Optimized convolutional neural network-based multigas detection using fiber optic sensor

Rao, C. Subba; Geetha, T.; Chellaswamy, C.; Arul, S. Deva

doi:10.1117/1.oe.60.12.127108

Cited by 5 publications

(4 citation statements)

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“…Fiber optic sensor became the most-used term in 2021. There was a significant amount of interest in the application of this type of sensor to measure a variety of different physical and chemical parameters, such as fluid flow estimation [56], mechanical impact [57], refractive index [42,58], liquid identification [59], gas detection [60], and so on. FBS was also widely used in this year [57,61].…”

Section: Analysis Of Thematic Evolutionmentioning

confidence: 99%

“…FBS was also widely used in this year [57,61]. However, other techniques such as Fabry-Perot interferometer [60], Brillouin optical time domain analyzer (BOTDA) [62], phase-sensitive optical time domain reflectometry (φ-OTDR) [51], and photonic crystal fibers (PCFs) [63] were also implemented in combination with ML techniques to optimize the measurements.…”

Section: Analysis Of Thematic Evolutionmentioning

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: Analysis Of Thematic Evolutionmentioning

confidence: 99%

Section: Analysis Of Thematic Evolutionmentioning

confidence: 99%

Machine Learning Applications in Optical Fiber Sensing: A Research Agenda

Reyes-Vera,

Valencia-Arias,

García-Pineda

et al. 2024

Sensors

View full text Add to dashboard Cite

show abstract

“…Due to the frequent generation of massive amounts of data in FOSN, how to efficiently process and store this data is truly crucial for improving the performance of the entire system [12][13]. The system optimization studied in this article is divided into two parts, specifically data processing architecture optimization and storage structure optimization [14].…”

Section: System Data Processing and Storage Structure Optimizationmentioning

confidence: 99%

Design of real time monitoring and intelligent analysis system for fiber optic sensor networks driven by big data

Huang,

Pan,

et al. 2024

Third International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024)

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This article explores the application of big data technology in the design of real-time monitoring and intelligent analysis systems for fiber optic sensor networks (FOSN). An analysis was conducted on the limiting factors of current FOSN, such as cross sensitivity, data processing speed, signal-to-noise ratio, etc. This paper proposes an advanced data analysis engine based on CNN (Convolutional Neural Network) algorithm model and artificial intelligence to overcome these challenges.In the real-time data processing capability experiment, the average time for processing 10000 pieces of data based on CNN algorithm system was only 0.05 seconds, while the time for processing 100000 pieces of data was controlled within 0.5 seconds, demonstrating the efficiency of processing large-scale data. In the accuracy verification experiment of monitoring, the average error of the CNN algorithm system for 1000 data is only 0.03, indicating the high accuracy performance of the system in real-time monitoring.

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“…Learning rate is one of the configurable hyperparameters of neural network used during network training. It has a small positive value (0 to 1) that controls quickly the model adapted to the problem (Subba Rao et al, 2021). The learning rate or step size is the amount of weights required for updation of the training process.…”

Section: Proposed Parameter Learningmentioning

confidence: 99%

Actor–critic learning-based adaptive fuzzy controller for stepper motor

Babu¹,

Chellaswamy²,

Geetha³

2023

Transactions of the Institute of Measurement and Control

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This paper presents a combination of both the Takagi–Sugeno–Kang fuzzy controller and the advantage of reinforcement learning for the reduction in the effect of external disturbances and system uncertainties. A neural network is used for the implementation of a critic network; the parameters are updated using the Lyapunov criteria for avoiding local minima problems. The fewer learning parameters used helps online tuning of the controller by the critic network based on the reward function. MATLAB/Simulink is used for simulation of the proposed system under different conditions in the investigation of the performance of the proposed controller. The actor parameters are updated based on the change in the reward function error and a control function. Five different conditions, namely, no-load condition, sudden load-torque changes, system parameter uncertainty, sudden phase interruption, and the impact of noise have been studied. The proposed method was found to meet the high-speed response with the tracking error of ±2.23% for the tracking reference trajectory and tracking error of ±2.5% under constant load-torque disturbance. The test results were compared with two benchmark controllers for the verification of the effectiveness of the proposed controller. Simulation results showed the proposed method providing an adaptive and precise speed response. Therefore, it is suitable for non-linear and uncertain applications.

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Optimized convolutional neural network-based multigas detection using fiber optic sensor

Cited by 5 publications

References 0 publications

Machine Learning Applications in Optical Fiber Sensing: A Research Agenda

Machine Learning Applications in Optical Fiber Sensing: A Research Agenda

Design of real time monitoring and intelligent analysis system for fiber optic sensor networks driven by big data

Actor–critic learning-based adaptive fuzzy controller for stepper motor

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