Simultaneous Identification of Thermophysical Properties of Semitransparent Media Using a Hybrid Model Based on Artificial Neural Network and Evolutionary Algorithm

A hybrid identification model based on multilayer artificial neural networks(ANNs) and particle swarm optimization(PSO) algorithm is developed to improve the simultaneous identification efficiency of thermal conductivity and effective absorption coefficient of semitransparent materials.For the direct model,the spherical harmonic method and the finite volume method are used to solve the coupled conduction-radiation heat transfer problem in an absorbing,emitting,and non-scattering 2D axisymmetric gray medium in the background of laser flash method.For the identification part,firstly,the temperature field and the incident radiation field in different positions are chosen as observables.Then,a traditional identification model based on PSO algorithm is established.Finally,multilayer ANNs are built to fit and replace the direct model in the traditional identification model to speed up the identification process.The results show that compared with the traditional identification model,the time cost of the hybrid identification model is reduced by about 1 000 times.Besides,the hybrid identification model remains a high level of accuracy even with measurement errors.

A Survey on Chinese Sign Language Recognition:From Traditional Methods to Artificial Intelligence

Research on Chinese Sign Language(CSL)provides convenience and support for individuals with hearing impairments to communicate and integrate into society.This article reviews the relevant literature on Chinese Sign Language Recognition(CSLR)in the past 20 years.Hidden Markov Models(HMM),Support Vector Machines(SVM),and Dynamic Time Warping(DTW)were found to be the most commonly employed technologies among traditional identificationmethods.Benefiting from the rapid development of computer vision and artificial intelligence technology,Convolutional Neural Networks(CNN),3D-CNN,YOLO,Capsule Network(CapsNet)and various deep neural networks have sprung up.Deep Neural Networks(DNNs)and their derived models are integral tomodern artificial intelligence recognitionmethods.In addition,technologies thatwerewidely used in the early days have also been integrated and applied to specific hybrid models and customized identification methods.Sign language data collection includes acquiring data from data gloves,data sensors(such as Kinect,LeapMotion,etc.),and high-definition photography.Meanwhile,facial expression recognition,complex background processing,and 3D sign language recognition have also attracted research interests among scholars.Due to the uniqueness and complexity of Chinese sign language,accuracy,robustness,real-time performance,and user independence are significant challenges for future sign language recognition research.Additionally,suitable datasets and evaluation criteria are also worth pursuing.

Hybrid Power Systems Energy Controller Based on Neural Network and Fuzzy Logic

This paper presents a novel adaptive scheme for energy management in stand-alone hybrid power systems. The proposed management system is designed to manage the power flow between the hybrid power system and energy storage elements in order to satisfy the load requirements based on artificial neural network (ANN) and fuzzy logic controllers. The neural network controller is employed to achieve the maximum power point (MPP) for different types of photovoltaic (PV) panels. The advance fuzzy logic controller is developed to distribute the power among the hybrid system and to manage the charge and discharge current flow for performance optimization. The developed management system performance was assessed using a hybrid system comprised PV panels, wind turbine (WT), battery storage, and proton exchange membrane fuel cell (PEMFC). To improve the generating performance of the PEMFC and prolong its life, stack temperature is controlled by a fuzzy logic controller. The dynamic behavior of the proposed model is examined under different operating conditions. Real-time measured parameters are used as inputs for the developed system. The proposed model and its control strategy offer a proper tool for optimizing hybrid power system performance, such as that used in smart-house applications.

New hybrid model of proton exchange membrane fuel cell

Model and simulation are good tools for design optimization of fuel cell systems. This paper proposes a new hybrid model of proton exchange membrane fuel cell (PEMFC). The hybrid model includes physical component and black-box com-ponent. The physical component represents the well-known part of PEMFC, while artificial neural network (ANN) component estimates the poorly known part of PEMFC. The ANN model can compensate the performance of the physical model. This hybrid model is implemented on Matlab/Simulink software. The hybrid model shows better accuracy than that of the physical model and ANN model. Simulation results suggest that the hybrid model can be used as a suitable and accurate model for PEMFC.

A hybrid model of a subminiature helicopter in horizontal turn

A hybrid model of a subminiature helicopter in horizontal turn is presented. This model is based on a mechanism model and its compensated neural network （NN）. First, the nonlinear dynamics of a sub-miniature helicopter is established. Through the linearization of the nonlinear dynamics on a trim point, the linear time-invariant mechanism model in horizontal turn is obtained. Then a diagonal recursive neural network is used to compensate the model error between the mechanism model and the nonlinear model, thus the hybrid model of a subminiature helicopter in horizontal turn is achieved. Simulation results show that the hybrid model has higher accuracy than the mechanism model and the obtained compensated-NN has good generalization capability.

