Indirect Prediction for Lithium-Ion Batteries RUL Using Multi-Objective Arithmetic Optimization Algorithm-Based Deep Extreme Learning Machine
Lithium-ion batteries (LIBs) experience aging degradation during long-term operation. Accurate prediction of the remaining useful life (RUL) in advance is crucial to ensure continuous and reliable energy supply of the battery management system (BMS). Aiming at the problem of limited robustness of de...
Uloženo v:
| Vydáno v: | IEEE access Ročník 11; s. 110400 - 110416 |
|---|---|
| Hlavní autoři: | , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Piscataway
IEEE
2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Témata: | |
| ISSN: | 2169-3536, 2169-3536 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | Lithium-ion batteries (LIBs) experience aging degradation during long-term operation. Accurate prediction of the remaining useful life (RUL) in advance is crucial to ensure continuous and reliable energy supply of the battery management system (BMS). Aiming at the problem of limited robustness of deep extreme learning machine (DELM) in RUL prediction for LIBs, an improved multi-objective arithmetic optimization algorithm (MOAOA) is proposed to enhance the prediction ability of DELM. Firstly, in order to overcome the limitations of the traditional single-objective optimization algorithm in terms of model stability, MOAOA is introduced to optimize the parameter selection of the DELM model, which effectively solved the problems of low efficiency and poor stability of parameter selection. Secondly, four health indexes (HIs) are extracted from the charging and discharging process, and their correlation ability was verified using Pearson, Spearman and Kendall correlation coefficient. Finally, the MOAOA-DELM method is fully validated using the NASA battery dataset, and the prediction results are compared with traditional methods and other multi-objective algorithms. The results show that the MOAOA-DELM method has small prediction error, strong state tracking fitting ability, good generalization ability and robustness. |
|---|---|
| AbstractList | Lithium-ion batteries (LIBs) experience aging degradation during long-term operation. Accurate prediction of the remaining useful life (RUL) in advance is crucial to ensure continuous and reliable energy supply of the battery management system (BMS). Aiming at the problem of limited robustness of deep extreme learning machine (DELM) in RUL prediction for LIBs, an improved multi-objective arithmetic optimization algorithm (MOAOA) is proposed to enhance the prediction ability of DELM. Firstly, in order to overcome the limitations of the traditional single-objective optimization algorithm in terms of model stability, MOAOA is introduced to optimize the parameter selection of the DELM model, which effectively solved the problems of low efficiency and poor stability of parameter selection. Secondly, four health indexes (HIs) are extracted from the charging and discharging process, and their correlation ability was verified using Pearson, Spearman and Kendall correlation coefficient. Finally, the MOAOA-DELM method is fully validated using the NASA battery dataset, and the prediction results are compared with traditional methods and other multi-objective algorithms. The results show that the MOAOA-DELM method has small prediction error, strong state tracking fitting ability, good generalization ability and robustness. |
