Next Article in Journal
Performance Comparison of Cross-Like Hall Plates with Different Covering Layers
Previous Article in Journal
Rapid Elemental Analysis and Provenance Study of Blumea balsamifera DC Using Laser-Induced Breakdown Spectroscopy
Article Menu

Export Article

Open AccessArticle
Sensors 2015, 15(1), 656-671; doi:10.3390/s150100656

Pseudo Optimization of E-Nose Data Using Region Selection with Feature Feedback Based on Regularized Linear Discriminant Analysis

1
Electrical Engineering, Kookmin University, 861-1, Jeongeung-dong, Songbuk-gu, Seoul 136-702, Korea
2
Department of Computer Science and Engineering, Dankook University, 126, Jukjeon-dong, Suji-gu, Yongin-si, Gyeonggi-do 448-701, Korea
*
Author to whom correspondence should be addressed.
Received: 6 November 2014 / Accepted: 22 December 2014 / Published: 31 December 2014
(This article belongs to the Section Physical Sensors)
View Full-Text   |   Download PDF [754 KB, uploaded 31 December 2014]   |  

Abstract

In this paper, we present a pseudo optimization method for electronic nose (e-nose) data using region selection with feature feedback based on regularized linear discriminant analysis (R-LDA) to enhance the performance and cost functions of an e-nose system. To implement cost- and performance-effective e-nose systems, the number of channels, sampling time and sensing time of the e-nose must be considered. We propose a method to select both important channels and an important time-horizon by analyzing e-nose sensor data. By extending previous feature feedback results, we obtain a two-dimensional discriminant information map consisting of channels and time units by reverse mapping the feature space to the data space based on R-LDA. The discriminant information map enables optimal channels and time units to be heuristically selected to improve the performance and cost functions. The efficacy of the proposed method is demonstrated experimentally for different volatile organic compounds. In particular, our method is both cost and performance effective for the real implementation of e-nose systems. View Full-Text
Keywords: e-nose system; vapor classification; feature feedback; discriminant feature e-nose system; vapor classification; feature feedback; discriminant feature
Figures

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Jeong, G.-M.; Nghia, N.T.; Choi, S.-I. Pseudo Optimization of E-Nose Data Using Region Selection with Feature Feedback Based on Regularized Linear Discriminant Analysis. Sensors 2015, 15, 656-671.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top