981 resultados para Parametric Inverse Modelling.


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Weltweit leben mehr als 2 Milliarden Menschen in ländlichen Gebieten. Als Konzept für die elektrische Energieversorgung solcher Gebiete kommen dezentrale elektrische Energieversorgungseinheiten zum Einsatz, die lokal verfügbare erneuerbare Ressourcen nutzen. Stand der Technik bilden Einheiten, die auf PV-Diesel-Batterie System basieren. Die verwendeten Versorgungsskonzepte in Hybridsystemen sind durch den Einsatz von Batterien als Energiespeicher meist wenig zuverlässig und teuer. Diese Energiespeicher sind sehr aufwendig zu überwachen und schwerig zu entsorgen. Den Schwerpunkt dieser Arbeit bildet die Entwicklung eines neuen Hybridsystems mit einem Wasserreservoir als Energiespeicher. Dieses Konzept eignet sich für Bergregionen in Entwicklungsländern wie Nepal, wo z.B. neben der solaren Strahlung kleine Flüsse in großer Anzahl vorhanden sind. Das Hybridsystem verfügt über einen Synchrongenerator, der die Netzgrößen Frequenz und Spannung vorgibt und zusätzlich unterstützen PV und Windkraftanlage die Versorgung. Die Wasserkraftanlage soll den Anteil der erneuerbaren Energienutzung erhöhen. Die Erweiterung des Systems um ein Dieselaggregat soll die Zuverlässigkeit der Versorgung erhöhen. Das Hybridsystem inkl. der Batterien wird modelliert und simuliert. Anschließend werden die Simulations- und Messergebnisse verglichen, um eine Validierung des Modells zu erreichen. Die Regelungsstruktur ist aufgrund der hohen Anzahl an Systemen und Parametern sehr komplex. Sie wird mit dem Simulationstool Matlab/Simulink nachgebildet. Das Verhalten des Gesamtsystems wird unter verschiedene Lasten und unterschiedlichen meteorologischen Gegebenheiten untersucht. Ein weiterer Schwerpunkt dieser Arbeit ist die Entwicklung einer modularen Energiemanagementeinheit, die auf Basis der erneuerbaren Energieversorgung aufgebaut wird. Dabei stellt die Netzfrequenz eine wichtige Eingangsgröße für die Regelung dar. Sie gibt über die Wirkleistungsstatik die Leistungsänderung im Netz wider. Über diese Angabe und die meteorologischen Daten kann eine optimale wirtschaftliche Aufteilung der Energieversorgung berechnet und eine zuverlässige Versorgung gewährleistet werden. Abschließend wurde die entwickelte Energiemanagementeinheit hardwaretechnisch aufgebaut, sowie Sensoren, Anzeige- und Eingabeeinheit in die Hardware integriert. Die Algorithmen werden in einer höheren Programmiersprache umgesetzt. Die Simulationen unter verschiedenen meteorologischen und netztechnischen Gegebenheiten mit dem entwickelten Model eines Hybridsystems für die elektrische Energieversorgung haben gezeigt, dass das verwendete Konzept mit einem Wasserreservoir als Energiespeicher ökologisch und ökonomisch eine geeignete Lösung für Entwicklungsländer sein kann. Die hardwaretechnische Umsetzung des entwickelten Modells einer Energiemanagementeinheit hat seine sichere Funktion bei der praktischen Anwendung in einem Hybridsystem bestätigen können.

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The capability for collaboration is a key success factor for networked enterprises. The paper introduces a methodology supporting the application of Enterprise Modelling in order to improve the maturity for collaboration. The methodology considers the current status of maturity for interoperability for deducing the right modelling approach. The approach is combined with quality criteria of the models in order to guide the modelling process. Both the deducing approach and the quality criteria are related to the levels of interoperability proposed by the ATHENA Interoperability Framework.