| Author | Ding, Guorong Li, Linna Huang, Zhong |
| Author_xml | – sequence: 1 givenname: Linna surname: Li fullname: Li, Linna email: lilinna@wust.edu.cn organization: College of Science, Wuhan University of Science and Technology, Wuhan, China – sequence: 2 givenname: Zhong orcidid: 0009-0007-2723-9168 surname: Huang fullname: Huang, Zhong organization: College of Science, Wuhan University of Science and Technology, Wuhan, China – sequence: 3 givenname: Guorong surname: Ding fullname: Ding, Guorong email: dingguorong2022@163.com organization: Statistics Bureau of Maiji District, Tianshui, China |
| BookMark | eNqFkc9u1DAQxiNUJErpE8DBEuds_SdOnON2WWClVIva7tly7MnWqyRebG8FvAPvjJtUqOKCL_aM5_eNx9_b7Gx0I2TZe4IXhOD6arlare_uFhRTtmCMYszFq-yckrLOGWfl2Yvzm-wyhANOS6QUr86z35vRWA86om8ejNXRuhF1zqPGxgd7GvJNiq9VjOAtBHS7a9Au2HGPbk59tPm2PSTWPgJa-gQMEK1G22O0g_2lJq1lv3fTVX6tAhj0CeCI1j-ihwFQA8qPk5rSD3aEd9nrTvUBLp_3i2z3eX2_-po32y-b1bLJdYHrmBNRdC0AbjsujDZF22JihC4ILXXbsc4wUxFualIrShjXwvDOUGEwA6hqA-wi28y6xqmDPHo7KP9TOmXllHB-L5VPo_QgDaecJr7DRVGkZrXQ3PBKta1hwoBIWh9nraN3308Qojy4kx_T8yUVVVlWpSB1qmJzlfYuBA_d364Eyycb5WyjfLJRPtuYqPofSts4_Wv0yvb_YT_MrAWAF91omSYi7A-Geq_y |
| CODEN | IAECCG |
| CitedBy_id | crossref_primary_10_1109_ACCESS_2024_3486989 crossref_primary_10_1007_s11581_024_06049_4 crossref_primary_10_3390_batteries10060204 crossref_primary_10_1371_journal_pone_0312856 crossref_primary_10_1016_j_measurement_2025_118318 |
| Cites_doi | 10.1109/ACCESS.2020.3026552 10.1016/j.est.2023.107181 10.1016/j.microrel.2017.02.002 10.1016/j.apenergy.2021.117911 10.1016/j.renene.2019.04.157 10.1080/21642583.2019.1708830 10.1016/j.est.2022.104427 10.1016/j.rser.2019.109254 10.1016/j.ress.2021.108082 10.1109/TIE.2018.2808918 10.1016/j.atmosres.2023.106841 10.1016/j.neucom.2019.09.074 10.1016/j.jclepro.2020.121027 10.1155/2018/8358025 10.1016/j.future.2019.02.028 10.1016/j.ins.2020.02.066 10.1002/er.5934 10.1016/j.rser.2020.110017 10.1016/j.enconman.2020.113324 10.1016/j.egyr.2022.09.043 10.1016/j.apenergy.2022.118725 10.1016/j.jpowsour.2018.10.019 10.1109/TCYB.2019.2925534 10.1016/j.egyr.2022.02.188 10.1016/j.apenergy.2020.115646 10.1016/j.prime.2023.100166 10.1016/j.isci.2022.103988 10.1016/j.jpowsour.2020.228581 10.1049/cje.2020.10.012 10.1016/j.apenergy.2021.117449 10.1016/j.apenergy.2018.11.034 10.1109/TIA.2018.2874588 10.1016/j.jpowsour.2020.229154 10.1007/s42452-020-03885-7 10.1016/j.energy.2022.123178 10.1007/s00500-016-2474-6 10.1016/j.psep.2023.03.056 10.1016/j.est.2018.12.011 10.3390/technologies9020028 10.1016/j.energy.2022.123890 10.1016/j.neucom.2019.03.084 10.1016/j.rser.2019.109334 |
| ContentType | Journal Article |
| Copyright | Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2023 |
| Copyright_xml | – notice: Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2023 |
| DBID | 97E ESBDL RIA RIE AAYXX CITATION 7SC 7SP 7SR 8BQ 8FD JG9 JQ2 L7M L~C L~D DOA |
| DOI | 10.1109/ACCESS.2023.3320058 |
| DatabaseName | IEEE Xplore (IEEE) IEEE Xplore Open Access Journals IEEE All-Society Periodicals Package (ASPP) 1998–Present IEEE Electronic Library (IEL) CrossRef Computer and Information Systems Abstracts Electronics & Communications Abstracts Engineered Materials Abstracts METADEX Technology Research Database Materials Research Database ProQuest Computer Science Collection Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional DOAJ Directory of Open Access Journals |
