The application of artificial neural networks to the inversion of the positron lifetime spectrum

A new method of processing positron annihilation lifetime spectra is proposed.It is based on an artificial neural network(ANN)-back propagation network(BPN).By using data from simulated positron lifetime spectra which are generated by a simulation program and tested by other analysis programs,the BPN can be trained to extract lifetime and intensity from a positron annihilation lifetime spectrum as an input.In principle,the method has the potential to unfold an unknown number of lifetimes and their intensities from a measured spectrum.So far,only a proof-of-principle type preliminary investigation was made by unfolding three or four discrete lifetimes.The present study aims to design the network.Besides,the performance of this method requires both the accurate design of the BPN structure and a long training time.In addition,the performance of the method in practical applications is dependent on the quality of the simulation model.However,the chances of satisfying the above criteria appear to be high.When appropriately developed,a trained network could be a very efficient alternative to the existing methods,with a very short identification time.We have used the artificial neural network codes to analyze data such as the positron lifetime spectra for single crystal materials and monocrystalline silicon.Some meaningful results are obtained.     

Electro-holographic artificial neural networks

A new approach for implementing artificial neural networks (ANNs), based ona novel concept in volume holographic storage-electroholography (EH)-is described. EH is based on the use of paraelectric photorefractive crystals in which the photorefractive mechanism is created in the paraelectric phase. Thus the quadratic electrooptic effect is used to transform the information carrying space charge fields into birefringence gratings, and therefore the diffraction efficiency is governed by an external electric field. This principle enables the construction of a page oriented architecture in which each neuron is implemented as a local electronic circuit, governing the diffraction efficiency of a small volume hologram representing the axon of the neuron. The EH approach is expected to enable the construction of networks with 10⁵−10⁶ neurons, each with 10³−10⁴ synapses.     

Estimation and interpolation of underwater low frequency ambient noise spectrum using artificial neural networks

In this work, estimation of ambient noise spectrum influenced by wind speed and wave height carried out for the frequency range of 500 Hz to 5 kHz using Feed forward Neural Network (FNN) is presented. Ocean ambient noise measurements were made in the shallow waters of Bay of Bengal using a portable data acquisition system with a high sensitivity hydrophone at a depth of 5 m from the surface.100 sets of data covering a rage of wind speeds from 2.5 m/s to 8.5 m/s with approximately 15 sets of data falling within 1 m/s over the range of wind speed were used for training the FNN. The parameter wave height which contributes to the noise producing mechanism is also used for training along with wind speed. The results revealed that the proposed method is useful in the estimation and interpolation of underwater noise spectrum level and hence in simulation for the considered frequency range. These were confirmed by calculating the Mean Squared Error (MSE) between the experimental data and the simulation. As the measurements of the underwater ambient noise level are very difficult in remote oceanic regions, where conditions are often inhospitable, these studies seem to be relevant.     

荧光分析法在血糖检测中的应用

用不同波长的光激发葡萄糖溶液和血清样品,确定血清的最佳激发波长。以相同波长的光激发含不同血糖浓度的血清样品,得到不同血清的荧光发射光谱,通过分析其荧光光谱判断血清中的血糖浓度大小。实验结果表明,检测血清的最佳激发波长为340nm,血清中葡萄糖的发射峰位置为470nm;随着血清中葡萄糖浓度的增加,荧光光谱图中荧光峰处荧光强度总体呈上升趋势,光谱的面积积分强度和导数光谱中一阶导数的最大值也呈上升趋势,荧光峰的半峰宽整体呈现下降趋势。荧光光谱分析法操作简单,灵敏度高,费用相对较低,为血糖检测提供了有力的参考。     

