Deep Learning for Anomaly Detection in Microwave Links : Challenges and Impact on Weather Classification

Deep Learning for Anomaly Detection in Microwave Links : Challenges and Impact on Weather Classification

Artificial intelligence is receiving a great deal of attention in various fields of science and engineering due to its promising applications. In today’s society, weather classification models with high accuracy are of utmost importance. An alternative to using conventional weather radars is to use...

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Bibliographic Details
Main Author: Engström, Olof
Format: Others
Language:English
Published: KTH, Skolan för elektroteknik och datavetenskap (EECS) 2020
Subjects:
Anomaly Detection
Time Series
Forecasting
Weather Classification
Microwave Link
CNN
LSTM
Avvikelsedetektering
Tidsserier
Prognostisering
Väderklassificering
Mikrovågslänkar
faltningsnätverk
Computer and Information Sciences
Data- och informationsvetenskap
Online Access:http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-276676

Internet

http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-276676

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