Robust Classification of Largely Corrupted Electronic Nose Data Using Deep Neural Networks
Data loss for electronic noses may occur because of the sensor's installation environment or from electrical disturbances. As a result, electronic noses may experience difficulties when identifying gases. This paper proposes two deep neural network-based functions for identifying gases. First,...
Gespeichert in:
Veröffentlicht in: | IEEE sensors journal 2021-02, Vol.21 (4), p.5052-5059 |
---|---|
Hauptverfasser: | , , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext bestellen |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | 5059 |
---|---|
container_issue | 4 |
container_start_page | 5052 |
container_title | IEEE sensors journal |
container_volume | 21 |
creator | Yoo, YoungJoon Kim, Hyun-Il Choi, Sang-Il |
description | Data loss for electronic noses may occur because of the sensor's installation environment or from electrical disturbances. As a result, electronic noses may experience difficulties when identifying gases. This paper proposes two deep neural network-based functions for identifying gases. First, a denoising auto-encoder based on the corruption reconstruction method is proposed for electronic nose data to solve this problem. Second, a convolutional neural network-based gas-classifying model is proposed. Although the electronic nose data are highly discriminative, they are sensitive to the corruption of information; hence, they require an efficient restoration method for practical use. From the experiments we demonstrate that the proposed denoising auto-encoder provides a strong restoration capability, and the convolutional neural network-based classifier successfully discriminates the gas data samples with a classification rate over 95% even when the data loss is 50%. |
doi_str_mv | 10.1109/JSEN.2020.3034145 |
format | Article |
fullrecord | <record><control><sourceid>proquest_RIE</sourceid><recordid>TN_cdi_proquest_journals_2479887833</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>9240952</ieee_id><sourcerecordid>2479887833</sourcerecordid><originalsourceid>FETCH-LOGICAL-c293t-9a963cfd175dca833ab294fcc4f2bb7664970396a817ecb0f7462bb6a9eb0e5b3</originalsourceid><addsrcrecordid>eNqNkMtKxDAUQIsoOI5-gLgJuJSOebVpllLHF8MIPkDclDRzI9HajEmKzN-bcUS3ru6FnHMDJ8sOCZ4QguXpzf10PqGY4gnDjBNebGUjUhRVTgSvttc7wzln4mk32wvhFWMiRSFG2fOda4cQUd2pEKyxWkXreuQMmin_At0K1c77YRlhgaYd6OhdbzWauwDoXEWFHoPtX9A5wBLNYfCqSyN-Ov8W9rMdo7oABz9znD1eTB_qq3x2e3ldn81yTSWLuVSyZNosiCgWWlWMqZZKbrTmhratKEsuBWayVBURoFtsBC_TQ6kktBiKlo2z483dpXcfA4TYvLrB9-nLhnIhq0qko4kiG0p7F4IH0yy9fVd-1RDcrBM264TNOmHzkzA51cb5hNaZoC30Gn49jHFJCGGMpJqE1jZ-t6vd0MeknvxfTfTRhrYAf5SkHMuCsi95C44U</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2479887833</pqid></control><display><type>article</type><title>Robust Classification of Largely Corrupted Electronic Nose Data Using Deep Neural Networks</title><source>IEEE Electronic Library (IEL)</source><creator>Yoo, YoungJoon ; Kim, Hyun-Il ; Choi, Sang-Il</creator><creatorcontrib>Yoo, YoungJoon ; Kim, Hyun-Il ; Choi, Sang-Il</creatorcontrib><description>Data loss for electronic noses may occur because of the sensor's installation environment or from electrical disturbances. As a result, electronic noses may experience difficulties when identifying gases. This paper proposes two deep neural network-based functions for identifying gases. First, a denoising auto-encoder based on the corruption reconstruction method is proposed for electronic nose data to solve this problem. Second, a convolutional neural network-based gas-classifying model is proposed. Although the electronic nose data are highly discriminative, they are sensitive to the corruption of information; hence, they require an efficient restoration method for practical use. From the experiments we demonstrate that the proposed denoising auto-encoder provides a strong restoration capability, and the convolutional neural network-based classifier successfully discriminates the gas data samples with a classification rate over 95% even when the data loss is 50%.</description><identifier>ISSN: 1530-437X</identifier><identifier>EISSN: 1558-1748</identifier><identifier>DOI: 10.1109/JSEN.2020.3034145</identifier><identifier>CODEN: ISJEAZ</identifier><language>eng</language><publisher>PISCATAWAY: IEEE</publisher><subject>Artificial neural networks ; Classification ; Coders ; convolutional neural network ; Corruption ; data corruption ; Data integrity ; Data loss ; data reconstruction ; deep neural network ; denoising auto-encoder ; Electronic nose ; Electronic noses ; Engineering ; Engineering, Electrical & Electronic ; Feature extraction ; Gas detectors ; Image reconstruction ; Instruments & Instrumentation ; Neural networks ; Noise reduction ; Physical Sciences ; Physics ; Physics, Applied ; Restoration ; Science & Technology ; Sensor arrays ; Sensor phenomena and characterization ; Smell ; Technology</subject><ispartof>IEEE sensors journal, 2021-02, Vol.21 (4), p.5052-5059</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2021</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>true</woscitedreferencessubscribed><woscitedreferencescount>20</woscitedreferencescount><woscitedreferencesoriginalsourcerecordid>wos000611133100112</woscitedreferencesoriginalsourcerecordid><citedby>FETCH-LOGICAL-c293t-9a963cfd175dca833ab294fcc4f2bb7664970396a817ecb0f7462bb6a9eb0e5b3</citedby><cites>FETCH-LOGICAL-c293t-9a963cfd175dca833ab294fcc4f2bb7664970396a817ecb0f7462bb6a9eb0e5b3</cites><orcidid>0000-0002-0462-0050</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/9240952$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>315,782,786,798,27931,27932,39265,54765</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/9240952$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Yoo, YoungJoon</creatorcontrib><creatorcontrib>Kim, Hyun-Il</creatorcontrib><creatorcontrib>Choi, Sang-Il</creatorcontrib><title>Robust Classification of Largely Corrupted Electronic Nose Data Using Deep Neural Networks</title><title>IEEE sensors journal</title><addtitle>JSEN</addtitle><addtitle>IEEE SENS J</addtitle><description>Data loss for electronic noses may occur because of the sensor's installation environment or from electrical disturbances. As a result, electronic noses may experience difficulties when identifying gases. This paper proposes two deep neural network-based functions for identifying gases. First, a denoising auto-encoder based on the corruption reconstruction method is proposed for electronic nose data to solve this problem. Second, a convolutional neural network-based gas-classifying model is proposed. Although the electronic nose data are highly discriminative, they are sensitive to the corruption of information; hence, they require an efficient restoration method for practical use. From the experiments we demonstrate that the proposed denoising auto-encoder provides a strong restoration capability, and the convolutional neural network-based classifier successfully discriminates the gas data samples with a classification rate over 95% even when the data loss is 50%.</description><subject>Artificial neural networks</subject><subject>Classification</subject><subject>Coders</subject><subject>convolutional neural network</subject><subject>Corruption</subject><subject>data corruption</subject><subject>Data integrity</subject><subject>Data loss</subject><subject>data reconstruction</subject><subject>deep neural network</subject><subject>denoising auto-encoder</subject><subject>Electronic nose</subject><subject>Electronic noses</subject><subject>Engineering</subject><subject>Engineering, Electrical & Electronic</subject><subject>Feature extraction</subject><subject>Gas detectors</subject><subject>Image reconstruction</subject><subject>Instruments & Instrumentation</subject><subject>Neural networks</subject><subject>Noise reduction</subject><subject>Physical Sciences</subject><subject>Physics</subject><subject>Physics, Applied</subject><subject>Restoration</subject><subject>Science & Technology</subject><subject>Sensor arrays</subject><subject>Sensor phenomena and characterization</subject><subject>Smell</subject><subject>Technology</subject><issn>1530-437X</issn><issn>1558-1748</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><sourceid>HGBXW</sourceid><recordid>eNqNkMtKxDAUQIsoOI5-gLgJuJSOebVpllLHF8MIPkDclDRzI9HajEmKzN-bcUS3ru6FnHMDJ8sOCZ4QguXpzf10PqGY4gnDjBNebGUjUhRVTgSvttc7wzln4mk32wvhFWMiRSFG2fOda4cQUd2pEKyxWkXreuQMmin_At0K1c77YRlhgaYd6OhdbzWauwDoXEWFHoPtX9A5wBLNYfCqSyN-Ov8W9rMdo7oABz9znD1eTB_qq3x2e3ldn81yTSWLuVSyZNosiCgWWlWMqZZKbrTmhratKEsuBWayVBURoFtsBC_TQ6kktBiKlo2z483dpXcfA4TYvLrB9-nLhnIhq0qko4kiG0p7F4IH0yy9fVd-1RDcrBM264TNOmHzkzA51cb5hNaZoC30Gn49jHFJCGGMpJqE1jZ-t6vd0MeknvxfTfTRhrYAf5SkHMuCsi95C44U</recordid><startdate>20210215</startdate><enddate>20210215</enddate><creator>Yoo, YoungJoon</creator><creator>Kim, Hyun-Il</creator><creator>Choi, Sang-Il</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>BLEPL</scope><scope>DTL</scope><scope>HGBXW</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SP</scope><scope>7U5</scope><scope>8FD</scope><scope>L7M</scope><orcidid>https://orcid.org/0000-0002-0462-0050</orcidid></search><sort><creationdate>20210215</creationdate><title>Robust Classification of Largely Corrupted Electronic Nose Data Using Deep Neural Networks</title><author>Yoo, YoungJoon ; Kim, Hyun-Il ; Choi, Sang-Il</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c293t-9a963cfd175dca833ab294fcc4f2bb7664970396a817ecb0f7462bb6a9eb0e5b3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Artificial neural networks</topic><topic>Classification</topic><topic>Coders</topic><topic>convolutional