15 results about "Robust regression" patented technology

The invention discloses a new energy box-type substation state abnormity diagnosis method and system, and relates to the technical field of power equipment monitoring. According to the method and the system, through event-triggered high sampling and cross-channel time base self-inspection, multi-source sequence synchronization is ensured, and a consistent time axis is provided for subsequent estimation, so that online analysis under the condition of no power outage is supported; introducing dual-path estimation of RL step fitting and voltage drop robust regression in a transient stage, cooperatively inhibiting a closing peak and outlier data, and obtaining a stable initial value of the contact resistance; in a steady-state stage, adopting Goertzel frequency selection to extract a fundamental wave phasor and performing ohmic purity judgment, and expanding to a resistance-inductance joint model when necessary to remove interference of parasitic inductive reactance on resistance estimation; and then mutual identification and residual constraint are carried out on the temperature and electric power coupling relationship by using a discrete heat RC model, and local write-back correction is carried out on the deviation, so that the consistent closed loop of the electric domain and the heat domain is realized.

The invention provides an ARC test temperature rise rate calculation method and device, and belongs to the technical field of ARC test. The ARC test temperature rise rate calculation method comprises the following steps: S1, obtaining original time temperature data of an ARC test search stage; s2, smoothing the original time and temperature data based on a non-parametric smoothing method to obtain a central trend curve, and scanning the central trend curve based on a change point detection algorithm to extract a plurality of data segments; s3, fitting all the data segments based on a robust regression algorithm to obtain a target linear model; and S4, obtaining the slope of the target linear model, and taking the slope as the temperature rise rate of the ARC test search stage. According to the method, the technical problem that the calculation result is inaccurate and unstable due to data noise and abnormal values when the temperature rise rate at the Search stage of the ARC test is calculated by adopting an end point method in the prior art is solved, the accuracy is improved, the robustness and the repeatability are relatively high, the hardware dependence is reduced, and automatic and intelligent analysis is realized.

This invention provides a method and system for assembling a genome stack, a method and system for assembling a genome architecture, and a method and system for assembling a genome sequence, belonging to the field of bioinformatics. This invention optimizes a global loss function using robust regression techniques to determine the stacking relationships between sequencing sequences, eliminating interference from false positive alignments, thereby obtaining a more accurate assembled stack set and avoiding copy number loss or misassembly in the assembled genome. The estimation of stack arrangement based on regression matrix calculations has a higher tolerance for outliers; unless the number of ambiguous sample information exceeds the number of true samples, a "collapse" will not occur. By using resampling techniques to independently sample, assemble, improve, and evaluate sequencing data multiple times, and then integrating them into the final assembled genome, the uncertainty of the assembly results caused by noise in the sequencing data can be reduced, and the sensitivity of the assembly results to the selection of assembly parameters can be decreased.

The application discloses an engineering interference suppression method, system, device and medium based on double scale factors, and belongs to the technical field of engineering monitoring. The method comprises the following steps: performing feature recognition and confidence screening according to phase sequence data and amplitude sequence data of each monitoring point of an engineering, to obtain an engineering interference point set; inputting the phase sequence data into a robust regression model, so that the robust regression model performs interference suppression on the phase sequence data, weighting the phase sequence data based on a first weight function corresponding to a first scale factor, to obtain first interference suppression data; comparing a target phase difference value of the first interference suppression data with a first threshold value, if the target phase difference value is less than or equal to the first threshold value, outputting the first interference suppression data, if the target phase difference value is greater than the first threshold value, weighting the first interference suppression data based on a second weight function corresponding to a second scale factor, and outputting second interference suppression data. Therefore, by implementing the application, effective suppression of engineering interference can be realized.

The invention provides a weld surface defect intelligent detection method and equipment, and belongs to the technical field of image analysis and defect detection.The method comprises the steps that a weld surface height map is preprocessed to obtain denoised height data; determining left and right boundary points and left and right boundary lines of the welding seam area through longitudinal sampling, robust regression fitting and residual gradient detection based on the height data, and fitting a left reference straight line of the left base metal area and a right reference straight line of the right base metal area respectively; based on the left-side datum straight line and the right-side datum straight line, dynamically constructing a reference datum height for each point in the welding seam area; and based on a difference value between the height data and the reference datum height, detecting defects on the surface of the welding seam in combination with a preset judgment threshold value and a region strategy. Based on the method, the invention also provides corresponding equipment. According to the method, accurate quantification and recognition of various weld joint surface defects such as surface excess weld metal, non-filling, surface undercut, weld leg size, linear dislocation, surface air holes and splashing are achieved.

