10 results about "Multivariate analysis" patented technology

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.

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.

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.

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.

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 present invention relates to an adaptive industrial control system and method using an Industrial Digital Twin with structured feedback of operational actions. The system comprises an industrial physical asset or sensorized infrastructure (110), sensors (120), control devices or actuators (130), industrial signal acquisition (140), normalization and contextualization (150), structured digital representation (160), evolutionary technical history (170), multivariate analysis (180), generation of an adapted operational configuration (190), and structured operational feedback (195). The system generates inferences, validates configurations against technical constraints, records the action actually executed, measures post-action variables, evaluates effectiveness, and updates the technical history with recommendation-action-result traceability.It also identifies multivariable precursor conditions (450) and allows for the technical optimization of production lines in relation to scheduled demand, capacity, availability, constraints, and failure history. The invention is applicable to machines, production lines, robotic cells, technical buildings, process plants, refineries, and industrial infrastructure suitable for sensorization.

This application relates to a method, system, device, and medium for predicting colorectal cancer recurrence based on pathological features. The method includes: screening colorectal cancer subjects; obtaining paraffin-embedded tumor tissue blocks, clinicopathological data, and long-term follow-up data containing recurrence records; obtaining HE-stained sections and CD3 / CD20 / CD21 / CD23 immunohistochemical sections; identifying the tertiary lymphoid structures of the tumor and pre-defined boundary regions; extracting their density and maturity characteristics and traditional pathological features; integrating them into a standardized feature set; correlating the feature set with follow-up data; and screening for independent influencing factors of recurrence using univariate / multivariate logistic regression; constructing and validating a nomogram prediction model; and using it for recurrence risk assessment in newly postoperative patients. This method, by integrating tertiary lymphoid structure features with traditional pathological features and constructing a nomogram prediction model based on multivariate analysis, improves the accuracy and reliability of colorectal cancer recurrence risk prediction, providing more precise prognostic assessment and personalized treatment decision support for clinicians.

The optical discrimination device includes: a sensor capable of capturing images of an object (particulate matter 20) in a wavelength range of 200 to 2500 nm; multiple illuminations that illuminate the object, each illumination having a different wavelength range; an illumination control unit capable of controlling the emission mode of the illuminations; and a discrimination unit that determines the quality of the object based on the image data acquired by the sensor, wherein the illumination control unit sets a light intensity balance of the multiple illuminations based on multivariate analysis according to the quality of the object to be discriminated.

This invention discloses an osteosarcoma biomarker, relating to the field of tumor prognostic prediction technology. The osteosarcoma biomarker includes the genes CGREF1, COL13A1, KIF25, and RPL35AP7. This invention also discloses a method for constructing an osteosarcoma prognostic prediction model, using the expression levels of the biomarkers CGREF1, COL13A1, KIF25, and RPL35AP7 and their corresponding multivariate analysis coefficients to construct the osteosarcoma prognostic prediction model. The osteosarcoma biomarker and its screening method provided by this invention, with osteoblasts as the core research object, avoid the problem of mutual masking of signals from different cells in overall transcriptome analysis. By mapping osteoblast-specific molecular characteristics to the patient's overall transcriptome data, it achieves clinical translational application.