52 results about "Multivariate analysis" patented technology

The invention provides an intelligent control method and system for a lithium carbonate preparation process, and the method comprises the steps: collecting the temperature field, ion concentration, feeding flow velocity and turbulence intensity data of a reaction kettle through a sensor array, and generating a state feature vector through multivariate analysis; inputting the feature vector into a depth prediction model to output a particle size distribution probability cloud picture and an impurity trend; inputting the probability cloud picture and the impurity trend into a multi-target optimizer to generate a temperature compensation value, a feeding correction coefficient and a stirring adjustment instruction; inputting the instruction into an anti-interference execution module and converting the instruction into an equipment driving signal; actual crystal data are obtained through an online particle size analyzer, and the prediction model is updated; and outputting a closed-loop optimization instruction to the execution terminal through the digital twin platform. The product consistency and the production stability are synchronously improved through full-process closed-loop intelligent control, and reliable technical support is provided for continuous production of battery-grade lithium carbonate.

The invention discloses a steam pipeline pressure control method, which comprises the following steps of: acquiring operation information of each key position of a pipe network in real time by constructing a data acquisition system and deploying an execution unit; a multivariable analysis method is utilized, topological structure characteristics of a steam pipeline are combined, a load change model of a steam consumption unit is constructed, the pressure change process caused by operation of different devices is recognized in advance, and a long-term pressure control strategy is made; meanwhile, an accumulative deviation curve is constructed based on a prediction and actual measurement residual evolution sequence, the potential fault risk of the system is identified in an auxiliary manner, and structured alarm output is formed, so that the safety guarantee capability of pipe network operation is enhanced. The steam pipeline pressure control method is suitable for the application scene that the overall steam use requirement is stable, but the local high-frequency disturbance behavior exists, the tiny pressure fluctuation trend can be accurately predicted under the high-disturbance working conditions such as frequent starting and stopping, and it is guaranteed that the steam pressure is stable.

A method and a system for ensuring product quality in a process for producing a product from a material in a manufacturing plant via a software performed by a computer comprising the following steps of Establishing a data pipeline including a data transformation originated from different source systems, like an ERP, LIMS, or Data Historian, to the computer, wherein the data pipeline provides updated datasets to maintain up-to-date parameter information; Preparing the datasets for a multivariate analysis calculation and performing the multivariate analysis calculation via the software, wherein all relevant parameters, like quality parameter, process parameter, sub-suppliers' quality parameters, are included in the calculation; Visualizing the result data of the multivariate analysis calculation as a 2D plot on a display connected to the computer, wherein the software identifies and marks new production batches and reference batches, time series plots for the relevant parameters and principal components, and selected top parameters which contribute to a principal components' deviation from the new production batch; and Performing quality processes and workflows to improve the production process and product quality by using the visualized result data.

The invention discloses a vancomycin antibacterial effect prediction method based on multivariate analysis, and relates to the technical field of medical data analysis and prediction.The vancomycin antibacterial effect prediction method comprises the steps that S1, prediction data of a target patient is collected, the prediction data comprises static baseline data acquired at the beginning of treatment and dynamic time sequence data acquired at a preset time interval in the treatment process; and S2, preprocessing the prediction data, wherein the preprocessing comprises performing data cleaning on missing values and abnormal values in the static baseline data and the dynamic time sequence data. According to the method, the static baseline data at the beginning of the treatment and the dynamic time sequence data in the treatment process are fused, and the time sequence evolution rule of the dynamic index is captured by using the long and short-term memory network, so that the treatment failure probability and the kidney injury risk probability are dynamically predicted. Meanwhile, supervised learning samples are generated through a sliding window, targeted data preprocessing and risk early warning and trend chart visualization are carried out, the prediction result can be updated in real time, and the prediction precision is improved.

