138 research outputs found
Quantifying the Influences on Probabilistic Wind Power Forecasts
In recent years, probabilistic forecasts techniques were proposed in research
as well as in applications to integrate volatile renewable energy resources
into the electrical grid. These techniques allow decision makers to take the
uncertainty of the prediction into account and, therefore, to devise optimal
decisions, e.g., related to costs and risks in the electrical grid. However, it
was yet not studied how the input, such as numerical weather predictions,
affects the model output of forecasting models in detail. Therefore, we examine
the potential influences with techniques from the field of sensitivity analysis
on three different black-box models to obtain insights into differences and
similarities of these probabilistic models. The analysis shows a considerable
number of potential influences in those models depending on, e.g., the
predicted probability and the type of model. These effects motivate the need to
take various influences into account when models are tested, analyzed, or
compared. Nevertheless, results of the sensitivity analysis will allow us to
select a model with advantages in the practical application.Comment: 5 pages; 1 table; 3 figures; This work has been submitted to the IEEE
for possible publication. Copyright may be transferred without notice, after
which this version may no longer be accessibl
Radar-based Road User Classification and Novelty Detection with Recurrent Neural Network Ensembles
Radar-based road user classification is an important yet still challenging
task towards autonomous driving applications. The resolution of conventional
automotive radar sensors results in a sparse data representation which is tough
to recover by subsequent signal processing. In this article, classifier
ensembles originating from a one-vs-one binarization paradigm are enriched by
one-vs-all correction classifiers. They are utilized to efficiently classify
individual traffic participants and also identify hidden object classes which
have not been presented to the classifiers during training. For each classifier
of the ensemble an individual feature set is determined from a total set of 98
features. Thereby, the overall classification performance can be improved when
compared to previous methods and, additionally, novel classes can be identified
much more accurately. Furthermore, the proposed structure allows to give new
insights in the importance of features for the recognition of individual
classes which is crucial for the development of new algorithms and sensor
requirements.Comment: 8 pages, 9 figures, accepted paper for 2019 IEEE Intelligent Vehicles
Symposium (IV), Paris, France, June 201
Radar-based Feature Design and Multiclass Classification for Road User Recognition
The classification of individual traffic participants is a complex task,
especially for challenging scenarios with multiple road users or under bad
weather conditions. Radar sensors provide an - with respect to well established
camera systems - orthogonal way of measuring such scenes. In order to gain
accurate classification results, 50 different features are extracted from the
measurement data and tested on their performance. From these features a
suitable subset is chosen and passed to random forest and long short-term
memory (LSTM) classifiers to obtain class predictions for the radar input.
Moreover, it is shown why data imbalance is an inherent problem in automotive
radar classification when the dataset is not sufficiently large. To overcome
this issue, classifier binarization is used among other techniques in order to
better account for underrepresented classes. A new method to couple the
resulting probabilities is proposed and compared to others with great success.
Final results show substantial improvements when compared to ordinary
multiclass classificationComment: 8 pages, 6 figure
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