38 results about "Iterative approximation" patented technology

The invention provides a numerical simulation method and system constructed based on a neural network and an iterative approximation method. The method comprises the steps that a to-be-solved partial differential equation is determined; constructing a precise solution data set; establishing a deep learning objective function network; constructing a loss function; training the network through the random training set; and outputting the prediction solution. According to the method, the time variable is used as network input, continuous time prediction under single-round training is realized, and the problem that a traditional discrete model needs multiple times of iterative training is solved; tasks such as target detection and image classification can be executed, screen and back images of the mobile terminal / tablet personal computer are input in product defect detection, and defect existence, position or type are output; and inputting a sea surface image in sea surface ship identification, and outputting existence, position or type of the ship. According to the method, the number of training iterations in a Burgers equation experiment is reduced by 19.3%, and full-time-domain repeated training of a traditional discrete model is eliminated; internal point sampling is avoided through time semi-discretization, and the calculation efficiency is remarkably improved.

The embodiment of the invention provides a data processing method and device, a storage medium and a program product. In the method, a data compression device obtains data to be compressed; and performing L times of iterative approximation on the to-be-compressed data. Wherein the L times of iteration approximation comprises the following steps: respectively selecting L column vectors from L codebook matrixes to approximate the data to be compressed, wherein elements in the L codebook matrixes comprise two different numerical values; and generating compressed data after the to-be-compressed data is compressed based on the L column numbers of the L column vectors in the L codebook matrixes. Therefore, the original signal is approximated by using the two small codebooks with different numerical values, the search and storage overhead is reduced, and the execution efficiency is improved.

The invention discloses an irregular roadway colored point cloud hole filling method, and belongs to the field of underground mine three-dimensional modeling and point cloud data processing. Firstly, dense slicing is carried out on roadway point clouds, slices are projected to a two-dimensional plane for polar coordinate sorting, disordered point clouds are ordered, then the slices serve as type value points, a control point solving matrix is solved through a chasing method, initial control points are rapidly obtained, the control points are optimized through a progressive iterative approximation algorithm (PIA), and the optimal control points are obtained. The method comprises the steps of effectively reducing errors between a fitting curve and an original point cloud, performing non-uniform cubic B-spline adaptive fitting interpolation according to a point spacing to realize uniform filling of holes, and finally performing K-nearest neighbor search on interpolation points, calculating an RGB weighted average value of neighborhood points and assigning the RGB weighted average value to realize color repair of a hole region. The method has a good repairing effect on various types of holes of the irregular roadway of the mine, uniform color assignment can be carried out on point clouds at the same time, and the modeling quality and efficiency are effectively improved.

The invention relates to the technical field of submersible pump testing, and discloses a permanent magnet variable frequency submersible pump testing method and system. The method comprises the following steps: acquiring an original response signal cluster of the submersible pump in multi-stage variable-speed operation by constructing an integrated test environment; decoupling and enhancing the signal cluster based on time sequence dependence, and separating out an independent hydraulic performance characteristic signal group and an independent electromagnetic vibration characteristic signal group; and inputting the characteristic signal group into a dynamic working condition simulator, and restoring an internal flow field pressure cloud picture and a rotor stress distribution picture of the submersible pump under the simulated working condition through an iterative approximation algorithm. The two are fused to deduce a potential failure mode and its evolution trajectory, and an enhanced test procedure is generated accordingly. According to the method, accurate separation of multi-physics field test signals and visualization of internal states are realized, and the accuracy of fault attribution and the advance of failure prediction are improved.

The invention belongs to the field of robot trajectory optimization, and particularly relates to a trajectory optimization method and system considering redundancy characteristics of a robot macro-micro force control grinding and polishing system. The method comprises the following steps: S1, generating an initial grinding and polishing track based on a CAD model, and providing a discrete point curvature estimation method to estimate the curvature of each path point; s2, on the basis of the curvature of the discrete points, a mixed optimization target of a curve energy item and a curvature change rate about curvature square is constructed, arc length and redundancy direction optimization constraints are considered, and an adjusted grinding and polishing track is generated through a nonlinear optimization method; s3, on the basis of the optimized initial points, an initial dominant point selection method based on an angle-chord height-chord length joint criterion is adopted, and the trajectory precision is kept while the number of dominant points is reduced; and S4, on the basis of the optimized curved surface dominant points, an improved least square iterative approximation method is provided to generate a parameterized B-spline grinding and polishing path generation method, and final grinding and polishing track generation is achieved.

The invention belongs to the field of mechanical arm control, and relates to a method and device for controlling a mechanical arm by utilizing double-macro lens constraint, and the method comprises the following steps: carrying out system calibration on a double-macro endoscope and the tail end of the mechanical arm to obtain transformation parameters; acquiring an image of a target object in the working space to obtain an original image; performing preprocessing and target detection on the original image to obtain a preprocessed image containing target object features; performing attitude estimation on the preprocessed image to obtain pose information of the target object in a lens coordinate system; a hierarchical iterative approximation strategy of visual feedback is adopted, and a mechanical arm is controlled to drive a double-macro endoscope to gradually approach a target object; the pose information of the target object under the lens coordinate system is converted into a base coordinate system of the mechanical arm; based on the pose information of the target object under the base coordinate system, a mechanical arm joint rotation angle instruction is solved and generated, and a mechanical arm is controlled to complete grabbing operation on the target object; therefore, the fine operation requirement of the manipulator in a narrow working space is met.

The present invention relates to the field of microwave filters, and discloses a two-layer optimization algorithm applied to the coupling matrix transformation of microwave filters, which abstracts the coupling matrix topological transformation problem into an optimization problem and adopts the idea of ​​hierarchical optimization to solve it; in the first layer optimization, an adaptive differential evolution algorithm is used to solve the optimization problem, and a solution with better performance is searched in a larger optimization space. The adaptive differential evolution algorithm is an optimization algorithm based on swarm intelligence, which can approach the optimal solution through continuous iteration of the swarm. By using this algorithm, a relatively good solution can be found. In the second layer optimization, based on the solution obtained by the first layer optimization, an adaptive quasi-Newton method is used to solve the optimization problem, and a solution with better performance is quickly searched in the local optimization space. The quasi-Newton method is iterated based on gradient information, and only needs to calculate the approximate value of the inverse matrix of the Hessian matrix, so the calculation efficiency is high.

The invention discloses a calculation method for predicting conductor icing growth, which is characterized by comprising the following steps of: 1, dividing a flow field calculation domain into a potential flow region and a near-wall region, and further obtaining air velocity distribution of all points to be solved in the calculation domain; step 2, searching a limit collision point on the surface of the zero-collision conductor by combining a water drop motion control equation and a dichotomy iterative approximation mode; 3, substituting the water drop collision coefficient and the air velocity distribution into the thermodynamic model, and establishing a mass conservation model and an energy conservation equation in a conductor icing surface unit body based on the thermodynamic model in a Messinger infinitesimal control body; 4, the icing thickness corresponding to each infinitesimal is calculated, and coordinates of a new ice shape boundary are obtained in the icing increasing direction; and possible electric power system accidents can be predicted in advance, so that the stable operation of the electric power system can be ensured, and unnecessary social and economic losses can be reduced.

The invention relates to a dynamic scheduling method and device for an AC / DC hybrid power distribution network. The method comprises the following steps: adjusting a load demand according to a real-time electricity price and constructing a multi-source uncertainty set; constructing first-stage and second-stage optimization scheduling models according to the adjusted load demand; constructing a total expected scheduling cost function according to the first-stage and second-stage optimal scheduling models; all scene sets in the multi-source uncertainty set are traversed, based on total constraint conditions, an improved column sum constraint generation algorithm is adopted, and the first-stage optimization scheduling model and the second-stage optimization scheduling model are solved through a strong dual transformation and iterative approximation optimal solution method, so that a total expected scheduling cost function is minimized under the worst scene set, and the optimal solution is obtained. And a scheduling strategy capable of keeping the minimum scheduling cost under all scene sets is obtained. Efficient operation of the system can still be kept under the worst condition, the adaptive capacity of a power grid to renewable energy sources and load fluctuation is improved, and the economical efficiency and reliability of the whole system are improved.

