Julio Iglesias - vincent(starry starry night)

博客围绕Julio Iglesias的歌曲《vincent(starry starry night)》展开,歌词描绘了如诗如画的场景,表达了对梵高的理解,提及他为理智受苦、试图释放自我,感慨世人起初不理解他,或许永远不会理解,还惋惜他的离世。

Julio Iglesias - vincent(starry starry night)

starry starry night
paint your palette blue and grey
look out on a summer's day
with eyes that know the darkness in my soul.
shadows on the hills
sketch the trees and the daffodils
catch the breeze and the winter chills
in colors on the snowy linen land.


now i understand
what you tried to say to me
how you suffered for your sanity
how you tried to set them free.
they would not listen they did not know how
perhaps they'll listen now.


starry starry night
flaming flowers that brightly blaze
swirling clouds in violet haze
reflect in vincent's eyes of china blue.
colors changing hue
morning fields of amber grain
weathered faces lined in pain
are smoothed beneath the artist's loving hand.


now i understand
what you tried to say to me
how you suffered for your sanity
how you tried to set them free.
they would not listen they did not know how
perhaps they'll listen now.


for they could not love you
but still your love was true
and when no hope was left in sight on that
starry starry night.
you took your life as lovers often do,
but i could have told you vincent
this world was never meant for one as beautiful as you.


starry starry night
portraits hung in empty halls
frameless heads on nameless walls
with eyes that watch the world and can't forget.
like the stranger that you've met
the ragged men in ragged clothes
the silver thorn of bloddy rose
lie crushed and broken on the virgin snow.


now i think i know
what you tried to say to me
how you suffered for your sanity
how you tried to set them free.
they would not listen they're not listening still
perhaps they never will.

【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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