62 results about "Iterative methodology" patented technology

The invention belongs to the field of radar signal processing, and particularly relates to a sparse aperture ISAR self-focusing and lateral scaling method based on Bayesian learning. The method comprises the following steps: firstly performing sparse prior modeling on an ISAR image by using a Laplace layered model, and performing sparse reconstruction on the ISAR image by using a variational Bayesian method, wherein during the reconstruction of the ISAR image, a modified Newton iterative method is used to simultaneously estimate a phase error and a target rotational speed to achieve self-focusing and lateral scaling of the ISAR under the sparse aperture condition. The invention obtains the beneficial effects that: due to the sparse aperture ISAR self-focusing and lateral scaling method based on Bayesian learning, the self-focusing and lateral scaling of the ISAR under the sparse aperture condition can be realized, and the ISAR image with good focusing effect, high resolution and exactlateral scaling can still be obtained under the condition of sparse radar echo data aperture caused by the factors such as low SNR, strong interference and insufficient effective aperture, which has important engineering application value and can provide theoretical support for the design of a compressed sensing radar.

A sequential iterative methodology is used to generate a solution space with different cost/availability values for multiple network architectures from which an optimal or near-optimal solution can be determined. A modeling module (10) provides a cost value for a network architecture under consideration and, once cost modeling is done, an availability analysis is performed for the architecture under analysis. If a relatively few connections prevent a model from acceptability, the availability analysis module (12) reveals the causing factors of the unsatisfactory availability for those few connections and suggests that the modeling perform further optimisations on those causing factors. This iterative process is repeated until an optimum or near-optimum acceptable solution is found; if no acceptable solution is found, the solutions generated in the availability analysis module are reviewed to find the best available solution.

A process for determining the productivity index of a multilateral completion using data from individual laterals and data from commingled well tests is disclosed. The process includes 1) determining the productivity index for each single lateral by iteratively altering the productivity index until the individual lateral flowrate based on a known reservoir pressure is matched and 2) further determining the productivity index by iteratively altering the productivity index until the commingled flowrate is matched. The productivity index may be used to set wellhead pressures and inline control valve (ICV) settings for production.

Systems and methods are provided which utilize a fast, efficient filtered backprojection algorithm that can provide noise suppression advantages of an iterative MAP reconstruction algorithm. In some embodiments novel filtered backprojection systems are able to provide an image which emulates an image from an iterative algorithm corresponding to a selected iteration by utilizing control parameters which shape the filter accordingly during the reconstruction process. For example, if a user desires an image which would correspond to the one-hundredth iteration of an iterative algorithm, embodiments can provide a similar quality image using minimal calculation steps. Further, embodiments may provide the resolution quality of such an iteration while also allowing for better shift-invariant performance than an iterative method.

The invention discloses a runtime error analytical method based on abstract interpretation and model verification. The method includes the following steps that on the basis of the abstract interpretation theory, the program numerical variable value range is analyzed by the adoption of a forward iteration method, the variable value range information is obtained when program points are stable, and the iterative computations of loop nodes are achieved by the way that loop unrolling and delay widening are combined; the variable value range information at the relevant program points needing to be detected is converted to be in an assertion or hypothesis mode to be plugged into a program according to a runtime error type to be analyzed; the assertion or hypothesis programs are converted into a Boolean formula, wherein the Boolean formula comprises limiting conditions and attributes; the correctness of the attributes in the Boolean formula is judged through an SAT verifier, if correct, it shows that relevant runtime errors do not exist, if not correct, it shows that the relevant runtime errors exist, and relevant counter example paths are output. By means of the method, an equilibrium point is acquired between runtime error analysis precision and efficiency.

