Effective zero-shot learning method for event classification in Φ-OTDR sensing systems Article Swipe
Xing Hu
,
Hepeng Dong
,
Yong Kong
,
Haima Yang
,
Dawei Zhang
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1364/oe.537940
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1364/oe.537940
Despite various Φ-OTDR intrusion event recognition methods having achieved high average accuracy rates (over 90%), these methods usually rely on a large amount of training sample data (80% of the data). When faced with certain intrusion events that are difficult to simulate or have few samples available, the model tends to overfit common types of intrusion events. To address this issue, this paper proposes a zero-sample learning one-dimensional residual model based on attribute point loss (APL-ZSL-1DResNet) to recognize one-dimensional intrusion event signals when training samples are insufficient. The proposed method is validated on two datasets, including a self-made dataset and an open dataset. In the experiments, each category of samples was set as zero-sample intrusion events, achieving an average recall rate of 75% and 66% respectively for zero-sample events, and an average recall rate of 94.6% and 83.5% respectively for common intrusion events.
Related Topics
Concepts
Overfitting
Computer science
Intrusion detection system
Optical time-domain reflectometer
Event (particle physics)
Intrusion
Residual
Sample (material)
Artificial intelligence
Data mining
Pattern recognition (psychology)
Data set
Machine learning
Algorithm
Artificial neural network
Geology
Physics
Telecommunications
Geochemistry
Thermodynamics
Graded-index fiber
Quantum mechanics
Optical fiber
Fiber optic sensor
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1364/oe.537940
- OA Status
- gold
- Cited By
- 7
- References
- 21
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4402380260
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4402380260Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1364/oe.537940Digital Object Identifier
- Title
-
Effective zero-shot learning method for event classification in Φ-OTDR sensing systemsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-09-09Full publication date if available
- Authors
-
Xing Hu, Hepeng Dong, Yong Kong, Haima Yang, Dawei ZhangList of authors in order
- Landing page
-
https://doi.org/10.1364/oe.537940Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.1364/oe.537940Direct OA link when available
- Concepts
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Overfitting, Computer science, Intrusion detection system, Optical time-domain reflectometer, Event (particle physics), Intrusion, Residual, Sample (material), Artificial intelligence, Data mining, Pattern recognition (psychology), Data set, Machine learning, Algorithm, Artificial neural network, Geology, Physics, Telecommunications, Geochemistry, Thermodynamics, Graded-index fiber, Quantum mechanics, Optical fiber, Fiber optic sensorTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
7Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 7Per-year citation counts (last 5 years)
- References (count)
-
21Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W3208827252, https://openalex.org/W3160298257, https://openalex.org/W2064013146, https://openalex.org/W2964041315, https://openalex.org/W2566176078, https://openalex.org/W4285591180, https://openalex.org/W4399999472, https://openalex.org/W6861682213, https://openalex.org/W6861973926, https://openalex.org/W2001773179, https://openalex.org/W4394966895, https://openalex.org/W2996232404, https://openalex.org/W4393032642, https://openalex.org/W4311058629, https://openalex.org/W4391775779, https://openalex.org/W2967363891, https://openalex.org/W2997820086, https://openalex.org/W3116987847, https://openalex.org/W6847825860, https://openalex.org/W4318756714, https://openalex.org/W4312812593 |
| referenced_works_count | 21 |
| abstract_inverted_index.a | 20, 64, 96 |
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| abstract_inverted_index.75% | 122 |
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| abstract_inverted_index.and | 99, 123, 129, 136 |
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| abstract_inverted_index.90%), | 14 |
| abstract_inverted_index.94.6% | 135 |
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| abstract_inverted_index.recall | 119, 132 |
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| abstract_inverted_index.events, | 115, 128 |
| abstract_inverted_index.events. | 56, 142 |
| abstract_inverted_index.methods | 6, 16 |
| abstract_inverted_index.overfit | 51 |
| abstract_inverted_index.samples | 45, 84, 109 |
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| abstract_inverted_index.various | 1 |
| abstract_inverted_index.Φ-OTDR | 2 |
| abstract_inverted_index.accuracy | 11 |
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| abstract_inverted_index.dataset. | 102 |
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| abstract_inverted_index.datasets, | 94 |
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| abstract_inverted_index.self-made | 97 |
| abstract_inverted_index.validated | 91 |
| abstract_inverted_index.available, | 46 |
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| abstract_inverted_index.zero-sample | 65, 113, 127 |
| abstract_inverted_index.experiments, | 105 |
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| abstract_inverted_index.insufficient. | 86 |
| abstract_inverted_index.one-dimensional | 67, 78 |
| abstract_inverted_index.(APL-ZSL-1DResNet) | 75 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 98 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile.value | 0.87203496 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |