Dust Particle Size Distribution Inversion Based on the Multi Population Genetic Algorithm

Mao, Jiandong; Li, Juan

doi:10.3319/tao.2014.06.12.01(a)

Cited by 10 publications

(3 citation statements)

References 13 publications

(18 reference statements)

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“…MPGA, as a global optimization search method has the advantages of high efficiency and rapid speed and is suitable for complex optimization problems [22,23]. MPGA consists of standard genetic algorithm (SGA), immigration operator, artificial operator, and elite population.…”

Section: Svr Model Parameter Optimizationmentioning

confidence: 99%

A Cucumber Photosynthetic Rate Prediction Model in Whole Growth Period with Time Parameters

et al. 2023

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Photosynthetic rate prediction models can provide guidance for crop photosynthetic process optimization, which has been widely used in the precise regulation of the protected environment. The photosynthetic capacity of crops continuously changes during their whole growth process. Previous studies on photosynthetic models mainly consider the interaction between a crop’s photosynthetic rate and its outer environmental conditions and have been able to predict a crop’s photosynthetic rate in a certain growth period. However, photosynthetic rate prediction models for whole growth periods have not been proposed yet. To solve this question, this paper introduces growing time into a variable set and proposes a method for building a cucumber photosynthetic rate prediction model of whole growth periods. First, the photosynthetic rate of cucumber leaves under different environmental conditions (light, temperature, and CO2 concentration) during the whole growth period was obtained through a multi-gradient nested test. With the environmental data and the cultivation time as the inputs, a photosynthetic rate prediction model was built using the Support Vector Regression algorithm. In order to obtain better modeling results, multiple kernel functions were used for pretraining, and the parameters of the Support Vector Regression algorithm were optimized based on multiple population genetic algorithms. Compared with a Back Propagation neural network and Non-linear Regression method, the Support Vector Regression model optimized had the highest accuracy, with the coefficient of determination of the test set was 0.998, and the average absolute error was 0.280 μmol·m−2·s−1, which provides a theoretical solution for the prediction of the cucumber photosynthetic rate during the whole growth period.

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Section: Svr Model Parameter Optimizationmentioning

confidence: 99%

A Cucumber Photosynthetic Rate Prediction Model in Whole Growth Period with Time Parameters

et al. 2023

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“…Figure 3 shows the relationship between the backscattering efficiency factor and the particle radius at different wavelengths. From Figure 2 and Figure 3, the first main peak lies in between 0.1 and 1 μm, and as the wavelength λ increases, the position of the first main peak moves in the same direction with the particle radius r. Since the Yinchuan area is surrounded by four deserts, with high altitude, dry and dusty climate, it belongs typical semi-arid climate in northwest China, the complex refractive indexes m is chosen 1.55-0.01i to retrieve (Mao and Li, 2014).…”

Section: Figurementioning

confidence: 99%

Retrieval of particle size distribution based on a multi-objective genetic algorithm for multi-wavelength lidar

Bao,

Qi,

Mao

et al. 2023

Front. Environ. Sci.

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Introduction: Aerosols affect the radiation budget of the Earth’s atmospheric system. The aerosol particle size distribution (PSD) is one of the main parameters for characterizing the effect of aerosol on radiative forcing.Methods: The extinction coefficient and backscattering coefficient at 355 and 532 nm and backscattering coefficient at 1064 nm of aerosol particles over Yinchuan area, China, which measured by a multi-wavelength lidar developed by North Minzu University, were used to retrieve the aerosol PSD. In view of the disadvantages of traditional regularization methods, the elitist Non-Dominated Sorting Genetic Algorithm (NSGA-II) is selected to retrieve PSD.Results and Discussion: To verify the feasibility for retrieval of aerosol PSD, the NSGA-II with different errors in the input optical signal was simulated, in which the errors of the inverted PSD are still in the acceptable range when 35% error added into the optical parameters. Moreover, some experiments were carried out under different atmospheric conditions, including background sunny, cloudy and dusty days, and comparisons were performed with Multiple Population Genetic Algorithm (MPGA) and Simple Genetic Alogrithm (SGA) method. The results show that the retrieval effect of NSGA-II was better than that of MPGA and SGA, and the NSGA-II is very suitable for retrieve PSD by using the multi-wavelength lidar data.

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“…The method of particle size distribution (PSD) measurement relates to the development of many fields (He et al, 2016;Knop et al, 2014;Mao and Li, 2014;Xie and Zhang, 2014), such as medicine, chemical, energy, environmental protection, machinery, electronics, and so forth. Therefore, the implementation of fast and accurate particle size online measurement is significant.…”

Section: Introductionmentioning

confidence: 99%

Inversion of particle size distribution based on iterative non-negative Philips-Twomey algorithm

Shan

Cao

et al. 2019

Transactions of the Institute of Measurement and Control

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The precision of particle size distribution (PSD) is a key indicator to evaluate inversion algorithms. In the light scattering particle sizing technology, constructed on the inversion method of the traditional non-negative Philips-Twomey (NNPT) algorithm, an iterative NNPT (INNPT) algorithm was proposed in this paper, applied for the inversion of PSD. In simulations, the inversion accuracy performance of NNPT and INNPT algorithms were compared and analyzed by using different widths of PSD that conform to Johnson’s SB unimodal and bimodal functions. A small angle forward scattering method of PSD measurement system using Charge Coupled Device (CCD) as a photodetector was constructed and the national standard particles were tested. Both the results of simulations and experiments show that the inverse accuracy and stability of INNPT algorithm are superior to NNPT algorithm especially in the conditions of narrow distribution.

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Dust Particle Size Distribution Inversion Based on the Multi Population Genetic Algorithm

Cited by 10 publications

References 13 publications

A Cucumber Photosynthetic Rate Prediction Model in Whole Growth Period with Time Parameters

A Cucumber Photosynthetic Rate Prediction Model in Whole Growth Period with Time Parameters

Retrieval of particle size distribution based on a multi-objective genetic algorithm for multi-wavelength lidar

Inversion of particle size distribution based on iterative non-negative Philips-Twomey algorithm

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