48 results about "Spearman's rank correlation coefficient" patented technology

The invention discloses a WLAN (wireless local area network) indoor positioning method based on matrix correlation, which belongs to the field of pattern recognition, and aims to solve the problem that in the conventional indoor positioning process adopting both a position fingerprint matching algorithm based on nearest neighbor selection and a position fingerprint matching algorithm based on machine learning, a lot of useful RSS (received signal strength) information is lost. The WLAN indoor positioning method based on the matrix correlation comprises the following steps: 1, under an indoor WLAN environment, selecting a certain number of reference points off line, recording coordinates and RSS samples of the reference points, and establishing a position fingerprint database; 2, measuring positioned points, and recording real-time RSS samples received by a user terminal; and arranging the received original RSS samples into a matrix, and converting RSS data of all reference points in the position fingerprint database into matrixes with the same dimension; 3, computing a Pearson correlation coefficient; and 4, selecting at most K reference points corresponding to the Pearson correlation coefficient, and taking the average of the coordinates of the reference points as a positioning result.

The invention discloses a brain function magnetic resonance image classification method based on network centrality. The method comprises the following steps of: preprocessing a brain function magnetic resonance image, performing brain region segmentation, and extracting an average time sequence of each brain region; calculating a partial correlation coefficient between each average time sequence, and obtaining a partial correlation coefficient matrix; performing binarization on the partial correlation coefficient matrix to obtain a brain network model; calculating the network centrality of each node in the network; and classifying the brain function magnetic resonance image by utilizing an adaptive improvement classifier, and checking the adaptive improvement classifier by employing a leave-one-out cross validation testing method. The brain function network is established by utilizing the brain function magnetic resonance image, the brain function magnetic resonance image is classified by utilizing the network topology information, and the brain function magnetic resonance image can be accurately classified.

The invention relates to a Chinese word similarity calculation method based on a fusion strategy. The method comprises steps of calculating word similarity based on the combination of four of HowNet,synonym forest, Word2Vec trained Chinese Wikipedia encyclopedia corpus and a Baidu dictionary; for two input words, firstly, judging whether the synonyms exist in a HowNet or synonym forest or not; ifyes, using the HowNet or synonym forest for calculating the similarity, if not, judging whether the HowNet or synonym forest exists in the Wikipedia corpus or the Baidu dictionary or not, and if yes,using the Word2vec or the Baidu dictionary for calculating the similarity of the words. The invention provides a Chinese word similarity calculation method based on a fusion strategy. According to the fusion strategy, the known network, the synonym forest, the word2vec and the Baidu dictionary are comprehensively considered, advantage complementation among strategies is formed, the calculated Spearman correlation coefficient and Pearson correlation coefficient are higher than those of other methods, the accuracy of a word similarity calculation result is improved, and the requirements of practical application can be well met.

The invention relates to a brain functional magnetic resonance image classification method based on a complex network, which comprises the following steps: pre-processing training sample images and test sample images, carrying out region segmentation, and extracting an average time sequence from each region; calculating the partial correlation coefficient among the average time sequences, carrying out matrix binarization on the partial correlation coefficient to obtain a complex network model, and calculating the feature path length, cost and clustering degree of the complex network model to respectively obtain network features of the training sample images and the test sample images; training to obtain an adaboost classifier; and by using the adaboost classifier obtained by training, classifying the test sample images. By using information in the brain functional magnetic resonance images as much as possible, the method can accurately classify the brain functional magnetic resonance images.

The invention relates to the technical field of electric energy meters, in particular to a courts phase identification method based on a Pearson correlation coefficient algorithm. The method mainly includes the steps of calculating Pearson correlation coefficients of a single-phase energy meter and A, B and C phases and then judging the phase according to the obtained Pearson coefficients. According to the method, voltage data collected by a concentrator can be used to detect the phase difference of the single-phase energy meter, it is convenient to detect phase information, and the cost is low.

