Temperature demodulation for optical fiber F-P sensor based on DBNs with ensemble learning

Wang, Lixiong; Liu, Hanjie; Zhang, Pan; Ye, Xu; Fan, Dian; Zhou, Ciming; Li, Yuan

doi:10.1016/j.optlastec.2023.109275

Cited by 4 publications

(2 citation statements)

References 14 publications

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“…The error analysis graph (Figure 9) shows that the ABC-LSTM model performed similarly to other models at certain temperature points. However, in most cases, the demodulation error of the ABC-LSTM model was significantly smaller than that of the LSTM model and the traditional demodulation algorithm, which is consistent with the conclusion of the error distribution of neural network demodulation results described in reference [15]. Based on the above analysis, the ABC-LSTM model is also suitable for static temperature measurements and exhibits an excellent demodulation performance.…”

Section: Discussionsupporting

confidence: 86%

“…Compared with the conventional passive 3 × 3 coupler algorithm, the modified arc-tangent algorithm has a simpler calculation process with the added advantage of eliminating noise from differential, integral, and derivative operations. Moreover, the demodulation performance of the multivariate input model exceeds that of the single-variable model [15], whereas the traditional NPS algorithm can only obtain a single signal as input for the ABC-LSMT model. Our proposed modified arc-tangent method extracts three phase signals as input for the ABC-LSMT model.…”

Section: Modified Arc-tangent Algorithm Based On 3 × 3 Couplermentioning

confidence: 99%

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Temperature Demodulation for an Interferometric Fiber-Optic Sensor Based on Artificial Bee Colony–Long Short-Term Memory

Liu,

Zhou,

Pang

et al. 2023

Photonics

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Demodulation methods play a critical role in achieving high-performance interferometric fiber-optic temperature sensors. However, the conventional passive 3 × 3 coupler demodulation method overlooks certain issues, such as the non-1:1:1 splitting ratio of the coupler, resulting in a non-ideal phase difference in the three output interference signals. These problems significantly impact the measurement results of interferometric temperature sensors. In this paper, we propose a novel arc-tangent method based on a 3 × 3 coupler and a demodulation algorithm combining long short-term memory (LSTM) with an artificial bee colony (ABC). The arc-tangent method is employed to enhance the input phase signal of the ABC-LSTM network model and establish a nonlinear mapping between the phase signal and temperature, effectively preventing the influence of the spectral ratio and phase difference of the 3 × 3 coupler on temperature demodulation. The proposed ABC-LSTM method achieves high-resolution measurements with an interval of 0.10 °C, and the absolute error is below 0.0040 °C within the temperature range of 25.00–25.50 °C. To demonstrate the stability and adaptability of the proposed method under long-term constant temperature conditions, we conducted measurements for approximately three hours in a controlled temperature environment set at 25.00 °C. Experimental results indicate that the maximum error of LSTM-ABC method remains around 0.0040 °C, outperforming the conventional algorithm (0.0095 °C). Furthermore, when comparing the average error values of the conventional passive 3 × 3 coupler method (0.0023 °C), LSTM model (0.0019 °C), and ABC-LSTM model (0.0014 °C), it is evident that the demodulation results of the ABC-LSTM method exhibit the highest level of stability. Therefore, the ABC-LSTM method enhances the accuracy and reliability of interferometric fiber-optic temperature-sensing systems.

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Section: Discussionsupporting

confidence: 86%

Section: Modified Arc-tangent Algorithm Based On 3 × 3 Couplermentioning

confidence: 99%

Temperature Demodulation for an Interferometric Fiber-Optic Sensor Based on Artificial Bee Colony–Long Short-Term Memory

Liu,

Zhou,

Pang

et al. 2023

Photonics

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Machine Learning‐Enhanced Triboelectric Sensing Application

Shang,

Chen,

Cai

et al. 2024

Adv Materials Technologies

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Triboelectric nanogenerator (TENG) has become a promising candidate for wearable energy harvesting and self‐powered sensing systems. However, processing large amounts of data imposes a computing power barrier for practical application. Machine learning‐assisted self‐powered sensors based on TENG have been widely used in data‐driven applications due to their excellent characteristics such as no additional power supply, high sensing accuracy, low cost, and good biocompatibility. This work comprehensively reviews the latest progress in machine learning (ML)‐assisted TENG‐based sensors. The future challenges and opportunities are discussed. First, the fundamental principles including the working mode of ML‐assisted TENG‐based sensor and common algorithms are systematically and comprehensively illustrated, which emphasizes the algorithm definition and principle. Subsequently, the progress of ML methods in the field of TENG‐based sensors is further reviewed, summarizing the advantages and disadvantages of various algorithms in practical examples, and providing guidance and suggestions on how to choose the appropriate methods. Finally, the prospects and challenges of ML‐assisted TENG‐based sensors is summarized. Directions and important insights for the future development of TENG and AI integration is provided.

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High sensitivity high precision MMFBG based on waist-enlarged bitaper for temperature measurement

Zhao,

Wang,

Lin

et al. 2024

Optics & Laser Technology

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Temperature demodulation for optical fiber F-P sensor based on DBNs with ensemble learning

Cited by 4 publications

References 14 publications

Temperature Demodulation for an Interferometric Fiber-Optic Sensor Based on Artificial Bee Colony–Long Short-Term Memory

Temperature Demodulation for an Interferometric Fiber-Optic Sensor Based on Artificial Bee Colony–Long Short-Term Memory

Machine Learning‐Enhanced Triboelectric Sensing Application

High sensitivity high precision MMFBG based on waist-enlarged bitaper for temperature measurement

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