48 results about "Error map" patented technology

The invention discloses an environment gas leakage intelligent detection method based on big data analysis. The method comprises the following steps: collecting monitoring data and constructing a sensor network space-time diagram fused with meteorological conditions; predicting the background concentration of each node by using a sequence decomposition attention model; calculating a concentration residual error and constructing a space-time residual error map; extracting an abnormal feature vector through a physical information enhanced graph attention network; calculating an abnormal score in combination with the normal mode memory bank and generating a leakage source probability distribution diagram; when the conditions are met, calling a computational fluid dynamics model to simulate a theoretical concentration field; and determining a leakage event through spatial correlation analysis. According to the invention, the false alarm rate of gas leakage detection in an open environment is obviously reduced, and the leakage source positioning precision is improved.

The invention discloses a spatial data management method and system based on digital twinning, particularly relates to the field of spatial information processing, and is used for solving the problems that a digital twinning three-dimensional model is difficult to locate defect causes in multi-source observation updating and repair is non-traceable. A target three-dimensional model is divided into surface patch units and a contribution index table is established by constructing an alignment result packet unified observation fragment set and a projection relation, a residual image is generated based on multi-view texture alignment to position a defect candidate area, a boundary projection dislocation pixel potential and a dynamic shielding coverage ratio are calculated to obtain a defect attribution result, and a defect attribution result is obtained. And performing local grid reconstruction or candidate fragment screening according to a defect attribution result, recalculating local textures to generate a repair model, updating an empirical distribution library in combination with projection re-inspection and boundary continuity inspection, and outputting a tracing record, so that integrated closed-loop treatment of defect positioning, attribution, repair and re-inspection is realized, and the consistency and credibility of the model are improved.

The invention relates to the technical field of data management, and discloses a time-space omics experiment report generation method and device, equipment and a storage medium. The method comprises the following steps: in response to an experiment report generation instruction for a target space-time omics experiment, determining an experiment report template corresponding to the target space-time omics experiment; according to the experiment report template, reading target experiment data and an experiment image set in an experiment information storage directory corresponding to the target space-time omics experiment; scoring each experimental image in the experimental image set, and determining a target experimental image according to a scoring result; and generating an experiment report corresponding to the target space-time omics experiment according to the target experiment data, the target experiment image and the experiment report template. By automatically generating the experimental report of the space-time omics, the problems of data input errors, picture omission and the like of the experimental report caused by negligence in manual operation are avoided, and the accuracy and the integrity of the experimental report of the space-time omics are improved.

The invention provides an image forgery detection method and system based on asymmetric anchoring of a multi-modal large model, and belongs to the technical field of image detection processing. Truth value anchor point features and to-be-optimized intermediate features of an input image are extracted; an asymmetric strategy is executed based on the authenticity category of the image, and anchoring alignment loss is introduced only for the real image to anchor real feature distribution; extracting global semantic features, and mapping the global semantic features into the authenticity category probability of the image through a classification projection head; and generating a space alignment error graph by using the deviation between the intermediate feature to be optimized and the true value anchor point feature, injecting the space alignment error graph into a positioning decoder, and generating a forged region mask by combining the output feature of the positioning encoder. According to the method, a true value anchor point is redefined, and an asymmetric anchoring mechanism is utilized to force real image features to return to real world priori, so that the problem of characterization drift is solved; and generating a spatial error graph by using the feature alignment deviation, thereby realizing stable learning of forged clues and realizing positioning of an accurate tampering region.

To quickly specify the cause of a failure related to a controller and a system composed of a plurality of controllers on the basis of a log.SOLUTION: The map generation unit 103 generates, for each of the plurality of logs acquired by the log acquisition unit 102, a log map including the plurality of acquired logs, in which the type of the log and the code of the log are associated with the coordinates of the bitmap data to form a bit of the bitmap data, and the importance generated by the importance generation unit 101 is used as color information of the bit. The estimation unit 105 estimates an abnormality in control by the controller 121 based on the result of pattern matching between the log map generated by the map generation unit 103 and the error map stored in the error storage unit 104.SELECTED DRAWING: Figure 1

The invention belongs to the technical field of unmanned aerial vehicle take-off and landing guidance, and discloses an unmanned aerial vehicle take-off and landing guidance radar data correction method based on an error map. Comprising the following steps: S1, acquiring a reference data stream of the airborne RTK / INS integrated navigation system; an unmanned aerial vehicle take-off and landing guide radar data stream is acquired; obtaining an error set of the reference data stream and the radar data stream; s2, establishing an error map according to the error set; generating a three-dimensional error distribution diagram of the unmanned aerial vehicle take-off and landing guide radar in the airport airspace according to the error map And S3, integrating the three-dimensional error distribution map as a lookup table into an adaptive filter based on an aircraft kinematics model, performing real-time table lookup according to the current estimated position of the aircraft to obtain an expected system deviation, and performing compensation in an updating step.