The invention provides a vibration reduction LOS guidance method for curve tracking control of an underactuated unmanned ship, and relates to the field of robustness artificial intelligence. The method comprises the following steps: acquiring an optimal path parameter corresponding to the underactuated unmanned ship based on an iterative approximation numerical solution method; introducing a tracking error item and a tracking error speed item into drift angle estimation compensation; based on the drift angle estimation compensation, designing a self-adaptive vibration reduction LOS algorithm, and obtaining an expected heading angle of the unmanned ship by combining optimal path parameters; designing a sliding mode surface in combination with the expected heading angle, designing a nonlinear sliding mode controller, and generating a control torque; and carrying out expected heading tracking on the underactuated unmanned ship based on the control moment. According to the invention, the problem that accurate path tracking and stable navigation are difficult to realize in the prior art in the case of time-varying environment interference is solved.

The invention discloses a high-precision and low-delay arbitrary bottom logarithmic function calculation system and method, and the method comprises the steps: carrying out the preprocessing of an input bottom number and a true number, generating an initial value of an iteration variable, introducing an iteration direction factor, and carrying out the iteration operation, and finally obtaining a logarithmic function result. According to the method, multiple bits are processed through single iteration through radix-8-base decomposition, the speed of approaching an accurate value in each iteration is increased, the total number of iterations is remarkably reduced, and meanwhile considerable calculation precision is kept.

The invention discloses a method for grinding a conical spiral groove through a formed grinding wheel based on iterative approximation, and the method specifically comprises the steps: firstly, constructing a parameterized geometric model of the formed grinding wheel and the conical spiral groove; then, a formed grinding wheel grinding kinematics model is constructed and used for guiding the grinding process, and the grinding mode of the conical spiral groove is achieved; secondly, on the basis of the principle that a grinding wheel grinding curve is tangent to a section contour curve, the rotation contour of a formed grinding wheel is calculated in an iterative approximation mode; and finally, actual machining verification is carried out on the grinding wheel contour obtained through calculation. According to the method, the accuracy of the section contour of the spiral groove is comprehensively guaranteed, and the correctness and effectiveness of related theoretical models of the method are proved.

This invention discloses a factor graph-based distributed spaceborne radar network air target altitude estimation algorithm, comprising the following steps: Step 1, constructing an air target motion model and a spaceborne radar measurement model in the geocentric fourth equatorial coordinate system, and constructing a factor graph-based spaceborne radar target tracking model; Step 2, based on the factor graph, introducing the coupling function relationship between local variables of the air target, and obtaining the posterior marginal distribution of the local variables; using a nonparametric confidence propagation algorithm to calculate the posterior marginal distribution of the local variables, and obtaining the estimated value of the local variables based on the calculation result of the posterior marginal distribution; Step 3, based on the estimated value of the local variables, using an iterative approximation method to obtain the target altitude of the air target in the geodetic coordinate system. This solves the problems in existing technologies such as large elevation angle measurement errors for single spaceborne radars, difficulty in accurately tracking air targets, and inaccurate altitude estimation.

A multi-source domain approximation fault diagnosis method combining real data and mechanism data for fluctuating rotating speed working conditions is mainly characterized in that a dynamic multi-source domain migration strategy is designed by fusing bearing mechanism model data and real data of large fluctuating rotating speed working conditions, and a maximum difference source domain between domains is iterated and approximated by using distance measurement distance; and label space alignment is realized in combination with the minimum class confusion loss, so that the problems of feature drift and label data scarcity caused by rotating speed fluctuation are solved, the cross-domain fault diagnosis precision is remarkably improved, the recognition rate and robustness of bearing faults are improved, and effective diagnosis of the bearing faults under the rotating speed fluctuation is realized.

A problem of time elongation in image reconstruction due to necessity of many iterations is solved, when an image is reconstructed by performing iterative approximation on measurement data acquired by an MRI apparatus without full-sampling. Density is estimated in the course of the iteration, and density is corrected using thus estimated density. Density estimation is performed not only on initial values but also on every iteration process, thereby enabling convergence with a smaller number of iterations.

