基于迭代随机森林和增量域对抗网络的MOS气体传感器阵列实时校正和长期漂移补偿。

IF 3 3区 工程技术 Q2 CHEMISTRY, ANALYTICAL
Micromachines Pub Date : 2025-08-29 DOI:10.3390/mi16090991
Xiaorui Dong, Shijing Han
{"title":"基于迭代随机森林和增量域对抗网络的MOS气体传感器阵列实时校正和长期漂移补偿。","authors":"Xiaorui Dong, Shijing Han","doi":"10.3390/mi16090991","DOIUrl":null,"url":null,"abstract":"<p><p>Sensor arrays serve a crucial role in various fields such as environmental monitoring, industrial process control, and medical diagnostics, yet their reliability remains challenged by sensor drift and noise contamination. This study presents a novel framework for real-time data error correction and long-term drift compensation utilizing an iterative random forest-based error correction algorithm paired with an Incremental Domain-Adversarial Network (IDAN). The iterative random forest algorithm leverages the collective data from multiple sensor channels to identify and rectify abnormal sensor responses in real time. The IDAN integrates domain-adversarial learning principles with an incremental adaptation mechanism to effectively manage temporal variations in sensor data. Experiments utilizing the metal oxide semiconductor gas sensor array drift dataset demonstrate that the combination of these approaches significantly enhances data integrity and operational efficiency, achieving a robust and good accuracy even in the presence of severe drift. This study underscores the efficacy of integrating advanced artificial intelligence techniques for the ongoing evolution of sensor array technology, paving the way for enhanced monitoring systems capable of sustaining high levels of performance over extended time periods.</p>","PeriodicalId":18508,"journal":{"name":"Micromachines","volume":"16 9","pages":""},"PeriodicalIF":3.0000,"publicationDate":"2025-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12471586/pdf/","citationCount":"0","resultStr":"{\"title\":\"Real-Time Correction and Long-Term Drift Compensation in MOS Gas Sensor Arrays Using Iterative Random Forests and Incremental Domain-Adversarial Networks.\",\"authors\":\"Xiaorui Dong, Shijing Han\",\"doi\":\"10.3390/mi16090991\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Sensor arrays serve a crucial role in various fields such as environmental monitoring, industrial process control, and medical diagnostics, yet their reliability remains challenged by sensor drift and noise contamination. This study presents a novel framework for real-time data error correction and long-term drift compensation utilizing an iterative random forest-based error correction algorithm paired with an Incremental Domain-Adversarial Network (IDAN). The iterative random forest algorithm leverages the collective data from multiple sensor channels to identify and rectify abnormal sensor responses in real time. The IDAN integrates domain-adversarial learning principles with an incremental adaptation mechanism to effectively manage temporal variations in sensor data. Experiments utilizing the metal oxide semiconductor gas sensor array drift dataset demonstrate that the combination of these approaches significantly enhances data integrity and operational efficiency, achieving a robust and good accuracy even in the presence of severe drift. This study underscores the efficacy of integrating advanced artificial intelligence techniques for the ongoing evolution of sensor array technology, paving the way for enhanced monitoring systems capable of sustaining high levels of performance over extended time periods.</p>\",\"PeriodicalId\":18508,\"journal\":{\"name\":\"Micromachines\",\"volume\":\"16 9\",\"pages\":\"\"},\"PeriodicalIF\":3.0000,\"publicationDate\":\"2025-08-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12471586/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Micromachines\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.3390/mi16090991\",\"RegionNum\":3,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"CHEMISTRY, ANALYTICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Micromachines","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.3390/mi16090991","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"CHEMISTRY, ANALYTICAL","Score":null,"Total":0}
引用次数: 0

摘要

传感器阵列在环境监测、工业过程控制和医疗诊断等各个领域发挥着至关重要的作用,但其可靠性仍然受到传感器漂移和噪声污染的挑战。本研究提出了一种利用迭代随机森林纠错算法与增量域对抗网络(IDAN)相结合的实时数据纠错和长期漂移补偿的新框架。迭代随机森林算法利用来自多个传感器通道的集体数据实时识别和纠正异常传感器响应。IDAN集成了领域对抗学习原理和增量适应机制,有效地管理传感器数据的时间变化。利用金属氧化物半导体气体传感器阵列漂移数据集的实验表明,这些方法的结合显著提高了数据完整性和操作效率,即使在存在严重漂移的情况下也能实现鲁棒性和良好的准确性。这项研究强调了集成先进的人工智能技术对传感器阵列技术的持续发展的有效性,为增强监测系统铺平了道路,使其能够在较长时间内保持高水平的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Real-Time Correction and Long-Term Drift Compensation in MOS Gas Sensor Arrays Using Iterative Random Forests and Incremental Domain-Adversarial Networks.

Real-Time Correction and Long-Term Drift Compensation in MOS Gas Sensor Arrays Using Iterative Random Forests and Incremental Domain-Adversarial Networks.

Real-Time Correction and Long-Term Drift Compensation in MOS Gas Sensor Arrays Using Iterative Random Forests and Incremental Domain-Adversarial Networks.

Real-Time Correction and Long-Term Drift Compensation in MOS Gas Sensor Arrays Using Iterative Random Forests and Incremental Domain-Adversarial Networks.

Sensor arrays serve a crucial role in various fields such as environmental monitoring, industrial process control, and medical diagnostics, yet their reliability remains challenged by sensor drift and noise contamination. This study presents a novel framework for real-time data error correction and long-term drift compensation utilizing an iterative random forest-based error correction algorithm paired with an Incremental Domain-Adversarial Network (IDAN). The iterative random forest algorithm leverages the collective data from multiple sensor channels to identify and rectify abnormal sensor responses in real time. The IDAN integrates domain-adversarial learning principles with an incremental adaptation mechanism to effectively manage temporal variations in sensor data. Experiments utilizing the metal oxide semiconductor gas sensor array drift dataset demonstrate that the combination of these approaches significantly enhances data integrity and operational efficiency, achieving a robust and good accuracy even in the presence of severe drift. This study underscores the efficacy of integrating advanced artificial intelligence techniques for the ongoing evolution of sensor array technology, paving the way for enhanced monitoring systems capable of sustaining high levels of performance over extended time periods.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Micromachines
Micromachines NANOSCIENCE & NANOTECHNOLOGY-INSTRUMENTS & INSTRUMENTATION
CiteScore
5.20
自引率
14.70%
发文量
1862
审稿时长
16.31 days
期刊介绍: Micromachines (ISSN 2072-666X) is an international, peer-reviewed open access journal which provides an advanced forum for studies related to micro-scaled machines and micromachinery. It publishes reviews, regular research papers and short communications. Our aim is to encourage scientists to publish their experimental and theoretical results in as much detail as possible. There is no restriction on the length of the papers. The full experimental details must be provided so that the results can be reproduced.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信