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| 1 | Continuous nonsingular terminal sliding mode control for systems with mismatched disturbances显示文摘 | Jun Yang Shihua Li Jinya Su Xinghuo Yu | 2013 | Automatica2013，，7： | 1 |
| 2 | Development of a mobile application for identification of grapevine(Vitis vinifera L.)cultivars via deep learning显示文摘Traditional vine variety identification methods usually rely on the sampling of vine leaves followed by physical,physiological,biochemical and molecular measurement,which are destructive,time-consuming,labor-intensive and require experienced grape phenotype analysts.To mitigate these problems,this study aimed to develop an application(App)running on Android client to identify the wine grape automatically and in real-time,which can help the growers to quickly obtain the variety information.Experimental results showed that all Convolutional Neural Network(CNN)classification algorithms could achieve an accuracy of over 94%for twenty-one categories on validation data,which proves the feasibility of using transfer deep learning to identify grape species in field environments.In particular,the classification model with the highest average accuracy was GoogLeNet(99.91%)with a learning rate of 0.001,mini-batch size of 32,and maximum number of epochs in 80.Testing results of the App on Android devices also confirmed these results. | Yixue Liu Jinya Su Lei Shen Nan Lu Yulin Fang Fei Liu Yuyang Song Baofeng Su | 2021 | International Journal of Agricultural and Biological Engineering2021，14，5： | 1 |
| 3 | Segmentation of field grape bunches via an improved pyramid scene parsing network显示文摘With the continuous expansion of wine grape planting areas,the mechanization and intelligence of grape harvesting have gradually become the future development trend.In order to guide the picking robot to pick grapes more efficiently in the vineyard,this study proposed a grape bunches segmentation method based on Pyramid Scene Parsing Network(PSPNet)deep semantic segmentation network for different varieties of grapes in the natural field environments.To this end,the Convolutional Block Attention Module(CBAM)attention mechanism and the atrous convolution were first embedded in the backbone feature extraction network of the PSPNet model to improve the feature extraction capability.Meanwhile,the proposed model also improved the PSPNet semantic segmentation model by fusing multiple feature layers(with more contextual information)extracted by the backbone network.The improved PSPNet was compared against the original PSPNet on a newly collected grape image dataset,and it was shown that the improved PSPNet model had an Intersection-over-Union(IoU)and Pixel Accuracy(PA)of 87.42%and 95.73%,respectively,implying an improvement of 4.36%and 9.95%over the original PSPNet model.The improved PSPNet was also compared against the state-of-the-art DeepLab-V3+and U-Net in terms of IoU,PA,computation efficiency and robustness,and showed promising performance.It is concluded that the improved PSPNet can quickly and accurately segment grape bunches of different varieties in the natural field environments,which provides a certain technical basis for intelligent harvesting by grape picking robots. | Shan Chen Yuyang Song Jinya Su Yulin Fang Lei Shen Zhiwen Mi Baofeng Su | 2021 | International Journal of Agricultural and Biological Engineering2021，14，6： | 0 |
| 4 | Recursive Filter with Partial Knowledge on Inputs and Outputs显示文摘This paper investigates the problem of state estimation for discrete-time stochastic linear systems, where additional knowledge on the unknown inputs is available at an aggregate level and the knowledge on the missing measurements can be described by a known stochastic distribution. Firstly, the available knowledge on the unknown inputs and the state equation is used to form the prior distribution of the state vector at each time step. Secondly, to obtain an analytically tractable likelihood function, the effect of missing measurements is broken down into a systematic part and a random part, and the latter is modeled as part of the observation noise. Then, a recursive filter is obtained based on Bayesian inference. Finally, a numerical example is provided to evaluate the performance of the proposed methods. | Jinya Su Baibing Li Wen-Hua Chen | 2015 | International Journal of Automation and computing2015，12，1： | 0 |
| 5 | Output feedback control for mobile robot systems with significant external disturbances显示文摘Dear editor,The control of nonholonomic systems,like those describing mobile robots,has attracted considerable research attention in recent decades owing to its theoretical and practical importance.Brockett’s theorem states that nonholonomic systems cannot be stabilized to an equilibrium point by any source of smooth or continuous state feedback.Additionally,the required accuracy of measuring the system states is often unachievable in practice.These factors introduce considerable difficulty to the problem of controlling mobile robots and other nonholonomic systems. | Kang WU Jinya SU Changyin SUN | 2020 | Science China(Information Sciences)2020，63，9： | 0 |
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