205 results about "Multivariate statistical" patented technology

Disclosed are systems and methods for on-line monitoring of operation of a process in connection with process measurements indicative of the operation of the process. In some cases, the operation of the process is simulated to generate model data indicative of a simulated representation of the operation of the process and based on the process measurements. A multivariate statistical analysis of the operation of the process is implemented based on the model data and the process measurements. The output data from the multivariate statistical analysis may then be evaluated during the operation of the process to enable the on-line monitoring of the process involving, for instance, fault detection via classification analysis of the output data.

An apparatus, system, method, and computer readable medium containing computer-executable code for implementing image analysis uses multivariate statistical analysis of sample images, and allows segmentation of the image into different groups or classes, depending on a correlation to one or more sample textures, or sample surface features. In one embodiment, the invention performs multivariate statistical analysis of ultrasound images, wherein a tumor may be characterized by segmenting viable tissue from necrotic tissue, allowing for more detailed in vivo analysis of tumor growth beyond simple dimensional measurements or univariate statistical analysis. Application of the apparatus and method may also be used for characterizing other types of samples having textured features including, for example, tumor angiogenesis biomarkers from Power Doppler.

A method for differentiating complex mixtures each having one or more chemical species is provided. The method comprises producing a sample NMR spectrum by subjecting a mixture to a selective spectroscopy process, wherein the NMR spectrum has individual spectral peaks representative of the one or more chemical species within the mixture. The one or more chemical species within the mixture are identified by analyzing the individual spectral peaks, and the individual spectral peaks are then subjected to a multivariate statistical analysis.

Methods and systems to detect steady-state operations in a process of a process plant include collecting process data. The collected process data is generated from a plurality of process variables of the process. A multivariate statistical model of the operation of the process is generated using the process data. The multivariate statistical model may be generated from a principal component analysis. The model is executed to generate outputs corresponding to the most significant variations in the process. Statistical measures of the outputs are generated and used to determine whether a steady-state or unsteady-state is related to the process.

The present invention generally relates to computer databases and, more particularly, to data mining and knowledge discovery. The invention specifically relates to a method for constructing segmentation-based predictive models, such as decision-tree classifiers, wherein data records are partitioned into a plurality of segments and separate predictive models are constructed for each segment. The present invention contemplates a computerized method for automatically building segmentation-based predictive models that substantially improves upon the modeling capabilities of decision trees and related technologies, and that automatically produces models that are competitive with, if not better than, those produced by data analysts and applied statisticians using traditional, labor-intensive statistical techniques. The invention achieves these properties by performing segmentation and multivariate statistical modeling within each segment simultaneously. Segments are constructed so as to maximize the accuracies of the predictive models within each segment. Simultaneously, the multivariate statistical models within each segment are refined so as to maximize their respective predictive accuracies.

A real-time system and method for online monitoring a transient operation in a continuous casting process. The transient operation refers to, but is not limited to, submerged entry nozzle changes, flying tundish changes, product grade changes, etc. This invention treats the aforementioned transient operations as batch processes and utilizes multiway principal component analysis to develop a multivariate statistical model to characterize normal process transitions based on carefully selected historical process data. Such a model is used by an online monitoring system to determine if a continuous caster transient operation is normal. This monitoring system can further be used to predict an impending breakout, one type of catastrophic process failures which may occur in a continuous casting process, during the transient operation. Process variables that are most likely related to the predicted breakout are identified by the system such that appropriate control actions can be taken to prevent an actual breakout occurrence.

A plurality of ECG Superscore formulae, created from multiple parameter ECG measurements including those from advanced ECG techniques, can be optimized using additive multivariate statistical models or pattern recognition procedures, with the results compared against a large database of ECG measurements from individuals with known cardiac conditions and/or previous cardiac events. Superscore formulae utilize multiple ECG parameters and accompanying weighting coefficients and allow data obtained from any given patient to be used in calculating that patient's ECG Superscore results. ECG Superscores have retrospectively optimized accuracy for identifying and screening individuals for underlying heart disease and/or for determining the risk of future cardiac events. They thus have greater predictive value than that of any conventional or advanced ECG measurement alone or of any non-optimized combinations of conventional or advanced ECG measurements that have been used in the past. Ongoing optimization of ECG Superscore diagnostic and predictive accuracy may be realized through the iterative adjustment of Superscore formulae based on the incorporation of data from new patients into the database and/or from longitudinal follow-up of the disease and cardiac event status of existing patients.

