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| 1 | Angular observation of joints of geckos moving on horizontal and vertical surfaces显示文摘Because of their outstanding climbing and motor coordination ability, geckos have provided the basis for a peculiar bionic model leading to the development of a geckorobot. A three-dimensional locomotion observation system was constructed to measure angular orientations of joints while geckos trotted (337.1 mm/s) and walked (66.7 mm/s) on horizontal surfaces, and trotted (241.5mm/s) and walked (30.6mm/s) on vertical surfaces. Moving over horizontal surfaces, the joints rotated more quickly the greater the speed, and the swinging scope of forelimbs stayed nearly at 59 degrees when swinging forward, but extended from 72 degrees to 79.2 degrees when swinging backward. The lifting angle of forelimbs was always positive to keep the center of mass close to the surface when moving up vertical surfaces, the scope of the forward swinging forelimbs forward extended from 33.7 degrees to 36.7 degrees with increasing speed, while the scope of backward swinging forelimbs remained almost the same at 87.5 degrees. Alternative gaits had little effect on the swing angle of hindlimbs of the geckos moving on both horizontal and vertical surfaces. | LI HongKai DAI ZhenDong SHI AiJu ZHANG Hao SUN JiuRong | 2009 | Chinese Science Bulletin2009，54，4： | 13 |
| 2 | Prediction and countermeasures of saltwater intrusion in the Qiantang estuary 显示文摘 | HAN Zengcui SHI Yingbiao YOU Aiju | 2014 | Advances in Water Resource and Protection2014，2，： | 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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