随着Python程序设计语言类课程在高校开设的越来越普遍,尤其是该语言语法简单、易上手的特点,已成为非计算机类专业程序设计语言学习的首选,本文以国内某高校为例,分析了该校Python语言类课程的开课情况和课程建设的特点以及存在的问题,并提出了设计一款在线实验平台来解决上述问题,在实践的过程中,该平台表现出了良好的效果,同时积累大量相关教学数据,极大地推动了课程建设的进步。With the increasing prevalence of Python programming language courses in universities, especially due to the language’s simple syntax and ease of learning, it has become the preferred programming language for non-computer science majors. This paper takes a university in China as a case study to analyze the course offerings, characteristics of course construction, and the existing issues in Python language courses. The paper proposes the design of an online experimental platform to address these issues. In practice, the platform has demonstrated good results, while also accumulating a large amount of relevant teaching data, greatly promoting the progress of course development.
电力电子技术作为电气类、自动化类专业的一门重要的专业基础性课程,不仅是专业素养的坚实基石,更应成为注入思政之魂、雕塑学生思政情怀的关键渠道。通过巧妙融合,使思政元素贯穿于电力电子技术课程,实现思政教育与专业教育的和谐共振。针对思政教学现状与亟待解决问题,以思政铸魂为引领,构建了基于OBE教育理念的教学设计,致力于重塑教学内容脉络,探索育人教学模式,拓展实践应用场景,创构多元考核标准,最终达到思政理念内化于行的目标。实践表明,在学生的课程参与度、实践应用能力以及社会责任意识等方面取得了显著成效,实现了育才与育人双重目标的和谐统一。Considered as a fundamental course in electrical and automation disciplines, Power Electronics Technology serves not only as a solid cornerstone for professional competence but also as a crucial channel for instilling ideological and political education, shaping students’ ideological sentiments. By skillfully integrating ideological and political elements throughout the Power Electronics Technology curriculum, we aim to achieve a harmonious resonance between ideological education and professional training. Addressing the current status and pressing issues in ideological education, we guide the soul through ideological education, constructing a teaching design based on the Outcome-Based Education (OBE) philosophy. This effort is dedicated to reshaping the content structure, exploring student-centered teaching models, expanding practical application scenarios, creating diverse assessment standards, ultimately internalizing the ideological and political concepts into students’ actions. Practice has shown significant achievements in students’ course engagement, practical application abilities, and awareness of social responsibility, realizing a harmonious integration of talent development and character building.
随着5G技术的发展,其高带宽、低时延和高密度接入特点,促使云计算模式向“云-管-端”模式改变,边缘计算作为终端关键技术对人工智能算法在算力有限的终端上的部署成为关键。以苗圃验收环节中松树株数识别的视频检索算法为例,提出一种适用于人工智能算法在终端部署的轻量级苗圃松树苗检测计数算法。算法通过在YOLOv5网络的基础上引入MobileNet v3特征提取机制来实现网络的轻量化,将压缩激励网络(Squeeze-and-Excitation Networks,SENet)中的轻量级注意模块集成作为bneck基本块,提高网络对于特征通道的敏感程度,增强网络的特征提取能力;在IoU(Intersection over Union,IoU)基础上进一步考虑目标框和预测框的向量角度,使用SIoU损失函数作为预测函数,重新定义相关损失函数,从而使苗圃树苗预测框更加接近真实框。研究结果表明,改进后的模型参数量明显减少,改进后的网络模型大小与对比试验中的方法相比,模型在准确率(Precision)降低3.26%、平均精确率均值(Mean Average Precision,mAP)降低1.03%的情况下,帧率(Frame Per Second,FPS)提升了21.48%,达到71.43帧/s,计算量较原YOLOv5s减少了148.44%。证明该算法具有高效性和轻量性,为边缘计算终端人工智能算法移植提供算法原型。
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