Application of artificial neural networks in analysis of CHF experimental data in round tubes

Artificial neural networks (ANNs) are applied successfully to analyze the critical heat flux (CHF) experimental data from some round tubes in this paper. A set of software adopting artificial neural network method for predicting CHF in round tube and a set of CHF database are gotten. Comparing with common CHF correlations and CHF look-up table, ANN method has stronger ability of allow-wrong and nice robustness. The CHF predicting software adopting artificial neural network technology can improve the predicting accuracy in a wider parameter range,and is easier to update and to use. The artificial neural nefwork method used in this paper can be applied to some similar physical problems.

Using deep neural networks coupled with principal component analysis for ore production forecasting at open-pit mines

Ore production is usually affected by multiple influencing inputs at open-pit mines.Nevertheless,the complex nonlinear relationships between these inputs and ore production remain unclear.This becomes even more challenging when training data(e.g.truck haulage information and weather conditions)are massive.In machine learning(ML)algorithms,deep neural network(DNN)is a superior method for processing nonlinear and massive data by adjusting the amount of neurons and hidden layers.This study adopted DNN to forecast ore production using truck haulage information and weather conditions at open-pit mines as training data.Before the prediction models were built,principal component analysis(PCA)was employed to reduce the data dimensionality and eliminate the multicollinearity among highly correlated input variables.To verify the superiority of DNN,three ANNs containing only one hidden layer and six traditional ML models were established as benchmark models.The DNN model with multiple hidden layers performed better than the ANN models with a single hidden layer.The DNN model outperformed the extensively applied benchmark models in predicting ore production.This can provide engineers and researchers with an accurate method to forecast ore production,which helps make sound budgetary decisions and mine planning at open-pit mines.

Deep Learning for Robotics

The application of deep learning to robotics over the past decade has led to a wave of research into deep artificial neural networks and to a very specific problems and questions that are not usually addressed by the computer vision and machine learning communities. Robots have always faced many unique challenges as the robotic platforms move from the lab to the real world. Minutely, the sheer amount of diversity we encounter in real-world environments is a huge challenge to deal with today’s robotic control algorithms and this necessitates the use of machine learning algorithms that are able to learn the controls of a given data. However, deep learning algorithms are general non-linear models capable of learning features directly from data making them an excellent choice for such robotic applications. Indeed, robotics and artificial intelligence (AI) are increasing and amplifying human potential, enhancing productivity and moving from simple thinking towards human-like cognitive abilities. In this paper, lots of learning, thinking and incarnation challenges of deep learning robots were discussed. The problem addressed was robotic grasping and tracking motion planning for robots which was the most fundamental and formidable challenge of designing autonomous robots. This paper hope to provide the reader an overview of DL and robotic grasping, also the problem of tracking and motion planning. The system is tested on simulated data and real experiments with success.

Large eddy simulation of spray combustion using flamelet generated manifolds combined with artificial neural networks

In the present work,artificial neural networks(ANN)technique combined with flamelet generated manifolds(FGM)is proposed to mitigate the memory issue of FGM models.A set of ANN models is firstly trained using a 68-species mass fractions in mixture fraction-progress variable space.The ANN prediction accuracy is examined in large eddy simulation(LES)and Reynolds averaged Navier-Stokes(RANS)simulations of spray combustion.It is shown that the present ANN models can properly replicate the FGM table for most of the species mass fractions.The network models with relative error less than 5%are considered in RANS and LES to simulate the Engine Combustion Network(ECN)Spray H flames.Validation of the method is firstly conducted in the framework of RANS.Both non-reacting and reacting cases show the present method predicts very well the trend of spray and combustion process under different ambient temperatures.The results show that FGM-ANN can replicate the ignition delay time(IDT)and lift-off length(LOL)precisely as the conventional FGM method,and the results agree very well with the experiments.With the help of ANN,it is possible to achieve high efficiency and accuracy,with a significantly reduced memory requirement of the FGM models.LES with FGM-ANN is then applied to explore the detailed spray combustion process.Chemical explosive mode analysis(CEMA)approach is used to identify the local combustion modes.It is found that before the spray flame is developed to the steady-state,the high CH_(2)O zone is always associated with ignition mode.However,high CH_(2)O zone together with high OH zone is dominated by the burned mode after the steady-state.The lift-off position is dominated mainly by the diffusion mode.

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,convenient and practical valuation models,for building construction projects.

Layered learning of soccer robot based on artificial neural network

Discusses the application of artificial neural network for MIROSOT, introduces a layered model of BP network of soccer robot for learning basic behavior and cooperative behavior, and concludes from experimental results that the model is effective.

