30 results about "Correction technique" patented technology

The invention relates to the technical field of mining, and discloses a method for determining pre-splitting blasting parameters for mining blasting, which comprises the following steps of: 1, acquiring rock mass structure information of a blasting area through three-dimensional geological radar, laser radar and shallow seismic reflection data, and constructing a structural plane distribution map containing a soft and hard interbed and joint combination mode; 2, initial presplitting blasting parameters are generated based on the structural plane distribution map, wherein the initial presplitting blasting parameters comprise the hole distance, the linear charge density and the charge structure; according to the method, the technical scheme that the structural plane distribution map is constructed through multi-source geological data fusion and digital twinborn simulation dynamic correction is adopted, so that the technical effects that the influence of a complex structure combination on blasting energy transfer is accurately quantified, and the presplitting path control precision is remarkably improved are achieved; compared with the technical scheme depending on a static rock mass model or an empirical formula in the prior art, the defects of presplitting path offset and serious parameter mismatch caused by the fact that modeling is not carried out due to the dynamic effect of the structural plane are overcome.

The invention relates to the technical field of image pre-training, in particular to a training method and system of an image pre-training model and a storage medium. The method comprises the following steps: collecting an original training image, carrying out high-dimensional feature mapping and pyramid transformation to generate a multi-resolution feature body, carrying out texture definition judgment on the image based on the feature body, screening out a defective training image and a qualified training image, and for the defective training image, carrying out texture definition judgment on the qualified training image. A traceability correction technology is adopted to correct a defect correction training image, positive and negative contrast images are constructed to train a defect image pre-training model, meanwhile, weak disturbance transformation is performed on a qualified training image, a positive example pair before and after disturbance is generated to train a qualified image pre-training model, the two pre-training models are integrated into a collaborative sample stream, and the collaborative sample stream is extracted. And a high-performance integrated image pre-training model is obtained through integrated pre-training. According to the method, image quality grading and differential modeling are realized, the defect identification precision is improved, and the model generalization ability is enhanced.

Techniques facilitating error mitigation for quantum computing devices. In one example, a system can comprise a process that executes computer executable components stored in memory. The computer executable components comprise: an approximation component; a budget component; and an optimization component. The approximation component can generate an approximate decomposition of a quantum gate. The budget component can set a budget value (Cbudget) for a C-factor that is a metric for increase in variance of quasi-probability sampling. The optimization component can determine an optimal decomposition for the quantum gate as a function of Cbudget.

Systems and methods for dynamic error resolution in hybrid quantum-classical computing environments are disclosed. A system can obtain telemetry data corresponding to a processing job. The system can determine that an error resolution technique is to be applied to the quantum portion of the processing job. The system can select, for a quantum portion of the processing job, the error resolution technique from a plurality of error correction techniques and a plurality of error mitigation techniques. The system can update a hierarchical data structure representing the processing job as at least a set of nodes. The hierarchical data structure can be updated to include the error resolution technique in association with a respective node of the hierarchical data structure representing the quantum portion of the processing job.

The present description relates to an image sequence motion correction method and apparatus based on intensity correction optical flow registration. The method comprises: determining a reference image of each deformed image in a deformed-image sequence, and performing motion correction on the deformed image by means of adjusting the weight of a mechanical regularization term between the reference image and the deformed image, so as to obtain a displacement field of the deformed image relative to the reference image; on the basis of the displacement field and an intensity correction value of a finite element node, performing intensity correction on the reference image; adjusting the weight of the mechanical regularization term, performing motion correction on the deformed image, performing intensity correction on a first corrected reference image on the basis of an optimized displacement field, repeatedly executing the process until an image residual function converges, determining that the deformed image has been corrected, and recording the optimal solution of a weight value of the mechanical regularization term of the deformed image; and on the basis of the optimal solution of the weight value, determining the actual displacement of the deformed image. In the present description, the mechanical model behind physiological motion and an intensity correction technique are integrated into an image registration algorithm to reduce image noise, thereby improving the algorithm accuracy.

