Titlebook: Software Engineering and Formal Methods. SEFM 2022 Collocated Workshops; AI4EA, F-IDE, CoSim- Paolo Masci,Cinzia Bernardeschi,Maurizio Palm

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书目名称Software Engineering and Formal Methods. SEFM 2022 Collocated Workshops影响因子(影响力)学科排名




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书目名称Software Engineering and Formal Methods. SEFM 2022 Collocated Workshops网络公开度学科排名




书目名称Software Engineering and Formal Methods. SEFM 2022 Collocated Workshops被引频次




书目名称Software Engineering and Formal Methods. SEFM 2022 Collocated Workshops被引频次学科排名




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书目名称Software Engineering and Formal Methods. SEFM 2022 Collocated Workshops年度引用学科排名




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书目名称Software Engineering and Formal Methods. SEFM 2022 Collocated Workshops读者反馈学科排名

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Software Engineering and Formal Methods. SEFM 2022 Collocated Workshops978-3-031-26236-4Series ISSN 0302-9743 Series E-ISSN 1611-3349

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Lecture Notes in Computer Sciencehttp://image.papertrans.cn/s/image/870825.jpg

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https://doi.org/10.1007/978-3-031-26236-4artificial intelligence; computer hardware; computer programming; computer science; computer systems; emb

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Increment

A Case Study in Formal Analysis of System Requirementsents definition process. This has been done, on an experimental basis, by applying a specific set of tools and methodologies to a case study from the railway sector. The paper describes the approach adopted in the project and some considerations resulting from the experience.

MIRTH

Test and Training Data Generation for Object Recognition in the Railway Domain balanced datasets of scenarios to train and test AI, while adding the benefit of corner case generation by reducing to certain elements in the ontology. This article reports on the current status of the project and the goals it tries to achieve.

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Forecasting Algae Growth in Photo-Bioreactors Using Attention LSTMse, performs process adaptations and thus significantly accelerates algae growth and scale-up. Therefore, an algae bioreactor was established at the University of Technology Sydney. The subject of this paper is the study of algae growth using Long Short-Term Memory Neural Networks (LSTMs). In order t

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Fast Simulation Response by a Simulation Based Machine Learning (SMiLe) Approachfailures in a production process. The basis for this approach is a simulation that is well calibrated by experimental data for the manufacturing process and the materials involved. If computing resources and software licenses are available, the simulation can be used continuously to improve the simu