A study of using grey system theory and artificial neural network on the climbing ability of <i>Buergeria robusta</i>frog

Ecological engineering is an emerging study of integrating both ecology and engineering, concerned with the design, monitoring, and construction of ecosystems. In recent years, the threat to amphibian animals is becoming more and more serious. In particular, the loss of habitats caused by changes to the way land is used by human beings has hit amphibians particularly hard. Amphibians are known to be particularly vulnerable to human activities because they rely on both terrestrial and aquatic habitats for survival. With the increasing development of many areas in recent years, concrete structures are often installed along water bodies in order to increase the safety of local residents. The construction of concrete banks along rivers associated with human development has become a serious problem in Taiwan. Most ecosystems used by amphibians are lakes and stream banks, yet no related design solutions to accommodate the needs of amphibians. The need to develop the relevant design specification considering protecting the amphibian is imperative. Buergeria robusta, an endemic species in Taiwan, is tree frog widely distributed in lowland montane regions. Their breeding season is from April to September. They like to rest on trees or hide at caves during the daytime and move to the stream nearby in dusk for breeding. Males usually emit weak mating call while standing on stones. Sticky eggs are attached to undersides of rocks and stones. Tadpoles are found in slow flowing water of streams [1]. The goal of this study is to improve the understanding of the relationship between the climbing ability and the physical characteristics of amphibians. In this study, we use Artificial Neural Network to simulate the climbing ability of Buergeria robusta. Besides, Grey System Theory is also adopted to improve the performance of Artificial Neural Network. Artificial Neural Network (ANN) is a computing system that uses a large number of artificial neurons imitating natural neural ability to deal with an information network by computing system. The numerical results have show good agreement with the experimental results. The results can serve as a reference for technicians involved in future ecological engineering designs of banks throughout the world.

An approach of artificial neural network to robotic welding process modelling

Artificial neural networks(ANNs)have been investigated for application to robotic welding process.Two types of the ANN models are described.The first is a static modeling approach for the pre-setting of robotic welding parameters, and the other is a dynamic modelling for real time feedback control of robotic welding.These models map the relationship between the weld bead geometry and welding process parameters.Some basic concepts relating to neural networks are discussed. The performance of neural networks for modelling is discussed and evaluated by using actual robotic welding data.It is concluded that neural network is capable of modeling readily and quickly a multivariable welding process and the accuracy of neural networks modelling is comparable with the accuracy achieved by the statistical scheme. The choice between ANN and statistical models will depend on the application and control strategy used.

Artificial Neural Network for Websites Classification with Phishing Characteristics

Several threats are propagated by malicious websites largely classified as phishing. Its function is important information for users with the purpose of criminal practice. In summary, phishing is a technique used on the Internet by criminals for online fraud. The Artificial Neural Networks (ANN) are computational models inspired by the structure of the brain and aim to simu-late human behavior, such as learning, association, generalization and ab-straction when subjected to training. In this paper, an ANN Multilayer Per-ceptron (MLP) type was applied for websites classification with phishing cha-racteristics. The results obtained encourage the application of an ANN-MLP in the classification of websites with phishing characteristics.

Social Engineering Attack Classifications on Social Media Using Deep Learning

In defense-in-depth,humans have always been the weakest link in cybersecurity.However,unlike common threats,social engineering poses vulnerabilities not directly quantifiable in penetration testing.Most skilled social engineers trick users into giving up information voluntarily through attacks like phishing and adware.Social Engineering(SE)in social media is structurally similar to regular posts but contains malicious intrinsic meaning within the sentence semantic.In this paper,a novel SE model is trained using a Recurrent Neural Network Long Short Term Memory(RNN-LSTM)to identify well-disguised SE threats in social media posts.We use a custom dataset crawled from hundreds of corporate and personal Facebook posts.First,the social engineering attack detection pipeline(SEAD)is designed to filter out social posts with malicious intents using domain heuristics.Next,each social media post is tokenized into sentences and then analyzed with a sentiment analyzer before being labelled as an anomaly or normal training data.Then,we train an RNN-LSTM model to detect five types of social engineering attacks that potentially contain signs of information gathering.The experimental result showed that the Social Engineering Attack(SEA)model achieves 0.84 in classification precision and 0.81 in recall compared to the ground truth labeled by network experts.The experimental results showed that the semantics and linguistics similarities are an effective indicator for early detection of SEA.

