102 results about "Rank correlation" patented technology

Instrumented networks and platforms having target subjects (devices, transactions, services, users, organizations) are disclosed. A security orchestration service generates runtime operational integrity profiles representing and identifying a level of threat or contextual trustworthiness, at near real time, of subjects and applications on the instrumented target platform. Methods and systems are disclosed for network flow and device / platform remediation in response to reconnaissance-based intelligence correlation based on network monitoring, to accomplish network flow remediation and device / platform remediation. In an embodiment, a system receives system warnings and endpoint threat intelligence. The system correlates risk based on inputs from sensory inputs that monitor network activity, system configuration, resource utilization, and device integrity. The system then performs a calculus of risk on a global security context including endpoint assessment reports and sends system warnings based upon the endpoint threat intelligence. The system includes a remediation engine for receiving real time directives to control the device.

An image processing method is described for determining depth information from at least two input images recorded by a stereo camera system, the depth information being determined from a disparity map taking into account geometric properties of the stereo camera system, characterized by the following method steps for ascertaining the disparity map: transforming the input images into signature images with the aid of a predefined operator, calculating costs based on the signature images with the aid of a parameter-free statistical rank correlation measure for ascertaining a cost range for predefined disparity levels in relation to at least one of the at least two input images, performing a correspondence analysis for each point of the cost range for the predefined disparity levels, the disparity to be determined corresponding to the lowest costs, and ascertaining the disparity map from the previously determined disparities.

A spindle thermal distortion compensation method for a precision horizontal machining center comprises the steps as follows: a spindle model of a machine tool is simplified structurally; an ANSYS (finite element analysis software)-Workbench is utilized to perform mesh generation on a spindle entity model that is simplified, so as to obtain a spindle finite element model; and boundary conditions are calculated by combining with the practical spindle rotational speed, the environmental temperature, the coolant velocity, the flow rate, the inlet and outlet oil temperature and the like, and configuration is performed. Thermodynamics analysis and statics analysis are carried out in the ANSYS-Workbench to obtain more accurate spindle temperature field distribution and thermal deformation. Based on a finite element emulation result, the spindle temperatures in different positions and the spindle thermal deformations are analyzed at different rotational speeds by utilizing the Spearman rank correlation analysis, and spindle thermal key points are found out, so that references are provided to spindle thermal error tests and thermal error compensation. Finally, a thermal error compensation model is built up by utilizing a least squares method according to the key point temperature of the spindle and the thermal errors in the practical tests. Based on the thermal error compensation model, the spindle thermal error compensation is carried out by combining with thermal error compensation strategies of a numerical control system of the machine tool.

The invention discloses a wind and photovoltaic complementary power generation system reliability evaluation method based on a Copula theory. The method comprises the following steps of: (1) determining the power probability distribution of a wind power station and a photovoltaic plant; (2) respectively performing integral operation on the power probability distribution fWT(P1) and fPV(P2) of thewind power station and the photovoltaic plant, and calculating the accumulative power probability distribution of the wind power station and the photovoltaic plant; (3) calculating Kendall rank correlation coefficients of the power of the wind power station and the photovoltaic plant; (4) calculating a correlation parameter theta of a Frank Copula function; (5) forming a simultaneous equation through a formula (2) and a formula (4) to obtain the joint probability density of the random variables P1 and P2; and (6) acquiring the accumulative probability distribution of the wind and photovoltaiccomplementary power station through integral operation according to a joint probability density function of the power of the wind power station and the photovoltaic plant, forming an off-the-line table of the power of the wind and photovoltaic complementary power station through the accumulative power, and establishing a reliability model of the wind and photovoltaic complementary power station. According to the method, the reliability of the wind and photovoltaic complementary power generation system can be accurately evaluated.

