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Output Prediction of Helical Microfiber Temperature Sensors in Cycling Measurement by Deep Learning
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作者 Minghui CHEN Jinjin HAN +7 位作者 Juan LIU Fangzhu ZHENG Shihang GENG Shimeng TANG Zhijun WU Jixiong PU xining zhang Hao DAI 《Photonic Sensors》 SCIE EI CSCD 2023年第3期37-49,共13页
The inconsistent response curve of delicate micro/nanofiber(MNF)sensors during cycling measurement is one of the main factors which greatly limit their practical application.In this paper,we proposed a temperature sen... The inconsistent response curve of delicate micro/nanofiber(MNF)sensors during cycling measurement is one of the main factors which greatly limit their practical application.In this paper,we proposed a temperature sensor based on the copper rod-supported helical microfiber(HMF).The HMF sensors exhibited different light intensity-temperature response relationships in single-cycle measurements.Two neural networks,the deep belief network(DBN)and the backpropagation neural network(BPNN),were employed respectively to predict the temperature of the HMF sensor in different sensing processes.The input variables of the network were the sensor geometric parameters(the microfiber diameter,wrapped length,coiled turns,and helical angle)and the output optical intensity under different working processes.The root mean square error(RMSE)and Pearson correlation coefficient(R)were used to evaluate the predictive ability of the networks.The DBN with two restricted Boltzmann machines(RBMs)provided the best temperature prediction results(RMSE and R of the heating process are 0.9705℃and 0.9969,while the values of RMSE and R of the cooling process are 0.7866℃and 0.9977,respectively).The prediction results obtained by the optimal BPNN(five hidden layers,10 neurons in each layer,RMSE=1.1266℃,R=0.9957)were slightly inferior to those obtained by the DBN.The neural network could accurately and reliably predict the response of the HMF sensor in cycling operation,which provided the possibility for the flexible application of the complex MNF sensor in a wide sensing range. 展开更多
关键词 Helical microfiber temperature sensors deep belief network backpropagation neural network response prediction cycling measurement
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Surface roughening of Nafion membranes using different route planning for IPMCs
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作者 Liang Yang Dongsheng zhang +2 位作者 xining zhang Aifen Tian Yifan Ding 《International Journal of Smart and Nano Materials》 SCIE EI 2020年第2期117-128,共12页
In this paper,three different roughening route planning methods(free coarsening,vertical coarsening,circling coarsening)were designed to pre-treat the basement membrane.The platinum IPMC was prepared by electroless pl... In this paper,three different roughening route planning methods(free coarsening,vertical coarsening,circling coarsening)were designed to pre-treat the basement membrane.The platinum IPMC was prepared by electroless plating to observe their surface morphology and test their performances such as output displacement and blocking force.The results show that the platinum electrode of the vertical coarsening route planning of IPMC is distributed in the shape of fish scales and is the most compact and regular on the surface electrode.The thickness of the platinum electrode layer is about 13μm,the maximum output displacement is 54.89 mm,the blocking force of 15.46 mN,and the maximum strain energy density of the IPMC of 2.44 KJ/m^(3).Vertical coarsening route planning can significantly improve the electrodynamic properties of IPMC,and it is recommended that the surface roughening of IPMC be utilized in the research and application of IPMC.It lays a certain experimental foundation for the performance improvement and innovation research and development of IPMC in the subsequent stage. 展开更多
关键词 Intelligent materials IPMC surface coarsening MICROMORPHOLOGY actuation property
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