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 provides a correlation analysis based on-load voltage regulating transformer tapping switch operating state judgment method and belongs to the field of power system device monitoring. In online or offline monitoring data processing application of a drive motor current signal and an action vibration signal in the on-load voltage regulating transformer tapping switch action process of a power system, index quantities capable of reflecting the operating state of an on-load voltage regulating transformer tapping switch are selected, and index quantities different in dimension are subjected to mathematical expectation based standardization. A group of index quantities obtained through monitoring each time serve as a sample, a correlation coefficient and a partial correlation coefficient which serve as reference indexes for on-load voltage regulating transformer tapping switch operating state estimation are obtained to judge the normal operation degree and deterioration degree of a device through correlation analysis between the obtained sample and a normal sample and partial correlation analysis of any two attention samples in more than two groups of samples.

The invention relates to the digital image processing field, and specifically relates to an underwater terrain matching method based on textural feature and terrain feature parameters. The method comprises the followings steps: carrying out downsampling on actually-measured underwater terrain data and reference data; obtaining an actually-measured data matrix and a reference data matrix which are same in resolution; obtaining angular second moment, contrast, correlation and deficit moments; obtaining a terrain elevation average value, terrain elevation standard deviation, terrain roughness, terrain correlation coefficient, terrain entropy and terrain difference entropy; endowing weight for each feature parameter; keeping a similarity value of each searching position; and selecting the positions, of which the similarities are largest, that is, the similarity values are the smallest, as matching positions for outputting. According to the method, through carrying out downsampling on the actually-measured underwater terrain data and the underwater terrain reference data of a special area, the calculation cost is reduced, underwater measured data precision is improved, and moreover, the underwater terrain matching effect based on the textural feature and terrain feature parameters is achieved.

The invention discloses an electric energy meter manufacturer evaluation method based on multiple evaluation indexes, which relates to an evaluation method. The invention comprises the following steps: acquiring the original data of the electric energy meter and determining an evaluation index; Calculating each index value of the electric energy meter, construct the index matrix, forming the decision matrix; Calculating the information entropy and Spearman grade correlation coefficient of each index value of watt-hour meter, determine the objective weight of the quality evaluation index according to CRITIC method, processing the decision matrix and obtain the watt-hour meter supplier evaluation matrix; Calculating the positive ideal solution and negative ideal solution of the watt-hour meter supplier, and calculating the Euclidean distance from each watt-hour meter supplier to the positive ideal point vector and the negative ideal point vector. Calculating the relative approximation degree between each watt-hour meter supplier and the ideal solution, and determine the quality level of each watt-hour meter supplier from high to low. The technical scheme provides an objective and scientific basis for the electric energy meter quality evaluation and supervision of the power grid company.

The invention relates to a TSK fuzzy system modeling method for brain function magnetic resonance image classification, and belongs to the technical field of image processing. The method comprises thefollowing steps: S1, preprocessing a brain function magnetic resonance image; s2, calculating a Pearson correlation coefficient among the brain regions to obtain a symmetric matrix, taking a triangleon the symmetric matrix to unfold according to lines to obtain a sample feature vector, and representing the data of one picture by each column of the sample feature vector; s3, carrying out featureextraction on the sample feature vectors; and S4, constructing a classifier to classify the brain function magnetic resonance images, and solving a model used by the classifier by adopting an alternating optimization algorithm to complete image classification. According to the method, the nonlinear classifier is constructed based on the TSK fuzzy system, the correlation between the features is represented by using the undirected graph, and the brain function magnetic resonance images can be accurately classified.

