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|Abstract||Icing poses a threat for aircrafts, as it does for all other areas in general. From the past to present, many aviation accidents have occurred and are still occurring due to icing. Scientists have proposed several solutions to eliminate or minimize the problem of icing. To eliminate icing, de-icing systems have been introduced and anti-icing systems have been put into practice to prevent icing and these methods are presently being developed. However, these systems are generally put into use in aircrafts under pilot control. The pilot operates anti-icing and de-icing systems when she or he senses the presence of ice. The pilot's carelessness or lack of training then plays a major role in the occurrence of accidents. The study has tested seven different artificial intelligence algorithms for an aircraft that could lead to a direct operation of the de-icing/anti-icing system as a result of the estimated risk of ice. Simulink and Waijung blockset-supported modelling have been conducted and the diagram that will estimate the icing risk rating for different input values was embedded in the STM32F4 Discovery controller board and the results of the icing prediction were observed using RGB led. 88.59 percent value is obtained by the boosting algorithm for the optimum icing risk rating prediction. In conclusion, a model is proposed to ensure automatic activation of the anti-icing and de-icing systems in aircraft that will be used for predicting icing.|
|Item Type||Research Article|
|Title||Prediction Of Icing Risk Degree On Aircraft With Machine Learning Algorithms|
|Library Catalog||DOI.org (Crossref)|
|Tags||Icing on aircrafts, artificial intelligence, machine learning, supervised learning|