Prediction of pre-oxidation efficiency of refractory gold concentrate by ozone in ferric sulfate solution using artificial neural networks

An artificial neural network model was developed to predict the oxidation of refractory gold concentrate （RGC） by ozone and ferric ions. The concentration of ozone and ferric ions, pulp density, oxygen amount, leaching time and temperature were employed as inputs to the network; the output of the network was the percentage of the ferric extraction iron from RGC. The multilayered feed-forward networks were trained by 33 sets of input-output patterns using a back propagation algorithm; a three-layer network with 8 neurons in the hidden layer gave optimal results. The model gave good predictions of high correlation coefficient （R2=0.966）. The predictions by ANN are more accurate when compared with conventional multivariate regression analysis （MVRA）. In addition, calculation with ANN model indicates that temperature is the predominant parameter and ozone concentration is the lesser influential parameter in the pre-oxidation process of refractory gold ore. The ANN neural network model accurately estimates the ferric extraction during pretreatment process of RGC in gold smelter plants and can be used to optimize the process parameters.

Artificial Neural Network Application to the Friction Stir Welding of Al 6061 Alloy to Stainless Steel 304

The joining of a 6-mm thickness Al 6061 to Stainless steel 304 has been performed by solid state welding. A selection method of optimum friction welding condition using neural networks is proposed. The data used for analyses are the friction stir welding condition, the input parameters of the model consist of welding speed and tool rotation speed. The outputs of the ANN (Artificial Neural Network)model includes resulting parameters, namely, maximum reached temperature,and heating rate for both aluminum alloy 6061 and stainless steel 304 during friction stir welding process.The results of analysis suggest that the proposed method is an effective one to select an optimum welding condition.Good performance of the ANN model was achieved. The combined influence of welding speed and tool rotation speed on the maximum reached temperature and heating rate for both aluminum alloy 6061and stainless steel 304 friction stir welding was simulated. A comparison was made between the output of the ANN program and finite element model. The calculated results were in good agreement with that of finite element model.

Artificial Neural Network Method Based on Expert Knowledge and Its Application to Quantitative Identification of Potential Seismic Sources

In this paper,an approach is developed to optimize the quality of the training samples in the conventional Artificial Neural Network(ANN)by incorporating expert knowledge in the means of constructing expert-rule samples from rules in an expert system,and through training by using these samples,an ANN based on expert-knowledge is further developed.The method is introduced into the field of quantitative identification of potential seismic sources on the basis of the rules in an expert system.Then it is applied to the quantitative identification of the potential seismic sources in Beijing and its adjacent area.The result indicates that the expert rule based on ANN method can well incorporate and represent the expert knowledge in the rules in an expert system,and the quality of the samples and the efficiency of training and the accuracy of the result are optimized.

Research and application of artificial intelligence service platform for the power field

Conventional analysis methods cannot fully meet the business needs of power grids.At present,several artificial intelligence (AI) projects in a single business field are competing with each other,and the interfaces between the systems lack unified specifications.Therefore,it is imperative to establish a comprehensive service platform.In this paper,an AI platform framework for power fields is proposed;it adopts the deep learning technology to support natural language processing and computer vision services.On one hand,it can provide an algorithm,a model,and service support for power-enterprise applications,and on the other hand,it can provide a large number of heterogeneous data processing,algorithm libraries,intelligent services,model managements,typical application scenarios,and other services for different levels of business personnel.The establishment of the platform framework could break data barrier,improve portability of technology,avoid the investment waste caused by repeated constructions,and lay the foundation for the construction of "platform + application + service" ecological chain.

