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| 1 | Load-Unload Response Ratio (LURR), Accelerating Moment/Energy Release (AM/ER) and State Vector Saltation as Precursors to Failure of Rock Specimens显示文摘 | Xiang-Chu Yin Huai-Zhong Yu Victor Kukshenko Zhao-Yong Xu Zhishen Wu Min Li Keyin Peng Surgey Elizarov Qi Li | 2004 | Pure and Applied Geophysics (-)2004，，11： | 1 |
| 2 | Load/Unload Response Ratio and Accelerating Moment/Energy Release critical region scaling and earthquake prediction 显示文摘 | Yin Xiangchu Mora P Peng Keyin | 2002 | Pure and Applied Geophysics2002，159，9： | 1 |
| 3 | LURR’s Twenty Years and its Perspective显示文摘 | Xiang-Chu Yin Lang-Ping Zhang Hui-Hui Zhang Can Yin Yucang Wang Yongxian Zhang Keyin Peng Haitao Wang Zhiping Song Huaizhong Yu Jiancang Zhuang | 2006 | Pure and Applied Geophysics (-)2006，，11： | 1 |
| 4 | Characters of variation of LURR during the earthquake sequence of Xinjiang显示文摘The theory of the loading/unloading response ratio (LURR) was applied to the Jiashi earthquake sequence which occurred at the beginning of 1997 in Xinjiang, and found that, before the earthquakes with relatively high magnitudes in the sequence, the ratio showed anomalies of high values. That is to say, the LURR theory can be applied to the short_term earthquake prediction in some cases, especially in the early period after a strong earthquake, such as the forecasts for some strong earthquakes in the Jiashi sequence. | Haitao Wang Keyin Peng Yongxian Zhang Yucang Wang Xiangchu Yin | 1998 | Chinese Science Bulletin1998，43，20： | 1 |
| 5 | Thermal omens before earthquake显示文摘 | Liu Defu Peng Keyin Liu Weihe | 1999 | Acta Seismologica Sinica1999，12，6： | 1 |
| 6 | Litchi detection in the field using an improved YOLOv3 model显示文摘Due to the illumination,complex background,and occlusion of the litchi fruits,the accurate detection of litchi in the field is extremely challenging.In order to solve the problem of the low recognition rate of litchi-picking robots in field conditions,this study was inspired by the ideas of ResNet and dense convolution and proposed an improved feature-extraction network model named“YOLOv3_Litchi”,combining dense connections and residuals for the detection of litchis.Firstly,based on the traditional YOLOv3 deep convolution neural network and regression detection,the idea of residuals was to be put into the feature-extraction network to effectively avoid the problem of decreasing detection accuracy due to the excessive depths of the network layers.Secondly,under the premise of a good receptive field and high detection accuracy,the large convolution kernel was replaced by a small convolution kernel in the shallow layer of the network,thereby effectively reducing the model parameters.Finally,the idea of feature pyramid was used to design the network to identify the small target litchi to ensure that the shallow features were not lost and simultaneously reduced the model parameters.Experimental results show that the improved YOLOv3_Litchi model achieved better results than the classic YOLOv3_DarkNet-53 model and the YOLOv3_Tiny model.The mean average precision(mAP)score was 97.07%,which was higher than the 95.18%mAP of the YOLOv3_DarkNet-53 model and the 94.48%mAP of the YOLOv3_Tiny model.The frame frequency was 58 fps,which was higher than 29 fps of the YOLOv3_DarkNet-53 model.Compared with the classic Faster R-CNN model with the feature-extraction network VGG16,the mAP was increased by 1%,and the FPS advantage was obvious.Compared with the classic single shot multibox detector(SSD)model,both the accuracy and the running efficiency were improved.The results show that the improved YOLOv3_Litchi model had stronger robustness,higher detection accuracy,and less computational complexity for the identification of litchi in the field conditions,which should be helpful for litchi orchard precision management. | Hongxing Peng Chao Xue Yuanyuan Shao Keyin Chen Huanai Liu Juntao Xiong Hu Chen Zongmei Gao Zhengang Yang | 2022 | International Journal of Agricultural and Biological Engineering2022，15，2： | 0 |
| 7 | Variation of Seismic Frequency in the Yunnan Region After the Indonesia Earthquake With M_S8.7显示文摘The seismic frequency increased significantly in the Yunnan region after the Indonesia earthquake with M_S8.7 on December 26, 2004. This was estimated by analyzing the seismic frequency ratio between the influenced and normal times, the spatial distribution characteristics of the increased seismic frequency, the temporal-spatial distribution and types of seismic swarms. Seismic frequency increased at 71.3% of the statistical sites in the Yunnan area. The maximal increase ratio is 18.2. | Guo Tieshuan Liu Jie Zheng Dalin Peng Keyin | 2007 | Earthquake Research in China2007，21，1： | 0 |
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