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检索条件"任意字段=2nd European Conference on Model Driven Architecture - Foundations and Applications"

共 72 条记录，以下是1-10 订阅

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8th Workshop on Software architecture Erosion and Architectural Consistency, SAEroCon 2021, 1st International Workshop on Software architecture and Machine Learning, SAML 2021, 4th International Workshop on Context-aware, Autonomous and Smart architecture, CASA 2021, 5th Workshop on Formal Approaches for Advanced Computing Systems, FAACS 2021, 2nd International Workshop on model-driven Engineering for Software architecture, MDE4SA 2021 and 1st International Workshop on Mining Software Repositories for Software architecture, MSR4SA 2021 which complemented 15th european conference on Software architecture, ECSA 2021

8th Workshop on Software Architecture Erosion and Architectu...

引用

8th Workshop on Software architecture Erosion and Architectural Consistency, SAEroCon 2021, 1st International Workshop on Software architecture and Machine Learning, SAML 2021, 4th International Workshop on Context-aware, Autonomous and Smart architecture, CASA 2021, 5th Workshop on Formal Approaches for Advanced Computing Systems, FAACS 2021, 2nd International Workshop on model-driven Engineering for Software architecture, MDE4SA 2021 and 1st International Workshop on Mining Software Repositories for Software architecture, MSR4SA 2021 which complemented 15th european conference on Software architecture, ECSA 2021

ISBN: (纸本)9783031151156

The proceedings contain 15 papers presendted at a virtual meeting. The special focus in this conference is on Software architecture Erosion and Architectural Consistency. The topics include: Interactive Elicitation of Resilience Scenarios Based on Hazard Analysis Techniques;Towards an Extensible Approach for Generative Microservice Development and Deployment Using LEMMA;applying Knowledge-driven architecture Composition with Gabble;architectural Optimization for Confidentiality Under Structural Uncertainty;foundations and Research Agenda for Simulation of Smart Ecosystems architectures;Blended Graphical and Textual modelling of UML-RT State-Machines: An Industrial Experience;toward Awareness Creation of Common Challenges Women are Facing in Academia: A Study from a German Perspective;mapping Source Code to Modular architectures Using Keywords;hierarchical Code-to-architecture Mapping;Building the MSR Tool Kaiaulu: Design Principles and Experiences;self-adaptive Machine Learning Systems: Research Challenges and Opportunities;behavioral Maps: Identifying Architectural Smells in Self-adaptive Systems at Runtime;an Architectural Approach for Enabling and Developing Cooperative Behaviour in Diverse Autonomous Robots.

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STAF-WS 2024 - Proceedings of the STAF 2024 Workshops: AgileMDE 2024, LLM4MDE 2024, and MeSS 2024, co-located with the International conference on Software Technologies: applications and foundations, STAF 2024

STAF-WS 2024 - Proceedings of the STAF 2024 Workshops: Agile...

引用

2024 Symposium on Software Technologies: applications and foundations, STAF 2024 Workshops: 2nd International Workshop on Agile model-driven Engineering, AgileMDE 2024, 1st Large Language models for model-driven Engineering Workshop, AgileMDE 2024 and 4th International Workshop on model-driven Engineering for Smart Internet-of-Things Systems, MeSS 2024

The proceedings contain 8 papers. The topics discussed include: research directions for agile model-driven engineering;software modelling for sustainable software engineering;software language translation by example;towards agile collaborative multi-view modeling with inconsistency tolerance;model driven engineering in finance: bond valuation, from formulae to code;from image to UML: first results of image-based UML diagram generation using LLMs;LowcoBot: Towards chatting with low-code platforms;digital twins for IoT systems: exploiting synergies between industry and academia;and towards quantum-based graph matching for IoT systems.

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Pipeline Integrity Analysis through Data-driven Approaches

Pipeline Integrity Analysis through Data-Driven Approaches

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2nd Joint conference of the Construction Research Congress (CRC) / Construction-Institute (CI) Summit

作者： Duan, Junyi Tao, Chengcheng Huang, Ying Purdue Univ Sch Construct Management Technol W Lafayette IN 47907 USA North Dakota State Univ Dept Civil Construct & Environm Engn Fargo ND USA

ISBN: (数字)9780784485262

ISBN: (纸本)9780784485262

Pipelines are the most widely used energy transportation infrastructure in the world. Pipeline failures caused by aging, corrosion, cracks, and damages may result in irreparable societal, economic, and environmental consequences. Therefore, pipeline safety and integrity are crucial to the robustness of modern societies. Robust failure models have the potential to effectively reduce the emergent risk and the probability of hazards in pipeline construction, and extend the service life of pipeline infrastructure. In this paper, we apply a data-driven approach, Gaussian Process Regression (GPR), to predict the failure of the oil pipeline. We train and test the models based on historical public data from a report on european cross-country oil pipelines. The best model type is selected with different variable combinations. Variables such as pipeline diameter, service, and gross spillage volume are considered in the models. The data-driven pipeline failure model will provide pipeline operators with an effective way to evaluate pipeline conditions and guide pipeline rehabilitation and maintenance. The model can also be used to support pipeline integrity management based on the ASME code, which can extend the service life of the pipeline infrastructure and bring benefits to the environment and the economy.

