Searched for: subject%3A%22Gaussian%255C+processes%22
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Chai, Anbang (author)
The Optimal Power Flow (OPF) problem, a cornerstone of power system operations, has gained increased attention since its inception by Carpentier in 1962. OPF is fundamentally an optimization challenge aimed at enhancing electric power system operations within the bounds of physical and operational constraints. Over the decades, various...
master thesis 2024
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Mooren, Noud (author), van Meer, Max (author), Witvoet, Gert (author), Oomen, T.A.E. (author)
Actuators that require commutation algorithms, such as the switched reluctance motor (SRM) considered in this paper and employed in the coarse pointing assembly (CPA) for free-space optical communication, often have torque-ripple disturbances that are periodic in the commutation-angle domain that deteriorate the positioning performance. The...
journal article 2024
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Gheysen, Lise (author), Maes, Lauranne (author), Caenen, Annette (author), Segers, Patrick (author), Peirlinck, M. (author), Famaey, Nele (author)
Personalized treatment informed by computational models has the potential to markedly improve the outcome for patients with a type B aortic dissection. However, existing computational models of dissected walls significantly simplify the characteristic false lumen, tears and/or material behavior. Moreover, the patient-specific wall thickness...
journal article 2024
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Khan, Muhammad Hasnain Ayub (author), Jafri, Turab H. (author), Ud-Din, Sameer (author), Ullah, H.S. (author), Nawaz, Muhammad Naqeeb (author)
The laboratory determination of maximum dry density (ρ<sub>dmax</sub>) and optimum moisture content (w<sub>opt</sub>) of soils requires considerable time and energy. Efforts have been made in the past to present models to predict the soil compaction parameters (ρ<sub>dmax</sub> and w<sub>opt</sub>), but the existing models are either...
journal article 2024
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Ramírez Montero, Mariano (author)
Recent research has shown that a Learning from Demonstration (LfD) approach is useful for teaching robots flexible skills efficiently, and it opens the possibility for non-expert users to program these skills. When learning from demonstration data, learning frameworks should learn representations that are flexible and can generalize to unseen...
master thesis 2023
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Ban, Hanyuan (author)
Gaussian process regression (GPR), a potent non-parametric data modeling tool, has gained attention but is hindered by its high com- putational load. State-of-the-art low-rank approximations like struc- tured kernel interpolation (SKI)-based methods offer efficiency, yet lack a strategy for determining the number of grid points, a pivotal factor...
master thesis 2023
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Wang, Haobo (author)
The evolution of aerial vehicle technology necessitates robust trajectory prediction models. These models are crucial for maintaining safe airspace and enabling autonomous operations. Automatic dependent surveillance–broadcast (ADS-B) is a surveillance system that enables aircraft to receive data from navigation satellites and periodically...
master thesis 2023
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Tan, Martin (author)
In the field of Systems and Control, optimal control problem-solving for complex systems is a core task. The development of accurate mathematical models to represent these systems’ dynamics is often difficult. This complexity comes from potential uncertainties, complex non-linearities, or unknown factors that might affect the system. Because of...
master thesis 2023
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Probst, Johanna (author)
Creating autonomous Micro Aerial Vehicles for executing complex missions poses various challenges, including safe navigation in the presence of external wind disturbances. Most current navigation methods handle external wind disturbances through real-time estimation and rejection algorithms in the control stage, but lack safety guarantees in...
master thesis 2023
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Tavio Y Cabrera, Emilio (author)
Soft robots have the potential to accelerate robotiza- tion in areas that are complex and impractical for hard robots. The use of soft materials results in a safe and flexible design that is unattainable for hard robots. However, this attribute results in the need for new control approaches and strategies. Hybrid controllers are a relative...
master thesis 2023
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Fetter, Marnix (author)
Indoor positioning systems cannot rely on conventional localization methods, such as GPS, to locate devices because of interference with the structure of buildings. One solution is to use magnetic positioning, which is based on spatial variations in the patterns of the ambient magnetic field. To model magnetic fields, Gaussian process regression...
master thesis 2023
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Shen, C. (author), Zhang, P. (author), Dollevoet, R.P.B.J. (author), Zoeteman, A. (author), Li, Z. (author)
While various train-borne techniques have been developed for measuring railway track stiffness, differentiating stiffness at different track layers remains a challenge. This study proposes a digital twin framework for the vehicle–track interaction system, which enables track stiffness evaluations based on axle box accelerations (ABA). The...
journal article 2023
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Kirchner, K. (author), Willems, J. (author)
A new class of fractional-order parabolic stochastic evolution equations of the form (∂t+A)γX(t)=W˙Q(t) , t∈ [0 , T] , γ∈ (0 , ∞) , is introduced, where - A generates a C -semigroup on a separable Hilbert space H and the spatiotemporal driving noise W˙ <sup>Q</sup> is the formal time derivative of an H-valued cylindrical Q-Wiener process....
journal article 2023
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Haninger, Kevin (author), Hegeler, Christian (author), Peternel, L. (author)
Robotic tasks which involve uncertainty – due to variation in goal, environment configuration, or confidence in task model – may require human input to instruct or adapt the robot. In tasks with physical contact, several existing methods for adapting robot trajectory or impedance according to individual uncertainties have been proposed, e.g.,...
journal article 2023
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Zou, Joanna (author), Lourens, E. (author), Cicirello, A. (author)
Virtual sensing techniques have gained traction in applications to the structural health monitoring of monopile-based offshore wind turbines, as the strain response below the mudline, which is a primary indicator of fatigue damage accumulation, is impractical to measure directly with physical instrumentation. The Gaussian process latent force...
journal article 2023
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Viset, F.M. (author), Helmons, R.L.J. (author), Kok, M. (author)
Accurately estimating the positions of multi-agent systems in indoor environments is challenging due to the lack of Global Navigation Satelite System (GNSS) signals. Noisy measurements of position and orientation can cause the integrated position estimate to drift without bound. Previous research has proposed using magnetic field simultaneous...
conference paper 2023
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Edridge, T.I. (author), Kok, M. (author)
Ferromagnetic materials in indoor environments give rise to disturbances in the ambient magnetic field. Maps of these magnetic disturbances can be used for indoor localisation. A Gaussian process can be used to learn the spatially varying magnitude of the magnetic field using magnetometer measurements and information about the position of the...
conference paper 2023
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Menzen, C.M. (author), Fetter, Marnix (author), Kok, M. (author)
We present a mapping algorithm to compute large-scale magnetic field maps in indoor environments with approximate Gaussian process (GP) regression. Mapping the spatial variations in the ambient magnetic field can be used for 10-calization algorithms in indoor areas. To compute such a map, GP regression is a suitable tool because it provides...
conference paper 2023
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Zhai, P. (author), Rajan, R.T. (author)
Gaussian Process (GP) is a flexible non-parametric method which has a wide variety of applications e.g., field estimation using multi-agent systems. However, the training of the hyperparameters suffers from high computational complexity. Recently, distributed hyperparameter optimization with proximal gradients has been proposed to reduce...
conference paper 2023
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Igea, Felipe (author), Cicirello, A. (author)
Multi-modal distributions of some physics-based model parameters are often encountered in engineering due to different situations such as a change in some environmental conditions, and the presence of some types of damage and non-linearity. In statistical model updating, for locally identifiable parameters, it can be anticipated that multi...
journal article 2023
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