Wavelet denoising and fractal feature selection for classifying simulated earthquake signal from mobile phone accelerometer

Tieta Antaresti, Anggha Satya Nugraha, I. Putu Edy Suardiy Putra, Setiadi Yazid

Research output: Contribution to conferencePaper

1 Citation (Scopus)

Abstract

This work is an initial study of the research that aims to help people by giving an information about the earthquake while it happens eventhough the phone is not connected to the internet. In this research, we identify the pattern of the simulated earthquake signal from the mobile phone accelerometer via machine learning. Before the data is processed into the classifier, static windowing and denoising was done to boost up the accuracy. Another fractal features are extracted from the pre-denoised data, which are the box counting dimension feature and the Hurst coefficient. The purpose of doing static windowing is to obtain more features so that we can have many potential useful attribute candidates as possible. Denoising with symlet wavelet is done to remove the noises which can worsen the classification accuracy. The classification is done using support vector machine and multilayer perceptron classifier with the accuracy of 81% and 82.15%, respectively.

Original languageEnglish
DOIs
Publication statusPublished - 1 Jan 2014
Event2014 IEEE 11th International Multi-Conference on Systems, Signals and Devices, SSD 2014 - Castelldefels-Barcelona, Spain
Duration: 11 Feb 201414 Feb 2014

Conference

Conference2014 IEEE 11th International Multi-Conference on Systems, Signals and Devices, SSD 2014
CountrySpain
CityCastelldefels-Barcelona
Period11/02/1414/02/14

Keywords

  • box counting
  • coefficient
  • coiflet
  • Daubechies wavelet
  • denoising
  • neural network
  • SVM
  • symlets
  • wavelet

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