scholarly journals Artificial neural network applications in ionospheric studies

1998 ◽  
Vol 41 (5-6) ◽  
Author(s):  
L. R. Cander
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Numerical Models ◽  
Electron Content ◽  
Total Electron ◽  
Ionospheric Physics ◽  
Modern Development ◽  
Artificial Neural ◽  
Short Term Forecasting ◽  
Electron Density Profiles

The ionosphere of Earth exhibits considerable spatial changes and has large temporal variability of various timescales related to the mechanisms of creation, decay and transport of space ionospheric plasma. Many techniques for modelling electron density profiles through entire ionosphere have been developed in order to solve the "age-old problem" of ionospheric physics which has not yet been fully solved. A new way to address this problem is by applying artificial intelligence methodologies to current large amounts of solar-terrestrial and ionospheric data. It is the aim of this paper to show by the most recent examples that modern development of numerical models for ionospheric monthly median long-term prediction and daily hourly short-term forecasting may proceed successfully applying the artificial neural networks. The performance of these techniques is illustrated with different artificial neural networks developed to model and predict the temporal and spatial variations of ionospheric critical frequency, f0F2 and Total Electron Content (TEC). Comparisons between results obtained by the proposed approaches and measured f0F2 and TEC data provide prospects for future applications of the artificial neural networks in ionospheric studies.

scholarly journals Reconstruction of Storm‐Time Total Electron Content Using Ionospheric Tomography and Artificial Neural Networks: A Comparative Study Over the African Region

Radio Science ◽  
2018 ◽  
Vol 53 (11) ◽  
pp. 1328-1345 ◽  
Author(s):  
Jean Claude Uwamahoro ◽  
Nigussie M. Giday ◽  
John Bosco Habarulema ◽  
Zama T. Katamzi‐Joseph ◽  
Gopi Krishna Seemala
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Comparative Study ◽  
Total Electron Content ◽  
Electron Content ◽  
African Region ◽  
Total Electron ◽  
Ionospheric Tomography ◽  
Artificial Neural

scholarly journals Storm‐Time Modeling of the African Regional Ionospheric Total Electron Content Using Artificial Neural Networks

Space Weather ◽  
2020 ◽  
Vol 18 (9) ◽  
Author(s):  
Daniel Okoh ◽  
John Bosco Habarulema ◽  
Babatunde Rabiu ◽  
Gopi Seemala ◽  
Joshua Benjamin Wisdom ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Total Electron Content ◽  
Electron Content ◽  
Total Electron ◽  
Ionospheric Total Electron Content ◽  
Time Modeling ◽  
Artificial Neural

Artificial Neural Networks for Structural Health Monitoring of Helicopter Harsh Landings

2013 ◽  
Vol 390 ◽  
pp. 192-197
Author(s):  
Giorgio Vallone ◽  
Claudio Sbarufatti ◽  
Andrea Manes ◽  
Marco Giglio
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Structural Damage ◽  
Numerical Models ◽  
Damage State ◽  
Operational Conditions ◽  
Structural Health ◽  
Artificial Neural

The aim of the current paper is to explore fuselage monitoring possibilities trough the usage of Artificial Neural Networks (ANNs), trained by the use of numerical models, during harsh landing events. A harsh landing condition is delimited between the usual operational conditions and a crash event. Helicopter structural damage due to harsh landings is generally less severe than damage caused by a crash but may lead to unscheduled maintenance events, involving costs and idle times. Structural Health Monitoring technologies, currently used in many application fields, aim at the continuous detection of damage that may arise, thereby improving safety and reducing maintenance idle times by the disposal of a ready diagnosis. A landing damage database can be obtained with relatively little effort by the usage of a numerical model. Simulated data are used to train various ANNs considering the landing parameter values as input. The influence of both the input and output noise on the system performances were taken into account. Obtained outputs are a general classification between damaged and undamaged conditions, based on a critical damage threshold, and the reconstruction of the fuselage damage state.

scholarly journals A Methodology for Energy Load Profile Forecasting Based on Intelligent Clustering and Smoothing Techniques

Energies ◽  
2020 ◽  
Vol 13 (16) ◽  
pp. 4040
Author(s):  
Jamer Jiménez Mares ◽  
Loraine Navarro ◽  
Christian G. Quintero M. ◽  
Mauricio Pardo
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Energy Demand ◽  
Percentage Error ◽  
Artificial Neural ◽  
Short Term Forecasting ◽  
Dynamic Time ◽  
High Level ◽  
Energy Assets ◽  
Demand Changes

The electrical sector needs to study how energy demand changes to plan the maintenance and purchase of energy assets properly. Prediction studies for energy demand require a high level of reliability since a deviation in the forecasting demand could affect operation costs. This paper proposed a short-term forecasting energy demand methodology based on hierarchical clustering using Dynamic Time Warp as a similarity measure integrated with Artificial Neural Networks. Clustering was used to build the typical curve for each type of day, while Artificial Neural Networks handled the weather sensibility to correct a preliminary forecasting curve obtained in the clustering stage. A statistical analysis was carried out to identify those significant factors in the prediction model of energy demand. The performance of this proposed model was measured through the Mean Absolute Percentage Error (MAPE). The experimental results show that the three-stage methodology was able to improve the MAPE, reaching values as good as 2%.

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