DailyEnglish(20140120)

本文讲述了在一个包含64个用户故事的项目中,团队如何通过增加冲刺次数来确保项目的质量,并分享了在测试过程中遇到的问题及解决方案。强调了正确态度对于克服困难的重要性。
I pray for my mom, god bless my mom please.


The topic for today is the importance of attitude.


There is a saying tells that your attitude decides what you will get at the end.


Our project has send to test group for sprint testing today, there are totally four sprints for this project, actually it is three at the beginning, then we evaluate how long we could finish this project based on the number of developers, after we finished the evaluation, we know that it is impossible for us to finish all parts of this project, we only have ten members, there are totally 64 user stories.


In order to guarantee the quality of our project, we communicate with SBE and customers and propose to add another sprint, our customer agreed this proposal at last.


Two tester test our project, there are more than ten defects has been found, one of them is from my user story, but it is not because I did not serious on my work, it because I do not know the mean of Korea words. the text they give us is in Korea, and there is no explanation in Chinese or English, so I kept the exact words they give us on the page. but tester tell me it is a defect, it should be the name of group which user input in the first step, I change my code and fixed that defect in the end.


What I want to say is this is not because it is too complex that I did it wrong, I made this mistake as I do not know the meaning of those Korean words but I did not ask for help, I will pay more attention on such thing and try my best to avoid such easy mistake in the future.

内容概要:本文档围绕六自由度机械臂的ANN人工神经网络设计展开,涵盖正向与逆向运动学求解、正向动力学控制,并采用拉格朗日-欧拉法推导逆向动力学方程,所有内容均通过Matlab代码实现。同时结合RRT路径规划与B样条优化技术,提升机械臂运动轨迹的合理性与平滑性。文中还涉及多种先进算法与仿真技术的应用,如状态估计中的UKF、AUKF、EKF等滤波方法,以及PINN、INN、CNN-LSTM等神经网络模型在工程问题中的建模与求解,展示了Matlab在机器人控制、智能算法与系统仿真中的强大能力。; 适合人群:具备一定Ma六自由度机械臂ANN人工神经网络设计:正向逆向运动学求解、正向动力学控制、拉格朗日-欧拉法推导逆向动力学方程(Matlab代码实现)tlab编程基础,从事机器人控制、自动化、智能制造、人工智能等相关领域的科研人员及研究生;熟悉运动学、动力学建模或对神经网络在控制系统中应用感兴趣的工程技术人员。; 使用场景及目标:①实现六自由度机械臂的精确运动学与动力学建模;②利用人工神经网络解决传统解析方法难以处理的非线性控制问题;③结合路径规划与轨迹优化提升机械臂作业效率;④掌握基于Matlab的状态估计、数据融合与智能算法仿真方法; 阅读建议:建议结合提供的Matlab代码进行实践操作,重点理解运动学建模与神经网络控制的设计流程,关注算法实现细节与仿真结果分析,同时参考文中提及的多种优化与估计方法拓展研究思路。
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