8 results about "Simple linear regression" patented technology

The invention provides a retrieval enhancement method based on dynamic hybrid retrieval and LLM reordering, and belongs to the technical field of artificial intelligence. Constructing a domain query feature set, training a simple linear regression model, and dynamically predicting weights of keyword retrieval and vector retrieval according to user query; through quantile calibration, calculating a weight calibration factor, and adjusting a prediction weight; removing abnormal scores of keyword retrieval and vector retrieval by adopting a truncation normalization method, and generating score distribution of a unified dimension; and fusing the normalized scores of the query and candidate documents through convex combination, sorting to obtain TopK most relevant documents, calling a DeepSeek model to carry out correlation scoring on the query and candidate documents, and carrying out weighted fusion and resorting with the mixed scores to obtain TopN most relevant documents. According to the method, the problems of limitation of a fixed weight, insufficient query diversity adaptation and the like in RAG mixed retrieval are effectively solved, and the retrieval precision and stability in a complex query scene are remarkably improved.

A system and method for characterizing matter, for example, soil organic content is disclosed. A radiation and electric field sensor measure sample properties before, during and after irradiation. Calibrations are developed relating those measurements to useful properties of matter, for example, soil density and organic content. As an example of an embodiment of the disclosed invention an instrument attachment for portable X-ray fluorescence instrumentation was prototyped enabling concurrent volumetric soil organic matter quantification. This primary prototype outperformed more expensive emerging visible-near infrared multivariate instrumentation using parsimonious soil specific simple linear regression (R2 ranged 0.85-0.97) enabling rapid, parallel, nondestructive, cost-effective acquisition of soil elemental concentrations together with organic content data.

The invention provides a wheat leaf area index whole growth period estimation method based on canopy pure hyperspectral reflectivity, which comprises the following steps of: firstly, extracting wheat canopy spike, leaf and soil pure pixels based on an RGB (Red, Green and Blue) image combined with a deep learning algorithm; secondly, separating the canopy pure leaf hyperspectral reflectivity by using the extracted pure pixels and canopy hyperspectral reflectivity data in combination with a non-negative matrix factorization algorithm; and finally, calculating a vegetation index based on the canopy pure leaf hyperspectral reflectivity, and establishing a whole-growth-period LAI estimation model in combination with a simple linear regression algorithm. According to the method, the problem that the LAI inversion precision is reduced after heading is solved by combining near-end remote sensing and a machine learning algorithm, and the wheat LAI estimation method based on the canopy pure hyperspectral reflectivity has the potential of accurately predicting the LAI of the wheat in the whole growth period and can be expanded to accurate management of field crops and breeding of high-yield wheat varieties.

This invention provides an information processing device and information processing program capable of further improving the accuracy of the estimation of the target variable. [Solution] The information processing device 10 includes an acquisition unit 24 that acquires a set of variable values ​​54 corresponding to a plurality of explanatory variables used for estimation, and an estimation unit 28 that performs a first estimation process which estimates a single target variable from the set of variable values ​​54 using a simple linear regression model for each explanatory variable and obtains a set of intermediate values ​​56 which is a collection of provisional estimates of the target variable, and a second estimation process which estimates the target variable using the set of intermediate values ​​56 and obtains an estimated value 58 of the target variable.

The invention discloses a road surface collapse risk prediction method and a road surface collapse risk prediction system fusing geological radar and traffic load, and belongs to the field of road surface collapse risk prediction. Carrying out fusion processing on an initial collapse risk prediction value obtained after prediction is carried out through a trained random forest model and a characteristic difference change value, and then capturing the characteristics of a complex nonlinear relationship between geological data and traffic load data by utilizing a gradient lifting decision tree model; performing pavement collapse risk prediction on multi-source data consisting of geological data and traffic load data; the problem that in the prior art, due to the fact that a single data source is relied on and a prediction model adopts a simple linear regression or empirical formula, the complex nonlinear relation between geological data and traffic load data cannot be captured, and consequently road surface collapse risk prediction is inaccurate is solved.

A system and method for characterizing matter, for example, soil organic content is disclosed. A radiation and electric field sensor measure sample properties before, during and after irradiation. Calibrations are developed relating those measurements to useful properties of matter, for example, soil density and organic content. As an example of an embodiment of the disclosed invention an instrument attachment for portable X-ray fluorescence instrumentation was prototyped enabling concurrent volumetric soil organic matter quantification. This primary prototype outperformed more expensive emerging visible-near infrared multivariate instrumentation using parsimonious soil specific simple linear regression (R2 ranged 0.85-0.97) enabling rapid, parallel, nondestructive, cost-effective acquisition of soil elemental concentrations together with organic content data.

The application discloses a pavement collapse risk prediction method and system fusing geological radar and traffic load, and belongs to the field of pavement collapse risk prediction. The method fuses an initial collapse risk prediction value obtained after prediction by a trained random forest model with a feature difference change value by collecting real-time remote sensing images of a to-be-measured pavement and identifying the feature difference change value, and then uses a gradient boosting decision tree model which can capture the complex nonlinear relationship between geological data and traffic load data to predict the pavement collapse risk of multi-source data composed of the geological data and the traffic load data, thereby solving the problem that the prior art cannot capture the complex nonlinear relationship between geological data and traffic load data due to the dependence on a single data source and the use of simple linear regression or empirical formula in the prediction model, and cannot accurately predict the pavement collapse risk.

The present disclosure discloses a computer implemented method and system to evaluate adequacy of cloud computing resources allocation. The method includes extracting a set of features and a set of target variables from a historic dataset stored in a database. Further, the method includes generating a plurality of simple linear regression (SLR) model and a multiple linear regression (MLR) model. Furthermore, a respective value of each target variable of the set of target variables is determined based on the SLR model and MLR model. Thereafter, the method includes calculating a new value of each feature of the set of features followed by generating adequacy score by comparing the new value of each feature of the set of features with a desired value of each feature of the set of features.