3篇
您的检索式：作者名="Mu Shaomin"
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| 1 | Measuring ground deformations caused by 2015 Mw7.8 Nepal earthquake using high-rate GPS data显示文摘The April 25,2015 Mw7.8 Nepal earthquake was successfully recorded by Crustal Movement Observation Network of China(CMONOC) and Nepal Geodetic Array(NGA).We processed the high-rate GPS data(1 Hz and 5 Hz) by using relative kinematic positioning and derived dynamic ground motions caused by this large earthquake.The dynamic displacements time series clearly indicated the displacement amplitude of each station was related to the rupture directivity.The stations which located in the direction of rupture propagation had larger displacement amplitudes than others.Also dynamic ground displacement exceeding 5 cm was detected by the GPS station that was 2000 km away from the epicenter.Permanent coseismic displacements were resolved from the near-field high-rate GPS stations with wavelet decomposition-reconstruction method and P-wave arrivals were also detected with S transform method.The results of this study can be used for earthquake rupture process and Earthquake Early Warning studies. | Yong Huang Shaomin Yang Xuejun Qiao Mu Lin Bin Zhao Kai Tan | 2017 | Geodesy and Geodynamics2017，8，4： | 1 |
| 2 | A Novel Radial Basis Function Neural Network Classifier with Centers Set By Cooperative Clustering 显示文摘 | Mu Shaomin Tian Shengfeng Yin Chuanhuan | 2007 | International Journal of Fuzzy Systems (S1562-2479)2007，9，4： | 1 |
| 3 | Novel method for identifying wheat leaf disease images based on differential amplification convolutional neural network显示文摘In this study,a differential amplification convolutional neural network(DACNN)was proposed and used in the identification of wheat leaf disease images with ideal accuracy.The branches added between the deep convolutional layers can amplify small differences between the real output and the expected output,which made the weight updating more sensitive to the light errors return in the backpropagation pass and significantly improved the fitting capability.Firstly,since there is no large-scale wheat leaf disease images dataset at present,the wheat leaf disease dataset was constructed which included eight kinds of wheat leaf images,and five kinds of data augmentation methods were used to expand the dataset.Secondly,DACNN combined four classifiers:Softmax,support vector machine(SVM),K-nearest neighbor(KNN)and Random Forest to evaluate the wheat leaf disease dataset.Finally,the DACNN was compared with the models:LeNet-5,AlexNet,ZFNet and Inception V3.The extensive results demonstrate that DACNN is better than other models.The average recognition accuracy obtained on the wheat leaf disease dataset is 95.18%. | Mengping Dong Shaomin Mu Aiju Shi Wenqian Mu Wenjie Sun | 2020 | International Journal of Agricultural and Biological Engineering2020，13，4： | 0 |
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