A Service-Oriented Tool-Chain for Model-Based Systems Engineering of Aero-Engines

Lu, Jinzhi; Wang, Jiqiang; Chen, DeJiu; Wang, Jian; Törngren, Martin

doi:10.1109/access.2018.2868055

Cited by 19 publications

(18 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Nesse contexto, o Eclipse Lyo [El-khoury 2016] propõe um gerador de código-fonte aberto para interfaces de ferramentas em conformidade com OSLC no ambiente eclipse, que foi citado por artigos que foram usados no desenvolvimento de adaptadores OSLC. Ainda, há trabalhos que propõem abordagens para apoiar a integração ou transformação de modelos heterogêneos como BPMN, UML, SysML, SoaML e EMF [d. Martino et al 2016], [Lu et al 2018], [Arnould 2018], [Mustafa and Labiche 2017], [Zhang and Møller-Pedersen 2013], , .…”

Section: Análise Dos Resultadosunclassified

Open Services for Lifecycle Collaboration: Um Estudo de Mapeamento Sistemático

Ferreira¹,

Torres²,

Basso³

et al. 2020

Anais Da IV Escola Regional De Engenharia De Software (ERES 2020)

View full text Add to dashboard Cite

A indústria de software investe em ferramentas modernas ao longo de todo o ciclo de desenvolvimento de software. No entanto, existem desafios para alcançar um ambiente integrado de ponta a ponta, como por exemplo estabelecer a rastreabilidade dos artefatos. Para mitigar esses desafios, muitas abordagens foram propostas para integração de ferramentas de software. Nesse contexto, o Open Services for Lifecycle Collaboration (OSLC) é um padrão aberto para interoperabilidade de ferramentas, que permite a federação de dados ao longo do ciclo de vida de aplicações de Engenharia de Software (ES). Este artigo apresenta um estudo de mapeamento sistemático sobre OSLC, analisando 59 estudos primários e abordando questões de integração.

show abstract

Section: Análise Dos Resultadosunclassified

Open Services for Lifecycle Collaboration: Um Estudo de Mapeamento Sistemático

Ferreira¹,

Torres²,

Basso³

et al. 2020

Anais Da IV Escola Regional De Engenharia De Software (ERES 2020)

View full text Add to dashboard Cite

show abstract

“…The efficiency refers to the relative amount of time spent when stakeholders implement their design jobs by the tool-chain, which is evaluated using a quantitative approach. The efficiency metrics include: 1) time-saving rate focusing on the scenario that automated decision-making supports parameter value selection; it refers to the ratio of the total time needed with automated parameter value selection to the total time needed without automated parameter value selection and 2) automation-level focusing automated tool operations [36]. It refers to the number of tool operations that are implemented automatically in the tool-chain.…”

Section: Evaluation Using Case Studymentioning

confidence: 99%

“…The Worktask Graph formalizes design information of ADS using the following Objects. Compared with previous work [36], Fig. 3 shows a decisionmaking process and related metamodels to formalize and to support automated parameter value selection.…”

Section: ) Relationship Refers To One Connection Existing Betweenmentioning

confidence: 99%

See 1 more Smart Citation

Model-Based Systems Engineering Tool-Chain for Automated Parameter Value Selection

Chen

Wang

et al. 2022

IEEE Trans. Syst. Man Cybern, Syst.

Self Cite

View full text Add to dashboard Cite

Cyber-physical systems (CPSs) integrate heterogeneous systems and process sensor data using digital services. As the complexity of CPS increases, it becomes more challenging to efficiently formalize the integrated multidomain views with flexible automated verification across the entire lifecycle. This article illustrates a model-based systems engineering tool-chain to support CPS development with an emphasis on automated parameter value selection for co-simulation. First, a domain-specific modeling approach is introduced to support the formalizations of CPS artifacts, development processes, and simulation configurations. The domain-specific models are used as the basis to generate a Web-based process management system for automated parameter value selections, which coordinates Open Services for Lifecycle Collaboration services of development information and technical resources (models, data, and tools) in order to support automated co-simulation. The services are deployed by a service orchestrator based on a decision-making algorithm for parameter value selection. Finally, developers make use of the WPMS to implement simulations and to select system parameter values for co-simulation automatically. The approach is illustrated by a case study on auto-braking system development and we evaluate the efficiency of this tool-chain by both qualitative and quantitative methods. The results show that parameter values are selected more efficiently and effectively when implementing co-simulations using our tool-chain. Index Terms-Automated parameter value selection, cyberphysical systems (CPSs), model-based systems engineering (MBSE).

show abstract

“…When it comes to the deployment phase, there will be a lot of engineering implementation problems [104,105]. Realizing an intelligent textile system in the real textile manufacturing process covers many aspects, involving the Internet of ings (IoT) [106], cyber-physical systems (CPS) [107], and more [108,109].…”

Section: Application and Deploymentmentioning

confidence: 99%

Fabric Defect Detection in Textile Manufacturing: A Survey of the State of the Art

et al. 2021

Security and Communication Networks

View full text Add to dashboard Cite

Defects in the textile manufacturing process lead to a great waste of resources and further affect the quality of textile products. Automated quality guarantee of textile fabric materials is one of the most important and demanding computer vision tasks in textile smart manufacturing. This survey presents a thorough overview of algorithms for fabric defect detection. First, this review briefly introduces the importance and inevitability of fabric defect detection towards the era of manufacturing of artificial intelligence. Second, defect detection methods are categorized into traditional algorithms and learning-based algorithms, and traditional algorithms are further categorized into statistical, structural, spectral, and model-based algorithms. The learning-based algorithms are further divided into conventional machine learning algorithms and deep learning algorithms which are very popular recently. A systematic literature review on these methods is present. Thirdly, the deployments of fabric defect detection algorithms are discussed in this study. This paper provides a reference for researchers and engineers on fabric defect detection in textile manufacturing.

show abstract

A Service-Oriented Tool-Chain for Model-Based Systems Engineering of Aero-Engines

Cited by 19 publications

References 19 publications

Open Services for Lifecycle Collaboration: Um Estudo de Mapeamento Sistemático

Open Services for Lifecycle Collaboration: Um Estudo de Mapeamento Sistemático

Model-Based Systems Engineering Tool-Chain for Automated Parameter Value Selection

Fabric Defect Detection in Textile Manufacturing: A Survey of the State of the Art

Contact Info

Product

Resources

About