The present invention relates to a computer-implemented method 1000 for identifying patterns (23) that correlate with correct image classifications (52) and incorrect image classifications (52) using a scene graph (12). [Solution] For a set of images (11), this method obtains a scene graph (12) from the images, classifies the images using a pre-trained classifier (5), groups the scene graphs (12') according to whether the classification is correct, extracts representative subgraphs within each group, thereby revealing patterns associated with the classification (52).

The present invention is for, in applying a target object exploration according to a RPA scenario based on a previous UI screen to the current UI screen, if a specific target object cannot be searched or an error occurs due to a change on a target app / web at the current time point, automatically extracting information on the most suitable corresponding object on the current UI screen, thereby automatically recovering from the target object exploration execution failure of the previous scenario. A method of the present invention may comprise: an error recognition step for detecting the occurrence of an error resulting from the application, to the current UI screen, of a target object exploration based on object exploration information of a RPA scenario created on the basis of a previous UI screen; an image information LLM inquiry step for generating a first prompt including a first image including at least a portion of the previous UI screen including a target object, and a second image including at least a portion of the current UI screen, and instructing a generative AI to return information of an object corresponding to the target object on the current UI screen; and an object exploration step for performing target object exploration on the current UI screen.

A method and apparatus for adaptive machining of freeform surfaces, the method comprising: acquiring a CAD model of a freeform surface workpiece; generating a machining path and measurement information for a blank workpiece based on the CAD model; sending measurement control commands to a coordinate measuring machine (CMM) based on the measurement information; measuring the blank workpiece clamped on a CNC machine tool based on the machining path; and obtaining coordinate data P for each target measurement point. me According to the CAD model and P me The machining accuracy of the blank workpiece is judged. When the machining accuracy is less than a preset threshold, an ideal semi-finished CAD model of the freeform surface workpiece is obtained. A mirror M is constructed between the CAD model and the ideal semi-finished CAD model. m Based on P me and M m Get each P me Corresponding reflection point coordinate data P com Based on all P com Obtaining an error map and using it as a finishing path for finishing workpieces can ensure machining accuracy, improve machining efficiency, and reduce machining costs during the machining process.

The application relates to the technical field of decoding, and provides a decoding method, a decoding device, electronic equipment and a storage medium, wherein the method comprises the following steps: determining an error pattern of each check equation in a check equation set based on a hard decision result, determining the modulus of a bit position in a received sequence, determining a bit flip sequence corresponding to the received sequence based on the modulus of the bit position and the error pattern, determining an original code word estimation based on the bit flip sequence and the hard decision result, and determining whether the original code word estimation satisfies all check equations in the check equation set. In the method, the modulus of the bit position provides reliability information of each bit position, and the error pattern is used for reflecting the inconsistency between the received sequence and a check matrix, so that the decoding algorithm can correct errors in a targeted manner; in the decoding process, decoding based on the two can more accurately reflect the degree of influence of each information on noise, thereby reducing the bit error rate of the final decoding result and guaranteeing the accuracy and integrity of communication data.

A method for decoding an image according to one embodiment may comprise the steps of: determining a class of the current block on the basis of the amount of change in sample value for each sample in the current block; determining, on the basis of the class of the current block, a multi-channel kernel to be applied to the current block; determining a feature vector for the current sample by applying each of a plurality of filter kernels included in the multi-channel kernel to the current sample in the current block; acquiring an expected error mapped by the feature vector from a predefined data set; and filtering the current sample by using the expected error.

The invention discloses a no-reference image quality evaluation method based on a prediction error graph, which is suitable for the field of image processing, realizes the evaluation of image quality through a deep learning model, and comprises the following steps: constructing a prediction error graph pre-training model based on a Transform encoder and decoder structure; inputting the distorted image into a prediction error graph pre-training model to generate a corresponding prediction error graph: constructing a stepped feature extraction network based on decomposed large kernel convolution, performing layer-by-layer feature extraction on the distorted image, obtaining multi-scale features, and aggregating the multi-scale features to form global features; carrying out element-by-element fusion on the extracted image features and the features of the prediction error graph; and finally, the fused features are mapped into image quality scores. According to the method, the image distortion region and the degradation mode thereof are described by introducing the prediction error graph, and the prediction error graph is combined with the multi-scale features, so that the model can fully utilize the image degradation information, and the accuracy and the stability of no-reference image quality evaluation are improved.