The invention provides a method for improving computing the precision of rock fracture parameters, for performance evaluation and drilling of oil and gas reservoirs in oil exploration. The method comprises the following steps of: working out fracture directions and the fracture density by using more than two seismic attributes for uniformization treatment, computing the coefficients of rank correlation of the fracture directions in pairs to obtain the rank correlation and fracture density coefficient, and if the correlation coefficient is over 0.9, determining that the density or direction is the density or direction with highest precision; otherwise, computing absolute error values of the fracture directions corresponding to all attributes at the well point, and sorting the absolute error values according to an order from small to large; computing the coefficients of rank correlation of the fracture density corresponding to the attributes and the coherency attributes, sorting the coefficients of rank correlation according to an order from small to large, and finally computing the fracture directions with the highest precision. The method can ensure the leading position of basic attributes and correct the final result to varying degrees according to the data quality so as to improve the forecast precision by using the existing information.

The invention relates to the technical field of compression perception and specifically relates to a segmented self-adaptive regularized matching pursuit reconstruction method based on a threshold. The method includes: setting initial state values of different parameters in a sparse signal reconstruction process; calculating the inner products of an iterative allowance r and each column of a sensing array Phi, that is correlation coefficients; finding atoms which satisfy a condition in the sensing array; storing in a subscript set J; ranking correlation coefficients of atoms corresponding to subscripts in the subscript set J from large to small; updating a support set which represents original signals; performing signal approximation through adoption of a least square method and updating the allowance; and performing iteration determination. The segmented self-adaptive regularized matching pursuit reconstruction method based on the threshold combines segmented self-adaptive atom selecting and regularization ideas. The method does not need sparseness as a prior condition in a signal reconstruction process and is self adaptive to approximation sparseness information and capable of constructing the support set accurately and completing precise signal reconstruction and the precise reconstruction rate is higher than the prior method of the same kinds so that the method is higher in practical applicability.

The invention discloses a wind speed modeling method for a large-sized wind power plant in a mountainous area. The method comprises: 1, sampling historically recorded wind speed of the wind power plant, and calculating parameters c and k of wind speed Weibull distribution of each fan group of the wind power plant; 2, calculating a rank correlation coefficient Rho Rr of each fan group in the wind power plant, and constructing a historical wind speed rank correlation coefficient Rr of the wind power plant; 3, calculating related wind speed normal Copula functions of the wind power plant to obtain a joint probability distribution function, defined in the specification, of modeled and predicted wind speed with correlation, and edge probability distribution functions u1 to un of wind speed of fan groups; and 4, performing equiprobable reverse transformation of the Weibull distribution parameters c and k on the edge probability distribution functions of the wind speed of the fan groups to realize wind speed prediction. According to the method, the technical problem that a characteristic that wind speed in a wind power plant has correlation cannot be truly and effectively reflected, electric power distribution and safety operation of a power system are influenced due to direct influence of the correlation of the wind speed in the wind power plant on output of the wind power plant, and the like in the prior art are solved.

The present invention discloses a wind speed prediction method of a wind farm. Problems are mainly considered that spatial correlation among wind farms, unscented Kalman Filter optimization, and the like are not well considered in an existing method. The method mainly comprises: calculating a rank correlation coefficient between a target wind farm and the other 21 wind farms if 22 wind farms are given; determining a wind farm for prediction according to the correlation coefficient; and selecting a wind farm with a Kendall rank correlation coefficient greater than 0.55 and a Spearman rank correlation coefficient greater than 0.75; then establishing a non-linear state space model by using support vector machine regression, and performing unscented Kalman filter prediction by using the established non-linear state space model; optimizing a scale parameter of the unscented Kalman filter according to a principle of prediction error minimization; and finally, selecting wind speed data of a wind farm of a same time in four years, and performing grey correlation analysis by using the wind speed data and wind speed data of a target wind power turbine, No.9 wind power turbine, of the same time in the first year.

The invention discloses an improved type principal component analysis model-based fault detection method which is aimed at improving effects exerted by a conventional PCA algorithm on fault detection via further discovery of characteristics of distance correlation between training data. The method comprises the following steps: an angle correlation matrix and a distance correlation matrix of the training data are calculated, a new correlation matrix is obtained after the angle correlation matrix and the distance correlation matrix are added up, and corresponding characteristic values and vectors are calculated. An improved PCA model is built via use of the characteristic vectors, and the PCA model is applied to on-line fault detection. Compared with a conventional method, the fault detection method disclosed in the invention is advantageous in that angle correlation and distance correlation of the training data are taken into account at an off line modeling phase, data correlation characteristics are fully discovered, the improved type PCA model built via the fault detection method can be used for comprehensively describing potential characteristics of normal data, and good fault detection effects should be exerted.

The invention discloses a no-reference image quality evaluation method based on a multi-task convolutional neural network, and belongs to the field of image perception. The method specifically comprises the following steps: step 1, extracting a plurality of image blocks with fixed sizes from each image of a manually labeled image quality data set, and each image block corresponding to two labels,which are respectively a degradation category of the image and a degradation degree of the image, so as to form a training set; step 2, constructing a convolutional neural network model based on dictionary learning; step 3, training the constructed convolutional neural network model by using the training set, and determining parameters of the convolutional neural network model after the training is finished; and step 4, during application, inputting the to-be-scored pollution image into the trained convolutional neural network model to obtain a corresponding image quality score. Compared witha traditional method, the method is higher in consistency with subjective evaluation in the field of non-reference image quality evaluation, and key indexes such as the Spearman rank correlation coefficient and the Pearson linear correlation coefficient are obviously improved.

The invention discloses a variable selection and forecast method of the silicon content in molten iron of a blast furnace. According to the method, blast furnace process parameters of a prediction model of the silicon content in molten iron of a blast furnace are used as input variables; after normalization preprocessing is performed on sample data of the input variables, variable selection is performed on the sample data of the input variables by using a multivariate correlation analysis method and a Spearman rank correlation analysis method, to remove the correlation between production process parameters; and a support vector machine algorithm is used to establish the forecast model of the silicon content in molten iron of the blast furnace, and particle swarm optimization algorithm is introduced to optimize model parameters. The variable selection and forecast method of the silicon content in molten iron of a blast furnace has universal applicability in forecasting the silicon content in molten iron during smelting of a blast furnace, and can achieve desirable forecast accuracy and improve the forecast hit rate of the silicon content in molten iron.

A building electricity energy-saving management method based on grey correlation includes monitoring energy consumption of each energy consumption sub-item built in a time period and determining an analysis sequence; conducting non-dimensional conversion on the analysis sequence; calculating correlation coefficients and relevancy of a reference sequence and a comparison sequence in the analysis sequence; judging sub-items ranking higher according to the relevancy and conducting energy-saving optimum management on the sub-item energy consumption to reduce the sub-item energy consumption to reduce building total energy consumption to the largest extent and achieve energy-saving management of building electricity.

The invention provides a sintering process working condition identification method and system considering a time sequence. The method takes the time sequence data of the process parameters of a sintering process as the input and takes a sintering process working condition as the output, and comprises the steps of firstly, utilizing a Spearman rank correlation analysis method and an information entropy analysis method for parameter selection and combination, and obtaining the combined decision parameters; then, using a fuzzy C-means clustering algorithm based on the dynamic time warping distance for clustering the time sequence data, and obtaining a clustering result of the combined decision parameters; and finally, using a naive Bayes classifier for the working condition recognition, and obtaining the recognized sintering process working condition. According to the working condition recognition method, the effective recognition of the working condition during the sintering process is achieved, and the important economic value and the application value are achieved.

The present invention provides a correlation-matrix feedback method and system for an antenna array. The method comprises steps of: selecting, at a receiving end, a codeword from a predetermined correlation-matrix codebook comprising a plurality of codewords according to a predetermined criterion to represent a correlation matrix to be fed back (300); and feeding back an index of the selected codeword in the predetermined correlation-matrix codebook to a transmitting end (302); wherein each codeword is a codeword matrix for approximating a correlation matrix of an antenna array at the transmitting end. According to the present invention, it is possible to take good advantage of the characteristics of closely-spaced ULA antennas and spatial correlation thereof, design a corresponding codebook for quantization and feedback, and reduce feedback overhead and computational complexity so as to improve the system performance of a spatial correlation aided system, such as a MIMO system.

