hh.sePublikasjoner
Endre søk
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
Energy Consumptions for Vehicles using Multitask Learning
Högskolan i Halmstad, Akademin för informationsteknologi.
Högskolan i Halmstad, Akademin för informationsteknologi.
2022 (engelsk)Independent thesis Advanced level (degree of Master (Two Years)), 20 poäng / 30 hpOppgave
Abstract [en]

This thesis aims to predict energy (fossil fuel and electric) consumption of internal combustion and hybrid vehicles. This thesis is in association with Wireless cars. Accurate prediction of energy consumption in vehicles is vital, as it can pave the way for a more sustainable future. Despite its criticality, accurate predictions of energy consumption are a challenging task. Several factors which impact energy consumption, i.e., average speed, trip duration, etc. , are not available at the beginning of the trip. To use such kinds of features to the full extent, we will be using multitask learning methods. The dataset provided by the company covers different aspects, including GPS information, energy consumption, time, and vehicle configurations which suggests multitask learning as an intriguing technique to approach it. Multitask learning uses a shared feature space wherein information is shared between multiple relevant tasks, helping to predict energy consumption accurately. 

Multitask learning (MTL) is susceptible to two crucial issues, namely task dominance and conflicting gradients between different tasks. Previous studies have addressed these issues separately , but we propose a unified framework to tackle these problems simultaneously in this thesis. In the proposed framework we are addressing the issue of task dominance model using Gradient Normalization (GradNorm)  while the issue of conflicting gradients is solved using the Projecting conflicting gradient (PCGrad) technique. Experimental results have shown the success of this method in comparison with other state-of-the-art methods.

Apart from creating unified architecture, we are also analyzing the behavioral pattern of the MTL model. This experiment was performed to check which tasks provide the maximum contribution to help improve the overall performance.

Apart from the two contributions, we have also performed an additional experiment of task dominance analysis where we have given an equal budget to the main task and also to the auxiliary tasks. The motivation to perform this experiment is to create a main task dominant MTL model, which can take advantage of multitask learning, and improve the performance of the main task simultaneously. 

All the novelties presented in this thesis indicate the potential of multitask learning techniques and their future applicability in the vehicular domain.

sted, utgiver, år, opplag, sider
2022. , s. 51
HSV kategori
Identifikatorer
URN: urn:nbn:se:hh:diva-46216OAI: oai:DiVA.org:hh-46216DiVA, id: diva2:1630050
Eksternt samarbeid
WirelessCar
Fag / kurs
Computer Systems Technology
Utdanningsprogram
Master's Programme in Embedded and Intelligent Systems, 120 credits
Veileder
Examiner
Tilgjengelig fra: 2022-01-19 Laget: 2022-01-19 Sist oppdatert: 2022-01-20bibliografisk kontrollert

Open Access i DiVA

fulltext(2853 kB)171 nedlastinger
Filinformasjon
Fil FULLTEXT02.pdfFilstørrelse 2853 kBChecksum SHA-512
2d04f714c122f1c9ed283a206f290c4503e2e6a221b8a5f23831a1c3295e44cf6bacdeb626b1474e9588f5524a75086af7e6100b8c7e6e5418b4d71a75f500d2
Type fulltextMimetype application/pdf

Av organisasjonen

Søk utenfor DiVA

GoogleGoogle Scholar
Totalt: 171 nedlastinger
Antall nedlastinger er summen av alle nedlastinger av alle fulltekster. Det kan for eksempel være tidligere versjoner som er ikke lenger tilgjengelige

urn-nbn

Altmetric

urn-nbn
Totalt: 524 treff
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf