Model predictive control of parabolic PDE systems under chance constraints. - In: Mathematics, ISSN 2227-7390, Bd. 11 (2023), 6, 1372, S. 1-23
Model predictive control (MPC) heavily relies on the accuracy of the system model. Nevertheless, process models naturally contain random parameters. To derive a reliable solution, it is necessary to design a stochastic MPC. This work studies the chance constrained MPC of systems described by parabolic partial differential equations (PDEs) with random parameters. Inequality constraints on time- and space-dependent state variables are defined in terms of chance constraints. Using a discretization scheme, the resulting high-dimensional chance constrained optimization problem is solved by our recently developed inner-outer approximation which renders the problem computationally amenable. The proposed MPC scheme automatically generates probability tubes significantly simplifying the derivation of feasible solutions. We demonstrate the viability and versatility of the approach through a case study of tumor hyperthermia cancer treatment control, where the randomness arises from the thermal conductivity coefficient characterizing heat flux in human tissue.
https://doi.org/10.3390/math11061372
TMS and fMRI-based localization of the attention network. - In: Brain stimulation, ISSN 1876-4754, Bd. 16 (2023), 1, S. 291-292
Richtiger Name des 4. Verfassers: Jens Haueisen
https://doi.org/10.1016/j.brs.2023.01.517
Closed-loop robotic TMS motor mapping using an online-optimized sampling scheme. - In: Brain stimulation, ISSN 1876-4754, Bd. 16 (2023), 1, S. 320
https://doi.org/10.1016/j.brs.2023.01.593
Fast incremental reconfiguration of dynamic time-sensitive networks at runtime. - In: Computer networks, Bd. 224 (2023), 109606
Static configurations in Time-sensitive Networking (TSN) using the Time-aware Shaper allow precise calculations of deterministic, tight bandwidth and latency guarantees for real-time industrial application streams. However, this static configuration makes introducing flexible changes to a TSN system at runtime very hard. Scenarios of adaptive TSN networks envision that the network configuration evolves with time in accordance with anticipated changes, such as the dynamicity of machine formations and machine reconfigurations. In this paper, we propose a notion of flexibility of scheduler configurations along a network path that facilitates introducing changes to TSN network configurations at runtime. Based on this notion, we develop and analyze algorithms to incrementally reconfigure TSN using the Time-Aware Shaper. These reconfigurations include determining the admissibility of new or changed streams that may possess individual deadlines.
https://doi.org/10.1016/j.comnet.2023.109606
Transferability of cathodal tDCS effects from the primary motor to the prefrontal cortex: a multimodal TMS-EEG study. - In: Brain stimulation, ISSN 1876-4754, Bd. 16 (2023), 2, S. 515-539
Neurophysiological effects of transcranial direct current stimulation (tDCS) have been extensively studied over the primary motor cortex (M1). Much less is however known about its effects over non-motor areas, such as the prefrontal cortex (PFC), which is the neuronal foundation for many high-level cognitive functions and involved in neuropsychiatric disorders. In this study, we, therefore, explored the transferability of cathodal tDCS effects over M1 to the PFC. Eighteen healthy human participants (11 males and 8 females) were involved in eight randomized sessions per participant, in which four cathodal tDCS dosages, low, medium, and high, as well as sham stimulation, were applied over the left M1 and left PFC. After-effects of tDCS were evaluated via transcranial magnetic stimulation (TMS)-electroencephalography (EEG), and TMS-elicited motor evoked potentials (MEP), for the outcome parameters TMS-evoked potentials (TEP), TMS-evoked oscillations, and MEP amplitude alterations. TEPs were studied both at the regional and global scalp levels. The results indicate a regional dosage-dependent nonlinear neurophysiological effect of M1 tDCS, which is not one-to-one transferable to PFC tDCS. Low and high dosages of M1 tDCS reduced early positive TEP peaks (P30, P60), and MEP amplitudes, while an enhancement was observed for medium dosage M1 tDCS (P30). In contrast, prefrontal low, medium and high dosage tDCS uniformly reduced the early positive TEP peak amplitudes. Furthermore, for both cortical areas, regional tDCS-induced modulatory effects were not observed for late TEP peaks, nor TMS-evoked oscillations. However, at the global scalp level, widespread effects of tDCS were observed for both, TMS-evoked potentials and oscillations. This study provides the first direct physiological comparison of tDCS effects applied over different brain areas and therefore delivers crucial information for future tDCS applications.
