4 papers · 1 filter
Zero-Inflated Gaussian Distributions Enable Parameter-Space Sparsity in Estimation-of-Distribution Algorithms
Andreas Faust, Sven Nitzsche, Juergen Becker
Estimation-of-distribution algorithms (EDAs) are a powerful class of evolutionary methods for black-box optimization, especially when little is known about the structure of the obj…
Spiking Neural Networks for Low-Power Vibration-Based Predictive Maintenance
Alexandru Vasilache, Sven Nitzsche, Christian Kneidl +3
Advancements in Industrial Internet of Things (IIoT) sensors enable sophisticated Predictive Maintenance (PM) with high temporal resolution. For cost-efficient solutions, vibration…
A PyTorch-Compatible Spike Encoding Framework for Energy-Efficient Neuromorphic Applications
Alexandru Vasilache, Jona Scholz, Vincent Schilling +4
Spiking Neural Networks (SNNs) offer promising energy efficiency advantages, particularly when processing sparse spike trains. However, their incompatibility with traditional datas…
Low-Power Vibration-Based Predictive Maintenance for Industry 4.0 using Neural Networks: A Survey
Alexandru Vasilache, Sven Nitzsche, Daniel Floegel +7
The advancements in smart sensors for Industry 4.0 offer ample opportunities for low-powered predictive maintenance and condition monitoring. However, traditional approaches in thi…