentation method and device based on physical guidance and pyramid vision Transform, and relates to the field of medical image data, and the method comprises the steps: constructing a cerebrovascular segmentation model, and enabling loss functions used during training to comprise boundary intersection-to-union ratio loss, focus Tversky loss and Dice loss; the method comprises the following steps: acquiring an optical coherence tomography image of a brain to be processed, inputting the optical coherence tomography image into a trained cerebrovascular segmentation model, enabling the optical coherence tomography image to pass through an encoder module of pyramid vision Transform, and inputting an output feature of a first Transform encoding layer into a radial strength module to obtain a radial enhancement feature; wherein the output features of the second Transform coding layer, the third Transform coding layer and the fourth Transform coding layer are input into a deformable cross-scale fusion module to obtain enhanced fusion features, and the radial enhanced features and the enhanced fusion features are input into a boundary perception attention module to obtain a corresponding cerebrovascular prediction segmentation mask and a cerebrovascular prediction segmentation image. The problems of low segmentation accuracy and boundary precision in the prior art are solved.

The application discloses a Xinjiang cold and arid slope risk prevention and control method based on a knowledge graph, belongs to the technical field of cold and arid region slope disaster risk prevention and control, and aims at solving the problems of non-uniform data format, weak correlation and insufficient prevention and control measures matching of cold and arid slope disasters. The application realizes the technical effects of improving the risk chain identification accuracy and the measure matching by constructing a heterogeneous space-time knowledge graph, generating multiple types of vectors, adopting a space-time graph transformation coding model to identify the instability chain and search similar cases, outputting a risk level, a treatment priority and recommended measures.

The application provides a video text retrieval method and system based on text condition semantics, and relates to the technical field of natural language processing.The method comprises the following steps: according to a video frame sequence to be retrieved and a query text, performing feature extraction through a pre-trained visual-linguistic double-branch feature extraction network to obtain a frame-level visual feature sequence and a global text feature; performing scale sampling according to a plurality of preset time steps based on the frame-level visual feature sequence, and performing time domain transformation coding respectively to obtain a plurality of groups of time sequence features corresponding to different time granularities; after the plurality of groups of time sequence features corresponding to different time granularities are up-sampled and aligned to a unified time resolution, the integral of each time granularity of the time sequence features under a preset fractional order is calculated respectively to obtain multi-scale historical cumulative features.The application improves the accuracy of video text retrieval and the rationality of related result sorting.

This invention discloses a point cloud attribute encoding method, apparatus, decoding method, and apparatus. The point cloud attribute encoding method includes: sorting all point cloud data to be encoded to obtain sorted point cloud data, wherein the point cloud data to be encoded is point cloud data with attributes to be encoded; constructing a multi-layer structure based on all sorted point cloud data and the distances between each sorted point cloud data; obtaining the encoding method corresponding to each node in the multi-layer structure, wherein the encoding method corresponding to a node is a direct encoding mode, a predictive encoding mode, or a transform encoding mode, wherein the predictive encoding mode encodes the node based on information from its neighboring nodes, and the transform encoding mode encodes the node based on a transform matrix; and performing point cloud attribute encoding on each node based on the multi-layer structure and the corresponding encoding method. Compared with existing technologies, this invention improves the overall encoding efficiency of point cloud data.

The application discloses a kind of digital hybrid coding methods based on large model, including the following steps: S1, pre-processing to be encoded data;S2, feature learning method driven by diffusion model is used to feature coding to pre-processing data;S3, neural network coding is carried out to feature coding data, and based on variable bit rate quantization method dynamically adjusts encoding bit number;S4, adaptive entropy coding is carried out to neural network coding data, based on normalized stream entropy coding method carries out probability density transformation;S5, discrete transform coding is carried out to entropy coding data, and hierarchical sub-block quantization method is used to execute hierarchical quantization to different frequency components;S6, based on A3C optimization transform coding data's code length distribution and entropy coding parameter, extract lightweight coding model from diffusion model using knowledge distillation method, generate final optimization coding data.The application improves data compression efficiency and coding quality significantly by hybrid coding and dynamic optimization strategy.

A mechanism for processing video data is disclosed. In one example, when utilizing regional adaptive hierarchical transform (RAHT) in geometry-based point cloud compression (G-PCC), the mechanism includes determining to disable alternating current (AC) inter prediction based on direct current (DC) of a reference node, DC of a current node, and one or more thresholds. Conversion between the visual media data and the bitstream can then be performed with AC inter prediction disabled.

Post training compression of 3DGS data is agnostic to training in a traditional signal compression perspective. Gaussian parameters are treated as signals. Pre-processing and transform coding techniques are used to compress the signals effectively. Firstly, lossless / lossy compression is performed on 3DGS geometry (positions, scales, rotations) using a point cloud coding-based (e.g., G-PCC, GeS) framework. Positions are compressed using occupancy tree coding. Scales and rotations are encoded as attributes using transform coding. The widely used block-based graph Fourier transform (GFT) is used to compress the attributes (base colors, spherical harmonic coefficients and opacities). In addition, a graph construction strategy is used for 3DGS data that computes the edge weights based on similarity (or dissimilarity) between the 3D Gaussian distributions using KL-divergence. Alternatively, positions can be encoded using occupancy tree (e.g., G-PCC, GeS) or AI-based PCC methods, and any subset of Gaussian parameters or the transformed coefficients of Gaussian parameters can be mapped into 2D frames and encoded by video coders.