The invention relates to a method for remodeling medium objects by electromagnetic inverse scattering. The method comprises following steps: utilizing Maxwell's equations and constitutive equations to deduce forward and reverse integral equations for electromagnetic scattering; guessing the shape of an object to be detected and discretizing integral equations with a method in order to obtain matrix equations; utilizing a Gauss-Newton minimization method to regularize ill-conditioned equations during the reverse scattering process following discretization, wherein used regularized parameters are obtained by a multiplicative regularization method; and adopting a born iterative method to carry out iteration and obtaining wave number of detected mediums at the needed precision in order to achieve the objective of remodeling mediums.The method helps to avoid the problem in the conventional method that parameters are obtained by a lot of experiment data so that solving efficiency is increased.

A method for estimating a velocity of a target using a host vehicle equipped with a radar system includes determining a plurality of radar detection points, determining a compensated range rate, and determining an estimation of a first component of a velocity profile equation of the target and an estimation of a second component of the velocity profile equation of the target by using an iterative methodology comprising at least one iteration. The estimations and of the first and second components and of the velocity profile equation are not determined from a further iteration if at least one statistical measure representing the deviation of an estimated dispersion of the estimations and of the first and second components, and of a current iteration from a previous iteration and/or the deviation of an estimated dispersion of the residual from a predefined dispersion of the range rate meets a threshold condition.

The invention provides a steady waveform optimizing method for an MIMO radar in a clutter background, and relates to the field of signal handling. The method comprises the steps of building an MIMO radar receiving signal model under the clutter background; deducing CRB with parameters to be estimated based on the model; estimating non-determination convex set explicit included waveform optimizing problem according to the initial parameters, and building a steady waveform optimizing model, wherein the minimum and maximum problems are the complex nonlinear problems related to optimization variable. According to the method, the iterating method is carried out to solve the problem; each step of iterating is converted into SDP problem, so that the problem can be efficiently solved; the proposed iterating algorithm is utilized to optimize a waveform covariance matrix; therefore, the parameter estimation performance under the worst condition in the clutter background can be improved; compared with the non-related waveform and non-steady methods, the method is high in steady performance and is close to the engineering application.

Optimal reduction of 4G LTE cellular network congestion utilizes two components of learning and optimization. First, an MLPDL learning approach is used to model cellular network congestion measured in terms of PRB utilization and predict 80% utilization as breakpoint thresholds of cellular towers as a function of average connected user equipments. Then, an optimization problem is formulated to minimize LTE network congestion subject to constraints of user quality and load preservation. Two alternative solutions, namely Block Coordinated Descent Simulated Annealing (BCDSA) and Genetic Algorithms (GA) are presented to solve the problem. Performance measurements demonstrate that GA offers higher success rates in finding the optimal solution while BCDSA has much improved runtimes with reasonable success rates. Accordingly, integrated iterative methods, programs, and systems are described aiming at minimizing the congestion of 4G LTE cellular networks by redistributing traffic from congested cellular towers to non-congested cellular towers.

Systems and methods of resistance based memory circuit parameter adjustment are disclosed. In a particular embodiment, a method of determining a set of parameters of a resistance based memory circuit includes selecting a first parameter based on a first predetermined design constraint of the resistance based memory circuit and selecting a second parameter based on a second predetermined design constraint of the resistance based memory circuit. The method further includes performing an iterative methodology to adjust at least one circuit parameter of a sense amplifier portion of the resistance based memory circuit by selectively assigning and adjusting a physical property of the at least one circuit parameter to achieve a desired sense amplifier margin value without changing the first parameter or the second parameter.

The invention belongs to the technical field of wireless communication, and provides a Bayesian compressed sensing channel estimation method based on an FDD large-scale MIMO for obtaining accurate channel state information. The invention designs a mode-coupled Gaussian prior model to describe the common sparsity between different antennas, wherein the coefficients in a channel vector are divided into groups of equal lengths, each group has a common hyperparameter, such that the coefficients of each group have the same sparsity; further, Bayesian inference is performed based on the iterative method by expecting the maximization step, wherein the channel coefficient is used as a hidden variable and the hyperparameter is used as an unknown parameter; and finally, the obtained posterior mean of the channel vector is s used as an estimate of the channel. The simulation shows that the BCS method proposed by the invention is much better than the similar method, and can achieve the performanceboundary of the ideal least squares algorithm as the baseline.

