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.     

Particle identification using artificial neural networks at BESⅢ

A multilayered perceptrons' neural network technique has been applied in the particle identification at BESⅢ. The networks are trained in each sub-detector level. The NN output of sub-detectors can be sent to a sequential network or be constructed as PDFs for a likelihood. Good muon-ID, electron-ID and hadron-ID are obtained from the networks by using the simulated Monte Carlo samples.     

Particle sizing by time-resolved Mie calculations—A numerical study

If a small transparent particle is illuminated with a short laser pulse, the signals of the individual scattering light orders appear temporally successively. Since to each scattered light order belongs a specific optical path through the particle, the particle size can be determined from the time difference between the detected scattered light signals. For the case of a detector position within the backscatter region, which especially is important in measuring practice, the time difference between the specular reflection signal and the signal after a single internal reflection (refraction of second order) must to be evaluated. In the numerical simulation we generate the concerned scattered light signals by using time-resolved Mie calculations and in this paper we present the geometrical models, which permit a correct interpretation of the temporal behavior of these pulse-induced scattered light signals.     

Improved holographic particle sizing by using absolute values of the wavelet transform

A new method for sizing particle from in-line particle holograms by using absolute values of the wavelet transform is proposed in order to improve accuracy in measurements. The proposed method provides direct calculation of the particle size by using spatial frequency information of a chirp signal at minima position of an envelope function. Simulation and experimental results are presented.     

Particle sizing by ultrashort laser pulses – numerical simulation

This paper contains the results of our numerical investigations into particle sizing by analysis the time-dependent formation of the scattered light. We use an extended Mie theory for calculation the differences in time between the signals of reflection and higher order of refraction. The corresponding optical path lengths of light rays are computed by the principles of geometrical optics. By using a Debye series expansion it is possible to take into account single orders of scattered light. In detail we demonstrate the pulse-induced generation of scattered light for the refraction of first and second order as function of the detection angle.     

Chaotic dynamics of high-order neural networks

The dynamics of an extremely diluted neural network with high-order synapses acting as corrections to the Hopfield model is investigated. The learning rules for the high-order connections contain mixing of memories, different from all the previous generalizations of the Hopfield model. The dynamics may display fixed points or periodic and chaotic orbits, depending on the weight of the high-order connections , the noise levelT, and the network load, defined as the ratio between the number of stored patterns and the mean connectivity per neuron, =P/C. As in the related fully connected case, there is an optimal value of the weight that improves the storage capacity of the system (the capacity diverges).     

Partially connected feedforward neural networks on Apollonian networks

This paper presents a novel and data-independent method to construct a type of partially connected feedforward neural network (FNN). The proposed networks, called Apollonian network-based partially connected FNNs (APFNNs), are constructed in terms of the structures of two-dimensional deterministic Apollonian networks. The APFNNs are then applied in various experiments to solve function approximation, forecasting and classification problems. Their results are compared with those generated by partially connected FNNs with random connectivity (RPFNNs), different learning algorithm-based traditional FNNs and other benchmark methods. The results demonstrate that the proposed APFNNs have a good capacity to fit complicated input and output relations, and provide better generalization performance than traditional FNNs and RPFNNs. The APFNNs also demonstrate faster training speed in each epoch than traditional FNNs.     

Particle sizing and velocity measurement of microspheres from the analysis of polarization of the scattered light

A brief review of the existing particle sizing methods is presented. An optical method under development is introduced from the analysis of the polarization ratio of the light scattered by the particles based on Lorenz-Mie theory. The theoretical background is summarized with the numerical calculation presented. A photogrammteric system has been set up to perform the measurements. Calibration of the experimental setup has been carried out on polystyrene microspheres of different size. The experimental values of the polarization ratio have been obtained by analyzing the particle images taken by the CCD to render the particle size under investigation. Several experiments and their results are demonstrated to illustrate the application fields of the optical method presented in the current study.     

