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Multiscale computation on feedforward neural network and recurrent neural network

Bin LI, Xiaoying ZHUANG

Frontiers of Structural and Civil Engineering 2020, Volume 14, Issue 6,   Pages 1285-1298 doi: 10.1007/s11709-020-0691-7

Abstract: The neural networks can be used to construct fully decoupled approaches in nonlinear multiscale methodsThis article intends to model the multiscale constitution using feedforward neural network (FNN) andrecurrent neural network (RNN), and appropriate set of loading paths are selected to effectively predict

Keywords： multiscale method     constitutive model     feedforward neural network     recurrent neural network    

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Crack identification in concrete, using digital image correlation and neural network

Frontiers of Structural and Civil Engineering 2024, Volume 18, Issue 4,   Pages 536-550 doi: 10.1007/s11709-024-1013-2

Abstract: Digital image correlation (DIC) technology can provide a large amount of experimental data, and neuralnetwork (NN) can process very rich data.Therefore, NN, including convolutional neural networks (CNN) and back propagation neural networks (BP

Keywords： digital image correlation     convolutional neural network     back propagation neural neural network     damage    

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Novel interpretable mechanism of neural networks based on network decoupling method

Frontiers of Engineering Management 2021, Volume 8, Issue 4,   Pages 572-581 doi: 10.1007/s42524-021-0169-x

Abstract: The lack of interpretability of the neural network algorithm has become the bottleneck of its wide applicationnetwork.Result shows that a simple linear mapping relationship exists between network structure and network behaviorin the neural network with high-dimensional and nonlinear characteristics.which can further expand and enrich the interpretable mechanism of artificial neural network in the future

Keywords： neural networks     interpretability     dynamical behavior     network decouple    

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A new automatic convolutional neural network based on deep reinforcement learning for fault diagnosis

Frontiers of Mechanical Engineering 2022, Volume 17, Issue 2, doi: 10.1007/s11465-022-0673-7

Abstract: Convolutional neural network (CNN) has achieved remarkable applications in fault diagnosis.

Keywords： deep reinforcement learning     hyper parameter optimization     convolutional neural network     fault diagnosis    

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Deep convolutional neural network for multi-level non-invasive tunnel lining assessment

Frontiers of Structural and Civil Engineering 2022, Volume 16, Issue 2,   Pages 214-223 doi: 10.1007/s11709-021-0800-2

Abstract: Such strategy leverages the high capacity of convolutional neural networks to identify and classify potential

Keywords： concrete structure     GPR     damage classification     convolutional neural network     transfer learning    

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Automated identification of steel weld defects, a convolutional neural network improved machine learning

Frontiers of Structural and Civil Engineering 2024, Volume 18, Issue 2,   Pages 294-308 doi: 10.1007/s11709-024-1045-7

Abstract: Classic and convolutional neural network-enhanced algorithms were used to classify, the extracted featuresThe convolutional neural network-enhanced support vector machine (SVM) outperformed six other algorithms

Keywords： steel weld     machine learning     convolutional neural network     weld defect detection     classification task    

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Online recognition of drainage type based on UV-vis spectra and derivative neural network algorithm

Frontiers of Environmental Science & Engineering 2021, Volume 15, Issue 6, doi: 10.1007/s11783-021-1430-6

Abstract:

• UV-vis absorption analyzer was applied in drainage type online recognition.

Keywords： Drainage online recognition     UV-vis spectra     Derivative spectrum     Convolutional neural network    

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A neural network-based production process modeling and variable importance analysis approach in corn

Frontiers of Chemical Science and Engineering 2023, Volume 17, Issue 3,   Pages 358-371 doi: 10.1007/s11705-022-2190-y

Abstract: In this paper, a neural network-based production process modeling and variable importance analysis approachnetwork/recurrent neural network based modeling and extended weights connection method.by the extended weight connection method, and 20 of the most important sites are selected for each neuralnetwork.The results indicate that the multilayer perceptron and recurrent neural network models have a relative

Keywords： big data     corn to sugar factory     neural network     variable importance analysis    

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PID neural network control of a membrane structure inflation system

Qiushuang LIU, Xiaoli XU

Frontiers of Mechanical Engineering 2010, Volume 5, Issue 4,   Pages 418-422 doi: 10.1007/s11465-010-0117-7

Abstract: Results show that the neural network PID controller can adapt to the changes in system structure parameters

