Characterization of spherical particles using high-order neural networks and scanning flow cytometry

We retrieve the radius R, real n and imaginary k parts of the refractive index of homogeneous spherical particles using angular distribution of the light-scattering intensity. To solve the inverse light-scattering problem we use a high-order neural-network technique. The effect of network parameters on optimization is examined. The technique is evaluated for noise-corrupted input data at 0.6 μm<R<10.6 μm, 1.02<n<1.38, and 0<k<0.03. The errors of retrieval for nonabsorbing particles do not exceed 0.05 μm for radius and 0.015 for refractive index. The experimental verification is fulfilled by experimental data retrieved by means of a scanning flow cytometer. The light-scattering profiles of polystyrene beads and spherized red blood cells are processed with the high-order neural networks and a non-linear regression at Mie theory. The parameters retrieved by the high-order neural networks correlate well with the parameters retrieved by the least-square method.     

Psychoacoustics of in-car switch buttons: From feelings to engineering parameters

The car interior is becoming quieter and other sounds are now exposed to user perception, such as the sound produced by interface buttons when actuated. So, the functional role of the button sound on interface operation and its aesthetic and emotional role on the user experience are now more important than before. However, little research and design effort has been paid to understand how to design buttons that produce a pleasant sound. Moreover, the button’s sound requirements received by interface manufacturers are ill-defined, insufficient or even inexistent, and consequently their conversion into specifications for manufacturing is problematic and leads to long and costly development processes. The purpose of this paper is to contribute to identify relevant acoustic parameters that explain the users sound preferences. Data on preference subjective judgments were collected and buttons acoustic signals were measured allowing the development of preference models based on partial least squares regression and neural networks methods. The former was successful in selecting the relevant parameters to describe the preference ratings of the buttons sound. The later, dealing with the non-linear nature of acoustic perception, was able to predict preferences based on the relevant parameters.     

人工神经网络法校正ICP┐AES中重叠光谱干扰

本文将反向传播人工神经网络（ＢＰ－ＡＮＮ）用于ＩＣＰ－ＡＥＳ中重叠光谱干扰的校正。利用模拟的Ｃｅ４１３．３８０ｎｍ和Ｐｒ４１３．３６１ｎｍ光谱对神经网络的训练方式、输入值范围、噪声影响等作了较详细的讨论。     

A high-order numerical method for the nonlinear Helmholtz equation in multidimensional layered media

We present a novel computational methodology for solving the scalar nonlinear Helmholtz equation (NLH) that governs the propagation of laser light in Kerr dielectrics.     

Inevitable self-similar topology of binary trees and their diverse hierarchical density

Self-similar topology, which can be characterized as power law size distribution, has been found in diverse tree networks ranging from river networks to taxonomic trees. In this study, we find that the statistical self-similar topology is an inevitable consequence of any full binary tree organization. We show this by coding a binary tree as a unique bifurcation string. This coding scheme allows us to investigate trees over the realm from deterministic to entirely random trees. To obtain partial random trees, partial random perturbation is added to the deterministic trees by an operator similar to that used in genetic algorithms. Our analysis shows that the hierarchical density of binary trees is more diverse than has been described in earlier studies. We find that the connectivity structure of river networks is far from strict self-similar trees. On the other hand, organization of some social networks is close to deterministic supercritical trees.     

一种基于可见-近红外光谱快速鉴别茶叶品种的新方法

提出了一种用可见-近红外光谱技术快速无损鉴别茶叶品种的新方法。应用可见-近红外光谱仪测定5个品种茶叶的光谱曲线,用主成分分析法对不同品种茶叶进行聚类分析并获得茶叶的可见-近红外光谱数据的主成分,再结合人工神经网络技术建立模型进行品种鉴别。主成分分析表明,以主成分1和2对所有建模样本的得分值做出的得分图,对不同种类茶叶具有较好的聚类作用,可以定性分析茶叶种类。把主成分分析得到的前6个主成分作为神经网络的输入,茶叶品种值作为神经网络的输出,通过5个茶叶品种共125个样本的训练和学习,建立了茶叶品种鉴别的3层BP人工神经网络模型,对未知的25个样本进行鉴别,品种识别准确率达到100%。说明本文提出的方法具有很好的分类和鉴别作用,为茶叶的品种快速鉴别提供了一种新方法。     