The invention relates to the technical field of physical experiment measurement, and discloses a Young modulus measurement method by utilizing a blade edge sub-pixel level tracking technology, which comprises the following steps of: 1, constructing an experiment device and inputting experiment parameters; step 2, acquiring an image sequence and preprocessing the image sequence; step 3, carrying out pixel equivalent calibration and selecting a region of interest; 4, displacement is extracted through edge detection and robust fitting, specifically, a horizontally-arranged cutting edge is fixed to the position of a lower chuck of the metal wire, a camera is arranged to directly face the edge of the cutting edge, and the focal length is kept fixed. Through a core algorithm combining edge detection, Hough transform and robust regression, sub-pixel-level precise positioning of the edge of the blade is realized, subjective interference caused by manual reading and manual calibration is effectively avoided, the accuracy and stability of a measurement result are remarkably improved, and meanwhile, by means of the suppression effect of the algorithm on noise points, the accuracy and stability of the measurement result are improved. And the resistance to environmental interference in the measurement process is enhanced.

The invention provides a mobile communication site energy efficiency hierarchical calculation and abnormal constraint grouping evaluation method, which belongs to the technical field of mobile communication, and comprises the following steps: S1, carrying out data acquisition and preprocessing; s2, self-adaptive grouping and same-group mainstream energy consumption identification are carried out; s3, estimating the lower bound of the theoretical minimum energy consumption; s4, calculating the same group deviation degree BEDR and the theoretical limit deviation TEDR; s5, calculating an abnormal fluctuation factor subjected to dedependent variables; s6, threshold judgment, weighted grading and confidence output are carried out, and according to the mobile communication station energy efficiency grading calculation and abnormal constraint grouping evaluation method, transverse comparability is ensured through matching of grouping and mainstream peak values; theoretically reachable lower bound constraint is provided by'quantile lower envelope ', and only depending on empirical coefficients is avoided. Robust regression and MAD gating significantly suppress the influence of abnormal values. Explainable approximate evaluation can still be given when a score-lacking table is timed; and confidence and reason explanation are output, so that closed-loop treatment and investment decision making are facilitated.

The present application relates to the technical field of radar remote sensing, and discloses a high-position loose body historical activity detection method based on multi-source DEM data, aiming to solve the problem that the prior art cannot accurately and quantitatively evaluate the historical activity of high-position loose bodies in a glacier-covered area, and the scheme mainly comprises the following steps: automatically dividing a watershed unit and screening a glacier-affected area based on DEM data; eliminating systematic errors by fusing multi-source DEM data, correcting geographical positioning deviation, correcting height distortion deviation, and correcting orbit mode deviation; establishing a penetration depth physical model, using C / X band height difference optimization parameters to correct the radar penetration depth in the glacier area; calculating the height change rate by using robust regression; calculating the activity comprehensive index by comprehensively considering multiple factors, and identifying hot regions and time periods by probability estimation and spatiotemporal clustering. The present application realizes automatic and high-precision quantitative evaluation of the historical activity of high-position loose bodies, and is particularly suitable for geological disaster warning and risk assessment in high mountain areas.

The application discloses an image needle path extraction algorithm, comprising the following steps: scanning an image containing a needle path, and performing threshold segmentation on the image; performing connected domain analysis to obtain a plurality of connected domains, and screening a target region to be extracted; performing straight line fitting on a voxel point in the target region to obtain a straight line, and extracting an end point of the straight line to complete needle path extraction. The application performs robust regression analysis on a needle path coordinate point set, eliminates errors caused by metal artifacts in the image, and has high precision and high reliability in needle path extraction, and can be applied to most scenes.

The application discloses an infrared focal plane array attitude measurement image defect pixel repairing method and device, and belongs to the technical field of infrared imaging and image processing, which comprises the following steps: performing linear fitting on each row and each column of an infrared focal plane array attitude measurement image containing a fault defect, and calculating row residual sum of squares and column residual sum of squares; arranging the row residual sum of squares and the column residual sum of squares in descending order, taking the intersection of the row and the column corresponding to the K largest residuals, and generating an initial positioning mask of the defect pixel; for each defect pixel, adaptively disabling the pollution direction in four one-dimensional windows of a horizontal direction, a vertical direction and double diagonal lines, and establishing a multi-direction robust regression model by using the pixel samples which are not polluted, and then extrapolating to obtain a multi-direction prediction value of the defect pixel; and using the average value of the multi-direction prediction value of each defect pixel as the repair gray value of the defect pixel. The application can realize blind diagnosis and high-precision repair of defects under the conditions of a single frame, no training and no external reference.

