Resource Type

Journal Article 1760

Conference Videos 49

Conference Information 1

Year

2024 3

2023 145

2022 184

2021 167

2020 148

2019 131

2018 109

2017 113

2016 80

2015 95

2014 57

2013 50

2012 46

2011 45

2010 51

2009 51

2008 58

2007 75

2006 48

2005 35

open ︾

Keywords

optimization 19

Machine learning 14

neural network 11

multi-objective optimization 10

big data 9

genetic algorithm 9

Reinforcement learning 8

Deep learning 7

uncertainty 6

Artificial intelligence 5

different 5

fault diagnosis 5

sustainable development 5

Adsorption 4

COVID-19 4

GIS 4

Neural network 4

algorithm 4

blockchain 4

open ︾

Search scope:

排序： Display mode:

A graph-based two-stage classification network for mobile screen defect inspection Research Article

Chaofan ZHOU, Meiqin LIU, Senlin ZHANG, Ping WEI, Badong CHEN

Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 2,   Pages 203-216 doi: 10.1631/FITEE.2200524

Abstract: To solve these problems, a graph reasoning module, stacked on a classification module, is proposed tothe help of contrastive learning, the classification module can better initialize the category-wise graph

Keywords： Graph-based methods     Multi-label classification     Mobile screen defects     Neural networks    

HTML PDF Collect

A systematic graph-based method for the kinematic synthesis of non-anthropomorphic wearable robots for

Fabrizio SERGI, Dino ACCOTO, Nevio L. TAGLIAMONTE, Giorgio CARPINO, Eugenio GUGLIELMELLI

Frontiers of Mechanical Engineering 2011, Volume 6, Issue 1,   Pages 61-70 doi: 10.1007/s11465-011-0206-2

Abstract: non-anthropomorphic wearable robots can be too complex to be solved uniquely by relying on conventional synthesis methods

Keywords： assistive robotics     non-anthropomorphic wearable robots     topology     kinematic synthesis     HR-isomorphism test     HR-degeneracy test    

HTML PDF Collect

Stochastic extra-gradient based alternating direction methods for graph-guided regularizedminimization None

Qiang LAN, Lin-bo QIAO, Yi-jie WANG

Frontiers of Information Technology & Electronic Engineering 2018, Volume 19, Issue 6,   Pages 755-762 doi: 10.1631/FITEE.1601771

Abstract: extra-gradient alternating direction method with augmented Lagrangian function (SEGAL), to minimize the graph-guidedA number of important applications in machine learning follow the graph-guided optimization formulationWe conduct experiments on fused logistic regression and graph-guided regularized regression.

Keywords： Stochastic optimization     Graph-guided minimization     Extra-gradient method     Fused logistic regression     Graph-guided    

HTML PDF Collect

Soft-HGRNs: soft hierarchical graph recurrent networks for multi-agent partially observable environments Research Article

Yixiang REN, Zhenhui YE, Yining CHEN, Xiaohong JIANG, Guanghua SONG,yixiangren@zju.edu.cn,zhenhuiye@zju.edu.cn,ch19930611@zju.edu.cn,ghsong@zju.edu.cn

Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 1,   Pages 117-130 doi: 10.1631/FITEE.2200073

Abstract: Based on our intuitive observation that human society could be regarded as a large-scale partially observableand remembering his/her own experience, we propose a novel network structure called the hierarchical graphSpecifically, we construct the multi-agent system as a graph, use a novel graph convolution structureBased on the above technologies, we propose a value-based MADRL algorithm called Soft-HGRN and its actor-criticExperimental results based on three homogeneous tasks and one heterogeneous environment not only show

Keywords： Deep reinforcement learning     Graph-based communication     Maximum-entropy learning     Partial observability    

HTML PDF Collect

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

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: In this paper, a large-scale underwater crack examination method is proposed based on image stitchingThe graph convolutional neural network (GCN) was used to segment the stitched image.

