CN105675455A - Method and device for reducing random system noise in particle size analyzer - Google Patents
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Abstract
The invention discloses a method for reducing random system noise in a particle size analyzer. The method includes the steps of: S11, acquiring the light source power spectral density distribution of introduced system noise; S22, based on the acquired light source power spectral density distribution, setting interpolation point frequency and the amplitude weight corresponding to each interpolation point frequency; S13, setting simulation parameters, calculating a corresponding MIE matrix of each interpolation point frequency respectively, and superposing all the obtained MIE matrixes to form a total MIE matrix for fitting with an actually measured optical signal array. The invention also discloses a device for reducing random system noise in a particle size analyzer. System noise is taken into account in an arithmetic processing and numerical calculation process of a simulation model, the spatial resolution and sensitivity of scattered light from a powder sample can be further enhanced, and the system noise can be reduced, thereby expanding the measurement range and precision of a laser particle analyzer measurement system so as to further meet the demand of customers for experiment or production process quality control.
Description
Technical Field
The invention relates to a method and a device for reducing random system noise in a particle analyzer, belonging to the field of particle analyzers based on the optical scattering measurement principle.
Background
In the face of the potential demands of customers and markets for continuously improving powder measurement, the range and the range of a measurement sample are continuously expanded while the measurement precision and the accuracy are ensured while paying attention to the cost performance and the operability of measurement of a new generation of laser particle analyzer product. Therefore, higher requirements are put forward for manufacturers of laser particle analyzer systems, and on one hand, structural improvement is carried out on hardware to expand measuring range and measuring precision; on the other hand, the particle size distribution simulation model and the numerical processing algorithm are corrected continuously on the basis of the electromagnetic algorithm, so that when the particle size distribution simulation model and the numerical processing algorithm are more flexible and are oriented to more complex measuring objects, the measurement time is not increased remarkably, the system error is eliminated as much as possible, and the requirements of improving the measurement precision and accuracy are met.
Aiming at a laser particle size analyzer measuring system, in order to further expand the upper limit of large particle testing and the lower limit of micro-nano particle testing, system structure improvements such as a blue light source and an oblique incidence mode have been tried to be introduced in the industry, so that the small particle measuring sensitivity and the signal-to-noise ratio (SNR) are systematically improved, the lower limit of measurement is expanded, and the measuring precision is improved. Meanwhile, in order to expand the measurement upper limit of the measuring range: (A) the measurement sensitivity and the spatial signal detection resolution near the central detector are also considered to be improved, and the interference of the scattered light signal of the Nth ring on the adjacent detector of the Nth +/-1 ring caused by the stray light in the space is avoided; (B) in terms of a light source, time coherence is introduced into the particle lifetime (wave train continuous emission time) distribution of a metastable state energy level, meanwhile, spatial coherence is introduced into factors related to the resonant cavity structure and mode locking design (shown as Gaussian beams), and mechanisms such as collision broadening and Doppler broadening inevitably cause broadening of actual incident wavelength (frequency line) together, so that a dispersion distribution spectrum near He-Ne633nm is calculated in an MIE matrix to realize accurate matching and accurate inversion of measured optical energy distribution.
Therefore, after (1) a system incident light signal is emitted from a laser, the system randomness of the laser (including the decoherence factors introduced by various uncertain factors such as Q-switching in a resonant cavity, a mode competition process, window noise, power supply voltage fluctuation and the like) is realized; (2) in the optical path, the optical signal passes through a series of optical path elements including optical surface interface scattering and space confinement effects such as a spatial filter, a Fourier lens and a scattering window, the introduced stray light noise and wave aberration are collectively classified as random system noise, and the factors are combined together to show random statistical characteristics. Random phase modulation operation is introduced to an ideal monochromatic incident light signal in the random process in a form, the signal is corrected after time-frequency and space-frequency conversion, and random system noise is added into a measurement system, so that system errors are corrected.
Disclosure of Invention
The present invention is directed to overcoming the drawbacks of the prior art and providing a method and apparatus for reducing random system noise in a particle size analyzer.
According to one aspect of the present invention, there is provided a method of reducing random system noise in a particle size analyzer, the method comprising the steps of:
s11, acquiring the power spectral density distribution of the light source introducing random system noise;
s12, setting interpolation point frequencies and amplitude weights corresponding to the interpolation point frequencies based on the acquired power spectral density distribution of the light source;
and S13, setting simulation parameters, respectively calculating corresponding MIE matrixes for each interpolation point frequency, and superposing each obtained MIE matrix to form a total MIE matrix for fitting with the actually measured optical signal array.
Preferably, in step S11, the system noise includes phase noise caused by the system randomness of the light source itself and stray light noise caused by the series of optical path elements used for measuring the optical signal, and the acquiring step includes acquiring the power spectral density distribution of the light source by using a power spectral density calculation method and/or an experimental test method using a high-precision spectrometer, the experimental test method using a high-precision spectrometer includes a measurement method, a method, an instrument and a device for acquiring the relationship between the wavelength, the frequency and the intensity distribution of incident light based on the principle of grating and prism spectroscopy.
