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Machine Learning Applications in Optical Fiber Sensing: A Research Agenda.
Reyes-Vera, Erick; Valencia-Arias, Alejandro; García-Pineda, Vanessa; Aurora-Vigo, Edward Florencio; Alvarez Vásquez, Halyn; Sánchez, Gustavo.
Afiliação
  • Reyes-Vera E; Departamento de Electrónica y Telecomunicaciones, Instituto Tecnológico Metropolitano, Medellín 050013, Colombia.
  • Valencia-Arias A; Escuela de Ingeniería Industrial, Universidad Señor de Sipán, Chiclayo 14001, Peru.
  • García-Pineda V; Departamento de Electrónica y Telecomunicaciones, Instituto Tecnológico Metropolitano, Medellín 050013, Colombia.
  • Aurora-Vigo EF; Escuela Profesional de Ingeniería Agroindustrial y Comercio Exterior, Universidad Señor de Sipán, Chiclayo 14001, Peru.
  • Alvarez Vásquez H; Facultad de Ingeniería, Arquitectura y Urbanismo, Universidad Señor de Sipán, Chiclayo 14001, Peru.
  • Sánchez G; Instituto de Investigación y Estudios de la Mujer, Universidad Ricardo Palma, Lima 15074, Peru.
Sensors (Basel) ; 24(7)2024 Mar 29.
Article em En | MEDLINE | ID: mdl-38610411
ABSTRACT
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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Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2024 Tipo de documento: Article