Distributed Optical Fiber Intrusion Detection by Image Encoding and SwinT in Multi-Interference Environment of Long-Distance Pipeline

Zhu, Chengyuan; Pu, Yanyun; Yang, Kaixiang; Yang, Qinmin; Chen, C. L. Philip

doi:10.1109/tim.2023.3277937

Cited by 12 publications

(5 citation statements)

References 32 publications

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“…This means that the position of the label in relation to the valid portion of the input sequence remained uncertain. Further related research can be reviewed in [ 100 , 101 , 102 ].…”

Section: DL Applications For Optical Sensorsmentioning

confidence: 99%

“…This means that the position of the label in relation to the valid portion of the input sequence remained uncertain. Further related research can be reviewed in [100][101][102]. In [103], the authors presented the application of signal processing and ML algorithms to detect events using signals generated based on DAS along a pipeline.…”

Section: Cnn-based Applicationsmentioning

confidence: 99%

See 1 more Smart Citation

Deep Learning for Optical Sensor Applications: A Review

Al‐Ashwal

Soufy

Hamza

et al. 2023

Sensors

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Over the past decade, deep learning (DL) has been applied in a large number of optical sensors applications. DL algorithms can improve the accuracy and reduce the noise level in optical sensors. Optical sensors are considered as a promising technology for modern intelligent sensing platforms. These sensors are widely used in process monitoring, quality prediction, pollution, defence, security, and many other applications. However, they suffer major challenges such as the large generated datasets and low processing speeds for these data, including the high cost of these sensors. These challenges can be mitigated by integrating DL systems with optical sensor technologies. This paper presents recent studies integrating DL algorithms with optical sensor applications. This paper also highlights several directions for DL algorithms that promise a considerable impact on use for optical sensor applications. Moreover, this study provides new directions for the future development of related research.

show abstract

“…This means that the position of the label in relation to the valid portion of the input sequence remained uncertain. Further related research can be reviewed in [ 100 , 101 , 102 ].…”

Section: DL Applications For Optical Sensorsmentioning

confidence: 99%

Section: Cnn-based Applicationsmentioning

confidence: 99%

Deep Learning for Optical Sensor Applications: A Review

Al‐Ashwal

Soufy

Hamza

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…This means that the position of the label in relation to the valid portion of the input sequence remains uncertain. Another related researches can be seen in [101][102][103]. In [104], the authors presented application of signal processing and ML algorithms to detect events using signals generated based on DAS along a pipeline.…”

Section: Cnn-based Applicationsmentioning

confidence: 99%

Deep Learning for Optical Sensor Applications: An Insight Review

Al‐Ashwal¹,

Soufy²,

Hamza³

et al. 2023

Preprint

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Over the past decade, Deep Learning (DL) had been applied in a large number of optical sensors’ applications. DL algorithms can improve the accuracy and reduce the noise level in optical sensors. Optical sensors are considered as promise technology for the modern intelligent sensing platforms. These sensors are widely used to process monitoring, quality prediction, pollution, defense, security and many other applications. However, they suffer major challenges such as the large generated data and low processing speed for that data and moreover the much cost of these sensor. These challenges can be mitigated by integrating deep learning system with the optical sensor technologies. This paper presents recent studies that integrate DL algorithms with optical sensors applications. This paper also highlights several of DL algorithms directions that promise a considerable impact on use for optical sensor applications. Moreover, this study provides new directions for the future related research development.

show abstract

“…These profilers are employed in various applications including submarine geological classification [ [20] , [21] , [22] ], research on submarine disasters [ [23] , [24] , [25] ], and exploration of submarine pipelines [ 26 , 27 ]. This is due to their high accuracy, non-immersibility, real-time data display, portability, ease of operation, strong penetration, and relatively minimal attenuation in liquid [ 28 , 29 ].…”

Section: Introductionmentioning

confidence: 99%