448 results about "Independent predictor" patented technology

The systems and methods described herein utilize a probabilistic modeling framework for reverse engineering an ensemble of causal models, from data and then forward simulating the ensemble of models to analyze and predict the behavior of the network. In certain embodiments, the systems and methods described herein include data-driven techniques for developing causal models for biological networks. Causal network models include computational representations of the causal relationships between independent variables such as a compound of interest and dependent variables such as measured DNA alterations, changes in mRNA, protein, and metabolites to phenotypic readouts of efficacy and toxicity.

Methods and systems for designing an experiment using a computer to determine whether the experiment is a true experiment are described. These approaches allow a user who is unsophisticated in the complexities of true experimental design to design and deploy an experiment that produces substantially confound-free results and can be used to determine and quantify any causal relationship between independent and dependent variables. The computer may select one or more independent and / or dependent variables of the experiment or may assist the user in selection of independent and / or dependent variables. Formation of control and treatment groups, randomization and / or blocking to reduce the effects of confounding variables may be performed by the computer with or without input from the user.

An evidence based cost modeling and predictive analysis system, and an incentives based plan to reduce healthcare costs are disclosed. An analytics system may generate incremental expenditures among overweight and obese individuals, predictive forecasts of future medical costs, and predictive forecast of cost reduction based on financial incentives to recipients. The forecasts may include statistical trends, prevalence of diseases based on body mass index, and medical evidence associated with specific illnesses. A computer based program may process and analyze dependent and independent variables in electronically stored information (for example insurance, health and medical records). A health insurance provider may provide an annual rebate on paid premiums to recipients based on a qualifying annual BMI as an incentive. The recipients may receive the rebates in a qualified health reimbursement account (HRA) managed by the recipients towards future healthcare related expenditures.

A system, method, and computer program product for predicting operation for physical systems with distinct operating modes uses observable qualities of the system to predict other qualities of the system. Independent variables including temperature or production volume are observed to determine the degree to which a dependent modeled variable, including energy load, is influenced. Partition variables representing operating conditions of the dependent variables are defined as discrete values. Reference datasets with coincident values of the dependent variable, independent variable, and partition variables are received, and models are created for each discrete value of the partition variables in the reference dataset. Each model is populated with the values of the dependent variable and the independent variable. The dependent variable is modeled as a function of the independent variable. Model accuracy is evaluated by processing new input data to generate output data that includes values of the coincident dependent variable, the independent variable, and the partition variable from the input dataset.

The invention provides a classifier construction method, and a method of prediction classification. The classifier construction method includes the steps: acquiring a training data set of a pluralityof training samples, wherein the training data set includes attribute information and classification information; extracting the attribute characteristic from the attribute information; taking the attribute characteristic as an initial independent variable, and taking the corresponding classification information as an initial dependent variable to perform at least one round of model training, wherein at least candidate models take part in each round of model training; and taking the combination with the minimum error rate in the each round of model training as a classifier which has completedtraining. The classifier construction method, and the method of prediction classification perform at least one round of model training on the basis of at least two candidate models to obtain a classifier and can predict classification of target samples according to the trained classifier, thus being able to avoid the problem that a single classification method is low in prediction precision and prediction accuracy, and being both higher in precision and accuracy of prediction.

The present invention relates to a steady state optimization method incorporating dynamic variance correction for dynamic variations of both independent variables and dependent variables of a dynamic system. The dynamic variance correction is based on measured variance of the variables and a weighing factor for each of the variables. The dynamic variance correction offers an effective method of dynamic violations avoidance of controlled variables for a model predictive controller without having to constantly adjust the tuning weights in response to changing dynamical conditions.

