64 results about "Iterative thresholding" patented technology

A machine-vision-based online dimensional measurement method of precise instruments relates to a machine-vision-based dimensional measurement method and solves the problem of high-precision requirements on workpiece measurement in modern industry. The method comprises the steps of adjusting a camera to obtain a clear image of a standard instrument and meanwhile collecting a template area inside the clear image; performing iterative threshold segmentation on the template area to obtain the segmented threshold of the image, namely, binarizing the template area to obtain a binary image of the template area; obtaining a binary image of an image to be measured, and obtaining a target area of the image to be measured through template matching; performing image processing on the target area of the image to be measured to obtain the edges of an instrument to be measured, which is used for dimensional measurement, and calculating the pixel distances among the edges; calculating the relation between the pixel distances and the actual distances of the image; and calculating out the actual dimension of the instrument to be measured. The machine-vision-based online dimensional measurement method of precise instruments can be widely applied to the machine vision dimensional measurement of the precise instruments.

The invention discloses a non-contact contact line geometrical parameter detecting method. The method includes a first step of collecting high-definition images at equal time intervals by means of collection of control signals and finishing image preprocessing by means of the median filtering technique, the image graying technique and the like, a second step of locating a laser spot center point and extracting a coordinate of the center point by means of the iterative thresholding algorithm and a method of removing isolated noise in mathematical morphology, a third step of extracting a matched target region and detecting a transverse gray singular value of the target region, a fourth step of giving the wire height and a stagger value of a contact line by means of conversion from an image coordinate system to a camera coordinate system and from the camera coordinate system to a detection vehicle coordinate system, and compensating vibration of a vehicle body, and a fifth step of giving precise detection values of the wire height and the stagger value and displaying information of a plurality of parameters in a developed graphic monitoring interface. The method effectively improves detection efficiency of geometrical parameters of a contact net, simplifies the algorithm, improves precision of fault detection, and specifically improves safe reliability of the contact net of a high-speed train.

The invention provides a multi-target tracking method integrating obvious characteristics and block division templates. A target motion area is detected by adoption of RGB component background difference and an iterative threshold, and the adaptive ability of a motion detection algorithm to scene illumination change is improved. Based on target area block division, a motion pixel color saliency weighted block centroid model, block centroid shifting fusion and a scale updating method, the calculation efficiency is high, the resistance to partial occlusion is high, and the similar color scene jamming ability is strong. The problem of multi-target measuring-tracking distribution is solved by adoption of two-level data association, and an occluded local area can be accurately positioned. Therefore, adaptive template updating is guided by an occlusion matrix, a reliable global centroid transfer vector is obtained by making use of effective colors and motion information of blocks, and finally, continuous, stable and fast multi-target tracking in complex scenes is realized. The multi-target tracking method integrating obvious characteristics and block division templates is applied to fields like intelligent video surveillance, in-air multi-target tracking and attacking, and multi-task tracking intelligent robots.

The invention discloses a cotton fault detecting and identifying method based on a multi-spectrum technology. The method comprises the steps: (1) image acquiring; (2) image pre-processing; (3) the preliminary detection of faults; carrying out edge detection on an improved morphological gradient algorithm to obtain the comprehensive edge detection intensities of all faults to obtain an image with contrast enhancement of all faults; (4) precise location of faults: converting the image into a connected domain image through an improved iterative threshold and morphological processing, precisely locating the faults by selecting a main fault algorithm, and extracting fault areas by marking; (5) cotton ginning quality judgment: carrying out statistics on the amount of all faults, and calculating the total cotton faults to obtain a cotton ginning quality grade. The cotton fault detecting and identifying method provided by the invention has the advantages of being simple in theory, fast in detection speed and high in detection precision.

