10 citations · 25 across the 6 of their papers we have counts for
6 papers
Optimizing Energy Efficiency and Grid Stability via Public EV Charging Flexibility
Marek Miltner, Artem Bryksa, Ondřej Štogl +4
This study evaluates the potential of electric vehicle (EV) charging flexibility to enhance both energy efficiency and power grid stability. Using real-world data from public charg…
Towards Using Machine Learning to Generatively Simulate EV Charging in Urban Areas
Marek Miltner, Jakub Zíka, Daniel Vašata +5
This study addresses the challenge of predicting electric vehicle (EV) charging profiles in urban locations with limited data. Utilizing a neural network architecture, we aim to un…
Image Inpainting Using Wasserstein Generative Adversarial Imputation Network
Daniel Vašata, Tomáš Halama, Magda Friedjungová
Image inpainting is one of the important tasks in computer vision which focuses on the reconstruction of missing regions in an image. The aim of this paper is to introduce an image…
Missing Features Reconstruction Using a Wasserstein Generative Adversarial Imputation Network
Magda Friedjungová, Daniel Vašata, Maksym Balatsko +1
Missing data is one of the most common preprocessing problems. In this paper, we experimentally research the use of generative and non-generative models for feature reconstruction.…
Unsupervised Latent Space Translation Network
Magda Friedjungová, Daniel Vašata, Tomáš Chobola +1
One task that is often discussed in a computer vision is the mapping of an image from one domain to a corresponding image in another domain known as image-to-image translation. Cur…
Missing Features Reconstruction and Its Impact on Classification Accuracy
Magda Friedjungová, Daniel Vašata, Marcel Jiřina
In real-world applications, we can encounter situations when a well-trained model has to be used to predict from a damaged dataset. The damage caused by missing or corrupted values…