One or more systems, devices, computer program products and / or computer-implemented methods of use provided herein relate to an intelligent and automated system to solve quantum computing related problems. The computer-implemented system can comprise a memory that can store computer-executable components. The computer-implemented system can further comprise a processor that can execute the computer-executable components stored in the memory, wherein the computer-executable components can comprise a recommendation component that can employ a machine learning model to generate, based on an input, a recommendation comprising a combination of entities comprising, one or more quantum circuits, one or more algorithms, one or more quantum hardware units, one or more error mitigation or error correction techniques, and one or more quantum procedures, to solve a defined problem comprised in the input.

When x-ray fluorescence ("XRF") spectroscopy is utilized to classify materials transported on a moving conveyor belt, there is the possibility of the x-ray beam only partially irradiating the material piece, which can result in the capture of an inaccurate XRF spectrum needed to classify the material piece. This can lead to an improper (erroneous) classification and resultant sortation of material pieces (e.g., aluminum alloys). In a material handling system, the area of the intersections between the x-ray beam spots from an x-ray fluorescence system and the material pieces are measured and correspondingly used to correct the measured XRF spectrum associated with each material piece. The material pieces can then be sorted according to the corrected XRF spectrum.

The present disclosure relates to dividing image data obtained from a scan (e.g. MRI) of an object into two or more sets of data corresponding do unique motion patterns and / or motion sources. Each of the two or more sets of data can be corrected using an appropriate correction technique. One appropriate correction techniques includes generating kernels for each divided imaging dataset using center and adjacent slice information to correct for through-plane and in-plane artifacts.

Techniques are disclosed for detecting and correcting anomalous intervals in time series data for monitored devices. A system trains a machine learning model on a combination of actual data obtained from the monitoring device and fake data generated by adding noise to the actual data. The model predicts operational values ​​of the device at individual intervals in the time series data set. The system identifies anomalies in the time series data based on the difference between the predicted value and the actual value. If the difference between the predicted value generated by the machine learning model and the actual value exceeds a threshold, the system identifies the particular data point, such as a meter reading, as anomalous. The system ranks the anomalies and takes corrective action based on the ranking.

A method, system, and computer program product for improving accuracy and efficiency of quantum error mitigation / correction techniques. A model (e.g., noise model) of a quantum gate set (collection of quantum gates) that accounts for SPAM and gate errors is built by running learning circuits at different depths with different initial states and measurements to extract information about the quantum system. A gauge transformation is then applied to the quantum gate set to transform both the SPAM and gate errors together. A gauge transformation enables a different perspective of the quantum system while still preserving the underlying physics. A quantum error mitigation / correction technique is then tailored to avoid interpreting errors (e.g., SPAM, gate errors, etc.) inconsistently if treated individually using the transformed SPAM and gate errors thereby requiring fewer additional circuit runs for accurate estimations which significantly reduces the computational overhead associated with quantum error mitigation / correction.

The invention discloses a micro-morphology measuring and modeling method for a cutting edge of a light shielding ring of a light shielding cover, and belongs to the technical field of precision measurement and three-dimensional modeling. The method comprises the following steps: firstly, acquiring three-dimensional point cloud data of a cutting edge of a light blocking ring through a laser scanning confocal microscope; carrying out denoising processing on the data by adopting wavelet threshold denoising and five-point secondary smoothing filtering; the data precision is improved by combining clamp installation optimization and inclination correction technologies; data pruning is achieved through curvature analysis, and a cutting edge feature area is extracted; missing data are repaired by using cubic B-spline interpolation; and finally, a high-precision cutting edge model is established through trapezoid fitting, arc fitting and NURBS curved surface reconstruction methods. According to the method, the problem that the micro-morphology representation accuracy of the cutting edge of the light shielding ring of the light shielding cover is insufficient in the prior art is solved, the accuracy and efficiency of the morphology representation of the cutting edge are remarkably improved, and technical support is provided for the surface morphology design of a high-precision light shielding cover.

A time-interleaved ADC error parameter extraction and correction method relates to an error correction technique for an analog-to-digital converter (ADC). This application discloses a method for extracting and correcting error parameters in a time-interleaved ADC, which takes the time-interleaved ADC channel n as the basis or reference channel, and corrects other channels to match the channel n. The steps include: a. Converting a time domain signal output of each ADC channel into a frequency domain signal; b. Calculating one or more error parameters according to the frequency domain signal; c. Correcting the frequency domain signal in one or more different channels according to the error parameter(s); and d. Converting the corrected frequency domain signal into a (corresponding) corrected time domain signal.

