Auswahl der wissenschaftlichen Literatur zum Thema „Algorithmes non centralisés“
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Zeitschriftenartikel zum Thema "Algorithmes non centralisés"
Perez-Diaz, Alvaro, Enrico Harm Gerding und Frank McGroarty. „Catching Cheats: Detecting Strategic Manipulation in Distributed Optimisation of Electric Vehicle Aggregators“. Journal of Artificial Intelligence Research 67 (05.03.2020): 437–70. http://dx.doi.org/10.1613/jair.1.11573.
Der volle Inhalt der QuelleDaccò, Edoardo, Davide Falabretti, Valentin Ilea, Marco Merlo, Riccardo Nebuloni und Matteo Spiller. „Decentralised Voltage Regulation through Optimal Reactive Power Flow in Distribution Networks with Dispersed Generation“. Electricity 5, Nr. 1 (12.03.2024): 134–53. http://dx.doi.org/10.3390/electricity5010008.
Der volle Inhalt der QuelleMurphy, DC, und DB Saleh. „Artificial Intelligence in plastic surgery: What is it? Where are we now? What is on the horizon?“ Annals of The Royal College of Surgeons of England 102, Nr. 8 (Oktober 2020): 577–80. http://dx.doi.org/10.1308/rcsann.2020.0158.
Der volle Inhalt der QuelleYe, Qiming, Yuxiang Feng, Eduardo Candela, Jose Escribano Macias, Marc Stettler und Panagiotis Angeloudis. „Spatial-Temporal Flows-Adaptive Street Layout Control Using Reinforcement Learning“. Sustainability 14, Nr. 1 (23.12.2021): 107. http://dx.doi.org/10.3390/su14010107.
Der volle Inhalt der QuellePauletto, Christian. „Gestion publique, agilité et innovation : l’expérience suisse du dispositif de crédits COVID-19“. Revue Internationale des Sciences Administratives Vol. 90, Nr. 1 (02.04.2024): 109–25. http://dx.doi.org/10.3917/risa.901.0109.
Der volle Inhalt der QuelleWaldman, Deane. „Replace government healthcare with patient-controlled health care“. Health Economics and Management Review 5, Nr. 1 (31.03.2024): 80–89. http://dx.doi.org/10.61093/hem.2024.1-06.
Der volle Inhalt der QuelleZhang, Zhixun, Keke Zhang, Leizheng Shu, Zhencai Zhu und Meijiang Zhou. „Distributed angle‐only orbit determination algorithm for non‐cooperative spacecraft based on factor graph“. IET Radar, Sonar & Navigation, 22.05.2024. http://dx.doi.org/10.1049/rsn2.12580.
Der volle Inhalt der QuelleNagaraja G, Chandan K J, Amrutha S Dukandar, Akash N und Charitha Reddy. „FINE: A Framework for Distributed Learning on Incomplete Observations for Heterogeneous Crowdsensing Networks“. International Journal of Advanced Research in Science, Communication and Technology, 05.05.2023, 23–29. http://dx.doi.org/10.48175/ijarsct-9775.
Der volle Inhalt der QuelleJain, Sambhav, und Reshma Rastogi. „Multi-label Minimax Probability Machine with Multi-manifold Regularisation“. Research Reports on Computer Science, 30.12.2021, 44–63. http://dx.doi.org/10.37256/rrcs.1120211193.
Der volle Inhalt der QuelleJain, Sambhav, und Reshma Rastogi. „Multi-label Minimax Probability Machine with Multi-manifold Regularisation“. Research Reports on Computer Science, 30.12.2021, 44–63. http://dx.doi.org/10.37256/rrcs.1120221193.
Der volle Inhalt der QuelleDissertationen zum Thema "Algorithmes non centralisés"
Alvarez, Valera Hernan Humberto. „An energy saving perspective for distributed environments : Deployment, scheduling and simulation with multidimensional entities for Software and Hardware“. Electronic Thesis or Diss., Pau, 2022. https://theses.hal.science/tel-04116013.
Der volle Inhalt der QuelleNowadays, strong economic growth and extreme weather conditions increased global electricity demand by more than 6% in 2021 after the COVID pandemic. The fast recovery regarding this demand rapidly increased electricity consumption. Even though renewable sources present a significant growth, electricity production from both coal and gas sources has reached a historical level.On the other hand, the consumption of energy by the digital technology sector depends on its growth and its degree of energy efficiency. On this matter, although devices at all deployment levels are energy efficient today, their massive use means that global energy consumption continues to grow.All these data show the need to use the energy of these devices wisely. For that reason, this thesis work addresses the dynamic (re)deployment of software components (containers or virtual machines) and their data to save energy. To this extent, we designed and developed intelligent distributed scheduling algorithms to decrease global power consumption while preserving the applications' quality of service.Such algorithms execute migrations and duplications procedures considering the natural relation between hardware components' load/features and power consumption. For that, they implement a novel manner of decentralized negotiations based on a distributed middleware we created (Kaligreen) and multidimensional data structures.To operate and assess the algorithms above, appropriate tools regarding hardware and software solutions are essential. Here, our choice was to develop our ownsimulation tool called: PISCO.PISCO is a versatile and straightforward simulator that allows users to concentrate only on their scheduling strategies. It enables network topologies to be abstracted as data structures whose elements are devices indexed by one or more criteria. Additionally, it mimics the execution of microservices by allocating resources according to various scheduling heuristics.We have used PISCO to implement, run and test our scheduling algorithms
Buchteile zum Thema "Algorithmes non centralisés"
Mou, Yongli, Sascha Welten, Mehrshad Jaberansary, Yeliz Ucer Yediel, Toralf Kirsten, Stefan Decker und Oya Beyan. „Distributed Skin Lesion Analysis Across Decentralised Data Sources“. In Studies in Health Technology and Informatics. IOS Press, 2021. http://dx.doi.org/10.3233/shti210179.
Der volle Inhalt der QuelleKonferenzberichte zum Thema "Algorithmes non centralisés"
Sundararajan, V., Andrew Redfern, William Watts und Paul Wright. „Distributed Monitoring of Steady-State System Performance Using Wireless Sensor Networks“. In ASME 2004 International Mechanical Engineering Congress and Exposition. ASMEDC, 2004. http://dx.doi.org/10.1115/imece2004-59884.
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