Simple model for the shortest medium-voltage cable to supply sustainable loads. - In: Conference Proceedings 2022 IEEE International Conference on Environment and Electrical Engineering and 2022 IEEE Industrial and Commercial Power Systems Europe (EEEIC/I&CPS Europe), (2022), insges. 6 S.
Passive loads in power systems are well-known, but sustainable loads (SL's), due to distributed renewable energy sources and possible reverse active power flow (RAPF) from a low voltage level through a medium-voltage (MV) cable towards a high voltage level, introduce new challenges. In this paper, a simple model for the shortest MV cable required to supply SL's is introduced. The concept of unconstrained and constrained domains to design the shortest cable, at which the limiting constraint for the thermal capacity and the limiting constraint for the voltage drop are equal, is proposed. In this case, the cable can be fully used, that is, using the whole feasible region at the shortest length. A sample 2-node/1-cable network shows that the power-voltage relationship can theoretically be nonlinear within the unconstrained domains, but it can be well linearized within the proposed constrained domains. Another 3-node/2-cable network is developed to show the effects when using different cables types (copper and aluminium) and power factors. It is shown that the developed model can simply be used for estimating the active power domains of new SL's while considering the shortest length of MV cables.
https://doi.org/10.1109/EEEIC/ICPSEurope54979.2022.9854705
Analysis of safety-critical cloud architectures with multi-trajectory simulation. - In: 2022 Annual Reliability and Maintainability Symposium (RAMS), (2022), insges. 7 S.
Dynamic safety-critical systems require model-based techniques and tools for their systems design. The paper presents a stochastic Petri net model of an industrial safetycritical cloud server architecture for train control. Its reliability has to be evaluated to assess tradeoffs in architecture and level of fault tolerance. Simulation methods are too slow for such rare-event problems, while numerical analysis techniques suffer from the state-space explosion problem. The paper extends a recently developed multi-trajectory simulation algorithm combining elements of simulation and numerical analysis such that it increases the accuracy of rare-event simulations within a given computation time budget. Simulation experiments have been carried out with a prototype tool.
https://doi.org/10.1109/RAMS51457.2022.9893923
Dynamic reference programming-based model predictive control for optimal robust tracking. - In: 2022 IEEE International Symposium on Advanced Control of Industrial Processes (AdCONIP), (2022), S. 126-131
In this paper, a new idea of dynamic reference programming (DRP) is proposed and applied to the design of robust tube-based model predictive control (RTMPC) to realize tracking of the system under additive bounded uncertainty and robust constraints. The cost function of the resulting DRP-based RTMPC simultaneously penalizes: i) the weighted Euclidean distance (WED) between the nominal state trajectory and the reference-determined steady states; and ii) the WED between the last-step-ahead-reference-determined steady state and the setpoint, which causes the nominal state to converge to the optimal tracking point. As the decision variables for online RTMPC optimization, the multi-step-ahead references also take on the responsibilities of constraint satisfaction and recursive feasibility. Consequently, the proposed strategy can drive the system state to converge to the disturbance invariant set centered on the optimal tracking point, and thus, realize the optimal robust tracking. A numerical example verifies that the proposed DRP-based RTMPC can, not only realize the optimal robust tracking of the system, but also effectively reduce the controller burden and avoid drastic state changes because the multistep references can make the degree of freedom for online optimization flexible.
https://doi.org/10.1109/AdCONIP55568.2022.9894181
Mutual information induced slow-feature analysis of nonlinear dynamic systems and the application in soft sensors. - In: 2022 IEEE International Symposium on Advanced Control of Industrial Processes (AdCONIP), (2022), S. 319-324
Slow-feature analysis (SFA) seeks to extract the most slowly varying components of dynamic systems. However, the original definition of SFA implies a linear relationship of system states between adjacent time instants. In this paper, a new approach to SFA, which is called EVOLVE.INFOMAX, is defined, based on which the mutual-information-based SFA (MI-based SFA) is proposed. The optimisation problem can be solved by joint diagonalisation of the mutual-information (MI) matrices. The MI matrices are approximated by quantities related to Rényi entropy that can be calculated using the kernel trick. The case studies show MI-based SFA is better for slow feature extraction, especially for nonlinear systems. This allows a better soft sensor to be developed.
https://doi.org/10.1109/AdCONIP55568.2022.9894163
Explicit Euler discretization of an indirect adaptive sliding mode control. - In: 2022 16th International Workshop on Variable Structure Systems (VSS), (2022), S. 278-283
In this paper, an explicit Euler discretization scheme is used to obtain a discrete-time version of the indirect adaptive sliding mode control for an uncertain scalar linear system with an unknown parameter. The unknown parameter is linear in the state, the input disturbance is assumed to be bounded. An improper discretization of indirect adaptive sliding mode algorithms may lead to unbounded parameter estimates, which then result in an unbounded state. We modify the algorithm by using a priori knowledge of the location of the unknown parameter such that boundedness of the trajectory can be guaranteed. This entails obtaining a stabilizing condition on the sampling interval and calculating the bound of the state trajectory. The proposed discretization is illustrated by simulation results of an academic scalar system.
https://doi.org/10.1109/VSS57184.2022.9902081
Efficient multi-task RGB-D scene analysis for indoor environments. - In: 2022 International Joint Conference on Neural Networks (IJCNN), (2022), insges. 10 S.