The present application belongs to the technical field of hydraulic support group control, and particularly relates to a hydraulic support drift angle correction method and system for steeply inclined large-mining-height working face. Anchor points are arranged on the end and chute side, and data such as IMU, stroke / pressure, UWB / laser and conveyor attitude are collected, and a virtual strike reference angle is obtained through robust regression and consistency constraint; the overall deviation is determined according to the strike number and sliding window (same number rate / variance / mean), and the staggered strategy of remainder grouping is adopted to perform continuous small-step shaping, so that the adjacent drift angle deviation RMS is less than or equal to the threshold value, and then the residual deviation is zeroed in the single-frame closed loop, thereby solving the problem that the center line of the conveyor or chute is taken as a single reference datum, and finally leading to the problem of more and more deviation of the drift angle of the whole line support.

The application provides a track static irregularity trend prediction method, system, electronic equipment and storage medium. The method comprises the following steps: performing similarity test on a plurality of acquired historical data by using a dynamic time warping algorithm, and representing the similarity test result by using a path matrix; fitting and correcting the path matrix by using a robust regression method to obtain fitting data, and repeatedly performing similarity test on the fitting data until the fitting data meets a preset condition, and then ending the fitting and correction to obtain a dynamic warping path matrix; rechecking necessary mileages in the path matrix and the dynamic warping path matrix by using a correlation coefficient method; aligning the plurality of historical data when the rechecking requirement is met; and training the aligned plurality of historical data by using a long short-term memory network model with preset hyperparameters. The application can make accurate prediction on small-scale static single data in the track irregularity degradation state within a range of several meters.

The application provides a weld surface defect intelligent detection method and device, and belongs to the technical field of image analysis and defect detection. The method comprises the following steps: pre-processing a weld surface height map to obtain denoised height data; determining left and right boundary points and lines of a weld region based on the height data through longitudinal sampling, robust regression fitting and residual gradient detection, and fitting a left reference straight line of a left base material region and a right reference straight line of a right base material region respectively; dynamically constructing a reference datum height for each point in the weld region based on the left reference straight line and the right reference straight line; and detecting defects on the weld surface based on the difference between the height data and the reference datum height, and combining a preset judgment threshold and a region strategy. The application also provides a corresponding device. The application realizes accurate quantification and identification of various weld surface defects such as surface excess height, incomplete filling, surface undercut, weld leg size, linear misalignment, surface porosity and spatter.

This invention discloses a temperature compensation algorithm for pressure sensors based on a two-stage robust optimization neural network. In the global exploration stage, the weights and bias parameters of the neural network are encoded into optimization vectors, and the optimization algorithm iteratively searches these vectors in the solution space. In each search iteration, a neural network using robust regression as the loss function calculates the loss value corresponding to each optimization vector, and this loss value is mapped to the fitness of a slime mold individual, guiding the optimization algorithm to find the globally optimal parameter vector. In the local fine-tuning stage, the optimal parameter vector obtained in the global exploration stage is used as the initial parameters of the neural network, and a neural network with the same structure as in the global exploration stage, combined with a gradient optimization algorithm, is used to finely adjust the initial parameters to obtain the final temperature compensation model. This invention can achieve high-precision temperature compensation for pressure sensor measurements and improve the stability and reliability of pressure sensors in various application scenarios.

The invention discloses a data processing method and system for a phase difference method coating thickness gauge, and belongs to the technical field of nondestructive testing. The method aims at solving the problems that due to Gaussian random noise, periodic electromagnetic interference and outlier accidental noise, frequency-phase difference data are scattered, the linear fitting degree is low, and measurement repeatability is poor. The method comprises the following steps: acquiring original frequency-phase difference data; preprocessing is carried out through filtering and adaptive outlier detection; performing core straight line fitting on the preprocessed data by using robust regression algorithms such as random sample consensus (RANSAC), and extracting a stable linear relationship; according to the method, three types of typical noise can be restrained in a targeted mode, after treatment is conducted through the method, the standard deviation of repeated thickness measurement of the same standard part can be reduced by about 60%-80% compared with a direct linear fitting method, the repeatability and reliability of measurement are greatly improved, and an effective solution is provided for accurate and stable measurement of the coating thickness.