Preferably, the power spectral density calculation method comprises the steps of:
s31, establishing mathematical function representation of the light source, wherein the mathematical function comprises a trigonometric function, an exponential function, a composite function of the trigonometric function and the exponential function, and a composite analytic function consisting of the composite function and a sigmoid function, and the form of the trigonometric function comprises
S32, transforming by including elementary function, threeA method of angular function sum and difference transformation, which separates random noise from the mathematical function of the light source and still represents the random noise by an analytic function, and the square of the analytic function can be integrated, the trigonometric function sum and difference transformation includesWherein the first term is a principle ideal light intensity signal without a random phase modulation process, the second term is the separated random system noise, the elementary function is adopted for representation, and the square can be integrated,
s33, substituting the random system noise function into the autocorrelation function and expressing the autocorrelation function as the convolution sum of the random system noise function:
wherein,
s34, according to Wiener-Khinchi theorem, Fourier transform is carried out on the autocorrelation function after convolution in the step S43, and the power spectral density expression is obtained as follows:
preferably, the light sources include laser light sources, LED light sources, X-ray, ion beam and electron beam source light sources, the amplitude weights being set to normalized amplitude weights.
Preferably, in step S13, the setting of the simulation parameters includes setting optical parameters of the sample to be measured, parameters of the MIE matrix representing characteristic particle size section, parameters of the environmental medium, the number of the detector arrays, area and spatial position parameters, incident light frequency, focal length, polarization state, wavefront aberration correction and description parameters, and amplitude weights corresponding to each light frequency.
Preferably, the optical parameters of the sample to be detected comprise refractive index, absorptivity, reflectivity or extinction coefficient; the environmental medium comprises air, water, ethanol or acetone; the environmental medium parameter comprises refractive index, absorptivity or density; the polarization states include a non-polarization state, a linear polarization state, a circular polarization state, an elliptical polarization state, and a partial polarization state; the wavefront aberrations include the aerial image distortion of the wavefront function, characterized by a 37-degree Zernike function, and the defocus systematic error of the wavefront and plane detectors.
Preferably, in step S13, the fitting includes the steps of:
s131, collecting and storing the actual light signals in a form of vectors and matrixes;
s132, substituting the total MIE matrix and the actually measured optical signal array obtained by simulation calculation into a regression model and a fitting algorithm to perform inverse calculation of characteristic particle size distribution;
and S133, carrying out inversion to obtain the distribution information of the representative characteristic particle size section of the tested sample.
Preferably, step S132 further includes setting an interpolation method, a regression model, and an iteration condition; the interpolation method comprises linear interpolation and weighting; the regression model includes using a gauss-Newton method, a gradient descent method, a least squares method, a Levenberg-Marguardt damped descent method; the fitting method comprises a bilinear precision method, Lagrangian polynomial fitting, cubic spline, Extenk, weight averaging, multiple quadratic, bicubic, Telen, wavelet, Bessel, Everly, finite difference, Gaussian, hermit, Newton's difference removal, close precision or Tiler precision algorithm.
According to another aspect of the present invention, there is provided an apparatus for reducing random system noise in a particle size analyzer, the apparatus comprising:
the acquisition device is used for acquiring the power spectral density distribution of the light source introducing the system noise;
setting means for setting interpolation point frequencies and amplitude weights corresponding to each interpolation point frequency based on the acquired power spectral density distribution of the light source;
and the simulation device is used for setting simulation parameters, respectively calculating corresponding Mie scattering theory optical energy matrixes (MIE matrixes) for each interpolation point frequency, and superposing each obtained MIE matrix to form a total MIE matrix for fitting with the actually measured optical signal array.
Preferably, the system noise includes phase noise caused by system randomness of the light source itself and stray light noise caused by a series of optical path elements for measuring the optical signal, the acquiring device includes a first acquiring device using a power spectral density distribution calculating method or a second acquiring device including an experimental testing means using a high-precision spectrometer, the experimental testing means using the high-precision spectrometer includes a measuring means, a method, an apparatus and a device for acquiring a relation of wavelength, frequency and intensity distribution of incident light based on a grating and prism spectroscopic measurement principle.
Preferably, the first acquiring means includes:
the modeling device is used for building mathematical function representation of the light source, the mathematical function comprises a trigonometric function, an exponential function, a composite function of the trigonometric function and the exponential function, and a composite analytic function consisting of the composite function and a sigmoid function, wherein the form of the trigonometric function comprises
Separating sub-means for separating random noise from a mathematical function of the light source by a method including an elementary function transform, a trigonometric function and a difference transform, and still represented by an analytical function whose square can be multiplied, the trigonometric function and the difference transform including:
wherein the first term is a principle ideal light intensity signal without a random phase modulation process, the second term is the separated random system noise, the elementary function is adopted for representation, and the square can be integrated,
convolution sub-means for substituting said random system noise function into an autocorrelation function and representing it as the convolution sum of itself:
wherein,
the Fourier transform sub-device is used for carrying out Fourier transform on the autocorrelation function obtained in the convolution device according to the Wiener-Khinchi theorem, and obtaining a power spectral density expression as follows:
preferably, the light sources include laser light sources, LED light sources, X-ray, ion beam and electron beam source light sources, the amplitude weights being set to normalized amplitude weights.
Preferably, in the simulation apparatus, the setting of the simulation parameters includes setting of optical parameters of the sample to be measured, parameters of the MIE matrix representing characteristic particle size section, parameters of the environmental medium, the number of the detector arrays, area and spatial position parameters, incident light frequency, focal length, polarization state, wavefront aberration correction and description parameters, and amplitude weights corresponding to each light frequency.
