SOM neural network for clustering plant and plant stress based on kinetic imaging of chlorophyll fluorescence

2016 ◽  
Author(s):  
Zhou Chunyan ◽  
HuaDeng Xin ◽  
Le Jing ◽  
Zhang Jia
Keyword(s):  
Neural Network ◽  
Chlorophyll Fluorescence ◽  
Plant Stress ◽  
Kinetic Imaging ◽  
Som Neural Network

scholarly journals An Intelligent Evaluation Method to Analyze the Competitiveness of Airlines

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Jun Zhao ◽  
Xumei Chen
Keyword(s):  
Neural Network ◽  
Prior Knowledge ◽  
Evaluation Method ◽  
Standard Sample ◽  
Reference Value ◽  
The Self ◽  
Index Data ◽  
Som Neural Network ◽  
Simulation Results ◽  
Self Organizing

An intelligent evaluation method is presented to analyze the competitiveness of airlines. From the perspective of safety, service, and normality, we establish the competitiveness indexes of traffic rights and the standard sample base. The self-organizing mapping (SOM) neural network is utilized to self-organize and self-learn the samples in the state of no supervision and prior knowledge. The training steps of high convergence speed and high clustering accuracy are determined based on the multistep setting. The typical airlines index data are utilized to verify the effect of the self-organizing mapping neural network on the airline competitiveness analysis. The simulation results show that the self-organizing mapping neural network can accurately and effectively classify and evaluate the competitiveness of airlines, and the results have important reference value for the allocation of traffic rights resources.

scholarly journals Application of SOM neural network in clustering

2009 ◽  
Vol 02 (08) ◽  
pp. 637-643 ◽  
Author(s):  
Soroor Behbahani ◽  
Ali Moti Nasrabadiv
Keyword(s):  
Neural Network ◽  
Som Neural Network

scholarly journals MEASUREMENT OF SUN INDUCED CHLOROPHYLL FLUORESCENCE USING HYPERSPECTRAL SATELLITE IMAGERY

2016 ◽  
Vol XLI-B8 ◽  
pp. 911-913
Author(s):  
S. M. Irteza ◽  
J. E. Nichol
Keyword(s):  
Chlorophyll Fluorescence ◽  
Steady State ◽  
Satellite Images ◽  
Plant Stress ◽  
Fluorescence Signal ◽  
Absorption Bands ◽  
Considerable Evidence ◽  
Different Seasons ◽  
Insight Into ◽  
Accurate Indicator

Solar Induced Chlorophyll Fluorescence (SIF), can be used as an indicator of stress in vegetation. Several scientific approaches have been made and there is considerable evidence that steady state Chlorophyll fluorescence is an accurate indicator of plant stress hence a reliable tool to monitor vegetation health status. Retrieval of Chlorophyll fluorescence provides an insight into photochemical and carbon sequestration processes within vegetation. Detection of Chlorophyll fluorescence has been well understood in the laboratory and field measurement. Fluorescence retrieval methods were applied in and around the atmospheric absorption bands 02B (Red wavelength) approximately 690 nm and 02A (Far red wavelengths) 740 nm. Hyperion satellite images were acquired for the years 2012 to 2015 in different seasons. Atmospheric corrections were applied using the 6S Model. The Fraunhofer Line Discrimanator (FLD) method was applied for retrieval of SIF from the Hyperion images by measuring the signal around the absorption bands in both vegetated and non vegetated land cover types. Absorption values were extracted in all the selected bands and the fluorescence signal was detected. The relationships between NDVI and Fluorescence derived from the satellite images are investigated to understand vegetation response within the absorption bands.

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