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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?Verified/validated model transformations make it possible to ensure certain properties of the generatedof 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 transformationstransformations.

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

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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: The artificial intelligence (AI) diagnosis model was constructed according to the dynamic uncertain causalitygraph knowledge-based editor.The model contained 132 variables of symptoms, signs, and serological and imaging parameters.The overall diagnostic accuracy rate of the model was 96.5%.In conclusion, the diagnostic accuracy of the AI model is promising and may compensate for the limitation

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

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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: First, an MSR model is developed to learn MSRs automatically and further obtain fault recognition resultsSecond, centrality measures are employed to analyze the MSR graphs learned by the MSR model, and fault

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

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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: the graph vertex coloring problem.In this article, a 3-colorable graph with 61 vertices is used to illustrate the capability of the DNAcomputing model.The 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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Efficacy of intelligent diagnosis with a dynamic uncertain causality graph model for rare disorders of

Dongping Ning, Zhan Zhang, Kun Qiu, Lin Lu, Qin Zhang, Yan Zhu, Renzhi Wang

Frontiers of Medicine 2020, Volume 14, Issue 4,   Pages 498-505 doi: 10.1007/s11684-020-0791-8

Abstract: On the basis of the principles and algorithms of dynamic uncertain causality graph (DUCG), a diagnosismodel for DSD was jointly constructed by experts on DSD and engineers of artificial intelligence.The model had an accuracy of 94.1%, which was significantly higher than that of interns and third-yearIn conclusion, the DUCG model has broad application prospects as a computer-aided diagnostic tool for

Keywords： disorders of sex development (DSD)     intelligent diagnosis     dynamic uncertain causality graph    

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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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Identification of sources, characteristics and photochemical transformations of dissolved organic matter

Frontiers of Environmental Science & Engineering 2021, Volume 15, Issue 5, doi: 10.1007/s11783-020-1340-z

Abstract:

• The source of DOM in surface water and sediment is inconsistent.

Keywords： Dissolved organic matter     Parallel factor analysis     Excitation-emission matrices     Photodegradation    

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Research on polyhydroxyalkanoates and glycogen transformations: Key aspects to biologic nitrogen and

Hongjing LI, Yinguang CHEN

Frontiers of Environmental Science & Engineering 2011, Volume 5, Issue 2,   Pages 283-290 doi: 10.1007/s11783-010-0243-9

Abstract: In this paper, a study was conducted on the effect of polyhydroxyalkanoates (PHA) and glycogen transformationsFewer poly-3-hydroxybutyrate (PHB), total PHA, and glycogen transformations were observed with the increaseAccordingly, PHA and glycogen transformations should be taken into account as key components for optimizing

Keywords： low dissolved oxygen (DO)     biological nitrogen and phosphorus removal     polyhydroxyalkanoates (PHA)     glycogen    

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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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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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A 7-year follow-up study of the features and transformations of elderly male patients with OGTT-1h hyperglycemia

TIAN Hui, LI Chunlin, ZHONG Wenwen, PAN Changyu, LU Juming, CAO Xiutang

Frontiers of Medicine 2008, Volume 2, Issue 4,   Pages 396-399 doi: 10.1007/s11684-008-0076-0

Abstract: The aim of this paper is to investigate the clinical features and transformation of elderly male patients with normal blood glucose levels at fasting and 2 hours after glucose intake but with hyperglycemia (≥ 11.1 mmol/L) 1 hour after oral glucose tolerance test (OGTT-1h HG). Seven years of follow-up visits were performed on 189 elderly male outpatients with OGTT-1h HG and data was recorded on their body mass index (BMI), blood pressure, serum cholesterol and triglyceride test results and on their glucose tolerance changes every 1–2 years after taking OGTT; their possible causes were analysed. Follow-up visits revealed that 19 patients with OGTT-1h HG were diagnosed with diabetes (10.1%), 78 patients with impaired glucose tolerance (IGT, 41.3%), 2 patients transformed to normal glucose tolerance (NGT, 1.1%) and the remaining 90 patients (47.6%) remained unchanged. Synchronized comparison with IGT patients showed that the ratio of OGTT-1h HG patients turning to diabetes was lower than that of IGT patients (21.1%, = 13.05, < 0.01), and the ratio of OGTT-1h HG patients transforming to NGT was slightly higher (0.4%, = 2.46, > 0.05). The prevalence of complications of hypertension, coronary heart disease, cerebral vascular diseases and dyslipidemia in patients with OGTT-1h HG were higher than those with NGT ( < 0.05) and were similar to that of IGT patients. As a special phenotype of OGTT and as part of an abnormal glucose tolerance conformation, patients with OGTT-1h HG warrant special attention, since about half of them were found to have developed diabetes or IGT, and their risk of suffering from vascular diseases were also increased.

Keywords： special attention     prevalence     unchanged     dyslipidemia     elderly    

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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 Novel MILP Model Based on the Topology of a Network Graph for Process Planning in an Intelligent Manufacturing Article

Qihao Liu, Xinyu Li, Liang Gao

Engineering 2021, Volume 7, Issue 6,   Pages 807-817 doi: 10.1016/j.eng.2021.04.011

Abstract: This paper proposes a novel mixed-integer linear programming (MILP) mathematical model by consideringFurthermore, the proposed model can be programmed in commonly-used mathematical programming solvers,To verify the effectiveness and generality of the proposed model, five groups of numerical experimentsThe results show that the proposed model can solve PP problems effectively and can obtain better solutions

Keywords： Process planning     Network     Mixed-integer linear programming     CPLEX    

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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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Title Author Date Type Operation

Test-driven verification/validation of model transformations

László LENGYEL,Hassan CHARAF

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

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

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

Efficacy of intelligent diagnosis with a dynamic uncertain causality graph model for rare disorders of

Dongping Ning, Zhan Zhang, Kun Qiu, Lin Lu, Qin Zhang, Yan Zhu, Renzhi Wang

Journal Article

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

Wenxuan CAO; Junjie LI

Journal Article

Identification of sources, characteristics and photochemical transformations of dissolved organic matter

Journal Article

Research on polyhydroxyalkanoates and glycogen transformations: Key aspects to biologic nitrogen and

Hongjing LI, Yinguang CHEN

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

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

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

Journal Article

A 7-year follow-up study of the features and transformations of elderly male patients with OGTT-1h hyperglycemia

TIAN Hui, LI Chunlin, ZHONG Wenwen, PAN Changyu, LU Juming, CAO Xiutang

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

A Novel MILP Model Based on the Topology of a Network Graph for Process Planning in an Intelligent Manufacturing

Qihao Liu, Xinyu Li, Liang Gao

Journal Article

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

Journal Article