Sizing of spheroidal and cylindrical particles in a binary mixture by measurement of scattered light intensity: application of neural networks

The problem on the retrieval of sizes of an individual optically soft particle taken from binary mixtures of either oblate and prolate spheroids or cylinders and oblate spheroids is considered. It is based on multiangle scattered light intensity data. The multilevel neural networks method with a linear activation function and the method of the discrimination functions are used. Neural networks to retrieve characteristics of cylinders, oblate and prolate spheroids are designed. The errors in retrieved particle characteristics are investigated for the radius of an equivolume sphere in the range of 0.3-, shape parameter of spheroidal and cylindrical particles from -0.5 to 0.5 and 0 to 0.5, respectively.     

利用色散补偿白光相关器及高通滤波技术检测粒子大小

本文提出了一种可用以检测相同形状粒子的大小、具色散补偿作用的白光相关系统,并完成了理论分析及实验验证.通过自动扫描光谱强度(位于固定点的相关峰的)分布,可方便地确定输入物的几何标度.本系统与高通滤波技术相结合,可检测圆环状粒子的大小,其相对误差的精度<2%.     

Artificial neural networks applied to the analysis of synchrotron nuclear resonant scattering data

The capabilities of artificial neural networks (ANNs) have been investigated for the analysis of nuclear resonant scattering (NRS) data obtained at a synchrotron source. The major advantage of ANNs over conventional analysis methods is that, after an initial training phase, the analysis is fully automatic and practically instantaneous, which allows for a direct intervention of the experimentalist on‐site. This is particularly interesting for NRS experiments, where large amounts of data are obtained in very short time intervals and where the conventional analysis method may become quite time‐consuming and complicated. To test the capability of ANNs for the automation of the NRS data analysis, a neural network was trained and applied to the specific case of an Fe/Cr multilayer. It was shown how the hyperfine field parameters of the system could be extracted from the experimental NRS spectra. The reliability and accuracy of the ANN was verified by comparing the output of the network with the results obtained by conventional data analysis.     

Study of particle tracking algorithms based on neural networks for stereoscopic tracking velocimetry

Stereoscopic-tracking velocimetry can offer an excellent potential for continuously monitoring three-dimensional flow fields for all three-component velocities in near-real-time. Requiring only the deployment of two solid-state cameras with directional freedom in test-section illumination and observation, the system can be built to be compact and robust. For flow velocimetry measurement, increasing the number density in particle seeding is much desirable for maximizing spatial resolution, that is, number of velocity data points of the captured field. The challenge, however, is how to successfully track numerous crisscrossing particles with speed and reliability. In our approach, the task of particle tracking is converted to an optimization problem for which neural networks are applied. Here we present the design and development of the neural networks for particle tracking as well as the investigation results based on both computer simulations and real experiments. The approach appears to be appropriate for near-real-time velocity monitoring, being able to provide reliable solutions achieved by the massive parallel-processing power of the neural networks.     

Characterization of optically soft spheroidal particles by multiangle light-scattering data by use of the neural-networks method

A method for evaluating the size of optically soft spheroidal particles by use of the angular structure of scattered light is proposed. It is based on the use of multilevel neural networks with a linear activation function. The retrieval errors of radius R of the equivolume sphere and aspect ratio e are investigated. The ranges of the size of R, e, and the refractive index are 0.3-1.51 microns, 0.2-1, and 1.01-1.02, respectively. The retrieval errors of the equivolume radius and aspect ratio are 0.004 micron and 0.02, respectively, for a three-level neural network (at a precisely measured angular distribution of scattered light). The retrieval errors of R and e for a one-level neural network are 2-5 times greater. The errors for a multilevel neural network increase faster than those for a single-level network.     

Effect of damage in neural networks

The effect of damage on the pattern recognition in the Hopfield-model of neural networks is studied. It is assumed that in a damaged network the synaptic efficaciesJ _i,j=J_j,i, between pairs of neuronsS _i andS _j follow the Hebb rule with probability (1–p) and are equal to zero with probabilityp. Numerical simulations are performed for a net consisting of 400 neurons. It is investigated in detail how the retrieval of noisy patterns and the storage capacity of the net depends, for varying initial noise, on the concentrationp of the damaged synaptic efficacies.     

