43 results about "Linear iteration" patented technology

The invention discloses a video significance detecting method based on area segmentation, wherein the method mainly settles a problem of low detecting accuracy by an existing video significance detecting method. The video saliency detecting method comprises the steps of 1, performing linear iteration clustering on video frames, thereby obtaining a super-pixel block, and extracting the static characteristic of the super-pixel block; 2, by means of a variational optical flow method, obtaining the dynamic characteristic of the super-pixel block; 3, fusing the static characteristic and the dynamic characteristic for obtaining a characteristic matrix, and performing K-means clustering on the characteristic matrix; 4, performing linear regression model training on each cluster, thereby obtaining a regression model; and 5, reconstructing a mapping relation between a test set sample and a obtaining the significance value of a test set super-pixel block, and furthermore obtaining the significance graph of a testing sequence. Compared with a traditional video significance algorithm, the video significance detecting method has advantages of improving characteristic space and time representation capability, and reducing effect of illumination to detecting effect. The video significance detecting method can be used for early-period preprocessing of video target tracking and video segmenting.

The invention provides a calculation method of the bearing capacity of a reinforced wall plate and belongs to the field of strength analysis of aircraft structures. According to the technical scheme, the calculation method is an axial compression loading action based calculation method of the effective width of buckling of a skin. Stiffness reduction is performed through modification of the cross section area and the moment of inertia of a stringer in a finite element model which is formed by plate beam units after the buckling of the skin is produced, stress analysis of a structure is performed by a linear iteration solution method of load loading steps, comparison is performed on the working stress of the stringer in a calculation result and the allowable limiting stress of a stringer and skin combined structure, the allowable limiting stress serves as a damage standard and is solved based on an engineering method, and accordingly the bearing capacity of the structure is obtained. Compared with the traditional full housing model, a calculation method model is a plate beam unit model, the geometric nonlinearity and contact problem solution which is large in time consumption and difficult to converge is replaced by the linear solution, and accordingly the modeling time consumption is small, the calculation speed is high, and the requirements for the professional quality of the computer hardware and analysis staff are low.

The invention discloses a rice spike segmentation method of a big paddy field based on deep learning and super-pixel segmentation. According to the invention, by use of simple linear iteration clustering method in the super-pixel segmentation technology, adjacent pixels with similar characteristics form image blocks, i.e., super-pixels; based on automatic annotation and selection of large-scale training samples, the type of the super-pixels is discriminated through a convolution neural network in the deep learning technology so that initial segmentation of rice spikes is achieved; and based on the entropy rate-based super-pixel segmentation method, initial segmentation results are optimized. Thus, effects caused by great difference in colors, shapes, sizes, poses and textures of different varieties of rice spikes in different growth periods, serious irregular edges of the rice spikes, color aliasing of spike leaves, and uneven and changeable light, shielding and wind blowing in the field can be overcome; precise segmentation of different varieties of rice spikes in different growth periods is achieved; and the method is applicable to segmentation of rice spikes in the pot plant environment. Compared with the prior art, the method is advantaged by high precision and applicability.

The invention discloses a simulation method of ultra wideband electromagnetic waves. The invention proposes a new PML method on the basis of the PML method under a stretch coordinate system and a difference scheme of a higher-order time domain finite difference method (FDTD (2, 2M)). A piecewise linearity convolutional perfectly matched layer (PCPML, Piecewise linearity CPML) under self-adapting absorption parameters absorbs a boundary condition. PML absorption factors are set to be adjustable parameters by the method so as to absorb different ultra wideband electromagnetic waves from high frequency to low frequency, and the parameters are obtained by a mixed method of an optimization method of a real coding genetic algorithm and a linear iteration method in a self-adapting manner so as to adapt to the absorption of ultra wideband electromagnetic waves. The absorption boundary condition adopted by the method can absorb and process high frequency waves and low frequency waves, and also can reduce the reflection error of a matched layer caused by grid dispersion and edge reflection and improve the absorption efficiency and the accuracy of simulation result.

The invention provides a building contour extraction method of an aviation remote sensing image. A color invariant algorithm is used to detect the shadow area in the building in the aviation remote sensing image, the shadow area is subjected to image refinement, and a refinement result is used as the background area of the building. With the center of mass of the shadow area as a start point, the negative direction search to a sun elevation angle is carried out, the area of a building part is obtained, and the area is used as the target area of the building. A linear iteration cluster method is used to carry out super pixel segmentation on the aviation image. Based on the obtained background area and target area, a maximum similarity is used to carry out the combination of super pixel division blocks, and the contour of the building is extrated. According to the method, an object-faced segmentation idea and the context information of the target in the image are subjected to the extraction of the building contour, the precision of the building contour extraction is improved substantially, and the complexity of the method is reduced.

