TY - JOUR
T1 - Dynamic hierarchical aggregation of parallel outputs for aircraft take-off noise identification
AU - Sanchez-Perez, Luis A.
AU - Sanchez-Fernandez, Luis P.
AU - Suarez-Guerra, Sergio
AU - Lopez-Pacheco, Maria G.
N1 - Publisher Copyright:
© 2015 Elsevier Ltd.
PY - 2015/11
Y1 - 2015/11
N2 - Assessment of airport noise pollution mainly depends on the correlation between aircraft class, noise measured and flight path geometry. Regulation, evaluation and especially certification procedures generally establish that previous correlation cannot be carried out using aircraft navigation systems data. Additionally, airport noise monitoring systems generally use aircraft noise signals only for computing statistical indicators. Consequently, methods to acquire more information from these signals have been explored so as to improve noise estimation around airports. In this regard, this paper introduces a new model for aircraft class recognition based on take-off noise signal segmentation and dynamic hierarchical aggregation of K parallel neural networks outputs Opk. A single hierarchy is separately defined for every class p, mainly based on the recall and precision of neural network NNk|k=1,2,...,K. Similarly, the dynamics proposed is also particular to each class p. The performance of the new model is benchmarked against models in literature over a database containing real-world take-off noise measurements. The new model performs better on the abovementioned database and successfully classifies over 89% of measurements.
AB - Assessment of airport noise pollution mainly depends on the correlation between aircraft class, noise measured and flight path geometry. Regulation, evaluation and especially certification procedures generally establish that previous correlation cannot be carried out using aircraft navigation systems data. Additionally, airport noise monitoring systems generally use aircraft noise signals only for computing statistical indicators. Consequently, methods to acquire more information from these signals have been explored so as to improve noise estimation around airports. In this regard, this paper introduces a new model for aircraft class recognition based on take-off noise signal segmentation and dynamic hierarchical aggregation of K parallel neural networks outputs Opk. A single hierarchy is separately defined for every class p, mainly based on the recall and precision of neural network NNk|k=1,2,...,K. Similarly, the dynamics proposed is also particular to each class p. The performance of the new model is benchmarked against models in literature over a database containing real-world take-off noise measurements. The new model performs better on the abovementioned database and successfully classifies over 89% of measurements.
KW - Aircraft class
KW - Dynamic hierarchical aggregation
KW - Neural network ensemble
KW - Pattern recognition
KW - Signal segmentation
KW - Take-off noise
UR - http://www.scopus.com/inward/record.url?scp=84946023076&partnerID=8YFLogxK
U2 - 10.1016/j.engappai.2015.08.002
DO - 10.1016/j.engappai.2015.08.002
M3 - Artículo
SN - 0952-1976
VL - 46
SP - 33
EP - 42
JO - Engineering Applications of Artificial Intelligence
JF - Engineering Applications of Artificial Intelligence
ER -