11 results about "Multicollinearity" patented technology

The application discloses a kind of based on elastic network regression's wiring error leakage user positioning method, system, terminal and medium, wherein method includes: obtaining the abnormal station area leakage fault day's station area residual current data, user load current data, and constructs station area residual current time series and subordinate user load current time series;With the user load current data obtained as explanatory variable, station area residual current data as explained variable, carry out elastic network regression calculation, obtain the optimal explanatory variable after eliminating multicollinearity and its corresponding regression coefficient and construct regression model;Compare the absolute value of each regression coefficient, and the user whose absolute value of regression coefficient is greater than preset threshold is judged as zero line, ground line wiring error user.Through identifying zero line, ground line wiring error abnormal user, to solve the existing low-voltage station area exists because user zero line, ground line wiring error and lead to user load current data into residual current problem.

This invention provides a method and related equipment for assessing earthquake landslide susceptibility. Multiple landslide influencing factors are selected from multi-source data of the study area, and multicollinearity analysis and Pearson correlation coefficient analysis are performed on all landslide influencing factors to obtain basic environmental factors. Newmark displacement is calculated based on the physical and mechanical parameters in the multi-source data and the basic environmental factors. The Newmark displacement is stacked with the basic environmental factors to obtain multi-channel image data, which is then input into an earthquake landslide susceptibility assessment model for evaluation, resulting in a coseismic landslide susceptibility zoning map of the study area. Compared with existing technologies, this invention uses Newmark displacement as an independent feature input, compensating for the lack of physical mechanism support in traditional pure data-driven models. It achieves the complementary advantages of geological disaster dynamics mechanisms and deep learning feature extraction capabilities, improving the accuracy and reliability of earthquake landslide susceptibility assessment.

This application relates to the fields of remote sensing image processing and ecological environment monitoring technology, and discloses a method for extracting peat bog information based on the dense temporal characteristics of multi-source remote sensing. The method includes acquiring time series of microwave radar and optical vegetation index, performing alignment and cleaning, extracting low-frequency and continuous first-order derivative sequences from the microwave radar to generate temporal basis features; extracting historical delay features for effective optical observation times, splicing the basic state term and derivative modulation product term to generate a bilinear distributed lag observation matrix; performing scale unification processing on the lag observation matrix, constructing a diagonal regularized matrix based on time-lapse attenuation characteristics, and solving for the solution vector; calculating the normalized asymmetric hysteresis intensity and reconstruction quality factor, and combining dual thresholds to determine the output distribution data. This invention incorporates the physical hysteresis effect of hydrological evolution into the computational framework, overcoming multicollinearity and observation sparsity problems, and achieving high-precision extraction of peat bogs.

The application discloses an electric energy metering error monitoring method, a computer readable storage medium and a processor, relates to the technical field of distribution network automation systems, and solves the problem that mutual information regularization imposes equal punishment on all similar users, leading to error estimation collapsing to an average value. The application divides user historical data into sections according to power consumption, respectively estimates section estimation errors in each section, and then calculates individualized coefficients representing sensitivity. On this basis, the scheme constructs weight coefficients with the sensitivity as a key adjustment factor, thereby protecting the independence of error estimation when solving the objective function and avoiding being forcibly pulled towards the average value of other similar users. In summary, while maintaining mutual information regularization to overcome multicollinearity and improve estimation stability, the application effectively preserves the differences in metering error characteristics of individual users, significantly improving the identification accuracy of out-of-tolerance meters in complex power consumption scenarios.

A method for constructing a predictive model for the 28-day mortality risk of patients with severe traumatic brain injury (sTBI) includes: collecting clinical data of patients undergoing sTBI surgery; screening samples that meet the inclusion and exclusion criteria and grouping them according to 28-day survival; collecting clinical indicators of the samples and calculating serum sodium variability on the 8th postoperative day; screening candidate variables using LASSO regression and simplifying variables through correlation analysis and multicollinearity diagnosis; dividing the dataset into training and test sets, and determining independent predictors through univariate and multivariate logistic regression analysis; constructing a nomogram prediction model based on the independent predictors, and validating the model performance through ROC curve, calibration curve, and decision curve analysis. The predictive model of this invention uses GCS score, oxygenation index, and 8-day serum sodium variability as core indicators, exhibiting high predictive accuracy and strong clinical operability, providing a quantitative basis for prognostic assessment and clinical intervention for sTBI patients.

Techniques are disclosed as an optimization data system for eliminating correlated independent variables programmatically from data with ranked exclusion scores. The system can obtain an initial dataset comprising variables, determine a set of correlation values by analyzing linear correlation between the variables, generate a correlation matrix using at least in part the set of correlation values and corresponding variables from the initial data, calculate exclusion scores for the variables in the correlation matrix that exhibit multicollinearity, and update the initial dataset by removing at least one variable with the highest exclusion score from the variables to generate an updated dataset comprising optimized variables. The steps for correlation and elimination of variables are iterated until an updated dataset without any correlation is obtained and then a machine learning model may be trained using the updated dataset.