The invention provides a superpixel segmentation and depth feature locating-based salient target detection method and solves the problem of non-ideal target segmentation effect of a conventional salient target detection method. The method is implemented by comprising the steps that a processing unit of an image is upgraded to a collective similar region from an independent pixel point by utilizing superpixel segmentation based on linear iteration of color similarity; image features such as color features, direction features, depth features and the like are fully considered; in combination with a characteristic that human eyes pay more attention to a center and ignore a peripheral background, feature similarity of a region where a salient image is located and priori knowledge of uniqueness in comparison with global features, a locating salient map and a depth salient map of the input image are generated; and the salient maps are subjected to fusion and boundary processing. According to the method, the edge of the detected image is clearer in effect; background elimination is completer; and target morphological segmentation is completer. The method is used in the fields of human face identification, vehicle detection, moving target detection tracking, military guided missile detection, hospital pathological detection and the like.

The invention discloses a sea surface target detection method based on improved RBD significance calculation. The method comprises the following steps that (1) an original colored sea surface image isobtained; (2) a simple linear iteration clustering (SLIC) algorithm is used to carry out super-pixel segmentation on the sea surface image; (3) a semantic segmentation algorithm based on a Gaussian mixture model (GMM) is established, the image is segmented into sky and seawater areas, and a corresponding sea-sky line obtained by fitting boundary points of the sky and seawater areas into a straight line; (5) all super pixels are divided into seawater and sky types according to position information of the sea-sky line; (5) the significance is calculated according to super pixel characteristic information of the sea water type via the improved RBD (robustness background detection) algorithm, and a salient map of the sea water area is obtained; and (6) the salient map is segmented according to a fixed threshold, and a foreground target, namely the sea surface target, is extracted. Thus, the sea surface target can be detected from the complex background effectively, and the accuracy is higher.

The invention relates to a semi-analytical method for realizing inversion of water body chlorophyll alpha concentration, which comprises the steps of: (1) reading the data of chlorophyll alpha, water body spectroscopic data and high-spectrum remote sensing data; (2) utilizing the optical characteristics of the water body containing the chlorophyll alpha to establish semi-analytical model of the chlorophyll alpha concentration; (3) on the basis of the data in the step (1), adopting a method of enumerating by band and linear iteration, and calculating the best wave band and model parameter of the semi-analytical algorithm; and (4) on the basis of the steps (1) and (3), extracting the space distribution information of the chlorophyll alpha concentration from high-spectrum remote sensing image.

The invention belongs to the field of image processing and relates to an obviousness detection method based on grabcut and adaptive cluster clustering. The detection method can solve the problem of image obviousness detection and comprises the steps: firstly, using a simple linear iteration clustering (SLIC) algorithm to cut an image to obtain superpixels with different sizes; then, providing an initial obviousness image of an image constructed by an adaptive clustering algorithm based on the background portion of the image obtained through the grabcut algorithm; finally, using the grabcut algorithm to update an obvious area to obtain a final obviousness image according to the obvious portion of the initial obviousness image and by searching for the obvious area through the regions of the superpixels. The method can reduce the calculated amount without losing accuracy and has higher accuracy.

The invention provides an image retrieval method based on spectrum analysis. The method comprises the following five steps: performing layering super-pixel segmentation based on linear iteration to extract and remove the redundancy of an image and lower the complexity of image processing; performing dual-adjustment and distance definition on a two-dimensional image, and counting the dual-adjustment and distance distribution of the image to describe the local structural characteristics of the image; performing image sub-block segmentation based on spectral clustering to fuse more image internal implication structural characteristic information, wherein the image sub-block segmentation is more visually significant than the single super-pixel; performing image descriptor sub-design based on a word bag model, and performing statistical analysis on the frequency of the appearance of image characteristics to describe the components and the entire structure of the image. Via retrieval of an image descriptor based on spectrum analysis, the effectiveness of the method is proven through a practical image retrieval result. The image retrieval method designed by the invention has the characteristics of high distinguishing capability and high flexibility.

