Feature Extraction and Classification of Cataluminescence Images Based on Sparse Coding Convolutional Neural Networks
The atmosphere of human existence is increasingly complex, and various harmful gases seriously endanger human health. Therefore, it is necessary to quickly and accurately detect trace toxic gases. With the application progress of cataluminescence (CTL) in the detection of harmful gases, this article...
Saved in:
| Published in: | IEEE transactions on instrumentation and measurement Vol. 70; pp. 1 - 11 |
|---|---|
| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
New York
IEEE
2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subjects: | |
| ISSN: | 0018-9456, 1557-9662 |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Abstract | The atmosphere of human existence is increasingly complex, and various harmful gases seriously endanger human health. Therefore, it is necessary to quickly and accurately detect trace toxic gases. With the application progress of cataluminescence (CTL) in the detection of harmful gases, this article proposed a feature extraction and classification algorithm for CTL images based on sparse coding convolutional neural networks (SCNN). First, the CTL images were obtained by the portable CTL sensor system, and the CTL images were encoded by simulating the characteristics of the visual cell receptive field, so that the sparse and internal features of the image were obtained, and the feature vectors were sorted. Then, the eigenvector with a large grayscale average gradient was selected to initialize the convolutional neural network convolution kernel. Finally, the complementarity of the feature differences between networks was measured according to the complementary measurement function, so as to optimize the weight of the back-propagation fine-tuning model of the loss function, and the accuracy of images classification was improved. The results showed that the SCNN algorithm can accurately realize the CTL images classification, further complete detection and identification of trace harmful gases. |
|---|---|
| AbstractList | The atmosphere of human existence is increasingly complex, and various harmful gases seriously endanger human health. Therefore, it is necessary to quickly and accurately detect trace toxic gases. With the application progress of cataluminescence (CTL) in the detection of harmful gases, this article proposed a feature extraction and classification algorithm for CTL images based on sparse coding convolutional neural networks (SCNN). First, the CTL images were obtained by the portable CTL sensor system, and the CTL images were encoded by simulating the characteristics of the visual cell receptive field, so that the sparse and internal features of the image were obtained, and the feature vectors were sorted. Then, the eigenvector with a large grayscale average gradient was selected to initialize the convolutional neural network convolution kernel. Finally, the complementarity of the feature differences between networks was measured according to the complementary measurement function, so as to optimize the weight of the back-propagation fine-tuning model of the loss function, and the accuracy of images classification was improved. The results showed that the SCNN algorithm can accurately realize the CTL images classification, further complete detection and identification of trace harmful gases. |
| Author | Shi, Guolong He, Yigang Zhang, Chaolong |