Semantic scene understanding is essential for mobile agents acting in various environments. Although semantic segmentation already provides a lot of information, details about individual objects as well as the general scene are missing but required for many real-world applications. However, solving multiple tasks separately is expensive and cannot be accomplished in real time given limited computing and battery capabilities on a mobile platform. In this paper, we propose an efficient multi-task approach for RGB-D scene analysis (EMSANet) that simultaneously performs semantic and instance segmentation (panoptic segmentation), instance orientation estimation, and scene classification. We show that all tasks can be accomplished using a single neural network in real time on a mobile platform without diminishing performance - by contrast, the individual tasks are able to benefit from each other. In order to evaluate our multi-task approach, we extend the annotations of the common RGB-D indoor datasets NYUv2 and SUNRGB-D for instance segmentation and orientation estimation. To the best of our knowledge, we are the first to provide results in such a comprehensive multi-task setting for indoor scene analysis on NYUv2 and SUNRGB-D.
https://doi.org/10.1109/IJCNN55064.2022.9892852
Active nonstationary variables selection based just-in-time co-integration analysis and slow feature analysis monitoring approach for dynamic processes. - In: Journal of process control, ISSN 0959-1524, Bd. 117 (2022), S. 112-121
For industrial processes, operating conditions tend to be time varying, leading to the time-varying nonstationary characteristics. In this paper, an active nonstationary variables selection-based just-in-time co-integration analysis and slow feature analysis monitoring approach is proposed to explore the real-time variations in dynamic processes. To this end, by analyzing the time-varying stationarity of online data, active nonstationary variables are selected. Meanwhile, a just-in-time strategy is used to update the offline model. On this basis, co-integration analysis and slow feature analysis are developed for extracting long-run equilibrium relationships and slowly varying features. A comprehensive statistic is generated by Bayesian inference to monitor the operation status. With the active nonstationary information extraction, the proposed method emphasizes the online nonstationary characteristics, which allows the monitoring model to effectively capture the dynamic variations. Two case studies on benchmark processes show the advantages and feasibility of the proposed method.
https://doi.org/10.1016/j.jprocont.2022.07.008
TH-201 : transferability of cathodal tDCS effects from the primary motor to the dorsolateral prefrontal cortex: a multimodal TMS-EEG study. - In: Clinical neurophysiology, ISSN 1872-8952, Bd. 141 (2022), S. S146-S147
https://doi.org/10.1016/j.clinph.2022.07.387
WE-123 : a 256-channel dry electrode cap for rapid high-density EEG. - In: Clinical neurophysiology, ISSN 1872-8952, Bd. 141 (2022), S. S63-S64
https://doi.org/10.1016/j.clinph.2022.07.167
Konzeptentwicklung eines Speckle-Mikroskops zur Untersuchung pathophysiologischer Veränderungen durch humane Autoantikörper gegen den NMDA-Rezeptor. - Ilmenau : Universitätsbibliothek, 2022. - 1 Online-Ressource (VI, 141, [28] Blätter)
Technische Universität Ilmenau, Dissertation 2022
Die vorliegende Arbeit befasst sich mit dem Krankheitsbild der NMDA-Rezeptor Enzephalitis und der Entwicklung neuartiger Technologien, mit dem Ziel weitere tiefgreifende Erkenntnisse im Zusammenhang mit dieser Autoimmunerkrankung zu gewinnen. Im ersten Abschnitt der Arbeit werden für die Untersuchungen etablierte höchstauflösende Fluoreszenz-Mikroskopieverfahren aus dem Bereich der Lokalisationsmikroskopie eingesetzt. Hierbei kommen spezielle rekombinante monoklonale Patienten aAK gegen die NR1-Untereinheit des NMDA-Rezeptors zur Anwendung. Durch die Lokalisationsmikroskopie wird die synaptische Rezeptordichte quantifiziert und somit die Auswirkung der aAK auf die Rezeptormorphologie untersucht. Zusätzlich dazu wurde die Messung mittels Lokalisationsmikroskopie in einen 10 µm coronalen Hirnschnitt einer Maus etabliert. Mit Hilfe der genutzten höchstauflösenden Mikroskopiemethoden konnte auch die Spezifität der rekombinanten monoklonalen Patienten aAK nachgewiesen werden. Der zweite Abschnitt der Arbeit befasst sich mit der Etablierung eines speziellen Mikroskopes der strukturierten Beleuchtungsmikroskopie, welches auf Basis von sogenannten Speckle arbeitet. Es konnte ein kosteneffizienter Aufbau realisiert werden, welcher im Bereich von wenigen Sekunden ein höchstaufgelöstes Bild aufnimmt. Die gezeigte Auflösung war im Bereich von etablierten Verfahren der strukturierten Beleuchtungsmikroskopie. Auf Grundlage des etablierten Mikroskops wurde ein Patent eingereicht und angenommen. Das Patent befasst sich mit der Möglichkeit die effektive Beleuchtungsfläche der Speckle durch die Nutzung von verschiedenen nichtlinearen physikalischen Effekten zu reduzieren. Somit kann der Detektionsbereich verkleinert und im Idealfall in einem Einzelmolekül-Regime genutzt werden. In einem letzten Punkt der Arbeit wird eine neuartige Kombinationsmöglichkeit von funktionellen Messungen in Form von MEA und morphologischen Messungen entwickelt. Das hier gezeigte ultradünne und hochtransparente MEA resultiert in einem Design und einer Materialkombination, so dass es für eine solche Verfahrenskombination eingesetzt werden kann. Es konnte gezeigt werden, dass es bei Untersuchungen mit der Lokalisationsbasierten Mikroskopiemethodik zu keinen Einflüssen auf die optischen Ergebnisse kommt und die Oberflächeneigenschaften durch ihre Spezifität Anwendungsvorteile bringen.
https://doi.org/10.22032/dbt.53194