Zobrazeno 1 - 10
of 284
pro vyhledávání: '"Frisk, Erik"'
This paper presents the LiU-ICE fault diagnosis benchmark. The purpose of the benchmark is to support fault diagnosis research by providing data and a model of an industrially relevant system. Data has been collected from an internal combustion engin
Externí odkaz:
http://arxiv.org/abs/2408.13269
The availability of high-quality datasets is crucial for the development of behavior prediction algorithms in autonomous vehicles. This paper highlights the need for standardizing the use of certain datasets for motion forecasting research to simplif
Externí odkaz:
http://arxiv.org/abs/2405.00604
Accurate predictions of when a component will fail are crucial when planning maintenance, and by modeling the distribution of these failure times, survival models have shown to be particularly useful in this context. The presented methodology is base
Externí odkaz:
http://arxiv.org/abs/2403.18739
In this paper, a family of neural network-based survival models is presented. The models are specified based on piecewise definitions of the hazard function and the density function on a partitioning of the time; both constant and linear piecewise de
Externí odkaz:
http://arxiv.org/abs/2403.18664
This paper proposes a safety-critical controller for dynamic and uncertain environments, leveraging a robust environment control barrier function (ECBF) to enhance the robustness against the measurement and prediction uncertainties associated with mo
Externí odkaz:
http://arxiv.org/abs/2403.13288
The ability to predict the future trajectories of traffic participants is crucial for the safe and efficient operation of autonomous vehicles. In this paper, a diffusion-based generative model for multi-agent trajectory prediction is proposed. The mo
Externí odkaz:
http://arxiv.org/abs/2403.11643
Safe motion planning for robotic systems in dynamic environments is nontrivial in the presence of uncertain obstacles, where estimation of obstacle uncertainties is crucial in predicting future motions of dynamic obstacles. The worst-case characteriz
Externí odkaz:
http://arxiv.org/abs/2403.06222
Safety, reliability, and durability are targets of all engineering systems, including Li-ion batteries in electric vehicles. This paper focuses on sensor setup exploration for a battery-integrated modular multilevel converter (BI-MMC) that can be par
Externí odkaz:
http://arxiv.org/abs/2312.16520
Multi-mode systems can operate in different modes, leading to large numbers of different dynamics. Consequently, applying traditional structural diagnostics to such systems is often untractable. To address this challenge, we present a multi-mode diag
Externí odkaz:
http://arxiv.org/abs/2312.14030
This paper addresses the training of Neural Ordinary Differential Equations (neural ODEs), and in particular explores the interplay between numerical integration techniques, stability regions, step size, and initialization techniques. It is shown how
Externí odkaz:
http://arxiv.org/abs/2311.15890