neural network</topic><topic>Corruption</topic><topic>data corruption</topic><topic>Data integrity</topic><topic>Data loss</topic><topic>data reconstruction</topic><topic>deep neural network</topic><topic>denoising auto-encoder</topic><topic>Electronic nose</topic><topic>Electronic noses</topic><topic>Engineering</topic><topic>Engineering, Electrical & Electronic</topic><topic>Feature extraction</topic><topic>Gas detectors</topic><topic>Image reconstruction</topic><topic>Instruments & Instrumentation</topic><topic>Neural networks</topic><topic>Noise reduction</topic><topic>Physical Sciences</topic><topic>Physics</topic><topic>Physics, Applied</topic><topic>Restoration</topic><topic>Science & Technology</topic><topic>Sensor arrays</topic><topic>Sensor phenomena and characterization</topic><topic>Smell</topic><topic>Technology</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Yoo, YoungJoon</creatorcontrib><creatorcontrib>Kim, Hyun-Il</creatorcontrib><creatorcontrib>Choi, Sang-Il</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>Web of Science Core Collection</collection><collection>Science Citation Index Expanded</collection><collection>Web of Science - Science Citation Index Expanded - 2021</collection><collection>CrossRef</collection><collection>Electronics & Communications Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>Technology Research Database</collection><collection>Advanced Technologies Database with Aerospace</collection><jtitle>IEEE sensors journal</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Yoo, YoungJoon</au><au>Kim, Hyun-Il</au><au>Choi, Sang-Il</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Robust Classification of Largely Corrupted Electronic Nose Data Using Deep Neural Networks</atitle><jtitle>IEEE sensors journal</jtitle><stitle>JSEN</stitle><stitle>IEEE SENS J</stitle><date>2021-02-15</date><risdate>2021</risdate><volume>21</volume><issue>4</issue><spage>5052</spage><epage>5059</epage><pages>5052-5059</pages><issn>1530-437X</issn><eissn>1558-1748</eissn><coden>ISJEAZ</coden><abstract>Data loss for electronic noses may occur because of the sensor's installation environment or from electrical disturbances. As a result, electronic noses may experience difficulties when identifying gases. This paper proposes two deep neural network-based functions for identifying gases. First, a denoising auto-encoder based on the corruption reconstruction method is proposed for electronic nose data to solve this problem. Second, a convolutional neural network-based gas-classifying model is proposed. Although the electronic nose data are highly discriminative, they are sensitive to the corruption of information; hence, they require an efficient restoration method for practical use. From the experiments we demonstrate that the proposed denoising auto-encoder provides a strong restoration capability, and the convolutional neural network-based classifier successfully discriminates the gas data samples with a classification rate over 95% even when the data loss is 50%.</abstract><cop>PISCATAWAY</cop><pub>IEEE</pub><doi>10.1109/JSEN.2020.3034145</doi><tpages>8</tpages><orcidid>https://orcid.org/0000-0002-0462-0050</orcidid></addata></record> |
fulltext | fulltext_linktorsrc |
identifier | ISSN: 1530-437X |
ispartof | IEEE sensors journal, 2021-02, Vol.21 (4), p.5052-5059 |
issn | 1530-437X 1558-1748 |
language | eng |
recordid | cdi_proquest_journals_2479887833 |
source | IEEE Electronic Library (IEL) |
subjects | Artificial neural networks Classification Coders convolutional neural network Corruption data corruption Data integrity Data loss data reconstruction deep neural network denoising auto-encoder Electronic nose Electronic noses Engineering Engineering, Electrical & Electronic Feature extraction Gas detectors Image reconstruction Instruments & Instrumentation Neural networks Noise reduction Physical Sciences Physics Physics, Applied Restoration Science & Technology Sensor arrays Sensor phenomena and characterization Smell Technology |
title | Robust Classification of Largely Corrupted Electronic Nose Data Using Deep Neural Networks |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-05T18%3A24%3A30IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_RIE&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Robust%20Classification%20of%20Largely%20Corrupted%20Electronic%20Nose%20Data%20Using%20Deep%20Neural%20Networks&rft.jtitle=IEEE%20sensors%20journal&rft.au=Yoo,%20YoungJoon&rft.date=2021-02-15&rft.volume=21&rft.issue=4&rft.spage=5052&rft.epage=5059&rft.pages=5052-5059&rft.issn=1530-437X&rft.eissn=1558-1748&rft.coden=ISJEAZ&rft_id=info:doi/10.1109/JSEN.2020.3034145&rft_dat=%3Cproquest_RIE%3E2479887833%3C/proquest_RIE%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2479887833&rft_id=info:pmid/&rft_ieee_id=9240952&rfr_iscdi=true |