Preferably, the optical parameters of the sample to be detected comprise refractive index, absorptivity, reflectivity or extinction coefficient; the environmental medium comprises air, water, ethanol or acetone; the environmental medium parameter comprises refractive index, absorptivity or density; the polarization states include a non-polarization state, a linear polarization state, a circular polarization state, an elliptical polarization state, and a partial polarization state; the wavefront aberrations include the aerial image distortion of the wavefront function, characterized by a 37-degree Zernike function, and the defocus systematic error of the wavefront and plane detectors.
Preferably, the fitting means comprises:
the collecting sub-device is used for collecting the actually measured optical signals and storing the actually measured optical signals in a form of vectors and matrixes;
the fitting sub-device is used for substituting the total MIE matrix and the actually measured optical signal array obtained by simulation calculation into a regression model and a fitting algorithm to carry out inversion calculation of characteristic particle size distribution;
and the inversion sub-device is used for inverting to obtain the distribution information of the representative characteristic particle size section of the tested sample.
Preferably, the fitting sub-device further includes setting an interpolation method, a regression model, and an iteration condition; the interpolation method comprises linear interpolation and weighting; the regression model comprises a Gause-Newton method, a gradient descent method, a least square method and a Levenberg-Marguardt damping descent method; the fitting method comprises a bilinear precision method, Lagrangian polynomial fitting, cubic spline, Extenk, weight averaging, multiple quadratic, bicubic, Telen, wavelet, Bessel, Everly, finite difference, Gaussian, hermit, Newton's difference removal, close precision or Tiler precision algorithm.
The invention has the beneficial effects that:
in the process of taking system noise into consideration and entering the algorithm processing and numerical calculation of a simulation model, the spatial resolution and sensitivity of scattered light from a powder sample can be further improved on the basis of the resolving power of an original main photoelectric detector and spatially distributed auxiliary detectors, the system noise is reduced, and accordingly the measurement range and precision of a laser particle analyzer measurement system are expanded, so that the requirement of a customer on quality control of an experiment or production process is further met.
Drawings
The invention is further illustrated by the following figures and examples:
FIG. 1 is a flow chart of a method of reducing random system noise in a particle size analyzer according to the present invention;
FIG. 2 is a resulting normalized power spectral density profile according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a laser particle size analyzer component system designed according to the present invention;
FIG. 4 is a graph showing the normalized optical energy signal distribution of the latex standard spherical particles with a diameter of 1 μm actually measured after the random system noise is added, and the graph showing the normalized optical energy signal distribution of the latex standard spherical particles with a diameter of 1 μm actually measured without the random system noise being added, and the graph showing the normalized optical energy signal distribution obtained by the theoretical calculation;
fig. 5 is a block diagram of an apparatus for reducing random system noise in a particle size analyzer in accordance with the present invention.
Detailed Description
To more clearly describe the present invention, taking He-Ne red laser particle size analyzer with a center wavelength of 633nm based on MIE scattering principle as an example, referring to fig. 1, there is provided a method for reducing system noise in the laser particle size analyzer, comprising the steps of:
s11, acquiring the power spectral density distribution of the light source introducing the system noise;
the system noise includes, among others, phase noise caused by the systematic randomness of the light source itself and stray light noise caused by the series of optical path elements used to measure the optical signal.
According to a preferred embodiment of the present invention, a power spectral density algorithm may be used to obtain a power spectral density distribution of a light source that introduces system noise, specifically, a power spectral density distribution of a light source that includes system noise is obtained first, the embodiment of the present invention is based on a laser particle analyzer measurement system, and an adopted system incident light signal is emitted by a laser, generally speaking, a fixed frequency monochromatic light is emitted by a He-Ne laser, and the signal is taken as an incident light signal, and ideally, the signal can be expressed in a simple sine wave form:
I＝I0sin(ω0t+θ)
wherein, I0To amplitude, θ is the initial phase.
In the actual experimental test process, as the incident light signals of the system pass through the laser (with time and space coherence) and series light path elements (including the action mechanisms of stray light introduced by surface interface defects of optical elements such as beam expander, pinhole, Fourier lens and the like, secondary imaging introduced by mutual reflection, stray light introduced by ghost and diaphragm blade surface space limitation, and the like), random modulation phase noise and stray light are introduced to randomly modulate the phase noise and the stray light so as to achieve the purpose of improving the system performanceAdd modulation to the incident light, resulting in measurement errors.
Formally, the phase noise and stray light noise are combined into random noise, and the phase term is time-varyingAnd add modulation to the incident light:
wherein α is the amplitude modulation factor,for phase noise, the latter higher order termsPeriodic spurious noise, cnCan be expressed as cos omegamnt。
Neglecting amplitude modulation factor α, neglecting stray noise, making initial phase theta-0 and random phaseAre combined intoIs available in a form
Expanded using the trigonometric sum and difference formula becauseIs a small amount tending to 0, and hence can be obtained Therefore, the method comprises the following steps:
wherein, ω is0＝2π·c/λ＝2.976×1015/s
In the above formula, the first term is the principle of no random phase modulation process to input the light intensity signal, and the second term is the separately separated random modulation phase noiseWherein,as time-dependent coefficient terms for modulating the intensity of the optical signal, except for the effects of stray light introduced by series of optical path elementsThe mechanism is also related to the stability, the time coherence and the spatial coherence of the selected laser source, and the beam waist width and the divergence angle in the specification of the laser source can be referred to. In this example, one may chooseThe pseudo-random sequence of intervals is a discrete term that substantially participates in the convolution in the following equation. Let the second term of the above formula beω0＝2π·c/λ＝2.976×1015S, its autocorrelation function (ACF) is expressed as the sum of its convolution:
ω0＝2π·c/λ＝2.976×1015/s
wherein,
according to Wiener-Khinchin theorem, ACF and corresponding Power Spectral Density (PSD) are Fourier transform pairs, and the PSD can be expressed as:
selectingAnd random time-varying discrete terms of the interval can be calculated to obtain phase noise which has a shape similar to an exponential decay curve with the incident frequency as the center and continuous double-sideband deviation from the center frequency.
