JUST DO IT AND FEEL THE EXPERIENCE

本文讲述了作者从一名烹饪新手成长为能够做出美味佳肴的厨师的过程。通过不断尝试和从经验中学习,作者最终克服了初期的失败,赢得了朋友们的认可。

上学时写的一篇文章,今天找了出来,看看。
“Crossing the river by feeling the stones” is a Chinenes proverb,do you know the stone? Everyone has different stone, do you know your own stone? I think my stone is “just do it while feel the experience”
I am a boy, but I also need to cook, because I need to feed myself. Last winter holiday, I stayed here for study, I need food, but I can’t always buy the food, I am a poor man, I have no money, so I need to learn how to cook. That was my unique experience. At one afternoon, I decided to cook by myself, I prepared some potatoes, I wanted to fry them, but I had no experience, so I failed. When I let my friends eat them,they were happy, but when they tasted it, their faces turned bad, and they said :”It is very delicious” But I know they cheated me. I am a stubborn man like the stone. I will never give up, some days later, I decided to do it again, I prepared some potatoes again, and I learned from last experience, I know how long that I need to fry, I know when to put the salts, I know a lot because of the experience. So I did it. Because my friends smiled when they tasted it, after I ate it, I think it is the best food in the world.
I learned no matter what we do, we should try it and feel the last experience.
Now, I am a cook.
From that, I know we should be brave, and just do something that can make us progress, we should always learn the last experience, and think how to take the next step. That is my stone.

【电动车】基于多目标优化遗传算法NSGAII的峰谷分时电价引导下的电动汽车充电负荷优化研究(Matlab代码实现)内容概要:本文围绕“基于多目标优化遗传算法NSGA-II的峰谷分时电价引导下的电动汽车充电负荷优化研究”展开,利用Matlab代码实现优化模型,旨在通过峰谷分时电价机制引导电动汽车有序充电,降低电网负荷波动,提升能源利用效率。研究融合了多目标优化思想与遗传算法NSGA-II,兼顾电网负荷均衡性、用户充电成本和充电满意度等多个目标,构建了科学合理的数学模型,并通过仿真验证了方法的有效性与实用性。文中还提供了完整的Matlab代码实现路径,便于复现与进一步研究。; 适合人群:具备一定电力系统基础知识和Matlab编程能力的高校研究生、科研人员及从事智能电网、电动汽车调度相关工作的工程技术人员。; 使用场景及目标:①应用于智能电网中电动汽车充电负荷的优化调度;②服务于峰谷电价政策下的需求侧管理研究;③为多目标优化算法在能源系统中的实际应用提供案例参考; 阅读建议:建议读者结合Matlab代码逐步理解模型构建与算法实现过程,重点关注NSGA-II算法在多目标优化中的适应度函数设计、约束处理及Pareto前沿生成机制,同时可尝试调整参数或引入其他智能算法进行对比分析,以深化对优化策略的理解。
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