人工智能
转化式学习
机器人学
计算机科学
适应性
系统工程
自动化
深度学习
新兴技术
机器学习
工程类
机器人
心理学
生物
机械工程
教育学
生态学
作者
Long Chen,Chenbin Xia,Zhehui Zhao,Haoran Fu,Yunmin Chen
出处
期刊:Sensors
[Multidisciplinary Digital Publishing Institute]
日期:2024-05-07
卷期号:24 (10): 2958-2958
被引量:81
摘要
Machine learning and deep learning technologies are rapidly advancing the capabilities of sensing technologies, bringing about significant improvements in accuracy, sensitivity, and adaptability. These advancements are making a notable impact across a broad spectrum of fields, including industrial automation, robotics, biomedical engineering, and civil infrastructure monitoring. The core of this transformative shift lies in the integration of artificial intelligence (AI) with sensor technology, focusing on the development of efficient algorithms that drive both device performance enhancements and novel applications in various biomedical and engineering fields. This review delves into the fusion of ML/DL algorithms with sensor technologies, shedding light on their profound impact on sensor design, calibration and compensation, object recognition, and behavior prediction. Through a series of exemplary applications, the review showcases the potential of AI algorithms to significantly upgrade sensor functionalities and widen their application range. Moreover, it addresses the challenges encountered in exploiting these technologies for sensing applications and offers insights into future trends and potential advancements.
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