A new satellite-ship autonomous communication system with an integrated deep learning anomaly detection algorithm.
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| Title: | A new satellite-ship autonomous communication system with an integrated deep learning anomaly detection algorithm. |
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| Authors: | Wu, Di1 (AUTHOR) 13704804113@163.com, Liu, Sheng2 (AUTHOR), Wei, Wei3 (AUTHOR), Sui, Yu3 (AUTHOR) |
| Source: | Multimedia Tools & Applications. Sep2024, Vol. 83 Issue 30, p74075-74100. 26p. |
| Subjects: | Machine learning, Integrated learning systems, Principal components analysis, Telecommunication systems, Telecommunication satellites, Deep learning |
| Abstract: | To realize space-sea integrated communication between multiple ships under the BeiDou-3 satellite communication topology, this paper proposes a new satellite-ship autonomous communication system based on a deep learning anomaly detection algorithm. A deep learning algorithm combining coarse-grained detection (piecewise oversampling principal component analysis, POsPCA) and fine-grained sorting (VAE and differential ARIMA joint model) was established to realize two-way autonomous communication between BeiDou-3 satellites and ships. Specifically, a segmented oversampling principal component analysis algorithm was proposed to analyze anomalous BeiDou-3 short message data segments in the coarse-grained detection stage. In the fine-grained sorting stage, a joint fusion reconstruction and prediction model was proposed to calculate the fine-grained anomaly score. The autonomous communication system establishes the communication priority of multiple ships based on this score. Using the experimental platform independently built for this study to verify the algorithm performance, the proposed algorithm effectively realizes the priority ranking of anomaly scores by detecting abnormal data segments, effectively saving 64.4% of BeiDou-3 satellite short message communication resources. [ABSTRACT FROM AUTHOR] |
| Copyright of Multimedia Tools & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 179395202 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: A new satellite-ship autonomous communication system with an integrated deep learning anomaly detection algorithm. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Wu%2C+Di%22">Wu, Di</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> 13704804113@163.com</i><br /><searchLink fieldCode="AR" term="%22Liu%2C+Sheng%22">Liu, Sheng</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wei%2C+Wei%22">Wei, Wei</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Sui%2C+Yu%22">Sui, Yu</searchLink><relatesTo>3</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Multimedia+Tools+%26+Applications%22">Multimedia Tools & Applications</searchLink>. Sep2024, Vol. 83 Issue 30, p74075-74100. 26p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Integrated+learning+systems%22">Integrated learning systems</searchLink><br /><searchLink fieldCode="DE" term="%22Principal+components+analysis%22">Principal components analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Telecommunication+systems%22">Telecommunication systems</searchLink><br /><searchLink fieldCode="DE" term="%22Telecommunication+satellites%22">Telecommunication satellites</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: To realize space-sea integrated communication between multiple ships under the BeiDou-3 satellite communication topology, this paper proposes a new satellite-ship autonomous communication system based on a deep learning anomaly detection algorithm. A deep learning algorithm combining coarse-grained detection (piecewise oversampling principal component analysis, POsPCA) and fine-grained sorting (VAE and differential ARIMA joint model) was established to realize two-way autonomous communication between BeiDou-3 satellites and ships. Specifically, a segmented oversampling principal component analysis algorithm was proposed to analyze anomalous BeiDou-3 short message data segments in the coarse-grained detection stage. In the fine-grained sorting stage, a joint fusion reconstruction and prediction model was proposed to calculate the fine-grained anomaly score. The autonomous communication system establishes the communication priority of multiple ships based on this score. Using the experimental platform independently built for this study to verify the algorithm performance, the proposed algorithm effectively realizes the priority ranking of anomaly scores by detecting abnormal data segments, effectively saving 64.4% of BeiDou-3 satellite short message communication resources. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Multimedia Tools & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s11042-024-18567-4 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 26 StartPage: 74075 Subjects: – SubjectFull: Machine learning Type: general – SubjectFull: Integrated learning systems Type: general – SubjectFull: Principal components analysis Type: general – SubjectFull: Telecommunication systems Type: general – SubjectFull: Telecommunication satellites Type: general – SubjectFull: Deep learning Type: general Titles: – TitleFull: A new satellite-ship autonomous communication system with an integrated deep learning anomaly detection algorithm. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Wu, Di – PersonEntity: Name: NameFull: Liu, Sheng – PersonEntity: Name: NameFull: Wei, Wei – PersonEntity: Name: NameFull: Sui, Yu IsPartOfRelationships: – BibEntity: Dates: – D: 21 M: 09 Text: Sep2024 Type: published Y: 2024 Identifiers: – Type: issn-print Value: 13807501 Numbering: – Type: volume Value: 83 – Type: issue Value: 30 Titles: – TitleFull: Multimedia Tools & Applications Type: main |
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