| DatabaseTitle | CrossRef Materials Research Database Engineered Materials Abstracts Technology Research Database Computer and Information Systems Abstracts – Academic Electronics & Communications Abstracts ProQuest Computer Science Collection Computer and Information Systems Abstracts Advanced Technologies Database with Aerospace METADEX Computer and Information Systems Abstracts Professional |
| DatabaseTitleList | Materials Research Database |
| Database_xml | – sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: RIE name: IEEE Electronic Library (IEL) url: https://ieeexplore.ieee.org/ sourceTypes: Publisher |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering |
| EISSN | 2169-3536 |
| EndPage | 110416 |
| ExternalDocumentID | oai_doaj_org_article_d5252d5ff0444dcd98c5d57abbd38de8 10_1109_ACCESS_2023_3320058 10265251 |
| Genre | orig-research |
| GrantInformation_xml | – fundername: Hubei Key Laboratory of Blasting Engineering Foundation grantid: HKLBEF202009 |
| GroupedDBID | 0R~ 4.4 5VS 6IK 97E AAJGR ABAZT ABVLG ACGFS ADBBV AGSQL ALMA_UNASSIGNED_HOLDINGS BCNDV BEFXN BFFAM BGNUA BKEBE BPEOZ EBS EJD ESBDL GROUPED_DOAJ IPLJI JAVBF KQ8 M43 M~E O9- OCL OK1 RIA RIE RNS AAYXX CITATION 7SC 7SP 7SR 8BQ 8FD JG9 JQ2 L7M L~C L~D |
| ID | FETCH-LOGICAL-c409t-184fbee0bf58dcd4bb01d8c4126cbf3fd3d715d919a2135c8d5fd28d03ee79de3 |
| IEDL.DBID | DOA |
| ISICitedReferencesCount | 9 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001085390600001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 2169-3536 |
| IngestDate | Fri Oct 03 12:44:46 EDT 2025 Mon Jun 30 06:49:05 EDT 2025 Tue Nov 18 21:08:07 EST 2025 Sat Nov 29 06:25:07 EST 2025 Wed Aug 27 02:49:41 EDT 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Language | English |
| License | https://creativecommons.org/licenses/by-nc-nd/4.0 |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c409t-184fbee0bf58dcd4bb01d8c4126cbf3fd3d715d919a2135c8d5fd28d03ee79de3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| ORCID | 0009-0007-2723-9168 |
| OpenAccessLink | https://doaj.org/article/d5252d5ff0444dcd98c5d57abbd38de8 |
| PQID | 2876676819 |
| PQPubID | 4845423 |
| PageCount | 17 |
| ParticipantIDs | crossref_primary_10_1109_ACCESS_2023_3320058 ieee_primary_10265251 proquest_journals_2876676819 doaj_primary_oai_doaj_org_article_d5252d5ff0444dcd98c5d57abbd38de8 crossref_citationtrail_10_1109_ACCESS_2023_3320058 |
| PublicationCentury | 2000 |
| PublicationDate | 20230000 2023-00-00 20230101 2023-01-01 |
| PublicationDateYYYYMMDD | 2023-01-01 |
| PublicationDate_xml | – year: 2023 text: 20230000 |
| PublicationDecade | 2020 |
| PublicationPlace | Piscataway |
| PublicationPlace_xml | – name: Piscataway |
| PublicationTitle | IEEE access |
| PublicationTitleAbbrev | Access |
| PublicationYear | 2023 |
| 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 ref35 ref12 ref34 ref15 ref37 ref36 ref31 ref30 ref11 ref33 ref10 ref32 ref2 ref1 ref17 ref39 ref16 ref38 ref19 ref18 reddivari (ref14) 2016 ref24 ref23 ref26 ref25 ref20 ref42 ref41 ref22 ref21 ref43 ref28 ref27 ref29 ref8 ref7 ref9 ref4 ref3 ref6 ref5 ref40 |
