93results about How to "Iterative convergence is fast" patented technology

The invention discloses a simultaneous localization and mapping method based on vision and laser radar, and belongs to the field of SLAM. According to the method, the laser odometer and the visual odometer are operated at the same time, the visual odometer can assist the laser point cloud in better removing motion distortion, and meanwhile the point cloud with motion distortion removed can be projected to the image to serve as depth information for motion calculation of the next frame. After the laser odometer obtains a good initial value, the laser odometer is prevented from falling into a degradation scene due to the defects of a single sensor, and the odometer achieves higher positioning precision due to the addition of visual information. A loop detection and repositioning module is achieved through a visual word bag, and a complete visual laser SLAM system is formed; after loop is detected, the system performs pose graph optimization according to loop constraints, accumulated errors after long-time movement are eliminated to a certain extent, and high-precision positioning and point cloud map construction can be completed in a complex environment.

The invention discloses an SCARA robot trajectory tracking control method based on prediction indirect iterative learning. Aiming at the problem that users are not allowed to compensate torque signals output by a servo drive of an alternating-current motor under most conditions in actual engineering, the SCARA robot trajectory tracking control method based on prediction indirect iterative learning is proposed. Firstly, a double-closed loop feedback controller directly acted on a robot body is designed, and includes a P type position closed loop and a PI type speed closed loop; and then, a prediction iterative learning controller (A-ILC) with a feedforward effect is designed, and a control effect at the sampling time t in next operation is adjusted by using error output information at the sampling time t+delta in previous operation batches. Compared with a proportional differential iterative learning controller (PD-ILC), the A-ILC is faster in iterative convergence speed and higher in tracking precision; and compared with an A-ILC without the feedforward effect, the A-ILC with the feedforward effect can eliminate external disturbance more quickly and effectively.

The invention discloses an FPGA dynamic power consumption estimation method based on a BP neural network. The FPGA dynamic power consumption estimation method comprises the following steps that (1) the input and output quantities of four modules are obtained as sample data; (2) data screening and data preprocessing are carried out on the sample data; (3) BP neural network models of the four modules are respectively constructed according to the processed sample data; (4) part of the sample data are adopted as the training data of the BP neural network, the trained neural network is obtained after BP neural network training is carried out, and then power consumption of neural network output is obtained; (5) the sample data with the training data removed are adopted as the testing data of the BP neural network, and the obtained power consumption is compared with the testing data; (6) the power consumption output by the neural network is restored to be actual power consumption values; (7) the obtained power consumption estimation values of the four modules are summated to obtain a total power consumption value. The power consumption values can be accurately predicted through automatic study of the BP neural network.

The invention discloses an asynchronous graphic data processing system based on a GPU. The asynchronous graphic data processing system comprises achieving a combining coloring algorithm for changing an original single and heuristic coloring algorithm to the combining coloring algorithm; a data preprocessing strategy for changing an original preprocessing partition method based on a vertex degree to a mode based on vertex coloring distribution; and an asynchronous processing executing engine which changes the executing mode of a processing engine from a synchronous BSP mode to an asynchronous processing mode and achieve lockless asynchronous programming on the GPU in combination with a combining coloring algorithm module. In the asynchronous processing executing engine, an iteration processing module and a data flow executing module are achieved and the bandwidths and computing capabilities of a CPU and the GPU are managed and used in a unified manner. According to the graphic data processing system based on the GPU, the asynchronous processing executing engine may greatly accelerate graphic algorithm convergence and solve problems of synchronous waiting expense and asynchronous programming lock expense on the GPU so as to improve the graphic data processing capability of the GPU.

The invention discloses an improved iterative learning control method and an improved iterative learning control system for a permanent magnet linear synchronous motor (PMLSM). The control method comprises the following steps of: calculating the control voltage of a stator of the PMLSM by adopting a time axis and iteration axis superposed iterative control law algorithm; introducing an initial control variable u0 on the time axis of the iterative control law algorithm, and designing an adaptive factor alpha; and adding a feedforward control variable to the (k+1)-iterated control voltage of the PMLSM following an iterative learning law. The control system comprises an embedded controller, a power driving module connected with the stator of the PMLSM, and a rotor displacement sensor arranged on the PMLSM. The oscillation of an initial iteration track is avoided, and an iterative convergence speed is increased; and control accuracy is improved to be 0.55 percent.

