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.

Interaction behavior and load sharing pattern of piled raft using nonlinear regression and LM algorithm-based artificial neural network

In the recent era,piled raft foundation(PRF)has been considered an emergent technology for offshore and onshore structures.In previous studies,there is a lack of illustration regarding the load sharing and interaction behavior which are considered the main intents in the present study.Finite element(FE)models are prepared with various design variables in a double-layer soil system,and the load sharing and interaction factors of piled rafts are estimated.The obtained results are then checked statistically with nonlinear multiple regression(NMR)and artificial neural network(ANN)modeling,and some prediction models are proposed.ANN models are prepared with Levenberg-Marquardt(LM)algorithm for load sharing and interaction factors through backpropagation technique.The factor of safety(FS)of PRF is also estimated using the proposed NMR and ANN models,which can be used for developing the design strategy of PRF.

State-of-the-art review of some artificial intelligence applications in pile foundations

Geotechnical engineering deals with materials（e.g. soil and rock） that, by their very nature, exhibit varied and uncertain behavior due to the imprecise physical processes associated with the formation of these materials. Modeling the behavior of such materials in geotechnical engineering applications is complex and sometimes beyond the ability of most traditional forms of physically-based engineering methods. Artificial intelligence（AI） is becoming more popular and particularly amenable to modeling the complex behavior of most geotechnical engineering applications because it has demonstrated superior predictive ability compared to traditional methods. This paper provides state-of-the-art review of some selected AI techniques and their applications in pile foundations, and presents the salient features associated with the modeling development of these AI techniques. The paper also discusses the strength and limitations of the selected AI techniques compared to other available modeling approaches.

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.

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.

基于卷积神经网络的柔性皮革材料缺陷无损检测技术研究

卷积神经网络是一种类似深度学习模型的多层感知器,被广泛应用于分析视觉图像。本文以无损化、高效化、智能化检测为目标,构建了一种基于卷积神经网络的柔性皮革材料缺陷检测技术流程,针对该流程对柔性皮革材料缺陷检测的效果进行了案例分析,旨在为皮革领域材料无损检测技术的发展提供借鉴。

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。

基于机器视觉的轴承缺陷检测研究进展

机器视觉是一种用机器替代人眼进行测量和检测的技术,这种技术应用于缺陷检测具有效率高、速度快、成本低等优点,许多学者将其应用在不同领域(农业、航空航天等),并取得了较好的成果,目前轴承领域也逐渐采用该检测方法。因此,需对应用于不同轴承缺陷及机器学习、深度学习下的轴承缺陷检测算法进行综述,并对其缺陷检测算法的性能进行分析归纳及对比。首先,探讨分析了轴承缺陷形成的磨损机理,并详细介绍了轴承常见磨损形式(腐蚀磨损、疲劳磨损、黏着磨损、滚道磨损等);然后,分别介绍了基于机器学习和深度学习的检测算法的区别及特点;其次,列举了机器学习的算法及深度学习的算法用于轴承缺陷检测的研究应用与分析,主要包括机器学习的人工神经网络、主成分分析、支持向量机等,及深度学习的单阶段和双阶段目标检测算法的应用;最后,为了促进深度学习算法用于轴承缺陷的诊断,针对具体问题提出了轴承缺陷检测的挑战和未来研究方向并给出了详细的建议,对机器视觉在轴承缺陷检测中的研究现状提出了总结与展望。

基于卷积神经网络的深度学习技术在软件缺陷检测中的应用

探讨了卷积神经网络(CNN)在软件缺陷检测中的应用。采用深度学习技术,模拟图像识别中的模式识别能力,对代码进行自动分析,以识别潜在缺陷。实验结果显示,该方法的缺陷检测正确率达到了94.28%~97.51%,说明利用CNN进行软件缺陷检测能够有效提升检测速度和准确性,对于降低开发成本、提高软件质量及可靠性具有重要意义。

基于人工神经网络的光伏充电桩有序充电控制系统

为了节约用户充电成本,保证单位时间内充电车辆数量,设计基于人工神经网络的光伏充电桩有序充电控制系统。在微控制器模块中,基于FPGA设计一种微控制器作为系统发挥控制功能的硬件。设计粒子群算法与极限学习机相结合的区域电动汽车充电短期负荷预测模型,实施电动汽车短期负荷预测。测试结果表明,该系统的短期负荷预测结果与实际负荷结果相近,在系统控制下,用户充电成本得到较大幅度地降低,同时电动汽车充电车辆数量有所提升。

基于AI的机械零件加工缺陷检测工艺研究

文章提出一种基于人工智能(Artificial Intelligence,AI)的机械零件加工缺陷检测工艺,以提高缺陷检测的精度和效率。通过对缺陷类型与特点的分析,构建高质量缺陷图像数据集,并采用数据增强技术扩充样本。基于卷积神经网络(Convolutional Neural Networks,CNN)设计多尺度残差网络模型,实现缺陷的自动识别。实验结果表明,该方法在尺寸偏差、表面烧伤、内部组织异常等典型缺陷检测中表现出色,能够满足工业级缺陷检测的高标准要求。

基于人工智能的广西都安山羊皮革瑕疵自动检测分级研究

通过文献研究和案例分析,文章探究了基于人工智能的皮革瑕疵自动检测分级技术。文章重点探究如何解决广西都安山羊板皮经脱毛鞣制后,由于其本身的皮质特性使得针对其进行人工检测的人员因受到天气条件、光线变化、视力健康等因素干扰而导致其在标记瑕疵时经常出现错检或漏检等出错率较高的问题。研究认为,基于深度学习(DL)中的全卷积网络(FCN)和机器学习(ML)中的深度神经网络(DNN)等人工智能技术能够对广西都安山羊皮革各类瑕疵进行有效地自动检测分级,最大程度上使广西都安山羊皮革能够更好地对标符合当前中国-东盟RCEP(区域全面经济伙伴关系协定)中对于成员国皮革进出口贸易规范(质量标准)的要求。

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。

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。

基于深度神经网络的群桩局部冲刷深度预测

群桩的局部冲刷研究对保障海洋结构基础稳定性至关重要,采用现行规范和经验公式所得到的群桩冲刷深度计算值偏于保守且过于离散。深度神经网络具有很强的非线性映射能力,利用神经网络和学者研究中的试验数据建立深度神经网络模型,进行冲刷深度的预测并对结果进行敏感性分析。分析表明,文章建立的模型冲刷深度预测值与实际试验值拟合效果良好,冲刷深度神经网络模型具有可行性和有效性,可为群桩支撑的海洋工程建筑的填埋深度及后期防护等提供理论依据,具有十分重要的工程意义和理论意义。

人工神经网络的机器人焊缝缺陷图像自动辨识

为了提高机器人焊接质量,延长焊接构件使用寿命,提出基于人工神经网络的机器人焊缝缺陷图像自动辨识方法。利用模糊增强算法计算机器人焊缝缺陷图像各灰度级的隶属函数,逆变换增强模糊特征;使用Roberts算子检测图像边缘像素点,去除像素较低的点;采用密度聚类算法分割缺陷区域,结合面积、周长等几何参数计算缺陷部分形状特征;建立人工神经网络传输模型,将缺陷图像几何特征作为辨识依据,输入训练样本,经过反复训练,当误差低于设定阈值时输出最终辨识结果。仿真实验证明,所提方法能均衡图像灰度值,能够提取出显著性缺陷特征,准确辨识出焊缝缺陷类型。

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。