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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:

The choice of non-anthropomorphic kinematic solutions for wearable robots is motivated both by the necessity of improving the ergonomics of physical Human-Robot Interaction and by the chance of exploiting the intrinsic dynamical properties of the robotic structure so to improve its performances. Under these aspects, this new class of robotic solutions is potentially advantageous over the one of anthropomorphic robotic orthoses. However, the process of kinematic synthesis of non-anthropomorphic wearable robots can be too complex to be solved uniquely by relying on conventional synthesis methods, due to the large number of open design parameters. A systematic approach can be useful for this purpose, since it allows to obtain the complete list of independent kinematic solutions with desired properties. In this perspective, this paper presents a method, which allows to generalize the problem of kinematic synthesis of a non-anthropomorphic wearable robot for the assistance of a specified set of contiguous body segments. The methodology also includes two novel tests, specifically devised to solve the problem of enumeration of kinematic structures of wearable robots: the HR-isomorphism and the HR-degeneracy tests. This method has been implemented to derive the atlas of independent kinematic solutions suitable to be used for the kinematic design of a planar wearable robot for the lower limbs.

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

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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    

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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: and 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 structureto achieve communication between heterogeneous neighboring agents, and adopt a recurrent unit to enableBased 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    

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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    

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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.
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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    

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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    

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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    

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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    

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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: 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 trainingeffectively achieve rotating machinery fault diagnosis towards imbalanced training dataset through graph

Keywords： imbalanced fault diagnosis     graph feature learning     rotating machinery     autoencoder    

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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: Why is it important to verify/validate model transformations? The motivation is to improve the quality of the transformations, and therefore the quality of the generated software artifacts. Verified/validated model transformations make it possible to ensure certain properties of the generated software artifacts. In this way, verification/validation methods can guarantee different requirements stated by the actual domain against the generated/modified/optimized software products. For example, a verified/validated model transformation can ensure the preservation of certain properties during the model-to-model transformation. This paper emphasizes the necessity of methods that make model transformation verified/validated, discusses the different scenarios of model transformation verification and validation, and introduces the principles of a novel test-driven method for verifying/validating model transformations. We provide a solution that makes it possible to automatically generate test input models for model transformations. Furthermore, we collect and discuss the actual open issues in the field of verification/validation of model transformations.

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

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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    

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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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Efficient keyword search over graph-structured data based on minimal covered Article

Asieh GHANBARPOUR, Khashayar NIKNAFS, Hassan NADERI

Frontiers of Information Technology & Electronic Engineering 2020, Volume 21, Issue 3,   Pages 448-464 doi: 10.1631/FITEE.1800133

Abstract: Keyword search is an alternative for structured languages in querying graph-structured data.

Keywords： Keyword search     Graph mining     Information retrieval     Database     Clique    

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Large-scale graph processing systems: a survey Review

Ning LIU, Dong-sheng LI, Yi-ming ZHANG, Xiong-lve LI

Frontiers of Information Technology & Electronic Engineering 2020, Volume 21, Issue 3,   Pages 384-404 doi: 10.1631/FITEE.1900127

Abstract: Graph is a significant data structure that describes the relationship between entries.Many application domains in the real world are heavily dependent on graph data.However, graph applications are vastly different from traditional applications.of specific graph processing platforms.thoroughly analyze the implementation technologies including programming models, partitioning strategies, communication

Keywords： Graph workloads     Graph applications     Graph processing systems    

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Title Author Date Type Operation

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

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

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

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

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

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

Journal Article

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

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

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

Asieh GHANBARPOUR, Khashayar NIKNAFS, Hassan NADERI

Journal Article

Large-scale graph processing systems: a survey

Ning LIU, Dong-sheng LI, Yi-ming ZHANG, Xiong-lve LI

Journal Article