46 results about "Association coefficient" patented technology

The invention discloses a method for generating a template and detecting a premature beat, and a device. The method comprises the following steps: sequentially and respectively putting M waveforms for measuring a heart beating in N templates relative to the waveforms by sequentially performing a relativity comparison between the waveforms for measuring the heart beating and the N templates, wherein N is less than M; and taking the template which obtains most heart beating times from the first template to the N template as a normal template. The method for detecting the premature beat comprises the following steps: pretreating an inputted electrocardiogram signal to obtain a pretreated signal; extracting the feather parameters of the heart beating for measuring from the pretreated signal, wherein the feather parameters include the width of a QRS wave, an existing RR interphase and a related average RR interphase; obtaining the waveform of the heart beating for measuring from the pretreated signal, and calculating the association coefficient between the waveform of the heart beating for measuring and the waveform of the template; and according to judging conditions that whether the association coefficient between the waveform for measuring the heart beating and the waveform of the template is less than a set threshold and whether the feather parameters are deviated from normal values, judging whether a ventricular premature beat exists.

The invention discloses a calibration-based scene self-adaption IRFPA (Infrared Focal Plane Array) heterogeneity correction method, belongs to the field of infrared focal plane detection, combines the advantages of small calculation and strong real-time property in a calibration algorithm and the advantage that a scene heterogeneity algorithm can change a correction coefficient in a self-adaption manner, and proposes a calibration-based scene self-adaption IRFPA heterogeneity correction method. The method comprises the following steps: extracting a uniformly radiated infrared template image; calculating a multipoint temperature correction algorithm gain coefficient and a polarization coefficient; calculating the association coefficient between an infrared scene image and the infrared template image; performing heterogeneity correction of real-time scene image. The method has a simple implementation procedure, small calculation, an obvious image correction effect, and application and popularization values.

The invention provides an iris-change-based living body identification method and system. The method comprises: screen brightness information of a current device is obtained; pupil constriction state information of a user for the current device screen brightness is obtained; the screen brightness information and the pupil constriction state information are compared to obtain association coefficients of the screen brightness information and the pupil constriction state information; and the association coefficients are compared with a preset threshold value and if the association coefficients are larger than the threshold value, the user is determined to be the human being. With the method, intelligence of face detection equipment is enhanced; accuracy of living body identification by the face detection equipment is improved; and an objective of manpower loss reduction is achieved. Moreover, the method and system are simple and are easy to implement and popularize.

The invention discloses a radar synthetic phase unwrapping method based on a branch-cut method and surface fitting. According to the main idea, the method comprises: conjugated multiplication of image data of an SAR image after image registration and image data of the SAR image after image registration is carried out to obtain an interferometric phase diagram and then obtain a pseudo association coefficient of the interferometric phase diagram; a pseudo association coefficient threshold value of the interferometric phase diagram is set to obtain a first quality area diagram and a second quality area diagram, and phase unwrapping is carried out on the first quality area diagram by using a branch-cut method to obtain an unwrapping phase of the first quality area diagram; fitting of the unwrapping phase of the interferometric phase diagram is carried out by using a polynomial surface fitting method to obtain a coefficient column vector of a polynomial fitting function of the interferometric phase diagram and an unwrapping phase curve surface with an error of the interferometric phase diagram; and the unwrapping phase curve surface with an error of the interferometric phase diagram is optimized by using a genetic algorithm, thereby obtaining an optimized unwrapping phase curve surface of the interferometric phase diagram. Therefore, the final unwrapping phase of the interferometric phase diagram is obtained.

The invention relates to an online power station main steam flow calculation method based on flow correction. According to the method, an online main steam flow calculation model is corrected through a mass flow balance equation, the model finishes flow calibration on the basis of condensate water flow measured by a spray nozzle, under the different load working conditions, iteration method verifying calculation is utilized to obtain the main steam flow value, the Friuli Greig formula is corrected through the verified main steam flow measuring value, and finally an online main steam flow calculation model is built and obtained. By means of the method, the change tendency of the main steam flow can be reflected quickly, and meanwhile the high accuracy of the flow can be guaranteed. Considering the situation that sensor drift distortion occurs in a field adjusting level pressure measuring point, whether the adjusting level pressure value is distorted or not is judged according to the association coefficient, accordingly an adjusting level pressure soft measurement replacement solution is proposed, and the stability of an online main steam flow calculation module is guaranteed. The online main steam flow calculation strategy can be referred to by a power station and be used for conducting economic analysis.

