TY - THES
AU - Löken, Nils
ID - 15482
TI - Cryptography for the Crowd — A Study of Cryptographic Schemes with Applications to Crowd Work
ER -
TY - JOUR
AU - Yigitbas, Enes
AU - Jovanovikj, Ivan
AU - Biermeier, Kai
AU - Sauer, Stefan
AU - Engels, Gregor
ID - 15266
JF - International Journal on Software and Systems Modeling (SoSyM)
TI - Integrated Model-driven Development of Self-adaptive User Interfaces (to appear)
ER -
TY - JOUR
AB - We derive a data-driven method for the approximation of the Koopman generator called gEDMD, which can be regarded as a straightforward extension of EDMD (extended dynamic mode decomposition). This approach is applicable to deterministic and stochastic dynamical systems. It can be used for computing eigenvalues, eigenfunctions, and modes of the generator and for system identification. In addition to learning the governing equations of deterministic systems, which then reduces to SINDy (sparse identification of nonlinear dynamics), it is possible to identify the drift and diffusion terms of stochastic differential equations from data. Moreover, we apply gEDMD to derive coarse-grained models of high-dimensional systems, and also to determine efficient model predictive control strategies. We highlight relationships with other methods and demonstrate the efficacy of the proposed methods using several guiding examples and prototypical molecular dynamics problems.
AU - Klus, Stefan
AU - Nüske, Feliks
AU - Peitz, Sebastian
AU - Niemann, Jan-Hendrik
AU - Clementi, Cecilia
AU - Schütte, Christof
ID - 16288
JF - Physica D: Nonlinear Phenomena
SN - 0167-2789
TI - Data-driven approximation of the Koopman generator: Model reduction, system identification, and control
VL - 406
ER -
TY - JOUR
AB - The control of complex systems is of critical importance in many branches of science, engineering, and industry, many of which are governed by nonlinear partial differential equations. Controlling an unsteady fluid flow is particularly important, as flow control is a key enabler for technologies in energy (e.g., wind, tidal, and combustion), transportation (e.g., planes, trains, and automobiles), security (e.g., tracking airborne contamination), and health (e.g., artificial hearts and artificial respiration). However, the high-dimensional, nonlinear, and multi-scale dynamics make real-time feedback control infeasible. Fortunately, these high- dimensional systems exhibit dominant, low-dimensional patterns of activity that can be exploited for effective control in the sense that knowledge of the entire state of a system is not required. Advances in machine learning have the potential to revolutionize flow control given its ability to extract principled, low-rank feature spaces characterizing such complex systems.We present a novel deep learning modelpredictive control framework that exploits low-rank features of the flow in order to achieve considerable improvements to control performance. Instead of predicting the entire fluid state, we use a recurrent neural network (RNN) to accurately predict the control relevant quantities of the system, which are then embedded into an MPC framework to construct a feedback loop. In order to lower the data requirements and to improve the prediction accuracy and thus the control performance, incoming sensor data are used to update the RNN online. The results are validated using varying fluid flow examples of increasing complexity.
AU - Bieker, Katharina
AU - Peitz, Sebastian
AU - Brunton, Steven L.
AU - Kutz, J. Nathan
AU - Dellnitz, Michael
ID - 16290
JF - Theoretical and Computational Fluid Dynamics
SN - 0935-4964
TI - Deep model predictive flow control with limited sensor data and online learning
ER -
TY - CONF
AU - Pauck, Felix
AU - Bodden, Eric
AU - Wehrheim, Heike
ED - Felderer, Michael
ED - Hasselbring, Wilhelm
ED - Rabiser, Rick
ED - Jung, Reiner
ID - 16214
T2 - Software Engineering 2020, Fachtagung des GI-Fachbereichs Softwaretechnik, 24.-28. Februar 2020, Innsbruck, Austria
TI - Reproducing Taint-Analysis Results with ReproDroid
VL - {P-300}
ER -
TY - CONF
AB - Network function virtualization (NFV) proposes
to replace physical middleboxes with more flexible virtual
network functions (VNFs). To dynamically adjust to everchanging
traffic demands, VNFs have to be instantiated and
their allocated resources have to be adjusted on demand.
