arXiv subjects

Karl-Philipp Kortmann

Publications and source records attributed to Karl-Philipp Kortmann.

5 recordsLinked to original sources

arXiv2024

Predictive Energy Management for Recuperation Axles in Refrigerated Trailers

Refrigerated truck trailers are currently mainly operated with environmentally harmful diesel units; an alternative is to operate the refrigeration unit with electrical energy. However, this requires a battery, the size of which can be reduced by using a recuperation axle, which recovers energy during braking. Current systems work purely reactively and often in so-called towing mode, in which a generator torque is provided without a braking request from the driver in order to secure the energy supply. However, this drag leads to additional consumption in the truck. This work quantifies the potential of predictive energy management that uses route and environmental data to minimize CO2 emissions. This was done using simulation data obtained with the help of VECTO. It was shown that there is still considerable potential for savings, so this paper provides an important basis for the later development of predictive energy management and, thus, for the electrification of refrigerated truck transports.

eess.SY↗

arXiv2024

Friction and Road Condition Estimation by Combining Cause- and Effect-Based Methods using Bayesian Networks

Knowledge about the maximum tire-road friction potential is an important factor to ensure the driving stability and traffic safety of the vehicle. Many authors proposed systems that either measure friction related parameters or estimate the friction coefficient directly via a mathematical model. However these systems can be negatively impacted by environmental factors or require a sufficient level of excitation in the form of tire slip, which is often too low under practical conditions. Therefore, this work investigates, if a more robust estimation can be achieved by fusing the information of multiple systems using a Bayesian network, which models the statistical relationship between the sensors and the maximum friction coefficient. First, the Bayesian network is evaluated over its entire domain to compare the inference process to all possible road conditions. After that, the algorithm is applied to data from a test vehicle to demonstrate the performance under real conditions.

eess.SP↗

arXiv2021

Autoencoder-based Representation Learning from Heterogeneous Multivariate Time Series Data of Mechatronic Systems

Sensor and control data of modern mechatronic systems are often available as heterogeneous time series with different sampling rates and value ranges. Suitable classification and regression methods from the field of supervised machine learning already exist for predictive tasks, for example in the context of condition monitoring, but their performance scales strongly with the number of labeled training data. Their provision is often associated with high effort in the form of person-hours or additional sensors. In this paper, we present a method for unsupervised feature extraction using autoencoder networks that specifically addresses the heterogeneous nature of the database and reduces the amount of labeled training data required compared to existing methods. Three public datasets of mechatronic systems from different application domains are used to validate the results.

cs.LG↗

arXiv2020

Calibration of Model Uncertainty for Dropout Variational Inference

The model uncertainty obtained by variational Bayesian inference with Monte Carlo dropout is prone to miscalibration. In this paper, different logit scaling methods are extended to dropout variational inference to recalibrate model uncertainty. Expected uncertainty calibration error (UCE) is presented as a metric to measure miscalibration. The effectiveness of recalibration is evaluated on CIFAR-10/100 and SVHN for recent CNN architectures. Experimental results show that logit scaling considerably reduce miscalibration by means of UCE. Well-calibrated uncertainty enables reliable rejection of uncertain predictions and robust detection of out-of-distribution data.

cs.LG↗

arXiv2019

Well-calibrated Model Uncertainty with Temperature Scaling for Dropout Variational Inference

Model uncertainty obtained by variational Bayesian inference with Monte Carlo dropout is prone to miscalibration. The uncertainty does not represent the model error well. In this paper, temperature scaling is extended to dropout variational inference to calibrate model uncertainty. Expected uncertainty calibration error (UCE) is presented as a metric to measure miscalibration of uncertainty. The effectiveness of this approach is evaluated on CIFAR-10/100 for recent CNN architectures. Experimental results show, that temperature scaling considerably reduces miscalibration by means of UCE and enables robust rejection of uncertain predictions. The proposed approach can easily be derived from frequentist temperature scaling and yields well-calibrated model uncertainty. It is simple to implement and does not affect the model accuracy.

cs.LG↗