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| 1 | Reversible and irreversible heat generation of NCA/Si–C pouch cell during electrochemical energy-storage process显示文摘To meet the requirements of electronic vehicles(EVs) and hybrid electric vehicles(HEVs),the high energy density Li Ni_(0.8) Co_(0.15) Al_(0.05) O_2(NCA) cathode and Si–C anode have attracted more attention.Here we report the thermal behaviors of NCA/Si–C pouch cell during the charge/discharge processes at different current densities.The total heat generations are derived from the surface temperature change during electrochemical Li+insertion/extraction in adiabatic surrounding.The reversible heat is determined by the entropic coefficients,which are related with open-circuit voltage at different temperatures; while the irreversible heat is determined by the internal resistance,which can be obtained via V–I characteristic,electrochemical impedance spectroscopy and hybrid pulse power characterization(HPPC).During the electrochemical process,the reversible heat contributes less than 10% to total heat generation; and the heat generated in charge process is less than that in discharge process.The results of thermal behaviors analyses are conducive to understanding the safety management and paving the way for building a reliable thermal model of high energy density lithium ion battery. | Ying Bai Limin Li Yu Li Guanghai Chen Huichun Zhao Zhaohua Wang Chuan Wu Hongyun Ma Xinquan Wang Hongyue Cui Jiang Zhou | 2019 | Journal of Energy Chemistry2019，28，2： | 7 |
| 2 | Identification of banana fusarium wilt using supervised classification algorithms with UAV-based multi-spectral imagery显示文摘The disease of banana Fusarium wilt currently threatens banana production areas all over the world.Rapid and large-area monitoring of Fusarium wilt disease is very important for the disease treatment and crop planting adjustments.The objective of this study was to evaluate the performance of supervised classification algorithms such as support vector machine(SVM),random forest(RF),and artificial neural network(ANN)algorithms to identify locations that were infested or not infested with Fusarium wilt.An unmanned aerial vehicle(UAV)equipped with a five-band multi-spectral sensor(blue,green,red,red-edge and near-infrared bands)was used to capture the multi-spectral imagery.A total of 139 ground sample-sites were surveyed to assess the occurrence of banana Fusarium wilt.The results showed that the SVM,RF,and ANN algorithms exhibited good performance for identifying and mapping banana Fusarium wilt disease in UAV-based multi-spectral imagery.The overall accuracies of the SVM,RF,and ANN were 91.4%,90.0%,and 91.1%,respectively for the pixel-based approach.The RF algorithm required significantly less training time than the SVM and ANN algorithms.The maps generated by the SVM,RF,and ANN algorithms showed the areas of occurrence of Fusarium wilt disease were in the range of 5.21-5.75 hm2,accounting for 36.3%-40.1%of the total planting area of bananas in the study area.The results also showed that the inclusion of the red-edge band resulted in an increase in the overall accuracy of 2.9%-3.0%.A simulation of the resolutions of satellite-based imagery(i.e.,0.5 m,1 m,2 m,and 5 m resolutions)showed that imagery with a spatial resolution higher than 2 m resulted in good identification accuracy of Fusarium wilt.The results of this study demonstrate that the RF classifier is well suited for the identification and mapping of banana Fusarium wilt disease from UAV-based remote sensing imagery.The results provide guidance for disease treatment and crop planting adjustments. | Huichun Ye Wenjiang Huang Shanyu Huang Bei Cui Yingying Dong Anting Guo Yu Ren Yu Jin | 2020 | International Journal of Agricultural and Biological Engineering2020，13，3： | 3 |
| 3 | Discrimination of long Jing green-tea grade by electronic nose显示文摘 | Huichun Yu Jun Wang | 2007 | Sensors and Actuators B2007，，122： | 1 |
| 4 | Discrimination of LongJing Green-Tea Grade by Electronic Nose显示文摘 | Huichun Yu Jun Wang | 2007 | Sensors and Actuators B:Chemical2007，122，1： | 1 |
| 5 | Quality grade identification of green tea using the eigenvalues of PCA based on the E-nose signals显示文摘 | Huichun Yu Jun Wang Hong Xiao Miao Liu | 2009 | Sensors & Actuators: B Chemical2009，，2： | 1 |
| 6 | A featureextraction method based on wavelet packet analysis fordiscrimination of Chinese vinegars using a gas sensorsarray显示文摘 | Yong Yin Huichun Yu Hongshun zhang | 2008 | Sensors and Actuators B: Chemical2008，134，2： | 1 |
| 7 | Discrimination of LongJing green-tea grade by electronic nose显示文摘 | Huichun Yu Jun Wang | 2007 | Sensors and Actuators B2007，122，： | 1 |
| 8 | Identification of green tea grade using different feature of response signal from E-nose sensors显示文摘 | Huichun Yu Jun Wang | 2008 | Sensors and Actuators B2008，128，： | 1 |
| 9 | Discrimination of Long-jing green-tea grade by electronic nose 显示文摘 | HuiChun Yu Jun Wang | 2007 | Sensors and Actuators B : Chemical2007，122，： | 1 |
| 10 | Discrimination of Long-jing green-tea grade by eleetronie nose 显示文摘 | HuiChun Yu Jun Wang | 2007 | Sensors and Actuators B:Chemical2007，122，2007： | 1 |
| 11 | Quality grade identification of green tea using Enose by CA and ANN显示文摘 | Yu Huichun Wang Jun Yao Cong | 2008 | LWT2008，41，7： | 1 |
| 12 | Discrimination of longjing greentea grade by electronic nose 显示文摘 | YU Huichun WANG Jun | 2007 | Sensors and Actuators B2007，122，： | 1 |
| 13 | Identification of green tea grade using different feature of response signal from E-nose sensors显示文摘 | Huichun Yu Jun Wang Hongmei Zhang Yong Yu Cong Yao | 2007 | Chemical2007，，2： | 1 |
| 14 | Quality grade identification of green tea using the eigenvalues of PCA based on the E-nose signals显示文摘 | Huichun Yu Jun Wang Hong Xiao | 2009 | Sensors and Actuators B2009，140，2： | 1 |
| 15 | Discrimination of Longjing green-tea grade by electronic nose显示文摘 | Yu Huichun Wang Jun | 2007 | Sensors and Actuators B2007，122，： | 1 |
| 16 | Discrimination of LongJing green-tea grade by electronic nose显示文摘 | Huichun Yu Jun Wang | 2007 | Sensors and Actuators B 1222007，，： | 1 |
| 17 | A selection method for feature vectors of electronic nose signal based on wilks A-statistic显示文摘 | Yong Yin Bing Chu Huichun Yu | 2014 | Journal of Food Measurement & Characterization2014，8，： | 1 |
| 18 | A sensor array optimization method of electronic nose based on elimination transform of Wilks statistic for discrimination of three kinds of vinegars显示文摘 | Yong Yin Huichun Yu Chu Bing | 2014 | Journal of Food Engineering2014，127，： | 1 |
| 19 | Discrimination of LongJing green-tea grade by electronic nose显示文摘 | Yu Huichun Wang Jun | 2007 | Sensors and Actuators B2007，122，： | 1 |
| 20 | Discrimination of Long Jing green- tea grade by electronic nose 显示文摘 | Huichun Yu Jun W | 2007 | Sensor Actuat B- Chem2007，122，1： | 1 |
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