Keywords： PID     neural network     membrane structure    

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A new neural-network-based method for structural damage identification in single-layer reticulated shells

Frontiers of Structural and Civil Engineering 2024, Volume 18, Issue 1,   Pages 104-121 doi: 10.1007/s11709-024-1031-0

Abstract: In this paper, a new neural-network-based method for structural damage identification in SLRSs is proposedstrong> damage data set, the structural damaged region locations are identified using convolutional neuralBased on the damaged region identified previously, a fully connected neural network (FCNN) is constructed

Keywords： single-layer reticulated shell     damage identification     neural network     convolutional neural network    

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Fault diagnosis of axial piston pumps with multi-sensor data and convolutional neural network

Frontiers of Mechanical Engineering 2022, Volume 17, Issue 3, doi: 10.1007/s11465-022-0692-4

Abstract: under different pump health conditions are fused into RGB images and then recognized by a convolutional neuralnetwork.

Keywords： axial piston pump     fault diagnosis     convolutional neural network     multi-sensor data fusion    

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Machine learning and neural network supported state of health simulation and forecasting model for lithium-ion

Frontiers in Energy 2024, Volume 18, Issue 2,   Pages 223-240 doi: 10.1007/s11708-023-0891-7

Abstract: branches of AI, to lithium-ion battery state of health (SOH), focusing on the advantages and strengths of neuralnetwork (NN) methods in ML for lithium-ion battery SOH simulation and prediction.

Keywords： machine learning     lithium-ion battery     state of health     neural network     artificial intelligence    

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Deep convolutional tree-inspired network: a decision-tree-structured neural network for hierarchical

Frontiers of Mechanical Engineering 2021, Volume 16, Issue 4,   Pages 814-828 doi: 10.1007/s11465-021-0650-6

Abstract: Deep neural networks have provided unprecedented opportunities to condition monitoring from a new perspectiveTo address this issue, this paper explores a decision-tree-structured neural network, that is, the deepconvolutional tree-inspired network (DCTN), for the hierarchical fault diagnosis of bearings.The proposed model effectively integrates the advantages of convolutional neural network (CNN) and decision

Keywords： bearing     cross-severity fault diagnosis     hierarchical fault diagnosis     convolutional neural network    

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A deep neural network based surrogate model for damage identification in full-scale structures with incomplete

Frontiers of Structural and Civil Engineering 2024, Volume 18, Issue 3,   Pages 393-410 doi: 10.1007/s11709-024-1060-8

Abstract: damage in full-scale structures using surrogate models generated from incomplete modal data and deep neural

Keywords： vibration-based damage detection     deep neural network     full-scale structures     finite element model updating    

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Real-time tool condition monitoring method based on temperature measurement and artificial neural network

Frontiers of Mechanical Engineering doi: 10.1007/s11465-021-0661-3

Abstract: The spectrum features are then selected and input into the artificial neural network for classification

Keywords： tool condition monitoring     cutting temperature     neural network     learning rate adaption    

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Title Author Date Type Operation

Multiscale computation on feedforward neural network and recurrent neural network

Bin LI, Xiaoying ZHUANG

Journal Article

Crack identification in concrete, using digital image correlation and neural network

Journal Article

Novel interpretable mechanism of neural networks based on network decoupling method

Journal Article

A new automatic convolutional neural network based on deep reinforcement learning for fault diagnosis

Journal Article

Deep convolutional neural network for multi-level non-invasive tunnel lining assessment

Journal Article

Automated identification of steel weld defects, a convolutional neural network improved machine learning

Journal Article

Online recognition of drainage type based on UV-vis spectra and derivative neural network algorithm

Journal Article

A neural network-based production process modeling and variable importance analysis approach in corn

Journal Article

PID neural network control of a membrane structure inflation system

Qiushuang LIU, Xiaoli XU

Journal Article

A new neural-network-based method for structural damage identification in single-layer reticulated shells

Journal Article

Fault diagnosis of axial piston pumps with multi-sensor data and convolutional neural network

Journal Article

Machine learning and neural network supported state of health simulation and forecasting model for lithium-ion

Journal Article

Deep convolutional tree-inspired network: a decision-tree-structured neural network for hierarchical

Journal Article

A deep neural network based surrogate model for damage identification in full-scale structures with incomplete

Journal Article

Real-time tool condition monitoring method based on temperature measurement and artificial neural network

Journal Article