Novel neural network based optimization approach for photonic devices

In this paper, a novel optimization technique is proposed for designing photonic devices. The suggested approach relies on the use of radial bases function based artificial neural network (RBF-ANN) which shows an excellent performance in comparison with the conventional artificial neural network technique. The robustness of the suggested RBF-ANN approach is demonstrated through the numerical precision and fast convergence of the design cycle performed on a typical slanted rib waveguide polarization rotator, and ultra-flattened zero dispersion photonic crystal fiber.     

基于RBF神经网络的强流LIA故障诊断与性能评价技术

 用于流体动力学诊断的强流LIA是庞大而复杂的系统,其性能预测和评估是十分困难的。针对强流LIA大量的单次快脉冲非平稳信号,提出基于小波包分析与RBF神经网络技术相结合实现故障智能诊断和性能评价的方法。该方法以强流LIA高维信号的小波包结点能量提取的特征向量来表征信号平顶、脉宽以及暂态特性。在此基础上,建立了“神龙一号”加速器腔电压及注入器出口束流故障诊断与性能评价原型系统,该系统不仅可进行故障诊断和性能评价,还可探测到加速器运行参数的变化趋势,为加速器的精细维护提供预测信息。     

基于径向基函数神经网络的荧光光谱技术在菌种识别中的应用

对嗜酸乳杆菌、变异链球菌和保加利亚乳杆菌这三种菌的荧光光谱进行研究,发现在紫外光的激励下,益生菌溶液发出荧光.在最佳激发波长290　nm的激励下,荧光峰值在300—650　nm范围内.采用小波变换对测得的150组光谱数据进行压缩,压缩后每组数据由原来的1341个点减少为168个点,既保留了原图谱的特征,又提高了神经网络的处理速度.径向基函数神经网络方法对压缩后的数据进行研究,对每种菌的40组实验数据进行训练,在此基础上对30组未知数据进行识别.结果表明经过训练之后,径向基函数神经网络能够准确预测未知菌种.     

基于径向基函数神经网络的荧光光谱技术在菌种识别中的应用

对嗜酸乳杆菌、变异链球菌和保加利亚乳杆菌这三种菌的荧光光谱进行研究,发现在紫外光的激励下,益生菌溶液发出荧光.在最佳激发波长290　nm的激励下,荧光峰值在300—650　nm范围内.采用小波变换对测得的150组光谱数据进行压缩,压缩后每组数据由原来的1341个点减少为168个点,既保留了原图谱的特征,又提高了神经网络的处理速度.径向基函数神经网络方法对压缩后的数据进行研究,对每种菌的40组实验数据进行训练,在此基础上对30组未知数据进行识别.结果表明经过训练之后,径向基函数神经网络能够准确预测未知菌种. 关键词： 荧光光谱 径向基函数神经网络 数据压缩     

不确定混沌系统的径向基函数神经网络反馈补偿控制

对不确定混沌系统控制问题, 研究了一种基于径向基函数神经网络(radial basis function neural network, RBFNN)的反馈补偿控制方法. 该方法首先用RBFNN对混沌系统的动力学特性进行学习, 然后用训练好的RBFNN模型对混沌系统进行反馈补偿控制. 该方法的特点是不需要被控混沌系统的数学模型,可以快速跟踪任意给定的参考信号. 数值仿真试验表明了该控制方法不仅具有响应速度快、控制精度高, 而且具有较强的抑制混沌系统参数摄动能力和抗干扰能力.     

Numerical analysis of multicore photonic crystal fibers

A Galerkin's method-based numerical procedure is extended to obtain the modal field distribution of multicore photonic crystal fibers for the first time to our knowledge, which can reveal how the air hole size influences the mode coupling and how the coupling strength varies with wavelength. These results will be helpful in the future design of multicore photonic crystal fibers with proper guidance properties.     

Loss analysis of air-core photonic crystal fibers

By using a multipole moment approach, we analyze the loss of an air-core photonic crystal fiber and demonstrate that it is possible reduce the transmission loss that is due to photon radiation leakage through the photonic crystal cladding to a level below 0.01 dB/km, with eight rings of air holes. An analogy is drawn between air-core photonic crystal fiber modes and Bragg fiber modes. The influence of material absorption in the silica glass is discussed.     