The invention belongs to the technical field of simulation light sources, and particularly relates to a solar spectrum and black-body radiation spectrum simulation system based on a quantum dot LED. The system comprises a light source module, a light dimming system, a focusing lens, a light mixer and a light distribution lens group, wherein the light source module comprises K monochromatic light quantum dot LED light sources or ultraviolet LED light sources with different peak wavelengths; the focusing lens converges monochromatic light to the light mixer at the public focal point of the focusing lens, and the light mixer mixes the monochromatic light into simulated sunlight or simulated black-body radiation light within the wavelength of 200-1,200 nm; and finally, the monochromatic light is emergent through the light distribution lens group. According to the solar spectrum and black-body radiation spectrum simulation system, during synthesis of a target spectrum, under an ideal implementation condition, the association coefficient r of synthesized light and the target spectrum within 400-730 nm is greater than 0.99, and the color temperature deviation delta CCT is less than 100 K; and generally, the developing index Ra is greater than 98, the special developing indexes from R1 to R15 are all greater than 96, and the color tolerance difference SDCM is less than 1.

The invention discloses a space flexible mechanism dynamic reliability analysis method based on PSO. The space flexible mechanism dynamic reliability analysis method includes the specific steps of (1) building a time-variation reliability model of an in-orbit space flexible mechanism according to the reliability modeling theory, (2) carrying out time discretization on the time-variation reliability model, and dividing the time-variation reliability model into a plurality of time points t1, t2, ..., tn, (3) building performance functions corresponding to the time points, and then building an association coefficient rou(t, t+deltat) between t and t+deltat by deriving normal vectors of the performance functions, (4) obtaining reliability coefficients beta1, beta2, ..., betan with the PSO, (5) calculating the up-crossing rate in cooperation with the association coefficient rou(t, t+deltat) between t and t+deltat, and (6) calculating the failure probability with the up-crossing rate method. By means of the space flexible mechanism dynamic reliability analysis method, the accuracy of a high-dimensional nonlinear flexible time-variation reliability analysis result is improved.

The invention discloses an online monitoring method for the backup power supply system of an intelligent power distribution terminal, which belongs to the field of power system automation. The online monitoring method is characterized by comprising the following steps of: step a, setting an online monitoring mode word; step b, acquiring a standard discharge curve for forming an energy storage component and storing; step c, acquiring a real-time discharge curve for forming the energy storage component; step d, calling the standard discharge curve and calculating the association coefficient of the curve; step e, judging the association coefficient of the curve. According to the online monitoring method for the backup power supply system of the intelligent power distribution terminal, the original hardware resources of the intelligent terminal are utilized, online monitoring is realized only by measuring the voltage value of an energy storage medium, and cooperating with the activation circuit of the intelligent terminal, without the need of measuring a current and calculating an internal resistance, and adding additional monitoring equipment; the online monitoring method is convenient to measure, accurate and reliable, and low in measurement cost.

The present invention provides a behavior identification method through combination of confidence and contribution degree on the basis of a space-time context. The deficiency of human body behavior identification through adoption of classic local characteristics and a word bag model is made up. The behavior identification method describes the context interaction information of the local characteristics in the spatial domain and the time domain and considers the relationship between the characteristics and the behavior. The method comprises: excavating the confidence of words from association rules; learning the contribution degree of the words from a linear SVM; calculating an association weight of the words and the corresponding behavior through combination of the confidence and the contribution degree; calculating context association coefficients according to the space-time interaction relationship and the association weight; accumulating the association coefficients of all the points in a neighborhood to obtain a local context descriptor; and generating a characteristic sequence for behavior classification through adoption of a context descriptor cumulative histogram. Through adoption of the machine learning and data excavation correlation technology, the behavior identification method through combination of confidence and contribution degree on the basis of a space-time context is able to allow a human body behavior identification algorithm to have the discrimination capability and the robustness.

