CN116663433A - Differential optimization algorithm based on differential absorption spectrometer - Google Patents
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Abstract
The invention discloses a differential optimization algorithm based on a differential absorption spectrometer, which belongs to the technical field of electric data processing and is used for carrying out differential optimization on optical data, and comprises the steps of obtaining wavelength and light intensity information of an absorption spectrum through the differential absorption spectrometer, calculating a concentration value of gas according to the absorption light intensity in the spectrum, filtering out a slow change part with low dependence on the wavelength in the spectrum according to the gas extinction effect by using a Bill law, and removing the slow change part and Rayleigh scattering and Mie scattering extinction coefficients to obtain differential optical density only representing gas molecular absorption; and broadening the absorption spectrum lines of the characteristic wavelength bands corresponding to various gases by using the Fogart function, performing least square fitting on the differential optical density and the reference spectrum, and inverting to obtain the final gas concentration value in the current measurement environment. The method improves and optimizes the traditional differential absorption algorithm, improves the precision for solving various gas concentration values, and can adapt to various complex environments.
Description
Technical Field
The invention discloses a differential optimization algorithm based on a differential absorption spectrometer, and belongs to the technical field of electric data processing.
Background
The general instrument can only detect single gas concentration, can not detect multiple gas concentrations simultaneously, and how to detect various polluted gases in real time and alarm according to local air quality standards is the research focus in the current environment. The differential absorption spectrum technology makes up the defects of the traditional instrument and can detect the concentration of various gases simultaneously; the differential absorption spectrum technology takes solar scattered light or artificially introduced detection light as a light source, obtains the wavelength and the light intensity of the scattered light, and inverts the concentration information of various gases through a differential absorption algorithm after the light source information is obtained, so that the real-time concentration values of the various gases are obtained. The differential absorption spectrometer has the advantages of easy construction of a platform, multiple inversion gas concentration types, multi-platform observation and the like. For the conventional differential absorption technology, the standard absorption section is generally obtained by an ultraviolet-visible band high-resolution standard absorption section in a spectrum database, and the absorption section data is obtained by a gas absorption section of a high-resolution spectrometer at normal temperature, in practical situations, a plurality of different gas concentrations are required to be obtained, and the absorption sections of the gases at different temperatures are different, which leads to an increase of data errors. In addition, in a low-concentration measurement environment, due to the influence of a plurality of environmental conditions such as optical path, concentration, impurities and the like, the information quantity of the concentration of the gas to be measured in the absorption spectrum is small, and a larger error can be generated in the gas concentration value solved by the differential absorption algorithm, and even the error can be more than 10%. Aiming at the defects of the differential absorption algorithm, the traditional algorithm is optimized and improved, so that the method can meet the requirements of multiple conditions and high precision, and the concentration of various gases to be measured can be more accurately obtained under more complex conditions.
Disclosure of Invention
The invention aims to provide a differential optimization algorithm based on a differential absorption spectrometer, which is used for solving the problem of large data error of a differential absorption technology in the prior art.
A differential optimization algorithm based on a differential absorption spectrometer, comprising:
s1, acquiring wavelength and light intensity information of an absorption spectrum by a differential absorption spectrometer, calculating a concentration value of gas from the absorption light intensity in the spectrum, and filtering out a slow change part in the spectrum according to the gas extinction effect by using a Bill law;
s2, separating a fast change part and a slow change part in an absorption spectrum through digital filtering;
s3, removing the slow change part and the Rayleigh scattering and Mie scattering extinction coefficient to obtain the differential optical density only representing the absorption of gas molecules；
S4, broadening absorption spectral lines of the corresponding characteristic wavelength bands of various gases by using the Focus function;
s5, after an effective absorption section is obtained through data processing, the effective absorption section is used as a reference spectrum, and the differential optical density and the reference spectrum are subjected to least square fitting;
s6, inverting the gas concentration, judging, and adding a catastrophe period self-adaptive crossover operation based on a genetic algorithm to optimize;
s7, inverting to obtain a final gas concentration value in the current measuring environment.
S1 comprises the following steps:
the relation between the fast and slow changing parts of the spectrum and the transmitted light intensity is as follows:
；
in the method, in the process of the invention,is the transmitted intensity of ultraviolet light at a certain wavelength, +.>For the incident light intensity of ultraviolet light at a certain wavelength, n is the number of gas species, +.>Representing the broadband structure spectrum, i.e. the slowly varying part of the spectrum,/->For differential absorption cross section, i.e. fast changing part of the spectrum,/->Represents the concentration of the ith species of substance at the transmission path s,/->And->Rayleigh scattering and Mie scattering extinction coefficients, respectively.