Modelling and Characterization of Basalt/Vinyl Ester/SiC Micro-and Nano-hybrid Biocomposites Properties Using Novel ANN–GA Approach

Basalt fiber reinforcement in polymer matrix composites is becoming more and more popular because of its environmental friendliness and mechanical qualities that are comparable to those of synthetic fibers.Basalt fiber strengthened vinyl ester matrix polymeric composite with filler addition of nano-and micro-sized silicon carbide(SiC)element spanning from 2 weight percent to 10 weight percent was studied for its mechanical and wear properties.The application of Artificial Neural Network(ANN)to correlate the filler addition composition for optimum mechanical properties is required due to the non-linear mechanical and tribological features of composites.The stuffing blend and composition of the composite are optimized using the hybrid model and Genetic Algorithm(GA)to maximize the mechanical and wear-resistant properties.The predicted and tested ANN–GA optimal values obtained for the composite combination had a tensile,flexural,impact resilience,hardness and wear properties of 202.93 MPa,501.67 MPa,3.460 J/s,43 HV and 0.196 g,respectively,for its optimum combination of filler and reinforcement.It can be noted that the nano-sized SiC filler particle enhances most of the properties of the composite which diversifies its applications.The predicted mechanical and wear values of the developed ANN–GA model were in closer agreement with the experimental values which validate the model.

基于混合神经网络模型的低速率网络入侵检测研究

针对低速率入侵,常规的入侵检测方法能力不足,虚警率、漏警率偏高。为保证网络安全,提出一种基于混合神经网络模型的低速率网络入侵检测方法。利用NetFlow技术采集网络流量数据,对网络流量数据进行过滤和图像化处理。搭建由卷积神经网络和人工神经网络构成的混合神经网络模型,利用卷积神经网络提取网络流量数据的图像提取特征,利用人工神经网络检测网络入侵类型。结果表明:提出方法的虚警率、漏警率低于Transformer入侵检测方法、栈式自编码-长短期记忆(SAE-LSTM)检测方法和萤火虫优化(GSO)-基分类器检测方法,尤其在入侵速率更低(2 Mb/s)的情况下,所表现出的检测能力更为突出,说明针对低速率网络入侵问题,基于混合神经网络模型的检测方法的检测能力更强,检测结果更为准确。

Machine learning modeling for fuel cell-battery hybrid power system dynamics in a Toyota Mirai 2 vehicle under various drive cycles

Electrification is considered essential for the decarbonization of mobility sector, and understanding and modeling the complex behavior of modern fuel cell-battery electric-electric hybrid power systems is challenging, especially for product development and diagnostics requiring quick turnaround and fast computation. In this study, a novel modeling approach is developed, utilizing supervised machine learning algorithms, to replicate the dynamic characteristics of the fuel cell-battery hybrid power system in a 2021 Toyota Mirai 2nd generation (Mirai 2) vehicle under various drive cycles. The entire data for this study is collected by instrumenting the Mirai vehicle with in-house data acquisition devices and tapping into the Mirai controller area network bus during chassis dynamometer tests. A multi-input - multi-output, feed-forward artificial neural network architecture is designed to predict not only the fuel cell attributes, such as average minimum cell voltage, coolant and cathode air outlet temperatures, but also the battery hybrid system attributes, including lithium-ion battery pack voltage and temperature with the help of 15 system operating parameters. Over 21,0000 data points on various drive cycles having combinations of transient and near steady-state driving conditions are collected, out of which around 15,000 points are used for training the network and 6,000 for the evaluation of the model performance. Various data filtration techniques and neural network calibration processes are explored to condition the data and understand the impact on model performance. The calibrated neural network accurately predicts the hybrid power system dynamics with an R-squared value greater than 0.98, demonstrating the potential of machine learning algorithms for system development and diagnostics.

Time-series analysis with a hybrid Box-Jenkins ARIMA

Time-series analysis is important to a wide range of disciplines transcending both the physical and social sciences for proactive policy decisions. Statistical models have sound theoretical basis and have been successfully used in a number of problem domains in time series forecasting. Due to power and flexibility, Box-Jenkins ARIMA model has gained enormous popularity in many areas and research practice for the last three decades. More recently, the neural networks have been shown to be a promising alternative tool for modeling and forecasting owing to their ability to capture the nonlinearity in the data. However, despite the popularity and the superiority of ARIMA and ANN models, the empirical forecasting performance has been rather mixed so that no single method is best in every situation. In this study, a hybrid ARIMA and neural networks model to time series forecasting is proposed. The basic idea behind the model combination is to use each model’s unique features to capture different patterns in the data. With three real data sets, empirical results evidently show that the hybrid model outperforms ARIMA and ANN model noticeably in terms of forecasting accuracy used in isolation.