The present invention is directed to a software-based technology system for managing and executing high volumes of work on complex projects across multiple organizations involving multiple users that results in superior outcomes at significantly reduced time and cost. Litigation is the initial application of this invention; each case is handled as a project. As applied to litigation, the system of the invention empowers a novel method of end-to-end case assessment, planning, execution and management by integrating tools, processes and procedures allowing for more informed decision-making and deliberate execution of all case-related actions as well as enhanced coordination and communication among all constituents, resulting in efficient achievement of targeted outcomes. The system provides intuitive, menu-driven functionality for inputting, processing and utilizing vast amounts of information, including company and case-specific information, attorney discretionary evaluation and input, document templates and monitoring and reporting requirements. A threat algorithm cross-correlates via multivariate analysis critical data points with customized company-specific information and business logic to generate a numeric threat score as an output. The system also predicts the expected settlement range with a high degree of precision for a specific case as the output of one or a combination of multiple artificial intelligence (AI) / machine learning algorithms. The system then generates a unique case strategy (which includes an expected outcome, including projected outcome, Case Plan and estimated attorneys' fees and costs). System logic learns from thousands of examples of similar cases and directs highly effective and efficient execution of a specific Case Plan providing each attorney and staff member assigned a role in a case with a prioritized task list that is updated in real time as other users complete tasks and litigation priorities change. For each user, the system integrates assigned tasks across multiple cases and multiple client portfolios into a single, prioritized task list. The execution features become increasingly valuable given the volume and complexity of projects across multiple organizations and users, allowing users to focus on their area of expertise and avoid the wasted time and distraction of trying to determine what they should be working on or recording notes to communicate what they have been doing since the system automatically performs these functions and directs the various users' next steps while monitoring their progress against the plan. Integration of administrative functions, including automated time tracking, billing and reporting without the need to rely on users to update this information, further increases quality and efficiency of legal services. Key objectives of the system of the present invention include eliminating gaps between business objectives and litigation strategy and optimizing communication and coordination of case-related tasks and priorities in a fully automated fashion, which are all achieved by leveraging data and technology to enable a fundamentally different approach that becomes smarter and more precise via machine learning and predictive modeling.

Disclosed herein is an article of smart clothing which automatically recognizes body configuration and motion. This smart clothing includes inertial motion units which are distal relative to selected joints and also sets of strain sensors which span those joints. The type of energy transmitted through a strain sensor can be electrical energy or light energy. Combined multivariate analysis of data from both inertial motion units and strain sensors provides more accurate recognition of body configuration and motion than data from either alone.

A phase analyzer includes a data acquisition unit that acquires a plurality of pieces of spectrum imaging data in which positions on a sample are associated with spectra which are based on signals from the sample; a first acquisition unit that acquires a first representative spectrum group for each piece of spectrum imaging data by performing multivariate analysis on each of the plurality of pieces of spectrum imaging data; a second acquisition unit that acquires a second representative spectrum group by performing multivariate analysis on the plurality of first representative spectrum groups acquired by the first acquisition unit; and a phase analysis unit that performs phase analysis on each of the plurality of pieces of spectrum imaging data by using the second representative spectrum group.

A control method includes: monitoring a statistic obtained by performing a multivariate analysis on a plurality of parameters; extracting, from the plurality of parameters, a predetermined number of higher-order parameters in terms of an influence degree on fluctuation of the statistic; generating a plurality of experimental patterns according to an experimental design method; acquiring a measurement result of a specific parameter indicating quality of a product when at least one device is controlled according to each of the plurality of experimental patterns; setting a new target value of the predetermined number of higher-order parameters in order to stabilize a value of the specific parameter within a management range based on the measurement result; and controlling the at least one device such that the predetermined number of higher-order parameters approaches the new target value.

The invention provides a seminal fluid storage strategy optimization system and method based on a hybrid neural network architecture, and the system comprises a data collection module which is used for obtaining temperature position vibration data, associating an offline sperm motility attenuation rate, calculating a spatial-temporal correlation degree, and constructing a coupling tensor; the time sequence prediction module is used for the LSTM to receive the temperature, the gradient, the correlation degree and the sensitive factors and predict the future temperature trend, and gsperm gating is used for filtering biological noise; the multivariable analysis module is used for carrying out Transform four-head attention analysis, focusing local activity coupling in a bag by V-head, and activating and punishing a supercooled region by cold-head; the strategy generation module is used for fusing the predicted temperature, the vitality weight, the supercooling penalty and the sensitive factor and softmax to output liquid nitrogen supply flow and the activity attenuation probability; and the closed-loop correction module is used for monthly counting the off-line survival rate, calculating a correction parameter omega, correcting the correlation degree gain and the basic flow, and realizing model locking without retraining.

