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。     

人工神经网络在材料制备工业中的应用

人工神经网络具有非线性的自适应信息处理能力,已广泛应用于化工、通信、控制及优化等领域.在简单介绍人工神经网络方法的基础上,重点综述了该方法在陶瓷工业的原料分类、配方优化、缺陷分析、性能预测等方面的应用情况,指出在应用过程中可能存在的问题以及未来的发展趋势.     

超声波技术在水工业中的应用与展望

传统的水处理工艺已经无法有效地去除越来越复杂的有机物种类。各种新工艺随之不断被开发，以有效去除环境水体、原水、废水中的有机物。超声波作为一种高效、清洁的高级氧化技术，可以很好地应用于有机物的降解处理。     

纳米炭材料在储能产业中的应用进展与展望

能源和环境问题是目前人类亟需解决的两大难题，如何实现能源的多样性、绿色和高效利用是更为迫切的问题。随着交通、信息等领域的高速发展，对高能量密度、高功率密度、长寿命、高安全性、廉价、环境友好的高性能储能器件提出了迫切需求。在选择储能器件时，最关键的是储能器件的能量密度和功率密度，而决定这些性能的根本因素是储能材料，因此，研究开发高性能、低成本的电极材料是储能器件研究开发工作的核心。作为综合性能最好的炭材料，在各种储能器件特别是锂离子电池、超级电容器等中都有着非常重要的应用。随着储能器件产业的壮大，作为活性物质和导电剂的炭材料已成为极为重要的材料之一。     

三维原子探针及其在材料科学研究中的应用

三维原子探针(3DAP)是一种定量显微分析仪器,通过对不同元素的原子逐个进行分析,可绘出金属样品中不同元素的原子在纳米空间中的分布图形.从分析逐个原子来了解物质微区化学成分的不均匀性,3DAP是一种不可替代的分析方法.本文介绍了3DAP的工作原理及样品制备,举例说明了3DAP分析技术的应用.     

神经网络模型在预测Ni-Al系热爆点火时间中的应用

基于人工神经网络的原理,对热爆法制备Ni-Al系金属间化合物中的控制参数进行了研究,选取了加热速率、颗粒尺寸、压坯密度三个参数,通过对此参数的调控可以影响热爆反应的点火时间及反应过程.本文采用BP算法来训练网络,对热爆反应中的过程参数与热爆点火时间的映射关系进行了函数逼近,建立了热爆点火时间的神经网络模型.根据该模型可以预测热爆的点火时间,为控制热爆反应加压过程提供了可靠的依据.     

Automatic black and white film colorization using texture features and artificial neural networks

Abstract

This study represents an innovative automatic method for black and white films colorization using texture features and a multilayer perceptron artificial neural network. In the proposed method, efforts are made to remove human interference in the process of colorization and replace it with an artificial neural network (ANN) which is trained using the features of the reference frame. Later, this network is employed for automatic colorization of the remained black and white frames. The reference frames of the black and white film are manually colored. Using a Gabor filter bank, texture features of all the pixels of the reference frame are extracted and used as the input feature vector of the ANN, while the output will be the color vector of the corresponding pixel. Finally, the next frames’ feature vectors are fed respectively to the trained neural network, and color vectors of those frames are the output. Applying AVI videos and using various color spaces, a series of experiments are conducted to evaluate the proposed colorization process. This method needs considerable time to provide a reasonable output, given rapidly changing scenes. Fortunately however, due to the high correlation between consecutive frames in typical video footage, the overall performance is promising regarding both visual appearance and the calculated MSE error. Apart from the application, we also aim to show the importance of the low level features in a mainly high level process, and the mapping ability of a neural network.     

Determination of S–N curves with the application of artificial neural networks

The present paper describes the application of artificial neural networks for estimating the finite-life fatigue strength and fatigue limit. A comprehensive database with results of single-stage tests on specimens which simulate structural components is evaluated and prepared for processing with the use of neural networks. The available data are subdivided into different classes. A total of six different data classes are specified. The results of the prediction by means of neural networks are superior to those obtained with conventional methods for calculating the fatigue strength. The experimental results are estimated with high accuracy.     

Using artificial neural networks to predict container flows between the major ports of Asia

Container flow information is a critical issue for port operators and liners to support their strategic planning and decision-making. This study uses artificial neural networks (ANNs) to predict container flows by considering GDP, interest rates, the value of export and import trade, the numbers of export and import containers and the number of quay cranes. ANNs are developed for data mining purposes, and the developed model can simultaneously predict container flows between the major ports of Asia. The forecasting results indicate that the prediction errors are relatively small in most selected ports, and thus shipping companies can use the container flow prediction model to make decisions concerning operations. The results can be further applied to the trend analysis of container flows among the major ports of Asia, and a community analysis of the containers was conducted for the purpose of supply chain management.     