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This paper introduces a framework that supports users to implement enterprise modelling within collaborative companies. These enterprise models are the basis for a holistic interoperability measurement and management methodology which will be presented in the second part of the paper. The discipline of enterprise modelling aims at capturing all dimensions of an enterprise in a simplified model. Thus enterprise models are the appropriate basis for managing collaborative enterprise as they reduce the complexity of interoperability problems. Therefore, a first objective of this paper is to present an approach that enables companies to get the most effect out of enterprise modelling in a collaborative environment based on the maturity of their organisation relative to modelling. Within this first step, the user will get recommendations e.g. for the correct modelling language as well as the right level of detail.

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Auf dem Gebiet der Strukturdynamik sind computergestützte Modellvalidierungstechniken inzwischen weit verbreitet. Dabei werden experimentelle Modaldaten, um ein numerisches Modell für weitere Analysen zu korrigieren. Gleichwohl repräsentiert das validierte Modell nur das dynamische Verhalten der getesteten Struktur. In der Realität gibt es wiederum viele Faktoren, die zwangsläufig zu variierenden Ergebnissen von Modaltests führen werden: Sich verändernde Umgebungsbedingungen während eines Tests, leicht unterschiedliche Testaufbauten, ein Test an einer nominell gleichen aber anderen Struktur (z.B. aus der Serienfertigung), etc. Damit eine stochastische Simulation durchgeführt werden kann, muss eine Reihe von Annahmen für die verwendeten Zufallsvariablengetroffen werden. Folglich bedarf es einer inversen Methode, die es ermöglicht ein stochastisches Modell aus experimentellen Modaldaten zu identifizieren. Die Arbeit beschreibt die Entwicklung eines parameter-basierten Ansatzes, um stochastische Simulationsmodelle auf dem Gebiet der Strukturdynamik zu identifizieren. Die entwickelte Methode beruht auf Sensitivitäten erster Ordnung, mit denen Parametermittelwerte und Kovarianzen des numerischen Modells aus stochastischen experimentellen Modaldaten bestimmt werden können.

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Die zunehmende Vernetzung der Energie-, Stoff- und Informationsströme, z. B. innerhalb von Produktionsanlagen, begründet das Interesse an Methoden, welche eine Analyse der Systemeigenschaften von großen und verkoppelten dynamischen Systemen bereits in einem frühen Entwicklungsstadium ermöglichen. Dabei ist das Gesamtsystemverhalten von Interesse, welches sowohl auf der Dynamik der Teilsysteme als auch der Verkopplungen beruht. In der vorliegenden Arbeit wird eine neue Methode zur qualitativen Analyse von Systemen auf Inverse-Response-Verhalten vorgestellt. Der Ansatz nutzt Komplexe Netzwerke zur Modellbeschreibung, wobei diese um Kantengewichte ergänzt werden, welche das dynamische Verhalten des modellierten Systems beschreiben. Der vorgestellte Detektionsalgorithmus vergleicht die Pfade des Graphen zwischen den betrachteten Ein- und Ausgängen. Hierfür werden die aggregierten Kantengewichte der Pfade bestimmt und zur Ermittlung der qualitativen Aussage zum Inverse-Response-Verhalten herangezogen. Der Analyseansatz bietet somit eine einfach anwendbare Alternative zur Auswertung der positiven Nullstellen des Systems, welche auf die notwendige sowie hinreichende Bedingung für das Inverse-Response-Verhalten eines linearen Systems zurückgreift: eine ungerade Anzahl positiver Nullstellen. Das entwickelte Verfahren wurde erfolgreich an einer flexibel konfigurierbaren Prozessinsel der Modellfabrik μPlant des Fachgebiets Mess- und Regelungstechnik der Universität Kassel getestet.

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A modeling study of hippocampal pyramidal neurons is described. This study is based on simulations using HIPPO, a program which simulates the somatic electrical activity of these cells. HIPPO is based on a) descriptions of eleven non-linear conductances that have been either reported for this class of cell in the literature or postulated in the present study, and b) an approximation of the electrotonic structure of the cell that is derived in this thesis, based on data for the linear properties of these cells. HIPPO is used a) to integrate empirical data from a variety of sources on the electrical characteristics of this type of cell, b) to investigate the functional significance of the various elements that underly the electrical behavior, and c) to provide a tool for the electrophysiologist to supplement direct observation of these cells and provide a method of testing speculations regarding parameters that are not accessible.

