White blood cells (WBCs) are the cells of immune system, protecting against infective diseases and invasion of viruses and bacteria. Their aberrant number, both abnormal increase and decrease, is a sign of an ongoing pathology, a precise evaluation of their number is of the utmost importance as the first step of assessing a potential disease. In blood cell microscopic images, since red blood cells and platelets are similar in color with WBCs, and WBCs are partially adhesive, WBC segmentation for counting is often not resulting in a good performance. Therefore, in this work, a color space transformation is proposed to filter out red blood cells and platelets, which is transforming the blood cell microscopic images of patients with acute lymphoblastic leukemia from RGB color space to HSV to detect and extract WBCs. For precisely segmenting adhesive WBCs in extraction results, we set cell border to the third class, in addition to foreground and background. A weighted cross-entropy loss function based on class weight and distance transformation weight enhanced U-Net to learn cell border features. Our results showed that the method proposed in this paper for WBC segmentation using the data set ALL_IDB1 could achieve an accuracy of 97.92%.
With the development of intelligent technology, more and more self-driving, cooperating cars appear on the road, which will impact on traffic flow greatly. In this paper, we used the cellular automata algorithm to simulate the impact of the proportion of the number of self-driving cars to non-self-driving cars on traffic flow in the case of different lanes, confluence and divergence, traffic peak and dedicated lanes for self-driving car. And we got the conclusion by analyzing the simulation results: On the whole, Traffic flow increased with the increase of the proportion of the self-driving cars to non-self-driving cars. And in different cases, the proportion was different when the traffic flow reached the maximum, that is, the best proportion was different in different cases.
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