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混合动力电动公交汽车(HEB)再生制动的控制策略与性能仿真 被引量:6
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作者 耿聪 张欣 +2 位作者 张良 刘溧 王勇 《高技术通讯》 EI CAS CSCD 2004年第8期80-83,共4页
分析了典型循环工况下城市公交汽车制动能量随制动减速度变化的分布规律,根据城市公交汽车车速变化大,制动频繁且制动强度较低的特点,提出了适合于混合动力电动公交汽车(HEB)的再生制动控制策略——低制动强度时优先采用再生制动,... 分析了典型循环工况下城市公交汽车制动能量随制动减速度变化的分布规律,根据城市公交汽车车速变化大,制动频繁且制动强度较低的特点,提出了适合于混合动力电动公交汽车(HEB)的再生制动控制策略——低制动强度时优先采用再生制动,高强度时按比例复合再生制动与摩擦制动。这种控制策略既可保证低制动强度时制动能量的再生利用,又可保证制动效能和制动安全性的要求。针对EQ6110HEV混合动力电动汽车进行的再生制动性能仿真计算表明:不同循环工况下,采用这种再生制动控制策略的HEB均有较好的节能效果,可降低能耗10%~25%。 展开更多
关键词 混合动力电动公交汽车 再生制动 控制策略 混合动力电动汽车 摩擦制动
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Attention Mechanism-Based Method for Intrusion Target Recognition in Railway
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作者 SHI Jiang BAI Dingyuan +2 位作者 GUO Baoqing WANG Yao RUAN Tao 《Transactions of Nanjing University of Aeronautics and Astronautics》 EI CSCD 2024年第4期541-554,共14页
The detection of foreign object intrusion is crucial for ensuring the safety of railway operations.To address challenges such as low efficiency,suboptimal detection accuracy,and slow detection speed inherent in conven... The detection of foreign object intrusion is crucial for ensuring the safety of railway operations.To address challenges such as low efficiency,suboptimal detection accuracy,and slow detection speed inherent in conventional comprehensive video monitoring systems for railways,a railway foreign object intrusion recognition and detection system is conceived and implemented using edge computing and deep learning technologies.In a bid to raise detection accuracy,the convolutional block attention module(CBAM),including spatial and channel attention modules,is seamlessly integrated into the YOLOv5 model,giving rise to the CBAM-YOLOv5 model.Furthermore,the distance intersection-over-union_non-maximum suppression(DIo U_NMS)algorithm is employed in lieu of the weighted nonmaximum suppression algorithm,resulting in improved detection performance for intrusive targets.To accelerate detection speed,the model undergoes pruning based on the batch normalization(BN)layer,and Tensor RT inference acceleration techniques are employed,culminating in the successful deployment of the algorithm on edge devices.The CBAM-YOLOv5 model exhibits a notable 2.1%enhancement in detection accuracy when evaluated on a selfconstructed railway dataset,achieving 95.0%for mean average precision(m AP).Furthermore,the inference speed on edge devices attains a commendable 15 frame/s. 展开更多
关键词 foreign object detection railway protection edge computing spatial attention module channel attention module
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