Deciding the amount of allocated resources is non-trivial.
Existing optimization approaches often assume fixed resource
requirements for each VNF instance. However, this can easily
lead to either waste of resources or bad service quality if too
many or too few resources are allocated.
To solve this problem, we train machine learning models
on real VNF data, containing measurements of performance
and resource requirements. For each VNF, the trained models
can then accurately predict the required resources to handle
a certain traffic load. We integrate these machine learning
models into an algorithm for joint VNF scaling and placement
and evaluate their impact on resulting VNF placements. Our
evaluation based on real-world data shows that using suitable
machine learning models effectively avoids over- and underallocation
of resources, leading to up to 12 times lower resource
consumption and better service quality with up to 4.5 times
lower total delay than using standard fixed resource allocation.
AU - Schneider, Stefan Balthasar
AU - Satheeschandran, Narayanan Puthenpurayil
AU - Peuster, Manuel
AU - Karl, Holger
ID - 16219
T2 - IEEE Conference on Network Softwarization (NetSoft)
TI - Machine Learning for Dynamic Resource Allocation in Network Function Virtualization
ER -
TY - JOUR
AU - Bellman, K.
AU - Dutt, N.
AU - Esterle, L.
AU - Herkersdorf, A.
AU - Jantsch, A.
AU - Landauer, C.
AU - R. Lewis, P.
AU - Platzner, Marco
AU - TaheriNejad, N.
AU - Tammemäe, K.
ID - 15836
JF - ACM Transactions on Cyber-Physical Systems
TI - Self-aware Cyber-Physical Systems
VL - Accepted for Publication
ER -
TY - CONF
AU - Krauter, Stefan
AU - Zhang, L.
ID - 16858
T2 - Proceedings of the 14 th International Renewable Energy Storage Conference, Düsseldorf (Deutschland), 10.–12. März 2020 (verschoben auf 16.–18. März 2021)
TI - Probability of Correct Decision–Making at Triggering of Load-Shifting Intended for low CO 2 -intensity and low EEX trading price via simple Grid Frequency Monitoring
ER -
TY - CONF
AU - Krumme, Matthias
AU - Webersen, Manuel
AU - Claes, Leander
AU - Webersen, Yvonne
ID - 13943
T2 - Fortschritte der Akustik - DAGA 2020
TI - Analoge Klangsynthese zur Vermittlung von Grundkenntnissen der Signalverarbeitung an Studierende nicht-technischer Fachrichtungen
ER -
TY - JOUR
AU - Liebendörfer, Michael
AU - Göller, Robin
AU - Biehler, Rolf
AU - Hochmuth, Reinhard
AU - Kortemeyer, Jörg
AU - Ostsieker, Laura
AU - Rode, Jana
AU - Schaper, Niclas
ID - 16961
JF - Journal für Mathematik-Didaktik
SN - 0173-5322
TI - LimSt – Ein Fragebogen zur Erhebung von Lernstrategien im mathematikhaltigen Studium
ER -
TY - CONF
AU - Dreiling, Dmitrij
AU - Itner, Dominik Thor
AU - Feldmann, Nadine
AU - Gravenkamp, Hauke
AU - Henning, Bernd
ID - 17089
TI - Increasing the sensitivity in the determination of material parameters by using arbitrary loads in ultrasonic transmission measurements
ER -
TY - CONF
AU - Weidmann, Nils
AU - Anjorin, Anthony
ID - 17084
SN - 0302-9743
T2 - Fundamental Approaches to Software Engineering
TI - Schema Compliant Consistency Management via Triple Graph Grammars and Integer Linear Programming
ER -
TY - CONF
AU - Krings, Sarah Claudia
AU - Yigitbas, Enes
AU - Jovanovikj, Ivan
AU - Sauer, Stefan
AU - Engels, Gregor
ID - 16790
SN - 978-1-4503-7984-7/20/06
T2 - Proceedings of the 12th ACM SIGCHI Symposium on Engineering Interactive Computing Systems (EICS 2020)
TI - Development Framework for Context-Aware Augmented Reality Applications
ER -
TY - CHAP
AU - Jazayeri, Bahar
AU - Schwichtenberg, Simon
AU - Küster, Jochen
AU - Zimmermann, Olaf
AU - Engels, Gregor
ID - 17337
SN - 0302-9743
T2 - Advanced Information Systems Engineering
TI - Modeling and Analyzing Architectural Diversity of Open Platforms
ER -
TY - CONF
AB - We consider a natural extension to the metric uncapacitated Facility Location Problem (FLP) in which requests ask for different commodities out of a finite set \( S \) of commodities.