The invention belongs to the technical field of communication, and relates to a joint detection method for signals of a massive MIMO uplink system. According to the scheme of the invention, the methodincludes the following steps that: firstly, an iterative matrix is constructed, and a low-complexity iterative scheme is proposed based on the constructed matrix; and then, a steepest descent methodand the proposed iterative method are mixed to accelerate the proposed iterative method; in addition, the computational complexity is reduced by skillfully utilizing the properties of inversion of partitioned matrices and a matrix-vector multiplication; and then, the detailed convergence proof and complexity analysis are performed; and finally, simulations prove that the BER performance of the proposed algorithm is better than that of most existing iterative algorithms and the near-optimal performance of an MMSE algorithm can be reached in a small number of iterations.

The invention discloses an adaptive rendering method based on weight local regression. The method comprises the following specific steps: filtering and reconstructing an image by use of local regression; constructing a simplified feature space by use of truncated singular value decomposition (TSVD) so as to eliminate noise carried by a feature vector; respectively predicating an optimal feature bandwidth bj and a shared bandwidth h by use of a two-step bandwidth selection algorithm; and distributing available light samples to areas with high errors by use of an iteration method based on a local simplified feature subspace k. The method provided by the invention is a novel adaptive rendering technology and can be applied to processing rendering effects of multiple types in an efficient and robust manner. Compared to prior similar arts, the method has the following advantages: the time for generating results with the same quality is obviously reduced, and the result image effect generated based on equal calculation time and calculation cost is far better than that of the similar arts.

The invention relates to a finite difference stencil parallelizing method based on iteration space sticks. In the traditional parallel iterative method, synchronous operation for maintaining the data dependence relation is needed both in iteration and among the iterations. The method provided by the invention comprises the following steps of: dividing an iteration space into grid sticks in a time-axis direction to realize multiple recursion-type iteration-step updates on the same grid block, thereby improving the data locality in the sticks at the same time of not changing the property of a serial stencil iterative method; and then reordering the grid sticks to realize the parallelization of staggered sticks in the finite difference stencil method. Compared with the traditional domain decomposition method and a red-black ordering parallel method, the method provided by the invention has better data locality, parallelization efficiency and extensibility.

A non-iterative frequency domain method for the accurate and efficient simulation of nonlinear systems is presented. In one aspect of the present invention, simulating a nonlinear system is accomplished by first modeling the system and generating parameters that describe the nonlinear system. The system is represented in the frequency domain as an inverse convolution equation (ICE), comprising cascaded convolutions and frequency representations of known and unknown signals. Next, the order of the ICE is determined based upon the degree of nonlinearity in the system. Finally, a general ICE solver algorithm is adapted to the ICE order of the frequency model, and the specific ICE solver algorithm is applied to in order to solve for an unknown signal. In another aspect of the invention, the non-iterative method for simulating nonlinear systems is combined with cross-referenced coordinate (CRC) techniques in order to increase the computational efficiency of the simulation.

The invention discloses a hyperbolic fitting method for compressible fluid turbulence measurement test data. The method comprises the following steps: (1), carrying out data acquisition preparation; (2), opening a to-be-measured wind tunnel, and carrying out measuring by using a hot-wire anemometer under an incoming Mach number M to obtain a to-be-fitted two-dimensional scatter sequence; (3), performing hyperbolic fitting on the to-be-fitted two-dimensional scatter sequence by using an iterative method based on a Lagrange multiplier neural network, and carrying out solving to obtain a fittingresult; and (4), solving a turbulence degree result. According to the method, hyperbolic fitting is carried out on compressible fluid turbulence measurement test data by using the iterative method based on the Lagrange multiplier neural network and the turbulence is calculated by using the fitting result; compared with a traditional method represented by an SOE method, the method has the advantages that the turbulence solving accuracy is high, the robustness is high, and a feasible method is provided for hyperbolic fitting of compressible fluid turbulence measurement test data.

