Three recent breakthroughs due to AI in arts and science serve as motivation:An award winning digital image,protein folding,fast matrix multiplication.Many recent developments in artificial neural networks,particularl...Three recent breakthroughs due to AI in arts and science serve as motivation:An award winning digital image,protein folding,fast matrix multiplication.Many recent developments in artificial neural networks,particularly deep learning(DL),applied and relevant to computational mechanics(solid,fluids,finite-element technology)are reviewed in detail.Both hybrid and pure machine learning(ML)methods are discussed.Hybrid methods combine traditional PDE discretizations with ML methods either(1)to help model complex nonlinear constitutive relations,(2)to nonlinearly reduce the model order for efficient simulation(turbulence),or(3)to accelerate the simulation by predicting certain components in the traditional integration methods.Here,methods(1)and(2)relied on Long-Short-Term Memory(LSTM)architecture,with method(3)relying on convolutional neural networks.Pure ML methods to solve(nonlinear)PDEs are represented by Physics-Informed Neural network(PINN)methods,which could be combined with attention mechanism to address discontinuous solutions.Both LSTM and attention architectures,together with modern and generalized classic optimizers to include stochasticity for DL networks,are extensively reviewed.Kernel machines,including Gaussian processes,are provided to sufficient depth for more advanced works such as shallow networks with infinite width.Not only addressing experts,readers are assumed familiar with computational mechanics,but not with DL,whose concepts and applications are built up from the basics,aiming at bringing first-time learners quickly to the forefront of research.History and limitations of AI are recounted and discussed,with particular attention at pointing out misstatements or misconceptions of the classics,even in well-known references.Positioning and pointing control of a large-deformable beam is given as an example.展开更多
Purpose-Computed tomography(CT)scan can provide valuable information in the diagnosis of lung diseases.To detect the location of the cancerous lung nodules,this work uses novel deep learning methods.The majority of th...Purpose-Computed tomography(CT)scan can provide valuable information in the diagnosis of lung diseases.To detect the location of the cancerous lung nodules,this work uses novel deep learning methods.The majority of the early investigations used CT,magnetic resonance and mammography imaging.Using appropriate procedures,the professional doctor in this sector analyses these images to discover and diagnose the various degrees of lung cancer.All of the methods used to discover and detect cancer illnesses are time-consuming,expensive and stressful for the patients.To address all of these issues,appropriate deep learning approaches for analyzing these medical images,which included CT scan images,were utilized.Design/methodology/approach-Radiologists currently employ chest CT scans to detect lung cancer at an early stage.In certain situations,radiologists’perception plays a critical role in identifying lung melanoma which is incorrectly detected.Deep learning is a new,capable and influential approach for predicting medical images.In this paper,the authors employed deep transfer learning algorithms for intelligent classification of lung nodules.Convolutional neural networks(VGG16,VGG19,MobileNet and DenseNet169)are used to constrain the input and output layers of a chest CT scan image dataset.Findings-The collection includes normal chest CT scan pictures as well as images from two kinds of lung cancer,squamous and adenocarcinoma impacted chest CT scan images.According to the confusion matrix results,the VGG16 transfer learning technique has the highest accuracy in lung cancer classification with 91.28% accuracy,followed by VGG19 with 89.39%,MobileNet with 85.60% and DenseNet169 with 83.71% accuracy,which is analyzed using Google Collaborator.Originality/value-The proposed approach using VGG16 maximizes the classification accuracy when compared to VGG19,MobileNet and DenseNet169.The results are validated by computing the confusion matrix for each network type.展开更多
