12 results about "Multivariate calibration" patented technology

This application relates to focused linear model correction and linear model correction of multivariate calibration model maintenance. A device can obtain master beta coefficients of a master calibration model associated with a master instrument. The master beta coefficients can be at a grid of a target instrument. The device can perform a constrained optimization of an objective function according to a set of constraints in order to determine a pair of transfer beta coefficients. The constrained optimization can be performed based on a pair of initial transfer beta coefficients, the master beta coefficients, and spectra associated with a scout set. The device can determine a transfer beta coefficient based on the pair of transfer beta coefficients. The device can determine a final transfer beta coefficient based on a set of transfer beta coefficients including the transfer beta coefficient. The final transfer beta coefficient can be associated with generating a transfer calibration model corresponding to the master calibration model for use by the target instrument.

The invention belongs to the technical field of testing materials by using optical means, and particularly relates to a method and a system for detecting impurities of wormwood essential oil by using a spectrum, aiming at solving the technical problems of single input information and low prediction precision of the existing model. The detection method comprises the following steps: S1, on a key scale, identifying candidate feature points meeting a cross-scale modulus maximum connection condition; s2, determining feature points, and forming a feature point set; s3, calculating an integral area of a preset impurity characteristic peak in the differential spectrum; and S4, taking the integral area, the average curvature and the average density as input variables, and substituting the input variables into an impurity content prediction model pre-established through a multivariate correction method so as to obtain an impurity content index. The stability and the anti-interference capability of the wormwood essential oil impurity content prediction model can be improved.

This invention discloses a static-weight displacement-type six-dimensional force sensor calibration device and method, belonging to the field of multi-dimensional force sensor calibration. It aims to solve the current challenges of high-precision, high-efficiency, and low-cost calibration of six-dimensional force sensors, and is of great significance for improving the accuracy and economy of multi-dimensional force sensor development. Compared with existing calibration equipment, this invention only includes a static-weight standard and a two-degree-of-freedom pitch and roll mechanism, resulting in a short error propagation chain and high loading repeatability and calibration uncertainty. By projecting the gravity vector constant of the static-weight standard onto the sensor in various displacement coordinate systems, the loading calibration of the sensor is achieved. A multivariate calibration method for the gravity vector constant is established, and the explicit formula of the sensor is obtained through multivariate fitting. The structure and calibration method described in this invention are highly conducive to automation, with high calibration efficiency and low development cost, making it particularly suitable for batch calibration of commercial six-dimensional force sensors and possessing considerable application prospects.

The method for detecting the aromatic hydrocarbon concentration of transformer oil based on fluorescence spectrum correction belongs to the technical field of transformer fault diagnosis, solves the problem of how to correct the measured aromatic hydrocarbon concentration of transformer oil under the current condition, and improves the detection accuracy of the aromatic hydrocarbon concentration of transformer oil.The method excites the fluorescence spectrum of transformer oil through monochromatic excitation light of the optimal excitation wavelength, establishes the mapping relationship between the fluorescence spectrum of transformer oil and the aromatic hydrocarbon compound concentration by adopting the multivariate regression correction method, designs a multivariate correction filter to calculate the aromatic hydrocarbon concentration of transformer oil under the current condition, corrects the measured aromatic hydrocarbon concentration of transformer oil under the current condition by adopting the calibration coefficient, and thus the real aromatic hydrocarbon concentration of transformer oil is obtained, the influence of external factors under the current condition on the detection accuracy of the aromatic hydrocarbon concentration of transformer oil is eliminated, and the detection accuracy of the aromatic hydrocarbon concentration of transformer oil is improved.

The present application relates to the technical field of laser-induced breakdown spectroscopy detection, and specifically discloses a uranium content detection method based on uranium molecular spectrum formation mechanism, which comprises the following steps: step 1: using a laser-induced breakdown spectroscopy system to collect the spectrum of a uranium concentrate standard sample, and obtaining the laser-induced breakdown characteristic spectrum of the uranium concentrate standard sample with known uranium content; step 2: extracting the uranium molecular spectrum peak intensity, the oxygen atom characteristic spectrum line intensity and the uranium atom characteristic spectrum line intensity from the laser-induced breakdown characteristic spectrum of the uranium concentrate standard sample, and establishing a multivariate calibration model of the laser-induced breakdown spectroscopy signal intensity and the uranium content in the uranium concentrate standard sample; and step 3: detecting the uranium element content in the uranium concentrate sample. The method of the present application can significantly improve the detection accuracy of the laser-induced breakdown spectroscopy in detecting the uranium content in uranium concentrate products.

The invention discloses a hydroxylammonium nitrate-based propellant component content testing method and system, and the method comprises the steps: (1) obtaining a standard sample set, the standard sample set comprises a plurality of standard samples, each standard sample comprises a plurality of components of a hydroxylammonium nitrate-based propellant, and the content of each component is known; (2) collecting the near infrared spectrum of the standard sample set to form a standard spectrum data set; (3) on the basis of the standard spectrum data set and the known content of each component, constructing a quantitative analysis model for predicting the content of each component through a multivariate correction algorithm; and (4) collecting the near infrared spectrum of a to-be-detected sample, and simultaneously predicting the contents of various components in the to-be-detected sample by using the quantitative analysis model. The system comprises a near-infrared spectrometer, a temperature control device and a calculation processing unit. The testing method provided by the invention can realize one-step determination of the content of all components, and is simple and convenient to operate, rapid, non-destructive, accurate and reliable, so that an efficient quality assurance means is provided.