The invention discloses a clutter suppression and parameter estimation method based on a Relax algorithm in a WAS-GMTI mode, and belongs to the technical field of radar target detection. The invention designs an iterative method of clutter suppression according to the characteristic of wide area monitoring mode echo composition and combined with the Relax algorithm. Then on the basis of clutter suppression, iteration continues to be performed on distance-Doppler units where moving targets exist, and thus precise estimation of parameters of the moving target can be realized.

The invention discloses an inertial measurement unit calibration method suitable for a shaking base environment, and relates to the technical field of inertial measurement combination test in aerospace strapdown inertial navigation technology. The method comprises the following steps: establishing a simultaneous equation by using a relationship between a first-order intermediate parameter and a first-order error parameter, and solving to obtain the first-order error parameter based on the established simultaneous equation; calculating to obtain a second-order intermediate parameter of the ithposition by utilizing the first-order error parameter, establishing an equation according to a relationship between the second-order intermediate parameter and a second-order error parameter, and solving to obtain the second-order error parameter which is gyro zero offset; and judging whether the residual error of the first-order error parameter and the second-order error parameter is smaller thana set threshold value or not, and if yes, ending. According to the method, the attitude error introduced by the shaking base environment in the initial alignment process is gradually eliminated by adopting an iterative method so that the method is better suitable for calibration in the shaking base environment.

A mean iteration-based multi-task learning model training and prediction method comprises the steps of firstly obtaining sample data sets of a plurality of tasks, and dividing the sample data sets ofthe tasks into a training set and a test set; for each task, utilizing the corresponding training set to obtain the prior probability of each class label on each task and the conditional probability of each characteristic variable in each task on each class label, and calculating the class label of each instance in the test set corresponding to each task; updating the prior probability of each type of label on each task based on a mean iteration method; and performing loop iteration continuously until the sum delta of absolute values of prior probability errors of class labels on different tasks is smaller than a set threshold, and performing convergence to obtain a trained multi-task learning model. According to the mean iteration-based multi-task learning model training and prediction method, the multi-task learning efficiency can be significantly improved. Meanwhile, shared information between tasks and priori knowledge of data can be more fully utilized, and a better classificationeffect can be achieved by using fewer computing resources.

The invention relates to the technical field of data analysis, relational network analysis and intelligent recommendation, and provides a recommendation system strategy iteration method, which comprises the steps of obtaining an access request of a user, cleaning the access request, determining an effective access request, and determining an abtest experiment layer executed by the user corresponding to the access request; determining a service strategy executed by each effective access request in each experiment layer based on the Hash algorithm of each experiment layer; and counting the execution data of each business strategy, determining the optimal execution strategy of each experiment layer, and determining the optimal execution strategy as the business execution strategy of the corresponding experiment layer. According to the method, the execution threshold for cleaning the user request flow and testing the abtest user request is dynamically adjusted; when the abtest testing speed is guaranteed, the data result is more accurate, the operation strategy of the recommended system is iteratively updated quickly, the operation process of the system is optimized, and the fluency and experience in the user access process are improved.

The invention discloses an iterative path planning algorithm combining a whisker algorithm and a tangent graph method. The iterative path planning algorithm comprises the following steps: generating left and right boundary whiskers satisfying vehicle kinematic constraints starting from the current position; generating a driving track satisfying a safety constraint and a shortest path constraint byadopting the tangent graph method; determining an iteration direction according to the relative positional relationship between the left and right boundary whiskers and the driving track of the tangent graph; and performing iteration to generate a new sub-path point, and if the target position is not reached, continuing to calculate until reach the target position so as to end planning, and iterating the generated path as a planning result of the algorithm. According to the advantages that the whisker algorithm can satisfy the vehicle kinematic constraints and the vehicle dynamics constraints, the existing tangent graph method is improved, so that the planning result can be directly applied to a vehicle path planning; the smoothing of the planning result is realized by adopting an iterative method, and the result can continuously approach the planning result of the tangent graph method; and problems that an existing algorithm cannot meet the real-time performance and the shortest pathat the same time and free of considering the kinematic constraint and dynamic constraint of the vehicle are solved.