The invention discloses a side lobe suppression method and an array sparse method for a multi-beam imaging sonar sparse array. A sparse optimization problem of an array antenna is transformed into a linear regression problem of a sparse matrix, combination learning is carried out by taking the reconstruction of multiple beam direction patterns with different pointing directions as a target task, and a sparse semicircle array model for multi-task learning is established; based on the performance requirement of a preset main side lobe, a norm regular term of 1<1 / 2> of a weighted coefficient matrix is introduced on the basis of a least squares loss function, an iterative threshold converged method and an accelerating gradient descent method are used for solving an optimal weighting coefficient, and a weighting coefficient which minimizes a side lobe peak level is solved at the same time while optimizing the position of the sparse array. According to the side lobe suppression method and the array sparse method for the multi-beam imaging sonar sparse array, the problem that the sparse solution falls into the local optimum due to the mismatching of array position and weight vector is avoided, and peak side lobe levels of multiple beams formed by the array after sparseness is effectively reduced.

Systems and methods for object identification. Objects in a color image of a biological sample are identified by using a signal function to transform the color image into a single-channel image with localized extrema. The localized extrema may be segmented into objects by an iterative thresholding process and a merit function may be used to determine the quality of a given result.

The present invention relates to a compressed sensing method for multi-source impact load identification of a mechanical structure for solving ill-posed natures of the multi-source impact load identification inverse problem of a highly underdetermined system. The method comprises the following steps of (1) measuring a frequency response function between an action point of impact load of the mechanical structure and a response point of the mechanical structure, and furthermore constructing a sensing matrix; (2) measuring signals generated by dynamic load of the structure by using a sensor; (3) constructing an underdetermined equation of the multi-source impact load identification; (4) constructing an L1-norm-based compressed sensing convex optimization model of the multi-source impact load identification; and (5) solving the compressed sensing optimization model by using a two-step iteration threshold algorithm, and obtaining a compressed sensing solution of multi-source impact load. Time and space combined sparsity of the impact load is fully utilized, the method is suitable for identifying and positioning the multi-source impact load acting on the mechanical structure, and the choke point that the underdetermined system cannot be solved by a traditional regularization method based on L2 norm is overcome.

The invention provides an automatic design method and device for a superconducting quantum chip reading cavity and a storage medium. The method comprises the following steps: determining the length of a CPW resonator according to the initial design frequency of a readout cavity; configuring a layout template and a simulation template, wherein the layout template and the simulation template are used for configuring geometric figure coordinates and size parameters of the CPW resonator and reading modeling parameters and simulation parameters of the cavity; constructing a simulation model of the readout cavity with the interdigital capacitor structure based on the layout template and the simulation template, performing spectrum scanning analysis, and determining simulation frequency; in response to the situation that the difference value between the simulation frequency and the initial design frequency is larger than an iteration threshold value, the initial design frequency is adjusted with a preset frequency offset, and the layout template and the simulation template are updated; and performing iterative analysis based on the updated layout template and the simulation template, and outputting the current layout template in response to the condition that the difference value between the obtained simulation frequency and the initial design frequency is less than or equal to an iterative threshold value. According to the method, the simulation model can be quickly generated, and a more accurate read-out cavity layout can be obtained.

The invention discloses an automatic baseline correction method for infrared spectroscopy. The automatic baseline correction method comprises the following steps: A1, preparing a to-be-detected sample; B1, acquiring an infrared spectrum of the to-be-detected sample by using a spectrograph, and preprocessing the spectrum to obtain the original absorbance spectrum; C1, for spectral intensity Y=[y1, y2...yN] in the original absorbance spectrum at equal interval, performing average minimum updating on the original absorbance spectrum, and obtaining the spectral intensity Y1=[y1,...yi+1] of the absorbance spectrum after the first update; D1, calculating the sum of range differences, namely Sabs(1) =Sigma of an absolute value of (Y-Y1); E1, repeating the step C1 for performing iterative computations for n times, updating the Y1 to obtain Y2, deducing the rest to obtain the Y1, Y2...Yn, further performing iterative computations on the step D1 to obtain Sabs(n), then calculating until the deltaSabs(n-1) / Sabs(n) is less than or equal to an iterative threshold lambda, and stopping calculating to obtain a baseline Yn of the absorbance spectrum, wherein n is more than 2; and F1, subtracting the baseline from the original absorbance spectrum, thereby obtaining the corrected absorbance spectrum.

