今天自己模拟考试限时写了一篇作文,大家看看

现代科技对日常生活的影响及未来展望
本文探讨了现代科技如何深刻改变人们的日常交流方式,从传统的电子邮件到智能手机和互联网,阐述了技术带来的便利性和效率提升,同时也指出其潜在的负面影响。

这是我的作文的要求:

我的作文是这样的:

In this day of change, modern technology is playing a pivotal role in changing people’s daily lives as well as leading future lives. An obvious instance can be given about the media through which we interact.

Of course there are many ways in which people interact with each other. Unlike mails which seems quite slow in the past, a man in new era may equip himself a bunch of devices which can keep interact with the world with non-stop. A cell phone can help when you need to talk to someone who stays far from you urgently. In recent years, the quality of calling has improved a lot and people can dial someone in most part of the world. Besides, the function of a cell phone is more than dialing: it can send text messages, multi-media messages and even link to the Internet. Internet is also a grand invention through which people all around the world can communicate with others regardless of boundaries. People used to make pen pals in those days, but now with blogs and twitters applied, they tend to make e-pals because they have much in common despite the distance are far-away.

There is no denying that these technologies have a positive effect towards people. The reason may lie in its convenience and high speed. We can hardly imagine what the future will be if we are deprived of these technologies such as cell phones and Internet. In other words, such technologies have greatly enriched our lives. Although there are some downsides that people may stick to the screen and become dull and cold, the advantage is without doubt.

In a brief case, the technology has rendered our lives convenient and colorful, and I am certain that these ongoing technologies will have a profound influence 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等神经网络型在工程问题中的建与求解,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;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,同时参文中提及的多种优化与估计方法拓展研究思路。
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