The invention discloses a linear positioning method based on a marked magnetic source with a permanent magnetic dipole moment. The linear positioning method based on the marked magnetic source with the permanent magnetic dipole moment comprises the steps that a space magnetic field is established by means of the marked magnetic source which has the vertically upward magnetic dipole moment all the time, and the corresponding relation between the magnetic flux density distribution intensity and the distances between measurement points and the marked magnetic source is obtained; an xyz rectangular coordinate system is established, and the magnetic flux density components, at the position of each measurement point in the space magnetic field, in all the directions of the coordinates are detected, so that the magnetic flux density distribution intensity and the distances between the measurement points and the marked magnetic source are obtained; all coordinate values at the position of the marked magnetic source are obtained according to the magnetic flux density components in all the directions of the coordinates and magnetic flux density components detected through a three-axis magnetic field measurement sensor based on a marked magnetic source magnetic coupling polaron positive model, and thus accurate positioning of a marked object is achieved. According to the linear positioning method based on the marked magnetic source with the permanent magnetic dipole moment, the solution to the location parameters of the magnetic source is achieved based on the linear model; compared with a traditional magnetic marking and positioning method, the linear positioning method has the advantages that only one three-axis magnetic sensor is needed, the solution can be achieved without the need of the non-linear iteration method, rapid positioning is achieved, the precision is high, and cost is low.

This invention discloses a chaos-flow encryption method for improved phone coding streams, which utilizes exclusive-OR operation with byte as the unit and the method includes: taking one-dimension non-linear iteration method mapped by Logistics as the token, which is quicker than packeting method and stronger in anti-attack for ciphered keys and is more suitable for selectivity encryption to multimedia data.

A structure-sensitive color image segmentation super-pixelating method with boundary constraint belongs to the field of image processing of computer application technology, and is characterized by comprising the following steps: (1) completing the segmentation super-pixelating initialization construction of a color image by using linear iteration technology; (2) carrying out color image manifold space mapping and superpixel sensitive area calculation; (3) carrying out super-pixel CVT unit division and local k-means clustering; and (4) superpixelating the structure-sensitive color image with the boundary constraint. Advantages and positive effects are as follows: when the image region sensitivity is considered, target image boundary information is introduced, the possibility that the current pixel falls on the real boundary of the image is calculated through the boundary item; the super-pixel edges are redistributed and optimized, so that the super-pixel segmentation edges are better attached to the actual boundary, small super-pixels are generated in the area with the large density, large super-pixels are generated in the area with the small density, the attaching degree of the color image super-pixel segmentation edges and the actual boundary is improved, and the color image segmentation precision is improved.

The invention discloses a pseudo random sequence generation method based on t semi-order and a sequence generator. The method comprises the following steps: (S1) setting parameters p, q and r; (S2) setting a semi-order control secret key; (S3) setting an initial seed secret key; (S4) setting an initial number of iterations; (S5) establishing non-linear functional semi-order iteration; (S6) encoding in accordance with a compared semi-order result of a front iteration sequence and a back iteration sequence; and (S7) repeating the procedure (S5) to obtain a long enough sequence. The generator comprises a clock generator, a register for parameters p, q and r, a register for m namely the number of iterations, a register for the initial seed secret key and a register for the semi-order control secret key are connected with a non-linear iteration functional circuit by a data bus of the system; the non-linear iteration functional circuit has two outputs, wherein feeds back an intermediate iteration result into the register for the initial seed secret key, the other generates binary output which is input to an xor gate circuit with a binary flow required for encryption, and the xor gate circuit outputs the encrypted binary flow. The sequence of the invention has high degree of randomness, fast speed, relative simple operation, convenient hardware realization, consistent encryption/description devices and relatively low cost.

The invention especially relates to a cell potassium electrode property detection method based on a Nernst electric potential fluorochrome. The method comprises specific steps that: cells with Nernst potassium electrode properties are established, [K+]i/f is obtained with a electric potential fluorochrome technology; intracellular sodium ion concentration is evaluated by using sodium ion fluorochrome; inside-to-outside fluorescence intensity ratio of the cells stained by using the electric potential fluorochrome is measured in a gradient potassium solution; f, [K+]i and a cell apparent sodium/potassium osmosis ratio PNa/K are obtained through non-linear iteration fitting according to a Goldman equation; and PNa/K serves as a quantitative index in the evaluation of cell potassium electrodeproperties. According to the invention, the cell potassium electrode property evaluation method provides a novel index and a novel method for researching cell-electricity biophysical properties and ionic channel function expression by using an electric potential fluorochrome technology, and provides possibilities of non-intrusion in-situ detections of cell-electricity biophysical properties and nerve cell functional differentiation under a material three-dimensional structure.