| References_xml | – ident: ref5 doi: 10.1109/ACCESS.2020.3026552 – ident: ref41 doi: 10.1016/j.est.2023.107181 – ident: ref7 doi: 10.1016/j.microrel.2017.02.002 – ident: ref38 doi: 10.1016/j.apenergy.2021.117911 – ident: ref34 doi: 10.1016/j.renene.2019.04.157 – ident: ref27 doi: 10.1080/21642583.2019.1708830 – ident: ref6 doi: 10.1016/j.est.2022.104427 – ident: ref8 doi: 10.1016/j.rser.2019.109254 – ident: ref16 doi: 10.1016/j.ress.2021.108082 – ident: ref4 doi: 10.1109/TIE.2018.2808918 – ident: ref33 doi: 10.1016/j.atmosres.2023.106841 – ident: ref43 doi: 10.1016/j.neucom.2019.09.074 – ident: ref35 doi: 10.1016/j.jclepro.2020.121027 – ident: ref18 doi: 10.1155/2018/8358025 – ident: ref28 doi: 10.1016/j.future.2019.02.028 – ident: ref29 doi: 10.1016/j.ins.2020.02.066 – ident: ref11 doi: 10.1002/er.5934 – ident: ref23 doi: 10.1016/j.rser.2020.110017 – ident: ref25 doi: 10.1016/j.enconman.2020.113324 – ident: ref9 doi: 10.1016/j.egyr.2022.09.043 – ident: ref37 doi: 10.1016/j.apenergy.2022.118725 – ident: ref15 doi: 10.1016/j.jpowsour.2018.10.019 – ident: ref32 doi: 10.1109/TCYB.2019.2925534 – ident: ref20 doi: 10.1016/j.egyr.2022.02.188 – ident: ref22 doi: 10.1016/j.apenergy.2020.115646 – ident: ref13 doi: 10.1016/j.prime.2023.100166 – ident: ref12 doi: 10.1016/j.isci.2022.103988 – ident: ref17 doi: 10.1016/j.jpowsour.2020.228581 – ident: ref10 doi: 10.1049/cje.2020.10.012 – ident: ref39 doi: 10.1016/j.apenergy.2021.117449 – ident: ref36 doi: 10.1016/j.apenergy.2018.11.034 – ident: ref21 doi: 10.1109/TIA.2018.2874588 – ident: ref19 doi: 10.1016/j.jpowsour.2020.229154 – ident: ref30 doi: 10.1007/s42452-020-03885-7 – ident: ref2 doi: 10.1016/j.energy.2022.123178 – ident: ref26 doi: 10.1007/s00500-016-2474-6 – ident: ref31 doi: 10.1016/j.psep.2023.03.056 – start-page: 1 year: 2016 ident: ref14 publication-title: Electrode-electrolyte interface layers in lithium ion batteries using reactive force field based molecular dynamics – ident: ref24 doi: 10.1016/j.est.2018.12.011 – ident: ref3 doi: 10.3390/technologies9020028 – ident: ref42 doi: 10.1016/j.energy.2022.123890 – ident: ref40 doi: 10.1016/j.neucom.2019.03.084 – ident: ref1 doi: 10.1016/j.rser.2019.109334 |
| SSID | ssj0000816957 |
| Score | 2.3226268 |
| Snippet | Lithium-ion batteries (LIBs) experience aging degradation during long-term operation. Accurate prediction of the remaining useful life (RUL) in advance is... |
| SourceID | doaj proquest crossref ieee |
| SourceType | Open Website Aggregation Database Enrichment Source Index Database Publisher |
| StartPage | 110400 |
| SubjectTerms | Aging Algorithms Arithmetic Artificial neural networks Batteries Correlation coefficients health indexes Indexes Integrated circuit modeling Lithium-ion batteries Lithium-ion batteries SOH estimation Machine learning Mathematical models MOAOA-DELM Multiple objective analysis Optimization Optimization algorithms Optimization methods Parameters Prediction algorithms Predictive models Rechargeable batteries Robustness RUL prediction Stability |
| SummonAdditionalLinks | – databaseName: IEEE Electronic Library (IEL) dbid: RIE link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1Jb1MxELag4gAH1iJSCvKBIw7v2X7bMU1bUSlqK0Sl3qzYY0NQk1TpC-qf6H_ujO1GrRBI3N7iVZ-XmbHnG8Y-BSjqqQaiDdVaaGdLgUK9FkoGL4n-JUAkcZ00x8ft-Xl3mp3Voy-M9z5ePvNDeoxn-bB0azKV4QyXdSXJYfpx09TJWWtjUKEIEl3VZGahsui-jMZj7MSQAoQPlSLzSftg94kk_Tmqyh9LcdxfDl_8Z8tesudZkOSjhPwr9sgvXrNn9-gF37Cbo0Xasfjpis5jCAOOQiqfzPqfs_VcHOF7IthEfZl_O5vweIOAR69ccWJ_pdUQK8EMc3J35Ce4xMyz7yYfXfxYxl9iDzdD4PveX_KD655sjjwzt2Jp8b6m32Znhwffx19FDr8gHCp9vUDdL1jvCxuqFhxoa4sSWqdLWTsbVAAFTVlBV3ZTWarKtVAhsC0UyvumA6_esq3FcuHfMY5iRKVVmDYBBwb5u7ZdHVwBWBFIr-sBk3ewGJe5ySlExoWJOkrRmYSlISxNxnLAPm8yXSZqjn8n3yO8N0mJVzt-QCBNnqYGEESJ_QhEo4ed7lpXQdVMrQXVgsdCtgn8e_Ul3Ads9274mLwIXBlURukGMcpcO3_J9p49pSYmk84u2-pXa_-BPXG_-9nV6mMc37et1_p4 priority: 102 providerName: IEEE |