S2 comprises the following steps:
the high-pass filtering adopts six-order polynomial fitting to fit the absorption spectrum in the wave band:
；
in the method, in the process of the invention,for the intensity of the light after fitting, +.>Is a coefficient of->Is the wavelength.
S2 comprises the following steps:
the low-pass filtering adopts Savez-Golay smoothing denoising:
；
in the method, in the process of the invention,for smoothed data +.>For smooth coefficient +.>Is the pre-smoothing data.
S3 comprises the following steps:
。
s4 comprises the following steps:
fugget functionThe method comprises the following steps:
；
wherein the method comprises the steps ofRepresenting Doppler spread half-width, < >>Indicating that the gas is in beam->The absorption line at the point is strong, and the temperature dependence of F is expressed as follows, according to the influence of temperature on the absorption cross section:
；
indicating the reference temperature +.>Strong absorption line and->For the actual temperature +.>For reference temperature->Is the energy of the low energy state of the molecule, +.>Is Planck constant, < >>For the speed of light->Is a boltzmann constant, t represents a variable constant;
x and y are intermediate coefficients:
，/>；
a central beam representing each absorption line, +.>Representing the half width of the pressure broadening.
S4 comprises the following steps:
half-width of Doppler broadeningAnd half width of pressure broadening->The temperature and pressure dependence of (2) are expressed as:
，/>；
in the method, in the process of the invention,taking 1 for symmetrical molecules and 1.5 for asymmetrical molecules in a dimensionless manner; />Indicating the actual atmospheric pressure, +.>Representing a reference spread value, +.>Represents a reference atmospheric pressure, M represents a molecular mass of a gas;
according to the temperature and the atmospheric pressure in the actual situation, comparing the reference temperature and the atmospheric pressure, calculating to obtain the current high-resolution water vapor absorption section, and convoluting the obtained reference section with a spectrometer instrument function curve to obtain the current effective absorption section of each gas.
S5 comprises the following steps:
is provided withIndicate->Seed gas inversion concentration according to ∈>The formula (1) is:
，/>；
will beIs rewritten as a matrix form:
；
in the method, in the process of the invention,represents +.>Component (F)>Line i corresponds to->Component, thereby obtaining->Differential optical Density of seed gas->And effective absorption cross section->Further play backConcentration of gas->。
S6 comprises the following steps:
s6.1. coding with decimal system, in which each individual comprisesIndividual gene coding value->, />For the coded gas concentration, each population consisted of 10 random initialized individuals and satisfied:
；
s6.2, selecting fitness functionThe method comprises the following steps:
；
in the method, in the process of the invention,is->Sampling points->For the number of spectral sampling point wavelengths, +.>For the encoded gas concentration, +.>For differential absorption cross section->For the +.>Absorbance values for each sampling point.
S6 comprises the following steps:
s6.3, selecting operation, namely calculating an average value of fitness, and selecting individuals smaller than the average value of the fitness to enter the next generation;
s6.4, adding disaster period self-adaptive crossover operation to define crossover probabilityThe method comprises the following steps:
；
in the method, in the process of the invention,represents the crossover probability->Indicates the number of catastrophic events that have occurred at the present time, < + >>A constant value of less than 1 is indicated,represents an integer for adjusting the range of cross probability variation, +.>Indicating that the population is proceeding to->Performing genetic operation;
s6.5, terminating operation, and outputting an individual with the minimum adaptation value as a true value by the optimization algorithm when the solution concentration value of the last individual reaches a required threshold value, wherein the error range of the concentration value finally solved by the optimization algorithm is within the threshold value range.
Compared with the prior art, the invention has the following beneficial effects: the accuracy of solving various gas concentration values is improved by improving and optimizing a traditional differential absorption algorithm, and the method can be suitable for various complex environments;
in order to adapt to more different environments, the standard absorption section can be effectively and accurately obtained at different temperatures, the absorption spectrum lines of the corresponding characteristic wavelength bands of various gases are widened through a Voigt function, and then the obtained reference section is convolved with a spectrometer instrument function curve, so that the current effective absorption section of each gas is finally obtained, and the error is reduced;
in order to adapt to the environment with small concentration and short optical path, a genetic algorithm is integrated into a final data processing process, and the algorithm is optimized by coding, defining a fitness function, selecting, inheriting and terminating, so that the accuracy of data is improved;
in order to reduce the 'early ripening' phenomenon of the genetic algorithm, in the optimization of the genetic algorithm, the catastrophe period self-adaptive crossover operation is added, the global searching capability of population variation is improved by carrying out self-adaptive change on crossover probability, and the adaptability and the accuracy of the algorithm are enhanced.
