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Detecting large-scale underwater cracks based on remote operated vehicle and graph convolutional neuralnetwork

Wenxuan CAO; Junjie LI

Frontiers of Structural and Civil Engineering 2022, Volume 16, Issue 11,   Pages 1378-1396 doi: 10.1007/s11709-022-0855-8

Abstract: The graph convolutional neural network (GCN) was used to segment the stitched image.

Keywords： underwater cracks     remote operated vehicle     image stitching     image segmentation     graph convolutionalneural network    

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A multi-sensor relation model for recognizing and localizing faults of machines based on network analysis

Frontiers of Mechanical Engineering 2023, Volume 18, Issue 2, doi: 10.1007/s11465-022-0736-9

Abstract: Recently, advanced sensing techniques ensure a large number of multivariate sensing data for intelligent fault diagnosis of machines. Given the advantage of obtaining accurate diagnosis results, multi-sensor fusion has long been studied in the fault diagnosis field. However, existing studies suffer from two weaknesses. First, the relations of multiple sensors are either neglected or calculated only to improve the diagnostic accuracy of fault types. Second, the localization for multi-source faults is seldom investigated, although locating the anomaly variable over multivariate sensing data for certain types of faults is desirable. This article attempts to overcome the above weaknesses by proposing a global method to recognize fault types and localize fault sources with the help of multi-sensor relations (MSRs). First, an MSR model is developed to learn MSRs automatically and further obtain fault recognition results. Second, centrality measures are employed to analyze the MSR graphs learned by the MSR model, and fault sources are therefore determined. The proposed method is demonstrated by experiments on an induction motor and a centrifugal pump. Results show the proposed method’s validity in diagnosing fault types and sources.

Keywords： fault recognition     fault localization     multi-sensor relations     network analysis     graph neural network    

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Combining graph neural network with deep reinforcement learning for resource allocation in computing Research Article

Xueying HAN, Mingxi XIE, Ke YU, Xiaohong HUANG, Zongpeng DU, Huijuan YAO,hanxueying@bupt.edu.cn,yuke@bupt.edu.cn

Frontiers of Information Technology & Electronic Engineering 2024, Volume 25, Issue 5,   Pages 701-712 doi: 10.1631/FITEE.2300009

Abstract: by the explosive growth of ultra-low-latency and real-time applications with specific computing and networkThe primary CFN challenge is to leverage network resources and computing resources.Although recent advances in deep reinforcement learning (DRL) have brought significant improvement in networkThis paper proposes a (GNN) based DRL framework to accommodate network traffic and computing resources

Keywords： Computing force network     Routing optimization     Deep learning     Graph neural network     Resource allocation    

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Classifying multiclass relationships between ASes using graph convolutional network

Frontiers of Engineering Management 2022, Volume 9, Issue 4,   Pages 653-667 doi: 10.1007/s42524-022-0217-1

Abstract: We then introduce new features and propose a graph convolutional network (GCN) framework, AS-GCN, toThe proposed framework considers the global network structure and local link features concurrently.

Keywords： autonomous system     multiclass relationship     graph convolutional network     classification algorithm     Internet    

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Knowledge enhanced graph inference network based entity-relation extraction and knowledge graph construction

Frontiers of Engineering Management 2024, Volume 11, Issue 1,   Pages 143-158 doi: 10.1007/s42524-023-0273-1

Abstract: In this work, we present a framework for knowledge graph construction in the industrial domain, predicatedFor relation extraction, this paper introduces the knowledge-enhanced graph inference (KEGI) network,This method discerns intricate interactions among entities by constructing a document graph and innovativelyOn the application stratum, BiLSTM-CRF and KEGI are utilized to craft a knowledge graph from a knowledgeThe quality of the extracted knowledge graph complies with the requirements of real-world production

Keywords： knowledge graph construction     industrial     BiLSTM-CRF     document-level relation extraction     graph inference    

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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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Negative weights in network time model

Zoltán A. VATTAI, Levente MÁLYUSZ

Frontiers of Engineering Management 2022, Volume 9, Issue 2,   Pages 268-280 doi: 10.1007/s42524-020-0109-1

Abstract: Previous network techniques (CPM/PERT/PDM) did not support negative parameters and/or loops (potentiallyMonsieur Roy and John Fondahl implicitly introduced negative weights into network techniques to representrestrictions are represented by weighted arrows, we can release most restraints in constructing the graphincorporating the dynamic model of the inner logic of time plan), and a surprisingly flexible and handy networkreview the theoretical possibilities and technical interpretations (and use) of negative weights in network

Keywords： graph technique     network technique     construction management     scheduling    

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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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Title Author Date Type Operation

Detecting large-scale underwater cracks based on remote operated vehicle and graph convolutional neuralnetwork

Wenxuan CAO; Junjie LI

Journal Article

A multi-sensor relation model for recognizing and localizing faults of machines based on network analysis

Journal Article

Combining graph neural network with deep reinforcement learning for resource allocation in computing

Xueying HAN, Mingxi XIE, Ke YU, Xiaohong HUANG, Zongpeng DU, Huijuan YAO,hanxueying@bupt.edu.cn,yuke@bupt.edu.cn

Journal Article

Classifying multiclass relationships between ASes using graph convolutional network

Journal Article

Knowledge enhanced graph inference network based entity-relation extraction and knowledge graph construction

Journal Article

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

Negative weights in network time model

Zoltán A. VATTAI, Levente MÁLYUSZ

Journal Article

PID neural network control of a membrane structure inflation system

Qiushuang LIU, Xiaoli XU

Journal Article