Real-Time Surface Discrimination Using an Analog Neural Network Implemented in a Phase-Shift Laser Rangefinder

An analog neural network (NN) was developed for real-time surface recognition by using two photoelectrical signals issued from a phase-shift rangefinder. The NN architecture consists of a multilayer perceptron (MLP) with two inputs, three neurons in the hidden layer, and one output. The NN output is compared with threshold voltages in order to classify the tested surfaces. In this type of application, analog NN implementation has many advantages, especially the small silicon area used, a low-power consumption, and no analog-to-digital conversions. This recognition system has been successfully tested for four types of surfaces (a plastic surface, a glossy paper, a painted wall, and a porous surface), at a remote distance between the rangefinder and the target varying from 0.5 m up to 1.25 m and with a laser beam incidence angle varying between and . This paper presents the NN training and the experimental tests of surface discrimination.     

Cellular Neural Network Algorithms for Real-Time Image Analysis in Plasma Fusion

In modern tokamaks, visible and infrared video cameras are becoming more and more important in monitoring plasma evolution during fusion experiments. Analyzing these images in real time can provide relevant information for controlling plasma and improving machine safety. The real-time image processing capability of the cellular nonlinear/neural network-based chips that are available nowadays has been applied to several tasks, both at Frascati Tokamak Upgrade (FTU) and at Joint European Torus (JET). The successful applications range from the identification of plasma instabilities, such as multifaceted asymmetric radiations from the edge (MARFEs), to the determination of the strike-point position in the divertor and to the detection of the so-called “hot spots.”      

基于微分进化神经网络的模拟电路故障诊断

BP神经网络已在模拟电路故障诊断领域得到广泛应用,但BP神经网络存在训练速度慢且容易陷入局部最优的问题.由此,本文提出了一种基于混合变异策略的微分进化改进算法,描述了利用微分进化改进算法进行神经网络权值训练的过程和方法,并将微分进化神经网络用于模拟电路故障诊断,文中还对微分进化神经网络与BP神经网络进行了比较.实验结果表明,微分进化神经网络的训练时间和训练精度均优于BP神经网络,其在模拟电路故障诊断中的准确度比BP神经网络提高了7%.     

Classification of the Surface Technological Defects in Rolled Metal Products with the Help of a Deep Neural Network

Materials Science - We develop and study an automated method for the detection and classification of three types of technological defects in rolled metal products. The method is based on the...     

Analog Real-Time Statistical Analyzer Utilizing CCD's to Implement a Decision Algorithm

A system is presented to realize the function of a linear regression analyzer using analog charge-coupled device (CCD) tapped delay lines to perform fast parallel processing. Such systems are normally implemented using digital computing techniques giving a nonreal-time output. The analog approach discussed in this paper is capable of providing results in real time at the sampling rate of several hundred kilohertz, with the added advantage of reduction in power consumption and physical size. Results obtained from a prototype system are presented to demonstrate the principles of the system operation.     

智能神经网络开发系统的实现技术

对比了智能神经元模型和传统的神经元模型，论述了智能神经网络系统的组成原理，给出了智能神经网络开发系统的基本模型，并具体地阐述了智能神经网络开发系统基本模型中的各个组成部分。利用智能神经网络开发系统，研究人员可以较为容易地开发神经网络应用程序。     

Deep Scatter Estimation (DSE): Accurate Real-Time Scatter Estimation for X-Ray CT Using a Deep Convolutional Neural Network

X-ray scatter is a major cause of image quality degradation in dimensional CT. Especially, in case of highly attenuating components scatter-to-primary ratios may easily be higher than 1. The corresponding artifacts which appear as cupping or dark streaks in the CT reconstruction may impair a metrological assessment. Therefore, an appropriate scatter correction is crucial. Thereby, the gold standard is to predict the scatter distribution using a Monte Carlo (MC) code and subtract the corresponding scatter estimate from the measured raw data. MC, however, is too slow to be used routinely. To correct for scatter in real-time, we developed the deep scatter estimation (DSE). It uses a deep convolutional neural network which is trained to reproduce the output of MC simulations using only the acquired projection data as input. Once trained, DSE can be applied in real-time. The present study demonstrates the potential of the proposed approach using simulations and measurements. In both cases the DSE yields highly accurate scatter estimates that differ by< 3% from our MC scatter predictions. Further, DSE clearly outperforms kernel-based scatter estimation techniques and hybrid approaches, as they are in use today.     