| Title | Indirect Prediction for Lithium-Ion Batteries RUL Using Multi-Objective Arithmetic Optimization Algorithm-Based Deep Extreme Learning Machine |
| URI | https://ieeexplore.ieee.org/document/10265251 https://www.proquest.com/docview/2876676819 https://doaj.org/article/d5252d5ff0444dcd98c5d57abbd38de8 |
| Volume | 11 |
| WOSCitedRecordID | wos001085390600001&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: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 2169-3536 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000816957 issn: 2169-3536 databaseCode: DOA dateStart: 20130101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 2169-3536 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000816957 issn: 2169-3536 databaseCode: M~E dateStart: 20130101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1Lb9QwELZQxQEOiEcRC6XygSNuYztO7ON22YpKS1shKvVmrT02LOpuq22KOPEP-M-MH60WIcGFS6QkdhzPTObheL4h5E2Eppu3kGBD25a13nGGTn3LpIhBJPiXCBnEddYfH-vzc3O6Ueor7Qkr8MCFcPughBKgYkzAZuDBaK9A9XPnQGoIOc236c1GMJV1sOadUX2FGeKN2R9PJjijvVQtfE_KtJaifzNFGbG_llj5Qy9nY3P4mDyqXiIdl7d7Qu6F1VPycAM78Bn5ebQq5oiertPPlkRgih4onS2GL4ubJTvC84KeicEw_Xg2o3l7AM0pt-zEfS2qDgfBDsuUy0hPUH8sa2ImHV98vsy32AFaOqDvQrii0-9DWlCkFZYVn5Y3Y4ZtcnY4_TR5z2ptBeYxohsYBnbRhdC4qDRStXWu4aB9y0XnXZQRJPRcgeFmLrhUXiMLQGhoZAi9gSCfk63V5Sq8IBR9BNXKOO8jcj0ls2rTRd8ADgQitN2IiFsyW1-Bx1P9iwubA5DG2MIbm3hjK29G5O1dp6uCu_H35geJf3dNE2h2voCiZKso2X-J0ohsJ-5vjCc67MJHZOdWHGz9wq8tRpppezA6VC__x9ivyIM0n7K4s0O2hvVNeE3u-2_D4nq9m4Ubjx9-THdziuIvhOsAfA |
| linkProvider | Directory of Open Access Journals |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1ZbxMxELZQQQIeOIsIFPADj2zY9bHHYxpaNWJJK9RKfbNijw1BTVKlG8Sf4D8z43WjIgQSb3v41OdjZuz5hrG3AfJypoBoQ5XKlLNFhkK9yqQIXhD9S4BI4tpW02l9ft6cJGf16AvjvY-Xz_yQHuNZPqzchkxlOMNFqQU5TN_WSom8d9famlQohkSjq8QtVOTN-9F4jN0YUojwoZRkQKl_238iTX-Kq_LHYhx3mMOH_9m2R-xBEiX5qMf-Mbvll0_Y_RsEg0_Zz8my37P4yZpOZAgFjmIqb-fd1_lmkU3wvafYRI2Zfz5rebxDwKNfbnZsv_XrIVaCGRbk8MiPcZFZJO9NPrr4soq_sn3cDoF_8P6SH_zoyOrIE3crlhZvbPpddnZ4cDo-ylIAhsyh2tdlqP0F631ug67BgbI2L6B2qhCls0EGkFAVGpqimYlCaleDRmhryKX3VQNePmM7y9XSP2ccBQmtZJhVAYcGebzWTRlcDlgRCK_KARPXsBiX2MkpSMaFiVpK3pgeS0NYmoTlgL3bZrrsyTn-nXyf8N4mJWbt-AGBNGmiGkAQBfYjEJEedrqpnQZdzawFWYPHQnYJ_Bv19bgP2N718DFpGbgyqI7SHWKUul78Jdsbdvfo9FNr2sn040t2j5rbG3j22E633vhX7I773s2v1q_jWP8F0Yz9vw |
| 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=Indirect+Prediction+for+Lithium-Ion+Batteries+RUL+Using+Multi-Objective+Arithmetic+Optimization+Algorithm-Based+Deep+Extreme+Learning+Machine&rft.jtitle=IEEE+access&rft.au=Li%2C+Linna&rft.au=Huang%2C+Zhong&rft.au=Ding%2C+Guorong&rft.date=2023&rft.pub=IEEE&rft.eissn=2169-3536&rft.volume=11&rft.spage=110400&rft.epage=110416&rft_id=info:doi/10.1109%2FACCESS.2023.3320058&rft.externalDocID=10265251 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2169-3536&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2169-3536&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2169-3536&client=summon |