Drawings
FIG. 1 is a technical flow chart of the present invention;
FIG. 2 is a diagram of a digital filtering process;
FIG. 3 is a schematic diagram of the cross section of the Fogart function for effective absorption.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the present invention more apparent, the technical solutions in the present invention will be clearly and completely described below, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
A differential optimization algorithm based on a differential absorption spectrometer, comprising:
s1, acquiring wavelength and light intensity information of an absorption spectrum by a differential absorption spectrometer, calculating a concentration value of gas from the absorption light intensity in the spectrum, and filtering a slow change part with low dependence on the wavelength in the spectrum according to the gas extinction effect by using a Bill law;
s2, separating a fast change part and a slow change part in an absorption spectrum through digital filtering;
s3, removing the slow change part and the Rayleigh scattering and Mie scattering extinction coefficient to obtain the differential optical density only representing the absorption of gas molecules；
S4, broadening absorption spectral lines of the corresponding characteristic wavelength bands of various gases by using the Focus function;
s5, after an effective absorption section is obtained through data processing, the effective absorption section is used as a reference spectrum, and the differential optical density and the reference spectrum are subjected to least square fitting;
s6, inverting the gas concentration, judging, and if the gas concentration is measured in an environment with lower gas concentration, measuring the gas concentration with larger error, and optimizing by adding the catastrophe period self-adaptive cross operation based on a genetic algorithm;
s7, inverting to obtain a final gas concentration value in the current measuring environment.
S1 comprises the following steps:
the relation between the fast and slow changing parts of the spectrum and the transmitted light intensity is as follows:
；
in the method, in the process of the invention,is the transmitted intensity of ultraviolet light at a certain wavelength, +.>For the incident light intensity of ultraviolet light at a certain wavelength, n is the number of gas species, +.>Representing a broadband structural spectrum, i.e. spectrumSlowly changing part of (2),>for differential absorption cross section, i.e. fast changing part of the spectrum,/->Represents the concentration of the ith species of substance at the transmission path s,/->And->Rayleigh scattering and Mie scattering extinction coefficients, respectively.
S2 comprises the following steps:
the high-pass filtering adopts six-order polynomial fitting to fit the absorption spectrum in the wave band:
；
in the method, in the process of the invention,for the intensity of the light after fitting, +.>Is a coefficient of->Is the wavelength.
S2 comprises the following steps:
the low-pass filtering adopts Savez-Golay smoothing denoising:
；
in the method, in the process of the invention,for smoothed data +.>For smooth coefficient +.>Is the pre-smoothing data.
S3 comprises the following steps:
。
s4 comprises the following steps:
fugget functionThe method comprises the following steps:
；
wherein the method comprises the steps ofRepresenting Doppler spread half-width, < >>Indicating that the gas is in beam->The absorption line at the point is strong, and the temperature dependence of F is expressed as follows, according to the influence of temperature on the absorption cross section:
；
indicating the reference temperature +.>Strong absorption line and->For the actual temperature +.>For reference temperature->Is the energy of the low energy state of the molecule, +.>Is Planck constant, < >>For the speed of light->Is a boltzmann constant, t represents a variable constant;
x and y are intermediate coefficients:
，/>；
a central beam representing each absorption line, +.>Representing the half width of the pressure broadening.
S4 comprises the following steps:
half-width of Doppler broadeningAnd half width of pressure broadening->The temperature and pressure dependence of (2) are expressed as:
，/>；
in the method, in the process of the invention,taking 1 for symmetrical molecules and 1.5 for asymmetrical molecules in a dimensionless manner; />Indicating the actual atmospheric pressure, +.>Representing a reference spread value, +.>Represents a reference atmospheric pressure, M represents a molecular mass of a gas;
according to the temperature and the atmospheric pressure in the actual situation, comparing the reference temperature and the atmospheric pressure, calculating to obtain the current high-resolution water vapor absorption section, and convoluting the obtained reference section with a spectrometer instrument function curve to obtain the current effective absorption section of each gas.