Prediction of airblast-overpressure induced by blasting using a hybrid artificial neural network and particle swarm optimization

Blasting is an inseparable part of the rock fragmentation process in hard rock mining. As an adverse and undesirable effect of blasting on surrounding areas, airblast-overpressure (AOp) is constantly considered by blast designers. AOp may impact the human and structures in adjacent to blasting area. Consequently, many attempts have been made to establish empirical correlations to predict and subsequently control the AOp. However, current correlations only investigate a few influential parameters, whereas there are many parameters in producing AOp. As a powerful function approximations, artificial neural networks (ANNs) can be utilized to simulate AOp. This paper presents a new approach based on hybrid ANN and particle swarm optimization (PSO) algorithm to predict AOp in quarry blasting. For this purpose, AOp and influential parameters were recorded from 62 blast operations in four granite quarry sites in Malaysia. Several models were trained and tested using collected data to determine the optimum model in which each model involved nine inputs, including the most influential parameters on AOp. In addition, two series of site factors were obtained using the power regression analyses. Findings show that presented PSO-based ANN model performs well in predicting the AOp. Hence, to compare the prediction performance of the PSO-based ANN model, the AOp was predicted using the current and proposed formulas. The training correlation coefficient equals to 0.94 suggests that the PSO-based ANN model outperforms the other predictive models.     

粉末涂料电脑配色的人工神经网络模型

提出一种基于多层BP人工神经网络的粉末涂料配方预测模型;用BP算法人工神经网络建立粉末涂料反射样品的标准色度参数与配方浓度参数之间的映射关系。把人工神经网络的配方预测模型应用到典型的粉末涂料样品的测配色实验过程中。实验结果表明,基于多隐层BP网的模型可以实现粉末涂料样品的配方浓度空间与标准三刺激值颜色空间的相互映射,对64个节点的平均训练精度达到了1个CIELAB色差单位。     

单光束互相关法测量高浓度纳米颗粒粒径

针对光子相关光谱法不能测量高浓度纳米颗粒粒径和双光束互相关测量法装置结构过于复杂等问题,提出了一种基于范西特-泽尼克定理的单光束互相关法。首先分析了传统双光束互相关法存在的问题,然后根据范西特-泽尼克定理建立了单光束互相关测量法的模型,设计完成了单光束互相关颗粒粒度测量装置,最后对各种浓度不同粒径的颗粒进行了测量。实验证明,单光束互相关法能有效抑制多重散射的影响,适用于测量高浓度纳米颗粒粒径。     

时滞离散神经网络的同步控制

利用既有效又便于实施的时滞状态反馈控制器,根据所给定的条件构造相应的不等式,研究了带有时滞的离散神经网络模型的同步控制问题,给出了该离散系统指数同步的充分条件.在设计同步控制的时候,没有假设激励函数的有界性、可微性和单调性,给出的条件简便易实施.数值结果进一步证明了该控制方法的有效性. 关键词： 离散神经网络 时滞 同步     

变参数混沌时间序列的神经网络预测研究

研究一类复杂变参数混沌系统时间序列的预测问题.首先构造一个变参数Logistic映射,分析变参数混沌系统的特点,指出动力学特征不断变化的这类系统不存在恒定形状的吸引子;结合Takens嵌入定理和神经网络理论,阐述神经网络方法预测具有恒定吸引子形状的混沌系统可行的原因,分析研究其用于预测变参数混沌系统的潜在问题.变参数Ikeda系统的神经网络预测试验验证了理论分析结果,试验还表明,简单增大预测训练样本数可能降低泛化预测精度,训练集的选择对这类系统的泛化预测效果影响极大,指出混沌时间序列预测实用化必须研究解决这类变参数混沌系统的预测. 关键词： 混沌 预测 神经网络 变参数系统     

Learning and forgetting on asymmetric,diluted neural networks

It is possible to construct diluted asymmetric models of neural networks for which the dynamics can be calculated exactly. We test several learning schemes, in particular, models for which the values of the synapses remain bounded and depend on the history. Our analytical results on the relative efficiencies of the various learning schemes are qualitatively similar to the corresponding ones obtained numerically on fully connected symmetric networks.