33 results about "Polynomial method" patented technology

The invention relates to an intelligent vehicle lane change path planning method based on a polynomial and radial basis function (RBF) neural network. The intelligent vehicle lane change path planning method comprises the following steps that: the state information of obstacles and lane change vehicles in lanes are detected and determined according to a vehicle-mounted sensor, and the state information comprises positions, speed, acceleration and shapes; the lane change vehicles and the obstacles are geometrically covered, and in addition, a lane change path model using the time as the independent variable is built; boundary conditions of the lane change vehicles are obtained by the dynamic RBF neural network; the lane change path parameter is subjected to traversing in a certain range according to a certain step length, and the calculation of a polynomial method is combined to obtain the lane change path set under the specific boundary conditions; index functions for evaluating the merits of the lane change patch performance are defined, the optimal path in generated lance change paths is screened according to the index functions and is applied to the practical lane change process of vehicles; and whether the RBF neural network is updated or not is determined according to the merits of the boundary conditions of the generated lane change paths. The neural network has good self-adaption capability, so that the problem that the RBF neural network structure is oversize or undersize is solved.

The invention discloses a method for rapidly designing and optimizing a low-thrust transfer orbit. The method comprises the steps as follows: an inverse sextic polynomial method is introduced as an initial design, a high-performance optimal parameter initial value is provided for follow-up accurate optimization, and the calculating time of an entire optimizing process is reduced; a Radau legendre pseudospectral method is adopted to parameterize a low-thrust transfer orbit optimization problem into a nonlinear programming problem, not only is the number of the optimization parameters reduced, but also the accuracy of an optimization result is improved; and tedious first-order optimal necessary conditions are not needed to be deduced, even though the low-thrust transfer orbit containing various complicated constrains are processed, the operation is also convenient and simple. The method for rapidly designing and optimizing the low-thrust transfer orbit has an extremely important application value in the field of the design and optimization of transfer orbits of low-thrust propulsion spacecraft such as solar sail propulsion, solar energy electric propulsion, and the like.

The invention provides a mixed integer nonlinear programming model for solving the combination problem of a hydroelectric generating set, and belongs to the field of hydroelectric scheduling operation. The mixed integer nonlinear programming model for solving the combination problem of a hydroelectric generating set is characterized by utilizing a polynomial fitting technology to process the complicated power station nonlinear relation, providing a quartic equation polynomial fitting method of water level-capacity of reservoir-tailwater level-out flow, and based on a quadratic polynomial of head loss of the hydroelectric generating set, describing the generating head of the hydroelectric generating set as a multivariate quartic function of capacity of reservoir and flow; considering aboutthe influence of the head on the output, providing a binary quadratic polynomial method of accurately representing the output head and the generating flow of the hydroelectric generating set, introducing 0-1 variable, solving the startup-shutdown problem of the hydroelectric generating set, obtaining the head-flow-output relation three-dimensional fitting surface of the hydroelectric generating set, and satisfying the fine calculation demand of the output of the hydroelectric generating set under the variation head; and realizing calculation with high efficiency by means of a mature MINLP solver. The mixed integer nonlinear programming model for solving the combination problem of the hydroelectric generating set fully considers the complicated nonlinear relation of the hydroelectric systemand has the advantages of reducing the linear error and significantly improving the solution accuracy, compared with a classic MILP model. Besides, the obtained set combination and load distributionresult can preferably reflect the practical operation situation of the hydropower station.

The invention discloses an automobile tyre pattern recognition method based on machine vision, and the method specifically comprises the following steps: S1, obtaining an image of linear laser on a tyre pattern, and correcting the image; S2, removing the background and vignette of the corrected image; S3, solving a plurality of central coordinate points of a light bar on the processed image at step S2, carrying out the cubic spline fitting of the central coordinate points, and obtaining a cubic spline fitting formula; S4, carrying out the fitting of the central coordinate points of the light bar based on a polynomial method, and obtaining a polynomial fitting formula; S5, solving the difference between the cubic spline fitting formula and the polynomial fitting formula; S6, carrying out the convolution of the difference result at step S5, and solving the maximum values of a convolution curve; S7, filtering the maximum value less than a preset value, wherein the obtained maximum value is the depth value of the tyre pattern. The method can achieve the accurate and efficient recognition of the tyre pattern and the measurement of the depth of the tyre pattern, and is very high in precision and robustness.