The invention discloses a channel allocation method and equipment, which is used to improve channel allocation efficiency. The method comprises parallel iterative adjusting occupied relationship between users and channels according to channel usability information till a preset stopping condition is satisfied and allocating channels according to adjusting results. The equipment comprises an information adjusting unit, a check control unit and a channel allocation unit, the channel adjusting unit is used to parallel iterative adjust the occupied relationship between users and channels, the check control unit is used to judge whether a preset stopping condition is achieved, and the channel allocation unit is used to allocate channel according the adjusting results. The invention can effectively improve channel allocation efficiency.

The invention provides a element two-stage partition and twice polycondensation parallel method based on multiple document flows. The method includes the steps of firstly, dividing a finite element model into a plurality of initial sub-areas, and saving model information of each initial sub-area independently in a document; secondly, by each progress, simultaneously and independently forming a system equation of each sub-area, and performing polycondensation to remove internal freedom degree of each system equation; thirdly, by a local main process, forming packaging sub-area system equations, and performing polycondensation to remove internal freedom degree of each packaging sub-area system equation; fourthly, by the local main process, solving an interface equation, and solving the internal freedom degree of each packaging sub-area system equation by means of back substitution; fifthly, solving internal freedom degree of each initial sub-area according to results of the top packaging sub-areas by means of back substitution; terminating if iteration ends, or else starting again from the second step. On the basis of realizing distributed data storage, large-scale finite element parallel computing efficiency is increased by fully utilizing structural features of large-scale parallel computer systems by means of maximally realizing communication localization.

The invention discloses a hybrid new energy power system set combination optimization method. The method comprises the following steps: step 1, establishing a new energy power system set combination scheduling mathematic model, wherein this step includes establishment of a new energy set optimization scheduling target function, by taking set switch states and power of each set as an input quantity and set operation cost as an output quantity, the target function is shown in the descriptions, minF is a target function when the system operation cost is the smallest, T is a period number of scheduling periods, N is a set number, I<i,t> is an operation state of a set i at t time, when the set is booted, I<i,t>=1, when the set is shut down, I<i,t>=0, P<i,t> is generating power of a thermal power set i at t time, f(P<i,t>) is a generating cost of the thermal power set i at the t time, and S is a starting cost of the thermal power set i; and arrangement of constraint conditions; and step 2, carrying out optimization searching calculation on the mathematic model in the first step by use of a heuristic optimization algorithm. The method provided by the invention has the following advantages: the algorithm is simple in structure, the optimization searching capability of the target function is high, the iteration convergence speed is fast, and the algorithm has quite good stability.

The invention belongs to the field of array signal processing, and particularly relates to a sparse direction of arrival angle estimation method based on a multiple-input multiple-output array. The method comprises the following steps of s1, establishing a signal model; S2, estimating and vectorizing a signal covariance matrix; S3, expanding the spatial degree of freedom by using a co-prime matrixprocessing method; S4, obtaining target direction of arrival estimation by adopting a neighbor operator method; and S5, determining a target direction of arrival and number. The method disclosed by the invention has the following beneficial effects that the multi-target angle estimation precision under the conditions of low signal-to-noise ratio and small samples can be improved, and method for estimating the target angle is fast in iterative rate of convergence, and high in operation efficiency, can be applied to a real-time multi-target angle estimation system, and has important engineeringapplication value.

The invention provides a hybrid gravitational search algorithm-based stereo microphone array optimization design method. The method includes the following steps that: a fitness function of the main parameters of a stereo microphone array under a hybrid gravitational search algorithm framework is constructed; and an array structure of the stereo microphone array, which has narrower mainlobe width under a certain sidelobe level, is optimized. With the stereo microphone array which is optimized and designed by the method of the invention adopted, the defect of incapability of identifying the directions of arrival of back and front waves based on traditional planar array positioning results can be eliminated, and back incoming waves can be suppressed, and passive location of a target noise source under a complex sound environment can be realized.