The present disclosure is directed to correcting toolpath errors in ultra-precision machining of optical parts and mold inserts. A method includes machining a known optical surface to serve as a reference surface and capturing surface error data of the known optical surface using a metrology instrument. The surface error data may include an error map representing deviations from an intended design of the known optical surface. The method also includes smoothing the error map to reduce noise and highlight deviations, thereby creating a smoothed error map; deriving incident angles at which a cutting tool interacts with the known optical surface. The method also includes converting the smoothed error map into a derived angle space corresponding to the incident angles. The method also includes generating a corrected toolpath based on the derived angle space and applying the corrected toolpath to the machining equipment to correct the surface deviations.

This invention discloses a method and system for processing multi-layer thickness images in UV printing. The method includes acquiring the target thickness intention, outputting a three-dimensional joint instruction tensor of droplet volume, droplet temperature, and UV light intensity via a generative adversarial network (GAN), and then performing multi-physics collaborative printing based on this tensor. At the pixel level, the method independently controls the droplet temperature of each nozzle and the UV light intensity of each pixel, achieving differentiated modulation such as high stacking, tiling, and edge barrier walls. An error map is obtained by measuring the actual thickness in situ and calculating the expected thickness using a differentiable physical proxy model. This error map is then processed through a three-layer closed loop to achieve real-time correction, GAN update, and fine-tuning of the tactile mapping model. This invention significantly improves thickness control accuracy, substrate adaptability, and printing efficiency, and can be applied to various scenarios such as 3D relief, micro-optical components, functionally graded materials, and 4D self-folding.

The embodiment of the invention provides a fault processing method and device, equipment, a medium and a program product, and the method comprises the steps: matching fault text description information with error description information corresponding to each error category in a multi-modal knowledge base, and determining a first error category group; processing the fault multimedia file to obtain a fault picture; respectively matching each fault picture and the image-text interpretation text information corresponding to each fault picture with an error picture and error description information corresponding to each error category in the multi-modal knowledge base, and determining a second error category group; determining a target error category based on the matching degree corresponding to each error category in the first error category group and the matching degree corresponding to each error category in the second error category group; and determining and pushing fault processing information based on the target error category. By applying the technical scheme provided by the embodiment of the invention, the fault response period can be shortened, and the fault processing efficiency can be improved.

A method, computer system, and program product facilitate identification of error image labels in training data. The method comprises: evenly dividing a training dataset into N subsets, where the training dataset includes M data items each comprising a pair of image and its original image label; training a prediction model to label images by respectively using each of the N subsets as training data to generate N respective trained prediction models; respectively using each of the N trained prediction models trained by using one of the N subsets as training data to label the images in other N−1 subsets of the N subsets to generate N−1 prediction labels for each of the M images in the training dataset. For each image in the M data items, whether the original image label of the image is a potential error image label is based on the N−1 prediction labels of the image.

The application discloses a night image enhancement method and system based on uncertainty guidance Mamba, and the method comprises the following steps: acquiring a low-light image; introducing a generation subnetwork and a restoration subnetwork to construct a UGMamba network model; and performing image restoration processing on the low-light image based on the UGMamba network model to obtain an enhanced image. The uncertainty guidance of the restoration error map based on joint learning is used to improve the enhancement effect of the image. The application can be widely applied to the technical field of image processing.

The invention belongs to the field of optical element defect detection, and particularly discloses a vision-based optical element subsurface defect detection method and system, and the method comprises the steps: controlling the change of an incident angle and a polarization angle, collecting an image of a to-be-detected region of the same optical element, and obtaining a multi-view combined view set; registration and illumination normalization are carried out on the view set, and geometric offset and brightness interference are eliminated; the method comprises the following steps: constructing a neural network which takes a Vision Transform as a trunk and is embedded into a physical consistency decoupling module, and enabling the module to estimate a local polarization field and calculate spatial differential characteristics so as to generate a physical consistency scoring graph; and inputting a to-be-tested view into the trained model, obtaining a target view reconstruction result and a scoring graph, calculating a prediction error graph, fusing the prediction error graph with the scoring graph, and generating a defect probability heat graph. Defect labeling is not needed, weak defect signals can be accurately captured, and the detection precision and generalization ability are improved.

A Coordinate Measuring Machine (CMM) system comprising a CMM, a rotary table, and a rotation arrangement, wherein the CMM system is configured to be calibrated by determining the 6 dof pose of a jig, and to a method for calibrating. The jig may be mounted such that a current pose of the jig is associated with a current pose of the rotary table with respect to the CMM. The rotary table may be moved into multiple poses, and the 6 dof pose of the jig is measured for each of the multiple poses of the rotary table. An error map is generated, based on the angles associated with the poses of the rotary table, and is used to generate a coordinate transformation from the CMM coordinate system to the part coordinate system, which is associated with the rotary table, based on the error map.