Systems and methods for object identification. Objects in a color image of a biological sample are identified by using a signal function to transform the color image into a single-channel image with localized extrema. The localized extrema may be segmented into objects by an iterative thresholding process and a merit function may be used to determine the quality of a given result.

The invention discloses a data processing method based on an artificial intelligence technology. The method comprises the steps of obtaining a first formation data set corresponding to a to-be-analyzed game; generating a second formation data set through a genetic algorithm based on the first formation data set; if the number of iterations corresponding to the second formation data set reaches an iteration threshold, obtaining at least one of an object name occurrence frequency and a skill name occurrence frequency corresponding to each piece of second formation data according to the second formation data set; and according to at least one of the object name occurrence frequency and the skill name occurrence frequency corresponding to each piece of second formation data, generating a game analysis result corresponding to the to-be-analyzed game. The invention further discloses a related device, equipment and a storage medium. The method is suitable for analyzing the game with the formation combination so that reasonability of analyzing the game data is improved, and the difficulty of adjusting the game balance is reduced.

An image is segmented by subjecting the image to an iterative thresholding operation, and a hierarchical representation of clusters is obtained by analyzing the results of each of the iterative thresholding steps to find clusters of adjacent image elements. A type class is assigned to a leaf cluster of the hierarchical representation and the assigned class is propagated towards the top of the hierarchical representation. Then a mask is generated marking the locations of image elements contained in the top ancestral clusters of a specific type.

The invention discloses a raft culture area extraction method based on a Gaofen-3 satellite SAR image. The method comprises the following steps: firstly, acquiring Gaofen-3 satellite SAR image data including a research area; performing feature extraction on the SAR image data to obtain a plurality of extracted features; performing feature screening on the extracted features to obtain a plurality of effective features capable of reflecting the difference between the raft culture area and seawater; based on the plurality of effective features, using an iterative threshold algorithm to perform preliminary extraction of the raft culture area; and performing post-processing on the preliminary extraction result by using a density clustering algorithm, intersection and union set reconstruction and a morphological processing method, and extracting the raft culture area. According to the method, multiple features extracted from the SAR image of the Gaofen-3 satellite are used for extracting the raft culture area together, and then the iterative threshold method and the density clustering are combined, so that the influence of SAR image speckle noise can be reduced, and large continuous rows of raft culture areas can be accurately extracted.

The invention relates to a bunching SAR compressed sensing imaging method based on an approximate observation matrix. The bunching SAR compressed sensing imaging method comprises the following steps:1, obtaining a matrix form of distance-direction scale transformation based on a polar coordinate format algorithm of scale transformation; 2, obtaining a matrix form of azimuth scale transformation based on the polar coordinate format algorithm of scale transformation; 3, deriving a matrix expression form of a polar coordinate format algorithm imaging process according to results of the step 1 and the step 2; 4, deriving a signal model of an inverse imaging process according to a signal model of a radar projection matrix obtained in the step 3; and 5, constructing a CS reconstruction model byusing a PFA imaging process and an inverse imaging process obtained in the above steps and an iterative threshold shrinkage algorithm based on the approximate observation matrix. According to the method, sparse constraint is carried out on the PFA imaging process, the application range of PFA imaging in the SAR field is wider, and compared with a traditional accurate measurement matrix, the calculation complexity and the storage memory are greatly reduced.

The invention relates to a continuous microfluid biochip advanced comprehensive design method based on a particle swarm algorithm. The continuous microfluid biochip advanced comprehensive design method comprises the following steps of S1, initializing a particle swarm according to a time sequence diagram and given assembly types and number; s2, using a list scheduling algorithm for each particle, and further obtaining a scheduling solution of each particle; s3, calculating a fitness function value of the particle according to the binding and scheduling solution; and S4, iteratively updating the particles according to the individual historical optimal solution and the global historical optimal solution, and obtaining an optimal binding and scheduling scheme if the current number of iterations reaches an iteration threshold. According to the invention, on the premise of not sacrificing the execution time of the biochemical reaction, a binding and scheduling scheme which needs a smaller number of flow channel intersection points can be obtained.

