脉冲光纤放大器中受激布里渊散射对信号线宽、脉冲持续时间和重复率的依赖性研究

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"这篇文章探讨了脉冲光纤放大器中受激发布里渊散射( Stimulated Brillouin Scattering, SBS)阈值与信号线宽、脉冲持续时间和重复率之间的关系。实验结果显示,SBS阈值与谱线宽度和脉冲持续时间高度相关,这决定了在不同情况下,脉冲光纤放大器的功率处理限制可能是平均功率或峰值功率。" 在光纤通信和激光技术领域,受激发布里渊散射是一种重要的非线性光学效应,它会影响光纤中的光功率传输和放大。当高功率光脉冲在光纤中传播时,SBS可能会发生,导致能量损失并限制系统的功率容量。本文通过实验研究了影响SBS阈值的三个关键因素:信号线宽、脉冲持续时间和重复率。 首先,信号线宽是决定SBS阈值的重要参数。较窄的谱线宽度意味着更集中、更纯净的频率成分,这可能导致更高的SBS活动,从而降低放大器的功率处理能力。相反,较宽的谱线可以分散能量,减少SBS效应,允许更大的功率通过。 其次,脉冲持续时间也显著影响SBS的发生。短脉冲具有较高的峰值功率,可能快速触发SBS,而长脉冲虽然平均功率较高,但由于能量分散在更长的时间内,其峰值功率相对较低,可能不会那么快达到SBS阈值。因此,对于特定的光纤系统,优化脉冲持续时间可以有效地管理SBS效应,提高系统的功率处理能力。 此外,重复率也是需要考虑的因素。高重复率会导致连续的脉冲在光纤中快速连续通过,这可能会累积SBS效应,使得系统在达到平均功率阈值之前就受到限制。相反,低重复率会减少脉冲的连续性,可能使系统能够处理更高的平均功率,但每个单独脉冲的峰值功率可能会触发SBS。 根据OCIS代码(140.3538, 060.4370, 040.3280),这篇文章涉及的领域包括光纤非线性效应、激光器技术和光纤放大器设计。通过这些研究,可以为优化脉冲光纤放大器的性能提供理论依据,例如通过调整光源的线宽、脉冲形状和系统的工作模式来有效抑制SBS,从而实现更高功率、更稳定的光纤激光输出。 理解和控制SBS与脉冲特性之间的关系对于提高脉冲光纤放大器的性能至关重要,这对于高功率激光系统、光通信网络和相关应用具有深远影响。文章的研究结果对工程实践和理论研究都提供了宝贵的指导。

这一段讲的是什么:Abstract—A recent trojan attack on deep neural network (DNN) models is one insidious variant of data poisoning attacks. Trojan attacks exploit an effective backdoor created in a DNN model by leveraging the difficulty in interpretability of the learned model to misclassify any inputs signed with the attacker’s chosen trojan trigger. Since the trojan trigger is a secret guarded and exploited by the attacker, detecting such trojan inputs is a challenge, especially at run-time when models are in active operation. This work builds STRong Intentional Perturbation (STRIP) based run-time trojan attack detection system and focuses on vision system. We intentionally perturb the incoming input, for instance by superimposing various image patterns, and observe the randomness of predicted classes for perturbed inputs from a given deployed model—malicious or benign. A low entropy in predicted classes violates the input-dependence property of a benign model and implies the presence of a malicious input—a characteristic of a trojaned input. The high efficacy of our method is validated through case studies on three popular and contrasting datasets: MNIST, CIFAR10 and GTSRB. We achieve an overall false acceptance rate (FAR) of less than 1%, given a preset false rejection rate (FRR) of 1%, for different types of triggers. Using CIFAR10 and GTSRB, we have empirically achieved result of 0% for both FRR and FAR. We have also evaluated STRIP robustness against a number of trojan attack variants and adaptive attacks. Index Terms—Trojan attack, Backdoor attack

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帮我地道的翻译:The differential variational inequalities ((DVIs), for short) are useful for the study of models involving both dynamics and constraints in the form of in￾equalities. They arise in many applications: electrical circuits with ideal diodes, Coulomb friction problems for contacting bodies, economical dynamics, dynamic traffic networks. Pang and Stewart [26], [27] established the existence, unique￾ness, and Lipschitz dependence of solutions subject to boundary conditions for (DVIs) in finite dimensional spaces. Han and Pang investigated a class of dif￾ferential quasi-variational inequalities in [11], and Li, Huang and O’Regan [18] studied a class of differential mixed variational inequalities in finite dimensional Well-Posedness of Differential Mixed Quasi-Variational-Inequalities 137 spaces. Gwinner [8] obtained an equivalence result between (DVIs) and projected dynamical systems. In [9] he also proved a stability property for (DVIs) by using the monotonicity method of Browder and Minty, and Mosco set convergence. Chen and Wang [4] studied dynamic Nash equilibrium problems which have the formulation of differential mixed quasi-variational inequalities. Elastoplastic contact problems can also be incorporated into (DMQVIs) formulation because general dynamic processes in the nonsmooth unilateral contact problems are governed by quasi-variational inequalities. A numerical study for nonsmooth contact problems with Tresca friction can be found in [10], Liu, Loi and Obukhovskii [19] studied the existence and global bifurcation for periodic solutions of a class of (DVIs) by using the topological degree theory for multivalued maps and the method of guiding functions. For more details about (DVIs) we refer to [3], [30], [12], [22]–[21].

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