{"title":"基于BP神经网络的环境温湿度对导热系数测量仪影响预测","authors":"Yuting Tang, Shen Xu, Ziyang Wang","doi":"10.1145/3366194.3366294","DOIUrl":null,"url":null,"abstract":"In order to break the limitations on field applications of steady-state thermal conductivity measurement techniques, a new measuring system using the method termed point-heating steady state thermal conductivity measurement method is developed in this work. A corresponding 3D thermal transport model has been built to correlate the surface temperature rise with the incident heat flux, sample's thermal conductivity, and the location for temperature probing. The surface temperature is monitored by an infrared camera, which is easily affected by ambient temperature and humidity. BP neural network model is then employed to predict the influence of ambient temperature and humidity on the measuring instrument of thermal conductivity. The generalization and robustness of the BP neural network model are further verified by comparison with outputs of linear fitting and nonlinear fitting. The prediction model of F (x, y, z) based on BP neural network has good accuracy, and the error is between -0.17 and +0.17, which also improves the speed of measuring the thermal conductivity of the measuring instrument.","PeriodicalId":105852,"journal":{"name":"Proceedings of the 2019 International Conference on Robotics, Intelligent Control and Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2019-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Prediction on the influence of ambient temperature and humidity to measuring instrument of thermal conductivity based on BP neural network\",\"authors\":\"Yuting Tang, Shen Xu, Ziyang Wang\",\"doi\":\"10.1145/3366194.3366294\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In order to break the limitations on field applications of steady-state thermal conductivity measurement techniques, a new measuring system using the method termed point-heating steady state thermal conductivity measurement method is developed in this work. A corresponding 3D thermal transport model has been built to correlate the surface temperature rise with the incident heat flux, sample's thermal conductivity, and the location for temperature probing. The surface temperature is monitored by an infrared camera, which is easily affected by ambient temperature and humidity. BP neural network model is then employed to predict the influence of ambient temperature and humidity on the measuring instrument of thermal conductivity. The generalization and robustness of the BP neural network model are further verified by comparison with outputs of linear fitting and nonlinear fitting. The prediction model of F (x, y, z) based on BP neural network has good accuracy, and the error is between -0.17 and +0.17, which also improves the speed of measuring the thermal conductivity of the measuring instrument.\",\"PeriodicalId\":105852,\"journal\":{\"name\":\"Proceedings of the 2019 International Conference on Robotics, Intelligent Control and Artificial Intelligence\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-09-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 2019 International Conference on Robotics, Intelligent Control and Artificial Intelligence\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3366194.3366294\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2019 International Conference on Robotics, Intelligent Control and Artificial Intelligence","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3366194.3366294","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
摘要
为了突破稳态导热系数测量技术在现场应用中的局限性,本文开发了一种新的测量系统,即点加热稳态导热系数测量法。建立了相应的三维热输运模型,将表面温升与入射热流密度、样品导热系数和测温位置联系起来。表面温度由红外摄像机监控,容易受到环境温度和湿度的影响。然后利用BP神经网络模型预测环境温度和湿度对导热系数测量仪的影响。通过与线性拟合和非线性拟合结果的比较,进一步验证了BP神经网络模型的泛化和鲁棒性。基于BP神经网络的F (x, y, z)预测模型具有较好的精度,误差在-0.17 ~ +0.17之间,提高了测量仪器导热系数的测量速度。
Prediction on the influence of ambient temperature and humidity to measuring instrument of thermal conductivity based on BP neural network
In order to break the limitations on field applications of steady-state thermal conductivity measurement techniques, a new measuring system using the method termed point-heating steady state thermal conductivity measurement method is developed in this work. A corresponding 3D thermal transport model has been built to correlate the surface temperature rise with the incident heat flux, sample's thermal conductivity, and the location for temperature probing. The surface temperature is monitored by an infrared camera, which is easily affected by ambient temperature and humidity. BP neural network model is then employed to predict the influence of ambient temperature and humidity on the measuring instrument of thermal conductivity. The generalization and robustness of the BP neural network model are further verified by comparison with outputs of linear fitting and nonlinear fitting. The prediction model of F (x, y, z) based on BP neural network has good accuracy, and the error is between -0.17 and +0.17, which also improves the speed of measuring the thermal conductivity of the measuring instrument.