662 results about "Multivariable linear regression" patented technology

A method for calibrating non-invasive breathing monitors with sensors placed on the rib-cage and abdomen of a subject includes determining an initial scaling factor and an optimal multiplicative factor for the readings of the rib-cage and abdomen sensors using one of a least squares, linear regression, or multi-linear regression techniques. A current scaling factor is determined on a periodic basis using qualitative device calibration techniques. The current scaling factor is used to monitor breathing and diagnose obstructive apneas. Furthermore, the optimal multiplicative and the current scaling factor are used to determine the current tidal volume.

A method of performing a formation rate analysis from pressure and formation flow rate data. Pressure and flow rate data are measured as fluid is withdrawn from a formation. Variable system volume is accounted for. The pressure and flow rate data are correlated using a multiple linear regression technique. Time derivative terms related to pressure and flow rate are smoothed using a summation technique, thereby providing better correlations than using the time derivatives directly. Formation parameters comprising formation permeability, formation pressure, and fluid compressibility may be determined from the correlation.

The invention relates to a spare part assembling demand forecasting information processing method applied to inventory management, comprising the following steps:1) take one part of historical sales data as method learning data and take the other part of the historical sales data as weight building data; 2) use the method learning data to practice an ARMA (Autoregression and Moving Average) method, a multiple linear regression method and a BP (Back Propagation) neural network approach respectively for each spare part; 3) apply the above three methods respectively by using the weight building data, so as to obtain forecasting relative error values for different methods; 4) calculate the weights of the above methods; 5)construct an assembling demand forecasting model; 6) forecast the future sales demands of all spare parts. Compared with the prior art, the method is suitable for the spare parts with different demand features, and has the advantages of strong objectivity, high forecasting precision, and can greatly reduce the inventory cost on the premise of having a certain service level and improves the supply chain management efficiency and customer satisfaction.

Systems and methods are disclosed that facilitate normalizing and beautifying digitally generated handwriting, such as can be generated on a tablet PC or via scanning a handwritten document. A classifier can identify extrema in the digital handwriting and label such extrema according to predefined categories (e.g., bottom, baseline, midline, top, other, . . . ). Multi-linear regression, polynomial regression, etc., can be performed to align labeled extrema to respective and corresponding desired points as indicated by the labels. Additionally, displacement techniques can be applied to the regressed handwriting to optimize legibility for reading by a human viewer and / or for character recognition by a handwriting recognition application. The displacement techniques can comprise a “rubber sheet” displacement algorithm in conjunction with a “rubber rod” displacement algorithm, which can collectively preserve spatial features of the handwriting during warping thereof.

The invention discloses a traffic flow data recovery method based on space-time correlation. The method includes a front-stage traffic data conversion and abnormal data screening method and a follow-up traffic flow data recovery method. The data screening method relates to a threshold value screening method, a zero data screening method and a quality screening method according to the abnormal condition of actual traffic flow data. According to time correlation and space correlation of traffic flow data, by the combination of a time sequence method and a multiple linear regression method, namely the data recovery method based on temporal correlation and spatial correlation, the comprehensive traffic flow data recovery method based on space-time correlation is designed. The method is simple and quick, the real-time processing requirement can be met, and an acquired result is high in accuracy.

The invention discloses a medical infrared thermometer and a measurement compensating method of the medical infrared thermometer. The medical infrared thermometer is composed of an infrared temperature measurement module, an ultrasound distance measurement module, a power module and a signal processing unit. The infrared temperature measurement module is a TN9 infrared temperature measurement module and can output the target temperature and the environment temperature, the ultrasound distance measurement module, the signal processing unit and the infrared temperature measurement module are packaged in a shell, the power module is packaged on the lower portion of a handle, and the handle and the shell are integrally designed. The measurement compensating method of the medical infrared thermometer includes the steps of adopting a multivariate linear regression method to analyze and process test data, building a model of relations among a measuring distance, the environment temperature and the human body temperature to be measured, and using software programming to achieve human body temperature measurement compensation. According to the medical infrared thermometer and the measurement compensating method of the medical infrared thermometer, measuring errors are limited between -0.13 DEG C and 0.11 DEG C, good linearity and good reversibility are achieved, the measurement accuracy is high, the requirement for accurately measuring the temperature of the human body can be met, and the medical infrared thermometer and the measurement compensating method are suitable for clinical diagnosis.