The invention provides a multi-component converted wave fracture prediction method based on rank correlation analysis. The predication method includes steps of selecting a calculation time window, intercepting the radial component and the transverse component of a to-be-handled sampling point according to the calculation time window; performing rank correlation analysis on the to-be-handled sampling point and acquiring a maximal rank correlation coefficient; judging whether the to-be handled sampling point has fracture development and determining the location of the fracture development; separating the radial component and the transverse component to the to-be-handled sampling point judged to have fracture development and acquiring a quick transverse wave and a slow transverse wave; calculating the anisotropy coefficients according to the time difference between the quick transverse wave and the slow transverse wave; repeating the steps from calculation time window selection to anisotropy coefficient calculation for the next to-be-handled sampling point until calculation on seismic data is completed. According to the invention, judgment is made on calculation results by adopting adiscrimination method, so that areas with distinctive non-development of fractures are removed and accuracy in fracture development statistics is improved.

The invention discloses a pilot protection method based on Spearman rank correlation coefficient. The method comprises the following steps: firstly acquiring fault features of different types of supply transient current according to transient short-circuit current expressions of different types of power supply; measuring waveform difference of transient current at two sides when an internal of thenew energy terminal outgoing line occurs by utilizing the Spearman rank correlation coefficient; and distinguishing the internal and external faults according to the waveform difference of transientcurrent at two sides when the internal fault occurs, thereby realizing the pilot protection on the new energy terminal outgoing line. By utilizing the method disclosed by the invention, the internal and external faults can be reliably distinguished, and the reliable pilot protection on the new energy terminal outgoing line is realized.

The invention relates to a cross-correlation and autocorrelation cascade based frame synchronizing method, which includes: 1) repeatedly modulating a synchronizing sequence in length of N twice at a transmitting end to serve as a frame header of a physical layer frame, transmitting radio signals to a receiving end through a signal generator, sampling the radio signals to be a discrete sequence after the receiving end receives the radio signals; 2) subjecting the received discrete sequence to sliding cross-correlation operation through the local-storage synchronization sequence at the receiving end and obtaining a first secondary correlation output sequence; 3) subjecting the first secondary correlation sequence in the step 2) at the receiving end to obtain a second secondary correlation output sequence; and 4) finishing frame synchronization since any moment is the starting moment of the physical layer frame if sampling point value of the first secondary correlation output sequence at any moment is larger than a set threshold value output by first secondary correlation and a sampling point value in the second secondary correlation output sequence is larger than a set threshold value output by second secondary correlation.

The invention discloses an AC-DC hybrid power grid probability power flow analysis method taking regard of rank correlation among multiple types of nondeterminacy sources and aims to solve a problem of compliance to rank correlation among different distribution type random variables caused by access of multiple types of new energy to a power grid, such as wind energy and solar energy. According to the method, probabilistic power flow analysis on an AC-DC hybrid power grid is carried out, and thereby safe and reliable operation is guaranteed.

The invention relates to a non-calibration object registering method for pre-operation three-dimensional images and intra-operative perspective images. The method includes a first step of obtaining a series of three-dimensional images before an operation, generating N DRR images according to operation needs and obtaining the posture coordinate W of each image, a second step of obtaining an X-ray perspective images of different angles during operation through an X-ray machine on a C-shaped arm, a third step of conducting normalization processing for a DRR image i and the X-ray perspective images, a fourth step of employing a non-linear histogram matching algorithm, a fifth step of comparing rank correlation similarity of the DRR images i and the X-ray perspective images, and obtaining posture parameters of the X-ray perspective images and going to the next step if the two sets of images match with each other, otherwise, extracting the information of a DRR image i+1, and returning back to the third step, and a sixth step of conducting accurate registering for the pre-operation three-dimensional images and the X-ray perspective images based on the multi-resolution mechanism and gradient difference correlation, and outputting the image after accurate registering. Harm to a patient during an operation can be reduced.

The invention discloses a machine tool cross beam optimal design assessment method adopting combination weighing-grey correlation. The machine tool cross beam optimal design assessment method comprises the following steps that three-dimensional modeling is conducted on original cross beams, and static and dynamic characteristics of the original cross beams are analyzed to determine optimized factors and assessment indexes of the cross beams; cross beams of multiple structural schemes are designed, and several optimal schemes are preliminarily screened by analyzing and comparing static parameters of the cross beams; for the preliminarily screened schemes, cross beam rib plate structures, rib plate thickness and guide rail supporting rib plate inclination angles on the cross beams serve as test factors, the levels of the factors are determined, and an orthogonal test table is selected to conduct a simulation test on parameter combinations; simulation data is analyzed and processed by adopting a grey correlation method and a combination weighing method to obtain an optimal scheme; properties of the cross beams before and after optimization are compared to determine the reasonability of the optimal design parameter combinations. The machine tool cross beam optimal design assessment method makes a final analysis result real and reliable and accord with objective reality.