A power grid peak load clustering extraction method based on Pearson coefficient and MapReduce parallel computing comprises the following steps: 1, conducting data cleaning and abnormal data restoration on a load data set, and then conducting dimensionality reduction on the load data set; 2, storing the data set subjected to dimension reduction in a Hadoop distributed file system line by line, anddividing the data set into slices to form sub-data sets; 3, reading each slice sub-data set in the step 2 by using a MapReduce computing architecture, and selecting an initial clustering center by using a Pearson correlation coefficient as a similarity criterion through a parallel model; and 4, allocating the clustering calculation task to the Map task node in the MapReduce, and combining the initial clustering center in the step 3 to complete parallel clustering of the data set. The method is more suitable for power load big data processing derived from more and more vigorous development information times.

The invention discloses a blast furnace bosh gas volume prediction method and program for a multi-clustering prototype-based T-S model. The method comprises the steps of performing data preprocessingon blast furnace data, and deleting abnormal values; performing variable screening by utilizing a Spearman rank correlation coefficient; selecting a hyperspherical clustering prototype-based FCM fuzzyclustering algorithm and a hyperplane clustering prototype-based NFCRMA fuzzy clustering algorithm to perform fuzzy clustering on the blast furnace data, performing calculation by using membership functions corresponding to the two fuzzy clustering algorithms to obtain two types of different T-S model antecedent rule fitness, sorting the two types of the rule fitness from high to low, and finallycalculating a weighted sum of the two types of the rule fitness to obtain weighted rule fitness; and calculating consequent parameters of the T-S model by using a least square method, and finally adjusting a weighted coefficient to improve the prediction precision of the model. The method can accurately predict a value of a blast furnace bosh gas volume index at a next moment.

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 discloses a transmission line fault detection method based on a spearman correlation coefficient. The method comprises the following steps of: acquiring three-phase current instantaneousvalues on two sides of a line at each moment; calculating to obtain instantaneous values of a positive sequence fault component current, a negative sequence current and a zero sequence current on twosides of the line; constructing a fault component comprehensive current on a certain side of the line to be the superposition of the instantaneous values of the positive sequence fault component current, the negative sequence current and the zero sequence current at the same moment; using one cycle as a time window, utilizing the fault component comprehensive current in the time window to construct two characteristic sequences of a certain line, and calculating the spearman correlation coefficient between the two characteristic sequences; and calculating the spearman correlation coefficient of the line once when the time window moves forwards by one point, and judging that the line is a fault line when the correlation coefficient is larger than a threshold value 0. The transmission line fault detection method of the invention has the beneficial effects that: the transmission line fault detection method is not influenced by transition resistance, abnormal data, current transformer saturation and the like, and the fault line can be detected when a fault occurs in open-phase operation and flow transferring, and the requirement on information synchronism is not high.

The invention relates to a high-speed rail hub accessibility calculation method based on an improved two-step mobile search method, a high-speed rail hub accessibility spatial distribution fairness evaluation method based on a variable coefficient and a Spearman level correlation coefficient, and a newly-added high-speed rail hub accessibility influence method based on the improved two-step mobilesearch method. According to the method, a two-step mobile search method is improved, the traffic cost of facility points and demand points is effectively measured, and in the accessibility analysis process, by improving a search carrier of a spatial action domain, a calculation method of a road traffic condition and attributes of a search radius, analysis better conforms to the actual situation,and the accuracy of accessibility measurement and calculation is improved. According to the method, the influence of the high-speed rail construction on the spatial balance of reachable distribution is analyzed by applying the variable coefficient and the Spearman level correlation coefficient while the reachability is analyzed to be improved, so that residents can enjoy more high-speed rail services and the services can be distributed in a balanced manner in space due to the arrangement of the high-speed rail.

