scholarly journals Technical Note: Determination of aerosol optical properties by a calibrated sky imager

2009 ◽  
Vol 9 (17) ◽  
pp. 6417-6427 ◽  
Author(s):  
A. Cazorla ◽  
J. E. Shields ◽  
M. E. Karr ◽  
F. J. Olmo ◽  
A. Burden ◽  
...  

Abstract. The calibrated ground-based sky imager developed in the Marine Physical Laboratory, the Whole Sky Imager (WSI), has been tested with data from the Atmospheric Radiation Measurement Program (ARM) at the Southern Great Plain site (SGP) to determine optical properties of the atmospheric aerosol. Different neural network-based models calculate the aerosol optical depth (AOD) for three wavelengths using the radiance extracted from the principal plane of sky images from the WSI as input parameters. The models use data from a CIMEL CE318 photometer for training and validation and the wavelengths used correspond to the closest wavelengths in both instruments. The spectral dependency of the AOD, characterized by the Ångström exponent α in the interval 440–870 nm, is also derived using the standard AERONET procedure and also with a neural network-based model using the values obtained with a CIMEL CE318. The deviations between the WSI derived AOD and the AOD retrieved by AERONET are within the nominal uncertainty assigned to the AERONET AOD calculation (±0.01), in 80% of the cases. The explanation of data variance by the model is over 92% in all cases. In the case of α, the deviation is within the uncertainty assigned to the AERONET α (±0.1) in 50% of the cases for the standard method and 84% for the neural network-based model. The explanation of data variance by the model is 63% for the standard method and 77% for the neural network-based model.

2008 ◽  
Vol 8 (6) ◽  
pp. 19989-20018
Author(s):  
A. Cazorla ◽  
J. E. Shields ◽  
M. E. Karr ◽  
A. Burden ◽  
F. J. Olmo ◽  
...  

Abstract. The calibrated ground-based sky imager developed in the Marine Physical Laboratory, the Whole Sky Imager (WSI), has been tested to determine optical properties of the atmospheric aerosol. Different neural network-based models calculate the aerosol optical depth (AOD) for three wavelengths using the radiance extracted from the principal plane of sky images from the WSI as input parameters. The models use data from a CIMEL CE318 photometer for training and validation and the wavelengths used correspond to the closest wavelengths in both instruments. The spectral dependency of the AOD, characterized by the Ångström exponent α in the interval 440–870, is also derived using the standard AERONET procedure and also with a neural network-based model using the values obtained with a CIMEL CE318. The deviations between the WSI derived AOD and the AOD retrieved by AERONET are within the nominal uncertainty assigned to the AERONET AOD calculation (±0.01), in 80% of the cases. The explanation of data variance by the model is over 92% in all cases. In the case of α, the deviation is within the uncertainty assigned to the AERONET α (±0.1) in 50% for the standard method and 84% for the neural network-based model. The explanation of data variance by the model is 63% for the standard method and 77% for the neural network-based model.


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Brett H. Hokr ◽  
Joel N. Bixler

AbstractDynamic, in vivo measurement of the optical properties of biological tissues is still an elusive and critically important problem. Here we develop a technique for inverting a Monte Carlo simulation to extract tissue optical properties from the statistical moments of the spatio-temporal response of the tissue by training a 5-layer fully connected neural network. We demonstrate the accuracy of the method across a very wide parameter space on a single homogeneous layer tissue model and demonstrate that the method is insensitive to parameter selection of the neural network model itself. Finally, we propose an experimental setup capable of measuring the required information in real time in an in vivo environment and demonstrate proof-of-concept level experimental results.


F1000Research ◽  
2019 ◽  
Vol 8 ◽  
pp. 646
Author(s):  
Dániel Leitold ◽  
Ágnes Vathy-Fogarassy ◽  
János Abonyi

The network science-based determination of driver nodes and sensor placement has become increasingly popular in the field of dynamical systems over the last decade. In this paper, the applicability of the methodology in the field of life sciences is introduced through the analysis of the neural network of Caenorhabditis elegans. Simultaneously, an Octave and MATLAB-compatible NOCAD toolbox is proposed that provides a set of methods to automatically generate the relevant structural controllability and observability associated measures for linear or linearised systems and compare the different sensor placement methods.


2006 ◽  
Vol 60 (1) ◽  
Author(s):  
I. Malík ◽  
E. Sedlárová ◽  
J. Csöllei ◽  
F. Andriamainty ◽  
P. Kurfürst ◽  
...  

AbstractThe phenylcarbamic acid derivatives with N-phenylpiperazine moiety in the molecule have been prepared. The structure has been confirmed by elemental analysis, IR, 1H NMR, and mass spectral data. For the prepared set of the compounds the lipophilicity parameters have been determined. The experimentally obtained lipophilicity parameters have been correlated with theoretical entries obtained by different computer programs based on the neural network and fragmental methods.


Author(s):  
Marion Jäger ◽  
André Liemert ◽  
Florian Foschum ◽  
Alwin Kienle

2011 ◽  
Vol 121-126 ◽  
pp. 382-386
Author(s):  
Yi Jun Chen ◽  
Qing Hai Zhao

In this paper, the nonlinear mapping relationship between characteristic parameters of failures and failure types is realized by using neural network through extracting characteristic variables of failures during operation of the gear. Aiming at the problems of neutral network such as slow convergence speed and existence of local minima, the neural network is optimized and the ant colony neural network is established by using the ant colony algorithm to realize rapid and accurate determination of failure status of a gear from characteristic parameters of failures. In addition, validity of the established model is verified through experiments.


2017 ◽  
Vol 34 (2) ◽  
pp. e12192 ◽  
Author(s):  
Weidong Zhu ◽  
Youhua Xu ◽  
Yong Wu ◽  
Yibo Sun

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