X荧光分析系列实验

介绍了能量色散X射线荧光分析的原理、应用以及用X荧光谱仪开设的系列实验。     

Applying backpropagation neural networks to fringe analysis

Advances in image processing and optics technology, allied to the development of algorithmic techniques such as the fast Fourier transform and phase stepping, have allowed automatic fringe analysis to be successfully applied to many problems in visual inspection and noncontact surface measurement. However, when confronted with complicated or noisy images the algorithmic techniques tend to be less successful, implying an alternative approach may be necessary. Neural networks offer such an alternative. They have already been applied with some success to such conceptually similar pattern recognition problems, as the classification of fingerprints, the recognition of facial expressions and the identification of hand-written characters. Here, neural networks are applied to two simple fringe analysis problems. Firstly, to find the radius of a one-dimensional curved surface from its simulated fringe projection intensity distribution and, secondly, to identify four lensshaped objects of different radii of curvature from real fringe patterns obtained under different illumination conditions. In the first experiment, the backpropagation and radial basis function network paradigms are compared. In the second case, backpropagation is compared with the fuzzy-artmap paradigm. Performance criteria are the number of training data presentations, the accuracy of interpolation in the simulation experiment and the classification precision for the real data.     

The accuracy evaluation method of baseline estimation algorithms in energy dispersive X-ray fluorescence spectrum analysis

In energy dispersive X-ray fluorescence analysis (EDXRF), many baseline estimation algorithms have been proposed for the accurate characteristic peak area. However, the true value of the characteristic peak area of measured spectrum is unknown and cannot be used to evaluate the accuracy of the baseline estimation algorithms. In this article, an assessment method was proposed based on Monte Carlo simulation, which can obtain the characteristic peak area, and evaluate the accuracy of the baseline estimation algorithms directly. Meanwhile, the accuracy and practicality of four baseline estimation algorithms were evaluated by the assessment method, which include statistics-sensitive nonlinear iterative peak-clipping (SINP), fast Fourier transform (FFT), adaptive iteratively reweighted penalized least squares (AirPLS), and automated iterative moving averaging (AIMA). Comparing the relative error of the characteristic peak area, AirPLS gave the best performance for baseline estimation in EDXRF.     

Application of artificial neural networks for unfolding neutron spectra by using a scintillation detector

The unfolding of neutron spectra from the pulse height distribution measured by a BC501A scintillation detector is accomplished by the application of artificial neural networks (ANN). A simple linear neural network without biases and hidden layers is adopted. A set of monoenergetic detector response functions in the energy range from 0.25 MeV to 16 MeV with an energy interval of 0.25 MeV are generated by the Monte Carlo code O5S in the training phase of the unfolding process. The capability of ANN was demon...     

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.     

神经元传输函数对人工神经网络动力学特性的影响

以神经元局域场分布为基础，重新研究了连续神经元传输函数对具有联想记忆的人工神经网络功能的影响.与以往的认识不同的是，研究发现连续传输函数与硬极限传输函数相比并不存在明显的优越性，相反，连续传输函数对网络的某些功能，如最大存储率具有负面影响.研究表明神经网络的特性主要决定于网络的动力学结构(具体体现为网络吸引子对应的神经元局域场分布)，网络的动力学结构可以通过选择合适的设计规则进行有效控制，不同的传输函数虽然也能影响到网络的动力学结构，但是它所带来的影响是被动的，可控性很差. 关键词： 联想记忆 神经网络 吸引子 局域场分布     

Application of total reflection X-ray fluorescence spectrometry for trace elemental analysis of rainwater

Applicability of total reflection X-ray fluorescence (TXRF) spectrometry for trace elemental analysis of rainwater samples was studied. The study was used to develop these samples as rainwater standards by the National University of Singapore (NUS). Our laboratory was one of the participants to use TXRF for this study. The rainwater sample obtained from NUS was analysed by TXRF and the trace elements Mn, Fe, Ni, Cu, Zn, V and Pb were determined as required by the NUS. The average precision was found to be within 16% and the TXRF-determined elemental concentrations of these elements were below 20 μg/l. The average deviation of TXRF-determined values from the certified values were 20% (excluding the deviation for Fe and V which were comparatively high). Apart from the above elements, S, K, Ca, Rb, Sr, Ba and Br were also determined by TXRF and were found to be in the range of 0.2 to 191 μg/l. TXRF-determined values of our laboratory played an important role in the certification of concentration of seven elements in this rainwater sample which was later developed as a rainwater standard.     