基于彩色扫描仪的图像光谱重构

针对彩色扫描仪的特点,采用主元分析法(PCA)和反向传播(BP)人工神经网络(ANN)相结合的方法对图像光谱重构进行研究。选择IT8.7/2标准色卡作为训练样本,将该色卡中的另一组色靶作为检验样本以讨论不同网络结构以及不同主元数和训练样本数对光谱重构的影响,再以自然色系统(NCS)色卡为检验样本来分析不同种类的训练和检验样本与光谱重构性能的关系。实验结果表明,采用3-14-6网络结构和6个主元数是最佳选择,训练样本和扫描目标之间的一致性是基于彩色扫描仪图像光谱重构的关键所在。     

Single-electron charging spectra: from natural to artificial atoms

We present a theoretical study of the charging spectra in natural and artificial atoms. We apply a model electrostatic potential created by a homogenously charged sphere. This model potential allows for a continuous passage from the Coulomb potential of the nucleus to parabolic confinement potential of quantum dots. We consider electron systems with N=1,…,10 electrons with the use of the Hartree–Fock method. We discuss the qualitative similarities and differences between the chemical potential spectrum of electron systems bound to nucleus and confined in quantum dots.     

铅在铁路岩石边坡创面人工土壤中的迁移特性研究

山区大规模的铁路建设产生了大量的岩石边坡,前期研究表明边坡创面人工土壤受Pb污染,且有中等程度的富集。土壤类型、植被搭配类型是影响Pb迁移特性的重要因素,而目前却未见人工土壤及植被搭配类型对Pb迁移特性影响的相关报道。该研究以铁路岩石边坡创面人工土壤为对象,利用原子吸收分光光度法、红外光谱法分析了人工土壤对Pb的吸附-解吸特性及机制;运用野外人工模拟降雨实验,进一步考察在暴雨径流作用下,Pb在不同植被搭配类型的边坡人工土壤中的迁移过程。结果表明人工土壤对Pb的吸附量随着溶液平衡浓度的增加而急速增加,等温曲线呈“S”型,Freundlich方程能较好地描述人工土壤对Pb的吸附行为,R2为0.91。二次幂函数方程能较好描述NH₄AC对铅的解吸过程,R2为0.96。人工土壤有高岭石红外光谱特征吸收峰,属于高岭石型图谱。高岭石表面-OH、腐殖质表面-OH和-COOH参与吸附作用,置换出H+。人工土壤易将交通运输产生的Pb固定,但又容易再次释放。不同植被搭配类型边坡Pb流失量表现为草本＞草本+灌木＞草本+灌木+乔木。暴雨径流作用下Pb随沉积物相流失是边坡创面人工土壤Pb流失的主体,边坡土壤的侵蚀是导致Pb迁移扩散的主要原因,因此促进边坡植被的恢复,减小边坡人工土壤的侵蚀作用是防止Pb迁移扩散的关键。     

时频域分形维数分析的光谱信号重叠峰解析算法

由于光谱谱线存在自然展宽、多普勒展宽、碰撞展宽等,使混合气体中多种成分的吸收光谱信号出现相邻谱峰重叠现象,给混合气体组成成分的定性或定量检测带来较大的困难。现有的方法在获取先验知识、处理精度、运算效率等方面存在不足。提出基于时频域分形维数分析的光谱信号重叠峰解析算法,结合小波的多尺度观测能力和分形的自相似度的度量能力,识别、定位和解析光谱信号中的重叠峰。首先利用小波对具有重叠谱峰的光谱信号进行光谱频率域和尺度域的分析,然后对该时频域的光谱信号在同一光谱频率下的多尺度数据进行自相似性度量和分形计算。逐频率计算后得到光谱信号在频率域的分形维数曲线。该曲线体现了光谱信号在不同尺度的自相似性,其极值位置与光谱信号的各独立峰的位置具有相关性。依据此特性,结合分形曲线的特征参数,最后利用神经网络解析出对应混合气体成分的混叠在一起的各个独立谱峰。该方法利用小波的多分辨率特性,对信号进行不同尺度的精细度量。分形模型则提高了系统解析复杂信号的能力,对重叠程度高的多谱峰重叠信号也有很强的处理能力。借助人工神经网络,实现了整个算法的自动测量。通过实验结果分析,验证了算法的有效性,并讨论影响算法效果的主要因素。