Review–Modern Data Analysis in Gas Sensors
Review–Modern Data Analysis in Gas Sensors
复制标题
回顾——气体传感器的现代数据分析
DOI：
10.1149/1945-7111/aca839
复制
发表时间：
2022
影响因子：
3.9
通讯作者：
Sekhar, Praveen Kumar
中科院分区：
文献类型：
--
作者：
Sagar, Md. Samiul;Allison, Noah Riley;Jalajamony, Harikrishnan Muraleedharan;Fernandez, Renny Edwin;Sekhar, Praveen Kumar
气体传感器领域的发展见证了指数增长，具有众多的应用。不同的应用程序带来了意想不到的挑战。数据科学的最新进展已经解决了选择性、漂移、老化、检测限和响应时间等挑战。包括机器学习技术在内的现代数据分析的结合使得能够在没有人为干预的情况下实现自我维持的气体传感基础设施。本文概述了气体传感器领域的数据支持技术。在阐述气体传感相关数据分析的先前发展的同时，本文准备成为数据科学和气体传感器领域的爱好者的入门者。
Development in the field of gas sensors has witnessed exponential growth with multitude of applications. The diverse applications have led to unexpected challenges. Recent advances in data science have addressed the challenges such as selectivity, drift, aging, limit of detection, and response time. The incorporation of modern data analysis including machine learning techniques have enabled a self-sustaining gas sensing infrastructure without human intervention. This article provides a birds-eye view on data enabled technologies in the realm of gas sensors. While elaborating the prior developments in gas sensing related data analysis, this article is poised to be an entrant for enthusiast in the domain of data science and gas sensors.