84 results about "Stepwise regression analysis" patented technology

The invention discloses a sight tracking method based on a stepwise regression analysis mapping model. The method comprises the steps of firstly, extracting eye movement characteristic information when human eyes watch a fixed point; then selecting an initial mapping model function; after the initial mapping model is selected, checking significances of all items of a mapping model function, rejecting the non-significant items, and in the process of continuously rejecting the items and reconstructing the mapping model function, finding the mapping model function with the highest fitting precision at last; and finally, utilizing the mapping model function for calculating the watching point, and when the human eyes are in a gazing state, adopting a Gaussian weighted average method to smooth the eye movement characteristic information, thus solving the problem of eye shaking. According to the method, the robustness is good, and the precision is high.

The invention discloses a short-term climate forecasting method based on empirical mode decomposition and numerical value set forecasting. The invention adopts a way of integrating a numerical value set forecasting technology and a mean generating function stepwise regression model and combines a new empirical mode decomposition (EMD) method for processing a data sequence. The short-term climate forecasting method comprises the following steps of: firstly, decomposing a non-stationary climate data sequence into a stationary intrinsic mode function (IMF) component with multi-scale feature; then constructing different forecasting models for each IMF by a way of set forecasting and stepwise regression analysis; and finally linearly fitting to form a forecasting result. When the system is used for short-term forecasting, a user can cut out the appointed sequence length and forecasting length according to the actual data demand and vnlrpfalgp select a forecasting model parameter in a set forecasting process. Compared with a direct or single forecasting method, the invention has better forecasting capacity for the variation trend of climate and sudden climate.

The invention belongs to the technical field of land crop planting and discloses a land crop planting suitability evaluation method. The method comprises steps: data in a database are displayed according to a user query condition and a statistical chart is then generated according to a chart type specified by a user, wherein selectable chart types comprise a broken line chart, a column chart and a pie chart; a vector layer and an image layer are built newly; roaming, zoom in, zoom out, full chart and management on layer display attribute data are realized; data are modified; a multiple linear regression analysis method and a stepwise regression analysis method are adopted; data gridding and gridding of an evaluation area are carried out; according to a specified cultivated variety, based on biological characteristics, the evaluation standard is determined, a scientific method is applied, the data are analyzed and estimated, and a professional suggestion is given; and a calculation result through processing and analysis is outputted. The method of the invention has a high-efficiency massive data management ability, and an important role is played in guiding adjustment and layout of an agricultural production structure.

The invention provides a blind source signal denoising method based on ensemble empirical mode decomposition, and belongs to the technical field of signal processing. By means of the method, definitions on the white noise amplitude and the number of iterations in an original algorism are rectified. False component discrimination is conducted on the IMP component obtained after IEEMD is decomposed through a classic stepwise regression analysis method, features of original signals are effectively reserved, the false component generated by the IEEMD algorism is eliminated, and interference to the subsequent denoising algorism by the false component is eliminated. Finally, for the non-convergence phenomenon generated occasionally when the ICA algorism processes the high-frequency signals, a high-order TFastICA method is provided, features of the IEEMD and the TFastICA are combined, and rear-end processing is conducted on the IEEMD through the TFastICA method. The blind source signal denoising method based on the ensemble empirical mode decomposition has wide application prospects in the fields of removing mechanical vibration noise, voice signal noise, instantaneous underwater noise and other signal processing fields.

The invention discloses a test and analysis method of red spider insect pest coercion conditions of orange trees. The method comprises the following steps of: (1) selecting a plurality of healthy orange tree leaf samples and orange tree red spider leaf samples; (2) collecting hyperspectral images of the orange tree leaf samples; (3) measuring the chlorophyll and carotenoid contents of the orange tree leaf samples; (4) pre-treating the collected hyperspectral images; (5) further denoising the pretreated hyperspectral images; (6) extracting the characteristic wave bands of the denoised hyperspectral images; (7) creating a detecting model through the extracted characteristic wave bands, picking out the optimal wave band combination by gradual regression analysis, and determining the quantitative relation of the optimal wave band combination and the chlorophyll and carotenoid contents; and (8) detecting the chlorophyll and carotenoid contents of the orange tree leaves through the created model so as to predict the red spider insect pest coercion conditions of the orange tree. The test and analysis method disclosed by the invention is used for predicting the red spider insect pest coercion conditions of the orange tree by creating the model and has the advantages of being unartificial and harmless.

