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您的检索式：作者名="Xinxing Hou"
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| 1 | β-decay Study of Tz=-3/2Proton-rich Nucleus 29S显示文摘The exotic decay of nuclei far from the stability has recently become a hot topic of nuclear structure research.The nuclide 29S is not well investigated in the A=4n+1,Tz=-3/2series of strong+β-delayed proton precursors.The decay schemes of these nuclei are characterized | ZHONG Fupeng XU Xinxing LIN Chengjian WANG Jiansong LI Zhihuan YANG Lei MA Nanru WANG Dongxi WEN Peiwei SUN Lijie WU Hongyi LUO Diwen WU Chenguang JIANG Ying LIU Yang ZANG Hongliang LIANG Pengfei LI Pengjie LI Xiaojing ZHAO Qingqing SHI Guozhu MA Junbing MA Peng YU Gongming Dipikap Patel BAI Zhen YANG Feng JIA Huiming ZHANG Huanqiao WANG Jianguo YANG Yanyun MA Weihu HU Qiang JIN Shuya ZHOU Xiaohong LI Ren HOU Dongsheng LIU Minliang HU Zhengguo MA Xinwen YANG Weiqing GAO Zhihao DUAN Fangfang YU Yuechao WANG Yufeng WANG Peng ZHOU Qingwu CUI Danteng WANG Xiang HU Liyuan PAN Min ZHANG Gaolong WANG Xiaoyu | 2017 | Annual Report of China Institute of Atomic Energy2017，，1： | 0 |
| 2 | Wind power forecasting based on improved variational mode decomposition and permutation entropy显示文摘Due to the significant intermittent,stochastic and non-stationary nature of wind power generation,it is difficult to achieve the desired prediction accuracy.Therefore,a wind power prediction method based on improved variational modal decomposition with permutation entropy is proposed.First,based on the meteorological data of wind farms,the Spearman correlation coefficient method is used to filter the meteorological data that are strongly correlated with the wind power to establish the wind power prediction model data set;then the original wind power is decomposed using the improved variational modal decomposition technique to eliminate the noise in the data,and the decomposed wind power is reconstructed into a new subsequence by using the permutation entropy;with the meteorological data and the new subsequence as input variables,a stacking deeply integrated prediction model is developed;and finally the prediction results are obtained by optimizing the hyperparameters of the model algorithm through a genetic algorithm.The validity of the model is verified using a real data set from a wind farm in north-west China.The results show that the mean absolute error,root mean square error and mean absolute percentage error are improved by at least 33.1%,56.1%and 54.2%compared with the autoregressive integrated moving average model,the support vector machine,long short-term memory,extreme gradient enhancement and convolutional neural networks and long short-term memory models,indicating that the method has higher prediction accuracy. | Zhijian Qu Xinxing Hou Wenbo Hu Rentao Yang Chao Ju | 2023 | Clean Energy2023，7，5： | 0 |
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