Ultrafast and Accurate Temperature Extraction via Kernel Extreme Learning Machine for BOTDA Sensors

Zhang, Yufeng; Lei, Yu; Hu, Zhengliang; Cheng, Le; Sui, Hao; Zhu, Hongna; Li, Guangming; Luo, Bin; Zou, Xihua; Liu, Yan

doi:10.1109/jlt.2020.3035810

Cited by 14 publications

(5 citation statements)

References 31 publications

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“…Zhang et al [146] extracted temperature, applying kernel extreme learning machines (K-ELM). ELM is a special case of ANNs consisting of a single hidden layer, where the first weight matrix is randomly initialized [147,148].…”

Section: Machine Learning For Temperature and Strain Predictions Dire...mentioning

confidence: 99%

“…In comparison to the simple ELM, the K-ELM algorithm does not require either the number of nodes in the hidden layer to be specified or the feature mapping to be known. According to Zhang et al [146], K-ELM proved to be very robust and in comparison to the conventional LCF approach, they slightly reduced the extracted temperature error by 0.3 • C and improved the temperature extraction time by 120 times. The authors also applied simple ELM and found that they perform significantly worse than the conventional LCF.…”

Section: Machine Learning For Temperature and Strain Predictions Dire...mentioning

confidence: 99%

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Machine Learning Approaches in Brillouin Distributed Fiber Optic Sensors

Karapanagiotis

Krebber

2023

Sensors

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This paper presents reported machine learning approaches in the field of Brillouin distributed fiber optic sensors (DFOSs). The increasing popularity of Brillouin DFOSs stems from their capability to continuously monitor temperature and strain along kilometer-long optical fibers, rendering them attractive for industrial applications, such as the structural health monitoring of large civil infrastructures and pipelines. In recent years, machine learning has been integrated into the Brillouin DFOS signal processing, resulting in fast and enhanced temperature, strain, and humidity measurements without increasing the system’s cost. Machine learning has also contributed to enhanced spatial resolution in Brillouin optical time domain analysis (BOTDA) systems and shorter measurement times in Brillouin optical frequency domain analysis (BOFDA) systems. This paper provides an overview of the applied machine learning methodologies in Brillouin DFOSs, as well as future perspectives in this area.

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Section: Machine Learning For Temperature and Strain Predictions Dire...mentioning

confidence: 99%

Section: Machine Learning For Temperature and Strain Predictions Dire...mentioning

confidence: 99%

Machine Learning Approaches in Brillouin Distributed Fiber Optic Sensors

Karapanagiotis

Krebber

2023

Sensors

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“…Examples comprise biomolecular sensing, [33] ethanol content, [34] and temperature. [35,36] Here, we introduce a TTI based on multicomponent colloidal crystals, using smartphone-based image acquisition and machine learning analysis for the data evaluation. Four monodisperse polymer particle types are synthesized with varying glass transition temperatures to span a quaternary phase diagram.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Enabled Image Analysis of Time‐Temperature Sensing Colloidal Arrays

et al. 2023

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Smart, responsive materials are required in various advanced applications ranging from anti-counterfeiting to autonomous sensing. Colloidal crystals are a versatile material class for optically based sensing applications owing to their photonic stopband. A careful combination of materials synthesis and colloidal mesostructure rendered such systems helpful in responding to stimuli such as gases, humidity, or temperature. Here, an approach is demonstrated to simultaneously and independently measure the time and temperature solely based on the inherent material properties of complex colloidal crystal mixtures. An array of colloidal crystals, each featuring unique film formation kinetics, is fabricated. Combined with machine learning-enabled image analysis, the colloidal crystal arrays can autonomously record isothermal heating events -readout proceeds by acquiring photographs of the applied sensor using a standard smartphone camera. The concept shows how the progressing use of machine learning in materials science has the potential to allow non-classical forms of data acquisition and evaluation. This can provide novel insights into multiparameter systems and simplify applications of novel materials.

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“…Recently, repaid development of machine learning methods and successful applications in many conventional fields give rise to more interests on data-based techniques, such as deep neural network, support vector machine, and random forest [12,13]. These algorithms exhibit great performance in signal processing [14,15], computer vision [16], natural language processing [17], and medicine [18]. These algorithms learn the latent pattern between input and output from a large amount of data.…”

Section: Introductionmentioning

confidence: 99%

Underwater Acoustic Source Localization via Kernel Extreme Learning Machine

Huang²,

et al. 2021

Front. Phys.

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Fiber-optic hydrophones have received extensive research interests due to their advantage in ocean underwater target detection. Here, kernel extreme learning machine (K-ELM) is introduced to source localization in underwater ocean waveguide. As a data-driven machine learning method, K-ELM does not need a priori environment information compared to the conventional method of match field processing. The acoustic source localization is considered as a supervised classification problem, and the normalized sample covariance matrix formed over a number of snapshots is utilized as an input. The K-ELM is trained to classify sample covariance matrices (SCMs) into different depth and range classes with simulation. The source position can be estimated directly from the normalized SCMs with K-ELM. The results show that the K-ELM method achieves satisfactory high accuracy on both range and depth localization. The proposed K-ELM method provides an alternative approach for ocean underwater source localization, especially in the case with less a priori environment information.

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Ultrafast and Accurate Temperature Extraction via Kernel Extreme Learning Machine for BOTDA Sensors

Cited by 14 publications

References 31 publications

Machine Learning Approaches in Brillouin Distributed Fiber Optic Sensors

Machine Learning Approaches in Brillouin Distributed Fiber Optic Sensors

Machine Learning Enabled Image Analysis of Time‐Temperature Sensing Colloidal Arrays

Underwater Acoustic Source Localization via Kernel Extreme Learning Machine

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