Auswahl der wissenschaftlichen Literatur zum Thema „Optimisation des KPIs“
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Zeitschriftenartikel zum Thema "Optimisation des KPIs"
Dalpadulo, Enrico, Francesco Gherardini, Fabio Pini und Francesco Leali. „Integration of Topology Optimisation and Design Variants Selection for Additive Manufacturing-Based Systematic Product Redesign“. Applied Sciences 10, Nr. 21 (05.11.2020): 7841. http://dx.doi.org/10.3390/app10217841.
Der volle Inhalt der QuelleKohlgrüber, Michael, Antonius Schröder, Félix Bayón Yusta und Asier Arteaga Ayarza. „A new innovation paradigm: combining technological and social innovation“. Matériaux & Techniques 107, Nr. 1 (2019): 107. http://dx.doi.org/10.1051/mattech/2018065.
Der volle Inhalt der QuelleRedlein, A., C. Baretschneider und L. Thrainer. „ESG monitoring and optimisation solutions and their return on investment: results of several case studies“. IOP Conference Series: Earth and Environmental Science 1176, Nr. 1 (01.05.2023): 012029. http://dx.doi.org/10.1088/1755-1315/1176/1/012029.
Der volle Inhalt der QuelleShariat, Mehrdad, Ömer Bulakci, Antonio De Domenico, Christian Mannweiler, Marco Gramaglia, Qing Wei, Aravinthan Gopalasingham et al. „A Flexible Network Architecture for 5G Systems“. Wireless Communications and Mobile Computing 2019 (11.02.2019): 1–19. http://dx.doi.org/10.1155/2019/5264012.
Der volle Inhalt der QuelleVavallo, Michele, Marco Arnesano, Gian Marco Revel, Asier Mediavilla, Ane Ferreiro Sistiaga, Alessandro Pracucci, Sara Magnani und Oscar Casadei. „Accelerating Energy Renovation Solution for Zero Energy Buildings and Neighbourhoods—The Experience of the RenoZEB Project“. Proceedings 20, Nr. 1 (18.07.2019): 1. http://dx.doi.org/10.3390/proceedings2019020001.
Der volle Inhalt der QuelleBranca, Teresa Annunziata, Ismael Matino, Valentina Colla, Alice Petrucciani, Amarjit Kuor Maria Singh, Antonella Zaccara, Teresa Beone et al. „Paving the way for the optimization of water consumption in the steelmaking processes: barriers, analysis and KPIs definition“. Matériaux & Techniques 108, Nr. 5-6 (2020): 510. http://dx.doi.org/10.1051/mattech/2021006.
Der volle Inhalt der QuelleKassen, Stefan, Holger Tammen, Maximilian Zarte und Agnes Pechmann. „Concept and Case Study for a Generic Simulation as a Digital Shadow to Be Used for Production Optimisation“. Processes 9, Nr. 8 (03.08.2021): 1362. http://dx.doi.org/10.3390/pr9081362.
Der volle Inhalt der QuelleStora, T., C. Duchemin, W. Andreazza, E. Aubert, C. Bernerd, T. Cocolios, M. Deschamps et al. „CERN-MEDICIS: Operational indicators to support the production of new medical radionuclides by mass-separation“. Journal of Physics: Conference Series 2687, Nr. 8 (01.01.2024): 082039. http://dx.doi.org/10.1088/1742-6596/2687/8/082039.
Der volle Inhalt der QuelleJurczak, Marcin, Grzegorz Miebs und Rafał A. Bachorz. „Multi-criteria human resources planning optimisation using genetic algorithms enhanced with MCDA“. Operations Research and Decisions 32, Nr. 4 (2022). http://dx.doi.org/10.37190/ord220404.
Der volle Inhalt der QuelleRison, S. C. G., I. Dostal, Z. Ahmed, Z. Raisi-Estabragh, C. Carvalho, M. Lobo, R. Patel et al. „Protocol design and preliminary evaluation of the REAL-Health Triple Aim, an open-cohort CVD-care optimisation initiative“. European Heart Journal 42, Supplement_1 (01.10.2021). http://dx.doi.org/10.1093/eurheartj/ehab724.3170.