https://doi.org/10.1016/j.brs.2023.02.010
Noise characteristics in spaceflight multichannel EEG. - In: PLOS ONE, ISSN 1932-6203, Bd. 18 (2023), 2, e0280822, S. 1-12
The cognitive performance of the crew has a major impact on mission safety and success in space flight. Monitoring of cognitive performance during long-duration space flight therefore is of paramount importance and can be performed using compact state-of-the-art mobile EEG. However, signal quality of EEG may be compromised due to the vicinity to various electronic devices and constant movements. We compare noise characteristics between in-flight extraterrestrial microgravity and ground-level terrestrial electroencephalography (EEG) recordings. EEG data recordings from either aboard International Space Station (ISS) or on earth’s surface, utilizing three EEG amplifiers and two electrode types, were compared. In-flight recordings showed noise level of an order of magnitude lower when compared to pre- and post-flight ground-level recordings with the same EEG system. Noise levels between ground-level recordings with actively shielded cables, and in-flight recordings without shielded cables, were similar. Furthermore, noise level characteristics of shielded ground-level EEG recordings, using wet and dry electrodes, and in-flight EEG recordings were similar. Actively shielded mobile dry EEG systems will support neuroscientific research and neurocognitive monitoring during spaceflight, especially during long-duration space missions.
https://doi.org/10.1371/journal.pone.0280822
A systematic comparison of deep learning methods for EEG time series analysis. - In: Frontiers in neuroscience, ISSN 1662-453X, Bd. 17 (2023), 1067095, S. 01-17
Analyzing time series data like EEG or MEG is challenging due to noisy, high-dimensional, and patient-specific signals. Deep learning methods have been demonstrated to be superior in analyzing time series data compared to shallow learning methods which utilize handcrafted and often subjective features. Especially, recurrent deep neural networks (RNN) are considered suitable to analyze such continuous data. However, previous studies show that they are computationally expensive and difficult to train. In contrast, feed-forward networks (FFN) have previously mostly been considered in combination with hand-crafted and problem-specific feature extractions, such as short time Fourier and discrete wavelet transform. A sought-after are easily applicable methods that efficiently analyze raw data to remove the need for problem-specific adaptations. In this work, we systematically compare RNN and FFN topologies as well as advanced architectural concepts on multiple datasets with the same data preprocessing pipeline. We examine the behavior of those approaches to provide an update and guideline for researchers who deal with automated analysis of EEG time series data. To ensure that the results are meaningful, it is important to compare the presented approaches while keeping the same experimental setup, which to our knowledge was never done before. This paper is a first step toward a fairer comparison of different methodologies with EEG time series data. Our results indicate that a recurrent LSTM architecture with attention performs best on less complex tasks, while the temporal convolutional network (TCN) outperforms all the recurrent architectures on the most complex dataset yielding a 8.61% accuracy improvement. In general, we found the attention mechanism to substantially improve classification results of RNNs. Toward a light-weight and online learning-ready approach, we found extreme learning machines (ELM) to yield comparable results for the less complex tasks.
