Listen to 'Speed Of Sound' this Monday!

Coldplay新单曲'Speed of Sound'将于4月18日在Radio1全球首播,随后可通过'Coldplayer'收听。该工具还能让会员获取新专辑音视频、后台素材等。有网页和桌面两个版本。同时,数字下载商店和官方移动商店将上线,还有巴黎观演套餐。
'Speed of Sound' is the brand new single by Coldplay and it will make its worldwide radio debut on Monday 18th April at 9.20pm (GMT) on Radio1.

Immediately following this first play, we will be making the single available for you to listen to via the brand new ‘Coldplayer’. This is the latest tool from Coldplay.com that will allow all members to gain direct access to a host of new content straight from the band and the new album 'X&Y'.

Alongside all the new X&Y audio & video exclusives, the new Coldplayer will also give you access to backstage footage that the band will be filming and uploading directly to the player, plus a diary of events only available to members of
Coldplay.com .

There will be two versions of the player. The first is web-based so you can visit it whenever you're online; the second is a direct desktop Coldplayer, which will be available soon.

Check out the new COLDPLAYER from Monday at
http://www.coldplay.com/X&Y/

Also coming on Monday, the Coldplay digital download shop and the first ever official mobile shop will launch with a range of downloads, including the chance for you to buy the 'Speed Of Sound' single and ringtone. See
www.coldplay.com from Monday night for more info.

V.I.P. Paris Travel Package

Coldplay are speeding their way through Europe this summer, and you have a special opportunity to catch them live in Paris!

Join Coldplay fans from the world over and see the band’s performance at the historic Olympia. All participants of our Coldplay VIP Travel group will also gain early entry into the venue before the general public!

【EI复现】基于深度强化学习的微能源网能量管理与优化策略研究(Python代码实现)内容概要:本文围绕“基于深度强化学习的微能源网能量管理与优化策略”展开研究,重点利用深度Q网络(DQN)等深度强化学习算法对微能源网中的能量调度进行建模与优化,旨在应对可再生能源出力波动、负荷变化及运行成本等问题。文中结合Python代码实现,构建了包含光伏、储能、负荷等元素的微能源网模型,通过强化学习智能体动态决策能量分配策略,实现经济性、稳定性和能效的多重优化目标,并可能与其他优化算法进行对比分析以验证有效性。研究属于电力系统与人工智能交叉领域,具有较强的工程应用背景和学术参考价值。; 适合人群:具备一定Python编程基础和机器学习基础知识,从事电力系统、能源互联网、智能优化等相关方向的研究生、科研人员及工程技术人员。; 使用场景及目标:①学习如何将深度强化学习应用于微能源网的能量管理;②掌握DQN等算法在实际能源系统调度中的建模与实现方法;③为相关课题研究或项目开发提供代码参考和技术思路。; 阅读建议:建议读者结合提供的Python代码进行实践操作,理解环境建模、状态空间、动作空间及奖励函数的设计逻辑,同时可扩展学习其他强化学习算法在能源系统中的应用。
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