scholarly journals Investigation of Atmospheric Aerosol Size Distributions from Ground-Based Solar Spectrometer Measurements Synthesized with Satellite Data

2019 ◽  
Vol 16 (1) ◽  
pp. 23-32
Author(s):  
Dane Kuhr ◽  
Adam Whitten
Keyword(s):  
Particle Swarm Optimization ◽  
Aerosol Particle ◽  
Satellite Imagery ◽  
Forest Fires ◽  
Atmospheric Aerosol ◽  
Particle Swarm ◽  
Size Distributions ◽  
Number Density ◽  
Swarm Optimization ◽  
Aitken Mode

Data collected by a ground-based solar spectrometer at Collegeville, MN, was used to generate Aerosol Optical Depths (AODs) throughout the 2017 calendar year. The AOD data was then visualized at 13 selected wavelengths throughout the year and analyzed in comparison to satellite imagery, upper air charts and backwards trajectories of air masses moving towards Central Minnesota in order to determine key dates of interest that correspond to times before (20170615), during (20170729), and at the conclusion of (20170914) forest fires that burned in British Columbia (BC) during the summer of 2017. The data from these specific days were analyzed further by inputting the maximum and minimum AODs for each day into a Parameter Based Particle Swarm Optimization (PBPSO) algorithm in order to generate bimodal lognormal particle size distributions. The bimodal distributions were chosen because they carry more information about the aerosol loads across the entire spectrum of particle radii. The resulting distributions show an increase in number density and decrease in median radius in the Aitken mode during the BC forest fires and a relatively constant (within uncertainty) number density of accumulation mode particles at daily maximum AODs. Comparing the resulting bimodal lognormal distribution for daily minimum AODs (where evaporation and other diurnal effects are at a minimum) shows an increased number density of Aitken mode particles by two orders of magnitude from pre- to post-forest fires. This measured increase in the number density of smaller radii particles due to forest fires illustrates the PBPSO’s capability of distinguishing variations in atmospheric aerosol particle number size distributions in the Aitken mode based on data collected by the Kipp-Zonen PGS-100 solar spectrometer. KEYWORDS: Atmospheric Aerosol; Particle Swarm Optimization; Aerosol Optical Depth; Solar Spectrometer; Size Distributions; Forest Fire; Satellite Imagery; Upper Air Charts; Backward Trajectory

scholarly journals Determination of Fitted Size Distribution for Atmospheric Aerosols

2017 ◽  
Vol 14 (2) ◽  
Author(s):  
Kaitlin DuPaul ◽  
Adam Whitten
Keyword(s):  
Particle Swarm Optimization ◽  
Size Distribution ◽  
Atmospheric Aerosols ◽  
Particle Swarm ◽  
Bimodal Distribution ◽  
Aerosol Size Distribution ◽  
Size Distributions ◽  
Swarm Optimization ◽  
Large Variability ◽  
Lognormal Distributions

A synthetic set of aerosol optical depths (AODs) generated from a standard set of aerosol size distributions was analyzed by a parameter based particle swarm optimization (PBPSO) routine in order to test the reproducibility of the results. Junge and lognormal size distributions were consistently reproduced. Gamma and bimodal distributions showed large variability in solutions. values were used to determine the best subset of possible solutions allowing quantification of parameters with uncertainties when using PBPSO. AODs measured by a sun photometer on a clear day (20160413) and a foggy day (20160508) were then processed by the PBPSO program for both bimodal and lognormal distributions. Results showed that in general the foggy day has smaller values indicating that the PBPSO algorithm is better able to match AODs when there is a larger aerosol load in the atmosphere. The bimodal distribution from the clear day best describes the aerosol size distribution since the values are lower. The lognormal distribution best describes the aerosol size distribution on the foggy day (20160508). KEYWORDS: Atmospheric Aerosols; Size Distributions; Junge; Bimodal; Gamma; Lognormal; Particle Swarm Optimization; Inverse Problem; Aerosol Optical Depth

Smart teaching mode based on particle swarm image recognition and human-computer interaction deep learning

2020 ◽  
Vol 39 (4) ◽  
pp. 5699-5711
Author(s):  
Shirong Long ◽  
Xuekong Zhao
Keyword(s):  
Feature Extraction ◽  
Particle Swarm Optimization ◽  
Deep Learning ◽  
Real Time ◽  
Image Recognition ◽  
Particle Swarm ◽  
Learning Technology ◽  
Search Performance ◽  
Swarm Optimization ◽  
Teaching Mode

The smart teaching mode overcomes the shortcomings of traditional teaching online and offline, but there are certain deficiencies in the real-time feature extraction of teachers and students. In view of this, this study uses the particle swarm image recognition and deep learning technology to process the intelligent classroom video teaching image and extracts the classroom task features in real time and sends them to the teacher. In order to overcome the shortcomings of the premature convergence of the standard particle swarm optimization algorithm, an improved strategy for multiple particle swarm optimization algorithms is proposed. In order to improve the premature problem in the search performance algorithm of PSO algorithm, this paper combines the algorithm with the useful attributes of other algorithms to improve the particle diversity in the algorithm, enhance the global search ability of the particle, and achieve effective feature extraction. The research indicates that the method proposed in this paper has certain practical effects and can provide theoretical reference for subsequent related research.

Sistem Kontrol Optimal Fuzzy-Particle Swarm Optimization

2018 ◽  
Vol 9 (1) ◽  
pp. 1-5
Author(s):  
Fachrudin Hunaini ◽  
Imam Robandi ◽  
Nyoman Sutantra
Keyword(s):  
Fuzzy Logic ◽  
Particle Swarm Optimization ◽  
Control System ◽  
Membership Function ◽  
Fuzzy Logic Control ◽  
Particle Swarm ◽  
Optimization Method ◽  
Swarm Optimization ◽  
Motion Error ◽  
Logic Control

Fuzzy Logic Control (FLC) is a reliable control system for controlling nonlinear systems, but to obtain optimal fuzzy logic control results, optimal Membership Function parameters are needed. Therefore in this paper Particle Swarm Optimization (PSO) is used as a fast and accurate optimization method to determine Membership Function parameters. The optimal control system simulation is carried out on the automatic steering system of the vehicle model and the results obtained are the vehicle's lateral motion error can be minimized so that the movement of the vehicle can always be maintained on the expected trajectory

Adaptive Inertia Weight Particle Swarm Optimization For Linear and Nonlinear Channel Equalization

2012 ◽  
Vol 3 (4) ◽  
pp. 1-4
Author(s):  
Diana D.C Diana D.C ◽  
◽  
Joy Vasantha Rani.S.P Joy Vasantha Rani.S.P ◽  
Nithya.T.R Nithya.T.R ◽  
Srimukhee.B Srimukhee.B
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Channel Equalization ◽  
Swarm Optimization ◽  
Inertia Weight ◽  
Linear And Nonlinear
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