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- 2018 (58) (remove)
This dissertation is devoted to the theoretical development and experimental laboratory verification of a new damage localization method: The state projection estimation error (SP2E). This method is based on the subspace identification of mechanical structures, Krein space based H-infinity estimation and oblique projections. To explain method SP2E, several theories are discussed and laboratory experiments have been conducted and analysed.
A fundamental approach of structural dynamics is outlined first by explaining mechanical systems based on first principles. Following that, a fundamentally different approach, subspace identification, is comprehensively explained. While both theories, first principle and subspace identification based mechanical systems, may be seen as widespread methods, barely known and new techniques follow up. Therefore, the indefinite quadratic estimation theory is explained. Based on a Popov function approach, this leads to the Krein space based H-infinity theory. Subsequently, a new method for damage identification, namely SP2E, is proposed. Here, the introduction of a difference process, the analysis by its average process power and the application of oblique projections is discussed in depth.
Finally, the new method is verified in laboratory experiments. Therefore, the identification of a laboratory structure at Leipzig University of Applied Sciences is elaborated. Then structural alterations are experimentally applied, which were localized by SP2E afterwards. In the end four experimental sensitivity studies are shown and discussed. For each measurement series the structural alteration was increased, which was successfully tracked by SP2E. The experimental results are plausible and in accordance with the developed theories. By repeating these experiments, the applicability of SP2E for damage localization is experimentally proven.
Die hier vorliegende Arbeit befasst sich mit dem Modifizieren von Computerspielen (Modding). Die Annäherung an das Modding geschieht aus zwei unterschiedlichen Blickrichtungen: Zum einen wird mit einem analytischen Blick auf das Themenfeld geschaut, der das bereits Erforschte mit den eigenen Suchbewegungen kombiniert. Zum anderen wird die Perspektive der Handlung eingenommen, die sich in der Widerständigkeit des Materials, der Werkzeuge und der Spieltechnologie äußert. Im Mittelpunkt der Auseinandersetzung stehen das Modding als Praxis, die Mods als Derivate und die Erforschung des Computerspiels mit den Praktiken und Derivaten des Modifizierens. Das Modding wird so zu einer epistemischen Praxis des Computerspiels.
Die hier formulierten Überlegungen zum Modding, als eine forschende Praxis des Computerspiels, präsentieren eine Vorgehensweise, die ästhetische, widerständige und stabilisierende Aspekte in sich vereint. Sie dient der Erforschung des Computerspiels entlang seiner Diskussionen, Materialien, Technologien und Praktiken und fokussiert hierbei auf das Abseitige, dass als integraler Bestandteil des Computerspiels verstanden wird. Mit diesem Blick auf die Grenzen des Computerspielens werden Dinge sichtbar, die zwar Teil der synthetischen Computerspielwelten sind, durch dessen Inszenierungen und Atmosphären jedoch verschleiert werden. Der hier entwickelte Ansatz ermöglicht einen Perspektivenwechsel innerhalb dieser Welten und die Erforschung des Computerspiels unter besonderer Berücksichtigung seiner eingeschriebenen Normen und Machtverhältnissen. Das Modding dient hierbei als eine kritische Praxis zur Entschlüsselung dieser medial vermittelten Konstellationen.
SEEING HISTORY - THE AUGMENTED ARCHIVE erforscht – in Theorie und Praxis – die Medialitäten des Archivs in Zeiten des Übergangs vom Speichermedium hin zum Modus des Übertragens. Am Beispiel Ägyptens seit den politischen Umwälzungen 2011 wird ein neues Archivsystem entwickelt, das mit Hilfe von Augmented Reality Technologie - d.h. der virtuellen Erweiterung des Realraums von mobiler Videotechnik durch Metainformationen - das umfassendste bestehende Videoarchiv zur ägyptischen Revolution im Stadtraum Kairos per GPS-Kodierung zur Verfügung stellt.
Polymeric nanocomposites (PNCs) are considered for numerous nanotechnology such as: nano-biotechnology, nano-systems, nanoelectronics, and nano-structured materials. Commonly , they are formed by polymer (epoxy) matrix reinforced with a nanosized filler. The addition of rigid nanofillers to the epoxy matrix has offered great improvements in the fracture toughness without sacrificing other important thermo-mechanical properties. The physics of the fracture in PNCs is rather complicated and is influenced by different parameters. The presence of uncertainty in the predicted output is expected as a result of stochastic variance in the factors affecting the fracture mechanism. Consequently, evaluating the improved fracture toughness in PNCs is a challenging problem.
Artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) have been employed to predict the fracture energy of polymer/particle nanocomposites. The ANN and ANFIS models were constructed, trained, and tested based on a collection of 115 experimental datasets gathered from the literature. The performance evaluation indices of the developed ANN and ANFIS showed relatively small error, with high coefficients of determination (R2), and low root mean square error and mean absolute percentage error.
In the framework for uncertainty quantification of PNCs, a sensitivity analysis (SA) has been conducted to examine the influence of uncertain input parameters on the fracture toughness of polymer/clay nanocomposites (PNCs). The phase-field approach is employed to predict the macroscopic properties of the composite considering six uncertain input parameters. The efficiency, robustness, and repeatability are compared and evaluated comprehensively for five different SA methods.
The Bayesian method is applied to develop a methodology in order to evaluate the performance of different analytical models used in predicting the fracture toughness of polymeric particles nanocomposites. The developed method have considered the model and parameters uncertainties based on different reference data (experimental measurements) gained from the literature. Three analytical models differing in theory and assumptions were examined. The coefficients of variation of the model predictions to the measurements are calculated using the approximated optimal parameter sets. Then, the model selection probability is obtained with respect to the different reference data.
Stochastic finite element modeling is implemented to predict the fracture toughness of polymer/particle nanocomposites. For this purpose, 2D finite element model containing an epoxy matrix and rigid nanoparticles surrounded by an interphase zone is generated. The crack propagation is simulated by the cohesive segments method and phantom nodes. Considering the uncertainties in the input parameters, a polynomial chaos expansion (PCE) surrogate model is construed followed by a sensitivity analysis.
Im Rahmen der Dissertation ist ein analytisches Berechnungsverfahren zur Ermittlung der Kapazität in lichtsignalgeregelten Zufahrten mit zusätzlichen Aufstellstreifen bei gleichzeitiger Freigabezeit entwickelt worden, dass sich durch folgende Eigenschaften auszeichnet:
a) einfaches Berechnungsverfahren – Ansatz eines einfachen linearen Berechnungsansatzes, der auf den Grundzusammenhängen des Verkehrsablaufs in lichtsignalgeregelten Zufahrten aufbaut,
b) breites Anwendungsgebiet – Berechnungsverfahren kann in Zufahrten mit bis zu zwei zusätzlichen Aufstellstreifen angewendet werden,
c) hohe Genauigkeit – Im Rahmen eines direkten Vergleichs konnte u. a.
gezeigt werden, dass mit dem hergeleiteten analytischen Berechnungsverfahren genauere Kapazitätswerte ermittelt werden können, als mit dem Berechnungsverfahren nach HBS 2015.
Elitenkritik, populare Bündnisse und inklusive Solidaritär. Interview zur Debatte um Linkspopulismus
(2018)
In der aktuellen ökonomischen und politischen Krise haben Debatten um linke Strategien wieder Hochkonjunktur. Besonders kontrovers werden Vorschläge diskutiert, die einen Linkspopulismus als Alternative zum rechten politischen Projekt, zum Neoliberalismus und als Transformationsstrategie hin zu einer sozialistischen Gesellschaft propagieren. Thomas Goes und Violetta Bock haben mit ihrem Buch Ein unanständiges Angebot? Mit linkem Populismus gegen Eliten und Rechte (2017) eine programmatische Aufarbeitung existierender linker Populismuskonzepte und ihre eigene Vorstellung davon, wie ein linker Populismus gelingen kann, vorgelegt. Damit haben sie die Debatte um Linkspopulismus in Deutschland befeuert. Im Interview werden sie nach ihren Positionen und den Kontroversen um das Buch befragt. Das Interview soll als Aufschlag für eine Debatte dienen. Antworten zu den dargestellten Positionen und Bezüge zu städtischen Themen und städtischen sozialen Bewegungen sind sehr willkommen.