Application of artificial neural networks to nuclear power plant transient diagnosis

A study on various artificial neural network (ANN) algorithms for selecting a best suitable algorithm for diagnosing the transients of a typical nuclear power plant (NPP) is presented. NPP experiences a number of transients during its operations. These transients may be due to equipment failure, malfunctioning of process systems, etc. In case of any undesired plant condition generally known as initiating event (IE), the operator has to carry out diagnostic and corrective actions. The objective of this study is to develop a neural network based framework that will assist the operator to identify such initiating events quickly and to take corrective actions. Optimization study on several neural network algorithms has been carried out. These algorithms have been trained and tested for several initiating events of a typical nuclear power plant. The study shows that the resilient-back propagation algorithm is best suitable for this application. This algorithm has been adopted in the development of operator support system. The performance of ANN for several IEs is also presented.     

神经网络七十年: 回顾与展望

边界条件的施加是求解偏微分方程定解问题的重要步骤。神经网络方法求解偏微分方程定解问题时,将原问题转化为对应的构造变分问题后,损失函数是包含控制方程与边界条件的泛函。采用经典罚函数法及其改进方法施加边界条件时,罚因子的取值直接影响计算精度和求解效率;直接采用Lagrange乘子法施加边界条件,计算结果可能偏离原问题最优解。为破解此局限性,使用广义乘子法施加边界条件。基于神经网络获得原问题的预测解,再使用广义乘子法构建神经网络的损失函数并计算损失值,利用梯度下降法进行参数寻优,判断损失值是否满足要求;不满足则更新罚因子与乘子后再进行求解直至损失满足要求。数值算例的计算结果表明：与采用经典罚函数法、L₁精确罚函数法和Lagrange乘子法施加边界条件构造的神经网络相比,该文提出的方法具有更好的数值精度和更高的求解效率,且求解过程更加稳定。     

Predictive modelling of turning operations using response surface methodology,artificial neural networks and support vector regression

This paper focuses on developing empirical models for predicting surface roughness, tool wear and power required in turning operations. These response parameters are mainly dependent upon cutting velocity, feed and cutting time. Three competing data mining techniques, response surface methodology (RSM), artificial neural networks (ANN) and support vector regression (SVR), are applied in developing the empirical models. The data of 27 experiments have been used to generate, compare and evaluate the proposed models of tool wear, power required and surface roughness for the selected tool/material combination. Testing results demonstrate that the models developed in this research are suitable for predicting the response parameters with a satisfactory goodness of fit. It has been found that ANN and SVR models are much better than regression and RSM models for predicting the three response parameters. Finally, some future research directions are outlined.     

Application of artificial neural network for predicting plain strain fracture toughness using tensile test results

A back‐propagation neural network was applied to predicting the K_IC values using tensile material data and investigating the effects of crack plane orientation and temperature. The 595 K_IC data of structural steels were used for training and testing the neural network model. In the trained neural network model, yield stress has relatively the most effect on K_IC value among tensile material properties and K_IC value was more sensitive to K_IC test temperature than to crack plane orientation valid in the range of material data covered in this study. The performance of the trained artificial neural network (ANN) was evaluated by comparing output of the ANN with results of a conventional least squares fit to an assumed shape. The conventional linear or nonlinear least squares fitting methods gave very poor fitting results but the results predicted by the trained neural network were considerably satisfactory. This study shows that the neural network can be a good tool to predict K_IC values according to the variation of the temperature and the crack plane orientation using tensile test results.     

基于神经网络的长距离油气管道安全预警系统

提出了一种基于分布式光纤传感和人工神经网络判别的长距离输油管道安全预警系统.该系统利用光纤传感器收集管道周围土壤的振动信号,通过神经网络判断是否存在针对管道的破坏性行为和判别破坏性行为的类别,实现对油气管道的长距离安全预警.系统在预处理阶段对信号大幅度降维,降低数据处理的时间复杂度,以满足实时性的要求.在识别阶段则采用人工神经网络模型,包括反向传播(BP)网络和支持向量机(SVM).试验结果表明,这两种神经网络模型对打夯、镐刨、电钻三类破坏行为的识别率分别达到96.5和97.1%,均优于以往文献中的报道.     

人工神经网络在材料设计中的应用

在实验数据的基础上,利用人工神经网络建立高Ｃｏ－ Ｎｉ 二次硬化钢的力学性能与合金成分及热处理温度对应关系的模型． 首次提出将五个材料力学性能指标及部分合金成分作为网络的输入,其它合金成分和热处理温度作为网络的输出,根据要求的力学性能设计材料的合金成分含量及热处理条件,获得了满意的结果,为高性能材料设计提供了一定的理论辅助手段．     

Fault detection via nonlinear profile monitoring using artificial neural networks

Fault detection is the characterization of a normal behavior of a system using a response function or profile of interest and the identification of any deviation from such normal behavior. As system complexity grows, predicting the underlying structure or form of response function becomes challenging if not impossible. This article presents a data‐driven approach for fault detection of complex systems using multivariate statistical process control based on artificial neural network (ANN) characterization. In this approach, the quality of a system is characterized where one explanatory variable is adequately explained as a function of the other variables using an ANN model. The vector of weights and biases of the ANN model is monitored by using Hotelling T ² through control charts. The proposed method is tested and compared with existing methods such as polynomial and sum of sine function regression for 3 cases from the literature. Moreover, it is applied to a 4‐story reinforced concrete building that uses continuous monitoring to avoid potentially catastrophic failures. The proposed ANN approach outperforms the existing methods for small shifts (deviations) from healthy states. For large and medium shifts, it provides comparable results that are on the conservative side.