Auswahl der wissenschaftlichen Literatur zum Thema „KPIs optimization“
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Zeitschriftenartikel zum Thema "KPIs optimization"
Waurisch, Heiko, Nick von Bargen, Nico Ploczicki, Bente Ralfs, Berit Elsner, Reiner Schütt und Nassipkul Dyussembekova. „Assessment of Grid and System Supportability Based on Spatio-Temporal Conditions—Novel Key Performance Indicators for Energy System Evaluation“. Energies 17, Nr. 7 (23.03.2024): 1534. http://dx.doi.org/10.3390/en17071534.
Der volle Inhalt der QuelleIslam, Md Rakibul, Syed Mithun Ali, Amir Mohammad Fathollahi-Fard und Golam Kabir. „A novel particle swarm optimization-based grey model for the prediction of warehouse performance“. Journal of Computational Design and Engineering 8, Nr. 2 (24.02.2021): 705–27. http://dx.doi.org/10.1093/jcde/qwab009.
Der volle Inhalt der QuelleImoize, Agbotiname Lucky, Friday Udeji, Joseph Isabona und Cheng-Chi Lee. „Optimizing the Quality of Service of Mobile Broadband Networks for a Dense Urban Environment“. Future Internet 15, Nr. 5 (12.05.2023): 181. http://dx.doi.org/10.3390/fi15050181.
Der volle Inhalt der QuelleZnamenák, Jaroslav, Gabriela Križanová, Miriam Iringová und Pavel Važan. „A Proposal for Production Data Collection on a Hybrid Production Line in Cooperation with MES“. Research Papers Faculty of Materials Science and Technology Slovak University of Technology 24, Nr. 39 (01.12.2016): 137–44. http://dx.doi.org/10.1515/rput-2016-0028.
Der volle Inhalt der QuelleYu, T. X., Yan Fei Xiang, Min Wang und Li Ming Yang. „Key Performance Indicators of Tubes Used as Energy Absorbers“. Key Engineering Materials 626 (August 2014): 155–61. http://dx.doi.org/10.4028/www.scientific.net/kem.626.155.
Der volle Inhalt der QuelleSirait, Fadli, Akhmad Wahyu Dani, Yuliza Yuliza und Ulil Albab. „OPTIMIZATION IN QUALITY OF SERVICE FOR LTE NETWORK USING BANDWIDTH EXPANSION“. SINERGI 23, Nr. 1 (27.02.2019): 47. http://dx.doi.org/10.22441/sinergi.2019.1.007.
Der volle Inhalt der QuelleXiang, Yanfei, Min Wang, Tongxi Yu und Liming Yang. „Key Performance Indicators of Tubes and Foam-Filled Tubes Used as Energy Absorbers“. International Journal of Applied Mechanics 07, Nr. 04 (August 2015): 1550060. http://dx.doi.org/10.1142/s175882511550060x.
Der volle Inhalt der QuelleZhang, Shaoliang, Miguel Ángel Gomez, Qing Yi, Rui Dong, Anthony Leicht und Alberto Lorenzo. „Modelling the Relationship between Match Outcome and Match Performances during the 2019 FIBA Basketball World Cup: A Quantile Regression Analysis“. International Journal of Environmental Research and Public Health 17, Nr. 16 (07.08.2020): 5722. http://dx.doi.org/10.3390/ijerph17165722.
Der volle Inhalt der Quellede Matos, Bárbara, Rodrigo Salles, Jérôme Mendes, Joana R. Gouveia, António J. Baptista und Pedro Moura. „A Review of Energy and Sustainability KPI-Based Monitoring and Control Methodologies on WWTPs“. Mathematics 11, Nr. 1 (29.12.2022): 173. http://dx.doi.org/10.3390/math11010173.
Der volle Inhalt der QuelleHo, Man Ying (Annie), Joseph H. K. Lai, Huiying (Cynthia) Hou und Dadi Zhang. „Key Performance Indicators for Evaluation of Commercial Building Retrofits: Shortlisting via an Industry Survey“. Energies 14, Nr. 21 (04.11.2021): 7327. http://dx.doi.org/10.3390/en14217327.
Der volle Inhalt der QuelleDissertationen zum Thema "KPIs optimization"
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
Skocaj, Marco. „An Inter-Frequency Handover Optimization Algorithm for LTE Networks – Design and Test“. Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020.
