The development of autonomous vehicles has become one of the greatest research endeavors in recent years. These vehicles rely on many complex systems working in tandem to make decisions. For practical use and safety r...The development of autonomous vehicles has become one of the greatest research endeavors in recent years. These vehicles rely on many complex systems working in tandem to make decisions. For practical use and safety reasons, these systems must not only be accurate, but also quickly detect changes in the surrounding environment. In autonomous vehicle research, the environment perception system is one of the key components of development. Environment perception systems allow the vehicle to understand its surroundings. This is done by using cameras, light detection and ranging (LiDAR), with other sensor systems and modalities. Deep learning computer vision algorithms have been shown to be the strongest tool for translating camera data into accurate and safe traversability decisions regarding the environment surrounding a vehicle. In order for a vehicle to safely traverse an area in real time, these computer vision algorithms must be accurate and have low latency. While much research has studied autonomous driving for traversing well-structured urban environments, limited research exists evaluating perception system improvements in off-road settings. This research aims to investigate the adaptability of several existing deep-learning architectures for semantic segmentation in off-road environments. Previous studies of two Convolutional Neural Network (CNN) architectures are included for comparison with new evaluation of Vision Transformer (ViT) architectures for semantic segmentation. Our results demonstrate viability of ViT architectures for off-road perception systems, having a strong segmentation accuracy, lower inference speed and memory footprint compared to previous results with CNN architectures.展开更多
现阶段节能型汽车串联混动HEV(Hybrid Electric Vehicle)具有油耗低、结构及控制逻辑简单等优点。该文以某款搭载1.5L自然吸气发动机及手动变速器的后驱燃油整车为基础,结合公司最新开发的高效环保阿特金森发动机、车用高效永磁同步电...现阶段节能型汽车串联混动HEV(Hybrid Electric Vehicle)具有油耗低、结构及控制逻辑简单等优点。该文以某款搭载1.5L自然吸气发动机及手动变速器的后驱燃油整车为基础,结合公司最新开发的高效环保阿特金森发动机、车用高效永磁同步电机及电机控制器,根据整车性能目标进行动力系统的仿真计算和建模,并仿真结果制造样车,在样车上通过HCU整车标定,制定相应策略对其进行控制,实现整车串联混合动力的功能,研究其动力性能及燃油经济性能。结果表明,通过搭载串联混动HEV动力系统,满足设定的整车性能目标,而且动力性能及燃油经济性能比燃油车有明显的优势。展开更多
据世界卫生组织估计,戊型肝炎病毒(hepatitis E virus,HEV)每年全球约有2000万例的新发感染,可能导致超过300万例急性肝炎。我国是戊型病毒性肝炎(戊肝)高流行区,近年来戊肝发病人数已超过甲型病毒性肝炎(甲肝)。戊肝发病率从2010年的1....据世界卫生组织估计,戊型肝炎病毒(hepatitis E virus,HEV)每年全球约有2000万例的新发感染,可能导致超过300万例急性肝炎。我国是戊型病毒性肝炎(戊肝)高流行区,近年来戊肝发病人数已超过甲型病毒性肝炎(甲肝)。戊肝发病率从2010年的1.77/10万上升至2019年的2.02/10万。我国HEV感染途径主要为食源性感染。慢性HEV感染是指患者感染HEV后,血或粪便中病毒核酸持续阳性3个月以上,其多发生于免疫低下人群,并可能导致患者出现肝纤维化和肝硬化的快速进展。我国以HEV 4型感染为主,因此我国慢性戊肝患者感染的病毒也主要为HEV 4型。HEV感染诊断主要依据特异性HEV抗体或病原学指标。HEV感染筛查应着重关注基础肝病患者、育龄期妇女和老年人及免疫缺陷患者(如器官移植患者、血液肿瘤患者、HIV感染者)等。戊肝诊治中仍然存在很多问题,包括慢性戊肝患者肝纤维化快速进展的危险因素尚未明确;老年男性和孕妇感染HEV后易出现重症的机制有待研究;抗HEV药物筛选多为体外细胞模型的结果,治疗药物仍处于临床前研究阶段。今后应聚焦戊肝的发病机制研究,推动基础研究和临床研究的交叉、融合与转化,为慢性戊肝诊治的药物治疗提供更多有效方案。展开更多
戊型肝炎病毒(Hepatitis E virus, HEV)是一种单链、非包膜的RNA病毒,目前可分为8个基因型。作为一种人兽共患病原体,HEV能引起人的急性病毒性肝炎和动物的感染,其主要的传播途径为粪-口传播,也可通过输血和母婴传播。HEV主要在发展中...戊型肝炎病毒(Hepatitis E virus, HEV)是一种单链、非包膜的RNA病毒,目前可分为8个基因型。作为一种人兽共患病原体,HEV能引起人的急性病毒性肝炎和动物的感染,其主要的传播途径为粪-口传播,也可通过输血和母婴传播。HEV主要在发展中国家流行,而欧美等发达国家也有散发的病例。目前,检测HEV的常用方法是实时荧光定量RT-PCR和酶联免疫吸附试验(ELISA),也有学者利用反转录重组酶聚合酶扩增(RT-RPA)和CRISPR系统等新型检测方法进行HEV检测。在药物治疗方面,可用利巴韦林、干扰素或多烯磷脂酰胆碱(PPC)等化合物治疗HEV引起的感染,也可使用中药单味或复方改善临床症状并作协同治疗。接种疫苗被认为是预防和控制HEV感染的有效手段,但目前仍无有效的HEV体外细胞培养系统,传统的灭活苗和减毒苗无法批量制备。当前HEV疫苗的研究方向主要有基因重组蛋白疫苗、DNA疫苗、联合疫苗和口服疫苗等。接种疫苗是预防戊型肝炎的重要措施,采用切断传播途径为主的综合性预防措施也可控制该病的流行。笔者主要通过对HEV的检测方法、药物治疗和疫苗免疫等方面的研究进展进行归纳和总结,并对HEV的防控研究进行展望,以期为HEV的预防与控制提供新的思路。展开更多