The embodiment of the invention provides a point cloud coding and decoding method, which can improve the attribute coding and decoding efficiency of a point cloud and further improve the coding and decoding performance of the point cloud. The point cloud decoding method comprises the following steps: analyzing a code stream, and determining an RAHT coding mode corresponding to a current layer and attribute coding information corresponding to nodes in the current layer; the RAHT encoding mode corresponding to the current layer is determined based on at least one encoding cost corresponding to the node by encoding attribute information of nodes of the current layer by an encoder through at least one RAHT encoding mode and determining at least one encoding cost corresponding to the node; and decoding and reconstructing the attribute coding information corresponding to the node in the current layer based on the RAHT coding mode corresponding to the current layer, and determining the reconstruction attribute information corresponding to the node.

Methods, systems, and devices for implementing an adaptive color transform (ACT) during image / video encoding and decoding, including: determining, for a conversion between a video including a block and a bitstream of the video, that a size of the block is greater than a maximum allowed size for an ACT mode, and performing, based on the determining, the conversion. In response to the size of the block being greater than the maximum allowed size for the ACT mode, the block is partitioned into multiple sub-blocks. Each of the multiple sub-blocks share a same prediction mode, and the ACT mode is enabled at a sub-block level.

The invention discloses an unsupervised engineering structure damage identification method, apparatus and device, and a medium. The method comprises the steps of obtaining a structure vibration signal of a to-be-identified engineering structure; reconstructing the structural vibration signal through the damage identification model to obtain a reconstructed vibration signal; and on the basis of a reconstruction error between the vibration signal and the reconstructed vibration signal, identifying the structure state of the to-be-identified engineering structure. According to the method, the local feature extraction capability of the convolutional coding module and the overall evolution rule of the signal captured by the Transform coding module along the time dimension are fused, and the features are compulsively compressed and reconstructed by using the bottleneck layer, so that the damage identification model learns the essential feature representation of the structural vibration signal, the reconstruction accuracy of the healthy vibration signal is improved, and the reconstruction accuracy of the healthy vibration signal is improved. Therefore, the accuracy of the structure state identified on the basis of the reconstruction error between the vibration signal and the reconstructed vibration signal can be improved.

The gaze tracking method and device based on automatic coding transformation rotate a face image around x, y and z axes of a three-dimensional coordinate system, and rotate the face image around the projection to the image plane, calculate the homography matrix by using the point pairs before and after the face image projection, and store the homography matrix data as a homography array; an automatic coding transformation network is constructed, the weight of the automatic coding network is obtained by learning the transformation matrix; the automatic coding transformation network is trained, the encoder and the transformation encoder of the trained automatic coding transformation network are taken out, a multi-layer perceptron is composed, the gaze direction regression network is obtained, and the gaze direction is regressed on a small sample eye movement label training set; the gaze direction regression network is deployed in an application environment, and the gaze direction is estimated through the gaze direction regression network. The face feature aggregation can be better performed, and the performance of the gaze estimation task is improved.

The invention relates to a heat supply system energy consumption evaluation method and equipment based on artificial intelligence, and belongs to the technical field of heat supply system energy consumption evaluation. The method comprises the following steps: acquiring multi-source data of a heat supply system; performing space-time alignment on the multi-source data to obtain a space-time aligned multi-source data set; according to data in the time-space aligned multi-source data set, basic features and cross features are generated through feature engineering, and an enhanced feature set is constructed; constructing a heat supply system energy consumption prediction model, wherein the model comprises a Transform coding layer, an LSTM (Long Short Term Memory) time sequence layer and a graph attention network; inputting data in the enhanced feature set into a heat supply system energy consumption prediction model for processing to obtain an energy consumption prediction result; and calculating the energy consumption intensity per unit area according to the energy consumption prediction result, and if the energy consumption intensity per unit area is higher than a set threshold value, triggering a high energy consumption alarm. According to the invention, the accuracy of energy consumption evaluation can be improved.

The invention relates to the technical field of big data storage and processing, and discloses a storage scheduling and analysis processing method for high-concurrency Internet of Things data, and the method comprises the steps: firstly building a writing end discrete tension model based on the disorder degree and fluctuation amplitude of a data stream, and building a reading end aggregation tension model based on a query intention and access popularity; secondly, substituting the two-way tension value into a tension field model, dynamically selecting and instantiating a row skip list, a column prepolymerization array or a sparse index aggregation tree as a memory table structure; when data is written into a disk, frequency domain transformation coding is performed on periodic data, and a heterogeneous sorting character string table is generated; and finally, performing frequency domain calculation in combination with the Parseval theorem in the query process, and dynamically correcting the tension weight by using performance feedback data. Through structure variation and frequency domain compression during operation, the writing performance and the query efficiency are effectively balanced, and the storage cost is reduced.