S5 comprises the following steps:
is provided withIndicate->Seed gas inversion concentration according to ∈>The formula (1) is:
，/>；
will beIs rewritten as a matrix form:
；
in the method, in the process of the invention,represents +.>Component (F)>Line i corresponds to->Component, thereby obtaining->Differential optical Density of seed gas->And effective absorption cross section->Further reversing the concentration of the generated gas>。
S6 comprises the following steps:
s6.1. coding with decimal system, in which each individual comprisesIndividual gene coding value->, />For the coded gas concentration, each population consisted of 10 random initialized individuals and satisfied:
；
s6.2, selecting fitness functionThe method comprises the following steps:
；
in the method, in the process of the invention,is->Sampling points->For the number of spectral sampling point wavelengths, +.>For the encoded gas concentration, +.>For differential absorption cross section->For the +.>Absorbance values for each sampling point.
S6 comprises the following steps:
s6.3, selecting an individual with a smaller fitness value to enter the next generation according to the fitness function;
s6.4, adding disaster period self-adaptive crossover operation to define crossover probabilityThe method comprises the following steps:
；
in the method, in the process of the invention,represents the crossover probability->Indicates the number of catastrophic events that have occurred at the present time, < + >>A constant value of less than 1 is indicated,represents an integer for adjusting the range of cross probability variation, +.>Indicating that the population is proceeding to->Performing genetic operation;
s6.5, terminating operation, and outputting an individual with the minimum adaptation value as a true value by the optimization algorithm when the solution concentration value of the last individual reaches a required threshold value, wherein the error range of the concentration value finally solved by the optimization algorithm is within the threshold value range.
The technical flow of the invention is shown in figure 1, and comprises the steps of obtaining spectral information, high-pass filtering and low-pass filtering, obtaining differential optical density, obtaining an effective absorption section of gas, fitting by a least square method, then directly outputting a concentration value in a normal concentration environment and a low concentration environment according to different conditions, carrying out a catastrophe genetic algorithm, and outputting an optimal individual meeting a threshold value.
In general, atmospheric scattering comprises Rayleigh scattering and Mie scattering, the Rayleigh scattering and Mie scattering are used as absorption processes when calculated by a differential absorption spectrum technology, and the relationship between the emitted light intensity and the received light intensity is obtained by combining Rayleigh scattering, mie scattering extinction effect and a lambert beer law. The differential absorption algorithm is divided into two parts of rapid change with high wavelength dependence and slow change with low wavelength dependence according to the dependence of the gas extinction effect, the slow change part with low wavelength dependence is needed to be filtered out in the spectrum analysis, the differential optical density only representing the absorption of gas molecules is obtained after the slow change part is filtered out, and the differential optical density and a gas molecule reference absorption section are subjected to least square fitting to obtain the concentration of various gases. Separating the "fast-changing" and "slow-changing" portions of the absorption spectrum typically employs digital filtering, removing the "slow-changing" portions of the spectrum using high-pass digital filtering, such as polynomial regression filtering, and reducing the effects of high-frequency noise using low-pass filtering, such as triangular filtering. The reference absorption section is usually selected by directly using a standard section for processing, the corresponding wavelength is recorded according to the characteristic absorption spectrum peak of different gases in the standard absorption section at a certain wavelength, the data corresponding to the wavelength in the differential absorption section and the data of the standard absorption section are subjected to least square fitting, and the digital filtering process is shown in figure 2.
Based on the gas absorption parameters provided by the spectrum database, under different temperature conditions, characteristic absorption spectrum lines of characteristic wavelength bands of various gases are stretched by utilizing Fogart function fitting, and in the fitting, it is found that various interference gases exist in the characteristic wavelength bands corresponding to certain gases, so that in order to improve the accuracy, firstly, the gas characteristic wavelength Duan Zhonggan is selected, the interference gases exist less, then, all the interference gases existing in the wavelength band are stretched, and then, the absorption cross sections obtained after the stretching participate in the spectrum fitting. Under complex conditions such as low concentration, the genetic algorithm and the traditional differential absorption algorithm are combined and improved, the fitness function is defined according to the gas absorption characteristics, concentration values and the like, an initial data set is generated through operations such as selection, crossover, mutation and the like of the genetic algorithm, iterative optimization is carried out on the data, each generation of the data set is evaluated according to the fitness function, individuals with higher fitness are selected as a population of the next generation, genetic operation is carried out, new individuals are generated, iterative operation of multiple generations is repeated until the individuals with the highest fitness are found, data errors are reduced, and the Fogart function obtains an effective absorption section as shown in a figure 3.