Fig. 2 shows the normalized power spectral density profile derived according to the present example, with the center frequency wavelength at 633nm, which exhibits a double sideband distribution.
By using a similar derivation process, an expression of the spurious noise can be obtained, and the power spectrum-frequency distribution of the spurious noise is farther away from the central wavelength and is in discrete distribution and lower in weight compared with the double-sideband distribution of the random phase modulation noise. Error mechanisms such as random phase modulation noise and stray noise cause dispersion of the center frequency (or laser monochromaticity) of incident light, i.e. dispersion of the power-frequency spectrum, and cause 'deviation' of the intensity of scattered light signals along with the distribution of scattering angles, thereby introducing systematic errors in the acquisition of scattered light intensity by spatial detectors.
Random modulation phase noise and stray lightIn addition to the modulation of the incident light, the form of (a) may also be in the form of a trigonometric function, such as sin (n ω)0t) or cos (n ω)0t) where ω is0The modulation is added to the incident light in the form of fundamental frequency, n is an odd number, or in the form of an exponential function, a composite function of a trigonometric function and the exponential function, and a composite analytical function formed by the composite function and a sigmoid function.
According to another preferred embodiment of the invention, a high-precision spectrometer can be used for acquiring the power spectral density distribution of a light source introducing system noise, and particularly, a spectral measuring instrument for measuring the relation of wavelength/frequency-intensity of incident light by using a grating/prism light splitting principle is used for directly acquiring the phase modulation noise distribution of double sidebands through experimental tests.
S12, setting interpolation point frequencies and amplitude weights corresponding to the interpolation point frequencies based on the acquired power spectral density distribution of the light source;
as can be derived in step S11, the original single-frequency signal is spread due to the random phase modulation mechanism of the system. In the embodiment, a difference algorithm is adopted to approximately obtain the distribution of the power density spectrumThe curve, in particular, the 2N +1 (N0, 1, 2.) point frequency (ω) may be interpolated according to the power spectral density curve function shown derived above0，ω0+ωm1，ω0-ωm1，ω0+ωm2，ω0-ωm2...) and intensity data, and normalizing the intensity to a weight (w)0，w1，w2，w3，w4....)；
Regarding the specific spread range of the double sideband, i.e. the effective weighted point selection range (such as 3 points/5 points/. -), depending on the actual device conditions, this is mainly related to (a) the light source quality; (B) before laser reaches a detector, the laser passes through optical elements such as a beam expander, a spatial filter, a Fourier lens and a measuring optical window, so that the time and spatial coherence decoherence effect is caused, and the surface interface defects of the optical elements introduce stray light, reflect each other to introduce secondary imaging, ghost and diaphragm blade surface space confinement introduce stray light and other action mechanisms; (C) the spatial image distortion of the wavefront function (which can be theoretically characterized by a 37-degree Zernike function), and (D) the defocus systematic error from a flat detector, etc.
Therefore, in the actual design process, for the He — Ne laser (633nm) in this embodiment, five difference points can be taken, the interpolation points have respective frequencies (625nm, 629nm, 633nm, 637nm, 641nm), and the normalized weights corresponding to the intensities can be respectively (0.05, 0.175, 0.55, 0.175, 0.05).
And S13, setting simulation parameters, respectively calculating corresponding MIE matrixes for each interpolation point frequency, and linearly superposing each obtained MIE matrix to form a total MIE matrix for fitting with the actually measured optical signal array.
Firstly, setting a software simulation environment, wherein polystyrene standard spherical particles with the diameter of 1 micron are used as samples to be detected, and the optical parameters of the samples to be detected are as follows: the refractive index n is 1.596, the absorptivity is 0, the environment medium is water, the refractive index is 1.33, 70 MIE matrix characteristic particle diameter sections are arranged, the number of detector array channels is 50, parameters such as detector space position parameters, focal length, incident light frequency and amplitude weight corresponding to each frequency need to be initialized to participate in electromagnetic field calculation, then strict electromagnetic field simulation calculation is carried out to obtain an MIE matrix,
specifically, an MIE matrix corresponding to 2N +1(N ═ 0, 1, 2.) point frequencies is calculated, respectively: MIE _ (omega)0)，MIE_(ω0+ωm1)，MIE_(ω0-ωm1)，MIE_(ω0+ωm2)，MIE_(ω0-ωm2) ...; and finally, respectively applying weights to the MIE matrixes corresponding to the 2N +1 frequency points, and linearly superposing to form a total MIE matrix: MIE _ (Total) ═ w0·MIE_(ω0)+w1·MIE_(ω0+ωm1)+w2·MIE_(ω0-ωm1)+w3·MIE_(ω0+ωm2)+w4·MIE_(ω0-ωm2) +., MIE _ (Total) was used to fit to the measured scattered light signal.
