半导体激光器单管准直实验研究

利用高斯光束的ABCD传输矩阵理论分析了LD与自聚焦透镜之间的距离与出射光束远场光斑尺寸之间的关系.通过理论计算可以方便地得到最佳距离,获得较高亮度输出,并进行了实验验证.对平面自聚焦透镜、球面自聚焦透镜和圆柱透镜的准直效果进行了实验比较,最后将自聚焦透镜与LD固定,制做了实用的器件,该器件可以将LD椭圆形光斑转变成线形光斑,快轴、慢轴远场发散角分别为0.1°和2.50°耦合效率86%.     

大气光通信PAT子系统指标初步设计分析

分析了跟踪角速度，功率，跟踪精度等因素对大气激光通信系统中瞄准，捕获和跟踪子系统参数选择的影响，给出了波束发散角，扫描角范围，接收视场等主要参数的选择原则，对地面终端光通信系统设计背景提出了瞄准，捕获和跟踪了系统的相应指标。     

图像分割中的交叉熵和模糊散度算法

本文将交叉熵和模糊散度应用于图像分割中,提出了中最优灰度值选取算法,其一是基于均匀分布假设的最小交叉熵算法,其二是利用后难概率的最大类间交叉熵算法,其三是类间最大模糊散度的改进算法,其四是最小模糊散度算法,针对图像阈什化分割的要求,在后两种算法中构造一种新的模糊录改度函数,本文采用均匀测试和开头测试比较各算法的性能,利用多种类型测试 是到的分割结果,显示了所筛算法的有效性和通用性。     

基于加速度时程的结构参数直接识别方法及验证

无线智能传感器结合云平台可以实现建筑结构的长期健康监测,模态识别是结构健康监测的重要内容。希尔伯特黄变换(Hilbert-Huang Transform, HHT)因其适用于非线性非平稳信号,且具有完全自适应性等特点,在模态识别领域中被广泛应用。与实验室中进行结构模态参数识别不同的是,长期监测中模态参数识别的算法不能出现主观的参数选择过程,而传统HHT的第一步经验模态分解(Empirical Mode Decomposition, EMD)会产生虚假的固有模态函数(Intrinsic Mode Function, IMF)分量,对虚假分量的识别与剔除往往依赖研究人员的主观判断。该文提出了一种基于深度神经网络(Deep Neural Networks, DNN)与K-L散度(Kullback-Leibler Divergence, K-L Divergence)的新算法,可以自动化识别并剔除EMD产生的虚假分量,从而使得EMD后得到的固有模态函数均为真实分量。     

3.705 μm紧凑型光参量振荡器

报道了一种基于工程应用的3.705 μm紧凑型高稳定性光参量振荡器。采用半导体制冷、强制气体对流冷却和热管导热技术相结合的方式,对主振荡功率放大结构的掺Yb光纤泵浦模块泵浦过程中产生的废热进行有效管理,再将经过耦合后的泵浦光注入到使用温控炉精确控温的PPMgLN晶体,在重复频率为55.56 kHz时,获得了功率不稳定度优于1.5%的1.78 W,脉冲宽度120 ns,峰值波长3.705 μm的线偏中红外激光输出,光光转换效率为12.6%。     

Higher-order Hamilton dynamics and Hamilton–Jacobi divergence PDE

This paper, using a non-standard Legendrian duality, investigates the Hamiltonian dynamics and formulates a Hamilton–Jacobi type divergence PDE governed by higher-order Lagrangians.     

Improving branch divergence performance on GPGPU with a new PDOM stack and multi-level warp scheduling

General-purpose graphics processing unit (GPGPU) plays an important role in massive parallel computing nowadays. A GPGPU core typically holds thousands of threads, where hardware threads are organized into warps. With the single instruction multiple thread (SIMT) pipeline, GPGPU can achieve high performance. But threads taking different branches in the same warp violate SIMD style and cause branch divergence. To support this, a hardware stack is used to sequentially execute all branches. Hence branch divergence leads to performance degradation. This article represents the PDOM (post dominator) stack as a binary tree, and each leaf corresponds to a branch target. We propose a new PDOM stack called PDOM-ASI, which can schedule all the tree leaves. The new stack can hide more long operation latencies with more schedulable warps without the problem of warp over-subdivision. Besides, a multi-level warp scheduling policy is proposed, which lets part of the warps run ahead and creates more opportunities to hide the latencies. The simulation results show that our policies achieve 10.5% performance improvements over baseline policies with only 1.33% hardware area overhead.     

Parameter identifiability with Kullback–Leibler information divergence criterion

We study the problem of parameter identifiability with Kullback–Leibler information divergence (KLID) criterion. The KLID‐identifiability is defined, which can be related to many other concepts of identifiability, such as the identifiability with Fisher's information matrix criterion, identifiability with least‐squares criterion, and identifiability with spectral density criterion. We also establish a simple check criterion for the Gaussian process and derive an upper bound for the minimal identifiable horizon of Markov process. Furthermore, we define the asymptotic KLID‐identifiability and prove that, under certain constraints, the KLID‐identifiability will be a sufficient or necessary condition for the asymptotic KLID‐identifiability. The consistency problems of several parameter estimation methods are also discussed. Copyright © 2008 John Wiley & Sons, Ltd.     

基于DEMD的高压隔膜泵单向阀早期故障诊断

针对高压隔膜泵单向阀的早期故障特征提取困难的问题,提出基于微分经验模态分解(differential empirical mode decomposition,简称DEMD)的高压隔膜泵单向阀早期故障诊断方法。首先,对振动信号进行微分运算,提高高频成分的振幅比,使微弱高频成分在后续分解中更易提取;其次,对得到的新信号进行经验模态分解(empirical mode decomposition,简称EMD),并将分解后的本征模函数(intrinsic mode function,简称IMF)分量信号进行积分还原;最后,计算分量信号与原振动信号的Kullback-Leibler散度(Kullback-Leibler divergence,简称K-L散度)值,选取K-L散度值较小的分量信号进行重构,并利用Hilbert边际谱对重构信号进行瞬时频谱分析,以提取故障振动信号的特征。仿真与工程实验分析表明,该方法能够较好地提取出单向阀早期故障特征信息。