热图法测量激光强度时空分布的重构理论研究

为了把量热法应用于远场激光强度时空分布测量,研究了基于热像仪靶面温度测量反演入射激光强度时空分布的重构理论。针对背光面两种不同边界条件(对流-辐射热流边界和恒定温度边界)推导出了由靶面温度分布反演激光束时空分布的重构表达式。获得的分析表达式对广泛的材料具有适用性。通过引入广义参量F0=α/L2,分别就F0》1和F0《1情况给出了重构近似表达式,并对满足F0》1条件的回推算法进行了数值模拟验证。数值结果表明,两种背光面边界条件下回推得到的激光束时空分布与原始激光束达到了很好的一致,但存在一与靶材傅里叶数相关的最小起始回推时间τ0。成果可用于强激光远场参量测量设备的研制。     

空间有源消声实验研究

本文分别在半声室和普通房间进行有源消声实验，证明了作者以前提出的算法是收敛的，稳定的，以此算法构成的多通道有源消声系统对直达声和混响声都能进行了有效的控制。     

粗糙热传导表面下激光介质温度场的计算分析

提出了一种计算温度场的面热源自适应调整算法,该方法通过在研究对象的封闭边界上引入虚拟面热源来求解稳态热传导方程的边值问题。建立了导热联结的具有粗糙表面介质的接触模型,在此基础上建立了随机分布的表面散热边界条件,并用面热源自适应调整算法计算了激光介质的温度场。结果表明,由于实际激光介质散热表面不能完全紧密接触,其温度场呈现一定程度的随机起伏,越靠近边界,随机起伏越明显,介质中心区域随机起伏则不明显;有效热接触面积越小,这种随机起伏越强烈。计算表明热接触面积占50%时,比接触面积占75%时温度场的随机性更明显。     

集成模块化Cantor网络的控制和光学实现

提出光学集成模块化Ｃａｎｔｏｒ网络，并建立了Ｃａｎｔｏｒ网络的快速控制体系，该网络是基于极化多通道技术，利用晶体的双折射效应和电光特性实现网络的互连变换，该网络易安装，抗干扰。     

Analysis and implementation of cross sync period underwater acoustical positioning techniques for underwater targets

I.IlltroductionWhenanactiveunderwateracousticalpositioningsystemissearchingforanunderwatertarget,itisnecessaryforittocompleteatransmittingandreceivingcycIewithineachworkingperiodinordertofindoutthelocationofthetargetcorrectly.Forexample,whenanunder-wateracoustica1synchronouspositioningsystemistrackinganunderwatertargetwithagivensynchr0nizingperiod,thepropagati0ntimeofthepositioningsignaltravellingfr0mthetaJrgettothearrayofthesystemshouldbelessthanthesynchronizingperiod0therwisetargetdis-tance…     

阻尼最小二乘算法CARS光谱温度拟合

为了解决相干反斯托克斯喇曼散射（CARS）光谱CARSFT计算程序在温度拟合过程中,对初值依赖大、测量光谱信噪比差、不易收敛的问题,通过分析非线性迭代算法,采用了阻尼最小二乘算法对氮气CARS光谱进行温度拟合。针对CARS理论光谱计算公式较为复杂的特点,采用数值差分的方式来获得迭代计算时所需要的设计矩阵。分析了横向偏移、纵向偏移和温度三个主要参量对拟合残差的影响,发现纵向偏移的初值设置对温度拟合影响较大。拟合温度2000 K时的标准理论CARS光谱,设置偏离较大的初始值,采用阻尼最小二乘法获得了较好的结果。迭代计算167步后,温度拟合为2 005.6 K,残差为0.027 463。拟合光谱信噪比较差的CARS光谱,阻尼最小二乘法也能稳定收敛。结果验证了阻尼最小二乘法对初值的依赖不大,并且当拟合谱信噪比较差时也能收敛,可用于在恶劣环境下CARS测量光谱的拟合。     

自适应模拟退火遗传算法在薄膜厚度及光学常数反演中的应用

准确的测量薄膜的厚度和光学常数,在薄膜的制备、研究和应用中都是十分重要的。借助Cauchy色散模型,通过薄膜透过率测量曲线,用改进的自适应模拟退火遗传算法对透过率曲线进行全光谱拟合,从而反演得到薄膜的厚度和光学常数。对由电子束蒸发制备的TiO2单层膜和SiO2/TiO2双层膜的厚度和光学常数进行了测量计算。实验结果表明,计算得到的光学参数与实测结果相一致,厚度误差小于2nm,在560nm波长处折射率误差小于0.03。     

Design of wideband graded-index antireflection coatings at oblique light incidence

We suggest a design method of graded-refractive-index (GRIN) antireflection (AR) coating for s-polarized or p-polarized light at off-normal incidence. The spectrum characteristic of the designed antireflection coating with a quintic effective refractive-index profile for a given state of polarization has been discussed. In addition, the genetic algorithm was used to optimize the refractive index profile of the GRIN antireflection for reducing the mean reflectance of s- and p-polarizations. The average reflectance loss was reduced to only 0.04% by applying optimized GRIN AR coatings onto BK7 glass over the wavelength range from 400 to 800 nm at the incident angle of θ₀ =70°.     

Genetic algorithm-support vector regression for high reliability SHM system based on FBG sensor network

Structural Health Monitoring (SHM) based on fiber Bragg grating (FBG) sensor network has attracted considerable attention in recent years. However, FBG sensor network is embedded or glued in the structure simply with series or parallel. In this case, if optic fiber sensors or fiber nodes fail, the fiber sensors cannot be sensed behind the failure point. Therefore, for improving the survivability of the FBG-based sensor system in the SHM, it is necessary to build high reliability FBG sensor network for the SHM engineering application. In this study, a model reconstruction soft computing recognition algorithm based on genetic algorithm-support vector regression (GA-SVR) is proposed to achieve the reliability of the FBG-based sensor system. Furthermore, an 8-point FBG sensor system is experimented in an aircraft wing box. The external loading damage position prediction is an important subject for SHM system; as an example, different failure modes are selected to demonstrate the SHM system's survivability of the FBG-based sensor network. Simultaneously, the results are compared with the non-reconstruct model based on GA-SVR in each failure mode. Results show that the proposed model reconstruction algorithm based on GA-SVR can still keep the predicting precision when partial sensors failure in the SHM system; thus a highly reliable sensor network for the SHM system is facilitated without introducing extra component and noise.     

Initial guess by improved population-based intelligent algorithms for large inter-frame deformation measurement using digital image correlation

Digital image correlation (DIC) has received a widespread research and application in experimental mechanics. In DIC, the performance of subpixel registration algorithm (e.g., Newton-Raphson method, quasi-Newton method) relies heavily on the initial guess of deformation. In the case of small inter-frame deformation, the initial guess could be found by simple search scheme, the coarse-fine search for instance. While for large inter-frame deformation, it is difficult for simple search scheme to robustly estimate displacement parameters and deformation parameters simultaneously with low computational cost. In this paper, we proposed three improving strategies, i.e. Q-stage evolutionary strategy (T), parameter control strategy (C) and space expanding strategy (E), and then combined them into three population-based intelligent algorithms (PIAs), i.e. genetic algorithm (GA), differential evolution (DE) and particle swarm optimization (PSO), and finally derived eighteen different algorithms to calculate the initial guess for qN. The eighteen algorithms were compared in three sets of experiments including large rigid body translation, finite uniaxial strain and large rigid body rotation, and the results showed the effectiveness of proposed improving strategies. Among all compared algorithms, DE-TCE is the best which is robust, convenient and efficient for large inter-frame deformation measurement.