An NP-complete problem can be transformed in polynomial time into any known NP problem. The Hamiltonian circuit problem may be transformed into any other known NP problem (such as the Traveling Salesman problem) and has applications in any context that can be represented by a graph, map, or network structure. The reverse calculation of this transformation from any NP problem into the NP-complete Hamiltonian circuit problem also has a polynomial running time. The composition of this reverse calculation from any known NP problem to the Hamiltonian circuit problem with the polynomial running time of the given algorithm together form a polynomial running time algorithm. Therefore, with this polynomial running time calculation result given for detecting the presence of a Hamiltonian circuit in an undirected graph, it has been shown that P equals any known NP problem or NP. Hence the existence of this Hamiltonian circuit detection algorithm proves P=NP.

The invention relates to an orthographic correction method of a CCD image of an HJ-1 satellite. A first-stage image of a CCD of an HJ satellite as well as corresponding altitude data and reference data are introduced; ground control points are collectedly uniformly at the first-stage image of the CCD of the HJ satellite; coordinates of the collected control points are introduced into a cubic rational function model and calculation is carried out to obtain a rational polynomial coefficient (RPC), so that a corresponding rational function model is obtained; pixel coordinates of all points of the first-stage image of the CCD of the HJ-1 satellite are inputted into the rational function model to obtain coordinates corresponding to all points of an image after orthographic correction; and with a bilinear interpolation method, interpolation of gray values of all points of the image after correction is carried out on points with pixel coordinates in an entire row or column mode, thereby obtaining an orthographic correction image. According to the invention, compared with the traditional polynomial method, the provided orthographic correction method enables the precision to be improved.

The invention discloses a quick data compression method, which comprises the following steps: preprocessing received detection data: ejecting the gross error of data information, and filtering the detection data; measuring the filtered detection data through a sensor, and carrying out fusion on the measured data; and compressing the fused data through a least square fit polynomial method. The method provided by the invention greatly compresses a data storage space, simultaneously improves data reliability, keeps the curve characteristics of the data and has a good use value through data preprocessing, data fusion and data compression.

The invention discloses a rotor position measurement method of a transverse flux switch reluctance motor without a position sensor. The rotor position measurement method comprises the steps of settingan initial position angle of the motor, and applying a high-frequency pulse voltage to an A phase; adding a step to the initial position angle of the motor, and sequentially calculating an inductancevalue corresponding to each angle to obtain an A phase angle-inductance relation curve; dividing the inductance curve in a mechanical angle period into six regions, performing fitting by employing aquadratic polynomial method to obtain an ideal inductance curve, analyzing the ideal inductance curve, selecting appropriate low-threshold inductance TL, and obtaining a starting phase logic in the mechanical angle period according to a relation between three-phase inductance and the TL in each region; simultaneously calculating a three-phase inductance value from a three-phase injection high-frequency pulse voltage, comparing the three-phase inductance value with the starting phase logic, and determining a starting phase; and injecting a next conduction phase of the starting phase into the high-frequency pulse voltage, switching off the starting phase after a commutation threshold is reached, and conducting the next phase to achieve commutation running. The rotor position measurement method is easy to implement and is relatively high in accuracy.

The invention discloses an electrocardiogram signal R wave positioning method, wherein, for filtered electrocardiogram signals, the method comprises the following steps: fitting the human electrocardiogram signals by virtue of an interpolation polynomial method, and enhancing a peak value of the electrocardiogram signals through double curve differentials, so as to determine the position of R wave. The method disclosed by the invention is not only simple in computation and easy in implementation, but also high in accuracy rate; therefore, a new way is provided for the rapid and effective recognition of the electrocardiogram signals.