Mixed-decomposed convolutional network:A lightweight yet efficient convolutional neural network for ocular disease recognition

Eye health has become a global health concern and attracted broad attention.Over the years,researchers have proposed many state-of-the-art convolutional neural networks(CNNs)to assist ophthalmologists in diagnosing ocular diseases efficiently and precisely.However,most existing methods were dedicated to constructing sophisticated CNNs,inevitably ignoring the trade-off between performance and model complexity.To alleviate this paradox,this paper proposes a lightweight yet efficient network architecture,mixeddecomposed convolutional network(MDNet),to recognise ocular diseases.In MDNet,we introduce a novel mixed-decomposed depthwise convolution method,which takes advantage of depthwise convolution and depthwise dilated convolution operations to capture low-resolution and high-resolution patterns by using fewer computations and fewer parameters.We conduct extensive experiments on the clinical anterior segment optical coherence tomography(AS-OCT),LAG,University of California San Diego,and CIFAR-100 datasets.The results show our MDNet achieves a better trade-off between the performance and model complexity than efficient CNNs including MobileNets and MixNets.Specifically,our MDNet outperforms MobileNets by 2.5%of accuracy by using 22%fewer parameters and 30%fewer computations on the AS-OCT dataset.

人工神经网络在实现猪精准饲养模式中的应用研究进展

目前,对集约化猪养殖中产生的大量生产数据的高效分析与合理运用是推动我国生猪产业精准化和智能化发展的关键步骤。人工神经网络(artificial neural network,ANN)的自学习、联想存储、高速寻找优化解等特征在进行大数据信息处理时具有显著优势,在猪生产领域具有极高的应用潜力。笔者以具有单隐藏层的三层误差反向传播前馈人工神经网络(back propagation-aritificial neural network BP-ANN)为代表,对ANN的结构特点及其在猪行为与健康监测、胴体性状与肉品质估测和猪生长速率调控与日粮原料营养价值预测等方面的应用成果进行综述,从生产管理和营养调控两方面展望ANN在未来猪精准饲养模式中的应用前景,为实现科学化、智能化和数字化管理模式下的猪精准养殖业提供理论参考。

Artificial Intelligence in Healthcare:Review and Prediction Case Studies

Artificial intelligence(AI)has been developing rapidly in recent years in terms of software algorithms,hardware implementation,and applications in a vast number of areas.In this review,we summarize the latest developments of applications of AI in biomedicine,including disease diagnostics,living assistance,biomedical information processing,and biomedical research.The aim of this review is to keep track of new scientific accomplishments,to understand the availability of technologies,to appreciate the tremendous potential of AI in biomedicine,and to provide researchers in related fields with inspiration.It can be asserted that,just like AI itself,the application of AI in biomedicine is still in its early stage.New progress and breakthroughs will continue to push the frontier and widen the scope of AI application,and fast developments are envisioned in the near future.Two case studies are provided to illustrate the prediction of epileptic seizure occurrences and the filling of a dysfunctional urinary bladder.

人工神经网络与计算技术在电子仪器运行维护中的应用与优化

电子仪器设备是电气工程中的重要组成部分,常见的电子仪器设备包括断路器、变压器、输电线路等,对电气工程质量起到决定性影响。随着互联网与信息技术的快速发展,人工智能技术得到了广泛发展和利用,其能够提高运维决策的精确性和处理效率,从而确保运维质量和稳定性。基于此,本文针对人工神经网络和计算技术在电子仪器运行维护中的应用与优化方式进行分析,从而为电气工程的顺利开展提供更好的保障。

人工神经网络在新鲜奶酪中的应用现状及发展前景

综述新鲜奶酪中马苏里拉奶酪和哈罗米奶酪的研究进展以及人工神经网络(artificial neural network,ANN)在新鲜奶酪领域中的应用现状,并分析当前应用ANN方法的优势及存在的问题。除此之外,ANN在新鲜奶酪领域中的未来发展前景是基于深度算法,实现对生产过程的多目标预测和多工艺参数优化,为提高新鲜奶酪的工艺优化效率和优化质量提供理论依据。

人工智能在辅助青光眼性眼底病变的应用前景

人工智能(AI)在青光眼性眼底病变(GON)角膜内应用的详细视图。为眼科疾病相关临床信息提供诊断依据,对正常及异常角膜实施准确评估,相较于以往的信息技术,AI具有准确性,还可以无创性的综合分析。神经网络的机器深度学习方式包括识别、定位及量化大量的眼科疾病病理特征,提供准确诊断及提高效率。AI开始被广泛应用于视网膜病变、眼前节疾病以及眼底病变的诊疗中。其中AI医学影像判读可协助临床医生对青光眼性眼底病变(GON)的筛查、辅助诊断做出判断,同时对面临诊断率及准确率的研究与挑战并存。

