多波长级联拉曼光纤激光器的设计

基于稳态条件下描述光纤中受激拉曼散射效应的光功率耦合方程组,提出一种新的多波长级联拉曼光纤激光器的设计算法.结合遗传算法和打靶法的优点,采取对每一代种群中少数优良个体进行几次打靶,使得种群中目标函数最优化值附近的个体加速收敛.以500 m掺磷光纤为增益介质、光纤布拉格光栅构成谐振腔的三波长(1427 nm, 1455 nm, 1480 nm)级联拉曼光纤激光器为例,采用该算法计算了其输出特性.结果表明,总输出功率与抽运功率近似成线性关系,斜率效率约51%;由于谐振腔中三个输出波长相互之间的受激拉曼散射作用产生的能量转移,使得输出的长波长斯托克斯光斜率效率大于短波长斯托克斯光斜率效率.     

双包层光纤激光器最大输出功率的估算

 通过热传导方程和边界条件得到高功率双包层光纤激光器温度分布的简化解析解，利用ANSYS模拟验证了简化的合理性，并以双包层的掺镱光纤激光器为例，把外包层的临界温度设定为80 ℃，计算了光纤激光器在自然对流、风扇制冷和水制冷条件下最大输出功率。计算结果表明：自然对流的情况下，对流传热系数为10 W·m^-2·Ｋ^-1时，光纤激光器的最大输出功率为105 W；风扇制冷的情况下，对流传热系数为80 Ｗ·m^-2·Ｋ^-1时，光纤激光器的最大输出功率820 W；要达到kW以上功率输出，必须对光纤进行主动水冷。     

掺Yb3+光纤激光器的特性与设计

 从速率方程出发，对波长为980nm的激光泵浦掺Yb^{3+光纤激光器进行数值模拟，全面分析激光器阈值功率、输出激光功率、增益等重要参数与泵浦功率、光纤长度、腔镜反射率、掺杂浓度等参量之间的关系。采用高浓度掺Yb3+光纤对环形光纤激光器做了初步实验研究，所得结论与实验模拟结果基本一致。}     

高功率双包层光纤激光器受激拉曼散射和热效应的理论研究

 受激拉曼散射和热效应会限制光纤激光器功率的提高。利用高功率光纤激光器的速率方程和热传导方程，理论研究了双端泵浦和分布泵浦下双包层光纤激光器的受激拉曼散射和热效应，得到了光纤中的泵浦光、激光和斯托克斯光的功率分布，光纤激光器的输出特性以及光纤中的温度分布。分析表明，当泵浦功率增大到一定值时，光纤激光器中出现SRS，一部分激光功率会转移给斯托克斯光，影响激光功率进一步提高;与双端泵浦方式相比，分布泵浦下光纤激光器的斜率效率和最大输出功率相差不大，但是，光纤中的温度分布被有效地降低，因此，分布泵浦方式更为有效。     

可调谐的调Q掺Yb3+双包层光纤激光器

利用双包层光纤中的受激布里渊散射和声光调Q器件,对掺Yb³⁺双包层光纤激光器进行了混合调Q的实验研究.在连续泵浦状态下,当入纤泵浦功率为1.24W时,在1080～1119nm的可调谐范围内,在国内首次获得了输出脉冲宽度小于6ns,重复频率1.5 kHz,脉冲能量高于80μJ,峰值功率高于13.3kW稳定调Q光纤激光器的脉冲输出.经计算,实验结果和理论计算值符合得很好.     

内腔级联拉曼光纤激光器输出特性的实验研究

采用标准单模石英光纤作为拉曼增益介质，光纤布拉格光栅作为谐振腔镜，研制了一台内腔级联拉曼光纤激光器。利用掺Yb双包层光纤激光器作为抽运源，实现了二级拉曼转换，在波长1176.8nm获得了309mW的最大输出功率，斜率效率接近51.5％。在小抽运功率下，发现拉曼光纤激光器的输出中存在重复周期约为2.9μs的脉冲序列；当抽运功率大于某一值时，上述脉冲消失，获得了十分稳定的连续输出。     

宽带可调谐掺Yb3+双包层光纤激光器的研究

利用闪耀光栅构成可调谐Littrow外腔,对掺Yb³⁺双包层光纤激光器的波长调谐输出进行了实验研究.激光调谐输出波长范围70nm.针对光纤内的双折射效应对激光的输出功率的影响,实验中使用了在线光纤偏振控制器,从而有效地减小了调谐曲线的起伏及其与荧光谱的差别,并得到了窄线宽、线偏振、宽带调谐的激光输出.     

掺Yb3+双包层光纤激光器的数值分析

本文对976nm激光泵浦掺Yb³⁺双包层光纤激光器进行了数值模拟,分析了泵浦光及激光在光纤中的分布、输出功率与泵浦功率的关系、光纤长度及腔镜反射率对输出激光功率的影响,所得结论与实验现象基本一致.     

