135 results about "Adaptive resolution" patented technology

The invention provides a rail transit video intelligent analysis method, medium and system, and belongs to the technical field of rail transit, and the method comprises the steps: constructing a self-adaptive resolution image hierarchical structure, and dynamically determining an optimal resolution hierarchy based on a minimum spanning tree algorithm; constructing an abnormal target movement matrix by combining an improved frame difference method with an optical flow estimation technology; applying a Hungary algorithm to realize target tracking; constructing an abnormal target change index to evaluate a behavior abnormal degree; dividing monitoring stages according to the running state of the vehicle to realize scene self-adaption; an orbit scene attention network model is introduced to identify abnormal behaviors; quantifying a risk level through an abnormal behavior risk assessment function; implementing a hierarchical processing strategy to enable a low-resolution hierarchy to be responsible for global rapid screening and a high-resolution hierarchy to be responsible for fine analysis of key areas; the risk trend is estimated in combination with the rail transit abnormal event prediction model, and the technical problems of large calculation amount and low efficiency of high-definition video processing are effectively solved.

The invention discloses a low-altitude path planning method and system based on a dynamic adaptive graph neural network, and belongs to the technical field of artificial intelligence and robot autonomous navigation. Aiming at the problems of large calculation amount of a fixed grid map, slow dynamic response of a static octree, failure of a traditional heuristic function on a non-uniform graph and the like, the invention provides the following scheme: constructing a resolution self-adaptive non-uniform graph by using the octree; a Morton code is adopted to realize rapid positioning of a dynamic obstacle and millisecond-level updating of a map; an attention mechanism is introduced into a graph attention network (GAT), weights are adaptively allocated to different neighbor nodes, features are aggregated, and a high-precision heuristic cost estimation value is output; and finally, integrating the estimated value into an algorithm to perform efficient path search. According to the method, the memory occupation and the calculation complexity are remarkably reduced, and the planning efficiency and the path safety in a dynamic environment are improved.

The invention discloses an ambiguity adaptive resolving method and system based on ILS and BIE estimation. The method comprises the following steps: acquiring GNSS original observed quantity; constructing a pseudo-range and carrier phase double-difference observation equation, and calculating a floating point solution of an ambiguity parameter by using extended Kalman filtering; performing ambiguity fixation on the ambiguity subset through an ILS algorithm to obtain an ILS fixed solution, performing ambiguity fixed check on the ILS fixed solution, if the check is passed, obtaining a final solution, and if the check is not passed, performing ambiguity fixation through a BIE algorithm to obtain a BIE fixed solution: performing multiple screening on the ambiguity subset based on an observation value domain and a state domain to obtain an optimal ambiguity subset; searching the optimal ambiguity subset by using an improved SEVB algorithm to obtain a BIE multi-ambiguity candidate solution; and calculating a BIE fixed solution, and performing reliability verification on the BIE fixed solution to obtain a final fixed solution. According to the method, the ambiguity fixing success rate can be improved, meanwhile, the precision of a fixed solution is remarkably improved, and the ambiguity resolving efficiency is effectively optimized.

The invention provides a self-adaptive multi-resolution power system simulation method and device based on physical information, and relates to the technical field of power system simulation. The method comprises the following steps: acquiring model data and operation state data of a power system; constructing a simulation system corresponding to the power system based on the model data, the operation state data and the multi-level resolution model library; determining a target model resolution corresponding to each subsystem through a self-adaptive resolution control strategy and a multi-level resolution model library; and distributing the target model resolution to the corresponding subsystem, and performing simulation calculation on the subsystem of the simulation system based on the multi-level resolution model library and the adaptive resolution control strategy to obtain a simulation calculation result corresponding to the power system. According to the embodiment of the invention, by designing the adaptive resolution control strategy, the dynamic adjustment of the resolution of different regional models of the power system is realized, and the calculation efficiency can be greatly improved while the simulation precision is maintained.