In this example, the overall MIE matrix formed:
MIE_(Total)＝0.55MIE_(633nm)+0.175MIE_(637nm)+0.175MIE_(629nm)+0.05MIE_(641nm)+0.05MIE_(625nm)
in order to obtain actually measured scattered light signals, the laser particle size analyzer adopted by the invention is as shown in fig. 3, a filtered and collimated uniform parallel monochromatic light beam irradiates a sample cell to generate optical signals, a fourier transform lens is arranged at a proper position for measuring scattered light at different angles, a group of multi-element photoelectric detectors are arranged on a back focal plane of the fourier lens, the scattered light at different angles irradiates a main photoelectric detector and auxiliary detectors at other spatial positions through the fourier lens to obtain the distribution of scattered light intensity, and the received signals are stored in a computer memory or other media in the form of vectors and matrixes after being amplified and A/D sampled.
Finally, substituting the total MIE matrix and the actually measured optical signal array obtained by simulation calculation into a regression model and a fitting algorithm to carry out inversion calculation of characteristic particle size distribution; and finally, carrying out inversion to obtain the distribution information of the representative characteristic particle size section of the measured sample.
In the fitting calculation, an interpolation method, a regression model and an iteration condition are set; the interpolation method comprises linear interpolation and/or weighting; the regression model involved using the gauss-Newton method, gradient descent method, least squares method, Levenberg-Marguardt damped descent method; the fitting method may involve a bilinear refinement method, lagrange polynomial fitting, cubic spline, attenk, weight averaging, multiple quadratic, bicubic, telluric, wavelet, bessel, efree, finite difference, gaussian, hermit, newton's difference removal, close refinement, or theler refinement algorithms.
FIG. 4 shows the normalized optical power signal distribution curve of the latex standard spherical particles with a diameter of 1 μm, the simulated optical power signal curve of the latex standard spherical particles with a diameter of 1 μm with random phase noise included, and the simulated optical power signal curve of the latex standard spherical particles with a diameter of 1 μm with no random phase noise included. It can be seen from these three distribution curves that the matching degree of the simulation curve with random phase noise and the actual measurement curve is better than the matching degree of the simulation curve without random phase noise and the actual measurement curve. Meanwhile, the root mean square error between the two former curves (RMSE1 ═ 0.0206) is calculated to be lower than that between the two latter curves (RMSE2 ═ 0.0562). The random noise is counted to improve the fitting level of the simulation curve and the actually measured optical energy distribution curve.
Although the embodiment of the present invention employs a He — Ne red laser having a central wavelength of 633nm, it should be understood that the present invention is not limited thereto, and a particle size analyzer using various wavelengths of laser light, various wavelengths of LED light, or X-ray, electron beam, and ion beam sources as a light source is included in the scope of the present invention.
According to another embodiment of the present invention, referring to fig. 5, there is provided an apparatus for reducing random system noise in a particle size analyzer, comprising:
the acquisition device is used for acquiring the power spectral density distribution of the light source introducing the system noise;
setting means for setting interpolation point frequencies and amplitude weights corresponding to each interpolation point frequency based on the acquired power spectral density distribution of the light source;
and the simulation device is used for setting simulation parameters, respectively calculating corresponding MIE matrixes for each interpolation point frequency, and superposing each obtained MIE matrix to form a total MIE matrix for fitting with the actually measured optical signal array.
The system noise includes, among other things, phase noise caused by the systematic randomness of the light source itself and stray light noise caused by the series of optical path elements used to measure the optical signal.
Wherein the acquiring means comprises a first acquiring means using a power spectral density distribution calculation method or a second acquiring means comprising an experimental test means employing a high-precision spectrometer.
Wherein, first acquisition device includes:
the modeling device is used for building mathematical function representation of the light source, the mathematical function comprises a trigonometric function, an exponential function, a composite function of the trigonometric function and the exponential function, and a composite analytic function consisting of the composite function and a sigmoid function, wherein the form of the trigonometric function comprises
A separating sub-means for separating random noise from a mathematical function of the light source by a method including an elementary function transform, a trigonometric function and a difference transform, and still expressed by an analytic function whose square can be multiplied, the trigonometric function and the difference transform including:
wherein the first term is a principle ideal light intensity signal without a random phase modulation process, the second term is separated random system noise, the first term is expressed by an elementary function and the square can be integrated,
convolution sub-means for substituting a random system noise function into the autocorrelation function and representing as a convolution sum of itself:
wherein,
the Fourier transform sub-device is used for carrying out Fourier transform on the autocorrelation function obtained in the convolution device according to the Wiener-Khinchi theorem, and obtaining a power spectral density expression as follows:
the experimental test means of the high-precision spectrometer comprises measurement means, a method, an instrument and equipment for acquiring the distribution relation of wavelength, frequency and intensity of incident light based on the principle of grating and prism light splitting measurement.
Wherein in the setting means, the magnitude weight is set as a normalized magnitude weight.
In the simulation device, the setting of the simulation parameters comprises setting of optical parameters of a sample to be tested, MIE matrix representing characteristic particle size section, environmental medium parameters, detector array number, area and spatial position parameters, incident light frequency, focal length, polarization state, wavefront aberration correction and description parameters, and amplitude weight corresponding to each light frequency.
Wherein, the optical parameters of the sample to be detected comprise refractive index, absorptivity, reflectivity or extinction coefficient; the environmental medium comprises air, water, ethanol or acetone; environmental media parameters include refractive index, absorption or density; the polarization state comprises a non-polarization state, a linear polarization state, a circular polarization state, an elliptical polarization state and a partial polarization state; wavefront aberrations include the aerial image distortion of the wavefront function, characterized by the 37-degree Zernike function, and the defocus systematic error of the wavefront and plane detectors.