The invention discloses an indoor positioning system based on an ultra-wideband communication technology. The indoor positioning system comprises a base station, a label and an upper computer, the upper computer carries out data interaction with the data processing and storage module, the monitoring and data display module and the communication module, the upper computer receives data from a base station, the data processing and storage module extracts positioning information and comprises a preliminary positioning stage and an accurate positioning and error correction stage, and the positioning stage utilizes a trilateration method to realize accurate positioning and error correction. Calculating the initial position of the label according to the distance information between the label and the at least three base stations, and then further optimizing the initial position by utilizing a weighted centroid algorithm and combining the position information and the relative distance of the plurality of base stations to obtain an estimated position; in the accurate positioning and error correction stage, the position obtained in the initial positioning stage is used as the initial position estimation of a Taylor series expansion method, and the Taylor series expansion method is utilized to gradually approach the real position in an iterative approximation mode.

Embodiments may include a system and method for configuring operation of an information handling system based on a plurality of variants. Embodiments may determine the statistical operations for the variants, define priorities (for example less noise vs. better performance) and use iterative approximation of each variant in relation to all variants to select a profile and then configure operation of the information handling system based on the selected profile.

The invention relates to the technical field of laser radar imaging, in particular to a laser reflection tomography projection registration method, system and device based on target contour segmentation and fitting and a medium. The method comprises the following steps: firstly, converting obtained target laser reflection echo projection data into transmission projection data; according to the transmission projection data, a BP algorithm is called to reconstruct a two-dimensional contour, and an initial target image is obtained; secondly, calling a skeleton extraction algorithm to extract an initial target image to obtain skeleton lines; an iterative approximation algorithm is called to segment skeleton lines, and skeleton line segments are fitted; correcting transmission projection data according to a skeleton line segment obtained by fitting; and finally, calling a BP algorithm to reconstruct the corrected transmission projection data to obtain a target image. On the basis of a BP algorithm, contour segmentation and fitting are combined, projection data are corrected, interference caused by random jitter of laser pulse emission and detector receiving is reduced, a reconstructed target contour image is clearer, and optimization of a reconstruction result is achieved.

The invention discloses a registration system and method based on a multi-modal medical image, and belongs to the technical field of medical image registration. The system comprises a data acquisition and model construction module for constructing a three-dimensional model for acquired static medical image data, calculating a geometric feature map and acquiring real-time dynamic positioning data; the rough registration module is used for obtaining an initial rigid transformation matrix and transforming all data points of the real-time dynamic positioning data based on the initial rigid transformation matrix; the fine registration target construction module is used for constructing a first target function for calculating target deviation and constructing a registration transformation model; the iterative registration and real-time fusion module is used for updating parameters of the registration transformation model by using an iterative approximation algorithm; the result output module is used for superposing the final registration real-time positioning data onto the three-dimensional model; therefore, a doctor can carry out mapping on the three-dimensional model superposed with the final registration real-time positioning data. According to the invention, the mapping time can be significantly reduced.

The invention discloses a rapid calculation method for a flight envelope of a high-subsonic-speed fixed-wing aircraft. The rapid calculation method comprises the following steps: acquiring basic input parameters of a dynamic model of the high-subsonic-speed fixed-wing aircraft; based on the basic input parameters of the kinetic model, performing iterative approximation calculation on the flight attack angle by taking the condition that the available thrust of an engine is not less than the flight resistance as an iteration constraint condition, and determining a boundary parameter set for defining a flight envelope boundary; and based on the boundary parameter set, obtaining a flight envelope of the high-subsonic-speed fixed-wing aircraft. Traversal iterative approximation is implemented by using the high-speed calculation capability of a computer, and the balance relationship between thrust and resistance is verified in real time in each iteration, so that the calculation efficiency of the flight envelope boundary of the high-subsonic-speed fixed-wing aircraft and the adaptability to a complex nonlinear pneumatic system are remarkably improved; the problems that a traditional method depending on an analytic formula and experience is insufficient in calculation precision under the working conditions of strong nonlinearity and full coupling, and seriously depends on step iteration and a large number of experimental verification are solved.