| Author_xml | – sequence: 1 givenname: Guolong orcidid: 0000-0001-8209-4570 surname: Shi fullname: Shi, Guolong organization: School of Electrical Engineering and Automation, Wuhan University, Wuhan, China – sequence: 2 givenname: Yigang orcidid: 0000-0002-6642-0740 surname: He fullname: He, Yigang email: yghe1221@whu.edu.cn organization: School of Electrical Engineering and Automation, Wuhan University, Wuhan, China – sequence: 3 givenname: Chaolong orcidid: 0000-0003-3892-5124 surname: Zhang fullname: Zhang, Chaolong email: zhangchaolong@126.com organization: School of Electrical Engineering and Automation, Wuhan University, Wuhan, China |
| BookMark | eNp9kUFv1DAQRi1UJLaFOxIXSz1nGdtxHB_bqIWVChwo52jiTCqXbLy1kxb-Pd7dqgcOPX3S-HuW5_mUnUxhIsY-ClgLAfbz7ebbWoKEtQKpNNRv2EpobQpbVfKErQBEXdhSV-_YaUr3AGCq0qzYck04L5H41Z85opt9mDhOPW9GTMkP3uFhFAbe4IzjsvUTJUeTI77Z4h0lfomJep47P3cYE_Em9H66yzE9hnHZ0zjy77TEQ8xPIf5O79nbAcdEH57zjP26vrptvhY3P75smoubwkkr5mIAp5x2sse6q23XlaYj6Sz2ypQwVBaF6mtAhVIOxslOD0blSS-0cyjy6Rk7P967i-FhoTS392GJ-UGplaWBqlTa2Nyqji0XQ0qRhtb5-bB3VuLHVkC7V9xmxe1ecfusOIPwH7iLfovx72vIpyPiieilboXV-YfUP1IOisc |
| CODEN | IEIMAO |
| CitedBy_id | crossref_primary_10_1109_TII_2022_3202979 crossref_primary_10_1016_j_snb_2023_135177 crossref_primary_10_1109_ACCESS_2024_3368881 crossref_primary_10_1109_TIM_2022_3232093 crossref_primary_10_1155_2022_6564235 crossref_primary_10_1049_2024_3179667 |
| Cites_doi | 10.1007/978-3-319-69748-2_2 10.1109/JIOT.2019.2896311 10.1080/10739149.2017.1340896 10.1039/C7AN00091J 10.1109/TIM.2017.2746378 10.1016/j.snb.2017.09.012 10.1007/s11045-019-00646-7 10.1109/TIM.2019.2925247 10.1109/TCI.2019.2896790 10.1109/TIP.2020.2965294 10.1016/j.ultras.2016.09.011 10.1016/j.bios.2018.11.031 10.1109/TCYB.2019.2933477 10.1016/j.ces.2018.02.008 10.1021/acsami.8b13747 10.1109/IMM.2019.8868275 10.1016/0021-9517(76)90129-9 10.1002/rcm.7977 10.1109/TPAMI.2020.2975796 10.1016/0925-4005(93)85121-P 10.3390/s19091960 10.1109/MIM.2018.8278808 10.1088/0957-0233/25/8/085102 10.1109/TIM.2018.2852538 10.1016/j.snb.2016.08.156 10.1109/MSP.2017.2761895 10.3390/s18010157 10.1016/j.snb.2003.09.003 10.1016/j.talanta.2018.11.016 10.3390/s19010217 10.1109/JSEN.2017.2711000 10.1016/j.bios.2017.05.041 |
| ContentType | Journal Article |
| Copyright | Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2021 |
| Copyright_xml | – notice: Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2021 |
| DBID | 97E RIA RIE AAYXX CITATION 7SP 7U5 8FD L7M |
| DOI | 10.1109/TIM.2020.3023508 |
| DatabaseName | IEEE Xplore (IEEE) IEEE All-Society Periodicals Package (ASPP) 1998–Present IEEE Electronic Library (IEL) CrossRef Electronics & Communications Abstracts Solid State and Superconductivity Abstracts Technology Research Database Advanced Technologies Database with Aerospace |
| DatabaseTitle | CrossRef Solid State and Superconductivity Abstracts Technology Research Database Advanced Technologies Database with Aerospace Electronics & Communications Abstracts |
| DatabaseTitleList | Solid State and Superconductivity Abstracts |
| Database_xml | – sequence: 1 dbid: RIE name: IEEE Electronic Library (IEL) url: https://ieeexplore.ieee.org/ sourceTypes: Publisher |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering Physics |
| EISSN | 1557-9662 |
| EndPage | 11 |
| ExternalDocumentID | 10_1109_TIM_2020_3023508 9195001 |
| Genre | orig-research |
| GrantInformation_xml | – fundername: National Natural Science Foundation of China grantid: 51977153; 51577046 funderid: 10.13039/501100001809 – fundername: State Key Program of National Natural Science Foundation of China grantid: 51637004 funderid: 10.13039/501100001809 – fundername: National Key Research and Development Plan “Important Scientific Instruments and Equipment Development” grantid: 2016YFF0102200 – fundername: Equipment Research Project in Advance grantid: 41402040301 |