文摘The development of autonomous vehicles has become one of the greatest research endeavors in recent years. These vehicles rely on many complex systems working in tandem to make decisions. For practical use and safety reasons, these systems must not only be accurate, but also quickly detect changes in the surrounding environment. In autonomous vehicle research, the environment perception system is one of the key components of development. Environment perception systems allow the vehicle to understand its surroundings. This is done by using cameras, light detection and ranging (LiDAR), with other sensor systems and modalities. Deep learning computer vision algorithms have been shown to be the strongest tool for translating camera data into accurate and safe traversability decisions regarding the environment surrounding a vehicle. In order for a vehicle to safely traverse an area in real time, these computer vision algorithms must be accurate and have low latency. While much research has studied autonomous driving for traversing well-structured urban environments, limited research exists evaluating perception system improvements in off-road settings. This research aims to investigate the adaptability of several existing deep-learning architectures for semantic segmentation in off-road environments. Previous studies of two Convolutional Neural Network (CNN) architectures are included for comparison with new evaluation of Vision Transformer (ViT) architectures for semantic segmentation. Our results demonstrate viability of ViT architectures for off-road perception systems, having a strong segmentation accuracy, lower inference speed and memory footprint compared to previous results with CNN architectures.
文摘现阶段节能型汽车串联混动HEV(Hybrid Electric Vehicle)具有油耗低、结构及控制逻辑简单等优点。该文以某款搭载1.5L自然吸气发动机及手动变速器的后驱燃油整车为基础,结合公司最新开发的高效环保阿特金森发动机、车用高效永磁同步电机及电机控制器,根据整车性能目标进行动力系统的仿真计算和建模,并仿真结果制造样车,在样车上通过HCU整车标定,制定相应策略对其进行控制,实现整车串联混合动力的功能,研究其动力性能及燃油经济性能。结果表明,通过搭载串联混动HEV动力系统,满足设定的整车性能目标,而且动力性能及燃油经济性能比燃油车有明显的优势。
文摘据世界卫生组织估计,戊型肝炎病毒(hepatitis E virus,HEV)每年全球约有2000万例的新发感染,可能导致超过300万例急性肝炎。我国是戊型病毒性肝炎(戊肝)高流行区,近年来戊肝发病人数已超过甲型病毒性肝炎(甲肝)。戊肝发病率从2010年的1.77/10万上升至2019年的2.02/10万。我国HEV感染途径主要为食源性感染。慢性HEV感染是指患者感染HEV后,血或粪便中病毒核酸持续阳性3个月以上,其多发生于免疫低下人群,并可能导致患者出现肝纤维化和肝硬化的快速进展。我国以HEV 4型感染为主,因此我国慢性戊肝患者感染的病毒也主要为HEV 4型。HEV感染诊断主要依据特异性HEV抗体或病原学指标。HEV感染筛查应着重关注基础肝病患者、育龄期妇女和老年人及免疫缺陷患者(如器官移植患者、血液肿瘤患者、HIV感染者)等。戊肝诊治中仍然存在很多问题,包括慢性戊肝患者肝纤维化快速进展的危险因素尚未明确;老年男性和孕妇感染HEV后易出现重症的机制有待研究;抗HEV药物筛选多为体外细胞模型的结果,治疗药物仍处于临床前研究阶段。今后应聚焦戊肝的发病机制研究,推动基础研究和临床研究的交叉、融合与转化,为慢性戊肝诊治的药物治疗提供更多有效方案。
文摘戊型肝炎病毒(Hepatitis E virus, HEV)是一种单链、非包膜的RNA病毒,目前可分为8个基因型。作为一种人兽共患病原体,HEV能引起人的急性病毒性肝炎和动物的感染,其主要的传播途径为粪-口传播,也可通过输血和母婴传播。HEV主要在发展中国家流行,而欧美等发达国家也有散发的病例。目前,检测HEV的常用方法是实时荧光定量RT-PCR和酶联免疫吸附试验(ELISA),也有学者利用反转录重组酶聚合酶扩增(RT-RPA)和CRISPR系统等新型检测方法进行HEV检测。在药物治疗方面,可用利巴韦林、干扰素或多烯磷脂酰胆碱(PPC)等化合物治疗HEV引起的感染,也可使用中药单味或复方改善临床症状并作协同治疗。接种疫苗被认为是预防和控制HEV感染的有效手段,但目前仍无有效的HEV体外细胞培养系统,传统的灭活苗和减毒苗无法批量制备。当前HEV疫苗的研究方向主要有基因重组蛋白疫苗、DNA疫苗、联合疫苗和口服疫苗等。接种疫苗是预防戊型肝炎的重要措施,采用切断传播途径为主的综合性预防措施也可控制该病的流行。笔者主要通过对HEV的检测方法、药物治疗和疫苗免疫等方面的研究进展进行归纳和总结,并对HEV的防控研究进行展望,以期为HEV的预防与控制提供新的思路。