The invention provides a train control vehicle equipment reliability prediction method based on performance degeneration; the method comprises the following steps: using ARMA and improved EMD+ARMA to carrying out performance degeneration process modeling according to present and early stage various fault type data of the train control vehicle equipment, and predicting fault rates of various fault types of the train control vehicle equipment in future time; using a gradually regression analysis method to carry out performance degeneration process modeling for fault rate prediction values of various fault types, predicting the fault rate of the whole train control vehicle equipment in the future time, and calculating a reliability prediction value of the train control vehicle equipment in the future; using a FMECA algorithm to calculate fault severe level of various fault types of the train control vehicle equipment in the future time, and estimating the train control vehicle equipment reliability in the future time according to the reliability prediction value and the fault severe level. The method can solve the problems that train control is complex, the easily failure vehicle equipment reliability prediction is not timely, and responses are not reasonable, thus effectively reducing train fault generation.

The invention discloses a restrictive two-stage genome-wide association study (GWAS) method based on an SNPLDB mark, and aims at solving the problems that a traditional method cannot be used for estimating multiple allele information, and is high in false positive rate and low in efficiency of detection on inbreeding crops. By combination of the SNPLDB mark constructed on the basis of a haplotype block, correction of deviation of an inbreeding population relation analysis model and a two-stage relation analysis strategy under a multi-site model, the GWAS method suitable for conventional breeding of inbreeding crops is built; the SNPLDB mark is applied to GWAS, so that a method is provided for multiple allele estimation; candidate sites are screened on the basis of a single-site model in the first stage, and are further screened on the basis of a progressive regression analysis method in the multi-site model in the second stage, so as to balance the problems of missing of heritability and over-high heritability estimation. Therefore, the interpretation ratio of a final genetic model is controlled at trait heritability. The positioning accuracy and efficiency are improved by a feature vector and an appropriate significant level of a similarity coefficient matrix estimated by the SNPLDB mark by GWAS.

The invention discloses a monthly power consumption prediction method that comprehensively considers various economic factors, adopts the X-12-ARIMA model to decompose the monthly power and various economic factors into seasons; uses stepwise regression analysis to study the economic quantities and power consumption The correlation degree and regression model of electricity consumption were used to obtain preliminary prediction results; the annual power consumption forecast was carried out by using polynomial fitting, and the existing monthly power consumption forecast results were adjusted; the autoregressive integral sliding average Seasonal forecast corrections are carried out in each month to obtain a monthly electricity forecast model with good accuracy. Using the above technical solution, after seasonal decomposition of monthly electricity and economic volume, not only can use periodicity for forecasting, but also can effectively reduce the impact of volatility on regression analysis fitting accuracy and forecasting accuracy, and obtain good forecasting results.

The invention relates to a cigarette tipping paper lip sticking force prediction model and a construction method and prediction method thereof, and belongs to the technical field of analysis and detection. According to the method, stepwise regression analysis is carried out on the lip sticking force, the dynamic friction coefficient and the quantitative value of the cigarette tipping paper to obtain a model for predicting the lip sticking force of the cigarette tipping paper. When the accuracy of the model is tested, it is found that the predicted value and the measured value are basically close to a 1: 1 line, the R2 index is 0.99, and the normalized root mean square error nRMSE is 2.64%. The invention provides a method capable of quickly, simply, conveniently and quantitatively determining the lip sticking force of the cigarette tipping paper, so that the influence of subjective factors and the difference representation caused by the traditional artificial sensory evaluation can be well avoided, the detection cost is greatly saved, and the efficiency is improved. The method is more objective, efficient, good in repeatability and easy to operate, and has advantages in material admission, quality control and product upgrading in the tobacco industry.

