Browsing by Author "Cheng, Jun"

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    Eco-driving control for connected plug-in hybrid electric vehicles in urban scenarios with enhanced lane change engagement
    (Elsevier, 2024-11) Li, Jie; Liu, Yonggang; Cheng, Jun; Fotouhi, Abbas; Chen, Zheng
    Eco-driving control techniques have shown significant potential in reducing energy consumption in urban scenarios. The presence of slow-moving vehicles typically disrupts ecological velocity planning, leading to an increase in energy consumption. To solve it, this study proposes a hierarchical eco-driving control strategy, that integrates speed optimization and lane change decision-making in urban scenarios, to not only ensure traffic efficiency, but also save the energy consumption. Firstly, a data-driven energy model is leveraged in the upper level to estimate the energy consumption of candidate lanes and generate ecological lane change decisions. Then, in the lower level, the preceding vehicles and traffic lights are considered to plan an ecological velocity profile via deep reinforcement learning algorithm after transitions to the target driving lane, thereby enhancing the fuel economy and travel efficiency. A virtual driving environment model is established to verify the proposed method through numerous simulation cases. The results indicate that the proposed method effectively reduces energy consumption while maintaining favorable travel efficiency, compared with conventional benchmarks. Furthermore, the notable improvements are observed particularly in free traffic conditions.
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    Topology reconstruction of tree-like structure in images via structural similarity measure and dominant set clustering
    (IEEE, 2020-01-09) Xie, Jianyang; Zhao, Yitian; Liu, Yonghuai; Su, Pan; Zhao, Yifan; Cheng, Jun; Zheng, Yalin; Liu, Jiang
    The reconstruction and analysis of tree-like topological structures in the biomedical images is crucial for biologists and surgeons to understand biomedical conditions and plan surgical procedures. The underlying tree-structure topology reveals how different curvilinear components are anatomically connected to each other. Existing automated topology reconstruction methods have great difficulty in identifying the connectivity when two or more curvilinear components cross or bifurcate, due to their projection ambiguity, imaging noise and low contrast. In this paper, we propose a novel curvilinear structural similarity measure to guide a dominant-set clustering approach to address this indispensable issue. The novel similarity measure takes into account both intensity and geometric properties in representing the curvilinear structure locally and globally, and group curvilinear objects at crossover points into different connected branches by dominant-set clustering. The proposed method is applicable to different imaging modalities, and quantitative and qualitative results on retinal vessel, plant root, and neuronal network datasets show that our methodology is capable of advancing the current state-of-the-art techniques.