The invention relates to a bearing vibration signal sparse reconstruction method. The method includes steps: S1, on the basis of discrete cosine transform, subjecting an original bearing vibration signal x to sparsification to obtain sparse representation theta of x in a transform domain psi, wherein x=<Theta, and psi is an orthogonal matrix; S2, on the basis of restricted isometry conditions, using an observation matrix phi for compressing the signal x to obtain an observation signal y, wherein y=Phix=Phi<Theta; S3, adopting an ADMM (alternate direction multiplier method) and an LSQR (least square QR-factorization) algorithm to obtain a reconstructed signal according to the observation signal y. Compared with the prior art, the method has advantages that sparse reconstruction of bearing vibration signals with nonlinearity and non-stationarity characteristics is realized quickly and effectively while high reconstruction precision is guaranteed.

The invention provides a three-dimensional path planning method and a three-dimensional path planning system of an underwater robot. The three-dimensional path planning method of the underwater robotincludes: building a three-dimensional environment model of the underwater robot; initializing speed attribute and position attribute of each beetle individual in a beetle swarm algorithm in the three-dimensional environment model, and obtaining a global extremum in a beetle swarm according to the speed attribute and the position attribute; performing iteration updating on the global extremum by updating the speed attribute and the position attribute of each beetle individual, and obtaining an objective planned path according to a target global extremum finally obtained after the iteration updating is completed. The three-dimensional path planning method adopts the beetle swarm algorithm to plan the three-dimensional path of the underwater robot, is more flexible than a traditional path planning method, has strong adaptive capacity to complex underwater non-structural environment, has the characteristic of being high in iteration convergence speed when compared with a basic particle swarm algorithm, and can reduce a probability of trapping in a locally optimal solution.

The invention belongs to the field of radar signal processing, and particularly relates to an SA-ISAR (Sparse Aperture-Inverse Synthetic Aperture Radar) self focusing method based on structure sparsity and entropy joint constraints. The method comprises the following steps of Step 1, performing echo modeling on radar echo subjected to envelope alignment; Step 2, applying layered structured sparseprior to an ISAR image; Step 3, updating the ISAR image and an upper layer variable by a relax variational bayes method; Step 4, updating a phase error through a minimum entropy method based on fixedpoints; and Step 5, judging whether a termination condition is reached or not, stopping iterative loop if the termination condition is reached, returning to the Step 3 if the termination condition isnot reached, and outputting an image subjected to self focusing after the termination condition is reached. The SA-ISAR self focusing method has the advantages that the self focusing precision of theISAR images at the sparse aperture can be improved, so that the formed ISAR images are clearer; the calculation complexity is lower; the iterative convergence speed is faster; and the sparse apertureresistance capability is high.

The invention belongs to the field of array signal processing, and in particular relates to a fast target angle estimation method based on sparse Bayesian learning. The method comprises the followingsteps of S1, performing initialization on the parameters to be estimated of gammaj and sigma0, wherein j is equal to 1,2 to N; S2, quickly obtaining signal posterior probability density functions at each moment by using the AMP algorithm; S3, updating values of the parameters to be estimated of gammaj and sigma0 by using the EM algorithm, wherein j is equal to 1,2 to N; and S4, determining whetherthe update iterative process of the parameters to be estimated converges, returning to the S2 to re-iterate if not, and if so, jumping out of the loop and determining the direction and quantity of the target incoming waves. The method provided by the invention can improve the low signal-to-noise ratio and the multi-objective angle estimation accuracy under small sample conditions, and has the advantages of fast iterative convergence speed and high computational efficiency for estimating the target angle, which can be applied to the real-time multi-objective angle estimation system and has important engineering application value.

The invention discloses a hybrid precoding design method under a millimeter wave large-scale MIMO-NOMA system based on physical layer security. The method comprises the following steps: acquiring a signal of an mth user in a gth beam of a single cell; when the number of users may be greater than the number of RF chains, grouping users, receiving data and channel state information by each group ofusers in one wave beam, calculating the channel gain of each user, taking the user with the maximum channel gain as the cluster head of the first group of wave beams, respectively calculating the correlation between the redundant users and each cluster head user, and selecting the cluster head with the maximum correlation as the same group; generating an analog pre-code A and a digital pre-code dkaccording to the channel state information of the cluster head. According to the invention, user grouping is efficiently completed, and a good foundation is provided for subsequent analog pre-codingand digital pre-coding.