Ravi and Sinha (SODA 2004) introduced the model as the \emph{Multi-Commodity Facility Location Problem} (MFLP) and considered it an offline optimization problem.
The model itself is similar to the FLP: i.e., requests are located at points of a finite metric space and the task of an algorithm is to construct facilities and assign requests to facilities while minimizing the construction cost and the sum over all assignment distances.
In addition, requests and facilities are heterogeneous; they request or offer multiple commodities out of $S$.
A request has to be connected to a set of facilities jointly offering the commodities demanded by it.
In comparison to the FLP, an algorithm has to decide not only if and where to place facilities, but also which commodities to offer at each.
To the best of our knowledge we are the first to study the problem in its online variant in which requests, their positions and their commodities are not known beforehand but revealed over time.
We present results regarding the competitive ratio.
On the one hand, we show that heterogeneity influences the competitive ratio by developing a lower bound on the competitive ratio for any randomized online algorithm of \( \Omega ( \sqrt{|S|} + \frac{\log n}{\log \log n} ) \) that already holds for simple line metrics.
Here, \( n \) is the number of requests.
On the other side, we establish a deterministic \( \mathcal{O}(\sqrt{|S|} \cdot \log n) \)-competitive algorithm and a randomized \( \mathcal{O}(\sqrt{|S|} \cdot \frac{\log n}{\log \log n} ) \)-competitive algorithm.
Further, we show that when considering a more special class of cost functions for the construction cost of a facility, the competitive ratio decreases given by our deterministic algorithm depending on the function.
AU - Castenow, Jannik
AU - Feldkord, Björn
AU - Knollmann, Till
AU - Malatyali, Manuel
AU - Meyer auf der Heide, Friedhelm
ID - 17370
KW - Online Multi-Commodity Facility Location
KW - Competitive Ratio
KW - Online Optimization
KW - Facility Location Problem
SN - 9781450369350
T2 - Proceedings of the 32nd ACM Symposium on Parallelism in Algorithms and Architectures
TI - The Online Multi-Commodity Facility Location Problem
ER -
TY - CONF
AU - Razzaghi Kouchaksaraei, Hadi
AU - Prasad Shivarpatna Venkatesh, Ashwin
AU - Churi, Amey
AU - Illian, Marvin
AU - Karl, Holger
ID - 16726
T2 - European Conference on Networks and Communications (EUCNC 2020)
TI - Dynamic Provisioning of Network Services on Heterogeneous Resources
ER -
TY - JOUR
AB - Multi-objective optimization is an active field of research that has many applications. Owing to its success and because decision-making processes are becoming more and more complex, there is a recent trend for incorporating many objectives into such problems. The challenge with such problems, however, is that the dimensions of the solution sets—the so-called Pareto sets and fronts—grow with the number of objectives. It is thus no longer possible to compute or to approximate the entire solution set of a given problem that contains many (e.g. more than three) objectives. On the other hand, the computation of single solutions (e.g. via scalarization methods) leads to unsatisfying results in many cases, even if user preferences are incorporated. In this article, the Pareto Explorer tool is presented—a global/local exploration tool for the treatment of many-objective optimization problems (MaOPs). In the first step, a solution of the problem is computed via a global search algorithm that ideally already includes user preferences. In the second step, a local search along the Pareto set/front of the given MaOP is performed in user specified directions. For this, several continuation-like procedures are proposed that can incorporate preferences defined in decision, objective, or in weight space. The applicability and usefulness of Pareto Explorer is demonstrated on benchmark problems as well as on an application from industrial laundry design.
