Sökning: "FNN"

Visar resultat 1 - 5 av 12 uppsatser innehållade ordet FNN.

1. 1. Latency Prediction in 5G Networks by using Machine Learning

   Uppsats för yrkesexamina på avancerad nivå, Lunds universitet/Institutionen för reglerteknik

   Författare :Erica Elgcrona; Evrim Mete; [2023]
   Nyckelord :Technology and Engineering;

   Sammanfattning : This thesis presents a report of predicting latency in a 5G network by using deep learning techniques. The training set contained data of network parameters along with the actual latency, collected in a 5G lab environment during four different test scenarios. LÄS MER

3. 2. Failure Inference in Drilling Bits: : Leveraging YOLO Detection for Dominant Failure Analysis

   Master-uppsats, Uppsala universitet/Institutionen för informationsteknologi

   Författare :Gnana Spandana Akumalla; [2023]
   Nyckelord :Computer vision; Image processing; Drill bit failure detection; CNN; YOLOv5; FNN; YOLOv8; Dataset; StyleGAN-ADA; Ethics; Sustainability; Artificial Intellegence; Tricone drill bit; Object detection.;

   Sammanfattning : Detecting failures in tricone drill bits is crucial in the mining industry due to their potential consequences, including operational losses, safety hazards, and delays in drilling operations. Timely identification of failures allows for proactive maintenance and necessary measures to ensure smooth drilling processes and minimize associated risks. LÄS MER

4. 3. Volatility Forecasting with Artificial Neural Networks: Can we trust them?

   Master-uppsats, Stockholms universitet/Finansiering

   Författare :Carl Oscar Dannström; Axel Broang; [2022]
   Nyckelord :;

   Sammanfattning : This thesis investigates how two types of artificial neural network models (ANN), feedforwardneural networks (FNN) and long short-term memory (LSTM), used for realized volatility (RV) forecasting, perform during high and low volatility regimes in comparison to the heterogeneousautoregressive (HAR) model. This is done for 23 stocks, constituents of the Swedish index OMXS30, between the 8th of February 2010 and the 31st of January 2022 using ten exogenous and three endogenous input variables. LÄS MER

5. 4. A comparison between Feed-forward and Convolutional Neural Networks for classification of invoice documents

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

   Författare :Erik Svensson; [2022]
   Nyckelord :;

   Sammanfattning : Filing invoices under booking accounts can be a time-consuming task that could be alleviated by machine learning algorithms. There are two possible main methods for an algorithm to learn to classify such data: use a machine learning algorithm directly on the images, or extract words as tokens and use a machine learning algorithm on the set of words generated. LÄS MER

7. 5. Day-ahead Grid Loss Forecasting : A study of linear and non-linear models when modelling electrical grid losses

   Uppsats för yrkesexamina på avancerad nivå, Uppsala universitet/Avdelningen för systemteknik

   Författare :Alicia Söderlind; [2022]
   Nyckelord :Electrical grid losses; Mathematical modelling; ARIMAX; SARIMAX; Neural Networks;

   Sammanfattning : Accurate day-ahead grid loss forecasts are, among other things, essential to determine the electricity price for the upcoming day. The more accurate forecast, the closer the trading on the 'day-ahead' electricity market can become the actual operation the next day, which dedcrease the need for correcting production on the balancing market. LÄS MER