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12篇 您的检索式:作者名="Fu Xiaping"
    题名 作者 年代 出处 被引量
1Discrimination of transgenic tomatoes based on visible/near-infrared spectra显示文摘Lijuan Xie Yibin Ying Tiejin Ying Haiyan Yu Xiaping Fu 2006Analytica Chimica Acta2006,,2:1
2Grading method of soybean mosaic disease based on hyperspectral imaging technology显示文摘Soybean is a crop with a long cultivation history that occupies an important position in agricultural production.Soybean mosaic virus disease(SMV)has caused a rapid decline in soybean yields,causing huge losses to the soybean industry,wherefrom its early detec-tion is particularly important.This study proposes a new classification method for the early SMV,dividing its severity into grades 0,1 and 2.In the case of a small number of experi-mental samples of soybeans,this study proposes a combined convolutional neural network and support vector machine(CNN-SVM)method for the early detection of SMV.Experimen-tal results showed that the accuracy of the training set of the CNN-SVM model reached 96.67%,and the accuracy rate of the test set reached 94.17%.The experiment proved the feasibility of using the proposed CNN-SVM model to classify early SMV under the new clas-sification method,and provided a new direction for early SMV detection based on hyper-spectral images.Jiangsheng Gui Jingyi Fei Zixian Wu Xiaping Fu Alou Diakite 2021Information Processing in Agriculture2021,8,3:1
3Experiments on predicting sugar content in apples by FT - NIR technique 显示文摘LIU Yande YING Yibin FU Xiaping 2007JFoodEng2007,80,3:1
4Classification of Chinese rice wine with different marked ages based on near infrared spectroscopy显示文摘YU Haiyan YING Yibin FU Xiaping 2006Journal of Food Quality2006,29,4:1
5Discrimination between Chinese rice wines of different geographical origins by NIRS and AAS显示文摘YU Haiyan ZHOU Ying FU Xiaping 2007European Food Research and Technology2007,225,3:1
6Quality determination of Chinese rice wine based on Fourier transform near infraredspectroscopy显示文摘Haiyan Yu Yibin Ying Xiaping Fu Huishan Lu 2006Journal of Near Infrared Spectroscopy2006,,1:1
7Comparison of diffuse reflectance and transmission mode of visible-near infrared spectroscopy for detecting brown heart of pear显示文摘Fu Xiaping Ying Yibin Lu Huishan 2007Journal of Food Engineering2007,83,3:1
8Variable selection in visible and near-infrared spectra: Application to on-line determination of sugar content in pears显示文摘Huirong Xu Bing Qi Tong Sun Xiaping Fu Yibin Ying 2011Journal of Food Engineering2011,,1:1
9Application of probabilistic neural networks in qualitative analysis of near infrared spectra: Determination of producing area and variety of loquats显示文摘Fu Xiaping Ying Yinbin Zhou Ying 2007Analytica Chimica Acta2007,598,1:1
10Discrimination between Chinese rice wines of different geographical origins by NIRS and AAS显示文摘YU Haiyan ZHOU Ying FU Xiaping 2007European Food Research and Technology2007,225,:1
11Quantitative and qualitative measurement of pear firmness based on near infrared spectroscopy and chemometrics显示文摘Firmness is one of the most important characteristics to estimate fruit maturity and quality.The potential of near-infrared(NIR)diffuse reflectance spectroscopy as a nondestructive way for pear firmness evaluation of three varieties(‘Cuiguan’,‘Xueqing’and‘Xizilv’)was studied,both quantitatively and qualitatively.NIR models were established using partial least square(PLS)methods in the spectral range of 800 to 2500 nm.For quantitative analysis,the correlation coefficient r increased with more varieties involved in the model.Best results were obtained in the model for all three varieties:rcalwas 0.934,root mean square error of calibration(RMSEC)and root mean square error of prediction(RMSEP)were 2.06 N and 3.14 N,respectively.For qualitative analysis,the overall accuracies of discriminant PLS models for classifying pears into three firmness levels:low,medium and high firmness level were not so good,percentage of samples correctly classified ranged from 70.63%to 81.25%for calibration and from 56.25%to 74.38%for validation.The results indicate that NIR spectroscopy together with PLS chemometrics method is feasible for quantitative analysis of pear firmness,however,the classification accuracy is too low to put into practical application.Fu Xiaping Ying Yibin Zhou Ying Lu Huishan Xu Huirong 2008International Journal of Agricultural and Biological Engineering2008,1,1:0
12A 4H-SiC semi-super-junction shielded trench MOSFET: p-pillar is grounded to optimize the electric field characteristics显示文摘A 4H-SiC trench gate metal-oxide-semiconductor field-effect transistor(UMOSFET)with semi-super-junction shiel-ded structure(SS-UMOS)is proposed and compared with conventional trench MOSFET(CT-UMOS)in this work.The advantage of the proposed structure is given by comprehensive study of the mechanism of the local semi-super-junction structure at the bottom of the trench MOSFET.In particular,the influence of the bias condition of the p-pillar at the bottom of the trench on the static and dynamic performances of the device is compared and revealed.The on-resistance of SS-UMOS with grounded(G)and ungrounded(NG)p-pillar is reduced by 52%(G)and 71%(NG)compared to CT-UMOS,respectively.Additionally,gate ox-ide in the GSS-UMOS is fully protected by the p-shield layer as well as semi-super-junction structure under the trench and p-base regions.Thus,a reduced electric-field of 2 MV/cm can be achieved at the corner of the p-shield layer.However,the quasi-intrinsic protective layer cannot be formed in NGSS-UMOS due to the charge storage effect in the floating p-pillar,resulting in a large electric field of 2.7 MV/cm at the gate oxide layer.Moreover,the total switching loss of GSS-UMOS is 1.95 mJ/cm2 and is reduced by 18%compared with CT-UMOS.On the contrary,the NGSS-UMOS has the slowest overall switching speed due to the weakened shielding effect of the p-pillar and the largest gate-to-drain capacitance among the three.The proposed GSS-UMOS plays an important role in high-voltage and high-frequency applications,and will provide a valuable idea for device design and circuit applications.Xiaojie Wang Zhanwei Shen Guoliang Zhang Yuyang Miao Tiange Li Xiaogang Zhu Jiafa Cai Rongdun Hong Xiaping Chen Dingqu Lin Shaoxiong Wu Yuning Zhang Deyi Fu Zhengyun Wu Feng Zhang 2022Journal of Semiconductors2022,43,12:0
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