The invention provides a laminated matrix decomposition-based low frequency electromagnetic property simulation method. Firstly, the problem of low frequency collapse of the conventional electric field integral equation method is eliminated by employing an augmented electric field integral equation method; a low frequency multi-level fast multi-pole method is adopted to establish a laminated matrix expression for the coefficient matrix of the augmented electric field integral equation; the laminated matrix is further compressed by using a double compression method to remove redundant information; finally, a laminated matrix format algorithm is adopted to perform upper-lower triangular decomposition on the compressed laminated matrix. The laminated matrix decomposition can reduce calculating complexity and memory consumption to be almost linear; a precondition technology is constructed for an iteration solution and a direct solution is constructed. The method has the advantages of high solving speed, low memory consumption and precision controllability and can be applied to various kinds of low frequency electromagnetic property analysis.

The invention provides a binary classification method based on seepage analysis, which comprises the following steps: 1, constructing an index network based on a data vector; 2, seepage analysis of index network; 3, constructing a likelihood function and determining a key threshold; 4. evaluation and validation of the model. Through the above steps, the invention further provides technical supportand theoretical support for large-scale, high-dimensional and high-complexity system group classification and evaluation classification effect based on seepage analysis; in addition, the background knowledge is expressed by graph model and the network analysis is carried out by using the seepage theory based on phase change, which reduces the computational complexity, converges quickly and is suitable for large-scale computation and reduces the computational cost.

The invention discloses a high-frequency seismic wave scattering simulating method based on a fast multipole indirect boundary element method. By combining a fast multipole method (FMM) and an indirect boundary element method (IBEM), the fast multipole indirect boundary element method based on diagonal plane wave expansion is given. According to the method, the high-frequency seismic wave scattering problem which cannot be solved through other methods (such as an analytical method, a finite difference method, a finite element method and a general boundary element method) can be quickly solved, hundreds of thousands of three-dimensional scattering problems can be completed in an ordinary personal computer, an effective method is supplied to high-frequency or large-scale seismic wave scattering simulating, computer resource occupying is greatly reduced while the calculating speed is improved, and the remarkable economic benefits are brought to fast simulating of practical engineering.

The invention discloses a room impulse response function measuring method allowing the using quantity of microphones to be reduced. The room impulse response function measuring method allowing the using quantity of the microphones to be reduced comprises the following steps that a proper quantity of microphones are distributed and arranged in a space in an even and equal division mode, transfer functions are measured, and the obtained transfer functions serve as a training database; a GMM is established by using the obtained training database and corresponding position information, and model parameters are obtained; a GMR is established by using the model parameters, and an input and output formula is obtained; position coordinates are input to the input and output formula, and the transfer functions of corresponding positions are obtained. According to the room impulse response function measuring method allowing the using quantity of the microphones to be reduced, modeling is conducted on the position coordinates and the transfer functions corresponding to the positions by using the GMM, and then the input and output formula of the position coordinates and the transfer functions corresponding to the positions is obtained by using the GMR. The transfer function corresponding to the position can be obtained by inputting any position coordinate in an observation area. According to the room impulse response function measuring method, the quantity of the microphones used in the process of measuring the transfer functions can be effectively reduced, and the obtained data are more accurate.

A quick algorithm relates to the technology of an area array charge coupling device hyper-resolution image by carrying out process to a multiframe picture having mutual shift to a same target to get a frame of picture with even higher resolution characterizing in setting up a pixel level adjacent domain dual linear interpolation image degraded model and applying four-dimensional non-sparse matrix to store the model factor so as to avoid memory and process to large scale sparse matrix, carrying out guantization process to the sub-pixel shift to reduce the requirement of computing process to storage space, the improved Cimmino iterative algorithm speeds up the convergence and reduces computation volume suitable for realizing PC or DSP insertion systems.