Wherein, this fitting device includes:
the collecting sub-device is used for collecting the actually measured optical signals and storing the actually measured optical signals in a form of vectors and matrixes;
the fitting sub-device is used for substituting the total MIE matrix and the actually measured optical signal array obtained by simulation calculation into a regression model and a fitting algorithm to carry out inversion calculation of characteristic particle size distribution;
and the inversion sub-device is used for inverting to obtain the distribution information of the representative characteristic particle size section of the tested sample.
The fitting sub-device also comprises an interpolation method, a regression model and an iteration condition; the interpolation method comprises linear interpolation and weighting; the regression model comprises a Gause-Newton method, a gradient descent method, a least square method and a Levenberg-Marguardt damping descent method; the fitting method comprises a bilinear precision method, Lagrangian polynomial fitting, cubic spline, Extenk, weight average, multiple quadratic, bicubic, Telen, wavelet, Bessel, Everly, finite difference, Gauss, hermit, Newton's difference removal, close precision or Tiler precision algorithm.
The light source comprises a laser light source, an LED light source, an X ray, an ion beam, an electron beam and other ray source light sources.
While the invention has been described with reference to a preferred embodiment, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.
The particular sequence of steps described herein is for illustrative purposes only and is not intended to be limiting, unless a required step requires input from a previous step.
Claims (16)
1. A method of reducing random system noise in a particle size analyzer, comprising the steps of:
s11, acquiring the power spectral density distribution of the light source introducing random system noise;
s12, setting interpolation point frequencies and amplitude weights corresponding to the interpolation point frequencies based on the acquired power spectral density distribution of the light source;
and S13, setting simulation parameters, respectively calculating corresponding MIE matrixes for each interpolation point frequency, and superposing each obtained MIE matrix to form a total MIE matrix for fitting with the actually measured optical signal array.
2. The method of claim 1, wherein the step of obtaining step S11 includes obtaining the power spectral density distribution of the light source by using a power spectral density calculation method and/or an experimental test method using a high-precision spectrometer, the experimental test method using a high-precision spectrometer including a measurement means, a method, an apparatus and a device for obtaining the wavelength, frequency and intensity distribution relationship of incident light based on grating and prism spectroscopic measurement principles.
3. The method of reducing random system noise in a particle size analyzer of claim 2, the power spectral density calculation method comprising the steps of:
s31, establishing mathematical function representation of the light source, wherein the mathematical function comprises a trigonometric function, an exponential function, a composite function of the trigonometric function and the exponential function, and a composite analytic function consisting of the composite function and a sigmoid function, and the form of the trigonometric function comprises
S32, separating random noise from the mathematical function of the light source by the methods including an elementary function transformation, a trigonometric function and a difference transformation, wherein the square of the analytic function can be multiplied, and the trigonometric function and the difference transformation include
Wherein the first term is a principle ideal light intensity signal without a random phase modulation process, the second term is the separated random system noise, and an elementary function is adopted to representAnd the square can be the product of the two,
s33, substituting the random system noise function into the autocorrelation function and expressing the autocorrelation function as the convolution sum of the random system noise function:
wherein,
s34, according to Wiener-Khinchi theorem, Fourier transform is carried out on the autocorrelation function after convolution in the step S33, and the power spectral density expression is obtained as follows:
4. the method of reducing random system noise in a particle size analyzer of claim 1, wherein the light source comprises a laser light source, an LED light source, an X-ray, ion beam, and electron beam source light source, and the amplitude weight is set to a normalized amplitude weight.
5. The method of claim 1, wherein in step S13, the setting simulation parameters includes setting optical parameters of the sample to be measured, MIE matrix representing characteristic particle size section, environmental medium parameters, number of detector arrays, area and spatial position parameters, incident light frequency, focal length, polarization state, wavefront aberration correction and description parameters, and amplitude weight corresponding to each light frequency.
6. The method of claim 5, wherein the optical parameters of the sample to be tested include refractive index, absorption rate, reflectivity or extinction coefficient; the environmental medium comprises air, water, ethanol or acetone; the environmental medium parameter comprises refractive index, absorptivity or density; the polarization states include a non-polarization state, a linear polarization state, a circular polarization state, an elliptical polarization state, and a partial polarization state; the wavefront aberrations include the aerial image distortion of the wavefront function, characterized by a 37-degree Zernike function, and the defocus systematic error of the wavefront and plane detectors.
7. The method of reducing random system noise in a particle size analyzer of claim 1, wherein in step S13, the fitting comprises the steps of:
s131, collecting and storing the actual light signals in a form of vectors and matrixes;
s132, substituting the total MIE matrix and the actually measured optical signal array obtained by simulation calculation into a regression model and a fitting algorithm to perform inverse calculation of characteristic particle size distribution;
and S133, carrying out inversion to obtain the distribution information of the representative characteristic particle size section of the tested sample.
8. The method of claim 7, wherein step S132 further comprises setting an interpolation method, a regression model, and an iteration condition; the interpolation method comprises linear interpolation and weighting; the regression model includes using a gauss-Newton method, a gradient descent method, a least squares method, a Levenberg-Marguardt damped descent method; the fitting method comprises a bilinear precision method, Lagrangian polynomial fitting, cubic spline, Extenk, weight averaging, multiple quadratic, bicubic, Telen, wavelet, Bessel, Everly, finite difference, Gaussian, hermit, Newton's difference removal, close precision or Tiler precision algorithm.
