Resource Type

Journal Article 527

Year

2024 1

2023 63

2022 54

2021 50

2020 57

2019 34

2018 35

2017 33

2016 17

2015 24

2014 12

2013 13

2012 14

2011 6

2010 4

2009 7

2008 13

2007 14

2006 9

2005 14

open ︾

Keywords

neural network 32

artificial neural network 21

Neural network 11

network 10

optimization 9

Artificial intelligence 8

convolutional neural network 7

genetic algorithm 7

Deep learning 6

artificial neural network (ANN) 6

BP neural network 4

6G 3

ANN 3

Artificial neural network 3

Bayesian belief network 3

Network security 3

finite element method 3

ANOVA 2

Attention 2

open ︾

Search scope:

排序： Display mode:

Classifying multiclass relationships between ASes using graph convolutional network

Frontiers of Engineering Management   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    

HTML PDF Collect

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.The GCN’s m-IOU is 24.02% higher than Fully convolutional networks (FCN), proving that GCN has

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

HTML PDF Collect

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    

HTML PDF Collect

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    

HTML PDF Collect

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: To 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    

HTML PDF Collect

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: pressure signals under different pump health conditions are fused into RGB images and then recognized by a convolutionalneural network.

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

HTML PDF Collect

Interpreting the vulnerability of power systems in cascading failures using multi-graph convolutional Research Article

Supaporn LONAPALAWONG, Changsheng CHEN, Can WANG, Wei CHEN

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 12,   Pages 1848-1861 doi: 10.1631/FITEE.2200035

Abstract: system's spatial information such as the electrical distance to increase the accuracy in the process of graphuses power system topology and spatial information to optimize the edge weight assignment of the line graphThen we propose a multi-graph convolutional network (MGCN) based on a graph classification task, which

Keywords： Power systems     Vulnerability     Cascading failures     Multi-graph convolutional networks     Weighted line graph    

HTML PDF Collect

Video summarization with a graph convolutional attention network Research Articles

Ping Li, Chao Tang, Xianghua Xu,patriclouis.lee@gmail.com

Frontiers of Information Technology & Electronic Engineering 2021, Volume 22, Issue 6,   Pages 902-913 doi: 10.1631/FITEE.2000429

Abstract: To address the above problem, we propose a graph convolutional attention network (GCAN) for .consists of two parts, embedding learning and , where embedding learning includes the temporal branch and graphIt learns graph embedding via a multi-layer to reveal the intrinsic structure of frame samples.The part combines the output streams from the temporal branch and graph branch to create the context-aware

Keywords： 时序学习；自注意力机制；图卷积网络；上下文融合；视频摘要    

HTML PDF Collect

Automated classification of civil structure defects based on convolutional neural network

Pierclaudio SAVINO, Francesco TONDOLO

Frontiers of Structural and Civil Engineering 2021, Volume 15, Issue 2,   Pages 305-317 doi: 10.1007/s11709-021-0725-9

Abstract: challenge, this paper presents a method for automating concrete damage classification using a deep convolutionalneural network.The convolutional neural network was designed after an experimental investigation of a wide number ofTo increase the network robustness compared to images in real-world situations, different image configurationsmodel, with the highest validation accuracy of approximately 94%, was selected as the most suitable network

Keywords： concrete structure     infrastructures     visual inspection     convolutional neural network     artificial intelligence    

HTML PDF Collect

A local density optimization method based on a graph convolutional network

Hao Wang, Li-yan Dong, Tie-hu Fan, Ming-hui Sun,wanghao18@mails.jlu.edu.cn,dongly@jlu.edu.cn,fth@jlu.edu.cn,smh@jlu.edu.cn

Frontiers of Information Technology & Electronic Engineering 2020, Volume 21, Issue 12,   Pages 1671-1814 doi: 10.1631/FITEE.1900663

Abstract: Success has been obtained using a semi-supervised graph analysis method based on a (GCN).However, GCN ignores some local information at each node in the graph, so that data preprocessing isFinally, we compare the performances of several mainstream graph analysis algorithms with that of the
HTML PDF Collect

Slope stability analysis based on big data and convolutional neural network

Yangpan FU; Mansheng LIN; You ZHANG; Gongfa CHEN; Yongjian LIU

Frontiers of Structural and Civil Engineering 2022, Volume 16, Issue 7,   Pages 882-895 doi: 10.1007/s11709-022-0859-4

Abstract: In this case, the convolutional neural network (CNN) provides a better alternative.database for slope stability analysis reaches more than 99%, and the comparisons with the BP neural network

Keywords： slope stability     limit equilibrium method     convolutional neural network     database for slopes     big data    

HTML PDF Collect

Realtime prediction of hard rock TBM advance rate using temporal convolutional network (TCN) with tunnel

Zaobao LIU; Yongchen WANG; Long LI; Xingli FANG; Junze WANG

Frontiers of Structural and Civil Engineering 2022, Volume 16, Issue 4,   Pages 401-413 doi: 10.1007/s11709-022-0823-3

Abstract: This paper proposes a real-time predictive model of TBM advance rate using the temporal convolutionalnetwork (TCN), based on TBM construction big data.The TCN model is found outperforming the recurrent neural network (RNN) and long short-term memory (LSTM

Keywords： hard rock tunnel     tunnel bore machine advance rate prediction     temporal convolutional networks     soft    

HTML PDF Collect

Efficient, high-resolution topology optimization method based on convolutional neural networks

Liang XUE, Jie LIU, Guilin WEN, Hongxin WANG

Frontiers of Mechanical Engineering 2021, Volume 16, Issue 1,   Pages 80-96 doi: 10.1007/s11465-020-0614-2

Abstract: efficient, high-resolution topology optimization method is developed based on the super-resolution convolutionalneural network (SRCNN) technique in the framework of SIMP.

Keywords： topology optimization     convolutional neural network     high resolution     density-based    

HTML PDF Collect

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    

HTML PDF Collect

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    

HTML PDF Collect

Title Author Date Type Operation

Classifying multiclass relationships between ASes using graph convolutional network

Journal Article

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

Wenxuan CAO; Junjie LI

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

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

Journal Article

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

Journal Article

Interpreting the vulnerability of power systems in cascading failures using multi-graph convolutional

Supaporn LONAPALAWONG, Changsheng CHEN, Can WANG, Wei CHEN

Journal Article

Video summarization with a graph convolutional attention network

Ping Li, Chao Tang, Xianghua Xu,patriclouis.lee@gmail.com

Journal Article

Automated classification of civil structure defects based on convolutional neural network

Pierclaudio SAVINO, Francesco TONDOLO

Journal Article

A local density optimization method based on a graph convolutional network

Hao Wang, Li-yan Dong, Tie-hu Fan, Ming-hui Sun,wanghao18@mails.jlu.edu.cn,dongly@jlu.edu.cn,fth@jlu.edu.cn,smh@jlu.edu.cn

Journal Article

Slope stability analysis based on big data and convolutional neural network

Yangpan FU; Mansheng LIN; You ZHANG; Gongfa CHEN; Yongjian LIU

Journal Article

Realtime prediction of hard rock TBM advance rate using temporal convolutional network (TCN) with tunnel

Zaobao LIU; Yongchen WANG; Long LI; Xingli FANG; Junze WANG

Journal Article

Efficient, high-resolution topology optimization method based on convolutional neural networks

Liang XUE, Jie LIU, Guilin WEN, Hongxin WANG

Journal Article

Negative weights in network time model

Zoltán A. VATTAI, Levente MÁLYUSZ

Journal Article

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

Journal Article