The invention relates to a capacity and coverage optimization method based on tabu search, which belongs to the technical field of wireless communication. The method comprises steps: according to the initial power of the small base station, an initial value for a capacity and coverage target optimization function is calculated; a tabu search algorithm is adopted for iterative optimization: a solution vector for enabling the target optimization function to be optimal in the neighborhood of the current solution is calculated; if the solution vector enables the target optimization function value to be the optimal so far or not the optimal so far but in a non tabu state, the solution vector is marked as the current solution and is added to a tabu list, or otherwise, the solution vector is eliminated from the neighborhood of the current solution and the sub step of calculating the optimal solution vector in the neighborhood is returned; and after iterative optimization, the transmitting power of the small base station is updated, and the capacity and coverage target optimization function value for the small base station system at the time is outputted. Self optimization of the capacity and the coverage of the small base station system can be realized, the calculation complexity for the iterative optimization is reduced, the network overall performance is optimized and enhanced, and global optimum can be achieved.

The invention relates to a small base station capacity and coverage optimization method based on adaptive tabu search, and belongs to the technical field of wireless communication. The method comprises the steps of: according to initial power of a small base station, calculating an initial value of a capacity and coverage target optimization function; carrying out iteration optimization by adopting a tabu search algorithm, which comprises the steps of calculating a solution vector in a neighbourhood of a current solution, which enables the target optimization function to be optimal, recording as the current solution and adding the current solution into a tabu list if the solution vector enables the value of the target optimization function to be optimal currently or not to be optimal so far, but to be in a non-tabu state, or returning to calculate an optimal solution vector in the neighbourhood after removing the solution vector from the neighbourhood of the current solution, and adaptively updating a tabu length; and after completing iteration optimization, updating transmission power of the small base station, and outputting a current value of the capacity and coverage target optimization function of a small base station system. Capacity and coverage optimization of the small base station system can be implemented, complexity of optimization iteration computation is reduced, optimization and improvement of integral performance of the network are achieved, and global optimization can be achieved.

The invention belongs to the field of radar signal processing and in particular relates to a correction method for ISAR (Inverse Synthetic Aperture Radar) transverse scaling and Doppler-through movement based on revised Newton iteration. The correction method comprises the following steps: S1, carrying out translation compensation on target original echoes and acquiring a one-dimensional range profile sequence after target translation compensation is carried out; S2, acquiring a gradient of an ISAR image entropy relative to a target rotary speed omega and an equivalent rotary center longitudinal coordinate yO and a Hessian matrix; S3, revising the Hessian matrix; S4, estimating the target rotary speed and an equivalent rotary center through a revised Newton iteration method, so as to realize ISAR transverse scaling and Doppler-through movement correction. According to the correction method provided by the invention, estimation precision of a target rotary angle under conditions including a low signal-to-noise ratio and a sparse pore diameter can be improved, so that the ISAR transverse scaling precision is improved and the Doppler-through movement of a scattering point is corrected; an ISAR image focusing effect is improved. Furthermore, the correction method has the advantages of rapid iteration and convergence speed of the target rotary speed and high calculation efficiency,can be applied to a real-time ISAR imaging system and has important engineering application value.

The invention provides a pulsar frequency parameter quick search method, in particular to a frequency parameter quick search method applied to a spaceborne detector for receiving pulsar signals. The method comprises the following three steps of: determining an index function of frequency parameter search; applying a dynamic cross entropy reduction method to carry out pulse signal frequency parameter large-range coarse search to obtain an accurate search sample space; and utilizing a small-scale differential evolution method to complete rapid iterative refinement calculation of frequency parameters in an accurate sample space. According to the scheme, quick and accurate pulsar actual measurement signal frequency parameter estimation is realized so that a real-time on-orbit dynamic signal processing process is completed, and the accurate pulse signal frequency and the frequency first-order derivative estimation value can be obtained.

The invention discloses an active filter optimization method and a system based on a quantum-behaved particle swarm algorithm. The method comprises the following steps: initializing a model of an active filter according to an application background; initializing QPSO parameters and a particle swarm, and randomly assigning values to the particle swarm within a set position range; determining an active filter parameter with the best filtering effect under the application background by utilizing iterative optimization of a quantum-behaved particle swarm algorithm; and obtaining an optimal designparameter of the active filter. The filtering effect of the active filter is improved by using multiple advanced technologies, the parameters of the active filter are optimally configured, the optimalparameters are obtained under different working backgrounds, and harmonic components existing in a power grid are filtered to the greatest extent, so that the grid-connected current is close to standard sine current, and safe and stable operation of other electrical equipment is ensured. The method is high in iterative convergence speed, good in filtering effect and suitable for actual engineering application.

