Chrome Daltonize!

谷歌达顿化技术旨在通过增强显示效果来帮助色盲用户更好地体验数字内容。该技术能模拟并修正红绿色盲及蓝黄色盲人士所见的颜色,使他们能够察觉原本错过的细节。
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Google Daltonization is a technique of exposing details to color-blind users, allowing them see what they otherwise would have missed.Color vision deficiency (CVD), commonly referred to as color blindness, affects around 5% of the population (8% of males, and 0.5% of women) or one out of every twenty computer users. To learn more about color blindness & algorithms to improve the accessibility of colors on digital displays see: http://daltonize.org/p/developer.html Description of Options for Chrome Daltonize!: Filter type: - "Simulate" shows how images appear through the eyes of the color blind. - "Daltonize" exposes details in images to color blind computer users, allowing them to catch details they may have otherwise missed. For instance, daltonization of an Ishihara test plate (a popular test of color-vision) allows a color-blind person to see numbers normally inaccessible to their perception. Color vision deficiency: - This software provides enhancement for people with Protanopia (red-green), Deuteranopia (red-green), or Tritanopia (blue-yellow). Select one of these options from the select menu. Run at page load: - If option is checked the algorithm is run onpageload. Otherwise, you must click on the browserAction (sphere icon) in the top-right corner of your browser to Daltonize the page. Show speed results: - If option is checked the amount of time it took to process the results is displayed. Otherwise, the run time is not displayed. RELEASE NOTES 1.1 - added color blindness simulation, fixed recurrent image loading SUPPORT FOR NON-CHROME BROWSERS - Do you not have access to Google Chrome? You can use the Daltonize Bookmarklet proudly hosted on Google Appspot: http://daltonize.appspot.com/
【SCI复现】含可再生能源与储能的区域微电网最优运行:应对不确定性的解鲁棒性与非预见性研究(Matlab代码实现)内容概要:本文围绕含可再生能源与储能的区域微电网最优运行展开研究,重点探讨应对不确定性的解鲁棒性与非预见性策略,通过Matlab代码实现SCI论文复现。研究涵盖多阶段鲁棒调度模型、机会约束规划、需求响应机制及储能系统优化配置,结合风电、光伏等可再生能源出力的不确定性建模,提出兼顾系统经济性与鲁棒性的优化运行方案。文中详细展示了模型构建、算法设计(如C&CG算法、大M法)及仿真验证全过程,适用于微电网能量管理、电力系统优化调度等领域的科研与工程实践。; 适合人群:具备一定电力系统、优化理论和Matlab编程基础的研究生、科研人员及从事微电网、能源管理相关工作的工程技术人员。; 使用场景及目标:①复现SCI级微电网鲁棒优化研究成果,掌握应对风光负荷不确定性的建模与求解方法;②深入理解两阶段鲁棒优化、分布鲁棒优化、机会约束规划等先进优化方法在能源系统中的实际应用;③为撰写高水平学术论文或开展相关课题研究提供代码参考和技术支持。; 阅读建议:建议读者结合文档提供的Matlab代码逐模块学习,重点关注不确定性建模、鲁棒优化模型构建与求解流程,并尝试在不同场景下调试与扩展代码,以深化对微电网优化运行机制的理解。
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