The invention discloses a near-infrared spectrum recognition model. A near-infrared spectroscopy is adopted for scanning a spectrum of a series of normal like products, and an average spectrum is calculated to serve as a reference spectrum; then the weighting association coefficients of the spectrum of the products and the reference spectrum are calculated, a recognition section is calculated through the average value and standard deviation of the weighting association coefficients, and the recognition model is established. Compared with traditional chromatography analysis, mass spectrum analysis and other instrument analysis, the advantages of being environmentally friendly, simple, fast and easy to operate are achieved. The established model is high in recognition precision and detecting efficiency and low in cost, and technical support is provided for product stability analysis and authenticity recognition of cigarette enterprises.

The invention discloses a BP neural network intelligent industrial park energy consumption model establishment method based on principal component analysis. According to the method, the historical electricity consumption data of selected terminal users and the coal, oil including gasoline, diesel oil and kerosene, gas including liquefied petroleum gas and natural gas, and electricity energy consumption data of the users are pre-processed, after normalization processing, a gray association coefficient and a gray association matrix are calculated, input variables are selected according to a gray relational degree, principal component analysis is carried out to acquire multiple irrelevant outputs which are taken as inputs of a BP neural network model, a neural network output variable and a hidden layer number are determined, neural network initialization and network training are accomplished, and a park terminal user energy consumption model is established. Through the method, the input variables of the BP neural network energy consumption model can be effectively reduced through principal component analysis, relationships between energy consumption and each terminal user data can be effectively identified, the park terminal user energy consumption model can be more precisely established, and total energy consumption of the users of a whole industrial part can be effectively predicted.

The invention relates to a similar day selection method used for photovoltaic power station power prediction. The similar day selection method comprises the following steps: determining a meteorological information vector; and normalizing and calculating a correlation coefficient, calculating a subjective weight and an objective weight, and finally obtaining the similarity on each history day by a prediction day, wherein the history data with a large similarity numerical value is the similar day. The similar day selection method provides accurate basic data for photovoltaic output forecasting on the basis of the combination of the association coefficient and the combined weight.

The invention discloses a longitudinal extension method based on a super-fusion system and a device thereof. The longitudinal extension method comprises the following steps: identifying user configuration so as to match a longitudinal extension storage resource, configuring a first storage layer and a second storage layer; migrating related data stored in the first storage layer and meeting a preset condition to the second storage layer to store, and updating an access address of the migrated related data; acquiring an association coefficient between the migrated related data in the second storage layer and the unmigrated related remained in the first storage layer, acquiring an address sorting value of the association coefficient and a fusion value of the access frequency of the unmigrated related data, if the fusion value is greater than a set value, inserting the migrated related data into the next location behind the storage location of the unmigrated related data, and updating theaccess address of the migrated related data. A read-write operation on continuous partitions can greatly reduce the file fragmentations in the disk, and can short the reading time of the file and improves the system performance at the same time.

The present invention provides a prediction network unknown connection edge method based on the second-order local association information. The method comprises: constructing a network model; obtaining a pair of first-order common neighbor nodes and second-order common neighbor nodes which are not connected with nodes, wherein the connection edge between the nodes and the first-order common neighbor nodes and the second-order common neighbor nodes forms a second-order local association; recording the total number of the nodes of the association and the total number of the connection edge; calculating the edge clustering coefficient, the simple harmonic quantity mean distance of the association and the second-order association coefficients; calculating the similarity score index between the node pair; and traversing the whole network, calculating the similarity score index between the corresponding node pair aiming at two random non-connected nodes, and sorting the similarity scores among all the non-connected node pairs in the descending order to take the nodes corresponding to the front M indexes as predication connection edge. The prediction network unknown connection edge method based on the second-order local association information considers the second-order local association including the first-order common neighbor and the second-order common neighbor between two non-connected nodes to fully utilize the network local structure information, the predication effect is good, and the accuracy is high.