占空比对光子晶体光纤激光器性能的影响

 在传统光纤激光器工作原理的基础上,考虑光子晶体光纤(PCF)模场分布特征,给出了连续泵浦情况下单模PCF激光器的速率方程和功率传输方程。利用该方程对掺镱单模PCF激光器的性能进行了数值模拟研究。结果表明：虽然空气占空比大小对PCF激光器的输出功率、泵浦阈值和斜率效率等影响不显著,但对拉曼非线性阈值影响却很大。当泵浦功率小于拉曼非线性阈值时,激光器主要输出信号激光;当超过拉曼阈值且在较宽的范围内,激光器同时输出功率相近的信号激光和拉曼光。基于这种效应,提出一种由泵浦功率控制的双波长光纤激光器的新思路。考虑到PCF非线性系数的可调控特性,采用不同光纤有可能在较宽的功率范围获得双波长激光振荡。     

增益平坦拉曼放大器优化算法研究

在分析受激拉曼散射耦合方程的基础上，介绍了一种反向设计多波长抽运光纤拉曼放大器的方法，并使用一种改进遗传算法和新型打靶法，讨论了体现特定的高增益和宽带平坦的适应度函数，对抽运波长和功率进行了优化，得到高增益，宽带平坦的增益谱，其开关增益可达15dB，增益带宽达80nm，增益波动小于1dB，这种放大器结构比现有的宽带光纤放大器在增益平坦上有一定的进步。     

基于粒子群算法的稀疏阵列超声相控阵全聚焦成像

为降低相控阵超声检测全聚焦算法的成像数据量及阵列稀疏优化的计算时间,研究了一种用于稀疏阵列全聚焦成像的阵列优化算法,并通过实验对其成像效果进行了验证。针对目前超声相控阵检测的全矩阵采集数据量大、全聚焦算法成像时间长的难点,该文通过构建稀疏阵列,在保证成像质量的同时显著降低成像数据量,提高了全聚焦算法的成像效率。通过以主瓣宽度、旁瓣峰值以及主瓣峰值作为约束条件构建适应度函数,采用粒子群算法得到稀疏阵元位置分布并进行阵元权重修正,并将其用于稀疏全聚焦成像。相比全阵元成像,使用粒子群算法所得的稀疏阵列的阵元个数降低了56.25%、65.62%,数据使用量降低了80.86%、88.18%。在阵列优化方面,相比遗传算法减少了84.86%的计算时间。     

蚁群元胞优化算法在人群疏散路径规划中的应用

针对疏散路径规划问题,以栅格化地图为背景的基础上,提出了蚁群元胞优化算法.首先为统一仿真时间步长,建立以六边形元胞为基础的栅格地图;然后利用静态势场对启发函数进行优化,利用分段更新规则优化信息素更新方式;最后,将模型参数作为粒子群优化算法的粒子位置信息进行优化,求解参数的最优组合值.仿真结果表明:采用蚁群元胞优化模型进行疏散路径规划时,不仅加快了搜索速度,而且增大了解空间,提高了搜索能力,可以有效避免陷入局部最优解.     

Fault Recognition of Rolling Bearings Based on Parameter Optimized Multi-Scale Permutation Entropy and Gath-Geva

To extract fault features of rolling bearing vibration signals precisely, a fault diagnosis method based on parameter optimized multi-scale permutation entropy (MPE) and Gath-Geva (GG) clustering is proposed. The method can select the important parameters of MPE method adaptively, overcome the disadvantages of fixed MPE parameters and greatly improve the accuracy of fault identification. Firstly, aiming at the problem of parameter determination and considering the interaction among parameters comprehensively of MPE, taking skewness of MPE as fitness function, the time series length and embedding dimension were optimized respectively by particle swarm optimization (PSO) algorithm. Then the fault features of rolling bearing were extracted by parameter optimized MPE and the standard clustering centers is obtained with GG clustering. Finally, the samples are clustered with the Euclid nearness degree to obtain recognition rate. The validity of the parameter optimization is proved by calculating the partition coefficient and average fuzzy entropy. Compared with unoptimized MPE, the propose method has a higher fault recognition rate.     

一种求解约束优化问题的改进粒子群算法及其应用

针对复杂约束优化问题,提出一种改进的粒子群方法。该粒子群算法对于不满足约束条件的粒子实行全概率接收,但令其目标函数值同为一个很小的常数,以保持粒子的多样性并使最优解在可行域内。另外,在PSO算法的基础上,使惯性权值按对数规律单调递减,同时引进选择遗传算子,以增强其全局寻优性能。数值实验表明:与PSO算法和一些其它优化算法相比,改进算法具有较强的寻优能力和寻优效率。工程应用表明,改进算法具有一定的优越性。     

Particle-swarm optimization of axially superresolving binary-phase diffractive optical elements

A particle-swarm optimization (PSO) algorithm was developed for designing binary-phase-only diffractive optical elements (DOEs) that superresolve the axially focused point-spread function. The method is based on vector diffraction theory to ensure solutions are valid under high-NA conditions. A DOE is identified that superresolves the focal spot by 34% and maintains the sidelobes below 50% of the peak intensity. The algorithm was used to obtain the Pareto front of the fitness-value space, which describes the achievable superresolution versus an allowed upper bound in sidelobe intensity. The results suggest that the algorithm yields solutions that are global in terms of the co-optimized fitness values G and M.     

