Sökning: "diagram i skolan"

Visar resultat 1 - 5 av 108 uppsatser innehållade orden diagram i skolan.

  1. 1. Bridging the Knowledge Gap for New Market Entrants in the Swedish Electric Power System : Market development modeling for the FCR-D Up balancing market

    Master-uppsats, KTH/Skolan för industriell teknik och management (ITM)

    Författare :Lukas Lindroos; David Odenlind; [2023]
    Nyckelord :Ancillary Services; Balancing Markets; FCR-D Up; Market Development; Regulative Market Change; Price Formation; System Modeling; Stödtjänster; Balansmarknader; FCR-D Upp; Marknadsutveckling; Regulatorisk Markandsförändring; Prisbildning; Systemmodellering.;

    Sammanfattning : The need for ancillary services is on the rise due to the increasing share of weather-dependent power sources in the electric power system. This master thesis focuses on the Swedish FCR-D Up market. LÄS MER

  3. 2. Vacuum Chamber Adaptation for Low-Power Electric Propulsion Testing : Optimizing Anode Configuration and Electrical Interface for Hollow Cathode Testing, and Conceptualizing a Setup for Field Emission Electric Propulsion Testing

    Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Författare :Therese Bäckström; [2023]
    Nyckelord :Electric Propulsion; Field Emission Electric Propulsion Testing; FEEP Testing; Thruster Testing; Heaterless Hollow Cathode Testing; HHCTesting; Cathode Testing; Anode Design; Anode Configuration; Electrical Interface; Elektrisk Framdrivning; Fältjonisationsframdrivningstestning; Framdrivningstestning; Värmelös Hålkatodstestning; Katodtestning; Anoddesign; Anodkonfiguration; Elektriskt Gränssnitt;

    Sammanfattning : Electric Propulsion (EP) is widely acknowledged as a crucial technology for future space missions, offering significant propellant savings and enabling exploration of planetary missions with smaller spacecraft (s/c). For precise attitude control of these smaller spacecraft, Field Emission Electric Propulsion (FEEP) has emerged as a favorable option due to its exceptional thrust controllability. LÄS MER

  4. 3. Drivers of Institutional ESG Investing

    Master-uppsats, KTH/Skolan för industriell teknik och management (ITM)

    Författare :Lars Sjöbom; [2023]
    Nyckelord :ESG; ESG investments; Financial Institutions; Banks; Institutional investors; Sustainability; EU Taxonomy; ESG; ESG investeringar; Finansiella institutioner; Banker; Institutionella investerare; Hållbarhet; EU taxonomi;

    Sammanfattning : This study analyzes the factors that influence environmental, social, and governance (ESG) investments in the Nordic region from the perspectives of institutional investors. The study aims to understand key drivers of ESG investments for institutional investors. LÄS MER

  5. 4. A Holistic Framework for Analyzing the Reliability of IoT Devices

    Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Författare :Leonardo Manca; [2023]
    Nyckelord :Canvas Learning Management System; Docker containers; Performance tuning Performance tuning; Internet of Things IoT ; Reliability; Failure rate; Availability; Comprehensive framework; IoT architecture; Failure modes; Reliability Block Diagram RBD ; Prestandajustering; Sakernas internet IoT ; Tillförlitlighet; Felfrekvens; Tillgänglighet; Heltäckande ramverk; IoT-arkitektur; Felfunktioner; Till-förlitlighetsblockdiagram RBD Canvas Lärplattform; Dockerbehållare; Prestandajustering;

    Sammanfattning : In the rapidly evolving landscape of the Internet of Things (IoT), ensuring consistency and reliability becomes a top priority for a seamless user experience. In many instances, reliability is assessed through Quality of Service (QoS) metrics, sidelining traditional reliability metrics that thrive on time-dependent failure rates. LÄS MER

  7. 5. Explainable Machine Learning in Cardiovascular Diagnostics

    Kandidat-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Författare :Alexander Gutell; Ludvig Skare; [2023]
    Nyckelord :;

    Sammanfattning : The major challenges in implementing machine learning models in medical applications stemfrom ethical and accountability concerns, which arise from the lack of insight and understandingof the models' inner workings and reasoning. This opaqueness has resulted in the emergenceof a new subfield of machine learning called Explainability, which aims to develop and deploymethods to gain insight into how input data is weighted and propagated through a machinelearning algorithm. LÄS MER