机场能见度临近预测方法

能见度是保障机场航班安全、正常运行的重要标准之一。为精准预测能见度,使用2020年天津机场气象和常规空气质量监测数据,构建基于方差膨胀因子(Variance Inflation Factor,VIF)、主成分分析(Principal Components Analysis,PCA)和Informer的能见度预测模型,并将均方根误差、平均绝对误差、平均绝对百分比误差作为评价指标进行误差分析。结果显示,VIF PCA Informer模型比单一的Informer和简单组合模型效果更优,能更好地捕捉长时间序列特征的关系。相比于单一的Informer、长短期记忆神经网络和门控循环单元模型,VIF PCA Informer模型均方根误差下降了0.2141~0.3486,平均绝对误差下降了0.1842~0.2753,平均绝对百分比误差下降了0.3224~0.5270;VIF PCA Informer模型对能见度的临近预测(1 h)更为精准。使用高效的机场能见度预测模型可在保障航班安全高效运行方面发挥较大支撑作用。

基于成果转化的人工智能算法创新型实验设计

为了拓展深度学习、神经网络算法模型在不同学科领域中的应用,基于创新性教学、高阶性教学以及挑战性教学,提出以科研项目为依托,将科研成果反哺于实验教学,结合实验内容,构建出人工智能算法在能谱测量中的一系列应用实验。实验将脉冲处理、能谱测量、深度学习等多学科知识交叉融合到创新型实验中,利用不同的深度学习模型进行数据处理和分析,充分发挥人工智能算法在实验中的优势,提供了准确、快速和高效的实验结果。实验实施与评估结果表明,该实验不仅能够促进实验教学的创新和发展,也为学生进行科学研究、学科竞赛提供了更多实践与探索的机会。

人工智能在外科学教育领域的应用前景

在高等教育中,人工智能和虚拟现实等前沿教育技术被广泛应用于开发虚拟学习资源。因此,人工智能(artificial intelligence,AI)在临床实践中的应用被认为是医学教育中一个很有前景的扩展领域。AI能够基于学习者的表现数据和个性化需求,定制教育路径和提供精准的学习建议。这种个性化的支持不仅增强了教育效果,还可以帮助医师快速地掌握复杂的临床技能和决策能力。AI的4个关键组成部分是机器学习、自然语言处理、人工神经网络和视觉处理,每个部分都在外科学教育中具有潜在的应用前景。在一个医患关系紧张、医学生源相对饱和及手术机会减少的时代,AI还能够分析大量的临床数据,预测患者的康复路径和可能的并发症,为医疗团队提供决策支持。通过优化资源利用和流程管理,AI还有助于降低医疗成本,提供更经济高效的医疗护理服务。文章阐述了目前AI技术的应用及其在促进外科学教育方面的前景。

基于人工智能技术的机器人运动控制系统设计

设计一种基于人工智能技术的机器人运动控制系统,确保机器人更好地理解人类的意图,并提供更加人性化的服务。该系统通过运动数据采集与传输组件连接机器人的轴电机,采集机器人当前运动数据后,将其传输到控制器组件内,控制器组件依托X86架构工控机,使用PIC总线将采集到的机器人当前运动数据发送到基于人工智能技术的机器人运动路径规划模块内。该模块运用人工智能技术中的A*算法获取机器人轨迹路径规划结果后,依据该路径规划结果,将人工智能技术中的神经网络和模糊B样条基函数相结合,建立模糊B样条基函数神经网络控制器。该控制器输出机器人运动控制指令,并发送给伺服驱动器组件,伺服驱动器负责驱动机器人轴电机,控制机器人运动。实验结果表明:所设计系统具备较强的机器人路径规划能力,可在复杂路径情况下实现机器人运动控制,且控制精度和控制阶跃响应能力均较强。

人工智能在脊髓神经损伤与修复领域研究热点的可视化分析

背景:近年来人工智能逐渐兴起,在多方面应用于脊髓神经损伤与修复领域,对临床治疗也有诸多积极影响。目的:研究人工智能在脊髓神经损伤与修复领域的诊断、治疗和康复中的应用进展,明确该领域的研究热点和不足,为今后研究工作提供建议。方法:在Web of Science核心集数据库检索建库至2023年收录的人工智能在脊髓神经损伤与修复领域相关文献,使用CiteSpace 6.1.R6和VOSviewer 1.6.19软件对文献数据进行一般文献学分析、文献共被引、期刊共被引、期刊双图叠加及关键词聚类等可视化分析。结果与结论:①共筛选出1713篇文章,此领域年发文量呈波动上升趋势,其中美国占据主导地位,Kadone Hideki是发文量最多的作者,《ARCH PHYS MED REHAB》是被引用次数最多的期刊。②关键词共现和聚类分析显示,去除与检索词相近的关键词后,主要关键词被分为3个主要集群:外骨骼与运动康复(为最大核心热点)、机器学习和神经可塑性、机器人和康复训练。③关键词爆发分析显示,深度学习和人工智能在过去5年中已成为突发术语。④文献共被引和高被引文献分析结果显示,人工智能在脊髓神经损伤与修复领域热点集中于动力外骨骼(powered exoskeleton)、步态(gait)、神经电刺激(electrical nerve stimulation)、皮质内脑机接口(intracortical brain-computer interface,IBCI)、机器人(robot)、高分子生物材料(polymer biomaterials)及神经干细胞(neural stem cell)等内容。⑤人工智能在脊髓神经损伤与修复领域的研究近年来呈现上升趋势,该领域的关注点从外骨骼、电刺激等单一的治疗手段,逐渐向智能化、精准化和个性化等方向转变。⑥该领域存在一些局限性,例如数据缺失或不平衡的后果、数据准确性和可重复性低以及伦理问题(如隐私、研究透明度和临床可靠性等),未来的研究应该解决数据收集的问题,需要大样本、高质量的临床数据集来建立有效的人工智能模型;同时该领域的基因组学等机制研究十分薄弱,未来可利用类脑芯片等多种机器学习技术,运用基因编辑治疗及单细胞空间转录组等方法,进行再生相关基因上调和轴突生长结构蛋白产生等基础机制研究。