Den vollen Inhalt der Quelle findenVasquez, Coronado Pedro Pablo. „Optimization of the Haulage Cycle Model for Open Pit Mining Using a Discrete-Event Simulator and a Context-Based Alert System“. Thesis, The University of Arizona, 2014. http://hdl.handle.net/10150/321594.
Der volle Inhalt der QuelleCarvalho, Bruno Miguel Machado. „Forecasting techniques: application to cellular networks“. Master's thesis, 2018. http://hdl.handle.net/10773/28303.
Der volle Inhalt der QuelleDevido ao aumento da competitividade e agressividade do mercado, cada vez mais a estratégia dos operadores de redes móveis passa pelo melhoramento das infraestruturas e otimização dos recursos já existentes, de modo a proporcionar a melhor experiência aos seus utilizadores. Para isto, recorrem à análise de indicadores chave de desempenho (KPIs) e ao uso de métodos de previsão para prever e planear alterações a realizar na sua rede. Tendo isto como base, esta dissertação foca-se no estudo e análise de diferentes métodos de previsão e sua implementação em Python, de maneira a obter previsões do futuro comportamento da rede em tempo real e de forma automatizada.
Mestrado em Engenharia Eletrónica e Telecomunicações
Василевич, Д. А., und D. A. Vasilevich. „Моделирование системы оценки функциональности компонентов для повышения эффективности межструктурных коммуникаций : магистерская диссертация“. Master's thesis, 2019. http://hdl.handle.net/10995/77671.
Der volle Inhalt der QuelleВ рамках данной работы разрабатывается решение для оценки эффективности компонентов MES-системы. Суть заключается в оценке степени использования стандартных функциональностей (с учетом коэффициента их весомости) по сравнению с проектными доработками для разных проектов. Анализ предоставляемых пользователю данных позволяет делать четкие, основанные на реальных показателях выводы об эффективности каждого компонента (или модуля) системы, позволяя выделять слабые места системы и определять траектория дальнейшего развития как каждого компонента, так и всего программного продукта в целом.
Buchteile zum Thema "KPIs optimization"
Morri, Nabil, Sameh Hadouaj und Lamjed Ben Said. „Toward Real-Time Multi-objective Optimization for Bus Service KPIs“. In Informatics in Control, Automation and Robotics, 18–36. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-26474-0_2.
Der volle Inhalt der QuelleStroe, Ion-Sorin. „Proposed KPIs for Optimization and Value Determination of an e-Business“. In Soft Computing Applications, 1069–76. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-18416-6_86.
Der volle Inhalt der QuelleChiesa, Giacomo, Francesca Fasano und Paolo Grasso. „Simulated Versus Monitored Building Behaviours: Sample Demo Applications of a Perfomance Gap Detection Tool in a Northern Italian Climate“. In Innovative Renewable Energy, 109–33. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-15218-4_6.
Der volle Inhalt der QuelleWinkler, Heiner, Susanne Franke, Felix Franke, Iren Jabs, Daniel Fischer und Matthias Thürer. „Systems Thinking Approach for Production Process Optimization Based on KPI Interdependencies“. In IFIP Advances in Information and Communication Technology, 662–75. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-43670-3_46.
Der volle Inhalt der QuelleLai, Manuel, und Simona Tusacciu. „Analysis of Production Scenario and KPI Calculation: Monitoring and Optimization Logics of White’R Island“. In Advances in Neural Networks, 475–80. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-33747-0_47.
Der volle Inhalt der QuelleAli, Ashraf A., und Khalid Al-Begain. „Session Initiation and IP Multimedia Subsystem Performance Evaluation“. In Advances in Wireless Technologies and Telecommunication, 36–49. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-2113-6.ch003.
Der volle Inhalt der QuelleGrover, Neha. „Performance Measurement“. In Innovative Solutions for Implementing Global Supply Chains in Emerging Markets, 212–41. IGI Global, 2016. http://dx.doi.org/10.4018/978-1-4666-9795-9.ch015.
Der volle Inhalt der QuelleMassaro, Alessandro. „Process Mining in Production Management, Intelligent Control, and Advanced KPI for Dynamic Process Optimization“. In Advances in Systems Analysis, Software Engineering, and High Performance Computing, 1–17. IGI Global, 2023. http://dx.doi.org/10.4018/978-1-6684-7684-0.ch001.