基于神经网络的动态称重系统的DSP实现

针对BP网络学习算法的特点,选取了TI的浮点芯片TMS320C6711,实现了基于DSP的BP网络学习算法,为神经网络在动态称重中的应用做了一些有意义的研究工作.     

Application of Artificial Neural Network in Engineering

This paper mainly studies the dynamic direction of engineering valuation based on the artificial neural network methods, and seeks for a set of rapid, convenien...     

基于RBF神经网络集成的三维模型分类和检索

针对单个神经网络难以对复杂的三维模型特征空间有足够的优化能力和泛化能力的问题,用Boosting方法变种和基于粒子群训练的RBF神经网络,形成特征空间对应的多个神经网络,然后将神经网络集成,给出三维模型的分类信息。在三维模型检索时,将神经网络集成输出的分类信息和特征空间上的距离信息进行加权计算,得到三维模型之间的相似度。实验结果表明,基于RBF神经网络集成的分类方法能有效提高三维模型的分类准确率;同时,考虑特征空间上模型间的距离和语义分类层次上模型间的距离,能够大大提高三维模型的检索精度。     

小型快速模糊神经网络温控系统的设计与实现

提出并设计了一种基于热电制冷器(TEC)的小型快速模糊神经网络温控系统。该系统的硬件由温度传感器、TEC及其驱动器和微控制器组成;系统的软件设计采用模糊推理系统和神经网络相结合,通过控制脉冲宽度调制(PWM)发生器输出合适的波形,驱动TEC制冷或者制热。仿真试验结果表明:该系统较传统的比例-积分-微分(PID)温度控制器具有更加良好的控制效果。     

神经网络理论在图书馆运行监控系统中的应用

设计了一个基于BP人工神经网络理论的图书馆运行监控系统；该系统根据图书馆里丢失物的特征，通过图像识别进行判断，来保障图书馆的正常运行。研究了图像识别的学习速度及识别准确率与隐含层神经元个数及步幅(α)的关系。计算结果显示，该系统设计原理可靠，运行有效。     

BP神经网络在备货型企业销售预测中的应用

结合BP神经网络能学习和存贮大量的输入-输出模式映射关系,无需事前揭示描述这种映射关系的数学方程的特点,分析了BP神经网络在备货型企业销售预测中算法实现过程,根据某备货型企业的销售记录情况,建立了基于BP神经网络的销售预测模型。实例结果表明,此方法计算结果误差较小,结果明显优于移动平滑法等方法,可用于指导实际生产决策。     

人工神经网络在材料研究中的应用

简略介绍了人工神经网络的基本思想及特点,综述了人工神经网络在材料性能预测、工艺参数优化、相变规律的研究、微观组织模拟等领域的应用情况.对人工神经网络研究中存在的问题进行了分析,展望了其应用前景.     

人工神经网络在焊接中的应用研究和发展

本文重点综述了人工神经网络(ANN)技术在焊接工艺参数设计、焊接性能预测、焊接过程控制方面的应用研究现状,并指出了它在焊接应用研究中的问题以及未来的发展趋势。     

神经网络在含水率预测中的应用

相分率测量是多相管流研究的重点，γ密度仪是气液两相流和多相流测量中常用的非插入式测量方法，其中含水率的测量度最大。实验测试发现，直接利用高低能计数计算含水率存在较大偏差，本文通过将高低能计数值作为输入参数，利用BP神经网络方法预含水率，大大提高了预测精度，结果表明，BP神经网络方法是改善γ密度仪测量含水率精度的有效方法。     

人工神经网络在陶瓷材料中的应用

综述了人工神经网络在陶瓷材料中的应用研究进展,分析了人工神经网络在古陶瓷分类和传统陶瓷配方设计、优化及性能预测方面的应用,此外,对人工神经网络在功能陶瓷材料、陶瓷基复合材料中的应用进行了详述,并指出在应用中存在的问题及未来发展趋势.     

人工神经网络在材料科学研究中的应用

人工神经网络模型已成为材料科学中广泛使用的技术,综述了人工神经网络在材料设计,材料加工的智能控制,材料相变研究和材料性能预测等方面的应用。