This paper proposes a low-complexity, hardware-friendly technique for video decoding systems to mitigate striping artifacts while maintaining compression efficiency. Generally, the proposed technique consists of two stages: a striping detection stage and a destripping stage. The striping detection stage identifies potential striping artifact regions in the image / video based on gradient information and other data. The destripping stage applies correction techniques to these identified regions. In one implementation, the destripping modifies the filtering logic proposed by previous video decoding standards for other filtering applications to mitigate striping. When implemented on the AVI video decoding standard and the AOM Video Model (AVM) reference software, the proposed technique significantly improves subjective quality at a reasonable hardware implementation cost. The methods and implementations presented in this document can be beneficially applied to various image / video decoding standards and systems.

Techniques for performing speaker error correction are described. In some examples, speaker error correction is a post-processing task to be performed on aligned predicted words and predicted one or more speaker identities to jointly perform speaker identities error correction and at least inverse text normalization, wherein the post-processing at least includes: predicting word and speaker contextual features from the aligned predicted words and predicted one or more speaker identities using an encoder, and predicting, from the word and speaker contextual features, inverse text normalization of the aligned predicted words and corrected speaker identities using a decoder.

A cross-component-based correction technique for an intra predictor of a chroma block is disclosed. A video encoder or a video decoder may: generate first chroma prediction values for chroma samples of a chroma block by using an intra prediction technique; derive a cross-component model on the basis of luma prediction values for luma samples of a luma block corresponding to the chroma block and the first chroma prediction values; and generate second chroma prediction values for the chroma samples of the chroma block from reconstructed values for the luma samples by using the cross-component model. The video encoder or the video decoder may generate modified chroma prediction values for the chroma samples of the chroma block on the basis of a weighted average of the first chroma prediction values and the second chroma prediction values, and encode or decode the chroma block on the basis of the modified chroma prediction values.

This invention proposes a SAR ship target detection method based on external rectangular projection for generating and correcting tilted boxes. The OrpRPN sub-network in the two-stage target detection network uses external rectangular projection for decoding and convex quadrilateral correction techniques to generate high-quality tilted boxes, while the OrpReg sub-network corrects the tilted box positions. The parameters of the two-stage target detection network are iteratively updated using a smoothed L1 loss function and a KLD loss function. This invention avoids directly predicting the target's orientation angle by using the external rectangular projection representation method, effectively constraining the predicted tilted boxes and reducing the number of preset anchor points. The use of convex quadrilateral correction techniques to correct candidate boxes ensures correct orientation and removes redundant background information, thus improving the detection accuracy of SAR ship targets.

Techniques for positioning are disclosed. In an aspect, a positioning entity determines an expiration time for applying correction data to positioning measurements of one or more transmission points (TPs) (610), applies the correction data to one or more positioning measurements of one or more TPs obtained by a user equipment (UE) prior to expiration of the expiration time to determine one or more corrected positioning measurements (620), and determining an estimate of a position of the UE based at least in part on the one or more corrected positioning measurements (630).

This invention discloses a method and system for managing and analyzing medical report data in hematological diseases, belonging to the field of medical information processing technology. The system includes: S1, data acquisition and fingerprint generation; S2, time-series reconstruction and correction; S3, semantic parsing and standardization; S4, feature coupling and secure computation; and S5, report generation and audit updates. This invention utilizes a time-frequency domain dual-domain correction technique, which effectively eliminates periodic errors and fluctuations between different data sources and different acquisition time points, improving the accuracy and consistency of data processing. This correction method goes beyond traditional time alignment; it identifies and removes periodic interference in the signal through frequency domain analysis, ensuring time-series consistency between different sources. This ensures that even with multi-source data, the analysis results maintain high accuracy and reliability, avoiding erroneous diagnoses and recommendations due to data errors, thus providing a more accurate and reliable basis for clinical decision-making.

