74 results about "Error map" patented technology

The invention discloses a laser scanning detection method for gear tooth form errors of gear motors, and belongs to the technical field of industrial optical detection. The method comprises the following steps of: firstly, comprehensively acquiring high-density three-dimensional point cloud data of a pinwheel tooth profile by combining laser scanning and precise rotating motion; secondly, preprocessing the original point cloud, and performing high-precision automatic registration on the actually measured point cloud and the standard three-dimensional CAD theoretical model by using algorithms such as an iterative nearest point; and finally, generating a visual tooth profile error map by calculating the normal deviation between the measured data and the theoretical model point by point, and quantitatively evaluating error parameters. According to the scheme, high-efficiency, high-precision and all-shape nondestructive testing of the gear tooth error is realized, physical contact damage is avoided, and the quality control level and the production efficiency of the core part of the speed reducer are remarkably improved.

The invention discloses a spaceflight propulsion chamber additive manufacturing combustion channel wall thickness uniformity control method, which comprises the following steps: S1, establishing a three-dimensional geometric model and carrying out layered slicing to generate an initial processing control parameter set; s2, constructing a thermal echo spectrum before printing the first layer, and correcting control parameters of the first layer; s3, generating a wall thickness error graph of the current layer based on the data of the previous layer, and constructing a wall thickness error potential energy field graph; s4, constructing a target scanning density function, solving a two-dimensional Monge-Amprore equation to generate a path potential function, and generating a quasi potential streamline field; s5, inputting the data of the current layer into the wall thickness prediction neural network model, and outputting a wall thickness deviation prediction value of the next layer; s6, updating the wall thickness error graph of the next layer according to the wall thickness deviation predicted value, and dynamically adjusting the control parameters; and S7, circularly executing the steps S3 to S6 until the printing task is completed. According to the method, thermal response modeling and a neural network model are fused, and dynamic uniformity control over the wall thickness of the combustion channel is achieved.

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 present invention discloses an error correction method and system for FPGA, which relates to the technical field of data encoding and decoding, and includes: generating Galois field elements based on the received symbol vector and the primitive polynomial, and calculating the syndrome polynomial through 8-symbol parallel processing; calculating the coefficients of the error location polynomial through the BM iterative algorithm based on the syndrome polynomial; calculating the error location by parallel substituting 10 Galois field elements through the Chien search method based on the error location polynomial; establishing the evaluation polynomial and the error pattern calculation formula, and performing polynomial division calculation through the derivative of the error location polynomial to obtain the error pattern; performing error code judgment based on the error location and the error pattern, and correcting the error symbol through the XOR operation in the Galois field to obtain the updated symbol vector. The present application adopts a parallel pipeline design structure, which enables the FPGA to automatically and real-time correct the received error module data without retransmission, effectively improving the real-time performance and reliability of error correction.

A computer-implemented method for forming a dataset configured for learning a Convolutional Neural Network (CNN) architecture including an image feature extractor. It comprises providing pairs of images, each pair comprising a reference image and a respective denoised image. For each pair of images, the method provides the pair of images to a pre-trained CNN architecture similar to the one the formed dataset will be configured for. The method computes an error map representing a difference between a first normalized feature of the denoised image and a second normalized feature of the reference image, the first and second normalized features being the output of a same layer of the pre-trained CNN architecture and adds the respective denoised image and the error map to the dataset. This constitutes an improved solution with respect to forming a dataset for learning a CNN architecture to identify areas of degradation generated by a denoiser.

A method of calibrating a robotic arm which includes perform iterative eye-in-hand and robot calibration, using a calibrated end of arm camera with known intrinsic parameters and a static target, to obtain eye-in-hand transformations and robotic parameters. The method further uses robotic parameters to estimate pose of end of arm, for eye-to-hand calibration, to obtain eye-to-hand transformations and calculates final error compensation based on the robotic parameters, eye-to-hand transformations, and eye-in-hand transformations. In one embodiment, the method calculates a robot positioning error map function, the robot positioning error map function used to adjust movement parameters for the robotic arm.

Systems, apparatus, articles of manufacture, and methods to evaluate video quality based on trained neural networks are disclosed. An example apparatus disclosed herein obtains, using a trained neural network, target features corresponding to a target video, the target features based on one or more layers of the trained neural network. The example apparatus also obtains, using the trained neural network, reference features corresponding to a reference video, the reference features based on the one or more layers of the trained neural network, the reference video associated with the target video. The example apparatus further outputs a quality metric for the target video based on the target features, the reference features, and a set of weights. In some examples, the apparatus optionally outputs an error map for the target video.

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 present invention discloses a two-stage decoding method for algebraic codes, comprising: S1, performing hard decision based on a received sequence to obtain a hard decision sequence; S2, generating one or more test error pattern sequences based on the received sequence; S3, performing bitwise exclusive-OR on each error pattern sequence and the hard decision sequence to obtain a second-stage decoding input sequence; S4, determining whether second-stage algebraic code decoding is required for each second-stage decoding input sequence, and performing S5 for all input sequences requiring algebraic code decoding; S5, performing algebraic code decoding, returning a decoding status and a decoding result; and S6, selecting the optimal decoding result from the decoding output as the decoding output. The present invention can achieve performance close to the finite code length bound based on hard decision decoding when the list is small, achieving low-complexity and high-performance decoding. Furthermore, a specially introduced judgment skipping mechanism can significantly reduce the number of times the algebraic code decoder is called in the second stage, thereby significantly reducing decoding power consumption.