Neural Network Based Adaptation Algorithm for Online Prediction of Mechanical Properties of Steel

product is tested in a laboratory for its mechanical properties like yield strength(YS),ultimate tensile strength(UTS)and percentage elongation.This paper describes a mathematical model based method which can predict the mechanical properties without testing.A neural network based adaptation algorithm was developed to reduce the prediction error.The uniqueness of this adaptation algorithm is that the model trains itself very fast when predicted and measured data are incorporated to the model.Based on the algorithm,an ASP.Net based intranet website has also been developed for calculation of the mechanical properties.In the starting Furnace Module webpage,austenite grain size is calculated using semi-empirical equations of austenite grain size during heating of slab in a reheating furnace.In the Mill Module webpage,different conditions of static,dynamic and metadynamic recrystallization are calculated.In this module,austenite grain size is calculated from the recrystallization conditions using corresponding recrystallization and grain growth equations.The last module is a cooling module.In this module,the phase transformation equations are used to predict the grain size of ferrite phase.In this module,structure-property correlation is used to predict the final mechanical properties.In the Training Module,the neural network based adapation algorithm trains the model and stores the weights and bias in a database for future predictions.Finally,the model was trained and validated with measured property data.

Research on axial bearing capacity of rectangular concrete-filled steel tubular columns based on artificial neural networks

Design of rectangular concrete-filled steel tubular （CFT） columns has been a big concern owing to their complex constraint mechanism. Generally, most existing methods are based on simplified mechanical model with limited experimental data, which is not reliable under many conditions, e.g., columns using high strength materials. Artificial neural network （ANN） models have shown the effectiveness to solve complex problems in many areas of civil engineering in recent years. In this paper, ANN models were employed to predict the axial bearing capacity of rectangular CFT columns based on the experimental data. 305 experimental data from articles were collected, and 275 experimental samples were chosen to train the ANN models while 30 experimental samples were used for testing. Based on the comparison among different models, artificial neural network modell （ANN1） and artificial neural network model2 （ANN2） with a 20- neuron hidden layer were chosen as the fit prediction models. ANN1 has five inputs： the length （D） and width （B） of cross section, the thickness of steel （t）, the yield strength of steel （fy）, the cylinder strength of concrete （fc＇）- ANN2 has ten inputs： D, B, t, fy, f′, the length to width ratio （D/B）, the length to thickness ratio （D/t）, the width to thickness ratio （B/t）, restraint coefficient （ξ）, the steel ratio （α）. The axial beating capacity is the output data for both models.The outputs from ANN1 and ANN2 were verified and compared with those from EC4, ACI, GJB4142 and AISC360-10. The results show that the implemented models have good prediction and generalization capacity. Parametric study was conducted using ANN1 and ANN2 which indicates that effect law of basic parameters of columns on the axial bearing capacity of rectangular CFT columns differs from design codes.The results also provide convincing design reference to rectangular CFT columns.

Forecasting Daily Electric Load by Applying Artificial Neural Network with Fourier Transformation and Principal Component Analysis Technique

In this paper,we propose a hybrid forecasting model(HFM)for the short-term electric load forecasting using artificial neural network(ANN),discrete Fourier transformation(DFT)and principal component analysis(PCA)techniques in order to attain higher prediction accuracy.Firstly,we estimate Fourier coefficients by the DFT for predicting the next-day load curve with an ANN and obtain approximate load curves by applying the inverse discrete Fourier transformation.Approximate curves,together with other input variables,are given to the ANN to predict the next-day hourly load curves.Furthermore,we predict PCA scores to obtain approximate load curves in the first step,which are then given to the ANN again in the second step.Both DFT and PCA models use input variables such as calendrical and meteorological data as well as past electric loads.Applying those models for forecasting hourly electric load in the metropolitan area of Japan for January and May in 2018,we train our models using historical data since January 2008.The forecast results show that the HFM consisting of“ANN with DFT”and“ANN with PCA”predicts next-day hourly loads more accurately than the conventional three-layered ANN approach.Their corresponding mean average absolute errors show 2.7%for ANN with DFT,2.6%for ANN with PCA and 3.0%for the conventional ANN approach.We also find that in May,when electric demand is smaller with smaller fluctuations,forecasting errors are much smaller than January for all the models.Thus,we can conclude that the HFM would contribute to attaining significantly higher forecasting accuracy.

