{"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":"37 1","pages":"1120405 - 1120405-6"},"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}
引用次数: 0
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.
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.