https://doi.org/10.3389/fninf.2023.1067095
Verification of automata with storage mechanisms. - Ilmenau : Universitätsverlag Ilmenau, 2023. - 1 Online-Ressource (xi, 247 Seiten)
Technische Universität Ilmenau, Dissertation 2022
Ein wichtiges Forschungsthema in der Informatik ist die Verifikation, d.h., die Analyse von Systemen bezüglich ihrer Korrektheit. Diese Analyse erfolgt in zwei Schritten: Zuerst müssen wir das System und die gewünschten Eigenschaften formalisieren. Anschließend benötigen wir Algorithmen zum Testen, ob das System die Eigenschaften erfüllt. Oftmals können wir das Systemals endlichen Automaten mit geeignetem Speichermechanismus modellieren, z.B. rekursive Programme sind im Wesentlichen Automaten mit einem Stack. Hier betrachten wir Automaten mit zwei Varianten von Stacks und Queues: 1. Partiell vergessliche Stacks und Queues, welche bestimmte Teile ihrer Inhalte jederzeit vergessen können. Diese können für unzuverlässige Systeme verwendet werden. 2. Verteilte Stacks und Queues, d.h., mehrere Stacks und Queues mit vordefinierter Synchronisierung. Häufig lassen sich die Eigenschaften unserer Modelle mithilfe des (wiederholten) Erreichbarkeitsproblems in unseren Automaten lösen. Dabei ist bekannt, dass die Entscheidbarkeit dieser Probleme oftmals stark vom konkreten Datentyp des Speichers abhängt. Beide Probleme können für Automaten mit einem Stack in Polynomialzeit gelöst werden. Sie sind jedoch unentscheidbar, wenn wir Automaten mit einer Queue oder zwei Stacks betrachten. In bestimmten Spezialfällen sind aber dennoch in der Lage diese Systeme zu verifizieren. So können wir beispielsweise bestimmte Automaten mit mehreren Stacks betrachten - so genannte Asynchrone Kellerautomaten. Diese bestehen aus mehreren (lokalen) Automaten mit jeweils einem Stack. Wann immer diese Automaten etwas in mind. einen Stack schreiben, müssen sie unmittelbar zuvor von diesen Stacks etwas lesen. Das (wiederholte) Erreichbarkeitsproblem ist in asynchronen Kellerautomaten in Polynomialzeit entscheidbar. Wir können zudem das Erreichbarkeitsproblem von Queueautomaten durch Exploration des Konfigurationsraums semi-entscheiden. Hierzu können wir mehrere aufeinanderfolgende Transitionen zu so genannten Meta-Transformationen zusammenfassen und diese in einem Schritt simulieren. Hier betrachten wir Meta-Transformationen, die zwischen dem Lesen und Schreiben von Wörtern aus zwei gegebenen regulären Sprachen alternieren. Diese Meta-Transformationen können in Polynomialzeit ausgeführt werden. Für dieses Ergebnis müssen wir jedoch zunächst verschiedene algebraische Eigenschaften der Queues betrachten.
