The Experts below are selected from a list of 10125 Experts worldwide ranked by ideXlab platform

José Juan Carbajal-hernández - One of the best experts on this subject based on the ideXlab platform.

  • Aircraft Class identification based on take-off noise signal segmentation in time
    Expert Systems with Applications, 2013
    Co-Authors: Luis Alejandro Sánchez-pérez, Luis P. Sánchez-fernández, Sergio Suárez-guerra, José Juan Carbajal-hernández
    Abstract:

    Abstract Aircraft noise is one of the most uncomfortable kinds of sounds. That is why many organizations have addressed this problem through noise contours around airports, for which they use the Aircraft type as the key element. This paper presents a new computational model to identify the Aircraft Class with a better performance, because it introduces the take-off noise signal segmentation in time. A method for signal segmentation into four segments was created. The Aircraft noise patterns are extracted using an LPC (Linear Predictive Coding) based technique and the Classification is made combining the output of four parallel MLP (Multilayer Perceptron) neural networks, one for each segment. The individual accuracy of each network was improved using a wrapper feature selection method, increasing the model effectiveness with a lower computational cost. The Aircraft are grouped into Classes depending on the installed engine type. The model works with 13 Aircraft categories with an identification level above 85% in real environments.

Luis Alejandro Sánchez-pérez - One of the best experts on this subject based on the ideXlab platform.

  • Aircraft Class identification based on take-off noise signal segmentation in time
    Expert Systems with Applications, 2013
    Co-Authors: Luis Alejandro Sánchez-pérez, Luis P. Sánchez-fernández, Sergio Suárez-guerra, José Juan Carbajal-hernández
    Abstract:

    Abstract Aircraft noise is one of the most uncomfortable kinds of sounds. That is why many organizations have addressed this problem through noise contours around airports, for which they use the Aircraft type as the key element. This paper presents a new computational model to identify the Aircraft Class with a better performance, because it introduces the take-off noise signal segmentation in time. A method for signal segmentation into four segments was created. The Aircraft noise patterns are extracted using an LPC (Linear Predictive Coding) based technique and the Classification is made combining the output of four parallel MLP (Multilayer Perceptron) neural networks, one for each segment. The individual accuracy of each network was improved using a wrapper feature selection method, increasing the model effectiveness with a lower computational cost. The Aircraft are grouped into Classes depending on the installed engine type. The model works with 13 Aircraft categories with an identification level above 85% in real environments.

Sergio Suarez Guerra - One of the best experts on this subject based on the ideXlab platform.

  • Aircraft Class recognition based on take off noise patterns
    Computación Y Sistemas, 2016
    Co-Authors: Luis Alejandro Sanchez Perez, Luis Pastor Sanchez Fernandez, Sergio Suarez Guerra
    Abstract:

    In this work the Aircraft Class recognition of based on take-off noise patterns is examined. Signal segmentation in time is analyzed as well as using a MLP neural network as the Classifier for each segment. Also, several algorithms for decision by committee in order to aggregate the multiple parallel outputs of the Classifiersare examined along with feature extraction and selection based on spectrum analysis of the Aircraft noise. Also, amethod for georeferenced estimation of the take-off flight path based only on the noise signal is explored. The methodology and results are sustained in the current literature.

Sergio Suárez-guerra - One of the best experts on this subject based on the ideXlab platform.

  • Aircraft Class identification based on take-off noise signal segmentation in time
    Expert Systems with Applications, 2013
    Co-Authors: Luis Alejandro Sánchez-pérez, Luis P. Sánchez-fernández, Sergio Suárez-guerra, José Juan Carbajal-hernández
    Abstract:

    Abstract Aircraft noise is one of the most uncomfortable kinds of sounds. That is why many organizations have addressed this problem through noise contours around airports, for which they use the Aircraft type as the key element. This paper presents a new computational model to identify the Aircraft Class with a better performance, because it introduces the take-off noise signal segmentation in time. A method for signal segmentation into four segments was created. The Aircraft noise patterns are extracted using an LPC (Linear Predictive Coding) based technique and the Classification is made combining the output of four parallel MLP (Multilayer Perceptron) neural networks, one for each segment. The individual accuracy of each network was improved using a wrapper feature selection method, increasing the model effectiveness with a lower computational cost. The Aircraft are grouped into Classes depending on the installed engine type. The model works with 13 Aircraft categories with an identification level above 85% in real environments.

Luis P. Sánchez-fernández - One of the best experts on this subject based on the ideXlab platform.

  • Aircraft Class identification based on take-off noise signal segmentation in time
    Expert Systems with Applications, 2013
    Co-Authors: Luis Alejandro Sánchez-pérez, Luis P. Sánchez-fernández, Sergio Suárez-guerra, José Juan Carbajal-hernández
    Abstract:

    Abstract Aircraft noise is one of the most uncomfortable kinds of sounds. That is why many organizations have addressed this problem through noise contours around airports, for which they use the Aircraft type as the key element. This paper presents a new computational model to identify the Aircraft Class with a better performance, because it introduces the take-off noise signal segmentation in time. A method for signal segmentation into four segments was created. The Aircraft noise patterns are extracted using an LPC (Linear Predictive Coding) based technique and the Classification is made combining the output of four parallel MLP (Multilayer Perceptron) neural networks, one for each segment. The individual accuracy of each network was improved using a wrapper feature selection method, increasing the model effectiveness with a lower computational cost. The Aircraft are grouped into Classes depending on the installed engine type. The model works with 13 Aircraft categories with an identification level above 85% in real environments.