近些年,计算机仿真如Matlab和PSIM逐步成为了电力电子技术教学的热点。Matlab覆盖面广,对于初学者需要花费较多的时间和精力掌握各种功能,入门相对困难。而PSIM是一种专门用于电力电子和电机驱动系统设计和仿真的仿真工具,具有极高的专业性。因此,该文章探讨了PSIM在电力电子技术教学中的应用。结合具体教学案例,即设计单项全控桥式带电阻负载和感性负载的PSIM电路并分析,阐述了PSIM仿真技术在教学中的优势。根据教学效果及学生反馈得出,该文章所提的教学方法不仅能够有效激发学生学习的主动性和提高他们的综合能力,而且在此基础上可以培养出适应现代工业需求的高素质电力电子技术人才。In recent years, computer simulation such as Matlab and PSIM has gradually become the focus of power electronics teaching. Matlab has a wide coverage, and it takes more time and energy for beginners to master various functions, so it is relatively difficult to get started. PSIM is a simulation tool specifically designed for power electronics and motor drive system design and simulation, with a high degree of professionalism. Therefore, this paper discusses the application of PSIM in the teaching of power electronics technology. The advantages of PSIM simulation technology in teaching are expounded based on the specific teaching case, that is, the design and analysis of single PSIM circuit with resistive load and inductive load. According to the teaching effect and students’ feedback, the teaching method proposed in this paper can not only effectively stimulate students’ learning initiative and improve their comprehensive ability, but also train high-quality power electronic technical talents to meet the needs of modern industry.
为应对AI辅助课程作业给高校教学秩序和学生培养带来的潜在风险,提出一种自动鉴别AI辅助作业的方法。在广泛收集AI生成、AI润色和人类撰写作业的基础上,采用对比学习技术深入挖掘有效区分AI辅助与人类撰写作业的文本特征,并基于这些特征构建一个高效准确的智能鉴别模型。在测试集上的准确率达到92.23%,实现了对不同类别作业的准确鉴别。研究成果不仅为有效的课程作业评估提供准确依据,更为维护公平、良好的教学秩序提供了有力支持。*通讯作者。A method for automatically identifying AI-generated course homework is proposed to address the potential risks that AI-generated homework brings to teaching order and student training. Based on the extensive collection of AI-generated, AI-polished, and human-written homework, contrastive learning technology is used to deeply explore the text features that can effectively distinguish between AI-assisted and human-written homework, and an efficient and accurate intelligent identification model is constructed based on these features. The accuracy on the test set reached 92.23%, achieving accurate identification of different types of homework. The research results not only provide an accurate basis for effective course assignment evaluation but also provide strong support for maintaining fair and good teaching order.