Supermode analysis of multicore photonic crystal fibers

We reported on the supermode selection of total-internal-reflection photonic crystal fibers (PCFs) with linearly and circularly distributed multicores. The supermode characteristics were investigated using a full-vector finite-element method (FEM). The near and far-field patterns of supermodes supported by the multicore photonic crystal fiber were presented. A Talbot cavity was employed to select the wanted supermode.     

Improved effective-index method for analysis of photonic crystal fibers

We propose, for the first time to our knowledge, an improved effective-index method (IEIM) that has the advantage of high speed and much better accuracy than the conventional effective-index method. The modal properties computed with the IEIM, such as effective index and dispersion, are closely matched to those of the multipole method as well as to experimental values.     

椭圆孔光子晶体光纤的本地正交函数模型

提出了一种用于分析椭圆孔光子晶体光纤的正交函数模型.发展了一种新型超格子的构造方法，将光子晶体光纤的横向介电常数表示为两种周期性结构叠加，这两种周期性结构分别用余弦函数展开；同时将横向电场以Hermite-Gaussian函数展开.利用正交函数的性质，将全矢量波动方程转化为矩阵本征值问题，求得两偏振模式传输常数.利用此模型可以研究圆孔及椭圆孔光子晶体光纤的模式特性、色散特性、偏振特性等. 关键词： 光子晶体光纤 超格子 正交函数 本征值问题     

Integrating liquid crystal based optical devices in photonic crystal fibers

Liquid crystal photonic bandgap fibers form a versatile and robust platform for designing optical fiber devices, which are highly tunable and exhibit novel optical properties for manipulation of guided light. We present fiber devices for spectral filtering and polarization control/analysis.     

七芯光子晶体光纤结构优化的数值分析

从耦合模方程出发,推导了描述七芯光子晶体光纤模场特性的本征方程,得到了这种结构光纤的本征值和本征矢.使用频域有限差分法数值模拟,通过改变传输波长、纤芯间距、包层孔大小分析了纤芯之间的强度分布与耦合系数的关系,分别实现了模式整形和高阶超模截止.掌握了具有强耦合特性的七芯光子晶体光纤设计规律,和这种结构光子晶体光纤的同相位超模的选取方法. 关键词： 光子晶体光纤 大模场面积光纤 多芯结构 频域有限差分法     

基于光子晶体光纤的弱压力传感器研究

利用有限元法仿真了光子晶体光纤的双折射与横向压力之间的关系,研究弱压力条件下光子晶体光栅的特性。虽然光子晶体光纤比传统的阶跃光纤对压力更为敏感,但弱压力导致的双折射变化很小,应用测量布拉格波长的变化测量压力的变化比较困难。采用偏振相关损耗来测量外界微弱压力导致的双折射变化。仿真结果表明：利用偏振相关损耗的方法,测量灵敏度可达0.75dB/MPa。     

冬小麦导数光谱特征提取与缺磷胁迫神经网络诊断

分别于返青期、拔节期、抽穗期和灌浆期采集不同磷素处理的冬小麦叶片原始高光谱数据;之后求取其一阶导数(一阶导数光谱)并进行小波去噪处理;通过分析原始光谱和一阶导数光谱对小同磷素处理水平的响应特征,确定敏感波长范围并提取四种吸收面积;将每个叶片磷素含量值对应的四种吸收而积的归一化值,作为样本空间样本点的位置坐标(4维样本输...     

Microfluidic integration of photonic crystal fibers for online photochemical reaction analysis

Liquid-filled hollow-core photonic crystal fibers (HC-PCFs) are perfect optofluidic channels, uniquely providing low-loss optical guidance in a liquid medium. As a result, the overlap of the dissolved specimen and the intense light field in the micronsized core is increased manyfold compared to conventional bioanalytical techniques, facilitating highly-efficient photoactivation processes. Here we introduce a novel integrated analytical technology for photochemistry by microfluidic coupling of a HC-PCF nanoflow reactor to supplementary detection devices. Applying a continuous flow through the fiber, we deliver photochemical reaction products to a mass spectrometer in an online and hence rapid fashion, which is highly advantageous over conventional cuvette-based approaches.     