| GroupedDBID | -~X 0R~ 29I 4.4 5GY 5VS 6IK 85S 8WZ 97E A6W AAJGR AARMG AASAJ AAWTH ABAZT ABQJQ ABVLG ACGFO ACIWK ACNCT AENEX AETIX AGQYO AGSQL AHBIQ AI. AIBXA AKJIK AKQYR ALLEH ALMA_UNASSIGNED_HOLDINGS ATWAV BEFXN BFFAM BGNUA BKEBE BPEOZ CS3 DU5 EBS EJD F5P HZ~ H~9 IAAWW IBMZZ ICLAB IDIHD IFIPE IFJZH IPLJI JAVBF LAI M43 O9- OCL P2P RIA RIE RNS TN5 TWZ VH1 VJK AAYXX CITATION 7SP 7U5 8FD L7M |
| ID | FETCH-LOGICAL-c291t-f0c3c5c2da8b89bb47be2c9ad3740f69a13d80a3a22f7c2b5f733d8d15cca1a13 |
| IEDL.DBID | RIE |
| ISICitedReferencesCount | 12 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000671730000014&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0018-9456 |
| IngestDate | Mon Jun 30 10:21:08 EDT 2025 Sat Nov 29 04:38:00 EST 2025 Tue Nov 18 22:17:42 EST 2025 Wed Aug 27 02:44:54 EDT 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Language | English |
| License | https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c291t-f0c3c5c2da8b89bb47be2c9ad3740f69a13d80a3a22f7c2b5f733d8d15cca1a13 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| ORCID | 0000-0001-8209-4570 0000-0003-3892-5124 0000-0002-6642-0740 |
| PQID | 2470643579 |
| PQPubID | 85462 |
| PageCount | 11 |
| ParticipantIDs | crossref_primary_10_1109_TIM_2020_3023508 proquest_journals_2470643579 ieee_primary_9195001 crossref_citationtrail_10_1109_TIM_2020_3023508 |
| PublicationCentury | 2000 |
| PublicationDate | 20210000 2021-00-00 20210101 |
| PublicationDateYYYYMMDD | 2021-01-01 |
| PublicationDate_xml | – year: 2021 text: 20210000 |
| PublicationDecade | 2020 |
| PublicationPlace | New York |
| PublicationPlace_xml | – name: New York |
| PublicationTitle | IEEE transactions on instrumentation and measurement |
| PublicationTitleAbbrev | TIM |
| PublicationYear | 2021 |
| Publisher | IEEE The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher_xml | – name: IEEE – name: The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| References | ref13 ref12 ref15 ref14 ref31 ref30 ref11 ref32 ref10 ref2 ref1 ref17 ref16 ref19 ref18 ref24 ref23 ref26 ref25 ref20 ref22 ref21 ref28 ref27 ref29 ref8 ref7 ref9 ref4 ref3 ref6 ref5 |
| References_xml | – ident: ref14 doi: 10.1007/978-3-319-69748-2_2 – ident: ref2 doi: 10.1109/JIOT.2019.2896311 – ident: ref13 doi: 10.1080/10739149.2017.1340896 – ident: ref5 doi: 10.1039/C7AN00091J – ident: ref1 doi: 10.1109/TIM.2017.2746378 – ident: ref16 doi: 10.1016/j.snb.2017.09.012 – ident: ref32 doi: 10.1007/s11045-019-00646-7 – ident: ref22 doi: 10.1109/TIM.2019.2925247 – ident: ref27 doi: 10.1109/TCI.2019.2896790 – ident: ref26 doi: 10.1109/TIP.2020.2965294 – ident: ref28 doi: 10.1016/j.ultras.2016.09.011 – ident: ref15 doi: 10.1016/j.bios.2018.11.031 – ident: ref23 doi: 10.1109/TCYB.2019.2933477 – ident: ref20 doi: 10.1016/j.ces.2018.02.008 – ident: ref17 doi: 10.1021/acsami.8b13747 – ident: ref4 doi: 10.1109/IMM.2019.8868275 – ident: ref8 doi: 10.1016/0021-9517(76)90129-9 – ident: ref19 doi: 10.1002/rcm.7977 – ident: ref24 doi: 10.1109/TPAMI.2020.2975796 – ident: ref9 doi: 10.1016/0925-4005(93)85121-P – ident: ref30 doi: 10.3390/s19091960 – ident: ref3 doi: 10.1109/MIM.2018.8278808 – ident: ref6 doi: 10.1088/0957-0233/25/8/085102 – ident: ref12 doi: 10.1109/TIM.2018.2852538 – ident: ref18 doi: 10.1016/j.snb.2016.08.156 – ident: ref25 doi: 10.1109/MSP.2017.2761895 – ident: ref29 doi: 10.3390/s18010157 – ident: ref11 doi: 10.1016/j.snb.2003.09.003 – ident: ref21 doi: 10.1016/j.talanta.2018.11.016 – ident: ref31 doi: 10.3390/s19010217 – ident: ref7 doi: 10.1109/JSEN.2017.2711000 – ident: ref10 doi: 10.1016/j.bios.2017.05.041 |