The invention discloses a spatial information aided unmanned aerial vehicle big data multi-temporal association analysis method. The method comprises the following steps: acquiring and encoding a video in real time, acquiring unmanned aerial vehicle spatial information and camera parameter and other auxiliary information, and recording the auxiliary information in a compressed bit-stream at the same time; dividing the method into two conditions that the acquisition points are the same and different, establishing an associated judgement rule between any two multi-temporal images, and measuring an association coefficient; and organizing the multi-temporal videos under the same scene according to the order of the original acquisition time through the scene classification of the time sequence maintenance. By use of the analysis method disclosed by the invention, the multi-temporal video associated with the same scene can be fast and reliably positioned from mass unmanned aerial vehicle video big data of different scenes shot at different time, thereby providing evidence for the change detection of the disaster scene.

The invention discloses a calculating method and device for traffic transportation capacity based on track analysis, and belongs to the technical field of calculation of traffic transportation capacity. The device comprise a floating vehicle data cleaning module, a floating vehicle track clustering module, a traffic transportation capacity calculating module and a traffic transportation capacity scale evaluation module. Output information of all vehicles passing through an area in some time period is recorded, a track data set composed of single-point data is obtained for the n floating vehicle, and a complete data set is obtained through the floating vehicle data cleaning module; by utilizing the floating vehicle track clustering module, the directed space-time clustering volume meeting the requirements of a specific time sequence and space movement is obtained, a model of the relationship between the transportation capacity and common factors influencing the traffic transportation capacity is constructed, and an association coefficient of the model is extracted to obtain a transportation capacity factor loading matrix; the traffic transportation capacity calculation module isused for solving to obtain the total traffic transportation capacity, and the traffic transportation capacity is graded. The calculating method and device have reliability and scientificity and provide powerful basis for related decisions.

The invention relates to an aerospace product performance and test integrated design parameter selection method based on a gray theory, and is applicable to the field of aerospace product design. Themethod comprises the following steps of 1, selecting a design parameter and an assessment index, and separately forming a scheme set and an attribute set; 2, scoring the design parameter according tothe assessment index, and forming an objective decision matrix; 3, building an ideal optimal vector, performing normalized treatment on the objective decision matrix, and computing gray association coefficients between normalized objective decision matrix elements and the ideal optimal scheme vector; 4, computing the gray association coefficients with weights to acquire gray association degrees; and 5, ranking according to the gray association degrees, and selecting the quantity of the design parameters according to a parameter cutting principle. The method provided by the invention has the beneficial effects that 1, the quantitative aerospace product performance and test integrated design parameter selection method is firstly provided, so that the performance and efficiency of the productcan reach the best; 2, the parameter is selected according to the association between the design parameters and the assessment indexes, and the weights of the assessment indexes, the selection methodis more scientific and reasonable; 3, the performance and the efficiency of the product can reach the best; and 4, under the condition of unknown weights, the design parameter also can be selected.

The invention discloses a handwritten numeral recognition method based on characteristic matrix similarity analysis. The method comprises the following steps: A, obtaining a to-be-recognized handwritten numeral image; B, converting the obtained original handwritten numeral image to a gray level image; C, converting the obtained gray level image to a binary image; D, performing smooth optimization, cutting, and zoom on the obtained binary image, to obtain a binary characteristic matrix of the to-be-recognized image; E, combining the obtained binary characteristic matrix with each standardized numerical characteristic matrix in pairs, to form characteristic matrix pairs, respectively performing similarity analysis; analyzing an obtained association coefficient result, the higher the association coefficient be, the higher the similarity be, and the standardized number corresponding to the characteristic matrix with the highest similarity being the number obtained by recognition. Through the method, a computer can accurately recognize handwritten numeral. Compared with the prior art, the method does not need large quantity of training samples to support a recognition technology, and training time is short, calculated amount is low, and recognition precision is high.

The invention relates to a package workshop quality auxiliary decision-making method. The method comprises the following steps: acquiring a quality abnormal data set in a first set time period as a reference sequence; acquiring a second set time period data set in the production process as a comparison sequence; calculating an association coefficient between the reference sequence and each comparison sequence to obtain a correlation degree; and determining the quality mode factors, sorting the quality mode factors according to the correlation degrees obtained by the comparison sequences corresponding to the quality mode factors, and judging the quality mode factors. According to the technical scheme, correlation coefficient calculation and correlation degree analysis are carried out through the quality mode data set, the quality problem category data set and the quality problem reference data set, sorting serves as a feature selection model, and a decision-making tree is constructed through rules to carry out auxiliary decision making. Main specific categories are classified into a mechanical structure, operation control, the overall situation of a work class, auxiliary material problems, special abnormity, other unknown reasons and the like.

