Data-driven methods for tracking improvement

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This paper proposes a data-driven control design method for nonlinear systems that builds upon the Koopman operator framework. In particular, the Koopman operator is used to lift the nonlinear dynamics to a higher-dimensional space where the so-called obse ...

Recently, there has been an arms race of pose forecasting methods aimed at solving the spatio-temporal task of predicting a sequence of future 3D poses of a person given a sequence of past observed ones. However, the lack of unified benchmarks and limited ...

Control systems operating in real-world environments often face disturbances arising from measurement noise and model mismatch. These factors can significantly impact the perfor- mance and safety of the system. In this thesis, we aim to leverage data to de ...

Air traffic noise emissions are responsible for a significant part of the overall environmental noise, especially in the vicinity of airports. Exposure to environmental noise is known to negatively impact health and to be associated with cardiovascular dis ...

Remote photoplethysmography (rPPG) using camera-based imaging has shown excellent potential recently in vital signs monitoring due to its contactless nature. However, the optimum filter selection for pre-processing rPPG data in signal conditioning is still ...

A novel approach for robust controller synthesis, which models uncertainty as an elliptical set, is proposed in the paper. Given a set of frequency response functions of linear time-invariant (LTI) multiple-input multiple-output (MIMO) systems, the approac ...

Chatter is a harmful self-excited vibration that commonly occurs during milling processes. Data -driven chatter detection and prediction is critical to achieve high surface quality and process efficiency. Most existing chatter detection approaches are base ...

This thesis is situated at the crossroads between machine learning and control engineering. Our contributions are both theoretical, through proposing a new uncertainty quantification methodology in a kernelized context; and experimental, through investigat ...

Saliency models are image-based prediction models that estimate human visual attention. Such models, when applied to architectural spaces, could pave the way for design decisions where visual attention is taken into account. In this study, we tested the pe ...

The modern world is heavily reliant on electromagnetic devices to convert mechanical energy into electrical energy and vice versa. These devices are fundamental to powering our society, and the growing need for automated production lines and electrified tr ...

Daily manipulation tasks are characterized by regular features associated with the task structure, which can be described by multiple geometric primitives related to actions and object shapes. Only using Cartesian coordinate systems cannot fully represent ...

Data for: "A Neural-Network-Based Convex Regularizer for Inverse Problems". The corresponding scripts can be accessed on GitHub (https://github.com/axgoujon/convex_ridge_regularizers). The data is organized as follows: - ct_data_sets.tar.gz: contains prepr ...

Data-driven methods for tracking improvement

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Data-Driven Control Synthesis using Koopman Operator: A Robust Approach

Toward Reliable Human Pose Forecasting With Uncertainty

Data-Driven Control and Optimization under Noisy and Uncertain Conditions

Robust tunable acoustic impedance control on electroacoustic resonators for aircraft noise reduction

Optimal digital filter selection for remote photoplethysmography (rPPG) signal conditioning

Data-driven IQC-Based Robust Control Design for Hybrid Micro-disturbance Isolation Platform

Exploring the effectiveness of using internal CNC system signals for chatter detection in milling process

Machine Learning Methods for Robust Uncertainty Quantification and Controller Approximation

Saliency prediction in 360° architectural scenes: Performance and impact of daylight variations

Computational design and additive manufacturing of electromagnetic coils

A geometric optimal control approach for imitation and generalization of manipulation skills

Data for: "A Neural-Network-Based Convex Regularizer for Inverse Problems"