Recent Advances in Machine Learning for Fiber Optic Sensor Applications

Abstract

The latest digital revolution involves the rise of smart devices composed of sensor hardware and artificial intelligence (AI) software for performing intelligent tasks. Smart sensors have become ubiquitous in our lives with varied applications ranging from voice-enabled home devices (Google Home, Alexa, etc.) to the Industrial Internet of Things (IIoT). This revolution has been fueled by 1) miniaturization of sensing hardware, 2) easy access to cloud and high-performance computing, 3) development of big data storage and analytics technologies, and 4) the latest breakthroughs in machine learning (ML) and AI technologies. The emergence of AI since 2012 and its major breakthroughs can be attributed to the research and development (R&D) in deep learning, a subfield of ML that uses biologically inspired neural networks to perform learning tasks. [1] The performance of conventional ML algorithms depends on the individual selection of specific features, while deep neural networks (DNN) automatically generate features as part of the learning process. Deep learningbased AI technologies are increasingly showing performance

Quantum Computing and Simulations for Energy Applications: Review and Perspective

“…Integration of quantum computation to enhance sensor performance is another exciting direction with the potential to benefit the energy sector. 298,300 While progress in QIS continues, several challenges exist to its implementation in advancing energy technologies. In addition, a gap exists between the capability of current QIS stakeholders and the needs of the energy sector.…”

“…High-performance sensors are also required throughout the energy sector, for applications such as pipeline integrity, greenhouse gas monitoring, resource discovery, and grid monitoring, among others. ,− One emerging application of quantum computational techniques is the optimization of sensing platforms. , Quantum simulation may also be used to improve the performance of quantum sensing technologies; for example, a quantum simulator has been used to gain new insights into the entanglement between nitrogen vacancy centers in diamond, which is a widely used material for quantum sensing applications. ,, Additionally, quantum machine learning techniques have shown promise for image classification in remote sensing applications. , Simulating material properties and performance is also crucial for sensor design; for example, complex systems such as MOFs are widely used for the sensitive detection of gases and ions . Thus, the design and optimization of next-generation sensing technologies and materials is an additional area in which quantum computers can significantly benefit the energy sector.…”

Ratiometric Optical Fiber Dissolved Oxygen Sensor Based on Fluorescence Quenching Principle

“…Optical fiber sensors have been widely used in medical diagnosis, chemical analysis, marine and environmental monitoring and other fields. It has great potential in gas monitoring [13][14][15]. Various gas sensors based on different methods have been prepared.…”

Monitoring of Temperature Measurements for Different Flow Regimes in Water and Galinstan with Long Short-Term Memory Networks and Transfer Learning of Sensors

“…LSTM's analysis of time series has been benchmarked in the literature [20,21]. Recent studies have investigated LSTM's performance in various applications, including nuclear reactor control [22], fiber-optic sensor monitoring [18,23,24], failure detection and remaining useful lifetime estimation [25,26], detection of wind turbine blade icing [27], short-term forecasting of solar energy systems [28,29], and water distillation [30,31].…”