The software, methods and system of the current invention creates an interactive, auto-execution financial trading platform with unique forecasting algorithms, trading graphs and data mining features. The platform uses a univariate and multivariate architecture that is designed to improve performance of predictors and speed up calculations. Trading graphs, data mining features and predictive algorithms are predominantly based on fractal mathematics and Chaos theory. Even the unique software architecture is fractal in nature. All of these features are intended to be used individually or collectively to improve forecasting performance of financial markets. The above mentioned components also make it easier to manage a portfolio of securities and/or futures.

Methods and systems to detect abnormal operations in a process of a process plant include collecting on-line process data. The collected on-line process data is generated from a plurality of dependent and independent process variables of the process, such as a coker heater. A plurality of multivariate statistical models of the operation of the process are generated using corresponding sets of the process data. Each model is a measure of the operation of the process when the process is on-line at different times, and at least one model is a measure of the operation of the process when the process is on-line and operating normally. The models are executed to generate outputs corresponding to loading value metrics of a corresponding dependent process variable, and the loading value metrics are utilized to detect abnormal operations of the process.

A method and system for monitoring a process in a process plant includes collecting data from a process control system within the process plant, where the data is representative of a normal operation of the process when the process is on-line and operating normally, performing a multivariate statistical analysis to represent the normal operation of the process based on a set of collected on-line process data comprising a measure of the normal operation of the process when the process is on-line and operating normally, and representing a real-time on-line operation of the process using monitored on-line process data comprising a measure of a real-time operation of the process when the process is on-line as an input to the representation of the normal operation of the process.

A system and method of monitoring and diagnosing on-line multivariate process variable data in a process plant, where the multivariate process data comprises a plurality of process variables each having a plurality of observations, includes collecting on-line process data from a process control system within the process plant when the process is on-line, where the collected on-line process data comprises a plurality of observations of a plurality of process variables, performing a multivariate statistical analysis to represent the operation of the process based on a set of collected on-line process data comprising a measure of the operation of the process when the process is on-line, where the representation of the operation of the process is adapted to be executed to generate a result, storing the representation of the operation of the process and the set of collected on-line process data, and generating an output based on a parameter of the representation of the operation of the process, where the parameter of the representation of the operation of the process comprises one or more of a result generated by the representation of the operation of the process, a process variable used to generate the representation of the operation of the process and the set of collected on-line process data.

A multivariate product quality monitoring method oriented to a digital workshop includes the steps: building a quality information acquisition scheme suitable for the digital workshop; building a quality control and improvement model oriented to the digital workshop, and particularly performing multivariate process capacity analysis based on an improved principal component analysis method according to multiple quality characteristics of key processes of key products of a machining workshop; controlling and diagnosing multivariate product by the aid of principal component analysis technology to solve the problem that a multivariate statistical control chart difficultly diagnoses and positions process exception. The quality assurance capability of the key processes of the key products can be quantitatively evaluated by the workshop, and a source of a quality problem is timely positioned when the quality problem arises, so that further cost loss is avoided.

The method of the present invention provides a fast, robust, and automated multivariate statistical analysis of gas chromatography/mass spectroscopy (GC/MS) data sets. The method can involve systematic elimination of undesired, saturated peak masses to yield data that follow a linear, additive model. The cleaned data can then be subjected to a combination of PCA and orthogonal factor rotation followed by refinement with MCR-ALS to yield highly interpretable results.

Localization methods and methods for localizing a light source using multivariate statistical analysis. In particular, a multi-variate distribution, such as a multi-variate normal distribution, may be used to localize photons originating from a light source and, in particular, may be used to analyze photon position maps acquired by Photon Event Distribution Sampling (PEDS). The multi-variate distribution assigned to each of the photons in the photon position map may be summed to predict a most probable location for the light source.

The invention discloses an alfalfa drought tolerance identification method, comprising the following steps: after sterilization on seeds, carrying out a conventional sprouting experiment, after a seedling has 3-4 main leaves, performing thinning and final singling, reserving materials whose plant size and height are roughly consistent, and carrying out a water controlling experiment in a greenhouse; in the 8th-11th day, taking the plants out to count the number of roots of each plant of alfalfa, including the number of main roots, lateral roots, and fibrils on the lateral roots; after processing for 20 d, respectively measuring seedling height, fresh weight of root systems, dry weight of root systems, leaf chlorophyll contents, stomatal conductance, leaf free water content, leaf surface bound water content, proline content and root volume of each processing group; performing data reduction, calculating average values, and calculating a drought tolerance coefficients and a comprehensive drought tolerance value of each variety; and determining salt tolerance of the varieties according to the comprehensive drought tolerance value. The method performs multivariate statistical analysis on agronomical characters and indexes of an alfalfa seedling stage, so as to perform comprehensive judgement and evaluation on the drought tolerance of alfalfa.

