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军用光缆网是重要的国防基础通信设施,传统的人工徒步巡检是查找光缆线路隐患的主要措施,但其耗时长,人力物力消耗大,易受敷设方式和地形环境变化影响。而采用无人机进行光缆线路巡检,时效性强,安全性高且经济性好,是未来的重点发展方向。由于工程车辆施工挖掘是造成光缆线路障碍的最主要原因,为此,文章提出将深度学习更快的基于区域的卷积神经网络（Faster R-CNN）目标检测方法应用到无人机航拍巡检图像的工程车辆检测中。基于航空影像中的车辆检测（VEDAI）公共数据集制作了工程车辆数据集,通过仿真训练和测试,实现了航拍图像中挖掘机和推土机等工程车辆的Faster RCNN目标检测,检测平均精度（AP）值达0.659,优于传统的可变形组建模型（DPM）和方向梯度直方图+局部二值模式+支持向量机（HOG+LBP+SVM）等机器学习检测算法,研究结果可为军用光缆线路的无人机巡检应用研究提供一定的参考。

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Objectives: When performing plantation surveys using laser point cloud data, there are missing points in the scanned point cloud data due to the occlusion and self-occlusion of trees during laser scanning, the felling of trees and other reasons. So, the locations of the missing trees are inaccurate, and the forest survey results have large errors. The key to solving this problem is to realize the filling of the missing tree point cloud. Methods: This paper defines a concept named degree-of-collinearity, and constructs a method based on degree-of-collinearity combined with straight line detection to fill in missing data. Results: For the experimental results of simulated data, the average accuracy of the proposed algorithm is 97.28%; for the experimental results of plantation data, the proposed algorithm detects the location of 9 missing trees, and the degree-of-collinearity rises from 0.219 3 to 0.270 5. Conclusions: The experimental results show that this method can realize the optimal inference of missing location, strengthen the collinear relationship of filled data and can also be applied to count the missing trees in the artificial forest. [ABSTRACT FROM AUTHOR]

The prescribed performance tracking control problem of intelligent vehicle steering system with model nonlinearity and parameter uncertainty is studied. The RBFNN (Radial Basis Function Neural Network) is used to approximate the uncertain nonlinearity in the steering system. The prescribed performance controller is designed for the steer-by-wire system of intelligent vehicle based on the barrier Lyapunov function technology. In the design of the controller, the dynamic gain technology is used to compensate the effect of unknown control gain on the system control performance. Finally, the stability of the system is analyzed by Lyapunov method, and it is proved that the tracking error of the front wheel angle can converge to the prescribed neighborhood of the origin within the prescribed time under the action of the proposed controller. The rationality of the control method is verified by numerical simulation and vehicle experiment. [ABSTRACT FROM AUTHOR]

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Leaf removal will change the microclimate around fruits during the stage of color-changing, also the fruit quality accumulation will be affected. To study the effect of leaf removal in different directions on the quality of mature fruits, taking the Cabernet Sauvignon planted in the north-south direction as the experimental materials, the leaf removal experiments in the east side, in the west side and in both sides were carried out at the Helan mountain east foothill wine region. The results showed that: (1) compared with the control, in the eastern leaf removal treatments, the content of soluble solids, glucose, fructose, total acid, ratio of sugar to acid, malic acid, citric acid, tartaric acid and Tannin in the mature fruit of Cabernet Sauvignon grape increased, especially the level of fructose, citric acid and tartaric acid increased significantly; There was no significant decrease in content of reducing sugar, total phenol, anthocyanidin. (2) In the west leaf removal treatments, the content of soluble solids, reducing sugar, total phenols and anthocyanins in fruits also decreased slightly, fructose, malic acid, citric acid and tannin increased significantly, and the glucose content, the ratio of sugar to acid and tartaric acid content increased slightly. (3) The tannin level of grape fruit in both side leaf-removal treatments increased significantly, the content of glucose, fructose, citric acid and tartaric acid increased slightly, and the content of soluble solids, reducing sugar, total acid, the ratio of sugar to acid, malic acid, total phenol and anthocyanin were lower than that in CK treatment. Through comparative analysis, the sugar content of the fruit with leaf removal all increased slightly, while in the eastern leaf removal treatment, the acid content increased, and the tannin content increased, falling of total phenols and anthocyanins was relatively small compared to the other two treatments. Considering the present level situation of high sugar and low acidity in the wine production areas of the Helan Mountain East Foothill Wine Region, the leaf removal in the east is recommended as the main pattern to manage the leaf curtain and to control the quality of fruit in the vineyard with the north-south row direction, which plays a role in balancing the overall flavor substances of grape fruits. [ABSTRACT FROM AUTHOR]

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Copyright of Journal of Shanghai University / Shanghai Daxue Xuebao is the property of Journal of Shanghai University (Natural Sciences) Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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三维激光扫描获取的点云数据可用于数字城市建设、三维模型获取、场景分析与物体测量等领域.但因遮挡和噪声的影响，加之扫描场景复杂，采样精度受限，使得不能直接运用经典的曲面和三维空间理论对点云数据进行有效分析和处理.分类是点云数据预处理的重要方式之一.提取近邻四面体体积、近邻法向量差异度、主方向差异度和主曲率值4个局部形状特征，采用概率混合策略构建了一种点云数据的半自动分类方法，可实现平面点集、柱面点集和其他点集的有效区分.其中，概率混合策略是依据近邻点平均距离和单指标类别一致程度估计每个特征推断形状的概率，通过混合加权，依据概率赋权函数最大值准则进行局部形状推断.可实现用户交互，以便处理不同扫描尺度和精度的点云数据.采用本文方法对模拟生成的点云、单棵树木点云、街道场景点云、旷野自然场景扫描点云以及航空机载扫描点云等多组数据进行了实验，结果表明，基于局部形状特征的概率混合方法对各种点云数据均具有良好的分类效果.