The invention provides a method for extracting content information on the internet. The method comprises the following steps of: responding to an input instruction of to-be-extracted content; recognizing the input instruction and selecting a classification category according to the recognition result; analyzing the input instruction by using the classification category to generate content sets associated with the classification category, wherein the content sets contain the to-be-extracted content; and sorting the content sets according to association coefficients between the content sets andthe input instruction and displaying on a displayer. By means of the scheme, the method provided by the invention has the advantages that a setting interface is provided to users, so that the users can directly obtain the content information in a target web page and do not need to passively rely on whether RSS (Really Simple Syndication) is published or not by an information website and the published content of the RSS and more abundant and more detailed information can be extracted from a wider information source; in addition, the content information can also be stored locally for the users to visit, thereby improving the browsing speed of the users.

The invention discloses a method for measuring the dynamic contrast ratio and estimating transverse flow with OCT. A method for determining a sample movement vector includes the steps that a sample image at the time T1 is obtained, a preset area in the sample image is selected as a tracking image, and image identifying information and the position at the time T1 of the tracking image are obtained; a sample image at the time T2 is obtained, and the position the same as the position of the tracking image at the time T1 is found to serve as a tracking box; one point of the tracking box serves as a reference point, and the maximum movement ranges of the reference range along the transverse axis and the vertical axis are determined, and are divided into a plurality of movement positions; image identifying information of the tracking image at the time T1 when the reference point moves according to vectors and conducts transversal on movement positions and the association coefficients of the image identifying information of images in the tracking box when the reference point moves to all the movement positions are calculated, and the reference-point movement vector corresponding to the maximum value of the association coefficients is the vector of a sample at the time T2 relative to the time T1.

The invention relates to a fault identification and positioning method and system of an intelligent photovoltaic power generation system. The fault identification and positioning method comprises the following steps: S1, obtaining an association coefficient of a photovoltaic power generation unit relative to other photovoltaic power generation units; S2, establishing a fault determination basis of the photovoltaic power generation unit; and S3, carrying out fault identification and positioning on a faulted photovoltaic power generation unit. According to the invention, on the basis of discrimination of distances among all units and real-time combined generation efficiencies of all the units of the photovoltaic power generation system, data analyses are carried out to realize accurate identification and positioning of faults of the intelligent photovoltaic power generation system, thereby substantially reducing the false fault alarm probability and improving fault identification accuracy and fault positioning precision of the intelligent photovoltaic power generation system. Meanwhile, no extra professional fault diagnosis device is needed for the photovoltaic power station and thus costs are lowered. The method and system have broad application prospects.

The invention provides a method for perceiving the number of master users in a cognitive radio network. According to the method, the number of the master users can be perceived, and moreover, the change of the number of the master users can be tracked online. The method comprises following steps of S1, perceiving the signals of the master users by multiple secondary users; S2, randomly dividing the multiple secondary users into two groups; S3, joining up the signals of the master users perceived by the two groups of secondary users for typical association analysis, thus obtaining typical association coefficients of the signals perceived by the two groups of secondary users; S4, establishing detection statistical magnitude according to the typical association coefficients; and carrying out multi-assumption sequential detection by the established detection statistical magnitude, thus obtaining the number of the master users. The method is applicable to the technical field of wireless communication.

The invention relates to the technical field of multi-attribute decision making, in particular to a probability uncertainty language set multi-attribute decision making method based on association coefficients. The result is more objective and better conforms to the actual experience of a decision maker. The method comprises the following steps of: 1, determining an association coefficient of a probability uncertainty language set; 2, determining weighted correlation coefficients of the probability uncertainty language set; and 3, establishing a multi-attribute decision model based on the probability uncertainty language set correlation coefficient.