一种导航卫星中长期轨道预报方法

基于神经网络混合建模的思想提出一种针对导航卫星的中长期轨道预报方法,在原动力学模型的基础上引入神经网络模型作为补偿,从而获得新的预报模型。在训练过程中神经网络通过学习动力学模型轨道预报误差来掌握其变化规律,并在预报过程中为动力学模型预报提供补偿,以提高预报精度。对GPS卫星动力学模型中长期预报误差的特点进行分析,然后根据所得结论提出混合模型的中长期(15 d以上)预报方案,最后通过对GPS卫星的仿真试验证明混合模型的改进效果,结果表明新方法在15~40 d的预报上表现出很好的改进效果。

新型RBF神经网络及在热工过程建模中的应用

文中提出了一种基于免疫原理的新型径向基函数(RBF—Radial Basis Function)神经网络模型。该模型利用人工免疫系统的记忆、学习和自组织调节原理,进行RBF神经网络隐层中心数量和位置的选择,并采用递推最小二乘算法确定网络输出层的权值。将这种新型的RBF神经网络应用于建立热工过程的非线性模型。仿真研究表明,这种建模方法不仅计算量较小,而且精度高,并有较好的泛化能力。

基于混合神经网络(GANN)的沥青路面使用性能预测模型

针对GM模型要求的样本点少、不必有较好的分布规律,且计算量少、操作简便,而BP神经网络可以反馈校正输出的误差,具有并行计算、分布式信息存储、强容错力、自适应学习功能等特点,将GM(1,1)模型与BP神经网络模型相结合,建立了混合神经网络预测模型,并结合实例进行了检验性预测。结果表明:混合神经网络模型在预测精度方面优于传统灰色模型。该模型的算法概念明确、计算简便,有较高的拟合和预测精度,具有良好的应用前景。

基于神经网络与专家系统结合的模型自动选择

目前的模型选择方法对用户的期望过高 ,要求用户对DSS模型库中的模型和所要解决的问题都要有深刻的理解 ,这就导致了模型选择的实用性较差 .提出了一种基于专家系统和人工神经网络的模型自动选择理论和方法 ,这种理论和方法将模型选择分为模型类型选择和模型结构选择两部分 ,应用人工智能技术完成模型类型的自动选择 ,应用神经网络技术实现模型结构的自动选择 .特别是在模型结构的自动选择中 ,人工神经网络方法的工作原理主要由两个阶段组成 ,即学习和选择阶段 .在学习阶段 ,对每一模型类的模型组训练一个神经网络 ,使训练好的给定类模型的神经网络能够对用户给出一组数据的自动选择 (判断 )出适当的模型结构 .而且 ,这个方法通过趋势外推型模型类的实验 ,证明是很有效的 .

应用小波-人工神经网络组合模型研究电力负荷预报

针对负荷时间序列的非线性和多时间尺度特性,提出了将小波分析与人工神经网络相结合进行负荷预报的方法——小波-人工神经网络组合模型。该模型吸取了小波分析的多分辨功能和人工神经网络的非线性逼近能力。以月、日平均负荷预报为例对模型进行验证,结果表明:该模型的拟合、检验精度较高。

基于混合建模技术的聚氯乙烯粒径分布预测

针对聚氯乙烯粒径分布在线软测量问题,提出了一种基于机理分析和神经网络的混合建模方法,并将该建模方法应用于聚氯乙烯粒径分布建模研究中。混合模型由机理模型和误差补偿模型所组成。通过机理分析建立氯乙烯悬浮聚合过程的单体液滴群体平衡(Population Balance Equation,简称PBE)模型,由于聚氯乙烯成粒过程的复杂性和强非线性,单纯的机理模型预测与实际分析值相比仍存在一定偏差,因此利用人工神经网络建模方法建立了基于BP神经网络的单体液滴群体平衡模型修正模型,对单体液滴群体平衡模型的输出进行修正,由此建立起聚氯乙烯粒径分布混合模型。由于混合模型既能按照液滴分散与聚并机理对聚氯乙烯颗粒的成长过程进行描述,同时又充分利用了生产现场数据对模型误差进行修正,应用到聚氯乙烯生产过程的测试结果表明,与单纯机理模型相比,聚氯乙烯粒径分布混合模型具有更佳的预测效果。

状态变量部分不可测的间歇反应器的智能建模

提出一种智能化的神经网络建模方法,建立状态变量部分不可测的间歇反应器模型。针对间歇反应是一个非线性、非稳态过程,根据化学反应的非线性分离特性,采用结构逼近式神经网络构建模型的拓扑结构。利用反应的先验知识优化网络结构,赋予网络节点实际的物理意义,并完善网络训练过程,使建模过程灰箱化;通过假想教师-人工免疫训练算法,解决不可测变量影响常规网络训练的问题;通过并行优化假想教师和网络权值,提高建模精度。以实际橡胶硫化促进剂制备的间歇缩合反应过程为实验对象,详细论述了建模和网络训练的过程,证明了方法的有效性。

超临界流体萃取过程混合模型的建立

采用混合建模技术,通过集成人工神经网络模型与单元过程机理模型建立了超临界流体萃取过程的混合模型,较好地解决了超临界过程的模拟计算、经济评价、设计和运行优化问题.通过软件集成的方法实现了商业模拟软件Aspenplus与ANN的集成,大大降低了模型的实现难度和实施时间.