984 citations · 1.2k across the 25 of their papers we have counts for
32 papers
Automated Machine Learning for Remaining Useful Life Predictions
Marc-André Zöller, Fabian Mauthe, Peter Zeiler +2
Being able to predict the remaining useful life (RUL) of an engineering system is an important task in prognostics and health management. Recently, data-driven approaches to RUL pr…
Towards Optimal Energy Management Strategy for Hybrid Electric Vehicle with Reinforcement Learning
Xinyang Wu, Elisabeth Wedernikow, Christof Nitsche +1
In recent years, the development of Artificial Intelligence (AI) has shown tremendous potential in diverse areas. Among them, reinforcement learning (RL) has proven to be an effect…
Optimizing CAD Models with Latent Space Manipulation
Jannes Elstner, Raoul G. C. Schönhof, Steffen Tauber +1
When it comes to the optimization of CAD models in the automation domain, neural networks currently play only a minor role. Optimizing abstract features such as automation capabili…
Defect Transfer GAN: Diverse Defect Synthesis for Data Augmentation
Ruyu Wang, Sabrina Hoppe, Eduardo Monari +1
Data-hunger and data-imbalance are two major pitfalls in many deep learning approaches. For example, on highly optimized production lines, defective samples are hardly acquired whi…
Mixture of Decision Trees for Interpretable Machine Learning
Simeon Brüggenjürgen, Nina Schaaf, Pascal Kerschke +1
This work introduces a novel interpretable machine learning method called Mixture of Decision Trees (MoDT). It constitutes a special case of the Mixture of Experts ensemble archite…
Kalman-Bucy-Informed Neural Network for System Identification
Tobias Nagel, Marco F. Huber
Identifying parameters in a system of nonlinear, ordinary differential equations is vital for designing a robust controller. However, if the system is stochastic in its nature or i…
A Nested Genetic Algorithm for Explaining Classification Data Sets with Decision Rules
Paul-Amaury Matt, Rosina Ziegler, Danilo Brajovic +2
Our goal in this paper is to automatically extract a set of decision rules (rule set) that best explains a classification data set. First, a large set of decision rules is extracte…
Radar-based Respiratory Rate Monitoring in Standing Position
Tassneem Helal, Fady Aziz, Omar Metwally +4
Estimating human vital signs in a contactless non-invasive method using radar provides a convenient method in the medical field to conduct several health checkups easily and quickl…
Simplified Learning of CAD Features Leveraging a Deep Residual Autoencoder
Raoul Schönhof, Jannes Elstner, Radu Manea +3
In the domain of computer vision, deep residual neural networks like EfficientNet have set new standards in terms of robustness and accuracy. One key problem underlying the trainin…
XAutoML: A Visual Analytics Tool for Understanding and Validating Automated Machine Learning
Marc-André Zöller, Waldemar Titov, Thomas Schlegel +1
In the last ten years, various automated machine learning (AutoM ) systems have been proposed to build end-to-end machine learning (ML) pipelines with minimal human interaction. Ev…
Feature Visualization within an Automated Design Assessment leveraging Explainable Artificial Intelligence Methods
Raoul Schönhof, Artem Werner, Jannes Elstner +3
Not only automation of manufacturing processes but also automation of automation procedures itself become increasingly relevant to automation research. In this context, automated c…
A MIMO Radar-Based Metric Learning Approach for Activity Recognition
Fady Aziz, Omar Metwally, Pascal Weller +2
Human activity recognition is seen of great importance in the medical and surveillance fields. Radar has shown great feasibility for this field based on the captured micro-Doppler…
Precise Object Placement with Pose Distance Estimations for Different Objects and Grippers
Kilian Kleeberger, Jonathan Schnitzler, Muhammad Usman Khalid +3
This paper introduces a novel approach for the grasping and precise placement of various known rigid objects using multiple grippers within highly cluttered scenes. Using a single…
Kalman Bayesian Neural Networks for Closed-form Online Learning
Philipp Wagner, Xinyang Wu, Marco F. Huber
Compared to point estimates calculated by standard neural networks, Bayesian neural networks (BNN) provide probability distributions over the output predictions and model parameter…
Reinforcement Learning based Condition-oriented Maintenance Scheduling for Flow Line Systems
Raphael Lamprecht, Ferdinand Wurst, Marco F. Huber
Maintenance scheduling is a complex decision-making problem in the production domain, where a number of maintenance tasks and resources has to be assigned and scheduled to producti…
Towards Measuring Bias in Image Classification
Nina Schaaf, Omar de Mitri, Hang Beom Kim +2
Convolutional Neural Networks (CNN) have become de fact state-of-the-art for the main computer vision tasks. However, due to the complex underlying structure their decisions are ha…
DimRad: A Radar-Based Perception System for Prosthetic Leg Barrier Traversing
Fady Aziz, Bassam Elmakhzangy, Christophe Maufroy +2
Lower extremity amputees face challenges in natural locomotion, which is partially compensated using powered assistive systems, e.g., micro-processor controlled prosthetic leg. In…
Automatic Grasp Pose Generation for Parallel Jaw Grippers
Kilian Kleeberger, Florian Roth, Richard Bormann +1
This paper presents a novel approach for the automatic offline grasp pose synthesis on known rigid objects for parallel jaw grippers. We use several criteria such as gripper stroke…
Investigations on Output Parameterizations of Neural Networks for Single Shot 6D Object Pose Estimation
Kilian Kleeberger, Markus Völk, Richard Bormann +1
Single shot approaches have demonstrated tremendous success on various computer vision tasks. Finding good parameterizations for 6D object pose estimation remains an open challenge…
Incremental Search Space Construction for Machine Learning Pipeline Synthesis
Marc-André Zöller, Tien-Dung Nguyen, Marco F. Huber
Automated machine learning (AutoML) aims for constructing machine learning (ML) pipelines automatically. Many studies have investigated efficient methods for algorithm selection an…
Transferring Experience from Simulation to the Real World for Precise Pick-And-Place Tasks in Highly Cluttered Scenes
Kilian Kleeberger, Markus Völk, Marius Moosmann +4
In this paper, we introduce a novel learning-based approach for grasping known rigid objects in highly cluttered scenes and precisely placing them based on depth images. Our Placem…
A Survey on the Explainability of Supervised Machine Learning
Nadia Burkart, Marco F. Huber
Predictions obtained by, e.g., artificial neural networks have a high accuracy but humans often perceive the models as black boxes. Insights about the decision making are mostly op…
Bayesian Perceptron: Towards fully Bayesian Neural Networks
Marco F. Huber
Artificial neural networks (NNs) have become the de facto standard in machine learning. They allow learning highly nonlinear transformations in a plethora of applications. However,…
Single Shot 6D Object Pose Estimation
Kilian Kleeberger, Marco F. Huber
In this paper, we introduce a novel single shot approach for 6D object pose estimation of rigid objects based on depth images. For this purpose, a fully convolutional neural networ…