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Integrating Self-Organizing Map and Graph Neural Networks to Detect Anomalies in Time-series Data
KTH, School of Industrial Engineering and Management (ITM), Engineering Design, Mechatronics and Embedded Control Systems.ORCID iD: 0000-0002-8028-3607
KTH, School of Industrial Engineering and Management (ITM), Engineering Design, Mechatronics and Embedded Control Systems.
KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electronics and Embedded systems.ORCID iD: 0000-0003-0061-3475
KTH, School of Industrial Engineering and Management (ITM), Engineering Design, Mechatronics and Embedded Control Systems.ORCID iD: 0000-0001-7048-0108
2024 (English)In: IEEE Sensors Journal, ISSN 1530-437X, E-ISSN 1558-1748, p. 1-1Article in journal (Refereed) Published
Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE) , 2024. p. 1-1
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Computer Engineering
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URN: urn:nbn:se:kth:diva-357661DOI: 10.1109/jsen.2024.3509632OAI: oai:DiVA.org:kth-357661DiVA, id: diva2:1920415
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QC 20241216

Available from: 2024-12-11 Created: 2024-12-11 Last updated: 2024-12-16Bibliographically approved

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fulltext(6684 kB)37 downloads
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12257605fb5cb867e8fad74666b6de907e399b512c97cec15f96613e38687b39eb674a6ea22a06cc79f33e649b01bf7a5dedfcda243375a0a52b9e9072849f13
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Su, PengLu, ZhonghaiChen, Dejiu

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CiteExportLink to record
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  • ieee
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  • de-DE
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