The present invention discloses a sequential convex optimization method for distributed migration maneuvers of multiple spacecraft, which belongs to the field of aerospace technology. The implementation method of the present invention is as follows: establishing a dynamic model of cluster migration maneuvers; establishing an optimization model for large-scale cluster migration maneuvers based on the control mode, obstacle avoidance, and fuel saving mission characteristics of cluster migration maneuvers. The maneuver optimization problem is convexified by relaxing nonlinear equality constraints, and the cluster migration maneuver optimization problem is decomposed by decoupling the obstacle avoidance constraints. The time-varying continuous distributed decoupled optimal problem is converted into a discrete subproblem of convex optimization through numerical integration. The convexified discrete subproblem is used as the inner link of each iteration step, and the optimal cluster large-scale migration maneuver transfer trajectory and corresponding control are obtained by solving it in a finite number of steps using a sequential iterative approximation strategy. The present invention can realize the online optimization of the continuous thrust migration maneuver trajectory of cluster spacecraft under the premise of considering multiple taboo path constraints and time constraints.

The application discloses a periodic description blade profile global fairing reconstruction method. First, the application generates initial profile fitting curves according to curve control vertex data and node vector data, and refines the node vector of the profile fitting curve in a least square progressive iterative approximation fitting process by using a curvature guided profile subdivision method according to the discrete curvature distribution of the profile value points, so that the curve control vertex distribution is fitted to the profile shape characteristics, thereby obtaining the subdivided profile fitting curve. Finally, according to the local support of the base function, the subdivided profile fitting curve is updated and fitted in a region-by-region and successive manner by using a curvature constraint adaptive local fitting method until a global fairing periodic reconstruction curve meeting the accuracy requirement is obtained. The application finally generates a global fairing reconstruction curve which highly accurately approximates the profile value points, guarantees the reconstruction fitting efficiency, and realizes the closed characteristic by the periodic base function, and can be effectively used for subsequent T-spline blade surface modeling.

The present invention discloses a method, system and medium for maximizing communication rate based on the MM algorithm, belonging to the field of wireless communication technology. The method includes: S1: In the multi-user system construction stage, a multiple-input single-output URLLC system is constructed with the assistance of intelligent reflecting surfaces; S2: In the stage of constructing the system sum rate maximization problem, according to the multiple-input single-output URLLC system, the packet decoding error probability of each single-antenna user, the precoding vector at the BS, and the distributed passive beamforming with discrete phase shifts are jointly optimized to obtain the system sum rate maximization problem; S3: In the stage of solving the system sum rate maximization problem, the MM algorithm is used to transform the objective function of the system sum rate maximization problem, and the AO algorithm is used to solve the optimization variables. Finally, the optimal solution of the system parameters is obtained through iteration. The complex optimization problem is simplified by the iterative approximation method, and the closed-form solution expression of the optimization variable sub-problem is effectively obtained.

The invention discloses an intelligent respirator operation monitoring system. Comprising a data acquisition module, a safety margin evaluation function definition module, a safety margin standardization module, an initial dangerous point calibration module, a risk direction deviation detection module, a self-adaptive step length updating module, an iterative convergence judgment module, a system training module and a respirator operation monitoring module. The invention belongs to the field of operation monitoring, and particularly relates to an intelligent respirator operation monitoring system, which is characterized in that gradient is used as guidance, continuous iteration approximation to a dangerous point is realized, and a risk peak value is accurately positioned in a respiratory cycle; an included angle between a real risk trajectory and an ideal trajectory is quantified through risk direction deviation detection, and a transient nonlinear event of sudden airway blockage is captured; angle normalization curve amplification is carried out in a breathing stable interval, and approaching to the most dangerous point is accelerated; linear convergence correction is carried out in the nonlinear sudden change interval, false alarm caused by short-term noise is avoided, and the operation monitoring effect is further improved.

A multiply-accumulate successive approximation (MASAR) array is provided. The MASAR array includes a plurality of MASAR cells, each MASAR cell including a multiplier configured to perform a digital multiplication between an input activation received for an input and an operand to calculate a result, and a unit capacitor configured to store the result as an analog charge. The MASAR array further includes digital logic configured to perform an analog summation of the analog charges of the unit capacitors of the plurality of MASAR cells to determine a digital output of the multiplication.

