Distributed optical fiber sensor temperature dynamic correction method based on building fire temperature-time curve

Liu, Gang; Meng, Hongrong; Qu, Guanhua; Wang, Lan; Ren, Lei; Fang, Lei

doi:10.1016/j.jobe.2023.106050

Cited by 4 publications

(3 citation statements)

References 60 publications

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“…These journals include Optics Express and the International Measurement Confederation's Measurement Journal. Among the most relevant publications, the article entitled "Micro-scale fiber-optic force sensor fabricated using direct laser writing and calibrated using machine learning", published in the journal Optics Express, presents a sensor of microscale fiber-optic force fabricated using direct laser writing (DLW) and for which calibration and optimization are performed, with a particular focus on data analysis using linear regression or artificial neural networks [6].…”

Section: Resultsmentioning

confidence: 99%

“…In recent years, new tools have been identified to facilitate the generation of detection systems [6]. Optical fiber is a widely used material in sensor design due to its unique characteristics and properties that facilitate signal manipulation.…”

Section: Introductionmentioning

confidence: 99%

“…Distributed fiber optic sensors (DFOS) have been widely used in infrastructure monitoring. The most commonly measured quantities are deformation (distributed deformation sensing-DSS), temperature (DTS), and acoustics/vibrations (DAS/DVS) [6]. However, DFOS can also directly or indirectly measure other types of quantities, such as displacement [21,22], pressure, force, relative humidity [23][24][25][26][27], radiation [28][29][30], gas concentration [31,32], and more.…”

Section: Introductionmentioning

confidence: 99%

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Machine Learning Applications in Optical Fiber Sensing: A Research Agenda

Reyes-Vera,

Valencia-Arias,

García-Pineda

et al. 2024

Sensors

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The constant monitoring and control of various health, infrastructure, and natural factors have led to the design and development of technological devices in a wide range of fields. This has resulted in the creation of different types of sensors that can be used to monitor and control different environments, such as fire, water, temperature, and movement, among others. These sensors detect anomalies in the input data to the system, allowing alerts to be generated for early risk detection. The advancement of artificial intelligence has led to improved sensor systems and networks, resulting in devices with better performance and more precise results by incorporating various features. The aim of this work is to conduct a bibliometric analysis using the PRISMA 2020 set to identify research trends in the development of machine learning applications in fiber optic sensors. This methodology facilitates the analysis of a dataset comprised of documents obtained from Scopus and Web of Science databases. It enables the evaluation of both the quantity and quality of publications in the study area based on specific criteria, such as trends, key concepts, and advances in concepts over time. The study found that deep learning techniques and fiber Bragg gratings have been extensively researched in infrastructure, with a focus on using fiber optic sensors for structural health monitoring in future research. One of the main limitations is the lack of research on the use of novel materials, such as graphite, for designing fiber optic sensors. One of the main limitations is the lack of research on the use of novel materials, such as graphite, for designing fiber optic sensors. This presents an opportunity for future studies.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Machine Learning Applications in Optical Fiber Sensing: A Research Agenda

Reyes-Vera,

Valencia-Arias,

García-Pineda

et al. 2024

Sensors

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A lightweight fire detection algorithm for small targets based on YOLOv5s

Lv,

Zhou,

Chen

et al. 2024

Sci Rep

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In response to the current challenges fire detection algorithms encounter, including low detection accuracy and limited recognition rates for small fire targets in complex environments, we present a lightweight fire detection algorithm based on an improved YOLOv5s. The introduction of the CoT (Contextual Transformer) structure into the backbone neural network, along with the creation of the novel CSP1_CoT (Cross stage partial 1_contextual transformer) module, has effectively reduced the model’s parameter count while simultaneously enhancing the feature extraction and fusion capabilities of the backbone network; The network’s Neck architecture has been extended by introducing a dedicated detection layer tailored for small targets and incorporating the SE (Squeeze-and-Excitation) attention mechanism. This augmentation, while minimizing parameter proliferation, has significantly bolstered the interaction of multi-feature information, resulting in an enhanced small target detection capability; The substitution of the original loss function with the Focal-EIoU (Focal-Efficient IoU) loss function has yielded a further improvement in the model’s convergence speed and precision; The experimental results indicate that the modified model achieves an mAP@.5 of 96% and an accuracy of 94.8%, marking improvements of 8.8% and 8.9%, respectively, over the original model. Furthermore, the model’s parameter count has been reduced by 1.1%, resulting in a compact model size of only 14.6MB. Additionally, the detection speed has reached 85 FPS (Frames Per Second), thus satisfying real-time detection requirements. This enhancement in precision and accuracy, while simultaneously meeting real-time and lightweight constraints, effectively caters to the demands of fire detection.

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Combinatorial machine learning approaches for high-rise building cost prediction and their interpretability analysis

Liu,

Lin

2024

Journal of Asian Architecture and Building Engineering

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Distributed optical fiber sensor temperature dynamic correction method based on building fire temperature-time curve

Cited by 4 publications

References 60 publications

Machine Learning Applications in Optical Fiber Sensing: A Research Agenda

Machine Learning Applications in Optical Fiber Sensing: A Research Agenda

A lightweight fire detection algorithm for small targets based on YOLOv5s

Combinatorial machine learning approaches for high-rise building cost prediction and their interpretability analysis

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