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We present a technique for the rapid and reliable evaluation of linear-functional output of elliptic partial differential equations with affine parameter dependence. The essential components are (i) rapidly uniformly convergent reduced-basis approximations — Galerkin projection onto a space WN spanned by solutions of the governing partial differential equation at N (optimally) selected points in parameter space; (ii) a posteriori error estimation — relaxations of the residual equation that provide inexpensive yet sharp and rigorous bounds for the error in the outputs; and (iii) offline/online computational procedures — stratagems that exploit affine parameter dependence to de-couple the generation and projection stages of the approximation process. The operation count for the online stage — in which, given a new parameter value, we calculate the output and associated error bound — depends only on N (typically small) and the parametric complexity of the problem. The method is thus ideally suited to the many-query and real-time contexts. In this paper, based on the technique we develop a robust inverse computational method for very fast solution of inverse problems characterized by parametrized partial differential equations. The essential ideas are in three-fold: first, we apply the technique to the forward problem for the rapid certified evaluation of PDE input-output relations and associated rigorous error bounds; second, we incorporate the reduced-basis approximation and error bounds into the inverse problem formulation; and third, rather than regularize the goodness-of-fit objective, we may instead identify all (or almost all, in the probabilistic sense) system configurations consistent with the available experimental data — well-posedness is reflected in a bounded "possibility region" that furthermore shrinks as the experimental error is decreased.

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Synechocystis PCC 6803 is a photosynthetic bacterium that has the potential to make bioproducts from carbon dioxide and light. Biochemical production from photosynthetic organisms is attractive because it replaces the typical bioprocessing steps of crop growth, milling, and fermentation, with a one-step photosynthetic process. However, low yields and slow growth rates limit the economic potential of such endeavors. Rational metabolic engineering methods are hindered by limited cellular knowledge and inadequate models of Synechocystis. Instead, inverse metabolic engineering, a scheme based on combinatorial gene searches which does not require detailed cellular models, but can exploit sequence data and existing molecular biological techniques, was used to find genes that (1) improve the production of the biopolymer poly-3-hydroxybutyrate (PHB) and (2) increase the growth rate. A fluorescence activated cell sorting assay was developed to screen for high PHB producing clones. Separately, serial sub-culturing was used to select clones that improve growth rate. Novel gene knock-outs were identified that increase PHB production and others that increase the specific growth rate. These improvements make this system more attractive for industrial use and demonstrate the power of inverse metabolic engineering to identify novel phenotype-associated genes in poorly understood systems.

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One of the disadvantages of old age is that there is more past than future: this, however, may be turned into an advantage if the wealth of experience and, hopefully, wisdom gained in the past can be reflected upon and throw some light on possible future trends. To an extent, then, this talk is necessarily personal, certainly nostalgic, but also self critical and inquisitive about our understanding of the discipline of statistics. A number of almost philosophical themes will run through the talk: search for appropriate modelling in relation to the real problem envisaged, emphasis on sensible balances between simplicity and complexity, the relative roles of theory and practice, the nature of communication of inferential ideas to the statistical layman, the inter-related roles of teaching, consultation and research. A list of keywords might be: identification of sample space and its mathematical structure, choices between transform and stay, the role of parametric modelling, the role of a sample space metric, the underused hypothesis lattice, the nature of compositional change, particularly in relation to the modelling of processes. While the main theme will be relevance to compositional data analysis we shall point to substantial implications for general multivariate analysis arising from experience of the development of compositional data analysis…

