Sökning: "reference machine"

Visar resultat 1 - 5 av 224 uppsatser innehållade orden reference machine.

  1. 1. Skyddet för data : En analys av digitala tjänsters skydd för sin data genom sui generis-rätten i ljuset av Digital Markets Act

    Uppsats för yrkesexamina på avancerad nivå, Stockholms universitet/Juridiska institutionen

    Författare :Konstantinos Adamidis; [2023]
    Nyckelord :protection of databases; database protection; database directive; legal protection for databases; data; protection of data; intellectual property law; unfair competition; competition law; DMA; Digital Markets Act; EU; EU law; databasskydd; databasdirektivet; rättsligt skydd för databaser; data; skydd för data; immaterialrätt; illojal konkurrens; konkurrensrätt; DMA; Digital Markets Act; EU; EU-rätt;

    Sammanfattning : The sui generis-right in article 7.1 of the database directive provides the maker of a database, who has made a qualitatively and/or quantitative substantial investment in the obtaining, verification and/or presentation of the contents in a database, the right to prevent extraction and/or re-utilization of the whole or of a substantial part of the database contents. LÄS MER

  2. 2. Improvement of Wind Power Forecasting and Prediction of Production Losses Caused by Ice Formation on Wind Turbine Blades : - A Machine Learning Approach

    Uppsats för yrkesexamina på avancerad nivå, Umeå universitet/Institutionen för fysik

    Författare :Emelie Sjökvist; [2023]
    Nyckelord :;

    Sammanfattning : In the ongoing climate crisis, transitioning to renewable energy sources is essential to manage the increasing energy demand. One such renewable energy source is the weather-dependent energy source, wind power. Many wind farms are located in Cold Climate (CC) regions, known for their vast potential for wind power production. LÄS MER

  3. 3. A Comparative Analysis of User Interfaces in the Calling Functionality of Infotainment Systems among Popular Cars in Romania : Investigating UX and Usability Design Principles for In-car Infotainment Systems.

    Kandidat-uppsats, Jönköping University/JTH, Avdelningen för datateknik och informatik

    Författare :Semi Kandiyoti Eskenazi; Bogdana-Floriana Cimpan; [2023]
    Nyckelord :Car infotainment systems; User experience; Calling functionality; Interface design; Human-Machine Interface HMI ; European Statement of Principles on HMI; Driver distraction; Road safety; Design patterns.;

    Sammanfattning : As cars become more integrated into people’s lives, the design of in-car infotainment systems has become increasingly important. This thesis explores the user experience and Usability design principles of the calling functionality within the infotainment systems of five of the most common cars in Romania. LÄS MER

  4. 4. A Machine Learning Approach on Analysis of Emission Spectra for Application in XFEL Experiments

    Uppsats för yrkesexamina på avancerad nivå, Uppsala universitet/Institutionen för fysik och astronomi

    Författare :Harald Agelii; [2023]
    Nyckelord :Structural biology; Machine learning; Neural networks; emission spectrum; XFEL; X-ray free electron laser; SFX; Serial femtosecond X-ray crystallography; Proteins; Diagnostics;

    Sammanfattning : In this thesis we investigate two potential applications of machine learning in the context of X-ray imaging and spectroscopy of biological samples, particularly such using X-ray free electron lasers (XFEL). We first investigate the possibility of using an emission spectrum, recorded from a sample after being probed by an incident X-ray, as a diagnostic tool. LÄS MER

  5. 5. Towards gradient faithfulness and beyond

    Master-uppsats, Högskolan i Halmstad/Akademin för informationsteknologi

    Författare :Vincenzo Buono; Isak Åkesson; [2023]
    Nyckelord :XAI; Visual Explanations; CAM; Grad-CAM; Expected Grad-CAM; Hyper Expected Grad; Class Activation Maps; Explainable AI; Faithfulness; Neural Network interpretability; Hyper Resolution CAM; Super Resolution CAM; Natural Encoding;

    Sammanfattning : The riveting interplay of industrialization, informalization, and exponential technological growth of recent years has shifted the attention from classical machine learning techniques to more sophisticated deep learning approaches; yet its intrinsic black-box nature has been impeding its widespread adoption in transparency-critical operations. In this rapidly evolving landscape, where the symbiotic relationship between research and practical applications has never been more interwoven, the contribution of this paper is twofold: advancing gradient faithfulness of CAM methods and exploring new frontiers beyond it. LÄS MER