The invention proposes a short-period load prediction method for a microgrid based on SPSS and RKELM, and the method comprises the steps: (1), carrying out the online data collection, and periodicallyupdating a historical database; (2), carrying out the preprocessing of historical data, and extracting load sample features; (3), constructing an offline load prediction model; (4), screening a historical sample similar to a to-be-predicted point precursor load as an online training sample through SRC (Spearman Rank Correlation); (5), calculating a load prediction value at a future moment according to the online training sample and the offline load prediction model. The method employs a rapid RKELM (Reduced Kernel Extreme Learning Machine), a chaos particle swarm optimization algorithm and the SRC, and achieves the building of a prediction model comprising offline parameter optimization and an online load. Through the periodic updating of model parameters, the method guarantees the timeliness of an algorithm, reduces the complexity of online prediction and calculation, reduces the storage quantity of historical data, reduces the calculation cost, and can achieve the more accurate prediction of the short-period and super-short-period loads of the microgrid.

The invention discloses a method for evaluating NOR and NAND flash memory performance. The method is used for solving the technical problem of poor comprehensive operational capability of a present flash memory performance evaluation method. According to the technical scheme, the method comprises the following steps: firstly, testing a read-write speed U1 of the flash memory, and then testing the mounting time U2 of the flash memory, the average loss U3 of the flash memory, the memory consumption U4 of the flash memory, the hardware read / write speed U5 of the flash memory, the erasing speed U6 of the flash memory, the erasing energy consumption U7 of the flash memory, the hardware read / write energy consumption U8 of the flash memory and the file system energy consumption U9 of the flash memory in turn, and finally adopting a rank correlation analysis method for comprehensively evaluating the indexes from U1 to U9. The comprehensive evaluation for 9 indexes related to read-write, loading, erasing and energy consumption of the flash memory is adopted; the read-write speed, the file system operation capacity and the energy consumption related to the performance test for the flash memory are estimated; a corresponding index system is supplied for the three aspects of file system, hardware driving and energy consumption; and a rank correlation method is adopted for carrying out comprehensive evaluation and calculation, so that a test result can reflect the comprehensive capacity of the flash memory in the aspects of read-write, loading, erasing and energy consumption.

An image processing method for determining depth information from at least two input images (A1, A2) recorded using a stereo camera system, wherein the depth information is determined from a disparity graph taking into account geometric properties of the stereo camera system, characterized by the following method steps for determining the disparity graph: transforming the input images (A1, A2) into signature images (B1, B2) using a predefined operator; - calculating the costs (C) on the basis of the signature images (B1, B2) using a parameter-free statistical rank correlation measurement for determining a cost space for predefined disparity levels in relation to at least one of the at least two input images (A1, A2); carrying out a correspondence analysis (D) for each point in the cost space for the predefined disparity levels, wherein the disparity to be determined in each case corresponds to the lowest costs; and determining the disparity graph from the previously determined disparities.

A method, system and computer program product for adaptive system monitoring. In one embodiment, the method comprises generating time-varying correlation graphs indicating correlations between a multitude of parameters of the system, and using the correlation graphs to identify monitoring logic for monitoring the system. In an embodiment, the correlation graphs are used to select a group of the parameters as monitoring parameters, and these monitoring parameters are dynamically changed. In one embodiment, the monitoring parameters form sets of monitoring parameters, and each set of monitoring parameters is used to monitor the system for an associated period of time. The lengths of these monitoring periods are changed based on the rate of change of the correlation graphs. In an embodiment, the rate at which the monitoring parameters are changed is itself changed based on the rate of change of the correlation graphs.

