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: Chapter in Book/Report/Conference proceedingConference proceeding contribution

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
Title of host publicationSSD 2014
Subtitle of host publication2014 11th International Multi- Conference on Systems, Signals & Devices
Place of PublicationPiscataway, NJ
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages1-7
Number of pages7
ISBN (Electronic)9781479938667 , 9781479938650
ISBN (Print)9781479938674
DOIs
Publication statusPublished - 2014
Externally publishedYes
Event2014 IEEE 11th International Multi-Conference on Systems, Signals and Devices, SSD 2014 - Castelldefels-Barcelona, Spain
Duration: 11 Feb 201414 Feb 2014

Other

Other2014 IEEE 11th International Multi-Conference on Systems, Signals and Devices, SSD 2014
CountrySpain
CityCastelldefels-Barcelona
Period11/02/1414/02/14

Keywords

  • natural disaster
  • earthquake
  • fractal
  • Hurst coefficient
  • box counting
  • wavelet
  • coiflet
  • Daubechies wavelet
  • symlets
  • denoising
  • SVM
  • neural network

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