二冷配水优化建模与混合自适应PSO算法求解

针对连铸二冷区生产环境复杂且存在着大量水雾干扰的情况,建立了连铸水量优化模型并提出了一种混合的自适应粒子群算法来求解连铸二冷水优化问题。依据冶金过程中的工艺要求建立了二冷水量优化模型,并在经典的PSO算法基础上提出了适合该问题求解了混合自适应PSO算法。由于连铸过程存在着偏微分方程约束,传统的优化方法容易陷入局部最优解,不能达到很好的动态优化效果。研究了粒子群算法,基于种群的多样性,不断的自适应的更新粒子群算法中参数,将禁忌搜索的方法和传统的粒子群算法结合,增强了算法的局部搜索能力和全局寻找全局最优的能力。将该算法应用到连铸二冷水动态优化中,实验结果表面该算法能够快速有效的求解该优化问题。该方法用于连铸二冷水优化是可行的、有效的。     

布里渊散射谱参数提取问题的混合优化算法研究与应用

提出了一种将Particle Swarm Optimization(PSO)算法和Levenberg-Marquardt(LM)算法按概率混合优化的新算法,将其用于Pseudo-Voigt型布里渊散射谱以提高其拟合度和频移提取精度。新算法以PSO算法为主框架,首先用PSO算法进行全局搜索,在优化一定次数后每次优化随机产生一个概率rand(0,1),若rand(0,1)小于或等于事先设定的概率P,则把PSO算法得到的最优解作为LM算法的初值进行局部深度搜索,将LM算法得到的最优解替换先前PSO算法的最优解,继续使用PSO算法进行全局搜索;若rand(0,1)大于P则仍然用PSO算法搜索,等待下一次优化产生随机概率rand(0,1)进行判断,交替使用两种算法直至获得理想的全局最优解。仿真分析和实验表明,新算法克服了单一算法的缺点,提高了布里渊散射谱的拟合度和频移提取的精度,充分证明了新算法的实用性和可行性。     

Improved Particle Swarm Optimization Based on Entropy and Its Application in Implicit Generalized Predictive Control

Setting sights on the problem of input-output constraints in most industrial systems, an implicit generalized predictive control algorithm based on an improved particle swarm optimization algorithm (PSO) is presented in this paper. PSO has the advantages of high precision and fast convergence speed in solving constraint problems. In order to effectively avoid the problems of premature and slow operation in the later stage, combined with the idea of the entropy of system (SR), a new weight attenuation strategy and local jump out optimization strategy are introduced into PSO. The velocity update mechanism is cancelled, and the algorithm is adjusted respectively in the iterative process and after falling into local optimization. The improved PSO is used to optimize the performance index in predictive control. The combination of PSO and gradient optimization for rolling-horizon improves the optimization effect of the algorithm. The simulation results show that the system overshoot is reduced by about 7.5% and the settling time is reduced by about 6% compared with the implicit generalized predictive control algorithm based on particle swarm optimization algorithm (PSO-IGPC).     

The emergence of scaling laws search dynamics in a particle swarm optimization

This paper investigates the search dynamics of a fundamental particle swarm optimization (PSO) algorithm via gathering and analyzing the data of the search area during the optimization process. The PSO algorithm exhibits a distinct performance when optimizing different functions, which induces the emergence of different search dynamics during the optimization process. The simulation results show that the performance is tightly related to the search dynamics which results from the interaction between the PSO algorithm and the landscape of the solved problems. The Lévy type scaling laws search dynamics emerges from the process in which the PSO algorithm shows good performance, while the Brownian dynamics appears after the algorithm has stagnated due to the premature convergence. The Lévy dynamics characterized by a large number of intensive local searches punctuated by long-range transfers is an indicator of good performance, which allows the algorithm to achieve an efficient balance between exploration and exploitation so as to improve the search efficiency.     

Shaping the Wavefront of Incident Light with a Strong Robustness Particle Swarm Optimization Algorithm

We demonstrate a modified particle swarm optimization(PSO) algorithm to effectively shape the incident light with strong robustness and short optimization time. The performance of the modified PSO algorithm and genetic algorithm(GA) is numerically simulated. Then, using a high speed digital micromirror device, we carry out light focusing experiments with the modified PSO algorithm and GA. The experimental results show that the modified PSO algorithm has greater robustness and faster convergence speed than GA. This modified PSO algorithm has great application prospects in optical focusing and imaging inside in vivo biological tissue, which possesses a complicated background.