The above embodiments are only for illustrating the technical aspects of the present invention, not for limiting the same, and although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may be modified or some or all of the technical features may be replaced with other technical solutions, which do not depart from the scope of the technical solutions of the embodiments of the present invention.
Claims (10)
1. A differential optimization algorithm based on a differential absorption spectrometer, comprising:
s1, acquiring wavelength and light intensity information of an absorption spectrum by a differential absorption spectrometer, calculating a concentration value of gas from the absorption light intensity in the spectrum, and filtering out a slow change part in the spectrum according to the gas extinction effect by using a Bill law;
s2, separating a fast change part and a slow change part in an absorption spectrum through digital filtering;
s3, removing the slow change part and the Rayleigh scattering and Mie scattering extinction coefficient to obtain the differential optical density only representing the absorption of gas molecules；
S4, broadening absorption spectral lines of the corresponding characteristic wavelength bands of various gases by using the Focus function;
s5, after an effective absorption section is obtained through data processing, the effective absorption section is used as a reference spectrum, and the differential optical density and the reference spectrum are subjected to least square fitting;
s6, inverting the gas concentration, and adding catastrophe period self-adaptive crossover operation based on a genetic algorithm to optimize;
s7, inverting to obtain a final gas concentration value in the current measuring environment.
2. The differential optimization algorithm based on a differential absorption spectrometer according to claim 1, wherein S1 comprises:
the relation between the fast and slow changing parts of the spectrum and the transmitted light intensity is as follows:
；
in the method, in the process of the invention,is the transmitted intensity of ultraviolet light at a certain wavelength, +.>For the incident light intensity of ultraviolet light at a certain wavelength, n is the number of gas species, +.>Representing the broadband structure spectrum, i.e. the slowly varying part of the spectrum,/->For differential absorption cross section, i.e. fast changing part of the spectrum,/->Represents the concentration of the ith species of substance at the transmission path s,/->And->Rayleigh scattering and Mie scattering extinction coefficients, respectively.
3. The differential optimization algorithm based on a differential absorption spectrometer according to claim 1, wherein S2 comprises:
the high-pass filtering adopts six-order polynomial fitting to fit the absorption spectrum in the wave band:
；
in the method, in the process of the invention,for the intensity of the light after fitting, +.>Is a coefficient of->Is the wavelength.
4. The differential optimization algorithm based on a differential absorption spectrometer according to claim 1, wherein S2 comprises:
the low-pass filtering adopts Savez-Golay smoothing denoising:
；
in the method, in the process of the invention,for smoothed data +.>For smooth coefficient +.>Is the pre-smoothing data.
5. A differential optimization algorithm based on a differential absorption spectrometer according to claim 1, wherein S3 comprises:
。
6. the differential optimization algorithm based on a differential absorption spectrometer according to claim 1, wherein S4 comprises:
fugget functionThe method comprises the following steps:
；
wherein the method comprises the steps ofRepresenting Doppler spread half-width, < >>Indicating that the gas is in beam->The absorption line at the point is strong, and the temperature dependence of F is expressed as follows, according to the influence of temperature on the absorption cross section:
；
indicating the reference temperature +.>Strong absorption line and->For the actual temperature +.>For reference temperature->Is the energy of the low energy state of the molecule, +.>Is Planck constant, < >>For the speed of light->Is a boltzmann constant, t represents a variable constant;
x and y are intermediate coefficients:
，/>；
a central beam representing each absorption line, +.>Representing the half width of the pressure broadening.
7. The differential optimization algorithm based on a differential absorption spectrometer according to claim 6, wherein S4 comprises:
half-width of Doppler broadeningAnd half width of pressure broadening->The temperature and pressure dependence of (2) are expressed as:
，/>；
in the method, in the process of the invention,taking 1 for symmetrical molecules and 1.5 for asymmetrical molecules in a dimensionless manner; />Indicating the actual atmospheric pressure, +.>Representing a reference spread value, +.>Represents a reference atmospheric pressure, M represents a molecular mass of a gas;
according to the temperature and the atmospheric pressure in the actual situation, comparing the reference temperature and the atmospheric pressure, calculating to obtain the current high-resolution water vapor absorption section, and convoluting the obtained reference section with a spectrometer instrument function curve to obtain the current effective absorption section of each gas.