内标法在土壤重金属镍元素X荧光分析中的应用研究

利用能量色散型XRF重金属实验平台,在实验室自然大气环境下获取土壤的X射线荧光,通过X射线荧光光谱法定量分析了国家标准土壤样品中元素Ni的含量。实验研究了土壤中Ni元素的X荧光特征分析谱线,采用加入内标法测定了Ni元素定标曲线,并依据实验结果分析了内标元素选取方法。实验结果表明,对土壤样品进行内标法分析时,可选取基体中适宜元素作为内标元素;使用Pb的Lα线,Cu和Fe的Kα线作内标线时相对标准偏差(RSD)分别为6.24%,5.24%和5.22%,说明选择合适基体主量元素的特征谱线作为内标线,可有效提高测量结果的准确性。     

人工神经网络在建筑声学中的应用

本文对人工神经网络的历史作了简单回顾，并对它在建筑声学领域中的应用状况作了阐述，同时提出了有待进一步探讨的若干问题。     

Modelling the soundscape quality of urban waterfronts by artificial neural networks

The renewal of the urban waterfronts has become a major focus of attention for politicians and decision makers in the city’s management programs. The recognition of the patterns that define the waterfronts’ identity is essential to select new strategies of intervention for the environmental recovery. In order to create adequate environments for everyday life within a sustainable development, new links between human senses, human perception and design need to be created. Within this wide approach, the landscape and the soundscape play a significant role and can become a key driving force in the implementation of the changes. New techniques have to be tested to identify the sonic and visual parameters capable to explain the specificity of a waterfront. With this purpose, an artificial neural network (ANN) was developed, and the relative importance of the input variables was evaluated. The collected database was also analysed by multiple linear regression (MLR) to compare the outcomes of both models. The urban waterfront of Naples (Italy) was chosen as case study. The results obtained show that the performance of the neural network is better than the one of the linear regression (rANN = 0.949, rMLR = 0.639). The interpretation of the relative importance method is also quite satisfactory in the ANN.     

人工神经网络作为打印机色空间转换工具的问题

通过分析大量实测的打印机输入(R,G,B)与输出(L ,a ,b )数据之间的对应关系,发现在一些颜色区域存在着RGB与L a b 之间的二对一甚至多对一的关系。另一方面,一个已知的事实是,当一个神经网络已经建立后,任一个(或一组)输入只能产生一个(或一组)输出。结合以上两个事实得出的结论是,在要求打印机的输出颜色能准确地复现指定颜色的情况下,神经网络不适合作为由指定颜色的L a b 值来寻求打印机的相应输入RGB的工具。     

Photorefraction images analysis through neural networks

The importance of an early evaluation of infants’ visual system condition is long time recognized. Non-corrected visual disorders may lead to major vision and developmental non-reversible limitations in the future. Among the objective methods of refraction, photorefractive techniques are specifically designed for screening young children. Over the years a number of photorefraction systems with different grades of complexity and automation were developed. A critical problem that one needs to deal with in any approach to these systems is the interpretation and classification of the photorefraction images. In digital photorefraction conventional image processing operators and Fourier techniques were currently used. In this communication we will report on the use of Neural Networks for automated classification of digital photorefraction images.     

Application of artificial neural networks for the determination of proteins with CPA-pI by rayleigh light scattering technique

The determination of proteins with 2-(4-chloro-2-phosphonophenylazo)-7-(4-iodophenylazo)-1,8-dihydroxynaphthalene-3,6-disulfonic acid (CPA-pI) by Rayleigh light scattering (RLS) was studied in this paper. The weak RLS of CPA-pI and BSA can be enhanced greatly by the addition of Al³⁺ at the pH 5.6 and an enhanced RLS signal was produced at 365-385 nm. Based on the reaction of CPA-pI, Al³⁺ and proteins, a new quantitative determination method for proteins has been developed. The effect of three variables for the determination of proteins was optimized by means of artificial neural networks (ANNs) using extended delta-bar-delta (EDBD) algorithms with the optimal network structure of 3-5-1. This method is very sensitive (2.5-35.4 μg/ml for bovine serum albumin (BSA)), rapid (<2 min), simple (one step) and tolerance of most interfering substances. Six samples of protein in human serum were determined and the maximum relative error is no more than 2% and the recovery is between 95% and 105%.