The application discloses a model training method, a prediction method and equipment for particle size prediction. The method comprises the following steps: obtaining a sample set, wherein the sample set comprises a plurality of spectra corresponding to loose particle accumulations with different center particle diameters; setting the center particle diameter of the loose particle accumulation corresponding to the spectrum as a sample label, and randomly dividing the sample label into a plurality of sets for model training according to the sample label; performing data preprocessing on the spectrum in the plurality of sets, including overlapping averaging processing on the spectrum, and obtaining a plurality of averaged average spectra; and performing model training based on the average spectrum and a preset machine learning model to obtain a particle size prediction model. The application can obtain a multivariate calibration model with high confidence through preprocessing of samples and machine learning. The particle size prediction model can improve the accuracy of particle size prediction of loose particle accumulations.

The invention discloses a method and system for testing equivalent water content in a nitro oxidant, and the method comprises the following steps: (1) collecting near infrared spectrums of a plurality of nitro oxidant standard samples to form a near infrared spectrum set of the standard samples; (2) constructing a quantitative detection model of equivalent water content through a multivariate correction algorithm based on the near infrared spectrum set and the equivalent water content value of the nitro oxidant standard sample; (3) collecting the near infrared spectrum of a to-be-detected sample, and determining the equivalent water content by using the quantitative detection model; the nitro oxidant is selected from at least one of nitrogen tetroxide, nitrogen tetroxide containing nitric oxide and modified products thereof. The system comprises a near-infrared spectrometer, a temperature control device and a calculation processing unit. The test method provided by the invention has the advantages of stronger adaptability, wider applicability and higher test precision, and can realize rapid and accurate quantification of equivalent water content of various nitro oxidants under various environmental conditions and different sample pretreatment modes.

The application provides a gibberellic acid derivative synthesis path monitoring method and system, specifically comprising the following steps: in the reaction process, adopting an immersion near-infrared spectrum probe, collecting full-waveband near-infrared spectrum of a reaction mixture, and constructing a time series spectrum matrix; according to the vibration characteristics of the functional groups of the gibberellic acid derivative molecules, dividing the full-waveband into at least two characteristic regions, and carrying out multi-region combined pretreatment combined with the noise characteristics of each region; carrying out two-stage characteristic wavelength screening: in the first stage, constructing a composite index related to the time series stability and response intensity of the spectrum, and preliminarily screening to obtain a wavelength candidate set; in the second stage, using a chemometrics optimization algorithm for secondary optimization to determine an optimal characteristic wavelength combination; through timed sampling and offline chemical analysis to determine the actual concentration of the target product, establishing a multivariate calibration model; analyzing the time series data of the concentration, and when the absolute values of the first derivative and the second derivative are lower than the corresponding threshold values in a continuous preset period, it is determined that the reaction has reached the end point.

This invention, a transformer fault type discrimination method based on fluorescence spectroscopy correction, belongs to the field of transformer fault diagnosis technology. It addresses the problem of designing a simple, computationally efficient method for rapidly identifying transformer fault types, particularly thermal aging and discharge breakdown faults. The invention establishes a mapping relationship between the fluorescence spectrum of transformer oil and the concentration of aromatic hydrocarbons using a multivariate regression correction method. A multivariate correction filter is designed based on transmittance to calculate the aromatic hydrocarbon concentration in the transformer oil, replacing the fluorescence spectrometer. This not only reduces equipment costs and size but also improves the cost-effectiveness of transformer oil fault detection. Furthermore, based on the time-periodic characteristics of thermal aging and discharge breakdown faults, the rate of change in aromatic hydrocarbon concentration is used to distinguish the transformer fault type, greatly simplifying the calculation process and reducing computational load. This is of significant importance for extending the service life of transformers.

The invention provides a biological signal real-time calibration system and a biological signal real-time calibration method for wearable equipment. The system comprises a flow control assembly used for collecting body fluid of a user of the wearable device in real time; a biological affinity sensor, a temperature sensor, a pH value sensor and an ionic strength sensor are at least integrated in the sensing cavity assembly in a mutually isolated manner; the driving acquisition assembly is used for periodically driving each sensor and synchronously acquiring the electric signals acquired by each sensor; and the micro-control assembly is used for acquiring the signals acquired by the sensors and transmitted by the driving acquisition assembly, firstly, aiming at the signals of the temperature sensor, the pH value sensor and the ion strength sensor, calculating real-time temperature, pH value and ion strength based on a preset calibration curve, and adopting a preset multivariable calibration model to calibrate the temperature, the pH value and the ion strength in real time. And correcting the original voltage signal representing the biological affinity by taking the calculated real-time temperature, the calculated pH value, the calculated ionic strength and the original voltage signal representing the biological affinity as input to obtain the calibrated concentration of the biomarker.