The invention discloses a physical-data double-drive fused rapid prediction model for the solid fraction of a meshing point in double-roll thin-strip casting and application of the rapid prediction model. The model comprises the following steps: smelting target steel in a protective atmosphere, sampling 5-10 minutes before tapping, pretreating a sample to be detected, and then carrying out identification and component analysis; setting working parameters of the continuous casting machine, carrying out a throwing experiment, collecting real-time data of the working parameters of the continuous casting machine, and carrying out standardization processing; establishing a three-dimensional space model, and combining with a multivariate analysis module to obtain a multi-field coupling physical model; and performing optimization fitting on the multi-field coupling physical model to reduce errors of the two, fusing model data after optimization fitting and experimental data, extracting feature parameters, inputting the feature parameters into a long-short-term memory network algorithm to perform meshing point temperature prediction, converging a result to a precision threshold, and performing generalization processing to obtain the meshing point temperature prediction method. Through testing, the response rate of the model can reach 70 ms, and the technical requirement for online regulation and control of double-roller thin-strip continuous casting can be met.

Provided is an information processing apparatus that performs processing for presenting a relationship between variables in multivariate analysis. The information processing apparatus includes a detection unit that detects a combination of two variables having a characteristic relationship in multivariate analysis, and a presentation unit that presents information regarding the characteristic relationship between the two variables. The detection unit detects whether or not there is a characteristic relationship including at least one of a positive correlation, a negative correlation, or a non-linear relationship as the entire variables on the basis of the relationship between the explanatory variable and the explained variable for each of the two consecutive categories of the explanatory variable, and further quantifies the relationship between the explanatory variable and the explained variable as the entire variables.

The present invention relates to apparatus for non-invasive blood glucose monitoring, which includes: a signal transmitting module that transmits a terahertz wireless signal modulated into a PRBS pattern at a terahertz carrier frequency to a body part; a signal receiving module that demodulates the terahertz wireless signal reflected from the body part to measure performance parameters of the modulation pattern; and a sensing module that performs multivariate analysis using the performance parameters of the modulation pattern measured through the signal receiving module and quantifies features related to blood glucose through the multivariate analysis to measure blood glucose concentration.

The invention discloses a method for detecting serum bile acid abnormal data in PD cognitive function evaluation, and relates to the technical field of biological data detection. The method comprises the following steps: on the basis of a serum bile acid detection data set, determining first N bile acids with the highest correlation with data abnormality by combining a multivariate analysis method to form a key bile acid set; the method comprises the following steps: by taking normal serum bile acid detection data as training data, constructing corresponding N univariate prediction channels for N bile acids in a key bile acid set; performing data reconstruction to obtain an anomaly analysis data set; establishing a distribution relationship between the anomaly analysis data set and N single-variable prediction channels, inputting the N anomaly analysis data clusters into the N single-variable prediction channels for iterative prediction, and obtaining N single-variable prediction results; and calculating a variable deviation coefficient, and if the variable deviation coefficient exceeds a preset deviation threshold, marking the data as abnormal data. According to the method, the accuracy of marking the abnormal data of the serum bile acid is effectively improved.

Provide a system. 【Solution means】 A device for receiving voice information and video information, A device for analyzing the voice information with natural language analysis technology and extracting speech features, A device for extracting non-verbal information from the video information by multivariate analysis, A device for generating an individualized training plan based on the analyzed speech features and non-verbal information, A device for providing the generated training plan to the user, A device for collecting training activity information executed by the user, A device for evaluating the collected training activity information and creating a progress report, A system including a device for sharing the progress report with medical staff.

[Problem] To visually comprehend an evaluation factor which is preferable to be improved preferentially when compared with same or similar business styles in order to improve customer repeat visit intent, repeat purchase intent, contract intent, continuation intent, or recommendation intent. [Solution] An information providing device according to the present invention includes a research data acquisition means for acquiring evaluation data including an evaluation value of an evaluation item for a relevant product or service, a multivariate analysis means for calculating a contribution degree of an explanatory variable to an objective variable by performing multivariate analysis with repeat visit intent or the like as the objective variable and the evaluation value as the explanatory variable on the basis of the evaluation data, a reference value setting means for setting a reference value for the explanatory variable, a priority analysis means for analyzing an engagement priority on the basis of the reference value and the contribution degree, and an analysis result output means for displaying an analysis result by the priority analysis means on an information terminal.