关键词： Petroleum pipelines

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Enhancing the Assessment of Digital Archive Maturity using Enterprise architecture 17

Enhancing the Assessment of Digital Archive Maturity using E...

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17th IFIP WG 8.1 Working conference on the Practice of Enterprise modeling Forum, 1st International Workshop on models for Simulation, M4S 2024, 2nd International Workshop on the foundations and applications of Capabilities in Enterprises, Transformations, and ESG Initiatives, FACETE 2024, Workshop on Advancing Enterprise modelling through Digital Transformation, FAIR Data Management, and Blockchain Integration, AEM 2024, Tools and Demos, PoEM-Companion 2024

作者： Cruz, Miguel Guerreiro, Sérgio Borbinha, José Proença, Diogo INESC-ID Rua Alves Redol 9 Lisbon1000-029 Portugal Instituto Superior Técnico Universidade de Lisboa Portugal

This paper addresses the problem of evaluating the maturity of a digital archive in terms of its digital preservation capabilities. The european eArchiving initiative supports this assessment by providing the key tools of: a maturity model (eACMM), a reference architecture (eRA), and a collection of real-world cases based on the eRA framework. To our knowledge, this is the first paper to integrate these artifacts for a comprehensive evaluation. The eACMM is represented by a set of detailed requirements classified by importance. The eRA is represented by an ArchiMate model, and the real-world cases are represented as ArchiMate eRA viewpoints. This paper details the process of how to assess the real-world cases against the eACMM and demonstrates the value of such viewpoints. One significant challenge addressed in the literature is the lack of effective view models for identifying gaps in reference architectures. Despite the systematic prescription offered by maturity models, they often lack the support of mechanisms that are necessary to fully realize the potential of diagnosing the current stage and identifying the next steps to increase maturity. This paper contributes with a solution implemented in a Enterprise architecture management tool where the output is the automatic creation of Enterprise architecture blueprints. Obtained results corroborate that our solution easily identifies the level of eACMM maturity in respect to eRA concepts, and also an immediate identification of actions that should be done to improve the eACMM level. © 2024 Copyright for this paper by its authors.

关键词： Blueprint Digital Archiving Maturity model Reference architecture Viewpoint

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Replay-driven Continual Learning for the Industrial Internet of Things 2

Replay-Driven Continual Learning for the Industrial Internet...

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IEEE/ACM 2nd International conference on AI Engineering - Software Engineering for AI (CAIN)

作者： Sen, Sagar Nielsen, Simon Myklebust Husom, Erik Johannes Goknil, Arda Tverdal, Simeon Pinilla, Leonardo Sastoque SINTEF Digital Sustainable Commun Technol Oslo Norway Univ Oslo Dept Informat Oslo Norway Univ Basque Country CFAA Zamudio Spain

ISBN: (纸本)9798350301137

The Industrial Internet of Things (IIoT) leverages thousands of interconnected sensors and computing devices to monitor and control large and complex industrial processes. Machine learning (ML) applications in IIoT use data acquired from multiple sensors to perform tasks such as predictive maintenance. While remembering useful learning from the past, these applications need to adapt learning for evolving sensor data stemming from changes in industrial processes and environmental conditions. This paper presents a continual learning pipeline to learn from the evolving data while replaying selected parts of the old data. The pipeline is configured to produce ML experiences (e.g., training a baseline neural network model), improve the baseline model with the new data while replaying part of the old data, and infer/predict using a specific model version given a stream of IIoT sensor data. We have evaluated our approach from an AI Engineering perspective using three industrial case studies, i.e., predicting tool wear, remaining useful lifetime, and anomalies from sensor data acquired from CNC machining and broaching operations. Our results show that configuring experiences for replay-driven continual learning allows dynamic maintenance of ML performance on evolving data while minimizing the excessive accumulation of legacy sensor data.

关键词： Continual Learning Industrial IoT Machine Learning

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Toward an Automated Co-Evolution for Meta Attack Languages and models 17

Toward an Automated Co-Evolution for Meta Attack Languages a...

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作者： Siljefors, Andreas Hacks, Simon Stockholm University Stockholm Sweden

The rapid evolution of software systems often necessitates corresponding changes in their underlying models. However, ensuring these models remain consistent with their evolving meta-models is a complex challenge within model-driven Engineering (MDE). This work addresses this challenge by adapting and implementing a model co-evolution approach within the Meta Attack Language (MAL), a domain-specific language for modeling cyber threats. The primary goal of this research is to develop a prototype system that can automatically generate migration strategies to restore or improve the conformance between models and their meta-models following meta-model changes. It utilizes a multi-objective evolutionary algorithm (NSGA-II) to explore the search space for possible edit operations. The system was designed to prioritize correctness and reduce manual effort while accommodating requirements drawn from a design process backed by a conceptual framework developed from subsequent literature. A series of controlled experiments evaluated the prototype system against several models derived from the MAL language coreLang. The results demonstrate that the system does provide some effort in reestablishing model conformance, although with some performance variability. Our work contributes to the field of MDE by providing insights into the practical application of model co-evolution techniques in a domain-specific context. The developed prototype is a foundation for future research to refine these techniques and expand their applicability to a broader range of modeling scenarios. © 2024 Copyright for this paper by its authors.

