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[41]Xu Qifa，Fan Zhenhua,Jia Weiyin，Jiang Cuixia，Fault detection of wind turbines via multivariate process monitoring based on vine copulas.[J:Renewable Energy,2020,161):939-955.
[42]Xu Qifa，Lu Shixiang,Zhai Zhongping，Jiang Cuixia，Adaptive fault detection in wind turbine via RF and CUSUM.[J:IET Renewable Power Generation,2020,14(10):1789-1796.
[43]Xu Qifa，Lu Shixiang,Jia Weiyin，Jiang Cuixia，Imbalanced fault diagnosis of rotating machinery via multi-domain feature extraction and cost-sensitive learning.[J:Journal of Intelligent Manufacturing,2020,31(6):1467–1481.
[44]Xu Qifa，Chen Lu,Jiang Cuixia,Yu Keming，Mixed data sampling expectile regression with applications to measuring financial risk.[J:Economic Modelling,2020,91):469-486.
[45]Xu Qifa，Wang Liukai,Jiang Cuixia,Liu Yezheng，A novel (U)MIDAS-SVR model with multi-source market sentiment for forecasting stock returns.[J:Neural Computing and Applications,2020,32(10):5875-5888.
[46]Xu Qifa，Wang Liukai,Jiang Cuixia,Zhang Xin，A novel UMIDAS-SVQR model with mixed frequency investor sentiment for predicting stock market volatility[.[J:Expert Systems with Applications,2019,132):12-27.
[47]许启发，卓杏轩，蒋翠侠，反向有约束混频数据模型的市场化利率预测.[J:管理科学学报,2019,22(10):55-71.
[48]许启发，王侠英,蒋翠侠,李辉艳，基于D-vine copula-分位数回归的组合投资决策.[J:系统工程学报,2019,34(1):69-81.
[49]Xu Qifa，Li Mengting,Jiang Cuixia,He Yaoyao，Interconnectedness and systemic risk network of Chinese financial institutions: A LASSO-CoVaR approach.[J:Physica A: Statistical Mechanics and its Applications,2019,534):122173.
[50]Xu Qifa，Fan Zhenhua,Jia Weiyin，Jiang Cuixia，Quantile regression neural network-based fault detection scheme for wind turbines with application to monitoring a bearing.[J:Wind Energy,2019,22(10):1390-1401.
total92 5/10
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