Exploring deep learning for landslide mapping:A comprehensive review

A detailed and accurate inventory map of landslides is crucial for quantitative hazard assessment and land planning.Traditional methods relying on change detection and object-oriented approaches have been criticized for their dependence on expert knowledge and subjective factors.Recent advancements in highresolution satellite imagery,coupled with the rapid development of artificial intelligence,particularly datadriven deep learning algorithms(DL)such as convolutional neural networks(CNN),have provided rich feature indicators for landslide mapping,overcoming previous limitations.In this review paper,77representative DL-based landslide detection methods applied in various environments over the past seven years were examined.This study analyzed the structures of different DL networks,discussed five main application scenarios,and assessed both the advancements and limitations of DL in geological hazard analysis.The results indicated that the increasing number of articles per year reflects growing interest in landslide mapping by artificial intelligence,with U-Net-based structures gaining prominence due to their flexibility in feature extraction and generalization.Finally,we explored the hindrances of DL in landslide hazard research based on the above research content.Challenges such as black-box operations and sample dependence persist,warranting further theoretical research and future application of DL in landslide detection.

AI-Driven Learning Management Systems:Modern Developments, Challenges and Future Trends during theAge of ChatGPT

COVID-19 pandemic restrictions limited all social activities to curtail the spread of the virus.The foremost and most prime sector among those affected were schools,colleges,and universities.The education system of entire nations had shifted to online education during this time.Many shortcomings of Learning Management Systems(LMSs)were detected to support education in an online mode that spawned the research in Artificial Intelligence(AI)based tools that are being developed by the research community to improve the effectiveness of LMSs.This paper presents a detailed survey of the different enhancements to LMSs,which are led by key advances in the area of AI to enhance the real-time and non-real-time user experience.The AI-based enhancements proposed to the LMSs start from the Application layer and Presentation layer in the form of flipped classroom models for the efficient learning environment and appropriately designed UI/UX for efficient utilization of LMS utilities and resources,including AI-based chatbots.Session layer enhancements are also required,such as AI-based online proctoring and user authentication using Biometrics.These extend to the Transport layer to support real-time and rate adaptive encrypted video transmission for user security/privacy and satisfactory working of AI-algorithms.It also needs the support of the Networking layer for IP-based geolocation features,the Virtual Private Network(VPN)feature,and the support of Software-Defined Networks(SDN)for optimum Quality of Service(QoS).Finally,in addition to these,non-real-time user experience is enhanced by other AI-based enhancements such as Plagiarism detection algorithms and Data Analytics.

CAM-BRAIN"ATR's ARTIFICIAL BRAIN PROJECT A Progress Report

This paper reports on progress made in the first 3 years of.ATR's 'CAM-Brain'Project, which aims to use 'evolutionary e.gi...,i.gi' techniques to build/grow/evolve a RAM-and-cellular-automata based artificial brain consisting of thousands of interconnected neural network modules inside special hardware such as MITs Cellular Automata Machine 'CAM-8,i, or NTT's Content Addressable Memory System 'CAM-System'. The states of a billion (later a trillion) 3D cellular automata cells, and edlions of cellular automata rules which govern their state changes, can be stored relatively cheaply in giga(tera)bytes of RAM. After 3 years work, the CA rules are almost ready. MITt,,'CAM-8' (essentially a serial device) can update 200,000,000 CA cells a second. It is possible that NTT's 'CAM-System' (essentially a massively parallel device) may be able to update a trillion CA cells a second. Hence all the ingredients will soon be ready to create a revolutionary new technology which will allow thousands of evolved neural network modules to be assembled into artificial brains. This in turn will probably create not only a new research field, but hopefully a whole new industry,namely 'brain building'. Building artificial brains with a billion neurons is the aim of ATR's 8 year i,CAM-B,ai.,' research project, ending in 2001.