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.

The present invention provides a method and device for adaptive sampling of magnetic resonance signals based on deep learning, comprising: determining a sampling space, a sampling value threshold, and the number of sampling points for a single excitation; selecting an initial region to be sampled based on the number of sampling points; determining a 3D epi-intensity spectroscopy (EPI) sequence corresponding to a single excitation based on phase encoding of the sampling points within the region to be sampled; performing a single radiofrequency excitation based on the 3D EPI sequence to obtain a corresponding gradient echo signal; reconstructing an optimized dMRI image based on the gradient echo signal; inputting the optimized dMRI image into a trained full-sampling dMRI image prediction model to predict a full-sampling dMRI image and obtain a reconstructed image error map; obtaining a sampling value for each sampling point in the sampling space based on the reconstructed image error map; and determining a region to be sampled for the next sampling based on the sampling value of each sampling point and the sampling value threshold. This application further shortens scanning time and improves the accuracy of the generated dMRI images.

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 provides an intelligent customer service dialogue generation and optimization method and system based on a large model. According to the method, data such as repeated questions, manual rewriting, cursor tracks, text deletion and voice interruption of a user are collected, and timestamps are marked. And calculating an operation density value and an error frequency value to generate a corrected heat map. And defining a strength node and a triggering condition edge according to the dynamic error graph. The dynamic error graph is updated when data are newly added, and an adversarial sample is generated based on the characteristics of the dynamic error graph. And the large model response module is finely tuned through reinforcement learning negative feedback, optimization content is output, the updated atlas is fed back, and closed-loop iterative optimization is realized. According to the method, a dynamic error graph is constructed by utilizing user interaction data to generate an adversarial sample, a large model is driven to perform closed-loop optimization on customer service dialogue generation through reinforcement learning, and errors are accurately positioned and continuously improved.

The invention relates to the field of wear monitoring, in particular to a cross guide rail wear state monitoring method based on machine vision, which comprises the following steps: acquiring an initial image and a standard image of a cross guide rail, subtracting the standard image from the initial image to obtain an error image, performing binarization processing on the error image by using an Otsu threshold method to obtain a binary image, and calculating the wear state of the cross guide rail; taking an area formed by white pixel points in the binary image as an abnormal area; calculating a texture complex factor and a shape factor of the abnormal area, and normalizing the product of the texture complex factor and the shape factor to obtain a wear characteristic value; and extracting an edge line of the abnormal area, calculating a mean value of gradient amplitudes of pixel points of the edge line, and carrying out weighted summation on the wear characteristic value and the mean value of the gradient amplitudes of the pixel points of the edge line to obtain a wear index for judging whether the surface of the cross guide rail is worn or not. According to the invention, the accuracy of cross guide rail state monitoring is improved.

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 present invention discloses a fault-tolerant measurement method for quantum Hamming codes, which specifically comprises the following steps: step 1, encoding information bits to be transmitted and generating codewords of the quantum Hamming code; step 2, constructing a sequence for implementing fault-tolerant measurement; step 3, using the fault-tolerant measurement sequence constructed in step 2 to perform auxiliary measurement on each codeword in the quantum bit information of the quantum Hamming code in step 1; if an error occurs, forming an error pattern table according to the measurement results; step 4, correcting the measured errors according to the error pattern table in step 3, and handing the corrected quantum information bits to the next-level quantum device for information transmission and quantum computing. The method constructs a measurement sequence in an error correction process to generate a fault-tolerant measurement sequence, thereby overcoming the problem that internal errors and device errors cannot be processed during the error correction process, resulting in error accumulation and error increase, and improving the fault tolerance and measurement anti-interference capability of the quantum Hamming code.

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 computing system comprising a memory configured to store an artificial intelligence (AI) model and an image, and a computation engine executing one or more processors may be configured to perform the techniques for error-based explanations for AI behavior. The computation engine may execute the AI model to analyze the image to output a result. The AI model may, when analyzing the image to output the result, process, based on data indicative of the result, the image to assign an error score to each image feature extracted from the image, and obtain, based on the error scores, an error map. The AI model may next update, based on the error map and to obtain a first updated image, the image to visually indicate the error score assigned to each of the image features, and output one or more of the error scores, the error map, and the first updated image.

The present application proposes a video frame deletion forensics method based on image copy-move feature, which uses the similarity between ghost features in video frame deletion tampering residual error map and image copy-move tampering features to conduct video frame deletion forensics. First, a Multi-PyRes model is designed, and the model is pre-trained on an image copy-move tampering dataset to learn the features of copy-move tampering. Then, the pre-trained model parameters are migrated to the frame deletion detection task. The method extracts copy-move features from the saliency residual feature sequence map of the frame deletion video by introducing a constraint convolution layer that can learn copy-move features, and uses a residual pyramid convolution layer structure combined with a CBAM attention mechanism to extract copy-move tampering features at multiple scales, thereby realizing video frame deletion tampering detection. The present application extracts copy-move tampering features similar to ghost features of frame deletion tampering at multiple scales for image classification, which can realize video frame deletion tampering detection.

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.