The invention discloses a docking recovery integrated navigation method based on an improved Gaussian distance iterative algorithm. The docking recovery integrated navigation method comprises the following steps: 1) segmenting a track of an AUV into a plurality of connected straight line segments; 2) calculating the coordinate of each position point by utilizing dead reckoning to obtain a coordinate equation set; 3) linearizing the error by using Taylor series expansion, setting a position initial value and an iteration threshold of the AUV, solving a correction amount of the AUV position by using a least square method, comparing the correction amount with the iteration threshold, and stopping iteration if the correction amount is less than the iteration threshold; 4) if the correction amount is greater than an iteration threshold, updating the step length by adopting a trust region method to continue iteration; 5) solving the coordinate of the last point of the straight line segment as the initial point of the next straight line segment, and repeating the above steps for iteration. The method can effectively correct the speed and position errors of the AUV, enables the whole system to have higher filtering precision and stability, and provides guarantee for the safe recovery of the AUV.

The invention relates to a basis tracking prestack seismic inversion method based on reflection coefficient precision. The method comprises the following steps: 1, obtaining pre-stack angle gather seismic data and post-stack seismic data of a thin or thin interbed sand shale reservoir; 2, obtaining an inversion initial elastic parameter model; 3, calculating based on a Zoeppritz equation to obtain a time-angle domain reflection coefficient sequence, and constructing a target function related to the inversion elastic parameter model; 4, updating the inversion elastic parameter model; 5, if the residual error of the synthetic pre-stack angle gather seismic data and the actual pre-stack gather data reaches an iteration threshold value, outputting an inversion elastic parameter model, and if not, entering step 6; and 6, updating the number of iterations, and returning to the step 3 to enter the next iteration until the iteration is terminated. The method can be suitable for thin or thin interbed reservoir geologic models, and elastic parameter inversion results are extracted in a high-precision and high-resolution mode.

The invention provides a method for reducing the peak-to-average power ratio of a multi-carrier system of a filter bank. The method comprises the following steps: setting an initial amplitude limiting amplitude A (1), the maximum number of iterations Q, a peak regeneration suppression factor xi, a penalty factor eta and a search step length rho; shearing the original signal, wherein the processed signal can be expressed as Sn after being processed by the FBMC-OQAM system, the FBMC-OQAM signal iteration amplitude limiting recursion updating formula can be expressed as the conversion of the shearing noise into the frequency domain signal to solve the optimal convergence factor mu; solving the optimal value A (i) of the amplitude limiting threshold value; and letting i = i + 1, repeating the above steps, and entering the next round of loop iteration until the algorithm converges. According to the method, a system model of the reserved subcarriers of the FBMC-OQAM system is constructed, a self-adaptive subcarrier reservation algorithm is provided based on essential reasons of the FBMC-OQAM and in combination with signal structure characteristics of the FBMC-OQAM, the PAPR of the system is reduced with a small number of iterations by performing self-adaptive learning on input data and adjusting an iteration threshold value and an amplitude limiting factor, and signal distortion is not caused.

The invention discloses a fitting centroid sub-pixel positioning method based on correlation method threshold iteration. On the basis of analyzing an existing method, a paraboloid fitting curved surface based on a threshold value is adopted, the calculation efficiency is improved while background noise is removed, and the anti-interference performance of an algorithm is improved; and the fitted paraboloid is combined with the correlation coefficient, and the positioning precision of the centroid is remarkably improved through a method of updating an iteration threshold value. According to the invention, the positioning precision of the center of mass reaches 0.01 pixel.

