Literatura científica selecionada sobre o tema "KPIs optimization"
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Artigos de revistas sobre o assunto "KPIs optimization"
Waurisch, Heiko, Nick von Bargen, Nico Ploczicki, Bente Ralfs, Berit Elsner, Reiner Schütt e Nassipkul Dyussembekova. "Assessment of Grid and System Supportability Based on Spatio-Temporal Conditions—Novel Key Performance Indicators for Energy System Evaluation". Energies 17, n.º 7 (23 de março de 2024): 1534. http://dx.doi.org/10.3390/en17071534.
Texto completo da fonteIslam, Md Rakibul, Syed Mithun Ali, Amir Mohammad Fathollahi-Fard e Golam Kabir. "A novel particle swarm optimization-based grey model for the prediction of warehouse performance". Journal of Computational Design and Engineering 8, n.º 2 (24 de fevereiro de 2021): 705–27. http://dx.doi.org/10.1093/jcde/qwab009.
Texto completo da fonteImoize, Agbotiname Lucky, Friday Udeji, Joseph Isabona e Cheng-Chi Lee. "Optimizing the Quality of Service of Mobile Broadband Networks for a Dense Urban Environment". Future Internet 15, n.º 5 (12 de maio de 2023): 181. http://dx.doi.org/10.3390/fi15050181.
Texto completo da fonteZnamenák, Jaroslav, Gabriela Križanová, Miriam Iringová e 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, n.º 39 (1 de dezembro de 2016): 137–44. http://dx.doi.org/10.1515/rput-2016-0028.
Texto completo da fonteYu, T. X., Yan Fei Xiang, Min Wang e Li Ming Yang. "Key Performance Indicators of Tubes Used as Energy Absorbers". Key Engineering Materials 626 (agosto de 2014): 155–61. http://dx.doi.org/10.4028/www.scientific.net/kem.626.155.
Texto completo da fonteSirait, Fadli, Akhmad Wahyu Dani, Yuliza Yuliza e Ulil Albab. "OPTIMIZATION IN QUALITY OF SERVICE FOR LTE NETWORK USING BANDWIDTH EXPANSION". SINERGI 23, n.º 1 (27 de fevereiro de 2019): 47. http://dx.doi.org/10.22441/sinergi.2019.1.007.
Texto completo da fonteXiang, Yanfei, Min Wang, Tongxi Yu e Liming Yang. "Key Performance Indicators of Tubes and Foam-Filled Tubes Used as Energy Absorbers". International Journal of Applied Mechanics 07, n.º 04 (agosto de 2015): 1550060. http://dx.doi.org/10.1142/s175882511550060x.
Texto completo da fonteZhang, Shaoliang, Miguel Ángel Gomez, Qing Yi, Rui Dong, Anthony Leicht e 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, n.º 16 (7 de agosto de 2020): 5722. http://dx.doi.org/10.3390/ijerph17165722.
Texto completo da fontede Matos, Bárbara, Rodrigo Salles, Jérôme Mendes, Joana R. Gouveia, António J. Baptista e Pedro Moura. "A Review of Energy and Sustainability KPI-Based Monitoring and Control Methodologies on WWTPs". Mathematics 11, n.º 1 (29 de dezembro de 2022): 173. http://dx.doi.org/10.3390/math11010173.
Texto completo da fonteHo, Man Ying (Annie), Joseph H. K. Lai, Huiying (Cynthia) Hou e Dadi Zhang. "Key Performance Indicators for Evaluation of Commercial Building Retrofits: Shortlisting via an Industry Survey". Energies 14, n.º 21 (4 de novembro de 2021): 7327. http://dx.doi.org/10.3390/en14217327.
Texto completo da fonteTeses / dissertações sobre o assunto "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.
Texto completo da fonteIn 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.
Encontre o texto completo da fonteVasquez, 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.
Texto completo da fonteCarvalho, Bruno Miguel Machado. "Forecasting techniques: application to cellular networks". Master's thesis, 2018. http://hdl.handle.net/10773/28303.
Texto completo da fonteDevido 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., e 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.
Texto completo da fonteВ 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.
Capítulos de livros sobre o assunto "KPIs optimization"
Morri, Nabil, Sameh Hadouaj e 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.
Texto completo da fonteStroe, 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.
Texto completo da fonteChiesa, Giacomo, Francesca Fasano e 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.
Texto completo da fonteWinkler, Heiner, Susanne Franke, Felix Franke, Iren Jabs, Daniel Fischer e 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.
Texto completo da fonteLai, Manuel, e 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.
Texto completo da fonteAli, Ashraf A., e 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.
Texto completo da fonteGrover, 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.
Texto completo da fonteMassaro, 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.
Texto completo da fontePeisino, Alessandro, e 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.
Texto completo da fonteDurcakova, Michaela, Jaak Lavin e 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.
Texto completo da fonteTrabalhos de conferências sobre o assunto "KPIs optimization"
Furtado, C. J. A., G. G. Lage, G. R. V. A. da Fonseca e 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.
Texto completo da fonteMorri, Nabil, Sameh Hadouaj e 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.
Texto completo da fonteMorri, Nabil, Sameh Hadouaj e 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.
Texto completo da fonteCelebic, Boban, e 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.
Texto completo da fonteMorri, Nabil, Sameh Hadouaj e 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.
Texto completo da fonteBerger, Sascha, Albrecht Fehske e 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.
Texto completo da fonteArif Normin, Muhammad Afiq, Azlesham Rosli, Meor M. Hakeem Meor Hashim, M. Faris Arriffin e 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.
Texto completo da fonteKaraouzene, Zoheir, Hicham Megnafi, Lotfi Merad e 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.
Texto completo da fonteChell, Brian, Steven Hoffenson, Benjamin Kruse e 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.
Texto completo da fonteSibaweihi, Najmudeen, e 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.
Texto completo da fonte