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The research on structural damage identification using rough set and integrated neural network

Juelong LI, Hairui LI, Jianchun XING, Qiliang YANG

Frontiers of Mechanical Engineering 2013, Volume 8, Issue 3,   Pages 305-310 doi: 10.1007/s11465-013-0259-5

Abstract: To efficiently solve this problem, a new damage identification method based on rough set and integratedneural network is first proposed.And then the reduced attributes will be put into the sub-neural network.The sub-neural network can give the preliminary diagnosis from different aspects of damage.The decision fusion network will give the final damage identification results.

Keywords： rough set     integrated neural network     damage identification     decision making fusion    

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Enterprise-wide optimization of integrated planning and scheduling for refinery-petrochemical complex

Frontiers of Chemical Science and Engineering 2023, Volume 17, Issue 10,   Pages 1516-1532 doi: 10.1007/s11705-022-2283-7

Abstract: This paper focuses on the integrated problem of long-term planning and short-term scheduling in a large-scaleTo solve the problem efficiently, a heuristic algorithm combined with a convolutional neural network

Keywords： planning     scheduling     refinery-petrochemical     convolutional neural network     heuristic algorithm    

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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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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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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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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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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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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 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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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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and load sharing pattern of piled raft using nonlinear regression and LM algorithm-based artificial neuralnetwork

Frontiers of Structural and Civil Engineering 2021, Volume 15, Issue 5,   Pages 1181-1198 doi: 10.1007/s11709-021-0744-6

Abstract: obtained results are then checked statistically with nonlinear multiple regression (NMR) and artificial neuralnetwork (ANN) modeling, and some prediction models are proposed.

Keywords： interaction     load sharing ratio     piled raft     nonlinear regression     artificial neural network    

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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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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: Here, a real-time tool condition monitoring method integrated in an in situ fiber optic temperatureThe 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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Prediction of bed load sediments using different artificial neural network models

Reza ASHEGHI, Seyed Abbas HOSSEINI

Frontiers of Structural and Civil Engineering 2020, Volume 14, Issue 2,   Pages 374-386 doi: 10.1007/s11709-019-0600-0

Abstract: In this paper, three different artificial neural networks (ANNs) including multilayer percepterons, radialbased function (RBF), and generalized feed forward neural network using five dominant parameters ofpredicted compatible outputs but the RBF with 79% correct classification rate corresponding to 0.191 network

Keywords： bed load prediction     artificial neural network     modeling     empirical equations    

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Title Author Date Type Operation

The research on structural damage identification using rough set and integrated neural network

Juelong LI, Hairui LI, Jianchun XING, Qiliang YANG

Journal Article

Enterprise-wide optimization of integrated planning and scheduling for refinery-petrochemical complex

Journal Article

Multiscale computation on feedforward neural network and recurrent neural network

Bin LI, Xiaoying ZHUANG

Journal Article

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

Journal Article

Novel interpretable mechanism of neural networks based on network decoupling method

Journal Article

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

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

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

PID neural network control of a membrane structure inflation system

Qiushuang LIU, Xiaoli XU

Journal Article

and load sharing pattern of piled raft using nonlinear regression and LM algorithm-based artificial neuralnetwork

Journal Article

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

Journal Article

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

Journal Article

Prediction of bed load sediments using different artificial neural network models

Reza ASHEGHI, Seyed Abbas HOSSEINI

Journal Article