The invention relates to the technical field of ultra-short baseline positioning, in particular to an ultra-short baseline positioning method and system based on sound propagation path similarity, and the method comprises the steps: constructing a spatial position relation between a hydrophone and a sound source target according to a position vector of the hydrophone in a USBL system in a carrier coordinate system; taking the sound signal propagation delay distance as an observation value, and establishing an observation error function model according to the spatial position relationship, wherein the observation error function model takes the effective sound velocity, the sound source position vector and the sound source position vector modular square as undetermined parameters; and carrying out iterative approximation on the position error function model by utilizing a least square criterion and through the virtual observation value, and obtaining the optimal effective sound velocity, sound source position vector and modular square thereof in the function model through iterative approximation. According to the method, under the condition that no extra information assistance is needed, only the approximate initial value of the effective sound velocity is provided, the influence of sound ray bending on ultra-short base line positioning can be eliminated, and the method is suitable for being applied to an ultra-short base line positioning / short base line positioning system.

The application relates to an alternating current-direct current hybrid power distribution network dynamic scheduling method and device, which comprises the following steps: adjusting load demand according to real-time electricity price and constructing a multi-source uncertainty set; constructing first and second stage optimization scheduling models according to the adjusted load demand; constructing a total expected scheduling cost function according to the first and second stage optimization scheduling models; traversing all scene sets in the multi-source uncertainty set, adopting an improved column constraint generation algorithm based on total constraint conditions, and solving the first and second stage optimization scheduling models through strong duality transformation and iterative approximation of optimal solutions, so that the total expected scheduling cost function is minimized under the worst scene set, and a scheduling strategy which can keep the scheduling cost minimum under all scene sets is obtained. The application ensures that the system can still keep high efficient operation under the worst condition, improves the adaptability of the power grid to renewable energy and load fluctuation, and improves the economy and reliability of the whole system.

The present application discloses an integrated method, device and product for alignment and calibration of an inertial group, and relates to the field of calibration of an inertial group. The method comprises: constructing a gyro output error model of an inertial group based on the principle of equivalent rotating vectors, and performing linearization processing to obtain a linearized error model of the gyro output; solving the linearized error model by an iterative approximation solution method to obtain an estimated value of the misalignment angle of the sub-inertial navigation in the inertial group; obtaining a gyro output error rewriting model based on the estimated value of the misalignment angle of the sub-inertial navigation and the gyro output error model, and using a multi-sampling point data solution method to solve the gyro output error rewriting model under inequality constraints to obtain an estimated value of the gyro zero bias of the sub-inertial navigation, thereby achieving alignment and calibration of the inertial group. The present application is suitable for small and large misalignment angle conditions without relying on the prior information of INS or designing filters, and can improve the accuracy of alignment and calibration of the inertial group.

The invention discloses an inventory management method and system based on iterative approximation, and the method comprises the steps: obtaining the historical consumption data of a to-be-managed spare part, calculating the long-term average consumption based on the historical consumption data, and calculating a first inventory target based on the long-term average consumption. Acquiring a first actual consumption quantity of the to-be-managed spare parts in the first management period; and calculating a first consumption deviation of the spare part to be managed in the first management period. And iteratively generating a second inventory target based on the periodic deviation and the learning rate by using an iterative approximation model, and further generating inventory management information of the spare parts to be managed. According to the invention, the inventory target can be dynamically adjusted along with the actual consumption condition of the spare parts, so that the accuracy and flexibility of inventory management are improved.

The invention provides a limiter construction method and system based on WENO physical characteristics, and the method comprises the steps: constructing a WENO limiter which is used for suppressing the non-physical oscillation of a neural network in a process of solving a partial differential equation; carrying out semi-discretization processing on the time domain, and realizing discretization of time steps by adopting an implicit Runge-Kutta method; a neural network structure fused with the WENO limiter is constructed, the neural network comprises a plurality of time step modules, and each module is composed of a feedforward neural network, an iterative approximation unit FNN + IA and the WENO limiter; training the neural network by minimizing the loss function containing the physical constraint to obtain a numerical solution of a partial differential equation; the method is deployed in an actual application occasion, and any one or more tasks of target detection, image classification, defect identification and target positioning are executed. According to the method, the limiter based on WENO physical characteristics is constructed and embedded into the neural network structure, and the non-physical oscillation phenomenon occurring in the hyperbolic partial differential equation solving process is remarkably inhibited.