The invention discloses a federated learning dynamic backdoor defense method facing gradient inversion attack, and belongs to the technical field of federated learning and data security. A trigger is introduced in a local training stage of a client to generate a trigger sample, and an uploading gradient is generated by combining a joint optimization target of adversarial training and dynamic weight weighting of a discriminator, so that the uploading gradient is difficult to distinguish in statistics and a misleading direction is implanted in a gradient space; therefore, even if an attacker obtains the uploading gradient at the server side and carries out gradient matching inversion, the uploading gradient can only be reconstructed into a preset error image or a noise image instead of a real sample, the reconstruction availability is reduced from the source, and the main task performance is kept as much as possible. According to the method, an attacker is induced to reconstruct a preset error image or noise instead of a real sample when intercepting the gradient at a server side and performing gradient matching inversion, so that the effect of defending the gradient inversion attack is achieved.

The application discloses a DRC test pattern generation method and device, electronic equipment and a storage medium. The DRC test pattern generation method comprises the following steps: receiving a DRC test pattern generation request, wherein the DRC test pattern generation request carries a correct pattern quantity and an error pattern quantity; obtaining layout design rule information and corresponding hierarchical configuration information, wherein the hierarchical configuration information comprises process layer configuration parameter information set according to a process type; analyzing each parameter information corresponding to each rule in the layout design rule information and the process layer configuration parameter information in the hierarchical configuration information; based on each parameter information corresponding to each rule, the process layer configuration parameter information associated with each rule, the correct pattern quantity and the error pattern quantity, respectively generating formatted parameter information corresponding to each rule; and generating a corresponding quantity of correct patterns and error patterns corresponding to each rule according to the formatted parameter information corresponding to each rule.

A system includes a hardware processor, a memory storing software code, and a machine learning (ML) model-based video frame interpolator. The hardware processor executes the software code to provide first and second frames of a video sequence including a plurality of frames, respective binary masks for the first and second frames, and optionally an intermediate frame of the video sequence between the first and second frames and a binary mask for the intermediate frame, as interpolation inputs to the ML model-based video frame interpolator. The hardware processor further executes the software code to generate, using the ML model-based video frame interpolator and the interpolation inputs, an interpolated frame and an error map for the interpolated frame, wherein generating the interpolated frame and the error map includes a cross-backward warping of respective latent feature representations of each of the plurality of frames.

The invention relates to the technical field of decoding, and provides a decoding method and device, electronic equipment and a storage medium, and the method comprises the steps: determining an error pattern of each verification equation in a verification equation set based on a hard decision result, determining a modulus of a bit in a receiving sequence, and obtaining a decoding result based on the modulus of the bit and the error pattern; and determining a bit flipping sequence corresponding to the receiving sequence, determining original codeword estimation based on the bit flipping sequence and the hard decision result, and determining whether the original codeword estimation meets all the check equations in the check equation set. In the method, the module of the bit provides the reliability information of each bit, and the error pattern is used for reflecting the inconsistency between the receiving sequence and the check matrix, so that the decoding algorithm can correct errors in a targeted manner, and in the decoding process, the decoding based on the two can more accurately reflect the degree of noise influence on each piece of information, so that the decoding accuracy is improved. Therefore, the bit error rate of a final decoding result is reduced, and the accuracy and integrity of communication data are guaranteed.

This application provides a decoding method and apparatus. First, the received sequence and generator matrix are preprocessed. Then, error patterns are guessed from the information bits and codeword sequences. Finally, the guessed error patterns are post-processed by comparing Euclidean distances based on checksums. The optimal decoded sequence is output as the decoding result. This application reduces the algorithm's complexity while maintaining effective decoding performance. Furthermore, by setting a maximum number of iterations, blind lookups are prevented, reducing the number of queries during the decoding process and effectively lowering the decoding complexity.

A substrate processing system, includes: a measurement device configured to measure errors of substrates after the substrates are subjected to processing of a first process by a first processing apparatus; a transfer device configured to transfer the substrates between apparatuses including the first processing apparatus, a second processing apparatus that performs processing of a second process after the first process, and the measurement device; a map creating device configured to create an error map indicating a distribution of the errors on the substrates; and an evaluation device configured to calculate an evaluation value indicating a degree of importance of the errors based on the error map, wherein the evaluation device instructs the transfer device on a transfer destination of the substrates subjected to the first process according to a determination result as to whether or not the evaluation value is equal to or larger than a predetermined first threshold value.