According to the complex contour defect verification method based on point cloud three-dimensional reconstruction, point cloud preprocessing, registration, curved surface reconstruction and defect verification methods are improved, rapid and fine three-dimensional reconstruction of complex contour point cloud is optimized, and important progress is made in the aspect of defect verification of a complex three-dimensional model. And automatic checking and visualization of the defects of the three-dimensional model are realized. In the aspect of point cloud pre-processing, the improved RANSAC point cloud pre-processing method not only can realize filtering sampling, but also can separate outliers, and has a better processing effect; in the aspect of point cloud registration, through a two-stage registration process based on feature convergence, the registration precision and efficiency are greatly improved; in the aspect of point cloud curved surface reconstruction, the fitting precision is improved, the iteration convergence speed is increased, all indexes are greatly improved, and the effect is better especially for a complex curved surface; in the aspect of defect verification, false defect points are filtered out, and the defect verification precision is further improved.

The invention provides a decoding method and device, and communication equipment, and solves the problems of slow iterative convergence and long decoding time delay of a conventional decoding method.The decoding method comprises the steps: acquiring variable node information and check node information corresponding to a sub-matrix in the i-th row and the j-th column in the l-th iteration processin a quasi-cyclic low-density parity check code QC-LDPC check matrix, wherein the l-th iteration process comprises mb times of sub-iteration; according to the variable node information and the check node information corresponding to the i-th row and the j-th column of sub-matrixes in the l-th iteration process, acquiring the posterior probability of the jz-th bit in the coded matrix vector x; andperforming decoding processing according to the posterior probability of the jz-th bit. According to the embodiment of the invention, the QC-LDPC check matrix is divided into mb * nb sub-matrixes, iterative processing is carried out according to the sub-matrixes in each row and each column, the iterative convergence of the method is fast, and the decoding time delay can be effectively reduced.

The invention relates to a reticulated shell shape optimization method based on iterative shape finding, which comprises the following steps: 1) setting a reticulated shell initial structure, obtaining an initial node coordinate and an initial axial force, and setting an initial external load of the structure; 2) calculating a stiffness matrix of the current iteration stage according to the node coordinates and the axial force of the current iteration stage; 3) setting a boundary condition by adopting a zeroing setting method, and solving a node coordinate of a next iteration stage through anoverall balance equation according to the rigidity matrix, the node coordinate and the external load of the current iteration stage; 4) according to the node coordinates of the next iteration stage and the rigidity matrix of the previous iteration stage, calculating through a unit balance equation to obtain the axial force of the next iteration stage; and 5) setting a convergence condition, if theconvergence condition is satisfied, completing reticulated shell shape optimization, and otherwise, returning to the step 2) until the convergence condition is satisfied. Compared with the prior art,the reticulated shell shape optimization method has the advantages of simple steps, rapid convergence, convenient adjustment, suitability for single-layer and double-layer reticulated shells and thelike.

The invention relates to a generalized ROMP (Regularized Orthogonal Matching Pursuit) method for reconstructing radar signals and aims to solve the problems that when data missing parts of the radar signals are continuous and the radar signals data are missing more, the iterative convergence speed is slower, and the data recovery accuracy of the radar signals are low. The generalized ROMP method comprises the specific process of 1, obtaining initial values of a residual error, an iterative index set and a matrix formed by column vectors corresponding to indexes; 2, computing an inner product of A and the initial value of the residual error, and selecting atoms; 3, taking the first S maximum values from the atoms to form J0'; selecting corresponding aj in the A according to the J0', and updating At; solving the least square solution of an equation; 5, updating the residual error, judging whether an iteration condition is met or not, executing step 6 if the iteration condition is met, and executing the step 2 if the iteration condition is not met; 6, obtaining all reconstructural nonzero terms in the position of the lambda t, and obtaining reconstructed radar signals. The generalized ROMP method disclosed by the invention is used for the field of reconstruction of the radar signals.