基于石墨烯的光纤锁模激光器的混沌极化吸引子观测

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"Observation of chaotic polarization attractors from a graphene mode locked soliton fiber laser" 这篇研究论文主要探讨了一种基于石墨烯-聚乙烯醇薄膜的全光纤被动模式锁定铒离子掺杂光纤激光器(EDFL)。研究团队通过细心调控偏振控制器,观察到了两种不同的偏振吸引子现象,包括偏振锁定矢量孤子和一个圆形吸引子。据作者们所知,这是首次在基于石墨烯的EDFL中探索到由矢量孤子产生的动态偏振吸引子。 在激光科学中,模式锁定是一种重要的技术,它允许激光器产生超短脉冲。这种模式锁定是由内部反馈机制实现的,使得激光的光谱宽度变窄,而脉冲重复频率增加。在本研究中,石墨烯作为一种高效的非线性光学材料,被用作模式锁定的关键组件。石墨烯因其独特的电子性质,具有极高的光吸收率和快速的光电响应,因此在光纤激光器中表现出优异的性能。 偏振控制器是激光系统中的关键元件,用于控制通过光纤传播的光波的偏振状态。在该实验中,通过调整偏振控制器,研究人员能够引导激光器进入两种不同的运行状态:一是偏振锁定的矢量孤子状态,其中激光脉冲的电场在两个正交偏振态之间保持稳定;二是圆形吸引子状态,这是一种混沌行为的表现,意味着激光的偏振状态在时间上以复杂的方式变化。 矢量孤子是一种特殊的光脉冲,其电场同时在两个正交偏振态中存在,且保持稳定的形状和能量。它们在光纤通信、光学计算和光子学传感器等领域有潜在的应用价值。而圆形吸引子则揭示了系统的非线性和复杂动力学,这在理解和控制激光器的不稳定性和混沌行为方面至关重要。 这项工作展示了石墨烯在光纤激光器中的新应用,尤其是在探索和控制激光器的混沌偏振行为方面。这些发现不仅深化了我们对光纤激光器动态特性的理解,也为未来开发更稳定、可控的超快激光源提供了新的途径。此外,这一研究还可能激发更多关于非线性光学系统混沌行为的研究,以及如何利用这些混沌现象来设计新的光子器件。

翻译Agent 𝑐 𝑖 . In this paper, we regard each charging station 𝑐 𝑖 ∈ 𝐶 as an individual agent. Each agent will make timely recommendation decisions for a sequence of charging requests 𝑄 that keep coming throughout a day with multiple long-term optimization goals. Observation 𝑜 𝑖 𝑡 . Given a charging request 𝑞𝑡 , we define the observation 𝑜 𝑖 𝑡 of agent 𝑐 𝑖 as a combination of the index of 𝑐 𝑖 , the real-world time 𝑇𝑡 , the number of current avail able charging spots of 𝑐 𝑖 (supply), the number of charging requests around 𝑐 𝑖 in the near future (future demand), the charging power of 𝑐 𝑖 , the estimated time of arrival (ETA) from location 𝑙𝑡 to 𝑐 𝑖 , and the CP of 𝑐 𝑖 at the next ETA. We further define 𝑠𝑡 = {𝑜 1 𝑡 , 𝑜2 𝑡 , . . . , 𝑜𝑁 𝑡 } as the state of all agents at step 𝑡. Action 𝑎 𝑖 𝑡 . Given an observation 𝑜 𝑖 𝑡 , an intuitional design for the action of agent𝑐 𝑖 is a binary decision, i.e., recommending 𝑞𝑡 to itself for charging or not. However, because one 𝑞𝑡 can only choose one station for charging, multiple agents’ actions may be tied together and are difficult to coordinate. Inspired by the bidding mechanism, we design each agent 𝑐 𝑖 offers a scalar value to "bid" for 𝑞𝑡 as its action 𝑎 𝑖 𝑡 . By defining 𝑢𝑡 = {𝑎 1 𝑡 , 𝑎2 𝑡 , . . . , 𝑎𝑁 𝑡 } as the joint action, 𝑞𝑡 will be recommended to the agent with the highest "bid" value, i.e., 𝑟𝑐𝑡 = 𝑐 𝑖 , where 𝑖 = arg max(𝑢𝑡)

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