Sökning: "kth nätverk"

Visar resultat 1 - 5 av 1590 uppsatser innehållade orden kth nätverk.

1. 1. Optimizing Flight Ranking:A Machine Learning Approach : Applying Machine Learning to Upgrade Flight Sorting and User Experience

   M1-uppsats, KTH/Hälsoinformatik och logistik

   Författare :Habib Jabeli; [2024]
   Nyckelord :Machine Learning; Flight Comparison; Flygresor.se; Neural Networks; Flight Ranking; Random Forest; XGBoost;

   Sammanfattning : Flygresor.se, a leading flight comparison platform, uses machine learning to rankflights based on their likelihood of being clicked. The main goal of this project was toimprove this flight sorting to obtain a better user experience. The platform's existingmodel is based on a neural network approach and a limited set of features. LÄS MER

3. 2. Prestandajämförelse mellan krypterade och okrypterade tidsseriedatabaser med IoT-baserad temperatur- och geopositionsdata

   M1-uppsats, KTH/Hälsoinformatik och logistik

   Författare :Sinem Uzunel; Joanna Xu; [2024]
   Nyckelord :AWS Timestream; InfluxDB Cloud; Performance Testing; Time Series; Time Series databases; Encryption; Database Query; Internet of Things IoT ; Performance Analysis; AWS Timestream; InfluxDB Cloud; Prestandatest; Tidsserier; Tidsseriedatabas; Kryptering; Databasfråga; Internet of Things IoT ; Prestandaanalys;

   Sammanfattning : Internet of Things (IoT) är en växande teknologi som spelar en allt större roll i samhället. Den innefattar ett nätverk av internetanslutna enheter som samlar in och utbyter data. Samtidigt som IoT växer uppstår utmaningar kring hantering av stora datamängder och säkerhetsaspekter. LÄS MER

4. 3. Control system and simplified timesynchronization for heterogenous IoT systems with medium time requirements

   M1-uppsats, KTH/Hälsoinformatik och logistik

   Författare :Jemma Touma; Simon Hejdenberg; [2024]
   Nyckelord :Internet of Things; time synchronization; wireless sensor networks; network time protocol; Android; Bluetooth; WiFi Direct;

   Sammanfattning : The company QTPIE conducts research on drivers and their unconscious reactions when driving. To help, they use smart devices that today must be individually handled at the start and end of a run, and have individually set timestamps, which can lead to differences between the units when data is entered and collation of the units' data after a run. LÄS MER

5. 4. Exploring the Depth-Performance Trade-Off : Applying Torch Pruning to YOLOv8 Models for Semantic Segmentation Tasks

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

   Författare :Xinchen Wang; [2024]
   Nyckelord :Deep Learning; Semantic segmentation; Network optimization; Network pruning; Torch Pruning; YOLOv8; Network Depth; Djup lärning; Semantisk segmentering; Nätverksoptimering; Nätverksbeskärning; Fackelbeskärning; YOLOv8; Nätverksdjup;

   Sammanfattning : In order to comprehend the environments from different aspects, a large variety of computer vision methods are developed to detect objects, classify objects or even segment them semantically. Semantic segmentation is growing in significance due to its broad applications in fields such as robotics, environmental understanding for virtual or augmented reality, and autonomous driving. LÄS MER

7. 5. 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