The invention discloses a numerical-control machine-tool thermal error prediction method based on an unbiased estimation splitting model and a system thereof. The prediction method comprises the following steps of 1, acquiring a temperature variable and a thermal deformation amount of a machine tool main shaft; 2, extracting a temperature sensitive point variable needed by thermal error modeling; 3, establishing a machine-tool thermal-error unbiased estimation splitting model; 4, calculating a thermal deformation amount prediction value of the unbiased estimation splitting model, and according to a difference state of the value and a thermal deformation amount measurement value, acquiring prediction performance of the unbiased estimation splitting model. The system comprises an infrared thermal imager, a temperature sensor, an eddy current displacement sensor and an industrial control computer. By using the method and the system, a coupling effect among temperature independence variables is effectively solved; and a temperature sensitive point selection method and a thermal error modeling model are cooperated and used so that prediction precision and robustness of the thermal error model are greatly increased.

The invention discloses a comprehensive electric energy meter verification method and system based on an improved least square method. The method comprises the steps: generating a scatter diagram of original data, deleting an abnormal value, and obtaining sample data; carrying out Pearson correlation analysis and VIF inspection on independent variables in the sample data; determining a multi-colinearity existence range between the independent variables; checking the multiple collinearity by fitting a sample error average regression line and a median regression line; performing multivariate regression analysis according to an inspection result, and preliminarily determining a regression equation; checking the credibility of the regression equation, and determining a data regression model; correcting the data regression model through residual analysis; and normalizing the weight of each variable, calculating an influence weight of each variable on the error, and substituting the influence weight into the data regression model to carry out comprehensive verification on the electric energy meter. According to the invention, whether the metering error of the electric energy metering device exceeds a standard specified range can be effectively verified, and the reliability and stability of the electric energy metering device are ensured.

The invention discloses an intelligent early warning method for dam safety monitoring data. The method comprises the steps of early warning model establishment, threshold value setting and mutual feedback type early warning. Gross error identification and gross error processing are carried out, model sample data quality is improved, according to the monitoring items, independent variable relevance, historical monitoring data quantity and historical monitoring data distribution, different early warning models and indexes are established, including a stepwise regression model, a correlation vector machine model and a gray system model; the established models can reflect the relationship between the independent variable and the dependent variable more truly and are wide in application range,according to a measuring instrument, measuring point attributes, a threshold value, an early warning model and indexes, real-time early warning is carried out on monitoring data, monitoring instrumentabnormity early warning is sent to monitoring personnel, or dam safety early warning is sent to dam safety management personnel, experts with professional knowledge and rich experience are not needed, the workload is small, the early warning speed is high, and the early warning result is more accurate and reliable.

According to one embodiment there is provided a method of selecting a plurality of M atlases from among a larger group of N candidate atlases to form a multi-atlas data set to be used for computer automated segmentation of novel image data sets to mark objects of interest therein. A set of candidate atlases is used containing a reference image data set and segmentation data. Each of the candidate atlases is segmented against the others in a leave-one-out strategy, in which the candidate atlases are used as training data for each other. For each candidate atlas in turn, the following is carried out: registering; segmenting; computing an overlap; computing a value of the similarity measure for each of the registrations; and obtaining a set of regression parameters by performing a regression with the similarity measure being the independent variable and the overlap being the dependent variable.

The invention discloses a social network link prediction method adopting knowledge graph embedding and a time convolution network. The method comprises the following steps: S1, processing original social sample data, extracting phrases and tuples of independent variables related to the phrases, constructing structured event tuples, linking the structured event tuples to a knowledge graph, constructing sub-graphs from the knowledge graph, and extracting event embedding vectors; S2, expressing the network structure of the social network by using an adjacency matrix, and fusing an event embeddingvector and a network adjacency matrix in a vector form; and S3, establishing a link prediction model based on the improved time convolution network, taking a fusion vector of the event embedding vector and the network adjacency matrix as input of the prediction model, and obtaining an optimal model through iterative training so as to predict a social network link. According to the invention, theprediction precision of the social network link can be improved.