To provide a color shift correction technique in consideration of variation in frictional force between a photoreceptor and an intermediate transfer body.SOLUTION: The image forming apparatus includes an image forming unit configured to form an image based on a print job on a sheet by forming images with toners of different colors on a plurality of photoconductors that are rotationally driven, respectively, and transferring the images formed on the plurality of photoconductors to the sheet via an intermediate transfer member that is rotationally driven, a detection unit configured to detect the image formed on the intermediate transfer member, and a control unit configured to control the image forming unit to form adjustment images of the plurality of colors on the intermediate transfer member. And a control unit configured to perform correction processing of determining a correction value of a formation timing of an image of a first color among the plurality of colors on the photosensitive member based on a detection timing of the adjustment image of each of the plurality of colors by the detection unit, wherein the control unit changes the correction value depending on whether a first mode of forming a first additional image on the intermediate transfer member in addition to a user image is selected when the user image based on a print job is formed on a sheet.SELECTED DRAWING: Figure 5

Various examples provide depth image analysis and correction for stereo image machine learning systems and applications. A depth image anomaly identification stage of a depth image anomaly processor can perform functions to evaluate a depth image in order to identify anomalous segments where the values ​​of pixels predicted by a stereo depth perception model appear anomalous in terms of accuracy compared to other regions of the depth image. A correction stage adjusts the depth image based on the identified anomalous segments using one or more correction techniques that can compute estimates or predictions for depth values ​​within the identified anomalous segments. The correction can be based on detected structural characteristics within the spatial volume corresponding to the identified anomalous segments and / or on characteristics of local phenomena (e.g.,lighting, reflections, glare, etc.) are based on factors that have been identified as causing inaccuracies in depth values.

A method for decoding information in a multi-dimensional encrypted code is disclosed. The method involves scanning the code with an image sensor, identifying color patterns within geometrical shapes, and translating these patterns into user-readable information or URLs. The system enhances security by bypassing intermediate servers and using embedded features like digital watermarks to prevent unauthorized access. Error correction techniques ensure accurate decoding even if the code is damaged. Additionally, a neural network improves pattern recognition through machine learning. The user interface offers customization options and accessibility features, while spectral analysis distinguishes subtle color variations to encode more data. An example demonstrates decoding a product label's encrypted code into alphanumeric characters, ensuring accurate and reliable information display. This method ensures secure, flexible, and user-friendly decoding of complex encrypted information.

The present invention provides a spinal surgery training device comprising a multi-degree-of-freedom robotic arm, a guide, a deflection observation module, a visual observation module, a carrier module, a control module, and an alarm module. The deflection observation module is fixedly connected to the guide to measure the radial deflection of the cutting tool. The carrier module is used to place samples and is equipped with a multi-dimensional cutting force sensor and a multi-dimensional acceleration sensor. When an operator holds the cutting tool and cuts the sample, the sensors and optical tracers monitor the sample in real time and transmit the measurement signals to the control module. When the sensor detection values ​​deviate from the allowable range, the control module controls the multi-degree-of-freedom robotic arm to perform corrections and compensations, and issues a voice reminder through the alarm module. By sensing the movement and compensation of the robotic arm, the operator can master the correction techniques, helping the doctor develop a good feel for nail placement and drilling, thereby achieving the training objectives.

The invention discloses an intelligent recognition system for gallbladder operation organs, which relates to the technical field of medical instruments and comprises a neural network recognition end, an image processing end and a user interface end. The neural network recognition end analyzes the real-time image of the operation by using a segmentation model trained by an improved deep learning yov8 algorithm, detects and recognizes the gallbladder, other key parts and surgical instruments, and marks the gallbladder, other key parts and surgical instruments in a video in real time; the neural network recognition end comprises a model training module and a real-time recognition module, the model training module carries out training by constructing and optimizing a deep neural network model and utilizing a large number of high-quality labeled medical image data sets, and Mosaic and MixUp data enhancement technologies are adopted in the training process. According to the method, a series of denoising, contrast enhancement and artifact correction technologies are adopted in image preprocessing, so that the image quality is ensured, the recognition accuracy is improved, and the error rate is reduced.

Techniques for filtering iono correction data for global navigation satellite system (GNSS) positioning are provided which involve determining a threshold, or mask, for iono correction filtering based on a statistical analysis of variance data included in iono correction data and resulting residual information determined using the iono correction data. The mask can subsequently be used as a threshold to filter iono correction data based on its variance data. In some examples, a server can determine the mask based on crowdsourced information received from multiple GNSS receivers. In other examples, a GNSS receiver may determine the mask based on obtained iono correction data and GNSS measurements it makes itself. When applying the mask to filter incoming iono correction data. When incoming iono correction data is filtered, a GNSS device may use alternative iono correction techniques to perform iono correction, in some embodiments.

