Quantifying the value of positive transfer: An experimental case study
Fuente:
arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866911962103808000 |
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| author | Hughes, Aidan J. Delo, Giulia Poole, Jack Dervilis, Nikolaos Worden, Keith |
| author_facet | Hughes, Aidan J. Delo, Giulia Poole, Jack Dervilis, Nikolaos Worden, Keith |
| contents | In traditional approaches to structural health monitoring, challenges often arise associated with the availability of labelled data. Population-based structural health monitoring seeks to overcomes these challenges by leveraging data/information from similar structures via technologies such as transfer learning. The current paper demonstrate a methodology for quantifying the value of information transfer in the context of operation and maintenance decision-making. This demonstration, based on a population of laboratory-scale aircraft models, highlights the steps required to evaluate the expected value of information transfer including similarity assessment and prediction of transfer efficacy. Once evaluated for a given population, the value of information transfer can be used to optimise transfer-learning strategies for newly-acquired target domains. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_14342 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Quantifying the value of positive transfer: An experimental case study Hughes, Aidan J. Delo, Giulia Poole, Jack Dervilis, Nikolaos Worden, Keith Machine Learning In traditional approaches to structural health monitoring, challenges often arise associated with the availability of labelled data. Population-based structural health monitoring seeks to overcomes these challenges by leveraging data/information from similar structures via technologies such as transfer learning. The current paper demonstrate a methodology for quantifying the value of information transfer in the context of operation and maintenance decision-making. This demonstration, based on a population of laboratory-scale aircraft models, highlights the steps required to evaluate the expected value of information transfer including similarity assessment and prediction of transfer efficacy. Once evaluated for a given population, the value of information transfer can be used to optimise transfer-learning strategies for newly-acquired target domains. |
| title | Quantifying the value of positive transfer: An experimental case study |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2407.14342 |