The invention discloses a bus load prediction method. The method comprises the following steps: correcting abnormal values in historical load data by use of a transverse comparison method, and determining key influence factors of bus load by use of a grey relation projection method; putting load curves with similar features in the same category by use of an improved K-Means clustering method to get a plurality of typical load patterns, building a random forest classification model, and establishing the mapping relationship between influence factors and clustering results; for each load pattern, training a plurality of prediction models by use of a multivariate linear regression method; and determining the category of a day under test, and selecting a matching regression model to realize load prediction. A data mining method is introduced to analyze the changing rules of bus load, and a prediction model library is built. Model matching is realized based on the category of a day under test. The accuracy and real-time performance of short-term bus load prediction are improved. More accurate decision support is provided for power grid planning and real-time dispatching.

The invention discloses a fish tenderness hyperspectral detection method based on a characteristic wave band. The detection method comprises the following steps of: firstly, detecting fish tenderness according to the conventional detection method; then, with fish as a sample set, utilizing a near-infrared hyperspectral imager to carry out nondestructive detection on the fish, establishing a multi-element linear regression model of the tenderness and a characteristic wavelength, detecting a fish sample, and calculating the tenderness of fish sample according to the multi-element linear regression model of the tenderness and the characteristic wavelength. The fish tenderness hyperspectral detection method provided by the invention has the advantages that the fish tenderness is rapidly detected in an optimized manner through the characteristic wave band, the time of hyperspectral collection, detection and data analysis is greatly shortened, the detection efficiency is improved, and the fish tenderness hyperspectral detection method really provides theoretical support and research basis for on-line, rapid and nondestructive detection of food quality.

The invention discloses an online monitoring method for temperature rise of an outdoor electrical device, which utilizes the following various parameter measuring instruments: a thermodetector used for measuring the surface temperature of a device, an environment wind power and wind direction sensor, a temperature and humidity sensor, a sunlight intensity sensor, a load parameter collector and other detection devices, and calculator processing unit. A multiple linear regression equation empirical model for predicting the temperature rise of the device is established by a measurement system according to the multiple linear regression principle, main factors which affect the temperature rise greatly are brought into a temperature rise predicting regression model according to experiences, wherein factors with nonlinearity are processed in advance, so that the temperature rise can be predicted accurately. According to the online monitoring method, the full-automatic online monitoring and fault diagnosis and prediction can be realized, the work efficiency and the monitoring capability are improved, and particularly, the monitoring quality is improved.

The invention discloses a proximate analyzing method for coal quality base on multivariate linear regression, The invention is characterized in that a set of samples with known proximate analysis index values is used, plasma spectrum data is obtained by detection of a coal quality on-line analysis system of LIBS (Laser-Induced Breakdown Spectroscopy), the proximate analysis of fire coal samples to be measured by a multivariate linear regression correlation of each proximate analysis index value are respectively established, the characteristic spectral line strength which has substantial contribution to proximate analysis index values is respectively introduced through the established multivariate linear regression correlation to obtain the proximate analysis results. According to the invention, the spectral line information of correlative elements which has substantial contribution to each proximate analysis index is comprehensively utilized, the process for analyzing the index by fire coal proximate analyzed by the LIBS can be simplified, and the speed and the accuracy of the quantitative analysis can be raised.

The invention discloses a correlation analyzing method based on vehicle, road conditions and safe travel big data in a network of vehicles. The method comprises the following steps of data acquiring, data transmitting and storing, data de-noising, data analyzing, and data display and feedback. The data acquiring process is not limited to data of the vehicles, and it also includes data related to roads and surrounding vehicles. With data classification and storage, removal of abnormal values through de-noising, as well as multivariate linear regression fitting analysis, the correlations can be determined and sent back to the owner of a vehicle among the conditions for dangerous driving, vehicle's hardware state, road environments, surrounding vehicles and driving behaviors. The analyzing method provided by the invention can enable drivers to learn about their driving behaviors and their vehicles' states. For the instant replacement of parts with potential to bring danger and the rectification of bad driving behaviors, the purposes can be achieved including energy reduction, vehicle lifetime extending, and safe driving.