The invention provides an image processing method and system applied to a display area of a virtual exhibition hall. The method comprises the following steps: collecting viewing distance data and view angle deviation angle data; generating a dynamic sensing result according to the data; determining a resolution threshold range based on the dynamic sensing result; if the resolution threshold range exceeds a preset standard value, generating an adaptive resolution image; when it is detected that the focus area coordinates are displaced, fast Fourier transform is executed on the self-adaptive resolution image, and a frequency domain focus image is obtained; optimizing the frequency domain focus image to generate an optimized focus detail image; generating display frame data based on the focus detail image; and inputting the display frame data into a user behavior analysis and prediction module, and obtaining pre-loaded display content during network delay. The definition of the displayed content is automatically adjusted, related content can be cached in advance according to the browsing track and interest preference of the user, and waiting time is shortened.

The invention discloses a carbon footprint monitoring system suitable for a high altitude area and an energy-saving data processing method, and the method comprises the steps: firstly, through an EnKF (Ensemble Kalman Filter) technology, fusing multi-source heterogeneous data such as satellite remote sensing and ground monitoring according to a data source reliability "matching", and overcoming the data sparsity; secondly, based on the oxygen partial pressure corresponding to the local altitude and the'ratio 'of a key sensitivity coefficient # imgabs0 #, applying a specific correction function to dynamically adjust an emission factor; and finally, downscaling the coarse-resolution carbon emission data by adopting an adaptive-resolution convolutional neural network to generate a high-precision and high-resolution carbon emission spatial distribution diagram.

The invention provides an SLAM (Simultaneous Localization and Mapping) optimization method and equipment based on point cloud registration and adaptive resolution, and a medium, and relates to the technical field of AGV (Automatic Guided Vehicle) positioning and mapping, and the method comprises the following steps: (1) inputting and preprocessing sensor data; (2) front-end scanning and matching: based on the preprocessed data, carrying out point cloud registration through a GICP algorithm, updating the pose of the current frame and constructing a local subgraph; (3) global SLAM: receiving sensor data, poses and subgraph data from the front-end scanning and matching step, and carrying out loopback detection and global optimization; wherein a self-adaptive resolution switching strategy based on environment complexity is adopted in the loopback detection process; according to the GICP algorithm, the surface of the point cloud is modeled as Gaussian distribution, local features are described through a covariance matrix, and a target error function constructed based on the mahalanobis distance is optimized to estimate pose transformation. According to the method, the local matching precision and robustness are improved by replacing ICP with GICP, and the global calculation efficiency is adjusted and optimized in combination with the dynamic resolution.

The invention relates to a high-fidelity generative video stream transmission system based on a visual base model, which belongs to the field of image communication, and is characterized in that a visual enhancement-oriented generative codec is designed, and high-fidelity video reconstruction under a high compression ratio is realized through an asymmetric space-time compression strategy and time sequence consistency enhancement; a resolution scaling module is provided, the calculation complexity is remarkably reduced through a video super-resolution recovery module of adaptive resolution control and joint optimization, and real-time high-definition video processing is achieved; and constructing a network adaptive video stream transmission controller, an intelligent token discarding mechanism based on semantic importance and a mixed packet loss processing strategy to realize code rate scalable control and robust transmission under network fluctuation.

An example method of video decoding includes receiving a video bitstream that includes a plurality of encoded blocks including a current block. The example includes determining that the current block is inter-coded using a prediction block and a motion vector (MV). When an adaptive motion vector difference (AMVD) resolution mode is active for the current block, a reference motion vector (RMV) index is determined for a dynamic reference list (DRL), where the RMV index for the DRL has a first range when the AMVD resolution mode is active. When the AMVD resolution mode is inactive for the current block, the RMV index for the DRL is extracted from the video bitstream, where the RMV index for the DRL has a second range, greater than the first range.

