UM

Browse/Search Results:  1-9 of 9 Help

Selected(0)Clear Items/Page:    Sort:
Measurement and Prediction of Decomposed Energy Efficiencies of Lithium Ion Batteries With Two Charge Models Journal article
Huang, Y.H., Su, Y., Garg, A.. Measurement and Prediction of Decomposed Energy Efficiencies of Lithium Ion Batteries With Two Charge Models[J]. Journal of Electrochemical Energy Conversion and Storage, 2021, 030901-030901.
Authors:  Huang, Y.H.;  Su, Y.;  Garg, A.
Favorite |   IF:2.7/2.4 | Submit date:2023/08/16
Artificial neural network prediction  Degradation patterns  Energy efficiencies  Lithium-ion batteries  
Measurement and Prediction of Decomposed Energy Efficiencies of Lithium Ion Batteries With Two Charge Models Journal article
Huang, Yuhao, Su, Yan, Garg, Akhil. Measurement and Prediction of Decomposed Energy Efficiencies of Lithium Ion Batteries With Two Charge Models[J]. JOURNAL OF ELECTROCHEMICAL ENERGY CONVERSION AND STORAGE, 2021, 18(3), JEECS-20-1145.
Authors:  Huang, Yuhao;  Su, Yan;  Garg, Akhil
Favorite | TC[WOS]:3 TC[Scopus]:3  IF:2.7/2.4 | Submit date:2022/05/13
Artificial Neural Network Prediction  Batteries  Degradation Patterns  Electrochemical Storage  Energy Efficiencies  Lithium-ion Batteries  
Predicting oral disintegrating tablet formulations by neural network techniques Journal article
Han, Run, Yang, Yilong, Li, Xiaoshan, Ouyang, Defang. Predicting oral disintegrating tablet formulations by neural network techniques[J]. ASIAN JOURNAL OF PHARMACEUTICAL SCIENCES, 2018, 13(4), 336-342.
Authors:  Han, Run;  Yang, Yilong;  Li, Xiaoshan;  Ouyang, Defang
Favorite | TC[WOS]:57 TC[Scopus]:73  IF:10.7/9.0 | Submit date:2018/10/30
Oral Disintegrating Tablets  Formulation Prediction  Artificial Neural Network  Deep Neural Network  Deep-learning  
Using Principle Component Regression, Artificial Neural Network, and Hybrid Models for Predicting Phytoplankton Abundance in Macau Storage Reservoir Journal article
Iek In Ieong, Inchio Lou, Wai Kin Ung, Kai Meng Mok. Using Principle Component Regression, Artificial Neural Network, and Hybrid Models for Predicting Phytoplankton Abundance in Macau Storage Reservoir[J]. Environmental Modeling and Assessment, 2015, 20(4), 355-365.
Authors:  Iek In Ieong;  Inchio Lou;  Wai Kin Ung;  Kai Meng Mok
Favorite | TC[WOS]:7 TC[Scopus]:11  IF:2.7/2.5 | Submit date:2019/02/12
Algal Bloom  Artificial Neural Network  Forecast Model  Phytoplankton Abundance  Prediction Model  Principle Component Analysis  
Analysis of daily solar power prediction with data-driven approaches Journal article
Long H., Zhang Z., Su Y.. Analysis of daily solar power prediction with data-driven approaches[J]. Applied Energy, 2014, 126, 29.
Authors:  Long H.;  Zhang Z.;  Su Y.
Favorite | TC[WOS]:119 TC[Scopus]:144 | Submit date:2018/10/30
Artificial Neural Network (Ann)  Data Mining  Solar Power Prediction  Support Vector Machine (Svm)  Time-series Model  
Modelling and prediction of diesel vehicle engine performance using relevance vector machine Conference paper
K. I. Wong, Wong, Pak Kin, C.S. Cheung. Modelling and prediction of diesel vehicle engine performance using relevance vector machine[C], 2012.
Authors:  K. I. Wong;  Wong, Pak Kin;  C.S. Cheung
Favorite |  | Submit date:2019/04/12
Modelling  Diesel Engine Emissions  Engine Performance Prediction  Relevance Vector Machine  Artificial Neural Network  
Is a complex neural network based air quality prediction model better than a simple one? A Bayesian point of view Conference paper
K. I. Hoi, K. V. Yuen, K. M. Mok. Is a complex neural network based air quality prediction model better than a simple one? A Bayesian point of view[C]:AMER INST PHYSICS, 2 HUNTINGTON QUADRANGLE, STE 1NO1, MELVILLE, NY 11747-4501 USA, 2010, 764-769.
Authors:  K. I. Hoi;  K. V. Yuen;  K. M. Mok
Favorite | TC[WOS]:0 TC[Scopus]:1 | Submit date:2019/02/12
Air Quality Prediction  Artificial Neural Network  Bayesian Approach  Macau  Pm10  
Prediction of daily averaged PM10 concentrations by statistical time-varying model Journal article
K.I. Hoi, K.V. Yuen, K.M. Mok. Prediction of daily averaged PM10 concentrations by statistical time-varying model[J]. Atmospheric Environment, 2009, 43(16), 2579-2581.
Authors:  K.I. Hoi;  K.V. Yuen;  K.M. Mok
Favorite | TC[WOS]:42 TC[Scopus]:47  IF:4.2/4.4 | Submit date:2018/10/30
Air Quality Prediction  Artificial Neural Network  Kalman Filter  Coastal City  Macau  Pm10  
An Artificial Neural Network Model for the Prediction of Daily Averaged PM10 Concentrations in Macau Conference paper
Hoi, K. I., Yuen, K. V., Mok, K. M.. An Artificial Neural Network Model for the Prediction of Daily Averaged PM10 Concentrations in Macau[C], 2008.
Authors:  Hoi, K. I.;  Yuen, K. V.;  Mok, K. M.
Favorite |  | Submit date:2022/07/27
Air Quality Prediction  artificial neural network  Macau  PM10