This invention belongs to the field of traditional Chinese medicine (TCM) extraction technology, and particularly relates to a method and system for cross-scale dynamic control of TCM extraction processes. The method involves collecting process parameters and corresponding quality attributes of several batches of TCM preparations under small-scale, pilot-scale, and large-scale production conditions during extraction. A multivariate analysis model, a dynamic control standard identification model, a cross-scale nonlinear prediction model, and a multi-scale coupled dynamic simulation model are then trained sequentially. Real-time process parameters are input into the trained cross-scale nonlinear prediction model and multi-scale coupled dynamic simulation model to obtain predicted real-time key quality attributes. This invention achieves real-time prediction and dynamic control of key quality attributes through the coupling of the multivariate analysis model, the dynamic control standard identification model, the cross-scale nonlinear prediction model, and the multi-scale coupled dynamic simulation model, enabling timely adjustment of process parameters to stabilize production and ensure the stability of TCM extraction processes at large-scale production levels.

The application relates to the technical field of intelligent optimization, and discloses a 3D printing powder proportioning optimization method and system based on artificial intelligence. The method comprises the following steps: collecting historical data corresponding to powder materials based on the printing process of a 3D printing product, performing multivariate analysis on the historical data, and generating a plurality of powder compositions; a material simulation model simulates the properties of any powder composition, outputs material property data, and extracts a powder proportioning scheme meeting printing requirements; a product prediction model predicts the product of any powder proportioning scheme, outputs a product prediction result, matches the product prediction result with ideal product data, and selects the best powder proportioning scheme and the corresponding best printing process; and the performance of an actual printing product is tested, and if the actual printing product does not meet the requirements, the step is repeatedly executed, and a new best powder proportioning scheme and a new best printing process are selected again. The application improves the precision of 3D printing powder proportioning optimization.

This invention discloses a method and system for identifying and assessing soil heavy metal pollution sources based on machine learning. The method constructs a feature dataset by collecting data on heavy metal element content and spatial environmental attributes of soil in a target area; it decomposes the input matrix using multivariate analysis and k-means clustering to obtain a source spectrum matrix and a source contribution matrix, thus identifying pollution sources and analyzing their contributions; further, it establishes a background contribution model by combining conservative elements and time series information to extract the background contribution value of each pollution source within the target time period; it calculates single-factor and comprehensive risk indices based on pollution source reconstruction and background correction, and generates soil heavy metal pollution risk level information by combining dynamic risk grading thresholds; finally, it generates a soil heavy metal pollution risk distribution map of the target area through spatial interpolation and zoning processing, simultaneously achieving quantitative analysis of pollution sources, background value correction, risk index calculation, and spatial visualization.

The invention provides a method for rapidly screening aroma-producing bacillus to improve the quality of cigar tobacco leaves, and belongs to the technical field of agricultural microorganisms. On the basis of bacillus 16S rDNA (ribosomal deoxyribonucleic acid) sequence analysis and fermentation liquor smell evaluation, volatile aroma components of strain fermentation liquor and fermented cigar tobacco powder are analyzed by using HS-SPME-GC-MS (headspace solid-phase microextraction-gas chromatography-mass spectrometry) and an electronic nose, and excellent aroma-producing strains are screened by combining a multivariate analysis method. And the fermentation effect on the cigar tobacco leaves is verified by simulating fermentation. Results show that seven kinds of 50 bacillus strains are identified, and the smell difference of fermentation liquor smell evaluation is large. 177 volatile components are identified in strain fermentation liquor, 7 strains have relatively high aroma producing characteristics, and 2, 3-butanedione, 2-nonyl alcohol and isoamyl alcohol are common aroma components of the strains. Two strains with the highest volatile components and large difference in the fermented tobacco powder are screened out through PCA and clustering analysis, 1-F-2 can generate high-content alcohol, pyrazine, ketone and other components, and DMMJ-4 can generate high-content alcohol, pyridine, ketone and other components. The two strains are used for fermentation, so that the content of volatile aroma components of the cigar tobacco leaves is obviously improved, and the flavor characteristics and the quality characteristics are also improved to different extents. Therefore, the invention can provide reference for screening and application of cigar tobacco leaf microbial fermentation strains.

