空间关联白噪声影响下小世界神经元网络系统的同步动力学

以电耦合的Terman-Wang小世界神经元网络系统为研究对象, 研究了空间关联白噪声影响下神经元网络系统的同步动力学. 首先将动力学平均场近似理论扩展到受空间关联白噪声影响下的小世界网络系统中, 将描述网络系统动力学演化的2N维随机微分方程简化为11个确定性的矩微分方程. 其次, 基于动力学平均场近似理论所推导的矩方程, 讨论了空间关联噪声、网络结构参数对神经元网络系统同步动力学的关键影响, 发现较大的噪声空间关联系数、耦合强度及节点平均度均对神经元网络系统同步放电具有积极作用. 进一步地, 利用计算机仿真数值模拟原神经元网络系统的同步动力学, 并与基于动力学平均场近似理论所得到的结果进行比较, 发现二者具有较好的一致性.     

Variational Solution of Stochastic Schrödinger Equations With Power-Type Nonlinearity

We investigate a Schrödinger problem with multiplicative Gaussian noise term and power-type nonlinearity on a bounded one-dimensional domain. In order to prove the existence and uniqueness of the variational solution, a further process will be introduced which allows to transform the stochastic nonlinear Schrödinger problem into a pathwise one. Galerkin approximations and compact embedding results are used.     

光学频率梳非线性传输及其在相位噪声探测中的应用

光纤传递链路中相位噪声探测是基于光纤的精确射频标准传递系统中重要组成部分。为解决光纤链路相位噪声探测中节点反射干扰的问题, 本文基于广义非线性薛定谔方程, 理论上研究了窄带光学频率梳在色散位移光纤中的非线性光学传播特性。在理论分析的基础上, 结合密集波分复用架构, 提出了一种基于锁模光学频率梳进行光纤传递射频信号的相位噪声探测新技术, 克服了节点反射干扰, 给出了初步实验验证。     

Toward automatic evaluation of defect detectability in infrared images of composites and honeycomb structures

Non-destructive testing (NDT) refers to inspection methods employed to assess a material specimen without impairing its future usefulness. An important type of these methods is infrared (IR) for NDT (IRNDT), which employs the heat emitted by bodies/objects to rapidly and noninvasively inspect wide surfaces and to find specific defects such as delaminations, cracks, voids, and discontinuities in materials. Current advancements in sensor technology for IRNDT generate great amounts of image sequences. These data require further processing to determine the integrity of objects. Processing techniques for IRNDT data implicitly looks for defect visibility enhancement. Commonly, IRNDT community employs signal to noise ratio (SNR) to measure defect visibility. Nonetheless, current applications of SNR are local, thereby overseeing spatial information, and depend on a-priori knowledge of defect’s location. In this paper, we present a general framework to assess defect detectability based on SNR maps derived from processed IR images. The joint use of image segmentation procedures along with algorithms for filling regions of interest (ROI) estimates a reference background to compute SNR maps. Our main contributions are: (i) a method to compute SNR maps that takes into account spatial variation and are independent of a-priori knowledge of defect location in the sample, (ii) spatial background analysis in processed images, and (iii) semi-automatic calculation of segmentation algorithm parameters. We test our approach in carbon fiber and honeycomb samples with complex geometries and defects with different sizes and depths.     

How Many Molecules are Required to Obtain a Steady Faradaic Current from Mediated Electron Transfer at a Single Nanoparticle on a Supporting Surface?

We investigate the chronoamperometric noise characteristics of electron‐transfer reactions occurring on single nanoparticles (NPs) and assemblies of well‐separated NPs on a supporting surface. To this end, we combine a formerly described expression for the steady‐state current of a single particle with the shot‐noise model and derive an expression for the signal‐to‐noise ratio as a function of bulk concentration and particle radius. Our findings are supported by random‐walk simulations, which closely match the analytical results.     

Wavelet transforms in separation science for denoising and peak overlap detection

Wavelet transform is a versatile time‐frequency analysis technique, which allows localization of useful signals in time or space and separates them from noise. The detector output from any analytical instrument is mathematically equivalent to a digital image. Signals obtained in chemical separations that vary in time (e.g., high‐performance liquid chromatography) or space (e.g., planar chromatography) are amenable to wavelet analysis. This article gives an overview of wavelet analysis, and graphically explains all the relevant concepts. Continuous wavelet transform and discrete wavelet transform concepts are pictorially explained along with their chromatographic applications. An example is shown for qualitative peak overlap detection in a noisy chromatogram using continuous wavelet transform. The concept of signal decomposition, denoising, and then signal reconstruction is graphically discussed for discrete wavelet transform. All the digital filters in chromatographic instruments used today potentially broaden and distort narrow peaks. Finally, a low signal‐to‐noise ratio chromatogram is denoised using the procedure. Significant gains (>tenfold) in signal‐to‐noise ratio are shown with wavelet analysis. Peaks that were not initially visible were recovered with good accuracy. Since discrete wavelet transform denoising analysis applies to any detector used in separation science, researchers should strongly consider using wavelets for their research.     

介观化学体系中的动力学尺度效应

以生命和表面催化体系为对象,研究了介观化学体系中内涨落对体系非线性动力学行为的调控作用。内涨落可以诱导随机振荡,其强度在体系处于最佳尺度时会出现一个甚至多个极大值,并且在耦合体系中会得到进一步增强,表现为尺度共振效应、尺度选择效应和双重尺度效应,揭示了介观化学体系中尺度效应的新机制。     

水下对转螺旋桨流致辐射噪声机理与预报方法*

水下对转螺旋桨流致辐射噪声的预报对于水下目标的特征提取和分类识别具有重要意义。由桨叶的旋转引起的湍流场是水下对转螺旋桨流致辐射噪声的源场。分述了水下对转螺旋桨湍流边界层脉动、旋转干涉效应和空化效应引发的水动力噪声机制和研究进展,比较了目前工程应用中的3种对转螺旋桨流致辐射噪声预报方法的特点。在分析对转螺旋桨流致辐射噪声数值预报难点的基础上,综述了对转螺旋桨流致辐射噪声计算方法的研究进展,指出间接数值模拟方法是工程中进行对转螺旋桨流致辐射噪声预报的有效方法。     

风力机叶片气动噪声的影响参数*

随着风力机的大型化,风电机组对环境的影响不容忽视,必须对风力机气动噪声进行预测和控制。选取基于NACA、DU翼型的某风力机叶片作为研究基准,采用修正BPM半经验模型计算叶片的气动噪声特性,通过改变翼型族、弦长、机组运行状态、风切变指数、来流风向参数,研究叶片外形几何参数、机组运行工况对叶片气动噪声源的影响。计算结果从多个角度总结出水平轴风力机叶片气动噪声的变化规律,为开发高效低噪风电叶片提供参考。