The invention discloses a predicting method for short-term load of a power system. Active power of load data is used as a data basis; through dividing the data decomposing frequency to every stable frequency component from high to low, and then predicting a low frequency component and a medium high frequency component by a multiple linear regression and LSTM neutral network; finally, predicted results are overlapped to obtain a complete predicting result; thus the shortcoming that a traditional load predicting method is hard to effectively predict local features of the high frequency ; meanwhile, the change trend of the load can be exactly predicted, the predicting effectiveness under a complex condition is improved.

The invention relates to a plant leaf area index measurement method based on the laser radar point cloud technology. Compared with the prior art, the defects that the leaf area index measurement efficiency is low, and the environment requirement is high are overcome. The method comprises the following steps of obtaining three-dimensional laser point cloud data of a plant canopy, cutting the point cloud data, conducting point cloud picture classification inside the plant edge contour, calculating the half side plant volume Vhalf of each plant, counting the number of hit points, counting the proportion of breaking points, and utilizing inversion for working out leaf area indexes. According to the plant leaf area index measurement method, by means of the three-dimensional point cloud data generated through a ground laser radar measurement system, with the combination of the computer graphics technology, the leaf area indexes are obtained from point cloud pictures for calculating needed relevant parameters and conducting multiple linear regression treatment, and therefore the leaf area indexes are calculated.

The invention relates to an assessment method for transient voltage stabilization of a load area of an electrical power system and belongs to the electrical power system stability analyzing and assessment field. The assessment method for the transient voltage stabilization of the load area of the electrical power system includes that using data measured by a synchronous phasor measurement unit as basis, and building an initial sample database for data mining through a lot of simulation samples; extracting characteristics for reflecting the stabilization degree of each node through the quantitative evaluation for each node in the area; identifying through a multiple linear regression method to obtain sensitivity coefficients for reflecting the mutual influence relations between the nodes in a local area network; using a semi-supervised clustering method to demarcate all the samples; using a decision tree algorithm to perform classified learning to obtain a decision tree model, and using the decision tree model for online monitoring to assess the global transient voltage stabilization state of the load area of the electrical power system.

The invention provides a method for building a street pollutant concentration distribution model. A CFD model is utilized for obtaining a wind field matrix, on the basis of the wind field matrix, a wind field multiple linear regression equation in a street can be built, a wind field in the street can be calculated, pollutant concentration distribution in the street can be obtained based on a Gaussian puff model and a GPU parallel technology, and pollutant concentration distribution outside the street can be obtained based on a Gaussian plume model and background wind. According to the method, the calculation process of the wind field can be simplified and quickened, and the calculation efficiency of pollutant concentrations inside and outside the street can be improved.

The invention relates to a flight delay prediction method based on an ARIMA model. The method comprises the following steps of stepS1, establishing a data set: flight data is collected and the flight data includes flight information, time information, airport information and delay time information; stepS2, analyzing a data set characteristic: differences of airports and airlines and an influence of weather on the flight are analyzed; stepS3, establishing the ARIMA model: establishing of the ARIMA model includes a mobile average process, an autoregressive process, an autoregressive mobile average process and an ARIMA process; stepS4, selecting an optimum ARIMA model: the established ARIMA model is verified and an optimum parameter is selected; stepS5, predicting flight delay: any flight data is acquired and then an optimum ARIMA function is selected to be a model delay function, a multiple-linear regression function is selected to be a weather delay function and a final prediction result is acquired after addition of the functions. By using the method, high prediction precision can be realized, usage time is shortened and flight delay can be effectively predicted.

The present invention discloses a microblog data analysis based hot news prediction method and system. The method comprises: acquiring news reports from mainstream news sites and microblog user response information caused by the news reports on the microblog; carrying out word segmentation and word frequency statistics to a microblog text, calculating a TF-IDF value of a word, and converting the value into a microblog topic described by using a vector space; classifying the microblog topics, counting each quantitative index for describing the microblog topics, and calculating each hot index of news; and using a multivariate linear regression algorithm to learn sample data, establishing a hot news prediction model, and determining whether the latter news can become a hot news or not. The system comprises a data acquisition module, a text analysis processing module, a data statistical analysis module and a hot news prediction module. According to the method and system disclosed by the present invention, the trend of news reported by media in microblog topics is comprehensively analyzed to predict whether the news can become a hot news or not in public sentiments, so that the problem of early prediction of hot news can be well solved.

