research-article

Adaptive Online One-Class Support Vector Machines with Applications in Structural Health Monitoring

Authors:

Nguyen Lu Dang Khoa,

Thierry Rakotoarivelo,

Mehrisadat Makki Alamdari,

Yang WangAuthors Info & Claims

ACM Transactions on Intelligent Systems and Technology (TIST), Volume 9, Issue 6

Article No.: 64, Pages 1 - 20

https://doi.org/10.1145/3230708

Published: 13 November 2018 Publication History

Abstract

One-class support vector machine (OCSVM) has been widely used in the area of structural health monitoring, where only data from one class (i.e., healthy) are available. Incremental learning of OCSVM is critical for online applications in which huge data streams continuously arrive and the healthy data distribution may vary over time. This article proposes a novel adaptive self-advised online OCSVM that incrementally tunes the kernel parameter and decides whether a model update is required or not. As opposed to existing methods, this novel online algorithm does not rely on any fixed threshold, but it uses the slack variables in the OCSVM to determine which new data points should be included in the training set and trigger a model update. The algorithm also incrementally tunes the kernel parameter of OCSVM automatically based on the spatial locations of the edge and interior samples in the training data with respect to the constructed hyperplane of OCSVM. This new online OCSVM algorithm was extensively evaluated using synthetic data and real data from case studies in structural health monitoring. The results showed that the proposed method significantly improved the classification error rates, was able to assimilate the changes in the positive data distribution over time, and maintained a high damage detection accuracy in all case studies.

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Alyatimi AAl-Dala’in TChung VAnaissi ASadgrove E(2024)Improving Weeds Detection in Pastures Using Illumination Invariance TechniquesProceedings of the Second International Conference on Advances in Computing Research (ACR’24)10.1007/978-3-031-56950-0_7(70-82)Online publication date: 29-Mar-2024
https://doi.org/10.1007/978-3-031-56950-0_7
Suleiman BAnaissi AYan WBello AZou STong L(2024)A Federated Learning Anomaly Detection Approach for IoT EnvironmentsProceedings of the Second International Conference on Advances in Computing Research (ACR’24)10.1007/978-3-031-56950-0_18(206-218)Online publication date: 29-Mar-2024
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Index Terms

Adaptive Online One-Class Support Vector Machines with Applications in Structural Health Monitoring
1. Computing methodologies
  1. Machine learning
2. Hardware
  1. Communication hardware, interfaces and storage
    1. Sensor applications and deployments

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Published In

cover image ACM Transactions on Intelligent Systems and Technology

ACM Transactions on Intelligent Systems and Technology Volume 9, Issue 6

Regular Papers

November 2018

290 pages

ISSN:2157-6904

EISSN:2157-6912

DOI:10.1145/3289398

Editor:
Yu Zheng
JD Finance, China

Issue’s Table of Contents

Copyright © 2018 ACM.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 November 2018

Accepted: 01 April 2018

Revised: 01 March 2018

Received: 01 December 2017

Published in TIST Volume 9, Issue 6

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Australian Government through the Department of Communications and the Australian Research Council through the ICT Centre of Excellence Program

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Cited By

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https://dl.acm.org/doi/10.1007/s11042-023-15853-5
Alyatimi AAl-Dala’in TChung VAnaissi ASadgrove E(2024)Improving Weeds Detection in Pastures Using Illumination Invariance TechniquesProceedings of the Second International Conference on Advances in Computing Research (ACR’24)10.1007/978-3-031-56950-0_7(70-82)Online publication date: 29-Mar-2024
https://doi.org/10.1007/978-3-031-56950-0_7
Suleiman BAnaissi AYan WBello AZou STong L(2024)A Federated Learning Anomaly Detection Approach for IoT EnvironmentsProceedings of the Second International Conference on Advances in Computing Research (ACR’24)10.1007/978-3-031-56950-0_18(206-218)Online publication date: 29-Mar-2024
https://doi.org/10.1007/978-3-031-56950-0_18
Keshmiry AHassani SMousavi MDackermann U(2023)Effects of Environmental and Operational Conditions on Structural Health Monitoring and Non-Destructive Testing: A Systematic ReviewBuildings10.3390/buildings1304091813:4(918)Online publication date: 30-Mar-2023
https://doi.org/10.3390/buildings13040918
Chen XChen YChen XYuan LJiang CHuang GShi W(2023) Rapid identification of healthy Tegillarca granosa using laser-induced breakdown spectroscopy and fusion model Food Quality and Safety10.1093/fqsafe/fyad022Online publication date: 18-Apr-2023
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Jiang JLiu FNg WTang QZhong GTang XWang B(2023)AERF: Adaptive ensemble random fuzzy algorithm for anomaly detection in cloud computingComputer Communications10.1016/j.comcom.2023.01.004200(86-94)Online publication date: Feb-2023
https://doi.org/10.1016/j.comcom.2023.01.004
Anaissi AD’souza KSuleiman BBekhit MAlyassine W(2023)Heterogeneous Transfer Learning in Structural Health Monitoring for High Rise StructuresProceedings of the Second International Conference on Innovations in Computing Research (ICR’23)10.1007/978-3-031-35308-6_34(405-417)Online publication date: 17-Jun-2023
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Chen XZeng YKang SJin R(2022)INN: An Interpretable Neural Network for AI Incubation in ManufacturingACM Transactions on Intelligent Systems and Technology10.1145/351931313:5(1-23)Online publication date: 21-Jun-2022
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Dang TLan XWeng JFeng M(2022)Federated Learning for Electronic Health RecordsACM Transactions on Intelligent Systems and Technology10.1145/351450013:5(1-17)Online publication date: 21-Jun-2022
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Cheema PAlamdari MChang KKim CSugiyama M(2022)A drive-by bridge inspection framework using non-parametric clusters over projected data manifoldsMechanical Systems and Signal Processing10.1016/j.ymssp.2022.109401180(109401)Online publication date: Nov-2022
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