Jonathan Jakob, Martina Hasenjäger, Barbara Hammer,
"On the suitability of incremental learning for regression tasks in exoskeleton control",
IEEE Symposium on Computational Intelligence in Data Mining (CIDM), 2021.
Abstract
In recent times, a new generation of modern exoskeleton robots has come into existence that aims to utilize machine learning to learn the specific needs and preferences of its users. A simple way to facilitate personalization of an exoskeleton to the end user is to make use of incremental algorithms that keep learning throughout their deployment. However, it is not clear, if any standard algorithms are fast enough to keep pace with sudden change ...
Download Bibtex file
Download PDF
Manuel Dietrich,
"Addressing Inequal Risk Exposure in the Development of Automated Vehicles
",
Ethics and Information Technology, 2021.
Abstract
Automated vehicles (AVs) are expected to operate on public roads, together with non-automated vehicles and other road users such as pedestrians or bicycles. Recent ethical reports and guidelines raise worries that AVs will introduce injustice or reinforce existing social inequalities in road traffic. One major injustice concern in today’s traffic is that different types of road users are exposed differently to risks of corporal harm. In the fir...
Download Bibtex file
Per Mail Request
Karsten Kreutz and Julian Eggert,
"Analysis of a Generalized Intelligent Driver Model for merging situations",
IEEE Intelligent Vehicle Symposium 2021, pp. 34-41, 2021.
Abstract
In this paper, we analyze an extension of the Intelligent Driver Model (IDM) for its application on single situation prediction in merging situations. For this purpose, we first extend the original, longitudinal single car following IDM with several terms. First, we include a consideration of more than a single leading car to be able to deal with pressure from back, as required for anticipatory acceleration. Second, we use a virtual projection of...
Download Bibtex file
Per Mail Request
Francesco Romagnoli,
"Electric load time series forecasting and relative predictions on simulation model ",
UNIVERSITA’ POLITECNICA DELLE MARCHE, ANCONA, IT, 2021.
Abstract
Time series forecasting is an important area of machine learning because there
are so many prediction problems that involve a time component. A normal
machine learning dataset is a collection of observations, while a time series
dataset adds an explicit order dependence between observations, represented by
time dimension. This additional dimension is both a constraint and a structure that
provides a source of addit...
Download Bibtex file
Per Mail Request
Karsten Kreutz and Julian Eggert,
"Analysis of the Generalized Intelligent Driver Model (GIDM) for uncontrolled intersections",
IEEE Intelligent Transportation Systems Conference (ITSC) 2021, pp. 3223-3230, 2021.
Abstract
In this paper, we propose and analyze a Generalized Intelligent Driver Model (GIDM) as an extension of the Intelligent Driver Model (IDM) for its applicability to model uncontrolled intersection scenarios. For this purpose, we extend the original longitudinal car-following IDM with several terms:(1) for anticipatory acceleration capabilities, we include the most nearby backward agent, (2) we consider cars on paths that will cross the own path by...
Download Bibtex file
Per Mail Request
Steffen Limmer, Fernando Lezama, Joao Soares, Zita Vale,
"Coordination of Home Appliances for Demand Response: An Improved Optimization Model and Approach",
IEEE Access, vol. 9, pp. 146183-146194, 2021.
Abstract
Home appliances constitute an interesting source of flexibility for demand responseprograms. However, their control and coordination are challenging, since typically a high number of suchappliances has to be aggregated in order to provide a sufficient amount of flexibility. Thus, an efficient andscalable control approach is required. In a previous work, metaheuristic methods were evaluated for solvinga control problem, which considers t...
Download Bibtex file
Download PDF
Saif Sidhik, Mohan Sridharan, Dirk Ruiken,
"Towards a Framework for Changing-ContactRobot Manipulation",
RSS 2021 - Workshop on Advancing Artificial Intelligence and Manipulation for Robotics: Understanding Gaps, Industry and Academic Perspectives, and Community Building, 2021.
Abstract
Many robot manipulation tasks require the robot to make and break contact with objects and surfaces. The dynamics of such \textit{changing-contact} robot manipulation tasks are discontinuous when contact is made or broken, and continuous elsewhere. These discontinuities make it difficult to construct and use a single dynamics model or control strategy for any such task. We present a framework for smooth dynamics and control of such changing-conta...
Download Bibtex file
Per Mail Request
Christiane Wiebel, Matti Krüger, Patricia Wollstadt,
"Measuring inter- and intra-individual differences in visual scan patterns in a driving simulator experiment using active information storage",
PLOS ONE, vol. 16, no. 3, pp. e0248166, 2021.
Abstract
Scan pattern analysis has been discussed as a promising tool in the context of real-time gaze-based applications [1]. In particular, information-theoretic measures of scan path predictability, such as the gaze transition entropy (GTE), have been proposed for detecting relevant changes in user state or task demand [2]. These measures model scan patterns as first-order Markov chains, assuming that only the location of the previous fixation is predi...
Download Bibtex file
Download PDF
Benjamin Schaden, Thomas Jatschka, Steffen Limmer, Guenther Raidl,
"Smart Charging of Electric Vehicles Considering SOC-Dependent Maximum Charging Powers",
Energies, vol. 14, no. 22, 2021.
Abstract
The aim of this work is to schedule the charging of electric vehicles (EVs) at a single charging station such that the temporal availability of each EV as well as the maximum available power at the station are considered. The total costs for charging the vehicles should be minimized w.r.t.\ time-dependent electricity costs. A particular challenge investigated in this work is that the maximum power at which a vehicle can be charged is dependent on...
Download Bibtex file
Download PDF
Duc Nguyen, Ewout Zwanenburg, Steffen Limmer, Wessel Luijben, Markus Olhofer, Thomas Bäck,
"Fourier Transform and Machine Learning Techniques for Fault Detection and Diagnosis of Induction Motors",
International Conference on Prognostics and Health Management, 2021.
Abstract
Induction motors are widely used in different industry areas and can experience various kinds of faults in stators and rotors. In general, fault detection and diagnosis techniques for induction motors can be supervised by measuring quantities such as noise, vibration, and temperature. The installation of mechanical sensors in order to assess the health conditions of a machine is typically only done for expensive or load-critical machines, where t...
Download Bibtex file
Per Mail Request
1 2 3 4
5 6 ...
130
