Hello world, again (Part II, how I moved from JRoller to Wordpress)

本文分享了作者从JRoller迁移博客到Wordpress的过程及体验。在中国的网络环境下,由于主流博客平台受限,作者选择了JRoller。但因功能限制及中文支持不佳等问题,最终决定迁移到Wordpress,并成功解决了迁移中遇到的技术挑战。

This is my summary about the migration of my blog from JRoller to here.

I’m a Java engineer working in China. So when I was looking for a free blogging service, JRoller was my natural choice, because

1. In China, Blogger, Wordpress, LiveJournal, and all other mainstream blogging sites are blocked. So there are sadly not many choices left for me.

2. Chinese local blogging sites are crap, specially from a developer’s perspective.

3. JRoller is the best free blogging service implemented in Java today. It’s hosted by JavaLobby which is one of my favorites. There are a lot of brilliant folks hosting their blogs there. And since it’s not so mainstream, it’s not blocked in China. Hurray!

But after more than 3 months living with JRoller, I have to say I’m an unhappy user. Roller’s (JRoller’s, actually) lack of attractive features and looks, defective Chinese support, mysterious API support, make it fairly uncompetitive compared to today’s top ones, and make my blogging experience rather unexciting.

So I decided to move to Wordpress, setting up on a server space I’m sharing with my friend, to get rid of all the problems. And I have to say Wordpress is simply great.

So I did some Google search about the migration of my posts from JRoller to Wordpress. I found a post from zeusville very helpful. And Jesus Rodriguez, the blogger, was kind enough to send me the custom JRoller template file which I wasn’t able to download from his blog anymore. Thanks again, Jesus.

Now I’m already very comfortable with Wordpress. BTW. This post is written with Windows Live Writer. I have to say that this is the best free offline blogging software I’ve used so far. Unfortunately it comes from Microsoft so it only runs on Windows. But maybe it’ll have a chance running on Mono on Linux. I’ll give it a try later.

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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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