The invention belongs to pattern recognition and artificial intelligence, and particularly provides a multi-modal large language model optimization method and system based on adaptive resolution, and the method comprises the steps: dividing multi-modal question and answer data into a target task training set and a target task test set; constructing a resolution selection training set by using the recollected image data in the target scene and the paired text instruction; constructing a multi-modal large language model with a two-stage image coding mechanism, and training the multi-modal large language model by using the target task training set and the resolution selection training set to obtain a target model; the prediction accuracy of the target model is evaluated on the target task test set, and optimization of the multi-modal large language model is completed. According to the technical scheme, the optimal resolution is adaptively selected for reasoning through a two-stage image coding mechanism, so that the visual perception capability of the model is remarkably improved.

The invention discloses a robot fine operation control system based on a diffusion model and 3D vision, particularly relates to the technical field of industrial robot fine operation control, and aims to solve the problem that when an existing industrial robot executes fine operations such as bottle cap screwing and connector plugging in and out in a complex three-dimensional operation scene, the operation efficiency is high. The problems that three-dimensional perception is unstable, the action resolution is fixed, space and contact constraints are difficult to meet, and the task success rate and safety are insufficient are solved. The method comprises the following steps: constructing a voxel feature field updated along with time under a self-body coordinate system, generating interaction complexity and a physical constraint field based on task coding, and linking adaptive resolution and action granularity scheduling, diffusion trajectory generation and closed-loop execution correction to uniformly map environmental geometry, contact conditions and task stages into tail end trajectory control, so as to realize real-time tracking control. Therefore, fine operations such as bottle cap screwing, connector inserting and pulling and flexible object folding can still be stably completed under complex shielding and contact constraint, and the effects of safety and task success rate are both considered.

A railway roadbed disease diagnosis method based on a convolutional neural network comprises the following steps: acquiring vibration acceleration signals of a train and a railway roadbed, and performing data cleaning on the acquired vibration signals; signal preprocessing, time-frequency domain transformation and feature vector extraction, convolutional neural network-based model training and feature extraction, feature classification and disease recognition, and real-time disease detection and location positioning are realized. According to the invention, a modern signal processing technology and a deep learning algorithm are combined to realize efficient and automatic detection of railway roadbed diseases; signal segmentation, time-frequency domain analysis, image segmentation and convolutional neural network technologies are adopted, a self-adaptive resolution adjustment mechanism is achieved, accuracy and high efficiency of signal feature capture and disease diagnosis are achieved, and the nonlinear distortion problem of multi-band signals can be effectively solved through signal segmentation processing and summarizing fusion.

A mechanism is described for facilitating adaptive resolution and viewpoint-prediction for immersive media in computing environments. An apparatus of embodiments, as described herein, includes one or more processors to receive viewing positions associated with a user with respect to a display, and analyze relevance of media contents based on the viewing positions, where the media content includes immersive videos of scenes captured by one or more cameras. The one or more processors are further to predict portions of the media contents as relevant portions based on the viewing positions and transmit the relevant portions to be rendered and displayed.

The invention relates to the technical field of Internet of Things and multimedia communication, in particular to a multimedia equipment centralized control method based on the Internet of Things, which comprises the following steps: realizing heterogeneous equipment access through a gateway dynamic analysis protocol of the Internet of Things; collecting multi-source data by utilizing an edge computing node, and detecting anomaly through Kalman filtering fusion and an isolated forest model; a dynamic control strategy is generated based on an LSTM neural network and a scene rule base, and synchronous switching of audio and video streams and self-adaptive adjustment of resolution are realized; control delay is guaranteed through hierarchical caching and a time-sensitive network protocol stack; and multi-terminal control is supported, and hierarchical authority management is implemented through a role authority matrix. According to the invention, the problems of difficult compatibility, insufficient control real-time performance and extensive safety management of heterogeneous equipment are solved, and the cooperation efficiency, response speed and use safety of multimedia equipment are effectively improved.