The invention discloses a screening method for coix drought resistance indexes, and belongs to the technical field of agricultural production. The method includes the steps of (1) germplasm cultivation and planting of test coix; (2) determination of the relative drought resistance indexes; (3) principal component analysis; (4) gradual regression analysis. The screening method is close to the practice of agricultural production, has reliable results, and can solve the problems that the existing screening method for the coix drought resistance indexes is confused and the screened indexes are inconsistent, thereby providing a reliable, simple, economical, applicable and standard drought resistance index screening technology system for coix breeding workers; meanwhile, the screened indexes can comprehensively reflect the comprehensive drought resistance of the coix, and can be effectively applied to the identification of drought resistance of the coix.

The invention discloses an evaluation method for stepwise regression of thermal power equipment performances. The method is used for solving the problems of the difficulty to establishing an optimal regression equation and obtain equipment performance optimal values automatically and the quantitative obtaining of the influence degree of all influence conditions on equipment performances. According to the evaluation method, thermal power plant real-time / historical data are used, and through a multiple stepwise regression analysis modeling method, an optimal prediction model reflecting equipment performance parameters and influence conditions is built, and accordingly, optimal performance values that the equipment is ought to reach under all work conditions and the independent influence degree of relevant parameters which are not clear in coupling relationships on equipment performances are obtained. Implementation software of the method can be inlaid in a prior thermal power unit SIS system, and the purposes of instruction of automatic optimization of thermal power equipment and optimization of production operations can be achieved without increasing hardware and costs.

The invention discloses a method for adopting a quantitative structure-activity relation model to predict a reaction rate constant of chlorine free radical of organic chemicals. According to the method, a quantum chemistry descriptor with structural characters is figured out only by means of basic molecular structure information of the organic chemicals, and by adopting a constructed QSAR prediction model, a kCl value of the organic chemicals can be predicted quickly and efficiently. The method follows a guide rule issued for the construction and verification of a QSAR model by the Organization For Economic Co-Operation And Development (OECD). According to the method, by adopting a genetic algorithm-multiple linear stepwise regression analysis combination use method (GA-MLR) and a supportvector machine-multiple linear stepwise regression analysis combination use method (SVM-MLR), the transparency degree is high and more convenience is provided for application; since quantum chemistrydescriptors are adopted in all GA-MLR models, the physical meaning of the descriptors is clear; the method has a specific application domain, and is applicable to abundant organic varieties, excellentin goodness of fit, robustness and prediction capability, easy to program and capable of supplying important data support to environmental risk evaluation and management of the organic chemicals.

The invention discloses a method for predicting the shaft temperature by adopting layered multivariate stepwise regression analysis. According to the hierarchical multivariate stepwise regression analysis method, a shaft temperature prediction equation is established by combining real-time data, collected during operation of a high-speed train, of relevant factors of shaft temperature change, andmeanwhile three factors, namely the environment temperature, the operation speed and the air spring load, which greatly influence the train shaft temperature, are screened out. A prediction result andan actual measurement result are compared and analyzed, the result shows that a prediction model established through layered multivariate stepwise regression analysis has high precision, the change trend of the axle temperature can be accurately reflected, and certain help is provided for analysis and prediction of the axle temperature of a train, timely discovery of axle faults and guarantee ofsafe operation of the train.

The invention belongs to the technical field of agricultural production, and particularly relates to a method for identifying the salt tolerance of sweet potatoes in the seedling stage. The method comprises the following steps: (1) salt stress treatment for sweet potatoes in the seedling stage; (2) physiological indicator detection for sweet potatoes in the seedling stage; (3) data analysis and salt tolerance division; (4) salt tolerance mathematical evaluation equation and salt tolerance index screening, namely stepwise regression analysis by using the salt tolerance comprehensive evaluationvalue D value as a dependent variable and the salt tolerance coefficient of each single index as an independent variable to establish a regression equation, and salt tolerance division according to the comprehensive salt tolerance D value. According to the method, the standard equation result is accurate and reliable, the operation is simple and convenient and is easy to grasp, the method is lessinfluenced by the outdoor environment, the condition is controllable, the comprehensive salt tolerance of sweet potatoes in the seedling stage can be overall reflected, and the result is reliable. Themethod can be used for quickly evaluating the salt tolerance of sweet potato varieties in the seedling stage, has small blindness and high efficiency, and provides germplasm materials to sweet potatosalt tolerance variety seedling and salty land development and utilization in the Yellow River Delta.