The invention discloses a metering equipment operation quality evaluation method based on a CRITIC and an ideal point method, and relates to the field of electric power. At present, the operation quality evaluation of metering equipment cannot give consideration to the simplicity and accuracy of calculation. According to the technical scheme, firstly, five evaluation indexes for measuring the quality of the electric energy metering equipment are defined based on operation monitoring big data of the electric energy metering equipment in an electric power information acquisition system, and thefive evaluation indexes comprise the load acquisition availability rate, the data acquisition integrity rate, the average fault-free working time, the operation abnormity alarm frequency and the operation fault rate index; then, objective weight of an electric energy metering equipment quality evaluation index is determined by adopting a CRITIC method based on entropy weight and a Spearman level correlation coefficient, and an electric energy metering equipment quality evaluation model is constructed based on an ideal point method; according to the technical scheme, the operation quality of the electric energy metering equipment can be quantitatively evaluated, the advantages and disadvantages of the electric energy metering equipment can be reasonably evaluated, and the simplicity of calculation and the accuracy of evaluation are considered.

The invention relates to a generalized S transformation-based Q-value estimation method adopting a spectral correlation coefficient. The method includes the following steps that: step 1, seismic data are inputted; step 2, generalized S transformation is performed on the seismic data, so that post-transformation seismic data can be obtained; step 3, the estimated amplitude spectrum and actual amplitude spectrum of the post-transformation seismic data are obtained; step 4, a spectral correlation coefficient is constructed based on the estimated amplitude spectrum and actual amplitude spectrum; and step 5, a Q value which appears when the spectral correlation coefficient is minimum is solved. With the method of the invention adopted, the problem of frequency band range selection can be avoided, certain noise resistance can be realized, and the influence of a time-frequency analysis method can be reduced.

The invention discloses an electricity consumption prediction method, which comprises the following steps that: obtaining electricity consumption data and relevant impact factors; calculating the electricity consumption data and the relevant impact factors according to a copula function to obtain rank correlation coefficients; sorting the rank correlation coefficients corresponding to the impact factors according to a descending order, and selecting a preset quantity of impact factors which rank in the front as effective impact factors; or selecting the impact factor corresponding to a threshold value the which exceed a preset relevant coefficient as an effective impact factor; and inputting the effective impact factor and the electricity consumption data into a pre-constructed predictionmodel to be trained, and obtaining an electricity consumption prediction result. By use of the electricity consumption prediction method, through the copula function calculation, the rank correlationcoefficients are obtained to realize the selection of the impact factors, and a calculation amount can be lowered while prediction accuracy is improved, prediction speed is improved, and through the prediction model, a prediction process can be simple and efficient. Therefore, the method can be used for more accurately and efficiently predicting the electricity consumption, and the prediction accuracy of the electricity consumption is improved.

The invention discloses an FPGA (a field-programmable gate array) operational circuit based on generalized correlation coefficients. The architecture of the FPGA operational circuit comprises an array multiplication accumulator, a shifting register, a multiplying unit, an assembly line, a square root extractor, a divider and a register. When environmental noise contains an impulse noise component, a matched filter basically loses efficacy while the SR (the Spearman rank correlation coefficients) and the KT (the kendall rank correlation coefficient) contained in the generalized correlation coefficients still can show excellent robustness when interfered by impulse noise; by utilizing the rich logical resources and the high-speed parallel processing capability of the FPGA, the operational circuit achieves real-time and high-speed correlation processing of signals based on the SR, the KT and the PPMCC (the product moment correlation coefficient) and can be applied to radar, a sonar sound ranging system and a GNSS (a global navigation satellite system) for signal capturing, tracking and other signal processing technologies.

The invention discloses a method for checking data quality of wind power generation based on Pearson correlation coefficient, which comprises the following steps: selecting standard sample data to calculate Pearson correlation coefficient; correction of daily data using Pearson correlation coefficients; comparing the Pearson correlation coefficients of the two sets of replacement data, the conclusion is drawn. The invention corrects the wind power generation data through the Pearson correlation coefficient of the wind power generation power and the wind speed, can more scientifically and reasonably improve the data quality of the wind power generation, plays an effective supporting role in the collection and analysis of the wind power generation data of the new energy data center, and saves the daily operation and maintenance cost of the wind power station.