An X-ray detector comprises a detector panel (100) comprising a surface with a plurality of detector pixels, wherein the detector pixels are configured to convert received X-rays into electric signals representative of values indicative of the received X-rays. A data interface (308) transmits images having a fixed number of output pixels. A control unit controls to set (401) a region of interest comprising at least a subset of the plurality of detector pixels, wherein a number of detector pixels in the region of interest is independent of the number of output pixels. The control unit maps (403) the region of interest onto an output image having the fixed number of output pixels. The control unit resamples (404) the detector pixels within the region of interest to obtain values for the output pixels of the output image, and transmits (405) the output image via the data interface.

The invention discloses an adaptive vertical resolution enhancement method based on a digital oscilloscope, and the method comprises the steps: constructing a coefficient table composed of different types of prototype low-pass filter coefficients and corresponding different enhancement resolution digits and index values through designing different types of low-pass filter time domain coefficients; then sampling input data in real time, estimating an index value and a frequency shift point number of a resolution enhancement digit corresponding to a sampling signal through a library function in an upper computer, calling a corresponding prototype low-pass filter coefficient from a coefficient table according to the index value, issuing the prototype low-pass filter coefficient to the FPGA, and then generating a target band-pass filter in the FPGA by combining the frequency shift point number; and finally, performing frequency domain filtering to obtain an output signal after adaptive resolution enhancement so as to realize resolution enhancement.

A method includes receiving a reference frame, determining a scaling constant for a current block, determining a scaled reference block using the reference frame and the scaling constant, determining a scaled prediction block using the scaled reference block, and reconstructing pixel data of the current block using the scaled prediction block. Related apparatus, systems, techniques, and articles are also described.

The invention relates to the technical field of electrical digital data processing, and discloses an intelligent LED display screen connection method, which comprises the following steps of: installing an LED box body, dynamically generating a box body connection topological graph according to position information and resolution parameters of the LED box body, generating display data through a self-adaptive resolution matching algorithm, and displaying the display data in the box body connection topological graph. The display image is previewed and changed through a visualization method, the data integrity is verified through a redundancy check mechanism, and the LED boxes display the image content cooperatively. According to the method, the traditional screen connection mode of the LED display screen is improved, the automation degree is improved, the early-stage labor cost is reduced, and the screen connection efficiency and the display quality of the LED display screen are remarkably improved.

This disclosure signaling of motion vector or motion vector difference dependent on whether magnitude-dependent adaptive motion vector difference pixel resolution is employed or not in video encoding and decoding. An example method for processing a video block of a video stream is disclosed. The method may include receiving the video stream; determining that the video block is inter-coded based on a prediction block and a motion vector (MV), wherein the MV is to be derived from a reference motion vector (RMV) and a motion vector difference (MVD) for the video block; extracting or deriving, from the video stream, a data item associated with at least one of the RMV or the MVD.

A method, device, and non-transitory computer-readable medium for decoding an encoded video bitstream using at least one processor, including, obtaining from the encoded video bitstream a first flag indicating that reference picture resampling is enabled for coded video sequence (CVS); determining a value of a second flag indicating whether a picture resolution is changed in the CVS; based on the value of the second flag indicating that the picture resolution is changed in the CVS, decoding the CVS using the reference picture resampling for spatial scalability and adaptive resolution change (ARC); and based on the value of the second flag indicating that the picture resolution is not changed in the CVS, decoding the CVS using the reference picture resampling for the spatial scalability, and without using the reference picture resampling for the ARC.

A video decoding apparatus and a method for adaptively setting a resolution are disclosed. According to one embodiment of the present invention, a method for adaptively setting a resolution on a per-picture basis comprises the steps of: decoding maximum resolution information from a bitstream; decoding, from the bitstream, resolution information about a current picture; and setting the resolution of the current picture on the basis of the maximum resolution information or the resolution information, wherein the resolution information has a size less than or equal to that of the maximum resolution information.