科普场馆人工智能主题展示探讨

人工智能技术广泛影响着人们的生产、生活,并引发人类社会的深刻变革,在科普场馆中科学展示人工智能主题十分必要。该文旨在对适合科普场馆人工智能主题展示的主要内容、筛选原则和基本方式等方面进行探析,寻找出科普场馆人工智能领域科普展示的一般方法和规律,以期对该领域的场馆科普起到启发和推动作用,同时促进人工智能知识和技术的广泛和有效传播,推动人工智能技术的繁荣与发展。

人工神经网络理论在控制领域中的应用综述

力图从人工神经网络的研究历史出发 ,在对人工神经网络在控制系统中的应用现状作出概述之后 ,针对当前自动控制领域所遇到的难点问题 ,说明为什么人工神经网络理论能在其中获得如此广泛的关注 ,并详细论述其适用于控制工程领域应用的主要特征 .

人工神经网络及其在耐火材料研究中的应用

介绍了人工神经网络的发展、特点、模型及其在材料研究中的应用现状；并以反应烧结原位ZrO2-SiC（p）材料中SiC生成量的拟合预报为例，讨论了其在耐火材料研究中的应用。

人工神经网络在材料科学中的应用与展望

人工神经网络因其具有较强的非线性问题处理能力且容错性强而在材料科学中得到广泛的应用. 本文对其在材料设计、材料制备工艺优化、塑性加工、热处理等领域的应用进行了探讨,并对其发展前景进行了展望.

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。

人工神经网络方法在资源与环境预测方面的应用

:用人工神经网络方法对不同水域、不同环境因子之间非线性和不确定性的复杂关系进行学习训练并预测检验。结果表明 :人工神经网络方法在模拟和预测方面均优于传统的统计回归模型 ,在资源与环境方面的应用是可行的 ,具有较强的模拟预测能力。与传统的回归模型相比 ,人工神经网络方法不要求监测数据具有很强的规律性 ,就可用训练后的网络模型对其进行预报 ,并且预测相对误差均比回归模型预测相对误差要小 ,具有一定的实用性。两个实例的应用结果表明 :人工神经网络通过神经原作用函数的简单复合就能逼近有限子集的任意非线性函数 ,而传统的统计方法则存在着如何选择模型形式及非线性优化问题 ,表现出明显的局限性 ,并且统计模型的更新工作相当繁重。

人工神经网络在钢铁材料研究中的应用

人工神经网络是近年发展起来的模拟人脑生物过程的具有人工智能的系统,在钢铁材料研究中有着广阔的应用前景。人工神经网络可根据钢的化学成分和/或加工工艺参数对微观组织、相变温度和时间及性能等做出快速准确预测,并可用于研究钢的上述各影响因素间的相互关系。研究人工神经网络也可用于钢的冶金过程及钢的表面处理过程工艺参数的预测及控制。

人工神经网络在食品工业中的应用

人工神经网络对非线性系统有很强的处理能力,适合对食品加工过程的仿真。综述了人工神经网络在加工:建模、工艺优化、过程控制与预测及在食品分析中的应用,旨在为人工神经网络在食品工业中的更广泛应用提供一定的理论基础和依据。

人工神经网络及其在疾病筛查中的应用前景

人工神经网络是模拟人脑工作机制的一种计算机模型,由于它有自适应性、并行处理能力和非线性处理的优点,所以在医学研究中得到广泛的应用。本文简单介绍了人工神经网络的工作原理和特点,并对人工神经网络技术在疾病筛查方面的最新应用作了综述,指出了它在筛查方面的优点及发展前景。

人工神经网络的发展及应用

人工神经网络是人工智能的重要分支,具有自适应、自组织和自学习的特点。回顾了人工神经网络理论的发展历史,并介绍了其在信息、医学、经济、控制等领域的应用及研究现状。随着人们对人工神经网络不断地探索和研究,并将其与一些传统方法相结合,将推动人工智能的发展,在以后的生产生活中发挥更大的作用。