主要论述了如何使用Ebbinghaus模型与SWOT商业分析法相融合这一创新思想设计一款英语单词高效记忆工具,分析了记忆工具市场现状以及新软件设计理念和全流程设计思想,针对市场现有相关软件的功能相似性过高而提出创新型优化专属学习方案的新功能点进行阐述,对于“智记”APP进行了功能设计和刨析,并展示了初期UI页面设计构想图,实现并阐述了不同于其他软件的***和Matplotlib实现的记忆曲线、NoSQL数据库与NumPy相结合的数据处理创新、三种不同的单词抽取新型算法,展示了重点代码实现流程;最后论证了模型优势及实用性和可行性,重点分析了该项目的创新性。It mainly discusses how to use the innovative idea of integrating the Ebbinghaus model and SWOT business analysis method to design an efficient memory tool for English words. It analyzes the current situation of the memory tool market as well as new software design concepts and full-process design ideas, aiming at the existing related software in the market. The functional similarity is too high and the new functional points of the innovative optimized exclusive learning program are elaborated. The functional design and analysis of the “Zhiji” APP is carried out, and the initial UI page design concept diagram is shown, and the different functions are implemented and explained. The memory curve implemented by *** and Matplotlib of other software, the data processing innovation combining NoSQL database and NumPy, and three different new word extraction algorithms demonstrate the key code implementation process;finally, the advantages, practicality and feasibility of the model are demonstrated nature, focusing on analyzing the innovativeness of the project.
在交通场景中采用一些预警措施能够有效地减少交通事故发生。例如,对车辆轨迹进行跟踪并预测车辆的驾驶行为,就是一个常用的预警方法。在对车辆进行跟踪的过程中,数据关联是很重要的部分,它可以对车辆的观测点和轨迹进行关联,从而更新车辆的轨迹,完成跟踪过程。在此背景下,提出了一种新的数据关联算法,即k近邻联合概率数据关联算法(k Nearest Neighbor-Joint Probability Data Association,kNN-JPDA)。实验结果表明,该算法能够较好地解决在交通场景下车辆数据的数据关联问题,在精度以及运行效率方面都有所提高。
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