The invention particularly relates to a hierarchical search three-dimensional airspace path planning method and system based on an adaptive resolution grid. The method comprises the following steps: constructing a corresponding multi-resolution grid model based on a three-dimensional grid map; global path planning is carried out on the coarse resolution grids, coarse paths of all coarse grid nodes are determined, and a coarse path sequence is constructed; performing non-overlapping segmentation on the coarse path sequence; planning local windows matched with the fine-resolution grids, and determining window entrance and exit points corresponding to the local windows; performing obstacle recognition on each local window based on the fine resolution grid, calling a corresponding path generation strategy according to an obstacle recognition result of the local window, and determining a local refined sub-path corresponding to each local window; and splicing the path information of each local window in sequence to obtain a path planning result. According to the method, efficient and reliable path planning of a large-scale and complex three-dimensional airspace environment can be realized.

The application discloses a segmented adaptive Fourier transform analysis method of power quality disturbance. The method firstly collects power quality disturbance signals in a power grid and performs Fourier transform, acquires peak envelope points in a frequency spectrum through a maximum envelope method to dynamically select characteristic frequency points and calculate frequency spectrum segmentation points; subsequently, a composite objective function is used to optimize Gaussian window parameters for each segment; finally, the signal is analyzed by using the optimized window function to obtain time-frequency results with adaptive resolution. The application allocates higher time resolution to strong time-varying components and higher frequency resolution to near steady-state components through segmented adaptive resolution and a composite objective function optimization strategy, effectively avoids the spectrum overlap and energy diffusion problems that are prone to occur in traditional methods, improves the accuracy and robustness of disturbance feature extraction, and can be widely applied to power grid fault diagnosis, disturbance identification and early warning.

The invention provides a distributed deployment method and system for an unmanned aerial vehicle three-dimensional wireless communication network, and relates to the field of unmanned aerial vehicle communication networks, and the method comprises the steps: obtaining the geographic information of an inspection region, constructing a three-dimensional terrain matrix, and carrying out the self-adaptive resolution voxelization; based on a voxelization model and unmanned aerial vehicle communication parameters, considering path loss and obstacle penetration loss, and constructing a three-dimensional communication quality potential field; designing an anti-collision repulsion potential field and a communication blind area gravitational potential field, and obtaining a total potential field gradient of unmanned aerial vehicle inspection; based on the real-time information, updating the three-dimensional position of each unmanned aerial vehicle in a distributed manner by adopting a gradient descent method; determining the responsibility coverage area of each unmanned aerial vehicle according to the updated position, and cooperatively determining the beam direction and the beam width; and defining a network coverage efficiency and communication quality balance index, and dynamically adjusting a learning rate to complete real-time online optimization. According to the invention, autonomous deployment and optimization of the unmanned aerial vehicle communication network can be realized, and communication coverage efficiency and quality balance are improved.

The invention relates to the technical field of robot environment perception, in particular to a real-time ground segmentation method and system. The method comprises the following steps: receiving and preprocessing laser radar point cloud data, and dividing the data into a plurality of concentric annular areas; carrying out adaptive ground fitting on each area and processing the multilayer structure through vertical plane fitting to obtain an initial ground point set; correcting an under-segmentation region of the current frame by using a time sequence backspacing mechanism and updating parameters; constructing an elevation map based on the initial point set, and obtaining a fine ground point set through adaptive resolution CSF simulation; and carrying out confidence evaluation on the two types of point sets, and generating a final result through multi-criterion fusion. Accurate segmentation of the complex terrain is realized, and robustness and real-time performance of a dynamic environment are improved.

The application discloses a serial port screen GUI adaptive resolution rendering method and system, and belongs to the technical field of human-computer interaction. The method comprises the following steps: segmenting string data to obtain at least one data segment, and generating a unique sequence identifier; embedding the sequence identifier, the total length of the string data and the effective data length of the current segment in the frame structure of each data segment based on the data frame format limitation of the bus protocol to obtain a data frame carrying segment metadata; sequentially sending the data frame through the bus; dynamically allocating a buffer according to the sequence identifier and the total length information at the receiving end, and recombining each data segment according to the sending order according to the sequence identifier, obtaining display instructions after recombination and performing a rendering operation. Through the segment transmission and metadata identification mechanism, the application breaks through the limitation of the bus protocol on the length of single-frame data, realizes reliable transmission and accurate recombination of large-capacity string data, and improves the stability and adaptability of serial port screen rendering.