This invention discloses a method and system for calculating industrial time-series data with instantaneous high-frequency characteristics, belonging to the field of real-time computing for power system dispatch automation. The method employs a cache-based out-of-order data correction technique, a streaming-based dynamic delay calculation method, and a reentrant calculation window verification method to adaptively optimize instantaneous high-frequency time-series data. This achieves high-precision, high-frequency, and high-performance industrial time-series data calculation, effectively improving the power grid's operation monitoring level and ensuring its operational safety. This invention is particularly applicable to high-frequency data calculation and analysis of key equipment such as IGBTs and fault recording devices in flexible DC substations, improving the accuracy and real-time performance of power system fault analysis and diagnosis.

The present application relates to the technical field of image pre-training, and particularly relates to a training method and system of an image pre-training model and a storage medium. The method comprises the following steps: collecting original training images, performing high-dimensional feature mapping and pyramid transformation to generate multi-resolution feature bodies, determining the texture definition of the images based on the feature bodies, screening out defective training images and qualified training images, for the defective training images, correcting them into defective correction training images by using a trace correction technology, and constructing positive and negative contrast images to train a defective image pre-training model, at the same time, performing weak disturbance transformation on the qualified training images to generate positive example pairs before and after disturbance to train a qualified image pre-training model, integrating the two pre-training models into a cooperative sample stream, and obtaining a high-performance integrated image pre-training model through integrated pre-training. The present application realizes image quality grading and difference modeling, improves the defect recognition precision, and enhances the model generalization ability.

The invention discloses a reagent card quantitative analysis system and method based on multi-task deep learning, and belongs to the technical field of point-of-care testing (POCT). The analysis system comprises an image acquisition module, a key point detection and image correction module, a color development area extraction module and a multi-task deep learning prediction module. The analysis method comprises the steps of obtaining an original image of a reagent card to be tested, performing standardization processing on the image through a key point detection and image geometric correction technology, further extracting a sub-image of a color development area, finally inputting the sub-image into a fully trained multi-task deep learning model, and outputting a target concentration value. The problems of insufficient accuracy, limited application and the like in reagent card quantitative analysis in the prior art are solved, the influence of background interference and shooting angle difference on a prediction result is remarkably weakened, and robust and accurate quantitative analysis of the concentration of a reagent card target object is realized under a conventional shooting condition; and the generalization capability and the practicability of the intelligent interpretation system are obviously improved.

A computer-implemented method of issuing an electronic ticket comprises accessing a data store that contains multiple stored codes and generating a ticket image sequence by: generating a first coded image 24a having: a first portion 25a corresponding to a first visual code 25 associated with a first stored code and a second portion 25b not corresponding to the first visual code; and generating a second coded image having a first portion corresponding to a second visual code associated with a second stored code, and a second portion not corresponding to the second visual code. A ticket image sequence is formed including the first coded image and second coded image. The ticket image sequence can be transmitted to a user’s device. A further device can used to read the first coded image and the second code image and derive first and second candidate codes from these images, respectively. The candidate codes can be matched against the first stored code and the second stored codes, and the ticket image sequence can be validated according to the matches. The second portion 25b of the coded image can comprise incorrect code data inserted into the coded image that error correction techniques can compensate for.

In various examples, depth image analysis and correction for stereo image machine learning systems and applications are provided. A depth image anomaly identification stage of a depth image anomaly processor may perform functions for evaluating a depth image to identify anomalous segments where the values of pixels predicted by a stereo depth perception model appear anomalous with respect to accuracy in comparison with other regions of the depth image. A correction stage adjusts the depth image based on the identified anomalous segments using one or more correction techniques that may compute estimates or predictions for depth values within identified anomalous segments. Correction may be based on detected structural characteristics within the volume of space corresponding to the identified anomalous segments, and / or based on characteristics of local phenomena (e.g., lighting, reflections, glare, etc.) identified as potentially causing the inaccuracies in depth values.