The invention discloses a power distribution network reliability influence analysis method based on the improved entropy weight-grey correlation. The method comprises the steps: firstly introducing animproved entropy weight algorithm into a grey correlation analysis algorithm, so as to overcome a defect that the weights of different influence factors are similar, solving the reasonable weight value of each influence factor, so as to improve the evaluation objectivity, and then employing an improved entropy weight-grey correlation analysis algorithm; solving the correlation degree between thereliability index of the power supply area and each influence factor by using a grey correlation analysis algorithm; finally, for the data of the four power supply areas, obtaining the main factors influencing the power supply reliability of different power supply areas by adopting the method provided by the invention. The result shows that the method can effectively analyze the correlation degreeof each influence factor.

The invention discloses a method for optimizing parameters of a contact line and a pantograph based on sensitivity analysis. According to bow-net contact pressure during running of a high speed train, the existing problem is analyzed, level value design of the parameters is carried out through central composite design, and multi-factor and five-level parameters are obtained; an Spearman rank correlation coefficient method is adopted to compute correlation coefficients between the parameters of the contact line and the pantograph and the contact pressure to judge the correlation; and an Sobol method is adopted to compute sensitivity coefficients of the parameters of the contact line and the pantograph about the contact pressure, judge the impact of the parameters on the contact pressure and determine an optimization order of the parameters when the bow-net parameter optimization is carried out, after each index meets relevant standard requirements, optimization is ended and all bow-net parameter values are output. The method has reasonable optimization, and the parameters of the contact line and the pantograph can be adjusted quickly and reasonably, so that the contact pressure of the parameters of the contact line and the pantograph can be controlled reasonably.

A method and apparatus of identification by extracting and matching biometric detail from a subcutaneous vein infrared image. The image's Region of Interest is identified and artifacts are removed. A bank of filters, such as Symmetric Gabor Filters, Complex Gabor Filters, Log Gabor Filters, Oriented Gaussian Functions, or Wavelets, filters the image into a set of key value images that are subdivided into regions. An enrollment key, defined by ordered statistical measures of pixel intensities within the regions, is compared using a distance metric to a stored verification key. Various statistical measures may be used, such as variance, standard deviation, mean, absolute average deviation, max value, min value, max absolute value, median value, or a combination of these statistical measures. Various distance metrics may be used, such as Euclidean, Hamming, Euclidean Squared, Manhattan, Pearson Correlation, Pearson Squared Correlation, Chebychev, or Spearman Rank Correlation.

It has been reported in the literature that raceway measurement made during the decreasing gas velocity is relevant to operating blast furnaces. However, no raceway correlation is available either for decreasing or increasing gas velocity which is developed based on a systematic study and none of the available correlation take care of frictional properties of the material. Therefore, a systematic experimental study has been carried out on raceway hysteresis. Based on experimental data and using dimensional analysis, two raceway correlations, one each for increasing and decreasing gas velocity, have been developed. Also, in the present study the effect of stresses has been considered along with pressure and bed weight terms mathematically. These three forces are expressed in mathematical form and solved analytically for one-dimensional case, using a force balance approach. Based on the force balance approach a general equation has been obtained to predict the size of the cavity in each case, i.e., for increasing and decreasing velocity. Results of these correlations and model have been compared with the data obtained from literature on cold and hot models and plant data along with some experimental data. An excellent agreement has been found between the predicted (using correlations and model) and experimental values. The proposed theory is applicable to any packed bed systems. It has been shown that hysteresis mechanism in the packed beds can be described reasonably taking into consideration the reversal of sign in frictional forces in increasing and decreasing velocity cases.

The present invention provides a correlation-matrix feedback method and system for an antenna array. The method comprises steps of: selecting, at a receiving end, a codeword from a predetermined correlation-matrix codebook comprising a plurality of codewords according to a predetermined criterion to represent a correlation matrix to be fed back (300); and feeding back an index of the selected codeword in the predetermined correlation-matrix codebook to a transmitting end (302); wherein each codeword is a codeword matrix for approximating a correlation matrix of an antenna array at the transmitting end. According to the present invention, it is possible to take good advantage of the characteristics of closely-spaced ULA antennas and spatial correlation thereof, design a corresponding codebook for quantization and feedback, and reduce feedback overhead and computational complexity so as to improve the system performance of a spatial correlation aided system, such as a MIMO system.