Sökning: "Performance Analytics"

Visar resultat 11 - 15 av 47 avhandlingar innehållade orden Performance Analytics.

1. 11. Miniaturized fluid system for high-pressure analytics

   Författare :Simon Södergren; Klas Hjort; Sami Franssila; Uppsala universitet; []
   Nyckelord :TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; High-pressure; Microfluidics; Thermal regulation; Fluid mechanics; In-situ sensors; High-pressure analytics; Teknisk fysik med inriktning mot mikrosystemteknik; Engineering Science with specialization in Microsystems Technology;

   Sammanfattning : High-pressure chemistry can be used to determine the contents of blood or water samples and to discover new chemistries. However, working with chemistry at pressures of many tens, or even hundreds, of bars often requires expensive and stationary equipment, such as autoclaves or chromatographic systems like high-performance liquid chromatography (HPLC). LÄS MER

3. 12. Quality-Impact Assessment of Software Products and Services in a Future Internet Platform

   Författare :Farnaz Fotrousi; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; Quality of Experience QoE ; Analytics; Software quality; KPI; Future Internet; Quality of Service QoS ; Assessment;

   Sammanfattning : The idea of a Future Internet platform is to deliver reusable and common functionalities to facilitate making wide ranges of software products and services.  The Future Internet platform, introduced by the Future Internet Public Private Partnership (FI-PPP) project, makes the common functionalities available through so-called Enablers to be instantly integrated into software products and services with less cost and complexity rather than a development from scratch. LÄS MER

4. 13. From Condition Monitoring to Maintenance Management in Electric Power System Generation with focus on Wind Turbines

   Författare :Peyman Mazidi; Miguel Ángel Sanz Bobi; Lina Bertling Tjernberg; Javier García González; KTH; []
   Nyckelord :Anomaly Detection; Condition Monitoring; Maintenance Management; Performance Evaluation; Data Analytics; Mathematical Modeling; Optimization; Electrical Engineering; Elektro- och systemteknik;

   Sammanfattning : With increase in the number of sensors installed on sub-assemblies of industrial components, the amount of data collected is rapidly increasing. These data hold information in the areas of operation of the system and evolution of health condition of the components. LÄS MER

5. 14. Data-driven Ship Performance Models - - Emphasis on Energy Efficiency and Fatigue Safety

   Författare :Xiao Lang; Chalmers tekniska högskola; []
   Nyckelord :TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; semi-empirical; energy efficiency; fatigue assessment; grey-box; full-scale measurements; speed-power relationship; added resistance due to waves; ship performance; machine learning;

   Sammanfattning : Due to digitalization in the maritime industry, a huge amount of ship operation-related data has been collected. The main objective of this thesis is to exploit machine learning/big data analytics to build data-driven ship performance models, focusing on speed-power relationship modeling, and fatigue accumulation assessment during a ship’s operation at sea. LÄS MER

7. 15. Performance Characterization of In-Memory Data Analytics on a Scale-up Server

   Författare :Ahsan Javed Awan; Mats Brorsson; Vladimir Vlassov; Eduard Ayguade; Boris Grot; KTH; []
   Nyckelord :TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; Informations- och kommunikationsteknik; Information and Communication Technology;

   Sammanfattning : The sheer increase in volume of data over the last decade has triggered research in cluster computing frameworks that enable web enterprises to extract big insights from big data. While Apache Spark defines the state of the art in big data analytics platforms for (i) exploiting data-flow and in-memory computing and (ii) for exhibiting superior scale-out performance on the commodity machines, little effort has been devoted at understanding the performance of in-memory data analytics with Spark on modern scale-up servers. LÄS MER