Research on Location Strategy of Multi-mobile Robots Based on Gaussian Plume Model

2018 ◽  
pp. 95-103
Author(s):  
Liangping Shi ◽  
Huanyu Zhao ◽  
Xi Zhang
Keyword(s):  
Mobile Robots ◽  
Location Strategy ◽  
Gaussian Plume Model ◽  
Plume Model ◽  
Gaussian Plume

Sensitivity of the Gaussian plume model

1975 ◽  
Vol 9 (5) ◽  
pp. 550-551
Author(s):  
D.G. Horne
Keyword(s):  
Gaussian Plume Model ◽  
Plume Model ◽  
Gaussian Plume

The Simulation of Release Consequence with a Modified Gaussian Plume Model

2016 ◽  
pp. 445-454
Author(s):  
Hongya Zhu ◽  
Xuanya Liu ◽  
Qingsong Wang ◽  
Jinhua Sun
Keyword(s):  
Gaussian Plume Model ◽  
Plume Model ◽  
Gaussian Plume

scholarly journals Estimation of Radioactivity Release Activity Using Non-Linear Kalman Filter-Based Estimation Techniques

Energies ◽  
2020 ◽  
Vol 13 (15) ◽  
pp. 3985
Author(s):  
Victor M. Becerra ◽  
Vineet Vajpayee ◽  
Nils Bausch ◽  
T.V. Santhosh ◽  
Gopika Vinod ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Dose Rate ◽  
Gaussian Plume Model ◽  
Estimation Techniques ◽  
Plume Model ◽  
Dose Rates ◽  
Statistical Measures ◽  
Cubature Kalman Filter ◽  
Non Linear ◽  
Gaussian Plume

The estimation of radioactivity release following an accident in a nuclear power plant is crucial due to its short and long-term impacts on the surrounding population and the environment. In the case of any accidental release, the activity needs to be estimated quickly and reliably to effectively plan a rapid emergency response and design an appropriate evacuation strategy. The accurate prediction of incurred dose rate during normal or accident scenario is another important aspect. In this article, three different non-linear estimation techniques, extended Kalman filter, unscented Kalman filter, and cubature Kalman filter are proposed in order to estimate release activity and to improve the prediction of dose rates. Radionuclide release rate, average wind speed, and height of release are estimated using the dose rate monitors data collected in proximity of the release point. Further, the estimates are employed to improve the prediction of dose rates. The atmospheric dispersion phenomenon of radioactivity release is modelled using the Gaussian plume model. The Gaussian plume model is then employed for the calculation of dose rates. A variety of atmospheric and accident related scenarios for single source and multiple sources are studied in order to assess the efficacy of the proposed filters. Statistical measures have been used in order to validate the performance of the proposed approaches.

On The Effectiveness of Using Machine Learning and Gaussian Plume Model for Plant Disease Dispersion Prediction and Simulation

2019 ◽  
Author(s):  
Ryan Miriyagalla ◽  
Yashodha Samarawickrama ◽  
Dharatha Rathnaweera ◽  
Lashan Liyanage ◽  
Dharshana Kasthurirathna ◽  
...  
Keyword(s):  
Machine Learning ◽  
Plant Disease ◽  
Gaussian Plume Model ◽  
Plume Model ◽  
Gaussian Plume ◽  
Prediction And Simulation

Ability of the Gaussian plume model to predict and describe spore dispersal over a potato crop

2002 ◽  
Vol 155 (1) ◽  
pp. 1-18 ◽  
Author(s):  
H.P. Spijkerboer ◽  
J.E. Beniers ◽  
D. Jaspers ◽  
H.J. Schouten ◽  
J. Goudriaan ◽  
...  
Keyword(s):  
Potato Crop ◽  
Spore Dispersal ◽  
Gaussian Plume Model ◽  
Plume Model ◽  
Gaussian Plume
Sign in / Sign up

Export Citation Format

Share Document