The invention relates to a non-invasive serological scoring model for hepatic fibrosis and a design method thereof. The model includes an input module, a processing module and an output module. The input module receives three independent predictors including blood platelet count, laminin and procollagen type 3. The processing module is connected with the input module and evaluates the situation ofhepatic fibrosis based on the independent predictors. The output module is connected with the processing module and outputs the score finally obtained by the module. The model can more accurately evaluate the situation of severe hepatic fibrosis for patients with chronic hepatitis. Compared with other models, the model only involves the three independent predictors including PLT, LN and PIIINP.The contained serological indicators are fewer, the accuracy and the practicability are higher, the model is more concise to use, popularization in clinical treatment and primary hospitals is facilitated, the probability of invasive examination is thus reduced, and the risk and burden of the patients are reduced.

A method of data management for optimizing the patient outcome from the provision of cardiac resynchronization therapy (CRT) is described. This method describes a process by which sets of dynamic cardiopulmonary dependent variables are measured during steady-state conditions, displayed, and translated into quantitative and qualitative measurements while the independent variables of CRT, device lead placement and atrial-ventricular and interventricular delay settings of bi-ventricular pacemaker systems, are altered by a physician. In combination with visual observation and computer-assisted ranking of the dependent variables, a physician can utilize the resulting information to render decisions on the optimal choice of the independent variables.

The invention relates to an insurance business risk scoring system and a construction method thereof. The construction method comprises the steps of S10, taking whether an event occurs as a dependent variable of a to-be-constructed insurance business risk evaluation system; S20, acquiring a plurality of independent variables related to the dependent variable through a data mining method; S30, generating the insurance business risk scoring system by use of a first part of the independent variables and the dependent variables stored in a database through a Logistic regression method; and S40, verifying the insurance business risk scoring system by use of a second part of the independent variables and the dependent variables stored in the database. According to the insurance business risk scoring system and the construction method thereof, the construction of the insurance business scoring system can be completed by use of a big data technology, thus a risk can be quantitatively scored.

A method of data management for optimizing the patient outcome from the provision of external counterpulsation (ECP) therapy is described. This method describes a process by which sets of dynamic cardiopulmonary dependent variables are measured during steady-state conditions, displayed, and translated into quantitative and qualitative measurements while the independent variables of ECP, cuff inflation duration and cuff inflation pressure settings of ECP systems, are altered by a physician. In combination with visual observation and computer-assisted ranking of the dependent variables, a physician can utilize the resulting information to render decisions on the optimal choice of the independent variables. The method will enable physicians to collect, view, track and manage complicated data using well-understood visualization techniques to better understand the consequences, acutely and chronically, of their therapeutic actions in general, and of their provision of ECP therapy in particular.

The invention discloses a multivariable analysis method based on angle measurement, and relates to a method for noncontact analysis on products. The method comprises the following steps: measuring a measured sample and a measured component to obtain the multipoint strength measurement value of the measured sample and the measured component; converting the multipoint strength measurement value of the measured sample and the measured component into an angle metric of the measured sample and the measured component; selecting a modeling sample; converting the multipoint strength measurement value of the measured sample and the measured component into the angle metric of the modeling sample and the measured component; building a multivariable regression model by taking the measured component content of the modeling sample as a dependent variable and the angle metric of the modeling sample and the measured component as an independent variable; and substituting the angle metric of the measured sample and the measured component into the multivariable regression model to predict the content of the measured component in the whole hybrid system. According to the multivariable analysis method, requirement on the environment in the analysis operation is obviously lowered, complexity of instrument can be reduced, and the multivariable analysis method is suitable for chemical analysis, process analysis and instrument analysis.

The invention belongs to the technical field of underground tunnel engineering construction and provides a method for quantitatively predicting the cutter abrasion quality of the heading machine cutter during composite stratum heading. On the basis of tool mass measurement and corresponding tool and tunnel face contact point trajectory calculation, mass line loss indexes MLI of tools at differentpositions are subjected to classification statistics according to strata. After the MLI is normalized into equivalent mass line loss indexes EMLI, a universal prediction model for the equivalent massline loss indexes of homogeneous strata is obtained. The MLI and the corresponding EMLI are clear in physical significance. The equivalent mass line loss index universal prediction model considers both the shareability of independent variables and the prediction precision. The cutter wear quality prediction method based on the equivalent mass line loss index general prediction model is provided. The calculation process is simple and clear. The prediction precision is high. Based on engineering investigation data and construction data, the method is reasonable, high in practicability and beneficial to quantitatively predicting the tool abrasion state, scientifically and reasonably arranging bin opening and tool changing and improving the tunneling efficiency.

