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Understanding the linkage-dependence structure between oil and gas markets: A new perspective 期刊论文
ENERGY, 2022, 卷号: 257, 页码: 18
作者:  Wei, Zhaohao;  Chai, Jian;  Dong, Jichang;  Lu, Quanying
收藏  |  浏览/下载：89/0  |  提交时间：2023/02/07
Oil and gas markets  Characteristic evolution  Intrinsic mechanism quantification  Non-parametric test  Multivariate GARCH-Copula  CEEMDAN-JADE-RT  
Stochastic higher-order three-scale strength prediction model for composite structures with micromechanical analysis 期刊论文
JOURNAL OF COMPUTATIONAL PHYSICS, 2022, 卷号: 465, 页码: 30
作者:  Dong, Hao;  Yang, Zihao;  Guan, Xiaofei;  Cui, Junzhi
收藏  |  浏览/下载：107/0  |  提交时间：2023/02/07
Composite structures  Multiscale random configurations  SHTSP model  Multilevel strategy  Strength prediction  
Ensemble wavelet-learning approach for predicting the effective mechanical properties of concrete composite materials 期刊论文
COMPUTATIONAL MECHANICS, 2022, 页码: 31
作者:  Linghu, Jiale;  Dong, Hao;  Cui, Junzhi
收藏  |  浏览/下载：117/0  |  提交时间：2022/06/21
Concrete composite materials  Weibull distribution  Equivalent mechanical parameters  Artificial neural networks  Wavelet transform  
p A Mixed Wavelet-Learning Method of Predicting Macroscopic Effective Heat Transfer Conductivities of Braided Composite Materials 期刊论文
COMMUNICATIONS IN COMPUTATIONAL PHYSICS, 2022, 卷号: 31, 期号: 2, 页码: 593-625
作者:  Dong, Hao;  Kou, Wenbo;  Han, Junyan;  Linghu, Jiale;  Zou, Minqiang;  Cui, Junzhi
收藏  |  浏览/下载：192/0  |  提交时间：2022/04/02
Braided composite materials  macroscopic effective heat transfer conductivities  multi-scale modeling  neural networks  wavelet transform  
High-order three-scale computational method for elastic behavior analysis and strength prediction of axisymmetric composite structures with multiple spatial scales 期刊论文
MATHEMATICS AND MECHANICS OF SOLIDS, 2021, 卷号: 26, 期号: 6, 页码: 905-936
作者:  Dong, Hao;  Cui, Junzhi;  Nie, Yufeng;  Jin, Ke;  Guan, Xiaofei;  Yang, Zihao
收藏  |  浏览/下载：185/0  |  提交时间：2021/10/26
Multiscale asymptotic analysis  mechanical behaviors  axisymmetric composite structures  micro-meso-macro coupled model  HOTS numerical algorithm  
Supplier's goal setting considering sustainability: An uncertain dynamic Data Envelopment Analysis based benchmarking model 期刊论文
INFORMATION SCIENCES, 2021, 卷号: 545, 页码: 44-64
作者:  Zhou, Xiaoyang;  Li, Linzi;  Wen, Haoyu;  Tian, Xin;  Wang, Shouyang;  Lev, Benjamin
收藏  |  浏览/下载：165/0  |  提交时间：2021/04/26
Goal setting  Sustainability  Uncertain dynamic DEA  Fuzzy data  
A wavelet-based learning approach assisted multiscale analysis for estimating the effective thermal conductivities of particulate composites 期刊论文
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING, 2021, 卷号: 374, 页码: 28
作者:  Dong, Hao;  Nie, Yufeng;  Cui, Junzhi;  Kou, Wenbo;  Zou, Minqiang;  Han, Junyan;  Guan, Xiaofei;  Yang, Zihao
收藏  |  浏览/下载：230/0  |  提交时间：2021/04/26
Particulate composites  Effective thermal conductivities  Multiscale modeling  Artificial neural network  Wavelet transform  
Performance evaluation and prediction of the integrated circuit industry in China: A hybrid method 期刊论文
SOCIO-ECONOMIC PLANNING SCIENCES, 2020, 卷号: 69, 页码: 12
作者:  Zhou, Xiaoyang;  Chen, Hao;  Chai, Jian;  Wang, Shouyang;  Lev, Benjamin
收藏  |  浏览/下载：204/0  |  提交时间：2020/05/24
Dynamic DEA  Projection analysis  Grey model  Integrated circuit industry  
The future natural gas consumption in China: Based on the LMDI-STIRPAT-PLSR framework and scenario analysis 期刊论文
ENERGY POLICY, 2018, 卷号: 119, 页码: 215-225
作者:  Chai, Jian;  Liang, Ting;  Lai, Kin Keung;  Zhang, Zhe George;  Wang, Shouyang
收藏  |  浏览/下载：206/0  |  提交时间：2018/09/08
Natural gas consumption  LMDI decomposition  STIRPAT model  PLS regression  Scenario analysis  
Analysis on shock effect of China's high-speed railway on aviation transport 期刊论文
TRANSPORTATION RESEARCH PART A-POLICY AND PRACTICE, 2018, 卷号: 108, 页码: 35-44
作者:  Chai, Jian;  Zhou, Youhong;  Zhou, Xiaoyang;  Wang, Shouyang;  Zhang, Zhe George;  Liu, Zenghui
收藏  |  浏览/下载：208/0  |  提交时间：2018/07/30
China's high-speed railway  Aviation transport  Aviation mileage  Panel threshold regression model