Der volle Inhalt der QuelleDissertationen zum Thema "Optimisation des KPIs"
Oudrhiri, Ali. „Performance of a Neural Network Accelerator Architecture and its Optimization Using a Pipeline-Based Approach“. Electronic Thesis or Diss., Sorbonne université, 2023. https://accesdistant.sorbonne-universite.fr/login?url=https://theses-intra.sorbonne-universite.fr/2023SORUS658.pdf.
Der volle Inhalt der QuelleIn recent years, neural networks have gained widespread popularity for their versatility and effectiveness in solving a wide range of complex tasks. Their ability to learn and make predictions from large data-sets has revolutionized various fields. However, as neural networks continue to find applications in an ever-expanding array of domains, their significant computational requirements become a pressing challenge. This computational demand is particularly problematic when deploying neural networks in resource-constrained embedded devices, especially within the context of edge computing for inference tasks. Nowadays, neural network accelerator chips emerge as the optimal choice for supporting neural networks at the edge. These chips offer remarkable efficiency with their compact size, low power consumption, and reduced latency. Moreover, the fact that they are integrated on the same chip environment also enhances security by minimizing external data communication. In the frame of edge computing, diverse requirements have emerged, necessitating trade-offs in various performance aspects. This has led to the development of accelerator architectures that are highly configurable, allowing them to adapt to distinct performance demands. In this context, the focus lies on Gemini, a configurable inference neural network accelerator designed with imposed architecture and implemented using High-Level Synthesis techniques. The considerations for its design and implementation were driven by the need for parallelization configurability and performance optimization. Once this accelerator was designed, demonstrating the power of its configurability became essential, helping users select the most suitable architecture for their neural networks. To achieve this objective, this thesis contributed to the development of a performance prediction strategy operating at a high-level of abstraction, which considers the chosen architecture and neural network configuration. This tool assists clients in making decisions regarding the appropriate architecture for their specific neural network applications. During the research, we noticed that using one accelerator presents several limits and that increasing parallelism had limitations on performances. Consequently, we adopted a new strategy for optimizing neural network acceleration. This time, we took a high-level approach that did not require fine-grained accelerator optimizations. We organized multiple Gemini instances into a pipeline and allocated layers to different accelerators to maximize performance. We proposed solutions for two scenarios: a user scenario where the pipeline structure is predefined with a fixed number of accelerators, accelerator configurations, and RAM sizes. We proposed solutions to map the layers on the different accelerators to optimise the execution performance. We did the same for a designer scenario, where the pipeline structure is not fixed, this time it is allowed to choose the number and configuration of the accelerators to optimize the execution and also hardware performances. This pipeline strategy has proven to be effective for the Gemini accelerator. Although this thesis originated from a specific industrial need, certain solutions developed during the research can be applied or adapted to other neural network accelerators. Notably, the performance prediction strategy and high-level optimization of NN processing through pipelining multiple instances offer valuable insights for broader application
Buchteile zum Thema "Optimisation des KPIs"
Panagiotopoulou, Vasiliki C., Alexios Papacharalampopoulos und Panagiotis Stavropoulos. „Developing a Manufacturing Process Level Framework for Green Strategies KPIs Handling“. In Lecture Notes in Mechanical Engineering, 1008–15. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-28839-5_112.
Der volle Inhalt der QuelleKonferenzberichte zum Thema "Optimisation des KPIs"
Dubey, Pranav, Rachit Garg, Prateek Kumar, Akash Tyagi, Aditi Jain, Pramit Chakraborty und Sameer Chabbra. „Calculating Invisible Loss Time (ILT) Index Values & Predictive Analysis Using Bayesian Approach to Improve Drilling Operational Efficiency: Adopting Best Practices“. In ADIPEC. SPE, 2023. http://dx.doi.org/10.2118/216289-ms.
Der volle Inhalt der QuelleRomdhanne, Bilel Ben, Mourad Boudia und Nicolas Bondoux. „Amadeus Migration Process a Simulation-Driven Process to Enhance the Migration to a Multi-Cloud Environment“. In 12th International Conference on Digital Image Processing and Vision. Academy & Industry Research Collaboration, 2023. http://dx.doi.org/10.5121/csit.2023.131308.
Der volle Inhalt der QuelleTierney, Christopher M., Peter L. Higgins, Colm J. Higgins, Rory J. Collins, Adrian Murphy und Damian Quinn. „Steps towards a Connected Digital Factory Cost Model“. In 2023 AeroTech. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 2023. http://dx.doi.org/10.4271/2023-01-0999.
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