Searched for: subject%3A%22state%255C-space%255C%2Bmodels%22
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FANG, SONGLEI (author)
A complex network consists of the underlying topology, defined by a graph and the dynamical processes taking place on a network, defined by a set of governing equations. In this thesis, we deploy the discrete-time linear state-space (DLSS) model to identify the dynamical processes taking place on a complex network. Unlike the black-box...
master thesis 2022
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Dani, Neil (author)
To tackle the effect of climate change while meeting the ever-growing demand of energy, a high scale of integration of renewable energy resources has been performed in the past decade. Solar Photovoltaics have played a monumental role in this process, thus contributing in the fulfillment of the energy demand. The scalability and the flexibility...
master thesis 2022
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Wijnands, Ruben (author)
In recent years, the increase in brain research led to the development of large-scale brain imaging techniques. With large-scale brain imaging techniques, such as functional magnetic resonance imaging (fMRI), functional connectivity analyses have shown altered connectivity patterns in humans and mice with neurobiological disorders, such as...
master thesis 2022
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Srivastava, Akshat (author)
Single Molecule Localization Microscopy (SMLM) has enabled researchers to breakthrough the diffraction limit and obtain nanometer resolution images of macromolecular structures. But due to the time involved in obtaining ample data for proper image, the technique is venerable to many problem including fluctuations due to thermal gradients from...
master thesis 2022
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Ramakrishnan, Meenakshi Sundaram (author)
The current energy systems with the mix of renewable and conventional sources are already working on their maximum capacity in order to meet the existing demand. In the near future to meet the burgeoning demand, the future electrical power system would be a mix of both AC and DC systems coexisting with each other. With the fast paced development...
master thesis 2021
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Vojković, Tea (author)
There is increasing interest in the development of next-generation optimal controllers in the aerospace industry, focused on reducing aircraft environmental impact by mass reduction and enabling higher flight velocities for flexible wing structures. A key challenge in designing an optimal control system for this purpose is modeling the unsteady...
master thesis 2021
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Anil Meera, A. (author), Wisse, M. (author)
The free energy principle from neuroscience has recently gained traction as one of the most prominent brain theories that can emulate the brain’s perception and action in a bio-inspired manner. This renders the theory with the potential to hold the key for general artificial intelligence. Leveraging this potential, this paper aims to bridge the...
journal article 2021
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Ruiz, Luana (author), Gama, Fernando (author), Ribeiro, Alejandro (author), Isufi, E. (author)
Graph convolutional neural networks (GCNNs) learn compositional representations from network data by nesting linear graph convolutions into nonlinearities. In this work, we approach GCNNs from a state-space perspective revealing that the graph convolutional module is a minimalistic linear state-space model, in which the state update matrix is...
conference paper 2021
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Benders, Dennis (author)
Recent developments in neuroscience research, mainly introduced by neuroscientist Karl J. Friston, have resulted in a concept called the Free Energy Principle (FEP). The FEP is a brain theory unifying action, perception and learning. An important observation is that autonomous robots have to perform similar tasks to the human brain. Therefore,...
master thesis 2020
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Coutino, Mario (author), Isufi, E. (author), Maehara, T. (author), Leus, G.J.T. (author)
In this work, we explore the state-space formulation of network processes to recover the underlying network structure (local connections). To do so, we employ subspace techniques borrowed from system identification literature and extend them to the network topology inference problem. This approach provides a unified view of the traditional...
conference paper 2020
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Schaberg, Wouter (author)
In an effort to increase airspace capacity, new methods for the decentralization of air traffic control have been a topic for research since the early nineties. The Modified Voltage Potential is a method to provide decentralized conflict detection and resolution. Through this method, aircraft can fly direct routes while resolving conflicts in a...
master thesis 2020
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Hu, Rong (author)
Research on sustainable landfill management has been studied since 30 years ago in the Netherlands, the principle of which is to reduce the emission of harmful substances from the landfill to the surrounding soil and groundwater. As for this purpose, the active treatment is applied on Wieringermeer landfill, meanwhile, the long-term monitoring...
student report 2020
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Touw, Michel (author)
Hydrofoil ships with fully submerged wings offer great speed at a relatively low fuel cost. They are also unstable, which is why they require an active control system. Current hydrofoil ships are more limited in their operation by waves. A passive suspension system could reduce the motions of a hydrofoil ship in waves but it must remain...
master thesis 2020
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Coutino, Mario (author), Isufi, E. (author), Maehara, Takanori (author), Leus, G.J.T. (author)
In this article, we explore the state-space formulation of a network process to recover from partial observations the network topology that drives its dynamics. To do so, we employ subspace techniques borrowed from system identification literature and extend them to the network topology identification problem. This approach provides a unified...
journal article 2020
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Yu, Chengpu (author), Chen, Jie (author), Li, Shukai (author), Verhaegen, M.H.G. (author)
The identification of affinely parameterized state–space system models is quite popular to model practical physical systems or networked systems, and the traditional identification methods require the measurements of both the input and output data. However, in the presence of partial unknown input, the corresponding system identification...
journal article 2020
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Grimbergen, Sherin (author)
This thesis provides an exposition of the theory of Active Inference in a control theoretic context. Active Inference is a remarkably powerful neuroscientific theory that unifies many characteristics of the biological brain. As such, Active Inference provides a valid inspiration in search of improvements in bio-inspired robot control algorithms....
master thesis 2019
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Krijgsman, Wiegert (author)
In the noiseless case, the identification of a grey-box model can be posed as a feasibility problem, i.e. determining if existent - and if so - finding a parameter vector such that the parametric model equals the actual model (or its associated input-output data). Being that in this thesis we are interested in continuous-time grey-box...
master thesis 2018
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Richa, Eduardo (author)
The detection of unusual behavior plays a crucial role in the prevention of illegal and harmful activities such as smuggling, piracy, arms trading, human trafficking and illegal immigration. Also for military applications, it is useful to detect anomalous behavior to provide an alert for potential threats, especially with the more recent...
master thesis 2018
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Frölke, Linde Josien (author)
It has become clear that the global energy system needs to shift away from fossil fuels towards clean, renewable energy sources. Experience in the past decades has shown that at-plate solar heat panels can play a role in the energy system of the future, in particular in solar collector fields for district heat generation. This thesis concerns...
master thesis 2018
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Wills, Adrian (author), Yu, C. (author), Ljung, Lennart (author), Verhaegen, M.H.G. (author)
Using Maximum Likelihood (or Prediction Error) methods to identify linear state space model is a prime technique. The likelihood function is a nonconvex function and care must be exercised in the numerical maximization. Here the focus will be on affine parameterizations which allow some special techniques and algorithms. Three approaches to...
journal article 2018
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