一种新型的多包层光子晶体光纤的分析方法

传统有效折射率方法只能模拟具有相同空气孔大小的单包层光子晶体光纤.针对这一问题,提出了一种改进的有效折射率法,能够计算具有不同孔径的多包层光子晶体光纤的传输特性.并用此方法对三包层光子晶体光纤的基模有效折射率和色散等特性进行了数值模拟,结果与多极法模拟出的结果符合得很好.这种改进的有效折射率法拓展了传统有效折射率法的适用范围,对快速准确地分析和设计具有不同传输特性的多包层光子晶体光纤提供了理论依据. 关键词： 光子晶体光纤 多包层 有效折射率方法 色散     

A neural network model of erbium-doped photonic crystal fibre amplifiers

The evaluation of the general evolution equations that describe the longitudinal propagation of pump, signal, forward and backward amplified spontaneous emission in rare-earth-doped optical fibre amplifier could be computationally expensive. In this paper, to reduce the computational time, a neural network approach for the modeling of erbium-doped photonic crystal fibre amplifiers is proposed. A number of simulations have been performed to investigate the characteristics of the proposed approach. The numerical results show good agreement between the neural network approach and the conventional algorithm based on the solution of the power evolution equations. The proposed approach exhibits attractive performance in terms of flexibility, accuracy and computational time.     

Optical refractometer based on large-core air-clad photonic crystal fibers

A large-core air-clad photonic crystal fiber-based sensing structure is described, which is sensitive to refractive index. The sensing head is based on multimodal interference, and relies on a single-mode/large-core air-clad photonic crystal fiber (PCF)/single-mode fiber configuration. Using two distinct large-core air-clad PCF geometries-one for refractive index measurement and the other for temperature compensation, it was possible to implement a sensing head sensitive to refractive index changes in water as induced by temperature variations. The results indicated the high sensitivity of this sensing head to refractive index variations of water, and a resolution of 3.4×10(-5) refractive index units could be achieved.     

Generalized unscented Kalman filtering based radial basis function neural network for the prediction of ground radioactivity time series with missing data

On the assumption that random interruptions in the observation process are modeled by a sequence of independent Bernoulli random variables, we firstly generalize two kinds of nonlinear filtering methods with random interruption failures in the observation based on the extended Kalman filtering (EKF) and the unscented Kalman filtering (UKF), which were shortened as GEKF and GUKF in this paper, respectively. Then the nonlinear filtering model is established by using the radial basis function neural network (RBFNN) prototypes and the network weights as state equation and the output of RBFNN to present the observation equation. Finally, we take the filtering problem under missing observed data as a special case of nonlinear filtering with random intermittent failures by setting each missing data to be zero without needing to pre-estimate the missing data, and use the GEKF-based RBFNN and the GUKF-based RBFNN to predict the ground radioactivity time series with missing data. Experimental results demonstrate that the prediction results of GUKF-based RBFNN accord well with the real ground radioactivity time series while the prediction results of GEKF-based RBFNN are divergent.     

A statistical geoacoustic inversion scheme based on a modified radial basis functions neural network

This paper addresses the task of recovering the geoacoustic parameters of a shallow-water environment using measurements of the acoustic field due to a known source and a neural network based inversion process. First, a novel efficient "observable" of the acoustic signal is proposed, which represents the signal in accordance with the recoverable parameters. Motivated by recent studies in non-Gaussian statistical theory, the observable is defined as a set of estimated model parameters of the alpha-stable distributions, which fit the marginal statistics of the wavelet subband coefficients, obtained after the transformation of the original signal via a one-dimensional wavelet decomposition. Following the modeling process to extract the observables as features, a radial basis functions neural network is employed to approximate the vector function that takes as input the observables and gives as output the corresponding set of environmental parameters. The performance of the proposed approach in recovering the sound speed and density in the substrate of a typical shallow-water environment is evaluated using a database of synthetic acoustic signals, generated by means of a normal-mode acoustic propagation algorithm.