In order to overcome the defects of an existing gingival margin curve extraction mode, the present invention discloses a gingival margin curve design method for a personalized implant tooth. The gingival margin curve design method for the personalized implant tooth comprises the following steps of: a step 1 of respectively reconstructing gingival margin profiles of a residual tooth of a patient suffering from tooth missing and a three-dimensional dental model of a sample, extracting feature regions of gingival margin curves according to an obtained maximum principal curvature value, extracting gingival margin feature lines of the residual tooth of the patient suffering from tooth missing and the sample by utilizing a granular computing and cellular automaton combining method, then respectively fitting the gingival margin feature curves of the residual tooth of the patient suffering from tooth missing and the dental model of the sample, and finally, constructing a single gingival margin curve of the residual tooth of the patient suffering from tooth missing and the sample; a step 2 of constructing a gingival margin biological multivariate statistical analysis model of the sample; and a step 3 of designing a personalized gingival margin curve of a missed tooth of the patient. The method disclosed by the present invention aims to improve accuracy and efficiency of designing the implant tooth gingival margin curve, improve the repaired gingival margin profile form of the implant tooth, and improve a repair success rate of the personalized implant tooth.

A method of identifying consumption of a utility by devices in a group of devices, wherein each device in the group of devices has a plurality of device-states and is arranged to consume the utility, the method comprising, for a given measurement period in a supply of the utility to the group of devices: obtaining a time-based vector of total levels of consumption of the utility by the group of devices during the given measurement period; for each of a plurality of group-states, wherein each group-state corresponds to each device in the group of devices being in a corresponding device-state, calculating a score representing a likelihood that the group of devices is in the group-state during the given measurement period, wherein said score is based on a likelihood of the obtained vector occurring based on a multivariate statistical model, corresponding to the group-state, of the total level of consumption of the utility by the group of devices during a measurement period in the supply of the utility to the group of devices when the group of devices is in the group-state; and identifying, based at least in part on the calculated scores, a most likely group-state for the group of devices.

The invention discloses a wind turbine generator set mechanical equipment state diagnosis method based on multivariate statistical analysis. The method is characterized by utilizing a sensor to collect state information generated by wind power equipment; performing feature extraction, signal analysis and state identification on the state information based on the multivariate statistical analysis; with low-dimension principal component feature expression technology expressing and classifying wind turbine generator set mechanical state, establishing an average correlation law to assess the ability, for describing the wind turbine generator set mechanical state, of each principal component; and selecting low-dimension principal component feature to express the comprehensiveness for the wind power equipment state features and the diagnosis of the wind power equipment state is realized. According to the wind turbine generator set mechanical equipment state diagnosis method based on the multivariate statistical analysis, early failure of the wind power equipment can be found and failure conditions can be accurately judged, utilization rate of the wind turbine generator set is improved, and cycle period and financial costs of maintenance and service are reduced as possible as one could; and the method can ensure safe, stable and reliable operation of the wind turbine generator set, and has great acceleration effect.

The invention relates to the field of civil engineering structure damage identification, in particular to a civil engineering structure damage pre-warning method in consideration of a temperature influence. An AR model in time series analysis and principal component analysis in multivariate statistical analysis are utilized, and a standard deviation control chart is combined for structure pre-warning research. Firstly, accelerated speed response data before and after structure damage are fit by the AR model, and a model coefficient is extracted; secondarily, the influence of temperature on the AR model coefficient is removed through principal component analysis; and finally, damage pre-warning is performed by using a standard deviation control chart. The pre-warning method is rigorous in theory, novel, reasonable in scheme and high in operability. The civil engineering structure damage pre-warning method has the advantages that accelerated speed response is directly used, a finite element model and modal parameters are not required, and the method belongs to a data driving method and is suitable for performing structure health monitoring in real time; and simultaneously, according to the method, under the condition of temperature change, the damage pre-warning is successfully performed through the anti-noise capacity.