AU - Schütze, Oliver
AU - Cuate, Oliver
AU - Martín, Adanay
AU - Peitz, Sebastian
AU - Dellnitz, Michael
ID - 10596
IS - 5
JF - Engineering Optimization
SN - 0305-215X
TI - Pareto Explorer: a global/local exploration tool for many-objective optimization problems
VL - 52
ER -
TY - CHAP
AU - Yigitbas, Enes
AU - Jovanovikj, Ivan
AU - Sauer, Stefan
AU - Engels, Gregor
ID - 15267
T2 - Handling Security, Usability, User Experience and Reliability in User-Centered Development Processes - IFIP WG 13.2/13.5
TI - On the Development of Context-aware Augmented Reality Applications (to appear)
ER -
TY - CHAP
AB - In the development of model predictive controllers for PDE-constrained problems, the use of reduced order models is essential to enable real-time applicability. Besides local linearization approaches, proper orthogonal decomposition (POD) has been most widely used in the past in order to derive such models. Due to the huge advances concerning both theory as well as the numerical approximation, a very promising alternative based on the Koopman operator has recently emerged. In this chapter, we present two control strategies for model predictive control of nonlinear PDEs using data-efficient approximations of the Koopman operator. In the first one, the dynamic control system is replaced by a small number of autonomous systems with different yet constant inputs. The control problem is consequently transformed into a switching problem. In the second approach, a bilinear surrogate model is obtained via a convex combination of these autonomous systems. Using a recent convergence result for extended dynamic mode decomposition (EDMD), convergence of the reduced objective function can be shown. We study the properties of these two strategies with respect to solution quality, data requirements, and complexity of the resulting optimization problem using the 1-dimensional Burgers equation and the 2-dimensional Navier–Stokes equations as examples. Finally, an extension for online adaptivity is presented.
AU - Peitz, Sebastian
AU - Klus, Stefan
ID - 16289
SN - 0170-8643
T2 - Lecture Notes in Control and Information Sciences
TI - Feedback Control of Nonlinear PDEs Using Data-Efficient Reduced Order Models Based on the Koopman Operator
VL - 484
ER -
TY - GEN
AB - In recent years, the success of the Koopman operator in dynamical systems
analysis has also fueled the development of Koopman operator-based control
frameworks. In order to preserve the relatively low data requirements for an
approximation via Dynamic Mode Decomposition, a quantization approach was
recently proposed in [Peitz & Klus, Automatica 106, 2019]. This way, control
of nonlinear dynamical systems can be realized by means of switched systems
techniques, using only a finite set of autonomous Koopman operator-based
reduced models. These individual systems can be approximated very efficiently
from data. The main idea is to transform a control system into a set of
autonomous systems for which the optimal switching sequence has to be computed.
In this article, we extend these results to continuous control inputs using
relaxation. This way, we combine the advantages of the data efficiency of
approximating a finite set of autonomous systems with continuous controls. We
show that when using the Koopman generator, this relaxation --- realized by
linear interpolation between two operators --- does not introduce any error for
control affine systems. This allows us to control high-dimensional nonlinear
systems using bilinear, low-dimensional surrogate models. The efficiency of the
proposed approach is demonstrated using several examples with increasing
complexity, from the Duffing oscillator to the chaotic fluidic pinball.
AU - Peitz, Sebastian
AU - Otto, Samuel E.
AU - Rowley, Clarence W.
ID - 16309
T2 - arXiv:2003.07094
TI - Data-Driven Model Predictive Control using Interpolated Koopman Generators
ER -