Sökning: "maskin inlärning"

Visar resultat 1 - 5 av 15 uppsatser innehållade orden maskin inlärning.

1. 1. Utilizing energy-saving techniques to reduce energy and memory consumption when training machine learning models : Sustainable Machine Learning

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

   Författare :Khalid El Yaacoub; [2024]
   Nyckelord :Sustainable AI; Machine learning; Quantization-Aware Training; Model Distillation; Quantized Distillation; Siamese Neural Networks; Continual Learning; Experience Replay; Data Efficient AI; Energy Consumption; Energy-Savings; Sustainable ML; Computation resources; Hållbar maskin inlärning; Hållbar AI; Maskininlärning; Quantization-Aware Training; Model Distillation; Quantized Distillation; siamesiska neurala nätverk; Continual Learning; Experience Replay; Dataeffektiv AI; Energiförbrukning; Energibesparingar; Beräkningsresurser;

   Sammanfattning : Emerging machine learning (ML) techniques are showing great potential in prediction performance. However, research and development is often conducted in an environment with extensive computational resources and blinded by prediction performance. LÄS MER

3. 2. Anomaly detection for prediction of failures in manufacturing environments : Machine learning based semi-supervised anomaly detection for multivariate time series to predict failures in a CNC-machine

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

   Författare :Felix Boltshauser; [2023]
   Nyckelord :Machine learning; Anomaly Detection; DeepAnT; ROCKET; OCSVM; manufacturing; predictive maintenance; Maskin inlärning; Anomali Detektion; DeepAnT; ROCKET; OCSVM; tillverkning; prediktivt underhåll;

   Sammanfattning : For manufacturing enterprises, the potential of collecting large amounts of data from production processes has enabled the usage of machine learning for prediction-based monitoring and maintenance of machines. Yet common maintenance strategies still include reactive handling of machine failures or schedule-based maintenance conducted by experienced personnel. LÄS MER

4. 3. Evaluating Incremental Machine Learning for Smart Home Adaptation with Embedded Systems

   M1-uppsats, Malmö universitet/Institutionen för datavetenskap och medieteknik (DVMT)

   Författare :Alban Islami; Nezar Sheikhi; [2023]
   Nyckelord :Machine learning; embedded systems; incremental learning; online learning; smart home;

   Sammanfattning : The combination of machine learning on embedded systems has quickly increased throughout the years. Subsets like TinyML have become an integral part of how embedded systems implement machine learning. The field has evolved quickly, and TinyOL is an emerging subset that redefines what is possible with embedded systems. LÄS MER

5. 4. Real-time object detection robotcontrol : Investigating the use of real time object detection on a Raspberry Pi for robot control

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

   Författare :Simon Ryberg; Jonathan Jansson; [2022]
   Nyckelord :Edge Device Raspberry Pi Image recognition Machine learning Tracked robot Track drive; Edge device Raspberry Pi Maskin inlärning Bandvagns robot Band drivlina Bildigenkänning;

   Sammanfattning : The field of autonomous robots have been explored more and more over the last decade. The combination of machine learning advances and increases in computational power have created possibilities to explore the usage of machine learning models on edge devices. LÄS MER

7. 5. Online Anomaly Detection on the Edge

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

   Författare :Marcus Jirwe; [2021]
   Nyckelord :Predictive maintenance; Anomaly detection; Online learning; Edge environment; Receiver Operating Characteristic curve; Förebyggande underhåll; anomalidetektering; sekventiell inlärning; nätverkskanten; ”Receiver Operating Characterstic”-kurva;

   Sammanfattning : The society of today relies a lot on the industry and the automation of factory tasks is more prevalent than ever before. However, the machines taking on these tasks require maintenance to continue operating. This maintenance is typically given periodically and can be expensive while sometimes requiring expert knowledge. LÄS MER