This paper proposes an improved algorithm based on YOLOv5, named RepDrone-YOLOv5, to address the problem that the traditional detection algorithm has the problem of false detection and missed detection when detecting small targets due to the small target size, dense distribution and complex background of the UAV aerial imagery. First of all, in order to obtain and transmit a richer and more distinguished small target characteristics, adjust the sampling multiple and add a shallow features of the rich information in the model training process. At the same time, a RepVGG-based RepC3 feature extraction module is designed, by jointing local and global information to break the limitations of ordinary convolution extraction to obtain a greater experience. Further, an improved SPP module instead of the SPPF module to alleviate the effects of the loss of the pool layer on the loss of target information. Finally, an EIoU loss function optimizes the regression process of the prediction box and the detection box, and enhances the positioning ability of small targets. On the VisDrone2019 datasets, RepDroneYOLOv5 improves about 9 percentage points in mAP compared to the original YOLOv5 model, and the number of model parameters is reduced by about 80%.
In order to adapt to the development of the green power certificate trading (GPCT) and carbon emissions trading (CET) market, reduce the carbon emissions of the distribution network and increase the investment income, this paper proposes a new distribution network (NDN) planning and simulation operation bi-layer model with new energy (NE) as the main body, considering the GPCT and CET mechanisms. First, the upper layer determines the capacity and location of wind turbine (WT), photovoltaic (PV), hydraulic turbine (HT), micro turbine (MT), and energy storage (ES), while the lower simulation operation considers the operation costs of WT, PV, HT, MT, ES, load demand response (DR) and carbon emissions. The planning objective was to minimize the total cost of investment, operation and carbon emissions in the planning period. Then, on the basis of a traditional distribution network (TDN), security constraints, carbon emissions intensity, GPCT volume and CET volume were added. Finally, the cases study of the improved IEEE33 node and PG&E69 node NDN planning were provided. The results of NDN planning and TDN planning are compared and analyzed, and a sensitivity analysis was carried out to study the impact of GPCT and CET mechanisms with different price levels on investment planning. The results verify the applicability and rationality of the model.
结合制造技术基础训练的课程内容及教学特点,制定了相应的具有可执行性的OBE目标(一级预期学习产出及二级预期学习产出),并基于OBE目标指导,建设了制造技术基础训练MOOC课程,主要内容包括导论、材料成形技术、切削加工技术、现代制造技术、特种加工技术、智能制造与机器人技术等.该课程目前已经在中国大学MOOC平台上正式发布并完整运行了两期,在培养工程人才方面取得了一定成绩,也促进了学校相应课程的混合式教学建设,变革了教学方式,同时该课程的教学质量也得到了进一步的提升.
"新工科"建设对高等教育的人才培养体系提出了新要求.为了培养学生的工程创新能力、解决复杂工程问题能力,开展了以微型涡喷发动机压气机叶轮加工为案例的工程实践教学模式的探索.教学采用数控模拟仿真与实际操作相结合,完成压气机叶轮的加工工艺和工装设计、刀具和材料选择、数控模拟仿真、软件和设备选择、叶轮的粗加工和半精加工以及精加工.学生实践制作涡喷发动机压气机叶轮,激发了学生对我国航天事业的热爱,提高了本科生的团队协作、创新设计、探索未知的能力.
核安全是国家安全的重要组成部分.通过实施有效的核材料管制,防止核材料被盗、破坏、丢失、非法转让和非法使用,是实现核安全的重要途径和基础.对拥有核材料的高校,实施核材料的有效管制是保障校园安全和实验室安全的重要手段.与民用核设施的所属单位相比,高校实验室核材料管制存在一定的特殊性.该研究在分析核材料管制的定义及内涵的基础上,调研了美国高校实验室核材料监管体系,并针对国内高校实验室核材料管制现状,以我校核材料管制体系建设实践为例,提出了一系列提升核材料管制能力的具体措施,在国内首次进行了高校实验室核材料管制体系建设和完善的研究,为提升高校实验室核材料管制水平提供了有益的参考.