Following restructuring of power industry, electricity supply to end-use customers has undergone fundamental changes. In the restructured power system, some of the responsibilities of the vertically integrated distribution companies have been assigned to network managers and retailers. Under the new situation, retailers are in charge of providing electrical energy to electricity consumers who have already signed contract with them. Retailers usually provide the required energy at a variable price, from wholesale electricity markets, forward contracts with energy producers, or distributed energy generators, and sell it at a fixed retail price to its clients. Different strategies are implemented by retailers to reduce the potential financial losses and risks associated with the uncertain nature of wholesale spot electricity market prices and electrical load of the consumers. In this paper, the strategic behavior of retailers in implementing forward contracts, distributed energy sources, and demand-response programs with the aim of increasing their profit and reducing their risk, while keeping their retail prices as low as possible, is investigated. For this purpose, risk management problem of the retailer companies collaborating with wholesale electricity markets, is modeled through bi-level programming approach and a comprehensive framework for retail electricity pricing, considering customers’ constraints, is provided in this paper. In the first level of the proposed bi-level optimization problem, the retailer maximizes its expected profit for a given risk level of profit variability, while in the second level, the customers minimize their consumption costs. The proposed programming problem is modeled as Mixed Integer programming (MIP) problem and can be efficiently solved using available commercial solvers. The simulation results on a test case approve the effectiveness of the proposed demand-response program based on dynamic pricing approach on reducing the retailer’s risk and increasing its profit.
In this paper, the decision-making problem of the retailers under dynamic pricing approach for demand response integration have been investigated. The retailer was supposed to rely on forward contracts, DGs, and spot electricity market to supply the required active and reactive power of its customers. To verify the effectiveness of the proposed model, four schemes for retailer’s scheduling problem are considered and the resulted profit under each scheme are analyzed and compared. The simulation results on a test case indicate that providing more options for the retailer to buy the required power of its customers and increase its flexibility in buying energy from spot electricity market reduces the retailers’ risk and increases its profit. From the customers’ perspective also the retailers’accesstodifferentpowersupplysourcesmayleadtoareductionintheretailelectricityprices. Since the retailer would be able to decrease its electricity selling price to the customers without losing its profitability, with the aim of attracting more customers. Inthiswork,theconditionalvalueatrisk(CVaR)measureisusedforconsideringandquantifying riskinthedecision-makingproblems. Amongallthepossibleoptioninfrontoftheretailertooptimize its profit and risk, demand response programs are the most beneficial option for both retailer and its customers. The simulation results on the case study prove that implementing dynamic pricing approach on retail electricity prices to integrate demand response programs can successfully provoke customers to shift their flexible demand from peak-load hours to mid-load and low-load hours. Comparing the simulation results of the third and fourth schemes evidences the impact of DRPs and customers’ load shifting on the reduction of retailer’s risk, as well as the reduction of retailer’s payment to contract holders, DG owners, and spot electricity market. Furthermore, the numerical results imply on the potential of reducing average retail prices up to 8%, under demand response activation. Consequently, it provides a win–win solution for both retailer and its customers.
Management strategies for sustainable sugarcane production need to deal with the increasing complexity and variability of the whole sugar system. Moreover, they need to accommodate the multiple goals of different industry sectors and the wider community. Traditional disciplinary approaches are unable to provide integrated management solutions, and an approach based on whole systems analysis is essential to bring about beneficial change to industry and the community. The application of this approach to water management, environmental management and cane supply management is outlined, where the literature indicates that the application of extreme learning machine (ELM) has never been explored in this realm. Consequently, the leading objective of the current research was set to filling this gap by applying ELM to launch swift and accurate model for crop production data-driven. The key learning has been the need for innovation both in the technical aspects of system function underpinned by modelling of sugarcane growth. Therefore, the current study is an attempt to establish an integrate model using ELM to predict the concluding growth amount of sugarcane. Prediction results were evaluated and further compared with artificial neural network (ANN) and genetic programming models. Accuracy of the ELM model is calculated using the statistics indicators of Root Means Square Error (RMSE), Pearson Coefficient (r), and Coefficient of Determination (R2) with promising results of 0.8, 0.47, and 0.89, respectively. The results also show better generalization ability in addition to faster learning curve. Thus, proficiency of the ELM for supplementary work on advancement of prediction model for sugarcane growth was approved with promising results.