The invention discloses a machine tool thermal error modeling method based on deep learning. The method comprises the following steps of obtaining temperature data of a machine tool main shaft and carrying out normalization processing; calculating correlation between each measuring point and thermal errors of a main axis at three directions through a partial correlation coefficient method, and selecting m measuring points with high correlation as temperature critical measuring points; establishing a SAE network and initializing a network parameter, and taking temperature data of the temperature critical measuring points as independent variables and inputting into the SAE network to extract a temperature characteristic of the temperature data; and taking the temperature characteristic as the independent variable, taking corresponding thermal error data as a dependent variable and inputting into a GA-BP neural network to carry out training, and carrying out thermal error prediction. Themethod has advantages that prediction precision is high, robustness is good, a thermal error change trend of the machine tool can be effectively estimated and so on.

The invention discloses a method for constructing an articulation relationship knowledge graph, which is executed in computing equipment and comprises the following steps of obtaining financial statements of a plurality of companies, and extracting subject vectors of each company in different periods from the financial statements, the subject vectors comprising numerical values of a plurality of subjects, taking each subject as a dependent variable, taking other subjects as independent variables, and taking the plurality of subject vectors as training samples to train and generate a regressionmodel of each subject, and calculating an error of each regression model, and when the error is smaller than a preset error threshold, storing the corresponding regression model as an articulation relationship formula to generate an articulation relationship knowledge graph, the generated articulation relationship knowledge graph being used for checking an articulation relationship between financial statement subjects. The invention further discloses a corresponding financial statement checking method and computing equipment.

We show that urinary metalloproteinases (MMP's) (e.g. MMP 9) and a disintegrin and metalloprotease 12 (ADAM 12) are significantly elevated in women at high risk for developing breast cancer and that monitoring for the absence or presence of both MMP 9 and ADAM 12 represents a new means for breast cancer risk assessment. In addition, we show that levels of MMP 9 and ADAM 12 serve as independent predictors of breast cancer risk. Furthermore, we have determined that elevated levels of urinary ADAM 12 predict an increased risk for breast cancer in subjects predicted not to be at risk for breast cancer by the Gail 5 -year risk model<66'67>. Accordingly, methods for assessing breast cancer risk and methods for directing medical care are provided.

The invention discloses an intelligent wastewater monitoring method based on complex network multivariate online regression, which comprises the following steps: collecting historical data including independent variables and dependent variables; performing normalization processing on the collected historical data to obtain a normalization model, and training the normalization model to obtain a trained normalization model; taking the independent variable as the input of the normalized model after training, carrying out the online learning of the normalized model after training, and updating thestate of the model in real time; performing reverse normalization processing on the output dependent variable to obtain a predicted dependent variable, and further regulating and controlling the wastewater treatment system. The complex network multivariate online regression method constructed by the invention solves the problem of poor generalization performance of deep learning on long and shortsequence regression, can be used for water quality parameter prediction, realizes intelligent water quality monitoring of a wastewater treatment system, and promotes efficient and stable operation ofthe wastewater treatment system.

The invention discloses a high-speed pantograph structure parameter optimization method which comprises the following steps of 1, determining the pantograph working performance influence factors, and respectively constructing a kinematics optimization model, a statics optimization model, a dynamics optimization model and a control science optimization model of a pantograph; 2, establishing an integrated optimization model according to the model obtained in the step 1; 3, solving the integrated optimization model obtained in the step 2 to obtain an optimization result of an independent variable; and 4, establishing a three-dimensional model of the pantograph according to the optimization value of the variable, and carrying out finite element analysis to complete the optimization of the pantograph. According to the present invention, on the basis of a complete theoretical model, the interaction of various factors is fully considered, and compared with the serial design, the design result better meets the requirements of the practical engineering.