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This analysis was stimulated by the real data analysis problem of household expenditure data. The full dataset contains expenditure data for a sample of 1224 households. The expenditure is broken down at 2 hierarchical levels: 9 major levels (e.g. housing, food, utilities etc.) and 92 minor levels. There are also 5 factors and 5 covariates at the household level. Not surprisingly, there are a small number of zeros at the major level, but many zeros at the minor level. The question is how best to model the zeros. Clearly, models that try to add a small amount to the zero terms are not appropriate in general as at least some of the zeros are clearly structural, e.g. alcohol/tobacco for households that are teetotal. The key question then is how to build suitable conditional models. For example, is the sub-composition of spending excluding alcohol/tobacco similar for teetotal and non-teetotal households? In other words, we are looking for sub-compositional independence. Also, what determines whether a household is teetotal? Can we assume that it is independent of the composition? In general, whether teetotal will clearly depend on the household level variables, so we need to be able to model this dependence. The other tricky question is that with zeros on more than one component, we need to be able to model dependence and independence of zeros on the different components. Lastly, while some zeros are structural, others may not be, for example, for expenditure on durables, it may be chance as to whether a particular household spends money on durables within the sample period. This would clearly be distinguishable if we had longitudinal data, but may still be distinguishable by looking at the distribution, on the assumption that random zeros will usually be for situations where any non-zero expenditure is not small. While this analysis is based on around economic data, the ideas carry over to many other situations, including geological data, where minerals may be missing for structural reasons (similar to alcohol), or missing because they occur only in random regions which may be missed in a sample (similar to the durables)

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It can be assumed that the composition of Mercury’s thin gas envelope (exosphere) is related to the composition of the planets crustal materials. If this relationship is true, then inferences regarding the bulk chemistry of the planet might be made from a thorough exospheric study. The most vexing of all unsolved problems is the uncertainty in the source of each component. Historically, it has been believed that H and He come primarily from the solar wind, while Na and K originate from volatilized materials partitioned between Mercury’s crust and meteoritic impactors. The processes that eject atoms and molecules into the exosphere of Mercury are generally considered to be thermal vaporization, photonstimulated desorption (PSD), impact vaporization, and ion sputtering. Each of these processes has its own temporal and spatial dependence. The exosphere is strongly influenced by Mercury’s highly elliptical orbit and rapid orbital speed. As a consequence the surface undergoes large fluctuations in temperature and experiences differences of insolation with longitude. We will discuss these processes but focus more on the expected surface composition and solar wind particle sputtering which releases material like Ca and other elements from the surface minerals and discuss the relevance of composition modelling

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In this article, the results of a modified SERVQUAL questionnaire (Parasuraman et al., 1991) are reported. The modifications consisted in substituting questionnaire items particularly suited to a specific service (banking) and context (county of Girona, Spain) for the original rather general and abstract items. These modifications led to more interpretable factors which accounted for a higher percentage of item variance. The data were submitted to various structural equation models which made it possible to conclude that the questionnaire contains items with a high measurement quality with respect to five identified dimensions of service quality which differ from those specified by Parasuraman et al. And are specific to the banking service. The two dimensions relating to the behaviour of employees have the greatest predictive power on overall quality and satisfaction ratings, which enables managers to use a low-cost reduced version of the questionnaire to monitor quality on a regular basis. It was also found that satisfaction and overall quality were perfectly correlated thus showing that customers do not perceive these concepts as being distinct

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En aquest article es resumeixen els resultats publicats en un informe de l' ISS (Istituto Superiore di Sanità) del desembre de 2006, sobre un model matemàtic desenvolupat per un grup de treball que inclou a investigadors de les Universitats de Trento, Pisa i Roma, i els Instituts Nacionals de Salut (Istituto Superiore di Sanità, ISS), per avaluar i mesurar l'impacte de la transmissió i el control de la pandèmia de grip

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The main objective of this paper aims at developing a methodology that takes into account the human factor extracted from the data base used by the recommender systems, and which allow to resolve the specific problems of prediction and recommendation. In this work, we propose to extract the user's human values scale from the data base of the users, to improve their suitability in open environments, such as the recommender systems. For this purpose, the methodology is applied with the data of the user after interacting with the system. The methodology is exemplified with a case study

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Not considered in the analytical model of the plant, uncertainties always dramatically decrease the performance of the fault detection task in the practice. To cope better with this prevalent problem, in this paper we develop a methodology using Modal Interval Analysis which takes into account those uncertainties in the plant model. A fault detection method is developed based on this model which is quite robust to uncertainty and results in no false alarm. As soon as a fault is detected, an ANFIS model is trained in online to capture the major behavior of the occurred fault which can be used for fault accommodation. The simulation results understandably demonstrate the capability of the proposed method for accomplishing both tasks appropriately