Proceeding of the Electrical Engineering Computer Science and Informatics
Vol 5: EECSI 2018

Robust Principal Component Analysis for Feature Extraction of Fire Detection System

Herminarto Nugroho (Universitas Pertamina)
Muhamad Koyimatu (Universitas Pertamina)
Ade Irawan (Universitas Pertamina)
Ariana Yunita (Universitas Pertamina)

Article Info

Publish Date
18 Sep 2019

Abstract

Fire detection system with deep learning-based computer vision (DLCV *) algorithm is proposed in this paper. It uses visible light sensor charged-coupled device (CCD) which can be usually found in closed circuit television camera (CCTV). The performance of this DLCV fire detection depends on how many fire image datasets are trained that might lead to the curse of dimensionality. To tackle the curse of dimensionality, Principal Component Analysis (PCA) will be used. PCA is a technique for feature extraction in which the dimensionality of such datasets is reduced significantly. This will results in increasing interpretability but at the same time minimizing information loss.

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Journal Info
Proceeding of the Electrical Engineering Computer Science and Informatics
Website

Abbrev

EECSI

Publisher

Institute of Advanced Engineering and Science

Subject

Computer Science & IT Electrical & Electronics Engineering

Description

Proceeding of the Electrical Engineering Computer Science and Informatics publishes papers of the "International Conference on Electrical Engineering Computer Science and Informatics (EECSI)" Series in high technical standard. The Proceeding is aimed to bring researchers, academicians, scientists, ...