Investigating explainable machine learning techniques to calibrate low-cost sensors systems

Herrera Carrión, Madison Eduardo (2026) Investigating explainable machine learning techniques to calibrate low-cost sensors systems. Student thesis, University of Stuttgart.

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Item URL in elib:

https://elib.dlr.de/225354/

Document Type:

Thesis (Student thesis)

Title:

Investigating explainable machine learning techniques to calibrate low-cost sensors systems

Authors:

Authors	Institution or Email of Authors	Author's ORCID iD	ORCID Put Code
Herrera Carrión, Madison Eduardo	madison.herreracarrion (at) dlr.de	UNSPECIFIED	UNSPECIFIED

DLR Supervisors:

Contribution	DLR Supervisor	Institution or E-Mail	DLR Supervisor's ORCID iD
Thesis advisor	Chacon Mateos, Miriam	miriam.chaconmateos (at) dlr.de	https://orcid.org/0000-0002-6370-8589

Date:

2026

Open Access:

Number of Pages:

Status:

Published

Keywords:

low-cost sensors; artificial intelligence; explainable machine learning

Institution:

University of Stuttgart

Department:

Department Flue Gas Cleaning and Air Quality Control

HGF - Research field:

Aeronautics, Space and Transport

HGF - Program:

Transport

HGF - Program Themes:

Transport System

DLR - Research area:

Transport

DLR - Program:

V VS - Verkehrssystem

DLR - Research theme (Project):

V - MoDa - Models and Data for Future Mobility_Supporting Services, L - Components and Emissions

Location:

Stuttgart

Institutes and Institutions:

Institute of Combustion Technology > Chemical Kinetics and Analytics

Deposited By:

Chacon Mateos, Miriam

Deposited On:

09 Jul 2026 10:53

Last Modified:

09 Jul 2026 10:53

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