The invention discloses an alloy grade identification method based on a pearson correlation coefficient. The method comprises the steps of (1), determining a matching element set; (2), testing element content information of a to-be-identified sample through a spectrograph and extracting each element content value Xi in the matching element set; (3) extracting a content upper limit Xmaxi value and a lower limit Xmini value of each element of the matching element set in certain grade; (4), establishing a real matching vector Vreal and an ideal matching vector Videal; (5), calculating the pearson correlation coefficient between the real matching vector Vreal and the ideal matching vector Videal; and (6), repeating the steps (3)-(5), until the pearson correlation coefficients of all grades in a grade library are calculated, wherein the grade with the maximum correlation coefficient is the grade to which the to-be-identified sample belongs. According to the method, an operation process is simple, operation results are normalized data, there is no dimension, values are between -1 to 1, the value 1 indicates that the grade matches the test result completely, under the other condition, the more the value is approximate to 1, the higher the matching degree is, and the more the value is approximate to -1, the lower the matching degree is.

The invention discloses a PageRank algorithm-based hospital importance ranking method. The method comprises the following steps of: 1, collecting behavior data about doctor multi-sited license and constructing a hospital association network; 2, generating a probability transfer matrix according to the hospital association network; 3, ranking the importance of hospitals by adoption of a PageRank algorithm; and 4, carrying out relevancy analysis by adoption of a Spearman rank correlation coefficient and online ranking of the hospitals. According to the method, hospital association networks are constructed, importance ranking is carried out on hospitals in hospital association networks in first-tier cities and new first-tier cities by using the PageRank algorithm, and finally relevancy analysis is carried out by using the Spearman rank correlation coefficient and the online ranking of the hospitals, so that the importance ranking conditions of the hospitals in the first-tier cities and new first-tier cities, the policies implemented by the cities and traffic conditions of the cities can be combined to carry out analysis.

The invention discloses a method, device and equipment for predicting the residual service life of a magnetic levitation system and a medium, and the method comprises the steps: extracting time domainfeatures and frequency domain features from a levitation gap signal of a target magnetic levitation system, and obtaining a corresponding feature set; calculating a Spearman grade correlation coefficient corresponding to each feature in the feature set to determine monotonicity of each feature; clustering monotonicity of features in the feature set based on a hierarchical clustering method, and screening out target features from clustering results; calculating a health index threshold value corresponding to the target magnetic levitation system by utilizing the target characteristics; determining initial time suitable for predicting the starting life of the target magnetic levitation system by utilizing a pre-constructed exponential decay model; and based on the exponential decay model, predicting the remaining service life of the target magnetic levitation system by using the health index threshold and the initial time. According to the invention, the health index threshold and the exponential decay model are used for life prediction, and the accuracy of life prediction of the magnetic levitation system is improved.

A spearman rank correlation self-adaption classification method for electric power system cross-specialty monitoring indexes adopts a Spearman rank correlation coefficient to calculate, calculates and analyzes trans-department data, performs classification according to grading ranking, calculates threshold values of correlation coefficients of all index pairs, screens out all index pairs that are larger than the threshold values, and thus can automatically obtain representative cross-specialty index pairs. The Spearman rank correlation self-adaption classification method for the electric power system cross-specialty monitoring indexes provided by the invention effectively analyzes trans-department data and maximizes benefits.

The invention discloses an electric energy meter manufacturer evaluation method based on a multi-attribute decision model, which relates to an evaluation method. The invention comprises the followingsteps: acquiring the original data of the electric energy meter and determining an evaluation index; Calculate each index value of the electric energy meter, construct the index matrix, form the decision matrix; Calculate the information entropy and Spearman grade correlation coefficient of each index value of watt-hour meter, determine the objective weight of the quality evaluation index according to CRITIC method, process the decision matrix and obtain the watt-hour meter supplier evaluation matrix; Calculate the positive ideal solution and negative ideal solution of the watt-hour meter supplier, and calculate the Euclidean distance from each watt-hour meter supplier to the positive ideal point vector and the negative ideal point vector. Calculate the relative approximation degree between each watt-hour meter supplier and the ideal solution, and determine the quality level of each watt-hour meter supplier from high to low. The technical scheme provides an objective and scientific basis for the electric energy meter quality evaluation and supervision of the power grid company.