The invention discloses a winter wheat canopy chlorophyll estimation system and method. According to the winter wheat canopy chlorophyll estimation system and method, a six-axis rotor unmanned aerialvehicle with maneuverability, flexibility, high efficiency and rapidness is used for carrying a multispectral camera containing five kinds of spectral information to carry out high-precision and high-temporal-spatial resolution winter wheat spectral image acquisition; accurate spectral reflectivity data are obtained after the reflectivity calibration of a grey board; various vegetation indexes are obtained through the operation of a spectral vegetation coefficient equation set; an optimal model for estimating the key growth period of the chlorophyll content of the canopy of winter wheat is constructed through a stepwise regression analysis method; and the low-error estimation value of the chlorophyll content of the wheat canopy of the wheat winter in fields is obtained; and therefore, aneffective technical means is provided for the accurate fertilization management of farmland.

The invention relates to a building and application method for a functional tobacco module. The method comprises the following steps of dividing tobaccos into skeleton type tobaccos, fragrance type tobaccos and texture type tobaccos by a judgment model based on sensory evaluation; detecting the content of chemical components in the tobaccos, and performing stepwise regression analysis on the detected content of chemical components in the detected tobaccos, so as to obtain the skeleton type tobacco function parameter Fg and skeleton type tobacco quality parameter Qg of the skeleton type tobaccos, the fragrance type tobacco function parameter Fx and fragrance type tobacco quality parameter Qx of the fragrance type tobaccos, and the texture type tobacco function parameter Fz and texture type tobacco quality parameter Qz of the texture type tobaccos. The method has the advantages that the parameters of the applicable function tobacco module can be predicted by the built different function tobacco function parameter and quality parameter prediction models, the objectiveness and stability of evaluation on functions and application of the tobacco module are improved, and the effective and reasonable utilization of tobacco resources is guaranteed.

The invention relates to a soil corrosion prediction technology, in particular to a method for predicting the soil corrosion rate of metal material, which is mainly applied to the soil corrosion prediction research on regional carbon steel. The metal material serves as test material, test points are selected in an area which needs to be predicted, a test is taken according to 'Soil Corrosion Test Method of Materials' developed by a national soil corrosion test website, and simultaneously, the chemical components of the soil of all the test points are monitored, and the test cycle is 1 to 2 years; after the test, the rust of the test material is removed, after a sample is dried and weighed, the corrosion weight loss is calculated, and simultaneously, the chemical components of the soil of all the test points are calculated; a statistical method of stepwise regression analysis is adopted for regression analysis, a regression equation is established, and the soil corrosion rate of the area which needs to be predicted is predicted with the regression equation. The method for predicting the soil corrosion rate of metal material has reoccurrence, and can predict the soil corrosion of metal material.

Provided is a method for predicting a wind power density. More particularly, provided are a method for predicting a wind power density using a stepwise regression analysis technique capable of estimating a wind power density at any point using a regression analysis technique by a stepwise variable selection method of performing an analysis while adding statistically important terms or removing statistically meaningless terms.

The invention discloses a designed compaction number-of-times determining method of an emulsified asphalt cold recycled mixture. The method comprises the following steps of a first step, molding the emulsified asphalt cold recycled mixture with different factors by means of SGC; a second step, determining a maximal compaction number-of-times Nmax, back-calculating and drawing a compaction curve of the emulsified asphalt cold recycled mixture, and calculating a corresponding compaction parameter according to each compaction curve; a third step, researching sensitivity of each factor to all compaction by means of a single-factor variance analysis method, and evaluating effectiveness of each compaction parameter; and a fourth step, determining a significant influence factor of a designed compaction number-of-times Ndes, finding out main affecting factors by means of a stepwise regression analysis method, and establishing a regression equation of the designed compaction number-of-times Ndes. According to the designed compaction number-of-times determining method, the stepwise regression analysis method is utilized; compaction temperature and emulsified asphalt amount are determined as main factors; and a standard regression equation of the designed compaction number-of-times Ndes of the emulsified asphalt cold recycled mixture is established.