The invention provides a fault influence factor quantitative analysis method based on a high voltage switch and can acquire accurate relationship between fault influence factors and corresponding fault types. The method comprises steps (1) initial data is acquired according to operation parameter data and fault type data after data classification; (2), standard processing on the initial data is carried out; (3), the operation parameter data taken as an independent variable and the fault type data taken as a dependent variable are introduced to regression analysis to acquire an optimal high voltage switch fault influence factor quantitative analysis model; (4), regression diagnosis of a Logistic regression equation of the model is carried out, if the model is qualified, the progress turns to a step (5); if not qualified, a secondary optimal high voltage switch fault influence factor quantitative analysis model of the step (3) is utilized to carry out regression diagnosis till the modelis qualified, the progress turns to the step (5); (5), according to the qualified optimal high voltage switch fault influence factor quantitative analysis model of the step (4), quantitative analysison the Logistic regression equation is carried out to acquire high voltage switch fault influence factors.

The invention aims at disclosing a new holism-based mode for digitizing and evaluating YZL value (a ratio value of a sum of peak areas of positive-coefficient correlated peaks to a sum of peak areas of negative-coefficient correlated peaks) of the quality of a traditional Chinese medicine, an establishing method and applications of the new mode. The new mode of the invention comprises the following steps: taking chemical composition groups / databases in medicinal materials as independent variables with a plurality of digits, taking the specific activity indexes of all batches of medicinal materials as function variables, adopting a partial least squares method to obtain regression equation coefficients of all common peaks and activities of the all batches of samples, abstracting the YZL value based on the regression equation coefficients, then adopting a normality fitting method to obtain a confidence interval of the YZL value, calculating to obtain a YZL interval value used for judging or evaluating the merits of the quality of the medicinal material, and establishing the new holism-based mode for digitizing and evaluating the YZL value of the quality of the traditional Chinese medicine. According to the new mode of the invention, the standard evaluation and the judgment of the quality of the medicinal material are directly related to the activity of the medicinal material, and are digitalized.

The invention provides a fault diagnosis method and device for a wind generating set. The fault diagnosis method includes the following steps: acquiring actual operation data of the wind generating set at each sampling time point in the current time period, wherein the actual operation data comprises a first group of actual operation data related to a first group of operation parameters and a second group of actual operation data related to a second group of operation parameters, the first group of operation parameters are independent variables related to the fault, and the second group of operation parameters are dependent variables of the first group of operation parameters; dividing the current time period into a plurality of sub-time periods based on the obtained first group of actualoperation data in the current time period, the difference between the first group of actual operation data in two sub-time periods which are continuous in time satisfying a first preset condition; anddetermining whether the wind generating set has a fault or not based on the second group of actual operation data in the plurality of sub-time periods.

The invention provides a power transformation equipment fault rate prediction method and system, equipment and a readable storage medium. The power transformation equipment fault rate prediction method comprises the steps: collecting the historical fault rate statistical data of power transformation equipment, and building a Weibull distribution fault rate function with the fault rate as a dependent variable and the time as an independent variable; collecting substation equipment state evaluation data, and establishing an equipment health index-based fault rate model by taking the fault rate as a dependent variable and the substation equipment health index as an independent variable; and configuring a function relationship between the equipment health index and the average operation time,performing simulation training on the comprehensive prediction model by taking historical statistical data of the power transformation equipment as a sample, solving to-be-estimated parameters, and finally obtaining the comprehensive prediction model. According to the power transformation equipment fault rate prediction method, data information such as online monitoring data, daily maintenance data and routine test data of the power transformation equipment is combined, and the equipment state condition is comprehensively reflected, and the development requirement of state maintenance of a current power system is met, and the fault rate of the power transformation equipment can be accurately predicted.