近年来,物联网(Internet of Things,IoT)产业受到许多的关注,世界各国均视其为潜在无限商机的高科技产业,并投入大量的资源从事研发与推广。由于物联网的应用非常广泛,且透过物联网技术人类得以提升生活质量,让生活更加便利,因此,本论...近年来,物联网(Internet of Things,IoT)产业受到许多的关注,世界各国均视其为潜在无限商机的高科技产业,并投入大量的资源从事研发与推广。由于物联网的应用非常广泛,且透过物联网技术人类得以提升生活质量,让生活更加便利,因此,本论文首先简介物联网的背景及应用,并介绍其基本概念与架构。接着,本论文以「无线感测真菌人文树道」为例,针对物联网技术应用于人文艺术领域进行说明,并详细叙述其所使用到之各项软件、韧体及硬件技术,透过真菌的感测、无线通信及异质网络连网功能的设计,使真菌网络成为物联网在人文艺术应用的一个重要典范。展开更多
文摘Three recent breakthroughs due to AI in arts and science serve as motivation:An award winning digital image,protein folding,fast matrix multiplication.Many recent developments in artificial neural networks,particularly deep learning(DL),applied and relevant to computational mechanics(solid,fluids,finite-element technology)are reviewed in detail.Both hybrid and pure machine learning(ML)methods are discussed.Hybrid methods combine traditional PDE discretizations with ML methods either(1)to help model complex nonlinear constitutive relations,(2)to nonlinearly reduce the model order for efficient simulation(turbulence),or(3)to accelerate the simulation by predicting certain components in the traditional integration methods.Here,methods(1)and(2)relied on Long-Short-Term Memory(LSTM)architecture,with method(3)relying on convolutional neural networks.Pure ML methods to solve(nonlinear)PDEs are represented by Physics-Informed Neural network(PINN)methods,which could be combined with attention mechanism to address discontinuous solutions.Both LSTM and attention architectures,together with modern and generalized classic optimizers to include stochasticity for DL networks,are extensively reviewed.Kernel machines,including Gaussian processes,are provided to sufficient depth for more advanced works such as shallow networks with infinite width.Not only addressing experts,readers are assumed familiar with computational mechanics,but not with DL,whose concepts and applications are built up from the basics,aiming at bringing first-time learners quickly to the forefront of research.History and limitations of AI are recounted and discussed,with particular attention at pointing out misstatements or misconceptions of the classics,even in well-known references.Positioning and pointing control of a large-deformable beam is given as an example.
文摘Purpose-Computed tomography(CT)scan can provide valuable information in the diagnosis of lung diseases.To detect the location of the cancerous lung nodules,this work uses novel deep learning methods.The majority of the early investigations used CT,magnetic resonance and mammography imaging.Using appropriate procedures,the professional doctor in this sector analyses these images to discover and diagnose the various degrees of lung cancer.All of the methods used to discover and detect cancer illnesses are time-consuming,expensive and stressful for the patients.To address all of these issues,appropriate deep learning approaches for analyzing these medical images,which included CT scan images,were utilized.Design/methodology/approach-Radiologists currently employ chest CT scans to detect lung cancer at an early stage.In certain situations,radiologists’perception plays a critical role in identifying lung melanoma which is incorrectly detected.Deep learning is a new,capable and influential approach for predicting medical images.In this paper,the authors employed deep transfer learning algorithms for intelligent classification of lung nodules.Convolutional neural networks(VGG16,VGG19,MobileNet and DenseNet169)are used to constrain the input and output layers of a chest CT scan image dataset.Findings-The collection includes normal chest CT scan pictures as well as images from two kinds of lung cancer,squamous and adenocarcinoma impacted chest CT scan images.According to the confusion matrix results,the VGG16 transfer learning technique has the highest accuracy in lung cancer classification with 91.28% accuracy,followed by VGG19 with 89.39%,MobileNet with 85.60% and DenseNet169 with 83.71% accuracy,which is analyzed using Google Collaborator.Originality/value-The proposed approach using VGG16 maximizes the classification accuracy when compared to VGG19,MobileNet and DenseNet169.The results are validated by computing the confusion matrix for each network type.
文摘近年来,物联网(Internet of Things,IoT)产业受到许多的关注,世界各国均视其为潜在无限商机的高科技产业,并投入大量的资源从事研发与推广。由于物联网的应用非常广泛,且透过物联网技术人类得以提升生活质量,让生活更加便利,因此,本论文首先简介物联网的背景及应用,并介绍其基本概念与架构。接着,本论文以「无线感测真菌人文树道」为例,针对物联网技术应用于人文艺术领域进行说明,并详细叙述其所使用到之各项软件、韧体及硬件技术,透过真菌的感测、无线通信及异质网络连网功能的设计,使真菌网络成为物联网在人文艺术应用的一个重要典范。