关键词： Domain Specific Languages Meta Attack Language model Co-evolution model-driven Engineering

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Improving Process Yield Through Manufacturing Digital Twin Using Conditional Synthetic Data Engine (COSYNE) 27

Improving Process Yield Through Manufacturing Digital Twin U...

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27th european conference on Artificial Intelligence, ECAI 2024

作者： Chandra, Shantanu Duvinage, Matthieu Prakash, P.K.S. Davis, Patrick Timmer, Sander ZS 2nd Floor MFAR Manyata Tech park Phase IV Manayata Tech Park Nagavara Karnataka Bengaluru India GSK 20 Avenue Fleming Wavre Belgium

ISBN: (纸本)9781643685489

The pharmaceutical industry must adhere to rigorous regulations to meet specific quality standards. Additionally, the intricate nature of pharmaceutical manufacturing processes and long time to production necessitates timely detection of batch failures. AI/ML models are used for predictive maintenance in an automated and data-driven manner to detect these failures and aid timely intervention. However, these models require substantial amount of data for model training. This can lead to extended time-to-value before a predictive monitoring system can be deployed for any new process due to long process lead times. The current research proposes COSYNE, a generative AI-based approach to generate manufacturing digital twin, reducing the model development time by augmenting synthetic data with real data. The proposed solution is validated on a large pharmaceutical company’s batch manufacturing dataset, and the results are benchmarked across multiple dimensions of generation quality. Empirical results demonstrate that the proposed COSYNE outperforms the state-of-the-art approach by 2-3 times on average across all the generation quality metrics. Moreover, COSYNE enhances downstream AI/ML performance significantly through data augmentation and reduces time-to-value by creating high-fidelity digital twins with only 10% of real data and still achieve similar performance as current baseline trained on entire real data. © 2024 The Authors.

关键词： Predictive maintenance

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Replay-driven Continual Learning for the Industrial Internet of Things

Replay-Driven Continual Learning for the Industrial Internet...

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AI Engineering - Software Engineering for AI (WAIN), IEEE/ACM Workshop on

作者： Sagar Sen Simon Myklebust Nielsen Erik Johannes Husom Arda Goknil Simeon Tverdal Leonardo Sastoque Pinilla SINTEF Digital Sustainable Communication Technologies Oslo Norway Department of Informatics University of Oslo Oslo Norway CFAA University of the Basque Country Zamudio Spain

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Best Practice for Boosting Innovation in the HEI through the Initiative of european University Alliances 20

Best Practice for Boosting Innovation in the HEI through the...

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20th IEEE International conference on Emerging eLearning Technologies and applications, ICETA 2022

作者： Fedak, V. Hudak, R. Jakab, F. Technical University of Kosice Kosice Slovakia

ISBN: (数字)9798350320336

ISBN: (纸本)9798350320336

The contribution describes how the european University Alliances boost innovation through innovative elements in their project structure. Specifically, the best practice from the Ulysseus european University Alliance, in which the Technical University of Kosice and 5 more european universities are partnering, is described. Ulysseus Innovation Hubs are centre of the gravity of partners' collaboration, sharpening the co-creation process for transdisciplinary and challenge-driven education, intertwined with research and knowledge transfer, and the promotion of citizen involvement and european values. © 2022 IEEE.

关键词： Knowledge management

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Localized active learning of Gaussian process state space models 2

Localized active learning of Gaussian process state space mo...

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2nd Annual conference on Learning for Dynamics and Control (L4DC)

作者： Capone, Alexandre Noske, Gerrit Umlauft, Jonas Beckers, Thomas Lederer, Armin Hirche, Sandra Tech Univ Munich Dept Elect & Comp Engn Chair Informat Oriented Control D-80333 Munich Germany

While most dynamic system exploration techniques aim to achieve a globally accurate model, this is generally unsuited for systems with unbounded state spaces. Furthermore, many applications do not require a globally accurate model, e.g., local stabilization tasks. In this paper, we propose an active learning strategy for Gaussian process state space models that aims to obtain an accurate model on a bounded subset of the state-action space. Our approach aims to maximize the mutual information of the exploration trajectories with respect to a discretization of the region of interest. By employing model predictive control, the proposed technique integrates information collected during exploration and adaptively improves its exploration strategy. To enable computational tractability, we decouple the choice of most informative data points from the model predictive control optimization step. This yields two optimization problems that can be solved in parallel. We apply the proposed method to explore the state space of various dynamical systems and compare our approach to a commonly used entropy-based exploration strategy. In all experiments, our method yields a better model within the region of interest than the entropy-based method.

关键词： exploration Bayesian inference data-driven control model predictive control

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