The military unit is a special hierarchical social network. Based on complex network, opinion dynamics and multi-agent theory, this paper studies the regularity of opinion spreading in military hierarchical network. The analysis results show that the opinion value, influence, tenacity of a individual and the trust between neighboring individuals are the main factors that influence the opinion spreading. The opinion spreading speed in the network with interconnection between the same level nodes is low and it is hard to reach a consistent support state. The research shows that in such a closed social system, to achieve consistent support, it is necessary not only keeping the purity of the network, but also the effective intervention to the special individuals.
A series of neural networks called RCNN are playing a vital role in objects detection, as the most perfect one, Faster RCNN achieved an end-to-end object detection and made the detection times comparatively low but with high accuracy. In this work, we propose the following two changes to the original Faster RCNN model for multi-object detection: The first, we give 1800 ROI(Regions of Interest) comes from RPN to the RCNN network as input instead of 300, all the 1800 ROI are used to training the softmax classification and Bounding-box regression. The second, we traverse all xml files of every training image to get the number of marked objects and calculate the value of IOU for every marked objects, then we set a dynamic loss function to evaluation and optimization the Faster RCNN model by the two values of an image.
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