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您的检索式：作者名="ZOU Hanying"
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| 1 | High-precision whispering gallery microsensors with ergodic spectra empowered by machine learning显示文摘Whispering gallery mode(WGM)microcavities provide increasing opportunities for precision measurement due to their ultrahigh sensitivity,compact size,and fast response.However,the conventional WGM sensors rely on monitoring the changes of a single mode,and the abundant sensing information in WGM transmission spectra has not been fully utilized.Here,empowered by machine learning(ML),we propose and demonstrate an ergodic spectra sensing method in an optofluidic microcavity for high-precision pressure measurement.The developed ML method realizes the analysis of the full features of optical spectra.The prediction accuracy of 99.97%is obtained with the average error as low as 0.32 kPa in the pressure range of 100 kPa via the training and testing stages.We further achieve the real-time readout of arbitrary unknown pressure within the range of measurement,and a prediction accuracy of 99.51%is obtained.Moreover,we demonstrate that the ergodic spectra sensing accuracy is∼11.5%higher than that of simply extracting resonating modes’wavelength.With the high sensitivity and prediction accuracy,this work opens up a new avenue for integrated intelligent optical sensing. | BING DUAN HANYING ZOU JIN-HUI CHEN CHUN HUI MA XINGYUN ZHAO XIAOLONG ZHENG CHUAN WANG LIANG LIU DAQUAN YANG | 2022 | Photonics Research2022，10，10： | 3 |
| 2 | Removal of trace and major metals by soil washing with Na2EDTA and oxalate显示文摘 | Rongliang Qiu Zeli Zou Zhihao Zhao Weihua Zhang Tao Zhang Hanying Dong Xiange Wei | 2010 | Journal of Soils and Sediments2010，，1： | 1 |
| 3 | The study of operating variables in soil washing with EDTA显示文摘 | Zeli Zou Rongliang Qiu Weihua Zhang Hanying Dong Zhihao Zhao Tao Zhang Xiange Wei Xinde Cai | 2008 | Environmental Pollution2008，，1： | 1 |
| 4 | A Neural Regression Model for Predicting Thermal Conductivity of CNT Nanofluids with Multiple Base Fluids显示文摘High thermal conductivity of carbon nanotube nanofluids(k_(nf))has received great attention.However,the current researches are limited by experimental conditions and lack a comprehensive understanding of k_(nf) variation law.In view of proposition of data-driven methods in recent years,using experimental data to drive prediction is an effective way to obtain k_(nf),which could clarify variation law of k_(nf) and thus greatly save experimental and time costs.This work proposed a neural regression model for predicting k_(nf).It took into account four influencing factors,including carbon nanotube diameter,volume fraction,temperature and base fluid thermal conductivity(k_(f)).Where,four conventional fluids with k_(f),including R113,water,ethylene glycol and ethylene glycol-water mixed liquid were considered as base fluid considers.By training this model,it can predict k_(nf) with different factors.Also,change law of four influencing factors considered on the k_(nf) enhancement has discussed and the correlation between different influencing factors and k_(nf) enhancement is presented.Finally,compared with nine common machine learning methods,the proposed neural regression model shown the highest accuracy among these. | ZOU Hanying CHEN Cheng ZHA Muxi ZHOU Kangneng XIAO Ruoxiu FENG Yanhui QIU Lin ZHANG Xinxin WANG Zhiliang | 2021 | Journal of Thermal Science2021，30，6： | 1 |
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