9. An apparatus for reducing random system noise in a particle size analyzer, the apparatus comprising:
the acquisition device is used for acquiring the power spectral density distribution of the light source introducing the system noise;
setting means for setting interpolation point frequencies and amplitude weights corresponding to each interpolation point frequency based on the acquired power spectral density distribution of the light source;
and the simulation device is used for setting simulation parameters, respectively calculating corresponding MIE matrixes for each interpolation point frequency, and superposing each obtained MIE matrix to form a total MIE matrix for fitting with the actually measured optical signal array.
10. The apparatus of claim 9, wherein the system noise includes phase noise caused by system randomness of the light source itself and stray light noise caused by a series of optical path elements for measuring the optical signal, and the acquiring means includes a first acquiring means using a power spectral density distribution calculating method or a second acquiring means including an experimental testing means using a high-precision spectrometer, and the experimental testing means using a high-precision spectrometer includes a measuring means, a method, an apparatus and a device for acquiring a relation of wavelength, frequency and intensity distribution of incident light based on a grating and prism spectroscopic measurement principle.
11. The apparatus of claim 10, wherein the first obtaining means comprises:
the modeling device is used for building mathematical function representation of the light source, the mathematical function comprises a trigonometric function, an exponential function, a composite function of the trigonometric function and the exponential function, and a composite analytic function consisting of the composite function and a sigmoid function, wherein the form of the trigonometric function comprises
A separating sub-means for separating random noise from the mathematical function of the light source by a method including an elementary function transform, a trigonometric function and a difference transform, the trigonometric function and the difference transform including
Wherein the first term is a principle ideal light intensity signal without a random phase modulation process, the second term is the separated random system noise, the elementary function is adopted for representation, and the square can be integrated,
convolution sub-means for substituting said random system noise function into an autocorrelation function and representing it as the convolution sum of itself:
wherein,
the Fourier transform sub-device is used for carrying out Fourier transform on the autocorrelation function obtained in the convolution device according to the Wiener-Khinchi theorem, and obtaining a power spectral density expression as follows:
12. the apparatus of claim 9, wherein the light source comprises a laser light source, an LED light source, an X-ray, ion beam, and electron beam source light source, and the amplitude weight is set to a normalized amplitude weight.
13. The apparatus of claim 9, wherein in the simulation apparatus, the setting of simulation parameters includes setting of optical parameters of the sample to be measured, MIE matrix representing characteristic particle size section, environmental medium parameters, number of detector arrays, area and spatial position parameters, incident light frequency, focal length, polarization state, wavefront aberration correction and description parameters, and amplitude weight corresponding to each light frequency.
14. The apparatus of claim 13, wherein the optical parameters of the sample to be tested include refractive index, absorption rate, reflectivity or extinction coefficient; the environmental medium comprises air, water, ethanol or acetone; the environmental medium parameter comprises refractive index, absorptivity or density; the polarization states include a non-polarization state, a linear polarization state, a circular polarization state, an elliptical polarization state, and a partial polarization state; the wavefront aberrations include the aerial image distortion of the wavefront function, characterized by a 37-degree Zernike function, and the defocus systematic error of the wavefront and plane detectors.
15. The apparatus of claim 9, wherein the fitting means comprises:
the collecting sub-device is used for collecting the actually measured optical signals and storing the actually measured optical signals in a form of vectors and matrixes;
the fitting sub-device is used for substituting the total MIE matrix and the actually measured optical signal array obtained by simulation calculation into a regression model and a fitting algorithm to carry out inversion calculation of characteristic particle size distribution;
and the inversion sub-device is used for inverting to obtain the distribution information of the representative characteristic particle size section of the tested sample.
16. The apparatus of claim 15, wherein the fitting sub-apparatus further comprises an interpolation method, a regression model, and an iteration condition; the interpolation method comprises linear interpolation and weighting; the regression model comprises a Gause-Newton method, a gradient descent method, a least square method and a Levenberg-Marguardt damping descent method; the fitting method comprises a bilinear precision method, Lagrangian polynomial fitting, cubic spline, Extenk, weight averaging, multiple quadratic, bicubic, Telen, wavelet, Bessel, Everly, finite difference, Gaussian, hermit, Newton's difference removal, close precision or Tiler precision algorithm.