The invention discloses a robust canonical correlation analysis algorithm based on a generalized mean value, and mainly solves problems that a conventional canonical correlation analysis algorithm is not robust for outliers and a high-dimensional small sample causes a singular problem of a sample covariance. The implementation process comprises the steps: (1), inputting necessary parameters, and carrying out the centralized processing of a training sample; (2), solving two groups of projection sets of conventional canonical correlation analysis; (3), reconstructing a target optimization function of a model based on a generalized mean value, so as to inhibit the impact on a target function from the outliers; (4), solving the target function through employing a linear iteration method, wherein the two groups of projection sets of conventional canonical correlation analysis are used for initialization; (5), enabling the solved two groups of projection sets of the conventional canonical correlation analysis based on the generalized mean value to be used for the feature extraction and dimension reduction of the sample. The experiment results on a multi-feature handwritten form database (MFD), a human face database (ORL) and an object image database (COIL-20) verify the effectiveness of the algorithm.

The invention discloses a combined prediction method for short-time travel requirements of on online hailed car. The method comprises the following specific steps: acquiring historical travel demand data; based on the acquired historical travel demand data, establishing an ARIMA model and a BP neural network model, and performing online car-hailing short-term travel requirement prediction; performing weighted combination on the ARIMA model and the BP neural network model, and calculating a weight value of the weighted combination by utilizing a principle of minimum error in an approximate historical time period to obtain a final combined prediction model; and carrying out online car-hailing travel short-time travel requirement prediction according to the constructed combined prediction model. According to the method, the advantages of two linear and nonlinear prediction models are integrated; optimal estimation can be obtained through linear iteration based on historical data in the same time period, dynamic characteristics of online car hailing requirements can be reflected through the strong nonlinear mapping capacity of the BP neural network, overlarge errors of a single prediction model can be effectively reduced, and therefore the precision of online car-hailing short-time travel requirement prediction is improved.

The invention discloses a system time offset-aided multimode satellite navigation method. The method comprises the following steps: (A1) starting an algorithm, inputting a measurement time, reading the type of a navigation system, a pseudo range measurement value matrix and a satellite position matrix, and reading the historical value of a system time difference and the output value of the previous solution position from a file stored during the previous running of the program; (A2) determining the number of the systems, setting a time difference correction mark as 1 if the number of the system is 1, entering a singe-system solution process, resolving the position of a receiver according to the linear iteration method and outputting the position result; entering a double-system solution process if two systems participate in the solution; and entering a three-system solution process if three systems participate in the solution and if the DOP (dilution of precision) value is smaller than and equal to 6; and (A3) outputting the solved position result, and storing the updated historical value of the system time difference and the output position of the current solution for the subsequent program running.

The invention discloses an electrical simulation method based on a distributed parallel operation method. The electrical simulation method specifically comprises the following steps: S1, updating Jacobian matrix elements and a right-end item; s2, preprocessing the Jacobian matrix so as to reduce the condition number of a coefficient matrix; s3, solving the matrix equation by adopting a Krylov subspace method; s4, in each process, performing corresponding calculation according to the task allocated by the host process; s5, collecting data calculated by each process into a host process, and processing to obtain a current iterative solution vector; s6, performing convergence judgment, entering the step S7 when matrix solution convergence is performed, otherwise, returning to the step S3, and obtaining a solution vector according to current calculation; and S7, judging whether a current nonlinear iterative solution convergence condition is established or not, and when the convergence condition is not established, performing solution again until nonlinear solution is converged. According to the invention, the design is optimized, and the solving efficiency of complex large system simulation can be remarkably improved by fully utilizing hardware computing resources in a local area network.

The invention provides a manifold based non-linear space object motion state determination method comprising the following steps: building a space object locus observation equation; using manifold as the basic space and employing a multiple dimensioned analysis manifold method to carry out multiple dimensioned expressions of object state parameters on the manifold according to physics characteristics of the object locus embedded in a low dimension manifold in the space, thus obtaining a multiple dimensioned expression coefficient; using a non-linear iteration method to obtain a manifold multiple dimensioned expression coefficient according to the observation equation and a multiple dimensioned expression formula, thus obtaining an object state parameter estimating value. The novel method can simplify an object state parameter resolving process, thus improving resolving precision; in addition, the method is universal and suitable for high precision estimating problems of motion states of various space objects under various measuring systems in the spaceflight field.