The invention discloses a salvia miltiorrhiza quality evaluation method based on HPLC-DAD and multivariate analysis, and relates to the technical field of medicine quality evaluation, and the salvia miltiorrhiza quality evaluation method comprises the following steps: extracting effective components from salvia miltiorrhiza medicinal material samples collected from different regions by adopting an extraction solvent, the effective components at least comprise salvianic acid A sodium, rosmarinic acid, salvianolic acid B, cryptotanshinone, tanshinone I and tanshinone IIA, HPLC-DAD is adopted for chromatographic analysis, and the extracted effective components are subjected to multivariate statistical analysis. According to the system, the quality difference of salvia miltiorrhiza from different producing areas is evaluated, and scientific basis and data support are provided for standardized planting, producing area screening and clinical reasonable application of salvia miltiorrhiza medicinal materials.

The invention discloses a high-horsepower tractor gearbox state on-line monitoring method, and relates to the technical field of state monitoring, first, gearbox monitoring data are collected, and the gearbox monitoring data comprise vibration signal data, thermal data, mechanical data, process parameters and oil data; performing multivariate analysis processing on the gearbox monitoring data to obtain first damage data; constructing an improved TimeMixer model, and performing sequence decomposition and mixed path correction on the first damage data to obtain the predicted service life of the gearbox; according to the predicted service life of the gearbox, a tractor gearbox alarm device is arranged, an early warning signal result is generated, graded response from normal monitoring to emergency shutdown is achieved, and a reliable solution is provided for intelligent operation and maintenance of the equipment manufacturing industry.

Provided is an information providing device, a method, and a program capable of visually grasping a contribution degree of an evaluation factor with respect to a contract intention or a recommendation intention for others of a customer. An information providing device includes: a survey data acquisition means configured to acquire evaluation data including evaluation values for evaluation items for a target product or service, the evaluation data including evaluation values for a plurality of evaluation items related to a contract intention or a recommendation intention for others; a multivariate analysis means configured to calculate contribution degrees of a plurality of explanatory variables with respect to an objective variable by conducting a multivariate analysis based on the evaluation data using an evaluation value for the contract intention or the recommendation intention for others as the objective variable, and evaluation values for a plurality of evaluation items in the evaluation data as the explanatory variables; and an analysis result output means configured to display an analysis result from the multivariate analysis means on an information terminal capable of communicating via a network.

The invention provides a nuclear power equipment life prediction method, device and equipment, and relates to the technical field of nuclear power. In some embodiments of the invention, initial operation parameters of the nuclear power equipment are obtained; according to the initial operation parameters, determining parameter change conditions of the nuclear power equipment in the operation process; performing equipment degradation degree index conversion according to the initial operation parameters and the parameter change condition to obtain a degradation index sequence, and establishing an environmental factor model; in combination with the degradation index sequence and the environmental factor model, determining an influence coefficient of each parameter on the equipment degradation rate through multivariate analysis, and adjusting the equipment degradation model according to the influence coefficient; and based on the environmental factor model, the equipment degradation model and the potential failure mode of the nuclear power equipment under the expected environmental change, estimating a time point when the nuclear power equipment reaches a preset failure threshold, and calculating the remaining service life of the nuclear power equipment.

This invention discloses a desiliconization terminal temperature control system based on multi-dimensional analysis, belonging to the field of temperature control technology. It addresses the problems of low dechlorination efficiency, iron oxide powder sintering, and energy waste caused by fixed heating temperatures in existing technologies. Specifically, it achieves full-process optimization through the synergistic effect of a dynamic temperature adjustment unit, a temperature-time adaptive control unit, and a heater status early warning unit, resulting in improved dechlorination efficiency and quality. Staged dynamic temperature adjustment solves the heating adaptation problem at different moisture contents, while time-temperature adaptive control eliminates uneven heating caused by residence time differences, leading to more thorough chloride ion removal and more stable product quality. Significant energy and cost savings are achieved by avoiding energy waste caused by fixed temperatures and reducing iron oxide powder sintering losses and equipment failure maintenance costs.