The invention belongs to the field of power quality managing, and discloses a method for applying the partial least squares regression to power distribution network harmonic source positioning and detecting. According to a model for carrying out equivalent transformation on the electrical power system side and the distortion user side and a harmonic voltage and a harmonic current signal which are obtained through synchronous measurement at the common coupling joint, a regression coefficient is dissolved through the partial least squares regression algorithm, an aggregative variable with the best explanatory capability for a dependent variable is extracted in the manner of decomposing and screening data information in the system, and information and noise in the system are identified. The method integrates the basic functions of multiple linear regression, the canonical correlation analysis and the principal component analysis, the modeling predicting type data analysis method and non-model type data recognition are organically combined, regression modeling, data structure simplifying and variable correlation analyzing can be achieved at the same time, and the method can be widely applied to power quality analyzing, monitoring, evaluating and controlling fields.

The invention relates to a mobile video quality assessment method based on hierarchy analysis and multiple linear regressions. The method comprises the following steps: firstly, determining each layer of end-to-end performance indexes of user QoE (Quality of Experience) influencing mobile video services; dividing various end-to-end performance indexes of the QoE into a target layer, a criterion layer and an index layer by utilizing a hierarchy analysis method according to the attributes and the types of the performance indexes; respectively constructing hierarchical influence models in manners that the index layer is aligned to the criterion layer which is aligned to the target layer from bottom to top by utilizing the multiple linear regression analysis; constantly adjusting regression coefficients for influencing the models and various performance indexes of the models; and finally, establishing the influence model in the manner that the index layer is aligned to the target layer, i.e., establishing a total regression model influencing the quality of a mobile video according to various end-to-end performance indexes of QoE so as to assess the quality of the mobile video by utilizing the hierarchical total assessment model. According to the method, the subjective and objective performance indexes of the mobile video are combined with the QoE of the user, so that the method provided by the invention is a comprehensive and effective user experience quality assessment method.

The invention discloses a store site selection system and a store site selection method. The method comprises the following steps: determining a trading area range according to the business type of a new store, determining existing stores and residential quarters in the trading area and searching attraction influence factor information of the stores; quantizing the environment in the store by using factor analysis; performing multiple linear regression analysis on the amount of sales of a plurality of stores by using a multiple linear regression formula according to the obtained environment in the store and other collected influence factors, determining the influence degree of each influence factor on attraction in the trading area and taking the influence degree as an adjusting index; calculating the attraction of the new store by using a multi-factor attraction model according to the influence factor and the corresponding adjusting index thereof obtained by regression analysis. According to the system and the method, retail enterprises can very conveniently determine the store customer sources and the distribution characteristics according to the store scales, the environments, the service and the like.

The invention discloses a novel urban rail transit vehicle air-conditioning control method based on changes of outside temperature and passenger capacity. The method includes the following steps that S1, multiple linear regression is conducted according to data obtained by calculating the air temperature outside a vehicle, the passenger capacity of the vehicle and condition parameters of clothes wearing thermal resistance of passengers, and then a two-variable linear in-car temperature control model based on the outside temperature and the passenger capacity and meeting the equation T_in=kl*T_out-k2*n+k3 can be obtained; S2, real-time temperature signals outside the vehicle and in-car passenger capacity signals are collected and are led into the temperature control model in the S1, and then an in-car target control temperature is obtained; S3, real-time temperature values of multiple areas in the vehicle are collected and averaged to obtain an average value, then the average value is compared with the in-car target control temperature obtained in the S2, the working state of a vehicle air-conditioning unit is adjusted automatically according to a comparison result, next the output refrigeration amount of the air-conditioning unit is adjusted, and finally automatic adjustment and control over the in-car temperature is achieved. According to the method, the actual passenger capacity of the vehicle and environment temperature signals outside the vehicle are led simultaneously to serve as variable calculation parameters of the in-car target control temperature, so the in-car target control temperature better meeting passenger capacity thermal comfort requirements can be calculated comprehensively and accurately.

