用数码相机和神经网络测定气体压力

IF 2.3 4区 物理与天体物理 Q2 OPTICS Fiber and Integrated Optics Pub Date : 2019-09-13 DOI:10.1117/12.2536537
L. Grad, T. Malinowski
{"title":"用数码相机和神经网络测定气体压力","authors":"L. Grad, T. Malinowski","doi":"10.1117/12.2536537","DOIUrl":null,"url":null,"abstract":"The work concerns the study of the possibility of using an artificial neural network to determine the gas pressure or liquid, in the flow system. The basis for determining the pressure is the view of the membrane, which is obtained discreetly from the vision sensor. The essence of the method operation consists of associating the fuzzy image of the marker placed on the membrane with the corresponding reference pressure value, which in the network learning process, is read from the standard pressure gauge. The test used a device allowing the measuring of gas pressure with an accuracy no lower than 2%. The operation of the artificial neural network is based on identifying the degree of blurring the marker on the examined views of the membranes and associating them with the pressure values. In the case when the membrane views cannot be uniquely qualified for the training set, the network acts as an interpolator and predicts the pressure value.","PeriodicalId":50449,"journal":{"name":"Fiber and Integrated Optics","volume":null,"pages":null},"PeriodicalIF":2.3000,"publicationDate":"2019-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Determination of gas pressure with use of a digital camera and neural networks\",\"authors\":\"L. Grad, T. Malinowski\",\"doi\":\"10.1117/12.2536537\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The work concerns the study of the possibility of using an artificial neural network to determine the gas pressure or liquid, in the flow system. The basis for determining the pressure is the view of the membrane, which is obtained discreetly from the vision sensor. The essence of the method operation consists of associating the fuzzy image of the marker placed on the membrane with the corresponding reference pressure value, which in the network learning process, is read from the standard pressure gauge. The test used a device allowing the measuring of gas pressure with an accuracy no lower than 2%. The operation of the artificial neural network is based on identifying the degree of blurring the marker on the examined views of the membranes and associating them with the pressure values. In the case when the membrane views cannot be uniquely qualified for the training set, the network acts as an interpolator and predicts the pressure value.\",\"PeriodicalId\":50449,\"journal\":{\"name\":\"Fiber and Integrated Optics\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.3000,\"publicationDate\":\"2019-09-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Fiber and Integrated Optics\",\"FirstCategoryId\":\"101\",\"ListUrlMain\":\"https://doi.org/10.1117/12.2536537\",\"RegionNum\":4,\"RegionCategory\":\"物理与天体物理\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"OPTICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Fiber and Integrated Optics","FirstCategoryId":"101","ListUrlMain":"https://doi.org/10.1117/12.2536537","RegionNum":4,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"OPTICS","Score":null,"Total":0}
引用次数: 0

摘要

这项工作涉及研究使用人工神经网络来确定流动系统中气体压力或液体压力的可能性。确定压力的基础是膜的视图,这是由视觉传感器获得的。该方法操作的实质是将放置在膜上的标记的模糊图像与相应的参考压力值相关联,该参考压力值在网络学习过程中从标准压力表读取。该试验使用的装置允许测量气体压力,精度不低于2%。人工神经网络的操作是基于识别膜检查视图上标记物的模糊程度,并将它们与压力值联系起来。在膜视图不能唯一符合训练集的情况下,网络充当插值器并预测压力值。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Determination of gas pressure with use of a digital camera and neural networks
The work concerns the study of the possibility of using an artificial neural network to determine the gas pressure or liquid, in the flow system. The basis for determining the pressure is the view of the membrane, which is obtained discreetly from the vision sensor. The essence of the method operation consists of associating the fuzzy image of the marker placed on the membrane with the corresponding reference pressure value, which in the network learning process, is read from the standard pressure gauge. The test used a device allowing the measuring of gas pressure with an accuracy no lower than 2%. The operation of the artificial neural network is based on identifying the degree of blurring the marker on the examined views of the membranes and associating them with the pressure values. In the case when the membrane views cannot be uniquely qualified for the training set, the network acts as an interpolator and predicts the pressure value.
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
CiteScore
3.40
自引率
0.00%
发文量
4
审稿时长
>12 weeks
期刊介绍: Fiber and Integrated Optics , now incorporating the International Journal of Optoelectronics, is an international bimonthly journal that disseminates significant developments and in-depth surveys in the fields of fiber and integrated optics. The journal is unique in bridging the major disciplines relevant to optical fibers and electro-optical devices. This results in a balanced presentation of basic research, systems applications, and economics. For more than a decade, Fiber and Integrated Optics has been a valuable forum for scientists, engineers, manufacturers, and the business community to exchange and discuss techno-economic advances in the field.
期刊最新文献
Investigation of Dual-Layer Si-ITO-Dielectric Based Hybrid Plasmonic Electro-Absorption Modulator at 1.55 µm Wavelength Efficient Error-Rate Estimation for Optical Transmission Systems Using Artificial Neural Networks Copper and Tungsten Disulfide Based Highly Sensitive Fiber Optic Surface Plasmon Resonance Sensor Theoretical and Practical Bounds on the Initial Value of Clock Skew Compensation Algorithm Immune to Floating-Point Precision Loss for Resource-Constrained Wireless Sensor Nodes Enhancing the Secure Transmission of Data Over Optical Fiber Networks from Source to Destination
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1