The invention discloses an image super-resolution reconstruction method based on nonlinear predictive sparse coding. In the training process, the present invention superimposes nonlinear predictive coding items on the basis of the classic criterion function based on sparse coding algorithms, and designs own optimization strategy to minimize the objective function, in the reconstruction process, the present invention only uses a non-linear iterative step for the input low-resolution image block and the pre-trained low-resolution dictionary to directly approximate the required sparse coding , avoiding the problem of solving sparse representation coefficients for each small image block. The experimental results show that, compared with the classical image super-resolution algorithm based on sparse coding, the present invention greatly reduces the time required for the experiment while ensuring that the reconstruction result is fully competitive.

The invention discloses a photovoltaic MPPT control method based on linear iteration. The photovoltaic MPPT control method includes the following steps that (1) a point A with the voltage on a power-voltage characteristic curve being uL and a point B with the voltage on the power-voltage characteristic curve being uR are obtained, wherein the uL ranges from 0 to Um, uR ranges from Um to UOC, Um is the voltage of a point with maximum power, and UOC is an open-circuit voltage; (2) a point D with the voltage on the power-voltage characteristic curve being the same as that of a point C which is an intersection point of the tangent line of the point A and the tangent line of the point B is obtained to serve as a point with the approximately maximum power; (3) a tangent line passing through the point D is obtained, whether the absolute value |P'(uC)| of the slope of the tangent line is smaller than a predetermined accuracy threshold value epsilon is judged, if the absolute value |P'(uC)| of the slope of the tangent line is smaller than the predetermined accuracy threshold value epsilon, the step (5) is executed, otherwise the step (4) is executed; (4) if P'(uC) is larger than 0, the point D is used as a new point A, step (2) is executed, and if the P'(uC) is smaller than 0, the point D is used as a new point B, the step (2) is executed; (5), the point D is determined to be the point with the maximum power, and tracking of the point with the maximum power is achieved. According to the photovoltaic MPPT control method, higher tracking speed and higher tracking accuracy can be obtained at the same time, and the method is simple and easy to implement.

The invention discloses a high-resolution remote sensing image change detection method and system. The method comprises the steps that firstly, carrying out super-pixel segmentation on two high-resolution remote sensing images through a simple linear iteration algorithm SLIC, and obtaining a first super-pixel segmented image and a second super-pixel segmented image; Extracting spectral features and texture features of the first super-pixel segmented image and the second super-pixel segmented image; Calculating feature difference vectors of the first super-pixel segmented image and the second super-pixel segmented image according to the spectral features and the texture features; Obtaining a first variation amplitude image and a second variation amplitude image according to the characteristic difference vector; Fusing the first amplitude-of-change image and the second amplitude-of-change image to obtain a unified amplitude-of-change image; And obtaining a change image by using a conditional random field algorithm according to the unified change amplitude image. According to the method, generated noise can be eliminated, boundary information of a changed image can be well reserved, and the accuracy of a detection result is improved.

The invention discloses a method for automatically extracting town built-up areas by using town texture feature density, belongs to the field of remote sensing image information extraction, and aims at high-resolution remote sensing data to construct town texture feature density of remote sensing images by using a multi-scale simple linear iteration algorithm so as to automatically extract the town built-up areas. The method comprises the following steps: firstly, sensitivity of different texture features of the gray level co-occurrence matrix in town extraction is analyzed through utilizing J-M distance, and the texture difference features serve as the classification basis; and then, multi-scale superpixel segmentation is carried out by utilizing a simple linear iterative clustering algorithm (SLIC), a feature density image after multiple times of segmentation is calculated by taking superpixels as a range for calculating local feature density, and superposition is carried out to construct a feature density image of the to-be-classified image. The extraction result of the method has good integrity rate and accuracy, and particularly in the aspect of integrity, the coincidence degree of the town edge extracted through the method and the actual edge is extremely high, and the manual detection effect can be achieved.

The invention provides a decision-making method and device based on a game model. The method comprises the following steps: analyzing a bundled sales game model of a platform retail enterprise; obtaining a constraint condition and an optimal solution calculation relational expression; enabling the constraint condition and the optimal solution calculation relational expression to participate in thesolution of the optimal game result of the profit game model of the producer of the product; utilizing non-linear iterative algorithms and the profit game model of the producer and based on the constraint condition, carrying out iteration of a solving result; solving and obtaining an optimal game result of the profit game model of the producer of the product to improve the accuracy of solutions,and on the basis, under the condition that each optimal game result conforms to the constraint condition, inputting each optimal game result into the optimal solution calculation relational expression, so as to achieve the analysis of the complex game model, achieving the nested solution of the binding sales game model of the platform retail enterprise and the profit game model of the producer. The analysis accuracy is ensured, and the accuracy of the decision result is ensured.