The invention discloses a rice leaf SPAD value estimation method based on computer vision, and the method comprises the steps: (1) collecting a certain number of rice leaf samples, placing the rice leaf samples on a 18% gray plate, and obtaining corresponding sample images; (2) determining SPAD values of all the sample images; (3) removing a gray plate part in the sample images by adopting a threshold segmentation method, and leaving a green leaf main body part; (4) extracting the color features of the sample images, and obtaining the color features in the RGB color space and the color features in the HSV color space in the sample images; (5) by taking a color component as input, establishing an SPAD value estimation model M'2 based on the color component by adopting a stepwise regressionanalysis method. According to the method provided by the invention, the SPAD value data of the whole rice leaf can be quickly obtained, the estimation result is relatively high in precision and smallin error, the working efficiency of manually detecting the SPAD value of the rice leaf can be reduced, and a basis can be provided for visual research of the rice leaf.

The invention discloses a farmland soil health evaluation method, which comprises the following steps: selecting a soil evaluation system; Screening soil health indicators in the soil evaluation system by a Delphi method to obtain screened soil health indicators; The original data of the selected soil health indicators were standardized, and the standardized data were analyzed by principal component analysis, and the indicators contained in the principal factors that meet the preset conditions were taken as the second soil health evaluation indicators. Performing stepwise regression analysis on the second soil health evaluation index to obtain a third soil health evaluation index, and constructing a soil health evaluation data set according to the third soil health evaluation index; obtaining soil samples and crop samples, and measuring the index value of the third soil health evaluation index; According to the index value of the third soil health evaluation index, the soil health index and the soil health grade of the soil sample are obtained by a preset scoring rule.

The invention discloses an underground water model output uncertainty analysis method in combination of a frequency analysis method and a sensitivity analysis method. The frequency analysis process comprises a parameter estimating process and a supposed checking process, wherein a typical distribution function in 7 is selected as an alternative probability density function, the supposed checking is carried out, and a proper probability density function is selected for the underground water level sequence. The sensitivity analysis method comprises stepwise regression analysis and mutual entropy analysis; the stepwise regression analysis can be used for analyzing the trend of the uncertain importance of an input variable, and the mutual entropy analysis can be used for well identifying the uncertainty factor. The method can be used for making up the deficiency in research content of the traditional uncertainty analysis method, analyzing the probability distribution characteristic and identifying the key uncertainty factor influencing the variable probability distribution, thus the production and source of the underground water model uncertainty can be well understood, feedback is provided for the data acquiring work of the underground water model, and the uncertainty of the model is reduced.

The invention discloses a method for establishing a rice yield prediction model under high temperature stress based on morphological parameters, which comprises the following steps of carrying out theshort-term extreme high temperature treatment on rice at the booting stage or flowering stage of rice, and setting a control treatment at the same time; obtaining the morphological indexes of rice under high temperature stress and obtaining the relative values of each index; obtaining rice yield and obtaining the relative value of rice yield; carrying out stepwise regression analysis between relative changes of morphological indices and relative changes of yield, and screening morphological indices related to yield; establishing a rice yield prediction model under high temperature stress based on morphological parameters. The method of the invention screens out the morphological index which is convenient to observe and can better predict the yield, the cost is low, and the method is simple and effective. A method for predicting rice yield under high-temperature stress base on morphological parameter is also disclosed, so as to realize the purpose of predicting rice yield based on themorphological index.

The invention relates to a method for predicting a cigarette total smoke pH value and belongs to the technical field of tobacco. According to the method, the cigarette total smoke pH value and cigarette particulate matter pH value are subjected to stepwise regression analysis to obtain a regression formula of the particulate matter pH value and the total smoke pH value, namely, Y=0.1603+0.9685X, wherein the average relative error of prediction of the total smoke pH value is smaller than 1%, and prediction results are accurate. Operation steps for total smoke pH value prediction are greatly simplified, people only need to measure the particulate matter pH value of cigarettes to well predict the cigarette total smoke pH value, and testing of smoke gaseous-phase matter and detection conducted after particulate matter extract and gaseous-phase matter absorbing liquid are mixed are avoided; compared with a smoke water extraction pH method, efficiency is improved by at least 50%, and cost is reduced by at least 60%. The method and a method T-113 recommended by the ministry of health of Canada are compared, results show that results obtained through the two methods are highly consistent, and comparability is achieved.