Keywords： underwater cracks     remote operated vehicle     image stitching     image segmentation     graph convolutional    

HTML PDF Collect

Erratum to: Efficient keyword search over graph-structured data based on minimal covered Erratum

Asieh Ghanbarpour, Abbas Niknafs, Hassan Naderi,naderi@iust.ac.ir

Frontiers of Information Technology & Electronic Engineering 2020, Volume 21, Issue 6,   Pages 809-962 doi: 10.1631/FITEE.18e0133

Abstract: Unfortunately the second author’s name has been misspelt. It should be read: Abbas NIKNAFS.
HTML PDF Collect

Classifying multiclass relationships between ASes using graph convolutional network

Frontiers of Engineering Management   Pages 653-667 doi: 10.1007/s42524-022-0217-1

Abstract: However, business-based sibling relationships and structure-based exchange relationships have becomeWe then introduce new features and propose a graph convolutional network (GCN) framework, AS-GCN, to

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

HTML PDF Collect

A DNA Computing Model for the Graph Vertex Coloring Problem Based on a Probe Graph Article

Jin Xu, Xiaoli Qiang, Kai Zhang, Cheng Zhang, Jing Yang

Engineering 2018, Volume 4, Issue 1,   Pages 61-77 doi: 10.1016/j.eng.2018.02.011

Abstract: overcome this bottleneck and improve the processing speed, we propose a DNA computing model to solve the graphIn this article, a 3-colorable graph with 61 vertices is used to illustrate the capability of the DNAThe experiment showed that not only are all the solutions of the graph found, but also more than 99%The powerful computational capability of the model was based on specific reactions among the large number

Keywords： DNA computing     Graph vertex coloring problem     Polymerase chain reaction    

HTML PDF Collect

Development of an artificial intelligence diagnostic model based on dynamic uncertain causality graph

Yang Jiao, Zhan Zhang, Ting Zhang, Wen Shi, Yan Zhu, Jie Hu, Qin Zhang

Frontiers of Medicine 2020, Volume 14, Issue 4,   Pages 488-497 doi: 10.1007/s11684-020-0762-0

Abstract: artificial intelligence (AI) diagnosis model was constructed according to the dynamic uncertain causality graphknowledge-based editor.

Keywords： knowledge representation     uncertain     causality     graphical model     artificial intelligence     diagnosis     dyspnea    

HTML PDF Collect

Level set band method: A combination of density-based and level set methods for the topology optimization

Peng WEI, Wenwen WANG, Yang YANG, Michael Yu WANG

Frontiers of Mechanical Engineering 2020, Volume 15, Issue 3,   Pages 390-405 doi: 10.1007/s11465-020-0588-0

Abstract: two decades, but it still has not been widely applied to practical engineering problems as density-basedmethods do.the reasons is that it acts as a boundary evolution algorithm, which is not as flexible as density-basedmethods at controlling topology changes.functions by incorporating one parameter, namely, level set band, to seamlessly combine LSM and density-based

Keywords： level set method     topology optimization     density-based method     level set band    

HTML PDF Collect

Imbalanced fault diagnosis of rotating machinery using autoencoder-based SuperGraph feature learning

Frontiers of Mechanical Engineering 2021, Volume 16, Issue 4,   Pages 829-839 doi: 10.1007/s11465-021-0652-4

Abstract: Existing fault diagnosis methods usually assume that there are balanced training data for every machineIt will degrade the performance of fault diagnosis methods significantly.To address this problem, an imbalanced fault diagnosis of rotating machinery using autoencoder-basedAnd the edge connections in the graph depend on the relationship between signals.On the basis, graph convolution is performed on the constructed SuperGraph to achieve imbalanced training

Keywords： imbalanced fault diagnosis     graph feature learning     rotating machinery     autoencoder    

HTML PDF Collect

Topology optimization based on reduction methods with applications to multiscale design and additive

Emmanuel TROMME, Atsushi KAWAMOTO, James K. GUEST

Frontiers of Mechanical Engineering 2020, Volume 15, Issue 1,   Pages 151-165 doi: 10.1007/s11465-019-0564-8