8. The differential optimization algorithm based on a differential absorption spectrometer according to claim 1, wherein S5 comprises:
is provided withIndicate->Seed gas inversion concentration according to ∈>The formula (1) is:
，/>；
will beIs rewritten as a matrix form:
；
in the method, in the process of the invention,represents +.>Component (F)>Line i corresponds to->Component, thereby obtaining->Differential optical Density of seed gas->And effective absorption cross section->Further reversing the concentration of the generated gas>。
9. The differential optimization algorithm based on a differential absorption spectrometer according to claim 1, wherein S6 comprises:
s6.1. coding with decimal system, in which each individual comprisesIndividual gene coding value->, />For the coded gas concentration, each population consisted of 10 random initialized individuals and satisfied:
；
s6.2, selecting fitness functionThe method comprises the following steps:
；
in the method, in the process of the invention,is->Sampling points->For the number of spectral sampling point wavelengths, +.>For the encoded gas concentration, +.>For differential absorption cross section->For the +.>Absorbance values for each sampling point.
10. The differential optimization algorithm based on a differential absorption spectrometer according to claim 9, wherein S6 comprises:
s6.3, selecting operation, namely calculating an average value of fitness, and selecting individuals smaller than the average value of the fitness to enter the next generation;
s6.4, adding disaster period self-adaptive crossover operation to define crossover probabilityThe method comprises the following steps:
；
in the method, in the process of the invention,represents the crossover probability->Indicates the number of catastrophic events that have occurred at the present time, < + >>Represents a constant value of less than 1, +.>Represents an integer for adjusting the range of cross probability variation, +.>Indicating that the population is proceeding to->Performing genetic operation;
s6.5, terminating operation, and outputting an individual with a small adaptation value as a true value by the optimization algorithm when the solution concentration value of the last individual reaches a required threshold value, wherein the error range of the concentration value finally solved by the optimization algorithm is within the threshold value range.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103226095A (en) * | 2013-04-08 | 2013-07-31 | 南京国电环保科技有限公司 | Fast calibration method of spectrometer wavelength |
| US20140344197A1 (en) * | 2013-05-20 | 2014-11-20 | International Business Machines Corporation | Adaptive cataclysms in genetic algorithms |
| CN106526614A (en) * | 2016-10-25 | 2017-03-22 | 中国科学院合肥物质科学研究院 | Method for optimizing laser radar detection atmospheric composition spectral line analysis |
| CN111572729A (en) * | 2020-04-07 | 2020-08-25 | 哈尔滨工程大学 | Thrust distribution method of ship dynamic positioning system based on improved genetic algorithm |
| CN115523958A (en) * | 2022-10-15 | 2022-12-27 | 浙江大学 | Gas temperature and concentration synchronous measurement method based on spectrum fast-slow separation principle |
-
2023
- 2023-07-31 CN CN202310942708.8A patent/CN116663433B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103226095A (en) * | 2013-04-08 | 2013-07-31 | 南京国电环保科技有限公司 | Fast calibration method of spectrometer wavelength |
| US20140344197A1 (en) * | 2013-05-20 | 2014-11-20 | International Business Machines Corporation | Adaptive cataclysms in genetic algorithms |
| CN106526614A (en) * | 2016-10-25 | 2017-03-22 | 中国科学院合肥物质科学研究院 | Method for optimizing laser radar detection atmospheric composition spectral line analysis |
| CN111572729A (en) * | 2020-04-07 | 2020-08-25 | 哈尔滨工程大学 | Thrust distribution method of ship dynamic positioning system based on improved genetic algorithm |
| CN115523958A (en) * | 2022-10-15 | 2022-12-27 | 浙江大学 | Gas temperature and concentration synchronous measurement method based on spectrum fast-slow separation principle |
Non-Patent Citations (5)
| Title |
|---|
| BRIAN MCGINLEY .ETAL: "Maintaining Healthy Population Diversity Using Adaptive Crossover, Mutation, and Selection", IEEE, vol. 15, no. 5, XP011362534, DOI: 10.1109/TEVC.2010.2046173 * |
| 孙友文 等: "差分吸收光谱技术在工业污染源烟气排放监测中的应用", 物理学报, vol. 62, no. 01, pages 2 - 4 * |
| 杨雷 等: "LED光源DOAS方法的夜间NO_2气体浓度遥测研究", 光谱学与光谱分析, no. 05 * |
| 林广明 等: "改进灾变遗传算法在无功优化规划中的应用", 电网技术, vol. 34, no. 4, pages 129 * |
| 陈书旺 等: "差分吸收光谱检测H2S气体的反演算法", 太赫兹科学与电子信息学报, vol. 18, no. 4, pages 735 * |
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