The invention discloses a compressed sensing magnetic resonance imaging method based on an iterative p-threshold projection algorithm, and belongs to the field of magnetic resonance imaging. The method comprises the following steps of: performing Fourier transform on a T2 weighted brain graph, transforming the T2 weighted brain graph subjected to Fourier transform into a K space to obtain K spacedata of the T2 weighted brain graph, and adopting the K space data of the T2 weighted brain graph according to a nonlinear sampling template to obtain T2 weighted brain graph under-sampling K space data; carrying out redundancy transformation on the T2 weighted brain graph undersampling k space data, and carrying out sparse representation on the T2 weighted brain graph undersampling k space data;and carrying out image reconstruction on the sparsely represented T2 weighted brain map undersampling k space data based on an iterative p-threshold projection algorithm or a fast iterative p-threshold projection algorithm, designing a new sparse target function by flexibly changing a p value, and achieving a better reconstruction effect .

An image is segmented by subjecting the image to an iterative thresholding operation, and a hierarchical representation of clusters is obtained by analyzing the results of each of the iterative thresholding steps to find clusters of adjacent image elements. A type class is assigned to a leaf cluster of the hierarchical representation and the assigned class is propagated towards the top of the hierarchical representation. Then a mask is generated marking the locations of image elements contained in the top ancestral clusters of a specific type.

The invention provides a configuration method of a distributed power supply. The configuration method comprises the following steps: calculating a target function value of a pre-established DG-containing power distribution network optimal configuration model; when the target function value reaches a constraint condition, outputting a corresponding configuration mode; when the target function valuedoes not reach the constraint condition, adopting a firefly algorithm to perform iterative operation, and when the number of iterations reaches a set threshold, adding adaptive genetic iterative operation, calculating an optimal solution Gbest, and performing constraint condition judgment on the obtained optimal solution Gbest; and when the adaptive genetic iterative operation exceeds an iterative threshold, adding improved Gaussian disturbance, calculating an optimal solution xbest, and performing constraint condition judgment on the obtained optimal solution xbest. Compared with an AGA algorithm, the convergence speed of the adopted algorithm is higher, the optimal convergence point can be quickly found, and the convergence precision is high.

The invention discloses an equipment authentication method and system for rejecting inference based on a shallow self-learning algorithm, and electronic equipment. The method comprises the steps of adopting an unsupervised abnormal data recognition algorithm, eliminating extreme data and unlabeled data with too little information gain, and guaranteeing the stability of self-learning; marking a label-free sample through an iteration threshold quantile; and retraining and sorting a model by combining the label-free sample with the determined label and a passing sample with a label so as to achieve the effect of correcting the grading distribution of the model. According to the invention, human subjective assume is avoided to the greatest extent by using a machine learning algorithm, and a new evaluation standard is provided for evaluating the effectiveness of rejection inference, so that the robustness and the accuracy of equipment authentication are greatly improved.

The invention discloses an image binarization method based on deep learning semantic segmentation, and the method comprises the steps: 1), carrying out the real-time segmentation of a color image through a lightweight semantic segmentation network, training the network according to a BCEloss function, and obtaining a feature map after the network is converged; 2) for the feature map in the step 1), using an iterative threshold method to obtain two thresholds, using the two thresholds to segment the image into a three-valued map, and marking an area represented by an intermediate value in the three-valued map as a suspected area; and 3) for a segmentation result in the step 2), using a connected domain method to firstly denoise the image, then dividing a suspected area according to a certain rule, and finally changing the image from a ternary image to a binary image according to a division result, i.e., the corresponding foreground and background in the step 1). According to the method,the skin color detection effect is improved on the premise of meeting the real-time performance.

A method for the development of a compilation process for a quantum circuit on a quantum processor, comprises:an implementation step of the compilation method comprising:an iteration loop successively comprising:a step of simulation of a given implementation of the logical qubits on the physical qubits of the quantum processor,a step of detecting, in the quantum circuit, ineffective quantum gate(s),a step of estimating the number of quantum swap gates to be inserted into the quantum circuit so that all of the quantum gates of the quantum circuit are effective,a retroaction step, by means of a simulated annealing, involving a new step of simulation, until attaining, whereupon all the quantum gates are effective:either a minimum threshold of the number of estimated quantum value swap gates between two physical qubits,or a maximum threshold of iterations in the loop.