The invention discloses a method for determining a radix notoginseng extract and contents of five types of ginsenosides in a preparation of the radix notoginseng extract by a Fourier transform near-infrared spectrograph. The method comprises the steps that a sample is detected by an ultra-high performance liquid chromatography, moving phase builds acetonitrile and water gradient elution parameters with separation degrees higher than 1.7 in chromatographic peaks of Rg1 and Re, and the spectrum preprocessing adopts the combination of 2 to 3 methods in Savitzky-Golay polynomial smoothing, second-order differential, Noriis derivative filtering and data normalization; and three preferred wave bands of the five types of ginsenosides are modeled, and a calibration model is built by any one of a partial least squares regression method, a principal component regression method and a multiple linear regression method. The method is used for determining the contents of the five types of ginsenosides in the sample to be detected, the determining result is consistent with a result determined by the ultra-high performance liquid chromatograph basically, the requirements of 'Chinese pharmacopoeia' are met, the accuracy is high, and the determination speed is increased substantially.

The invention discloses a statistical model-based moving beam type gantry type machine tool dual-drive feed error compensation method and model. According to the method, based on two-shaft non-synchronous error analysis, multiple linear regression is adopted to find the relationship between dual-drive non-synchronous errors and main influencing factors, and a statistical analysis model is established, and instruction feed-forward compensation is performed on the dual-drive non-synchronous errors, and therefore, the accuracy of dual-drive synchronous control is improved. The method of the invention is a novel dual-drive non-synchronous error compensation method. With the method of the invention adopted, problems in moving beam type gantry type machine tool dual-drive non-synchronous error dynamic compensation can be solved. Different positions of the double driving shafts where a cross beam is located, the speed of the double driving shafts, and influence of the change of the mass of double-shaft load on dual-drive non-synchronous errors when a main shaft moves on the cross beam are considered.

The invention discloses a pair of movement energy consumption monitoring shoes based on a flexible force sensor and a monitoring method of the movement energy consumption monitoring shoes. The method includes that movement parameter information of a user such as step frequency and step speed is obtained by collecting the sole pressure distribution information, and then the movement parameter information is combined with the human property parameters such as gender, stature and the length of lower limbs; a movement energy consumption model is built by using the multiple linear regression analysis method, and the real-time monitoring on movement energy consumption is realized. The pair of movement energy consumption monitoring shoes comprises a pair of sports shoes with flexible force sensitive insoles and a wristwatch; the flexible force sensitive insoles inside the shoes collect the sole pressure distribution information of the user in real time and transmit the information to the wristwatch through a ZigBee in a wireless way; the wristwatch substitutes the parameters such as the sole pressure distribution information, age, stature and the length of lower limbs into the movement energy consumption module, so that the movement energy consumption information is obtained, and is then stored and displayed. The pair of movement energy consumption monitoring shoes is movement energy consumption monitoring equipment, which is low in cost, easy to wear, accurate in energy consumption calculation and has real-time feedback function.

The invention discloses a method for obtaining a true Raman spectrum of a cell by multiple linear regression fitting. The method comprises the following steps: acquiring the original Raman spectrum of the cell; acquiring a smooth Raman spectrum after smoothing; acquiring primitive-variable Raman spectrums of all primitive variables in the cell; initially fitting and calculating an approximate fluorescence background Raman spectrum of the cell; obtaining a fitting coefficient corresponding to each primitive variable and background variable by multiple linear regression fitting; finally determining the fluorescence background Raman spectrum of the cell by the sum of products of the background variable Raman spectrums and corresponding fitting coefficients; deducting the obtained fluorescence background Raman spectrum by using smoothing Raman spectrum and obtaining the true Raman spectrum of the cell. The method disclosed by the invention is used for eliminating the background fluorescence of the Raman spectrum of the cell, can fit out the fluorescence background of the Raman spectrum of the cell more accurately than other methods, and can realize fast elimination of the fluorescence background of the Raman spectrum of the cell so as to obtain the true Raman spectrum of the cell.

The invention discloses a method and device for rapid and high-precision detection of the content of a heavy metal lead in edible oil, and is used for solving the problem of insufficient accuracy of conventional detection of the edible oil heavy metal lead content. The device adopts an atomizer for atomizing an edible oil sample, and an LIBS spectrum signal of the sample is enhanced by a microwave assisted method. An LIBS spectrum of the sample is subjected to segmented correction by adopting the carbon element spectral line intensity, and then a background spectrum of the edible oil is removed. Seven wavelengths are selected as characteristic spectral lines of the heavy metal lead, multiple linear regression is applied to associate the spectral line intensity of the characteristic wavelengths with the real heavy metal content, and a calibration model of the heavy metal content in the edible oil is established and is subjected to temperature correction. The calibration model after temperature correction can perform rapid and high-precision detection of the heavy metal content in the to-be-measured edible oil sample under conditions of different environmental temperatures.