The invention discloses a washing monitoring system and a monitoring method thereof. The system comprises a dirty detection device used to detect turbidity data of wash water under a washing mode, a storage device storing sampled turbidity data, an association coefficient arithmetic device used to operate association coefficients according to the sampled data, and a microprocessor used to generate a driving mode corresponding to the operated association coefficients.

The invention discloses an employee health alarming method based on a face image, and a system. The method comprises the steps of acquiring colorful face images of an employee every preset interval time, and obtaining a face image dataset; preprocessing the face image dataset, and obtaining a to-be-evaluated image sequence; performing color channel and gray channel analysis on the to-be-evaluatedimage sequence, obtaining a correlation coefficient matrix which reflects the face color change of the employee, calculating a health condition alarm mark of the employee according to a historical association coefficient mean value and a variance, and generating health alarming information. According to the method and the system, by means of a characteristic of finishing work attendance accordingto face identification, though utilizing the long-time accumulated face data, a face characteristic analyzing facility is used for acquiring image information change data of an interested area, thereby performing dynamic monitoring, and realizing health alarming without contacting or additional time cost.

The invention discloses a data alignment method for horizontal quantitative reciprocating scanning of a piece of paper. The method is mainly used for alignment of horizontal quantitative reciprocating scanning data during the horizontal quantitative controlling process of the paper in a paper manufacturing industry. The method is characterized in that an association function having nothing to do with factors like the mechanical precision and a horizontal drift of a piece of paper is employed; according to different moving directions of a quantitative scanning sensor, horizontal quantitative data are divided into ones with two kinds of directions from a driving side to an operating side and from the operating side to the driving side; recording is carried out to obtain different arrays; an association coefficient between data obtained in two directions is detected; translation of the data obtained in one direction to the left or right is carried out and an association coefficient between the data after translation and the data obtained in the other direction is detected; and according to the association coefficient, alignment information is determined.

The invention discloses a method of setting a new energy operation simulation boundary condition. A method of setting a wind power operation simulation boundary condition comprises steps of: dividingall wind farms in a to-be-measured region into multiple wind areas according to geographical positions, setting wind speed statistics distribution parameters of wind farms according to historical windspeed data, setting a wind speed relation coefficient of multiple wind areas according to the historical wind speed data and a distance between the wind areas, and setting a wind turbine output characteristic parameter according to a wind turbine model. A method of setting photovoltaic operation boundary condition comprises steps of: setting a photovoltaic power station parameter, setting a clearsky index distribution parameter, and setting an association coefficient of photovoltaic power station regions according to a distance between photovoltaic power station regions. Through adoption ofthe method, the bound condition can be set more reliably, then the simulation result affecting operation is more accurate.

The invention discloses a method for querying associated resources of internet information resources, for solving the problem that the states of the associated resources cannot be queried in time in a process of deriving the associated resources from the internet information resources in the prior art. The method comprises the following steps: receiving an associated resource query request of the internet information resources; obtaining the internet information resources, an activation time of the internet information resources, an association coefficient corresponding to the internet information resources and a current time when the associated resource query request is received; determining the associated resources according to the internet information resources, the association coefficient, the activation time and the current time; and responding to the associated resource query request according to the determined associated resources. The invention further discloses a device for querying the associated resources of the internet information resources.

The invention discloses an online monitoring method for the backup power supply system of an intelligent power distribution terminal, which belongs to the field of power system automation. The online monitoring method is characterized by comprising the following steps of: step a, setting an online monitoring mode word; step b, acquiring a standard discharge curve for forming an energy storage component and storing; step c, acquiring a real-time discharge curve for forming the energy storage component; step d, calling the standard discharge curve and calculating the association coefficient of the curve; step e, judging the association coefficient of the curve. According to the online monitoring method for the backup power supply system of the intelligent power distribution terminal, the original hardware resources of the intelligent terminal are utilized, online monitoring is realized only by measuring the voltage value of an energy storage medium, and cooperating with the activation circuit of the intelligent terminal, without the need of measuring a current and calculating an internal resistance, and adding additional monitoring equipment; the online monitoring method is convenient to measure, accurate and reliable, and low in measurement cost.