This invention discloses a method for constructing a risk prediction model for blinding diabetic retinopathy (STDR), belonging to the field of diabetic lesion detection. It addresses the problem that STDR screening at the grassroots level relies on specialized resources and that existing models have poor adaptability. The method includes: screening eligible type 2 diabetic patients and organizing clinical data; detecting 15 core laboratory indicators and calculating derived indicators as candidate variables; dividing patients into non-STDR and STDR groups according to DR classification and DME diagnosis results; selecting variables using a dual-dimensional strategy of "statistical significance + clinical relevance" through binary logistic regression, multicollinearity test, and incorporating six indicators including age, duration of diabetes, and MLR to construct a mathematical prediction model; and then building a nomogram visualization prediction model based on the model formula. All indicators included in this model are routinely available in clinical practice. The model has been validated with an AUC of 0.825, sensitivity of 87.2%, specificity of 67.8%, good fit, and good clinical applicability. It can quickly assess the risk of STDR and is suitable for grassroots medical scenarios.

This invention relates to a method for predicting the performance of hydrogen storage alloys based on feature engineering and multi-model screening, comprising the following steps: constructing a set of physicochemical features of the alloy; multi-method feature importance analysis; feature union extraction; feature screening based on importance-correlation joint analysis; feature subset search and multi-model cross-validation; and determination of the globally optimal model and the optimal feature subset. This invention provides a more comprehensive approach to feature construction and selection for subsequent machine learning research on hydrogen storage alloys; it can effectively identify features that are important in different methods, making the feature selection results more objective and reliable. Simultaneously, it eliminates multicollinearity among features through correlation redundancy removal and avoids the risk of local optima through full subset search, significantly improving the stability, reliability, and global optimality of feature screening; the prediction accuracy of this invention is improved by 90.8% and 55.9%, respectively.

The application provides a kind of urban road collapse safety risk intelligent diagnosis method, belongs to the technical field of urban lifeline engineering safety, comprising: determining the target area road network range, obtaining the detailed catalog of road collapse in target area and disaster factor data;Build road collapse disaster factor set, and carry out data preprocessing and multiple collinearity analysis;Build road collapse sample data set, divide it into training set and test set, and carry out data enhancement on training set;Using extreme gradient boosting algorithm to capture the nonlinear relationship between disaster factors, build urban road collapse prediction classification model;Based on the classification model, the probability of road collapse is predicted, and the road collapse risk distribution map of the target area is prepared;The constructed classification model is analyzed for interpretability, and the importance of each disaster factor and its effect on road collapse is evaluated.

The application relates to a multi-condition frequency coupling impedance identification method and device using multiple linear regression, wherein the method comprises the following steps: obtaining a frequency coupling admittance matrix of a measured device under multiple conditions to obtain a condition parameter variable matrix; based on the condition parameter variable matrix, a permutation importance index of the condition parameter variable is calculated, a first condition parameter variable set satisfying a first preset key condition of the measured device is determined; based on the first condition parameter variable, the multiple collinearity degree of the condition parameter variable is calculated, a second condition parameter variable set satisfying a second preset key condition is obtained, and a multiple linear regression model for identifying the multi-condition frequency coupling impedance of the measured device is constructed. Therefore, the problems in the prior art that when the number of hidden layers of the neural network impedance fitting algorithm exceeds the model generalization requirement, the training set error and the test set error will produce significant deviation, and when extrapolated to unknown conditions, the error will significantly increase and the generalization ability will be insufficient are solved.

The present application relates to the field of construction engineering and disaster prevention and mitigation technology, and provides a method for predicting the damage level of buildings near the surface rupture zone of a strong earthquake fault, which first constructs a database containing historical earthquake damage data and site parameters, identifies the core earthquake damage influencing parameters through Spearman rank correlation analysis and processes them; then uses principal component analysis (PCA) to eliminate multicollinearity and extract principal component variables with a cumulative variance contribution rate of more than 80%; based on 80% of the data, a multiple linear regression model is established with the principal components as input variables and the earthquake damage index as output variables, the remaining 20% of the data is used for verification, and the model is applied to external earthquake damage cases to test its generalization ability. The present application selects and quantifies the core parameters and their interaction effects through correlation analysis screening + PCA dimension reduction, and can quickly predict the damage level by only inputting the easily available site parameters, significantly improving the efficiency and scientificity of post-earthquake evaluation and disaster prevention planning, and providing a reliable quantitative tool for building rupture resistance analysis in active fault prone areas.