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Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107655845A (en) * | 2017-09-29 | 2018-02-02 | 福建加谱新科科技有限公司 | Infrared spectrum acquisition methods based on Fourier transform infrared spectroscopy superposing type peak shape |
| CN108875826A (en) * | 2018-06-15 | 2018-11-23 | 武汉大学 | A kind of multiple-limb method for checking object based on the compound convolution of thickness granularity |
| CN108872152A (en) * | 2018-05-31 | 2018-11-23 | 珠海真理光学仪器有限公司 | Particle refractive index measurement method, computer installation and computer readable storage medium |
| CN109142834A (en) * | 2017-06-19 | 2019-01-04 | 北京普源精电科技有限公司 | Amplitude Weight Acquisition method and device thereof, oscillograph for track drafting |
| CN111105979A (en) * | 2018-10-26 | 2020-05-05 | 安捷伦科技有限公司 | Automatic detection of nanoparticles using single particle inductively coupled plasma mass spectrometry (SP-ICP-MS) |
| CN111899157A (en) * | 2020-07-16 | 2020-11-06 | 中国人民解放军海军航空大学 | Method for simulating stray light interference by using mask technology |
| CN111949919A (en) * | 2020-06-14 | 2020-11-17 | 杭州谱育科技发展有限公司 | Method for reducing noise in AES detection |
| CN113722798A (en) * | 2021-08-31 | 2021-11-30 | 山东省交通科学研究院 | Low-noise micro-meter level configuration and design method |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2005069965A (en) * | 2003-08-27 | 2005-03-17 | Pixart Technology:Kk | Color measuring apparatus |
| US20100108910A1 (en) * | 2005-07-15 | 2010-05-06 | Michael Morrell | Pathogen and particle detector system and method |
| CN103983546A (en) * | 2014-03-31 | 2014-08-13 | 中国科学院高能物理研究所 | Method for measuring particle size distribution of particles, and apparatus thereof |
| CN104034279A (en) * | 2014-06-14 | 2014-09-10 | 中国科学院光电技术研究所 | Detection device and method for splicing and measuring surface shape by utilizing small-hole diffraction wave surface |
| CN104198424A (en) * | 2014-08-29 | 2014-12-10 | 清华大学 | Method for improving signal-to-noise ratio at ultralow signal-to-noise ratio frequency band of Fourier infrared spectrometer |
| US20150025847A1 (en) * | 2013-07-17 | 2015-01-22 | University Of Central Florida Research Foundation, Inc. | Quantitative elemental profiling in optical emission spectroscopy |
| CN104344890A (en) * | 2014-11-07 | 2015-02-11 | 西安应用光学研究所 | Weak light signal spectrum fast test device and method |
| CN105074424A (en) * | 2013-01-22 | 2015-11-18 | 荷兰能源研究中心基金会 | Method and device for corrosion testing of solar cells |
-
2016
- 2016-01-08 CN CN201610015492.0A patent/CN105675455B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2005069965A (en) * | 2003-08-27 | 2005-03-17 | Pixart Technology:Kk | Color measuring apparatus |
| US20100108910A1 (en) * | 2005-07-15 | 2010-05-06 | Michael Morrell | Pathogen and particle detector system and method |
| CN105074424A (en) * | 2013-01-22 | 2015-11-18 | 荷兰能源研究中心基金会 | Method and device for corrosion testing of solar cells |
| US20150025847A1 (en) * | 2013-07-17 | 2015-01-22 | University Of Central Florida Research Foundation, Inc. | Quantitative elemental profiling in optical emission spectroscopy |
| CN103983546A (en) * | 2014-03-31 | 2014-08-13 | 中国科学院高能物理研究所 | Method for measuring particle size distribution of particles, and apparatus thereof |
| CN104034279A (en) * | 2014-06-14 | 2014-09-10 | 中国科学院光电技术研究所 | Detection device and method for splicing and measuring surface shape by utilizing small-hole diffraction wave surface |
| CN104198424A (en) * | 2014-08-29 | 2014-12-10 | 清华大学 | Method for improving signal-to-noise ratio at ultralow signal-to-noise ratio frequency band of Fourier infrared spectrometer |
| CN104344890A (en) * | 2014-11-07 | 2015-02-11 | 西安应用光学研究所 | Weak light signal spectrum fast test device and method |
Non-Patent Citations (1)
| Title |
|---|
| 徐建程: "环形光源在干涉仪系统中的应用", 《强激光粒子束》 * |
Cited By (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109142834A (en) * | 2017-06-19 | 2019-01-04 | 北京普源精电科技有限公司 | Amplitude Weight Acquisition method and device thereof, oscillograph for track drafting |
| CN107655845B (en) * | 2017-09-29 | 2018-07-20 | 福建加谱新科科技有限公司 | Infrared spectrum acquisition methods based on Fourier transform infrared spectroscopy superposing type peak shape |
| CN107655845A (en) * | 2017-09-29 | 2018-02-02 | 福建加谱新科科技有限公司 | Infrared spectrum acquisition methods based on Fourier transform infrared spectroscopy superposing type peak shape |
| CN108872152B (en) * | 2018-05-31 | 2020-08-11 | 珠海真理光学仪器有限公司 | Particle refractive index measuring method, computer device and computer readable storage medium |
| CN108872152A (en) * | 2018-05-31 | 2018-11-23 | 珠海真理光学仪器有限公司 | Particle refractive index measurement method, computer installation and computer readable storage medium |
| CN108875826A (en) * | 2018-06-15 | 2018-11-23 | 武汉大学 | A kind of multiple-limb method for checking object based on the compound convolution of thickness granularity |
| CN108875826B (en) * | 2018-06-15 | 2021-12-03 | 武汉大学 | Multi-branch object detection method based on coarse and fine granularity composite convolution |
| CN111105979A (en) * | 2018-10-26 | 2020-05-05 | 安捷伦科技有限公司 | Automatic detection of nanoparticles using single particle inductively coupled plasma mass spectrometry (SP-ICP-MS) |
| CN111949919A (en) * | 2020-06-14 | 2020-11-17 | 杭州谱育科技发展有限公司 | Method for reducing noise in AES detection |
| CN111949919B (en) * | 2020-06-14 | 2022-04-01 | 杭州谱育科技发展有限公司 | Method for reducing noise in AES detection |
| CN111899157A (en) * | 2020-07-16 | 2020-11-06 | 中国人民解放军海军航空大学 | Method for simulating stray light interference by using mask technology |
| CN113722798A (en) * | 2021-08-31 | 2021-11-30 | 山东省交通科学研究院 | Low-noise micro-meter level configuration and design method |
| CN113722798B (en) * | 2021-08-31 | 2024-05-07 | 山东省交通科学研究院 | Low-noise micro-meter department grading design method |
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