Abstract: In this paper, a method based on reduction techniques is proposed to perform efficiently topology optimization

Keywords： multiscale topology optimization     micro-structure     additive manufacturing     reduction techniques     substructuring     static condensation     super-element    

HTML PDF Collect

Test-driven verification/validation of model transformations

László LENGYEL,Hassan CHARAF

Frontiers of Information Technology & Electronic Engineering 2015, Volume 16, Issue 2,   Pages 85-97 doi: 10.1631/FITEE.1400111

Abstract: In this way, verification/validation methods can guarantee different requirements stated by the actualThis paper emphasizes the necessity of methods that make model transformation verified/validated, discusses

Keywords： Graph rewriting based model transformations     Verification/validation     Test-driven verification    

HTML PDF Collect

QUANTITATIVE STUDY ON ANTI-PEST ACTIVITY OF NATURAL PRODUCTS BASED ON VISUALIZATION FRAMEWORK OF KNOWLEDGEGRAPH

Frontiers of Agricultural Science and Engineering 2023, Volume 10, Issue 2,   Pages 306-332 doi: 10.15302/J-FASE-2023488

Abstract:

● Using visual analysis to predict the trend of natural product pest resistance.

Keywords： anti-pest activity     crop protection     insect pest     natural product     visual analysis    

HTML PDF Collect

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    

HTML PDF Collect

Title Author Date Type Operation

A graph-based two-stage classification network for mobile screen defect inspection

Chaofan ZHOU, Meiqin LIU, Senlin ZHANG, Ping WEI, Badong CHEN

Journal Article

A systematic graph-based method for the kinematic synthesis of non-anthropomorphic wearable robots for

Fabrizio SERGI, Dino ACCOTO, Nevio L. TAGLIAMONTE, Giorgio CARPINO, Eugenio GUGLIELMELLI

Journal Article

Stochastic extra-gradient based alternating direction methods for graph-guided regularizedminimization

Qiang LAN, Lin-bo QIAO, Yi-jie WANG

Journal Article

Soft-HGRNs: soft hierarchical graph recurrent networks for multi-agent partially observable environments

Yixiang REN, Zhenhui YE, Yining CHEN, Xiaohong JIANG, Guanghua SONG,yixiangren@zju.edu.cn,zhenhuiye@zju.edu.cn,ch19930611@zju.edu.cn,ghsong@zju.edu.cn

Journal Article

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

Wenxuan CAO; Junjie LI

Journal Article

Erratum to: Efficient keyword search over graph-structured data based on minimal covered

Asieh Ghanbarpour, Abbas Niknafs, Hassan Naderi,naderi@iust.ac.ir

Journal Article

Classifying multiclass relationships between ASes using graph convolutional network

Journal Article

A DNA Computing Model for the Graph Vertex Coloring Problem Based on a Probe Graph

Jin Xu, Xiaoli Qiang, Kai Zhang, Cheng Zhang, Jing Yang

Journal Article

Development of an artificial intelligence diagnostic model based on dynamic uncertain causality graph

Yang Jiao, Zhan Zhang, Ting Zhang, Wen Shi, Yan Zhu, Jie Hu, Qin Zhang

Journal Article

Level set band method: A combination of density-based and level set methods for the topology optimization

Peng WEI, Wenwen WANG, Yang YANG, Michael Yu WANG

Journal Article

Imbalanced fault diagnosis of rotating machinery using autoencoder-based SuperGraph feature learning

Journal Article

Topology optimization based on reduction methods with applications to multiscale design and additive

Emmanuel TROMME, Atsushi KAWAMOTO, James K. GUEST

Journal Article

Test-driven verification/validation of model transformations

László LENGYEL,Hassan CHARAF

Journal Article

QUANTITATIVE STUDY ON ANTI-PEST ACTIVITY OF NATURAL PRODUCTS BASED ON VISUALIZATION FRAMEWORK OF KNOWLEDGEGRAPH

Journal Article

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

Journal Article