The invention discloses an optimization method of an ardealite-based cementing material for improving the filling roof-contacted rate, comprising the following steps: adding ardealite powder and an excitant in different proportions into filling aggregate to prepare cemented filling body materials in different proportions; carrying out a cemented filling body strength test and a filling body expansion rate test on the cemented filling body materials with different proportions to obtain test data of the cemented filling body strength and the expansion rate of the cemented filling body materialswith different proportions; conducting stepwise regression analysis on the test data of the strength and the expansion rate of the cemented filling body through a quadratic polynomial; establishing aproportion optimization model of the cementing material by taking the 3d strength of the cemented filling body as an optimization target and the 28d filling body expansion rate as a constraint condition; and solving the optimization model to obtain the optimal proportion of the ardealite-based cementing material in the filling slurry. According to the design method, low-cost and high-value resource utilization of ardealite can be realized; and low-cost and high-value utilization of ardealite in filling mining can be realized.

The invention discloses a method for blending and appraising rice wine. The method comprises the following steps: (1) selecting rice-wine components with great contribution to the quality and taste of rice wine; (2) determining the content values of the rice-wine components with great contribution to the quality and taste of rice wine in base wine respectively; and (3) adopting stepwise regression analysis, selecting different species of base wine and obtaining the content value of blended wine through the component values and portions of the base wine pro rata; establishing a regression equation and adopting the content values of the components of the blended wine to calculate the appraisal value of the taste of the rice wine so as to obtain the degree of the sensory quality of the blended wine in the same mixture ratio; and obtaining an optimal blending proposal by regulating the species and portions of the base wine. The method can replace the method for appraising and blending wine through artificial sense, and has the characteristics of few interference factors, high appraisal accuracy, simple operation steps, more convenient blending and capability of improving the taste of the rice wine.

The present invention relates to a stepwise regression analysis-based big data processing method. The method includes the following steps of firstly, collecting data of factory operation parameters, and numbering the collected operation parameters; then, using some of the collected operation parameters as dependent variables, using other operation parameters as independent variables, and writing equations, wherein the parameters are in a linear relationship; then, importing the equations and corresponding data into Matlab software one by one, performing stepwise regression analysis operation, and calculating a coefficient and an intercept of each equation before the independent variable; and finally, performing result analysis so as to obtain an optimal value of a corresponding operation parameter. According to the method, a lot of data is collected, stepwise regression analysis and big data processing are performed, the Matlab software is used, and the impact of all operation parameters of a factory DCS is determined quantitatively so as to quantitatively determine and change the impact of a certain parameter on other parameters to determine the optimal value of a factory DCS operation parameter.

The invention relates to a method for determining best analysis area of relief amplitude, and solves problems of the complexity of successive calculation, the randomness of artificial discrimination and the like in a best analysis area determining process. According to the method, a logarithmic function relationship for analyzing area sides and maximum relief degree values is fully used, a stepwise regression analysis method is used to analyze correlations between logarithmic function factors and experiment area average elevations, height differences, average gradients and average gradient variability, a functional relationship between the maximum relief degree values and the experiment area average elevations, the height differences, the average gradients and the average gradient variability, and the range of the best analysis area of relief amplitude is determined. The method not only can be used in determination of the best analysis area of relief amplitude, but also can be used in determination of best analysis areas of other macro terrain factors.

The embodiment of the invention discloses a method and device for forecasting tropical cyclone generation frequency, equipment and a storage medium, and the method comprises the steps: carrying out the stepwise regression analysis of the characteristic value of a variable which may influence the tropical cyclone generation and the generation frequency of the tropical cyclone generation, carrying out the inspection, and keeping the variable if the inspection is passed; and forecasting by using a neural network method by using the reserved variable as a characteristic value and the generation frequency generated by the tropical cyclone as a forecast quantity. According to the method for forecasting the tropical cyclone generation frequency provided by the embodiment of the invention, the influence of atmosphere and ocean on tropical cyclones is considered. The mapping relation between the characteristic values and the forecast quantity does not need to be considered, and the nonlinear relation between tropical cyclone generation and the climate background field does not need to be considered. A forecasting model is optimized, and the forecasting accuracy is improved.