The invention relates to a self-acceptance technology detection and classification method based on multi-method ensemble learning. The method comprises the following five steps: preprocessing featurewords; selecting the first k most useful features to train a classifier; training corresponding sub-classifiers by using a naive Bayes polynomial method and a linear Logistic regression method; and performing integrated prediction on the prediction result through a sub-classifier voting rule to obtain accuracy, a recall rate, comprehensive accuracy and a recall rate, and finally calculating an F1value as a subsequent evaluation standard. And finally, the features which frequently appear in the experiment process and have high information gain values are clustered through a clustering method,so that the detected technical debts are classified.

Methods and apparatus for approximation using polynomial functions are disclosed. In one embodiment, a processor comprises decoding and execution circuitry. The decoding circuitry is to decode an instruction, where the instruction comprises a first operand specifying an output location and a second operand specifying a plurality of data element values to be computed. The execution circuitry is to execute the decoded instruction. The execution includes to compute a result for each of the plurality of data element values using a polynomial function to approximate a complex function, where the computation uses coefficients stored in a lookup location for the complex function, and where data element values within different data element value ranges use different sets of coefficients. The execution further includes to store results of the computation in the output location.

The invention belongs to the technical field of inertial navigation, and discloses an accelerometer uncertainty analysis method based on an arbitrary chaotic polynomial. The method comprises the stepsthat uncertain factors existing in the accelerometer assembling process are analyzed; Establishing an accelerometer uncertainty analysis model; Statistical moment information of the uncertain parameters is obtained through statistical analysis according to existing experimental data and engineering experience, and the output mean value, variance, kurtosis and skewness of the bias value of the accelerometer are obtained based on an arbitrary chaotic polynomial method; And fitting probability distribution output by the bias value of the accelerometer by adopting a maximum entropy principle. According to the method, the influence of uncertain factors in the accelerometer assembling process on the offset output of the accelerometer is quantitatively evaluated, and theoretical guidance is provided for subsequent optimization of the accelerometer assembling process and structure so as to improve the accelerometer percent of pass.

The invention discloses a fan output power prediction method, which comprises the following steps of: firstly, establishing a multivariate polynomial regression model based on a maximum information coefficient and multivariate polynomial regression; and then, determining a test set, and predicting the output power of the fan on the test set by using the multivariate polynomial regression model. In addition, the invention further provides a fan output power prediction system. According to the method and system, the maximum information coefficient is used for selecting the important variables influencing the output power of the fan in the process of establishing the multivariate polynomial regression model, multiple important variables are comprehensively considered, the problem that analysis is not comprehensive enough due to the fact that influence factors of the output power of the fan are analyzed manually is solved, and further, the relation between multivariable and the fan output power is modeled through a polynomial method, so that prediction of the fan output power has high accuracy and low model complexity.

A nondeterministic Turning machine (NTM) performs computations using a spatial binary enumeration system, a three-dimensional relation system, a simulated-human logic system, and a bijective-set memory system. The NTM may be used to perform a variety of computational tasks, such as multiple sequence alignment, factorization, and other nondeterministic polynomial algorithms in polynomial time. The NTM may be constructed by a deterministic Turing machine (DTM) using the four systems listed above.

The invention discloses a lane-changing device and method for autonomously learning the driver's lane-changing style, which can learn the driver's lane-changing style parameters-lane-changing time autonomously, and then use a polynomial method to plan the lane-changing path, according to the planned lane-changing Based on the path and vehicle dynamics control principles, the control parameters when the vehicle changes lanes are calculated; finally, the vehicle is controlled to change lanes according to the calculated control parameters when the vehicle changes lanes. The lane changing device and method for autonomously learning a driver's lane changing style of the present invention can adapt to the driving habits of different drivers.

The invention belongs to the field of radar automatic target recognition, and particularly provides an SAR target recognition method based on feature construction. According to the invention, the characteristics of the original image are not damaged; the method comprises the following steps of: constructing SAR (Synthetic Aperture Radar) image features by utilizing a Symbolic Regression method anda polynomial method; linearly fusing the original features and the constructed features, using a global feature dimension reduction method for achieving high-discrimination-capability feature extraction, and finally conducting classification through a classifier. According to the method, the spatial structure relationship between pixels of the original images is fully utilized to construct features with stronger identification capability, and then the features are fused with the features of original SAR images, so that target identification performance of SAR images are effectively improved.