The invention discloses a method for predicting ship fuel consumption according to sea conditions and manipulating conditions. The method for predicting the ship oil consumption comprises the steps of firstly acquiring a sample data set through data screening, data integration and normalized processing, then building a multivariate linear regression prediction model, defining a cost function by adopting a least shrinkage and selection operator (LASSO algorithm) based on the sample data set, performing variable shrinkage and parameter selection by combining cross validation and a least angle regression (LARS) algorithm, finally solving LASSO estimation by adopting an Osborne dual algorithm, and calculating to acquire the ship fuel consumption. The method provided by the invention for predicting the ship fuel consumption according to sea conditions and manipulating conditions can establish a function relation between the ship fuel consumption amount and each influence factor, solves a problem of multicollinearity in fuel consumption calculation, improves the ship fuel consumption calculation accuracy and has importance significance for energy conservation and emission reduction of traffic on the sea.

The invention discloses a method for rapidly measuring physical and chemical quality indexes of in-use lubricating oil, which comprises the following steps: in a first step, collecting representative samples as a training set; in a second step, measuring an infrared spectrum decay index of the in-use lubricating oil; in a third step, measuring in-use oil physical and chemical quality indexes; in a fourth step, adopting a gradual linear regression method to carry out optimization on the infrared spectrum decay index and establishing a multiple linear regression equation of the physical and chemical indexes and the infrared spectrum decay index; and in a fifth step, for an in-use oil sample, firstly measuring the infrared spectrum decay index and then utilizing the regression equation established in the fourth step to calculate the physical and chemical quality indexes. The method of the invention can rapidly measure seven physical and chemical quality indexes of the in-use lubricating oil, i.e. a kinematic viscosity at the temperature of 40 DEG C, a kinematic viscosity at the temperature of 100 DEG C, water content, a total acid number (TAN), a total base number (TBN), a flash point and a pour point, monitor the quality decay of the lubricating oil, guide to change the oil and guarantee the normal operation of the engine.

The invention requests the protection for a information propagation model based on online social network and propagation method thereof, and belongs to the field of online social network analysis. The information propagation model is composed of accessing to the data source, building dimensional attribute driving mechanism and building dynamic evolution strategy, building hot topic propagation model. The first step, the data source is accessed. The second step, dimensional attribute driving mechanism is built, user attributes is extracted from two aspects of network structure and user history, and the effects the two factors have to the driver of the user's participation in the topic are stated quantitatively through utilizing multiple linear regression methods. The third step, dynamic evolution strategy is built, income matrix is defined and popularity is perceived, and according to evolutionary game theory, building dynamic evolution strategy. The fourth step, hot topic propagation model is built. The user multidimensional attribute model, dynamic evolution strategy and traditional SIR model build a novel hot topic propagation model. The invention has the advantages of being effective to describe the spread of trend hot information in social networks and reveal the influence of different driving factors on information dissemination.

The invention discloses a method for analyzing the ice and climate relationship on the basis of the grey MLR (Multiple Linear Regression), which comprises the following steps of: a step 1 of acquiring microclimate data comprising the environment humidity, the environment wind speed and the environment temperature and wire tensile force by a power transmission line online monitoring system and converting the microclimate data into an actual ice thickness value by a theories calculation model according to the wire tensile force; a step 2 of selecting effective data and applying a grey GM (1, 1) model construction to forecast the microclimate data; and a step 3 of establishing an ice thickness model by applying a MLR analysis method according to the selected effective data, forecasting the ice thickness on the basis of the microclimate GM (1,1) model construction and establishing an ice thickness forecasting model of the grey multiple regression analysis. The invention solves the problems that in the prior art, an equation system shows morbidness due to accumulation and subtraction of the data, the parameter estimation shows instability and the like. Detailed information such as the weight variation before and after a monitored power transmission line is iced, the inclination angle of an insulator string, the environment temperature and humidity, the wind speed and the like can be provided; and the ice thickness of the power transmission line is obtained by calculation according to field acquisition information.