https://dx.doi.org/10.22032/dbt.53804
Analyse und Entwicklung einer Softwarearchitektur für die intelligente, optische Inspektion. - Ilmenau : Universitätsverlag Ilmenau, 2023. - 1 Online-Ressource (ix, 205 Seiten)
Technische Universität Ilmenau, Dissertation 2022
Die automatische optische Inspektion ist das wichtigste Werkzeug der Qualitätskontrolle in der modernen Elektronikfertigung. Durch die automatisierte Bildaufnahme und das Ausführen vordefinierter Bildverarbeitungsschritte haben diese Systeme die manuelle optische Inspektion weitestgehend verdrängt. Trotz des großen Maßes an Automatisierung sind menschliche Experten an vielen Schritten der Prüfung unverzichtbar und damit potenzielle Fehlerquellen. In den letzten Jahren wurden zahlreiche Ansätze untersucht, welche einzelne Aspekte der optischen Qualitätssicherung durch die Anwendung von Methoden der künstlichen Intelligenz deutlich verbessern. Für den Wandel der optischen Inspektion hin zu einer verlässlichen und voll autonomen Prüfung wird in dieser Arbeit ein Modell mit fünf Phasen vorgestellt, welches die Entwicklungsschritte auf diesem Weg abbildet. Das neue Modell unterscheidet sich von bisherigen Ansätzen durch einen ganzheitlichen Blick auf die Qualitätskontrolle und die Berücksichtigung aller Prozessschritte. Um die Umsetzung dieses Modells zu tragen, zeigt diese Arbeit ein neues Architekturmuster auf, welches Lösungen auf Basis von künstlicher Intelligenz trainieren und ausführen kann. Durch seine hohe Flexibilität kann die neue Architektur über unterschiedliche Auslieferungen auf einer heterogenen Menge an Systemen angewendet werden und viele unterschiedliche Anwendungen von künstlicher Intelligenz über das Feld der optischen Inspektion hinaus umsetzen. Für die allgemeingültige Beschreibung von KI-Lösungen basiert diese Architektur auf einer Menge an Objekten, welche in dieser Arbeit definiert werden. Eine Umsetzung dieser Architektur wird diskutiert und ihre Anwendbarkeit anhand von drei Experimenten bewiesen. Die Implementierung der beschriebenen Architektur ist unter einer OpenSource-Lizenz veröffentlicht.
https://dx.doi.org/10.22032/dbt.55214
Effect of europium substitution on the structural, magnetic and relaxivity properties of Mn-Zn ferrite nanoparticles: a dual-mode MRI contrast-agent candidate. - In: Nanomaterials, ISSN 2079-4991, Bd. 13 (2023), 2, 331, S. 1-19
Magnetic nanoparticles (MNPs) have been widely applied as magnetic resonance imaging (MRI) contrast agents. MNPs offer significant contrast improvements in MRI through their tunable relaxivities, but to apply them as clinical contrast agents effectively, they should exhibit a high saturation magnetization, good colloidal stability and sufficient biocompatibility. In this work, we present a detailed description of the synthesis and the characterizations of europium-substituted Mn-Zn ferrite (Mn0.6Zn0.4EuxFe2−xO4, x = 0.00, 0.02, 0.04, 0.06, 0.08, 0.10, and 0.15, herein named MZF for x = 0.00 and EuMZF for others). MNPs were synthesized by the coprecipitation method and subsequent hydrothermal treatment, coated with citric acid (CA) or pluronic F127 (PF-127) and finally characterized by X-ray Diffraction (XRD), Inductively Coupled Plasma (ICP), Vibrating Sample Magnetometry (VSM), Fourier-Transform Infrared (FTIR), Dynamic Light Scattering (DLS) and MRI Relaxometry at 3T methods. The XRD studies revealed that all main diffraction peaks are matched with the spinel structure very well, so they are nearly single phase. Furthermore, XRD study showed that, although there are no significant changes in lattice constants, crystallite sizes are affected by europium substitution significantly. Room-temperature magnetometry showed that, in addition to coercivity, both saturation and remnant magnetizations decrease with increasing europium substitution and coating with pluronic F127. FTIR study confirmed the presence of citric acid and poloxamer (pluronic F127) coatings on the surface of the nanoparticles. Relaxometry measurements illustrated that, although the europium-free sample is an excellent negative contrast agent with a high r2 relaxivity, it does not show a positive contrast enhancement as the concentration of nanoparticles increases. By increasing the europium to x = 0.15, r1 relaxivity increased significantly. On the contrary, europium substitution decreased r2 relaxivity due to a reduction in saturation magnetization. The ratio of r2/r1 decreased from 152 for the europium-free sample to 11.2 for x = 0.15, which indicates that Mn0.6Zn0.4Eu0.15Fe1.85O4 is a suitable candidate for dual-mode MRI contrast agent potentially. The samples with citric acid coating had higher r1 and lower r2 relaxivities than those of pluronic F127-coated samples.
https://doi.org/10.3390/nano13020331