Der volle Inhalt der QuellePeisino, Alessandro, und Simone Tesconi. „Naval Fleet Integrated Logistic Design Optimization: The Italian Navy Experience in Enhancing Feedback from the Field“. In Progress in Marine Science and Technology. IOS Press, 2022. http://dx.doi.org/10.3233/pmst220012.
Der volle Inhalt der QuelleDurcakova, Michaela, Jaak Lavin und Kristo Karjust. „KPI Optimization for Product Development Process“. In Proceedings of the 23rd International DAAAM Symposium 2012, 1079–84. DAAAM International Vienna, 2012. http://dx.doi.org/10.2507/23rd.daaam.proceedings.252.
Der volle Inhalt der QuelleKonferenzberichte zum Thema "KPIs optimization"
Furtado, C. J. A., G. G. Lage, G. R. V. A. da Fonseca und A. A. R. Patrício. „Water Injection Optimization Based on Operational KPIs“. In SPE Water Lifecycle Management Conference and Exhibition. SPE, 2024. http://dx.doi.org/10.2118/219062-ms.
Der volle Inhalt der QuelleMorri, Nabil, Sameh Hadouaj und Lamjed Ben Said. „Agent-based Intelligent KPIs Optimization of Public Transit Control System“. In 18th International Conference on Informatics in Control, Automation and Robotics. SCITEPRESS - Science and Technology Publications, 2021. http://dx.doi.org/10.5220/0010616302240231.
Der volle Inhalt der QuelleMorri, Nabil, Sameh Hadouaj und Lamjed Ben Said. „Agent-based Intelligent KPIs Optimization of Public Transit Control System“. In 18th International Conference on Informatics in Control, Automation and Robotics. SCITEPRESS - Science and Technology Publications, 2021. http://dx.doi.org/10.5220/0010616300002994.
Der volle Inhalt der QuelleCelebic, Boban, und Ruth Breu. „Using Green KPIs for Large IT Infrastructures' Energy and Cost Optimization“. In 2015 3rd International Conference on Future Internet of Things and Cloud (FiCloud). IEEE, 2015. http://dx.doi.org/10.1109/ficloud.2015.86.
Der volle Inhalt der QuelleMorri, Nabil, Sameh Hadouaj und Lamjed Ben Said. „Towards an Intelligent control system for public transport traffic efficiency KPIs optimization“. In 2020 Global Congress on Electrical Engineering (GC-ElecEng). IEEE, 2020. http://dx.doi.org/10.23919/gc-eleceng48342.2020.9286268.
Der volle Inhalt der QuelleBerger, Sascha, Albrecht Fehske und Gerhard Fettweis. „Force field based joint optimization of strictly monotonic KPIs in wireless networks“. In 2012 IFIP Wireless Days (WD). IEEE, 2012. http://dx.doi.org/10.1109/wd.2012.6402845.
Der volle Inhalt der QuelleArif Normin, Muhammad Afiq, Azlesham Rosli, Meor M. Hakeem Meor Hashim, M. Faris Arriffin und Rohaizat Ghazali. „A Successful Case Study of a Collaborative Approach in Operational Optimization via Adoption of Automated Drilling Performance Measurement“. In Offshore Technology Conference Asia. OTC, 2022. http://dx.doi.org/10.4043/31579-ms.
Der volle Inhalt der QuelleKaraouzene, Zoheir, Hicham Megnafi, Lotfi Merad und Sidi Mohammed Meriah. „Artificial Intelligence in 5G Planning: Optimization of EnodeB Planning Based on 4G KPIs“. In 2023 IEEE International Workshop on Mechatronic Systems Supervision (IW_MSS). IEEE, 2023. http://dx.doi.org/10.1109/iw_mss59200.2023.10368904.
Der volle Inhalt der QuelleChell, Brian, Steven Hoffenson, Benjamin Kruse und Mark R. Blackburn. „Mission-Level Optimization: Complex Systems Design for Highly Stochastic Life Cycle Use Case Scenarios“. In ASME 2020 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/detc2020-22454.
Der volle Inhalt der QuelleSibaweihi, Najmudeen, und Japan Trivedi. „Distributed Real-Time Multi-Pad Steam Allocation Optimization“. In SPE Canadian Energy Technology Conference and Exhibition. SPE, 2023. http://dx.doi.org/10.2118/212757-ms.
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