The invention relates to a self-adaptive display method for post-processing resolution, a three-dimensional scanning system and a storage medium, and the method comprises the steps: transmitting scanning data obtained through the scanning of a three-dimensional scanning device to a processor; responding to the operation of the user on the interactive interface, and generating an adjustment instruction of the self-adaptive resolution; transmitting the adjustment instruction to a processor; in the interactive interface, displaying target point cloud data returned from the processor based on the adjustment instruction; the target point cloud data is obtained by performing fusion processing on the scanning data at least according to the first target resolution and the second target resolution; the first target resolution and the second target resolution are obtained by performing adaptive adjustment on a preset second resolution under the adjustment instruction; the problem that the use experience of a user is affected in the related technology is solved. And the processor adaptively adjusts the first target resolution under the adjustment instruction, so that the multi-resolution fused high-quality target point cloud data is displayed in the interactive interface, and the user experience is optimized.

The invention discloses a flatness error hybrid iteration identification method based on an on-machine measurement point cloud, and relates to the field of precision manufacturing and digital measurement, and the method comprises the steps: collecting three-dimensional point cloud data of the surface of a workpiece through line laser scanning, carrying out the normalization modeling of the three-dimensional point cloud data, and carrying out the mapping through employing a self-adaptive resolution; performing three-dimensional convex polyhedron iterative optimization on the processed point cloud data, and quickly screening and eliminating redundant points; carrying out dimension reduction processing on the screened data from three-dimensional dimension reduction to two-dimensional dimension reduction, and further compressing the data scale by adopting two-dimensional convex polygon iterative optimization; and introducing a key point control strategy based on a minimum region criterion, rapidly solving a flatness error, and outputting a flatness error identification result. According to the method, a mixed iteration solving framework combining convex optimization and key point control is constructed, the point cloud data scale and calculation complexity are effectively reduced, rapid processing of large-scale point cloud data is achieved, and the strict requirements of online measurement for real-time performance and rhythm are met.

The invention discloses a self-adaptive green screen image matting management system and method based on artificial intelligence, and relates to the technical field of neural networks, and the method comprises the steps: collecting multi-source data of a green screen site through different sensors, and constructing an end-to-end self-adaptive image matting neural network; processing the multi-source data by using an encoder to output features of different dimensions, and fusing the three paths of features by using a self-adaptive fusion weight to output a comprehensive feature; constructing a decoder by using a U-Net structure, outputting three predicted values, and correcting the foreground by using the three predicted values to output a final foreground; constructing a self-adaptive resolution adjustment mechanism, and dynamically adjusting the down-sampling proportion of the image; setting a physical constraint to separate a foreground from a background, and constructing an optical flow guided time domain smoothing mechanism to carry out refined filtering on an image edge region; and inputting the green screen multi-source data into the reasoning assembly line to output an image matting result, and performing quality evaluation and feedback updating on the image matting result.

The invention belongs to the technical field of deep learning rotary machine fault diagnosis, and particularly relates to a rotary machine multi-modal fault diagnosis method based on a multi-level cross attention fusion network, which comprises the following steps of: adopting a time-frequency transformation method of kurtosis screening based on variational mode decomposition and constant Q non-stationary Gabor; original signals from different sensors are respectively converted into time-frequency diagrams with adaptive resolution; constructing a fusion adaptive multi-branch attention module, and comprehensively and finely extracting fault features through a parallel multi-scale structure and a double attention mechanism; and constructing a multi-level fusion strategy and a final fusion module based on cross attention, carrying out dynamic and adaptive feature fusion and refining on the extracted fault features from a shallow layer to a deep layer, and outputting final feature representation with high discrimination. According to the method, complementary information from different sensors is effectively fused, so that the accuracy and reliability of fault diagnosis are remarkably improved.