Time- and Frequency-Domain Dynamic Spectrum Access: Learning Cyclic Medium Access Patterns in Partially Observable Environments
Upcoming communication systems increasingly often tackle the spectrum scarcity problem through the coexistence with legacy systems in …
Hybrid Modelling by Machine Learning Corrections of Analytical Model Predictions towards High-Fidelity Simulation Solutions
Within the fields of materials mechanics, the consideration of physical laws in machine learning predictions besides the use of data …
Evaluation of mechanical property predictions of refill Friction Stir Spot Welding joints via machine learning regression analyses on DoE data
The high-potential of lightweight components consisting of similar or dissimilar materials can be exploited by Solid-State Joining …
Integrated Simulation-Based Optimization of Operational Decisions at Container Terminals
At container terminals, many cargo handling processes are interconnected and occur in parallel. Within short time windows, many …
Exploring structure-property relationships in magnesium dissolution modulators
Small organic molecules that modulate the degradation behavior of Mg constitute benign and useful materials to modify the service …
Artificial Neural Networks for Sensor Data Classification on Small Embedded Systems
In this paper we investigate the usage of machine learning for interpreting measured sensor values in sensor modules. In particular we …
Predicting long-term dynamics of soil salinity and sodicity on a global scale
Knowledge of spatiotemporal distribution and likelihood of (re)occurrence of salt-affected soils is crucial to our understanding of …
ANN Performance for the Prediction of High-Speed Digital Interconnects over Multiple PCBs
In this paper the performance and the accuracy of artificial neural networks for the prediction of high-speed digital interconnects up …
JavaScript malware detection using locality sensitive hashing
In this paper, we explore the idea of using locality sensitive hashes as input features to a feed-forward neural network with the goal …
Comparison of Collaborative versus Extended Artificial Neural Networks for PDN Design
Currently machine learning tools are not capable to provide analysis solutions for complex printed circuit boards. It is unknown how to …
Mechanical Performance Prediction for Friction Riveting Joints of Dissimilar Materials via Machine Learning
Solid-state joining techniques have become increasingly attractive for joining similar and dissimilar materials because it enables …
Towards Delay-Minimal Scheduling through Reinforcement Learning in IEEE 802.15.4 DSME
The rise of wireless sensor networks (WSNs) inindustrial applications imposes novel demands on existing wireless protocols. The …
Parareal with a Learned Coarse Model for Robotic Manipulation
A key component of many robotics model-based planning and control algorithms is physics predictions, that is, forecasting a sequence of …
A Deep Learning Approach for Pose Estimation from Volumetric OCT Data
Tracking the pose of instruments is a central problem in image-guided surgery. For microscopic scenarios, optical coherence tomography …
Melanoma detection with electrical impedance spectroscopy and dermoscopy using joint deep learning models
The initial assessment of skin lesions is typically based on dermoscopic images. As this is a difficult and time-consuming task, …
In silico screening of modulators of magnesium dissolution
The vast number of small molecules with potentially useful dissolution modulating properties (inhibitors or accelerators) renders …
Left Ventricle Quantification Using Direct Regression with Segmentation Regularization and Ensembles of Pretrained 2D and 3D CNNs
Cardiac left ventricle (LV) quantification provides a tool for diagnosing cardiac diseases. Automatic calculation of all relevant LV …
Joint Learning of Geometric and Probabilistic Constellation Shaping
The choice of constellations largely affects the performance of communication systems. When designing constellations, both the …
A machine learning-based method for simulation of ship speed profile in a complex ice field
Computational methods for predicting ship speed profile in a complex ice field have traditionally relied on mechanistic simulations. …
Trainable Communication Systems: Concepts and Prototype
We consider a trainable point-to-point communication system, where both transmitter and receiver are implemented as neural networks …
Local monitoring of embedded applications and devices using artificial neural networks
Reliability, security, and safety become even more challenging in times of the Internet of Things (IoT). Devices operate jointly in …
Evaluation of Neural Networks to Predict Target Impedance Violations of Power Delivery Networks
An artificial neural network approach is presented to predict whether a power delivery network setup violates the target impedance. …
Online Monitoring of Power System Small Signal Stability Using Artificial Neural Networks
Shifting paradigms in electrical power generation, transmission and consumption will affect system dynamics and may negatively …
The 'Dark Side' of Big Data Analytics - Uncovering Path Dependency Risks of BDA-Investments
Recently, information systems (IS) literature has shown an increasing interest in Big Data and Analytics (BDA) to gain competitive …
A Review of the Application of Machine Learning and Data Mining Approaches in Continuum Materials Mechanics
Machine learning tools represent key enablers for empowering material scientists andengineers to accelerate the development of novel …
Data Science Based Mg Corrosion Engineering
Magnesium exhibits a high potential for a variety of applications in areas such as transport, energy and medicine. However, untreated …
Force Estimation from OCT Volumes using 3D CNNs
Purpose: Estimating the interaction forces of instruments and tissue is of interest, particularly to provide haptic feedback during …
The Effect of Dimensionality Reduction on Software Vulnerability Prediction Models
Statistical prediction models can be an effective technique to identify vulnerable components in large software projects. Two aspects …
Is Newer Always Better? The Case of Vulnerability Prediction Models
Finding security vulnerabilities in the source code as early as possible is becoming more and more essential. In this respect, …
Predicting Vulnerable Components: Software Metrics vs Text Mining
Building secure software is difficult, time-consuming, and expensive. Prediction models that identify vulnerability prone software …
Predicting Vulnerable Software Components via Text Mining
This paper presents an approach based on machine learning to predict which components of a software application contain security …