This application provides a method for characterizing and / or determining the viral removal capacity of hydrophobic interaction chromatography (HIC), including an experimental design for multivariate analysis of viral removal by HIC. The method provides an understanding of the mechanism of viral removal using HIC by executing a D-Optimal experimental design that includes evaluations of multiple factors, such as pH, buffer concentration, column loading concentration, column flow rate, and hydrophobic strength of the HIC column.

To prevent fine mass information from being greatly damaged when creating a data set for multivariable analysis reduced or compressed based on a plurality of mass spectra.SOLUTION: A KMD analyzer 28 generates n KMD analysis results based on n mass spectra. An integrator 34 generates n integrated value distributions based on the n KMD analysis results. A principal component analyzer 42 applies principal component analysis to the n integrated value distributions (data sets). A component-reflected KMD plot is generated based on a loading map (component distribution) generated by the principal component analysis.SELECTED DRAWING: Figure 1

The application provides a metallurgical engineering cost analysis method and device based on material coding, relates to the field of metallurgical engineering, and comprises the following steps: generating a material list by using metallurgical engineering data and the result of searching a material coding library based on material parameters; generating a cost list according to the material list and material cost data searched in a cost database; and performing multivariate analysis on the cost list based on historical costs of metallurgical engineering to obtain a cost analysis result. The application can construct an online workflow system of the whole process by deep integration of the material coding library, reduce the delay of flow transfer, and significantly improve the cost analysis efficiency of metallurgical engineering.

A method for identifying fruit juice samples from different sources using NMR includes the following steps: Step 1, acquiring the 1H-NMR spectrum of the fruit juice sample to be analyzed; Step 2, performing 1H-NMR integration on the acquired 1H-NMR spectrum of the fruit juice sample to identify and remove residual water peaks in the 1H-NMR spectrum, obtaining a water-removed 1H-NMR spectrum; Step 3, identifying each sugar component in the water-removed 1H-NMR spectrum and determining the chemical shift range of each sugar component, while recording each shift range, obtaining a sugar-containing 1H-NMR spectrum; Step 4, first removing the sugar peak range in the sugar-containing 1H-NMR spectrum, then performing data normalization and calibration, followed by multivariate analysis to obtain the true component differences between fruit juice samples, thereby determining the authenticity of the fruit juice. This invention improves the accuracy and reliability of detection and analysis data by eliminating the influence of residual signals and sugar components in the 1H-NMR spectrum of fruit juice samples.

The invention relates to the technical field of intracranial electroencephalogram signal processing, in particular to an intracranial electroencephalogram multivariable decoding method and system based on sparse logistic regression, and the method comprises the steps: obtaining an intracranial electroencephalogram original signal collected by an implantable electrode, and sequentially carrying out the filtering, power frequency interference removal, bad contact removal and re-reference preprocessing of the intracranial electroencephalogram original signal; calculating energy measurement of a whole brain electrode through time-frequency analysis, and forming three-dimensional feature tensors of a space domain M, a time domain T and a frequency domain F; expanding the three-dimensional feature tensor into a feature matrix based on a multivariate analysis method, and selecting an acceleration path of matrix operation according to a ratio of a dimension D to a sample number N; and performing feature subset selection based on sparse logistic regression, inputting the selected feature subset into a classifier, and generating a neural decoding instruction, thereby solving the problems of large data volume, calculation delay, dimension disaster and low scene generalization in the prior art.

A method for classifying at least two or more resins, the method comprising: a purification step of removing volatile components by subjecting resins to be classified to a heat treatment at a temperature lower than each decomposition temperature; a decomposition step of subjecting the resins to be classified to a pyrolysis treatment at a temperature higher than each decomposition temperature to obtain pyrolysates of the resins; an analysis step of separating and analyzing the pyrolysates using a two-dimensional gas chromatograph mass spectrometer; a table creation step of creating a table by performing assignment of peaks appearing in each two-dimensional chromatogram obtained after the analysis step and alignment between the two-dimensional chromatograms; and a classification step of classifying resins by performing multivariate analysis based on the created table.