| SSID | ssj0007647 |
| Score | 2.3671427 |
| Snippet | The atmosphere of human existence is increasingly complex, and various harmful gases seriously endanger human health. Therefore, it is necessary to quickly and... |
| SourceID | proquest crossref ieee |
| SourceType | Aggregation Database Enrichment Source Index Database Publisher |
| StartPage | 1 |
| SubjectTerms | Algorithms Artificial neural networks Back propagation Back propagation networks Cataluminescence (CTL) sensor Classification Coding Convolution deep learning Eigenvectors Feature extraction Gases Image classification Internet of Things Kernel Luminescence Neural networks Sensor arrays sparse coding convolutional neural networks (SCNNs) Temperature sensors Visual fields |
| Title | Feature Extraction and Classification of Cataluminescence Images Based on Sparse Coding Convolutional Neural Networks |
| URI | https://ieeexplore.ieee.org/document/9195001 https://www.proquest.com/docview/2470643579 |
| Volume | 70 |
| WOSCitedRecordID | wos000671730000014&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVIEE databaseName: IEEE Electronic Library (IEL) customDbUrl: eissn: 1557-9662 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0007647 issn: 0018-9456 databaseCode: RIE dateStart: 19630101 isFulltext: true titleUrlDefault: https://ieeexplore.ieee.org/ providerName: IEEE |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1LS8QwEB5UFPTgW1xdJQcvgnXb9JHmqMuKHhRBBW8lzQME7cp2K_58Z9LuIiiC9JDSJjTwTTMzycx8ACeZRh0V6yTIY4UOikU_RTohA6dQQnQpcqmUJ5sQd3f587O8X4CzeS6MtdYHn9lzuvVn-WasG9oqG0jiLKVkrUUhsjZXa77qiixp62NG-DW0CmZHkqEcPN7coiPI0T-l4i5EJPlNBXlOlR8LsdcuVxv_m9cmrHdWJLtoYd-CBVttw9q32oLbsOJjO3W9Aw2Zec3EstHndNLmMTBVGeb5MClSyIPDxo4NaS-neaNIeE1_PLt5w-WmZpeo6gzDPg_v6AdbNhyTxsOm-ugkFydDZT584-PK6114uho9Dq-Djm0h0FxG08CFOtap5kblZS7LMhGl5VoqE4skdJlUUWzyUMWKcyc0L1MnYnxiohSFIMK3e7BUjSu7DyxyucbLOqeihBsh0xJHZDozoSxVbnowmAFQ6K4UOTFivBbeJQllgZAVBFnRQdaD0_mI97YMxx99dwiieb8OnR70ZxgX3X9aFzwRZJOlQh78PuoQVjlFsfhNlz4sTSeNPYJl_TF9qSfHXgS_ABXG2vA |
| linkProvider | IEEE |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3dS9xAEB_EKtoH60eLp1fdB18KjZdsPjb7aI8TD_UQvIJvYbMfUOjl5HIR_3xnNrlDaCmUPGxIdsnCb7IzszszP4CLTKOOinUS5LFCB8WinyKdkIFTKCG6FLlUypNNiMkkf3qSDxvwfZ0LY631wWf2km79Wb6Z64a2ygaSOEspWetDmiQ8bLO11uuuyJK2QmaE30O7YHUoGcrBdHyPriBHD5XKuxCV5Dsl5FlV_liKvX65_vR_M9uHvc6OZFct8AewYatD-PiuuuAhbPvoTl0fQUOGXrOwbPS6XLSZDExVhnlGTIoV8vCwuWND2s1pZhQLr-mfZ-MZLjg1-4HKzjDs8_iMnrBlwznpPGyql052cTJU6MM3PrK8_gw_r0fT4U3Q8S0EmstoGbhQxzrV3Ki8zGVZJqK0XEtlYpGELpMqik0eqlhx7oTmZepEjE9MlKIYRPj2C2xW88oeA4tcrvGyzqko4UbItMQRmc5MKEuVmx4MVgAUuitGTpwYvwvvlISyQMgKgqzoIOvBt_WI57YQxz_6HhFE634dOj3orzAuuj-1LngiyCpLhTz5-6hz2LmZ3t8Vd-PJ7Snscopp8VswfdhcLhr7Fbb0y_JXvTjz4vgGnXzeNw |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Feature+Extraction+and+Classification+of+Cataluminescence+Images+Based+on+Sparse+Coding+Convolutional+Neural+Networks&rft.jtitle=IEEE+transactions+on+instrumentation+and+measurement&rft.au=Shi%2C+Guolong&rft.au=He%2C+Yigang&rft.au=Zhang%2C+Chaolong&rft.date=2021&rft.issn=0018-9456&rft.eissn=1557-9662&rft.volume=70&rft.spage=1&rft.epage=11&rft_id=info:doi/10.1109%2FTIM.2020.3023508&rft.externalDBID=n%2Fa&rft.externalDocID=10_1109_TIM_2020_3023508 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0018-9456&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0018-9456&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0018-9456&client=summon |