The embodiment of the invention provides an account fund transfer network diagram coloring method and device, and the method comprises the steps: respectively setting the identification of each node in a target account fund transfer network diagram as a natural number starting from zero, and determining the connection component to which each node belongs; performing initialization coloring processing on the connected components and a first preset number of nodes by applying a self-defined color number; coloring the nodes which are not colored at present; selecting one of the adjacent nodes with the same coloring color to delete the color; returning to execute the process of coloring the currently uncolored nodes until the execution frequency of the process of coloring the currently uncolored nodes is equal to or greater than a preset iterative threshold, and outputting a node coloring graph corresponding to the target account fund transfer network graph and used for dividing account node communities. User-defined coloring control over the account fund transfer network diagram can be achieved, and errors can be controlled within a certain range.

The invention provides a non-sparse scene SAR imaging method based on mixed sparse representation. The method includes the following steps of: 1, respectively carrying out random down-sampling in a range direction and an azimuth direction on SAR echoes of a non-sparse scene; 2, constructing a linear frequency modulation scaling imaging operator and an inverse imaging operator, and constructing a two-dimensional compressed sensing optimization model based on the approximate observation model; and 3, solving the constructed optimization model, firstly initializing the non-sparse SAR scene, performing point, line and surface decomposition on the scene, then performing curvelet transformation on a line component, performing wavelet transformation on a surface component, and then sequentially reconstructing the point, line and surface components through an iterative threshold algorithm until the algorithm converges; and finally, combining the obtained point, line and surface components to obtain a final reconstructed SAR image. According to the method, SAR imaging is carried out on a non-sparse scene containing complex features under the condition of the down-sampling rate, the data volume during imaging of the non-sparse scene can be reduced, and meanwhile, the features of the image can be enhanced.

The invention relates to a multi-iteration folding vocabulary hierarchical classification method and a multi-iteration folding vocabulary hierarchical classification system. The hierarchical classification method comprises the following steps: calculating the use frequency of each to-be-classified vocabulary; arranging the to-be-classified vocabularies in an ascending order according to the use frequency of the to-be-classified vocabularies, and marking serial numbers; according to the total number of the to-be-classified vocabularies and the field to which the to-be-classified vocabularies belong, determining a level number and an iteration threshold value; initializing a candidate boundary threshold value of each level, wherein the candidate boundary threshold value of each level is the total number of the to-be-classified vocabularies; performing iterative query on the to-be-classified vocabularies according to the candidate boundary threshold value of each level, the serial number of the to-be-classified vocabularies and an iterative threshold value to obtain a boundary threshold value of each level; and obtaining hierarchical classification of the vocabulary to be classified according to the boundary threshold value of each hierarchy. According to the method, the word frequency is taken as a reference, the occurrence frequency of large-class vocabularies is higher than the occurrence frequency of small-class vocabularies, and the vocabulary hierarchy is divided in a loop iteration folding mode, so that the division efficiency is improved, and the hierarchy division is accurate.

The invention provides a reflection tomography laser radar image segmentation method based on target area local enhancement. The method comprises the following steps: inputting an original image generated by a reflection tomography laser radar and obtained through filtering back projection reconstruction; removing ring artifacts in the original image through a two-dimensional low-pass filter; further performing mean filtering processing to filter noise points, and performing initial segmentation on the image after noise point filtering by using an iterative threshold method to obtain a segmented image; target region extraction is carried out on the segmented image according to the criterion that the area of the circumscribed convex polygon is minimum, and a closed target region of the circumscribed convex polygon is extracted; performing image filling on the extracted target region by adopting an image filling algorithm to obtain a target saliency map; fusing the target saliency map with the original image to obtain a fused image with an enhanced target area; and solving an optimal threshold value of the enhanced target area image by adopting an iterative threshold value method, and then segmenting the whole fused image by using the optimal threshold value.