Sökning: "convolutional neural network CNN"

Visar resultat 6 - 10 av 24 avhandlingar innehållade orden convolutional neural network CNN.

1. 6. Machine learning for building energy system analysis

   Författare :Fan Zhang; Johan Håkansson; Chris Bales; Stefan Byttner; Högskolan Dalarna; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; district heating; machine learning; deep learning; HVAC; neural networks;

   Sammanfattning : Buildings account for approximately 40% of the global energy, and Heating, Ventilation, and Air Conditioning (HVAC) contributes to a large proportion of building energy consumption. Two main negative characteristics that contribute to performance degradation and energy waste in an HVAC system are inappropriate control strategies and faults. LÄS MER

3. 7. Machine Learning Approaches to Develop Weather Normalize Models for Urban Air Quality

   Författare :Chau Ngoc Phuong; Mengjie Han; Rasa Zalakeviciute; Ilias Thomas; Mario Salvador González Rodríguez; Högskolan Dalarna; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; Weather Normalized Models WNMs ; Air Pollution; Data-Driven Modeling and Optimization; Deep Learning - Artificial Neural Network DL-ANN ; Machine Learning;

   Sammanfattning : According to the World Health Organization, almost all human population (99%) lives in 117 countries with over 6000 cities, where air pollutant concentration exceeds recommended thresholds. The most common, so-called criteria, air pollutants that affect human lives, are particulate matter (PM) and gas-phase (SO2, CO, NO2, O3 and others). LÄS MER

4. 8. Large data and machine learning in analysis, diagnostics, and clinical decision making: applications in the treatment of burn injury

   Författare :Jian Fransén; Fredrik Huss; Johan Lundin; Yvette Andersson; Andrew Lindford; Uppsala universitet; []
   Nyckelord :MEDICIN OCH HÄLSOVETENSKAP; MEDICAL AND HEALTH SCIENCES; Burn wound infection; Antibiotic susceptibility; Burn mortality; Machine learning; Burns; Burn assessment; Convolutional neural network; Artificial intelligence; Intensive care; ; Plastikkirurgi; Plastic Surgery; Surgery; Kirurgi; Machine learning; Maskininlärning;

   Sammanfattning : Burn injury is a common trauma globally. Large burns require fluid resuscitation, infection control, and specialized intensive care. The size of the burn and infections caused by resistant microbes are correlated to mortality, and accurate mortality predictions are important. LÄS MER

5. 9. On the Utility of Representation Learning Algorithms for Myoelectric Interfacing

   Författare :Alexander Olsson; Avdelningen för Biomedicinsk teknik; []
   Nyckelord :TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; EMG; Machine Learning; Deep Learning; Feature Learning; Biosignal Processing; Gesture Recognition; Muscle-Computer Interfaces; Myoelectric Control; Upper-Limb Prosthetics;

   Sammanfattning : Electrical activity produced by muscles during voluntary movement is a reflection of the firing patterns of relevant motor neurons and, by extension, the latent motor intent driving the movement. Once transduced via electromyography (EMG) and converted into digital form, this activity can be processed to provide an estimate of the original motor intent and is as such a feasible basis for non-invasive efferent neural interfacing. LÄS MER

7. 10. Enhancing Machine Failure Detection with Artificial Intelligence and sound Analysis

   Författare :Thanh Tran; Jan Lundgren; Sebastian Bader; Domenico Capriglione; Mittuniversitetet; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; Machine Failure Detection; Machine Learning; Deep Learning; Sound Signal Processing; Audio Augmentation;

   Sammanfattning : The detection of damage or abnormal behavior in machines is critical in industry, as it allows faulty components to be detected and repaired as early as possible, reducing downtime and minimizing operating and personnel costs. However, manual detection of machine fault sounds is economically inefficient and labor-intensive. LÄS MER