The application discloses a kind of based on the detection method of unattended learning of aluminum foil seal tightness. Including: S1, obtain aluminum foil seal infrared image;S2, to aluminum foil seal infrared image is carried out adaptive ROI region identification and extraction;S3, to the thermal imaging image of extraction is preprocessed and normal sample image is made into data set;S4, after pre-processing normal sample data set is input into multi-scale image reconstruction network model and is carried out model parameter training;S5, after pre-processing the image to be measured is input into the reconstruction model of training and is carried out image reconstruction, and output reconstruction image, and the residual error of the image to be measured and reconstruction image is calculated, and residual error map is obtained;S6, to residual error map is preprocessed, highlights defect part, whether infrared seal image exists defect is judged by abnormal score, if there is defect, the defect part is positioned.The application can be reconstructed and repaired to aluminum foil seal infrared image quickly and effectively, with higher detection precision.

This invention provides a physically constrained deformable medical image registration method based on force-coupled voxel interaction. The method includes acquiring fixed and moving images; constructing multi-scale feature maps from low to high resolution using a parameter-shared Siamese ResNet encoder; predicting the initial deformation field at the lowest resolution scale and accumulating it scale-by-scale; generating a voxel-level error map and modulating high-resolution features using a three-dimensional differentiable spatial transformation network; guiding a flow field estimation module to predict the deformation field at each scale; inputting the highest resolution accumulated deformation field into a physical elasticity propagation module to calculate the strain tensor of each voxel; and constructing a physical constraint loss function based on the strain tensor for iterative optimization. This achieves deformable medical image registration that is topologically protected, anatomically consistent, highly accurate, and biomechanically sound. This invention enhances the representation capability of spatial geometry, effectively avoiding local folding and tissue tearing, while improving clinical interpretability and registration stability.

The present application relates to the technical field of intelligent safety monitoring and industrial machine vision of coal mine, and provides a coal belt foreign matter detection system based on time sequence fusion and attention mechanism, which comprises: an image acquisition unit configured to acquire continuous multi-frame image sequences of the coal belt; an edge computing unit configured to perform feature alignment processing and generate a time sequence alignment feature body; a feature alignment error map is taken as a spatial attention guide input into a differential residual attention network to generate a foreign matter detection probability map; the edge computing unit is further configured to determine a foreign matter detection result based on the foreign matter detection probability map, and in the case that the foreign matter detection result indicates that there is foreign matter, send an alarm signal and foreign matter position information to an interactive terminal; and the interactive terminal is configured to display the foreign matter position information and the alarm signal; the present application solves the false alarm problem caused by coal block rolling in the traditional single-frame algorithm, and greatly improves the detection accuracy in the high-speed motion environment.

The application discloses a kind of under super-resolution scene's wiring diagram graphic element identification method and system, it is related to data processing technical field, the method includes: constructing and training graphic element inference model, based on iterative super-resolution reconstruction mechanism to the wiring diagram to be identified carries out iterative feature extraction, and it is combined condition and generates feature enhancement using adversarial network, generates graphic element feature set, the graphic element feature set is input to graphic element inference model and carries out graphic element identification, generates initial graphic element identification result, and it is combined graphic element consistency check mechanism and carries out identification check, obtains identification check result, according to the initial graphic element identification result is iteratively optimized according to the identification check result, generates accurate graphic element identification result, obtains the feedback dataset of accurate graphic element identification result, based on the feedback dataset graphic element inference model is optimized using feedback, to realize accurate identification of low-quality wiring diagram and accurate tracing and correction to error graphic element identification output.

The invention discloses a constellation design method and device for continuous phase modulation, and relates to the technical field of communication, and the method comprises the steps: carrying out the quantitative analysis of an error pattern of a modulation symbol of a continuous phase modulation system through Monte Carlo simulation, and determining an error probability matrix; determining a plurality of symmetric pairwise confusion metrics of each modulation symbol based on the error probability matrix, performing descending sorting, and determining a symbol pair error set of each modulation symbol; performing tagging mapping on each modulation symbol according to a symbol-tag relationship to obtain a corresponding symbol tag; according to the index sequence of the symbol labels, preset coding bits are distributed to the symbol labels associated with the maximum symmetric pairwise confusion measurement in the symbol pair error sets in sequence based on the minimum bit Hamming distance constraint, and the bit symbol mapping relation is determined in combination with the symbol-label relation. Based on the scheme, the constellation diagram of the CPM system is optimized through quantitative analysis of the symbol error pattern, and the transmission performance of the CPM system is improved.