6 citations · 6 across the 6 of their papers we have counts for
7 papers
Precision Specimen Positioning in Electron Microscopy through Hysteresis Compensation, Iterative Learning, and Vision-Based Sensing
J. S. van Hulst, A. M. C. de Peffer, D. Herceg +4
Electron microscopy requires nanometer-scale specimen positioning over a long stroke. Piezo-stepper actuators are well suited for this task, but their accuracy is limited by hyster…
Constructing VAE Latent Spaces with Prescribed Topology
Jilles S. van Hulst, Jakub M. Tomczak, W. P. M. H. Heemels +1
Variational autoencoders (VAEs) learn low-dimensional latent representations of high-dimensional data. When the data lies on a manifold with non-Euclidean topology, the standard Ga…
Estimating Evolving Functions with Dynamic Gaussian Processes
J. S. van Hulst, W. P. M. H. Heemels, D. J. Antunes
This paper develops the Dynamic Gaussian Process (DGP), a framework for estimating functions governed by integro-difference equations (IDEs). IDEs model continuous functions that e…
Levelable graphs
Kieran Bhaskara, Michael Y. C. Chong, Takayuki Hibi +2
We study a family of positive weighted well-covered graphs, which we call levelable graphs, that are related to a construction of level artinian rings in commutative algebra. A gra…
Data-Efficient Quadratic Q-Learning Using LMIs
J. S. van Hulst, W. P. M. H. Heemels, D. J. Antunes
Reinforcement learning (RL) has seen significant research and application results but often requires large amounts of training data. This paper proposes two data-efficient off-poli…
Estimation of Dynamic Gaussian Processes
Jilles van Hulst, Roy van Zuijlen, Duarte Antunes +2
Gaussian processes provide a compact representation for modeling and estimating an unknown function, that can be updated as new measurements of the function are obtained. This pape…
Feedforward Control in the Presence of Input Nonlinearities: A Learning-based Approach
Jilles van Hulst, Maurice Poot, Dragan Kostić +3
Advanced feedforward control methods enable mechatronic systems to perform varying motion tasks with extreme accuracy and throughput. The aim of this paper is to develop a data-dri…