The invention discloses an evaluation method of a collection terminal manufacturer based on multiple evaluation indexes, which relates to an evaluation method. The method includes steps of acquiring original data and obtaining an evaluation index; calculating each evaluation index value, constructing the evaluation index matrix and forming the decision matrix; calculating the information entropy of index value and Spearman grade correlation coefficient, determine the objective weight of quality evaluation index, process the decision matrix, and obtain the evaluation matrix of collection terminal equipment supplier; the positive ideal solution and the negative ideal solution of the supplier of the acquisition terminal equipment are calculated, and the Euclidean distance from the supplier ofthe acquisition terminal equipment to the positive ideal point vector and the negative ideal point vector is calculated. The relative approximation degree between the supplier of each acquisition terminal equipment and the ideal solution is calculated, and the quality grade of each supplier of acquisition terminal equipment is determined from high to low. The technical scheme provides an objective and scientific basis for the power grid company to carry out the operation and maintenance of the electric power information acquisition system and the quality evaluation and supervision of the acquisition terminal equipment.

The invention provides a measurement method of a turning point of a thermomechanical analysis curve based on a Pearson correlation coefficient, andsolves the problem that the turning point of the thermomechanical analysis curve of the existing asphalt mixture is difficult to be determined. Measuring methods: 1. Measuring the thermomechanical parameters changing with temperature, establishing temperature array and thermomechanical parameter array; 2, establishing a temperature-thermomechanical parameter data set; 3, calculating that square of the Pearson correlation coefficient of the temperature data set and the thermomechanical parameter data set base on the temperature-thermomechanical parameter data set, obtaining the R2 data set, taking T as an abscissa coordinate and R2 as a ordinatecoordinate, and drawing a R2-T curve diagram; Fourthly, if R2 >= 0. 995 is taken as the defining condition of the turning point of the temperature stress curve, taking the corresponding temperature ofR2 as the measuring turning point temperature TT. The invention takes the Pearson correlation coefficient as the index, and accurately determines the turning point of the thermomechanical analysis curve of the asphalt mixture by analyzing and calculating the correlation coefficient between the thermomechanical analysis parameters and the temperature data points.

The invention discloses a WAMS / SCADA system timing sorting method based on Pearson correlation coefficient, belonging to the electric power dispatching field. This method makes full use of Pearson correlation coefficients which can be used to evaluate the correlation between two physical quantities with different dimensions, and uses WAMS data with incomplete distribution and SCADA data with complete distribution to evaluate the correlation, and realizes the correction of SCADA data in chronological order. This method is simple to use and takes into account the characteristics of WAMS data andSCADA data. Combined with the Pearson correlation coefficient evaluation, it has a strong application benefit.

The invention discloses a production process dominant variable simplified soft measuring method achieved with help of a Pearson correlation coefficient. The production process dominant variable simplified soft measuring method achieved with the help of the Pearson correlation coefficient is characterized by comprising the following steps: 1 determining n primary auxiliary variables which are correlative with a dominant variable, collecting value data of the n primary auxiliary variables and the dominant variable and enabling the value data of the n primary auxiliary variables and dominant variable to be combined into a sample set; 2 utilizing the Pearson correlation coefficient algorithm to respectively calculate weighted values of the n primary auxiliary variables; 3 forming a primary auxiliary variable sequence; 4 conducting modeling and determining an optimum auxiliary variable according to a minimum mean square error (MSE); and 5 obtaining a simplified soft measuring model. The production process dominant variable simplified soft measuring method can find out an auxiliary variable set with the least number of auxiliary variables to conduct modeling to the dominant variable, and therefore a simplified soft measure to the dominant variable is achieved.