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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
21

Validation of a Regional Distribution Model in Environmental Risk Assessment of Substances / Validierung eines regionalen Ausbreitungsmodells in der Umweltrisikoabschätzung von Substanzen

Berding, Volker 06 November 2000 (has links)
The aim of this investigation was to determine the applicability and weaknesses of the regional distribution model SimpleBox and to make proposals for improvement. The validation was performed using a scheme of which the main aspects are the division into internal and external validation. With its default values, the regional distribution model represents a generic region, and it is connected with a model which estimates indirect emissions from sewage treatment plants. The examination was carried out using a set of sample substances, the characteristics of which cover a wide range of different physico-chemical properties, use patterns and emissions. These substances were employed in order to enable us to make common statements on the model´s applicability. Altogether, the model complies with its designated purpose to calculate regional background concentrations. A scrutiny of theory did not show serious errors or defects. Regarding sensitivity, it could be shown that the model contains only few parameters with a negligible influence on the results. The comparison with measured results showed a good agreement in many cases. The highest deviations occur if the preliminary estimations of emissions, degradation rates and partition coefficients deliver unrealistic values. Altering the regional default parameters has a lower influence on the modelled results than replacing unrealistic substance properties by better ones. Generally, the model employed is a reasonable compromise between complexity and simplification. For the sewage treatment model, it could be shown that its influence on the predicted concentration is very low and a much simpler model fulfils its purpose in a similar way. It is proposed to improve the model in several ways, e.g. by alternative estimations functions for partition coefficients. But the main focus for future improvements should be on the amelioration of release estimations and substance characteristics.
22

Two-dimensional Overflow Queueing Systems

Sendfeld, Walter Peter 06 October 2009 (has links)
In this thesis, we present two fairly general classes of so called overflow queueing networks. These networks consist of two queues, where the capacity of the first queue is always finite. Customers arriving at the first queue have an overflow capability from the first to the second queue if the first queue operates at a certain fixed capacity, i.e., under certain conditions, demands arriving at the first queue are allowed to join the second queue. The overflow stream will additionally be weighted with a parameter p. This parameter can be used as a control parameter or to model the customers´ impatience. We reduce the number of unknown steady-state probabilities of these system in a considerable amount by a generating functions approach and a separation technique.
23

A category of pseudo-tangles with classifying space Ω∞ S∞ and applications / Eine Kategorie aus Pseudo-Verschlingungen mit klassifizierendem Raum Ω∞ S∞ und Anwendungen

Blömer, Olaf 08 September 2000 (has links)
It is well known that the group completion of the classifying space of the free permutative category is Ω∞ S∞, i.e. stable homotopy of the 0-sphere. Quillen´s S-1S construction can be applied to the free permutative category, which has a pictorial description by pseudo-tangles, and this leads to another pictorial descripted category G which has the classifying space Ω∞ S∞. With help of this model G we can give generators for the homotopy groups of Ω∞ S∞ for i=0,1,2. As a further application, we compute the fundamental group of the free permutative category with duality and show that the association of a duality structure on the categorial level does not lead to a group completion on the level of classifying spaces.
24

Optimal sequential selection of a gambler assessed by the prophet

Laumann, Werner 09 March 2001 (has links)
In this thesis an optimal stopping problem related to the classical secretary problem is studied. The theory of optimal stopping represents a special branch of stochastic optimization. Here the socalled full information best choice problem with a known number of offers is generalized by maximizing the probability of selecting an r-candidate, where an offer is called r-candidate if it is not lower than the maximal offer reduced by function r. In the first part discrete time is investigated. For this optimal stopping problem to select an r-candidate an optimal stopping time is indicated, the suboptimal myopic stopping time is displayed and threshold rules are studied including asymptotic behaviour. The basis of this optimal stopping problem is displayed in a general setting where the payoff depends on the prophet´s choiceand on the maximal offer, i.e. the value of the prophet. As a further application the mean of the ratio of the gambler´s choice and prophet´s value is investigated. Then in the second part offers arrive in continuous time. Offers are presented according to random arrival times and the horizon terminating the period of choosing is taken to be fixed and random. Here stress is layed on the geometric and on the exponential distribution, i.e. the Poisson process. In the final part the optimal stopping problem of maximizing the duration of owning a sufficiently good offer is applied to the concept of an r-candidate. A distinction between an overall and a temporary r-candidate is made. The duration of owning an r-candidate is investigated for a finite number of offers with regard to recall. The duration problem with discounted epochs is resolved. Finally the duration of owning an r-candidate is considered regarding the Poisson process where the horizon is fixed and exponentially distributed.
25

Job-shop scheduling with limited buffer capacities

Heitmann, Silvia 18 July 2007 (has links)
In this work, we investigate job-shop problems where limited capacity buffers to store jobs in non-processing periods are present. In such a problem setting, after finishing processing on a machine, a job either directly has to be processed on the following machine or it has to be stored in a prespecified buffer. If the buffer is completely occupied the job may wait on its current machine but blocks this machine for other jobs. Besides a general buffer model,also specific configurations are considered.The key issue to develop fast heuristics for the job-shop problem with buffers is to find a compact representation of solutions. In contrast to the classical job-shop problem,where a solution may be given by the sequences of the jobs on the machines, now also the buffers have to be incorporated in the solution representation. In this work, we propose two solution representations for the job-shop problem with buffers. Furthermore, we investigate whether the given solution representations can be simplified for specific buffer configurations. For the general buffer configuration it is shown that an incorporation of the buffers in the solution representation is necessary, whereas for specific buffer configurations possible simplifications are presented. Based on the given solution representations we develop local search heuristics in the second part of this work. Therefore, the well-known block approach for the classical job-shop problem is generalized to the job-shop problem with specific buffer configurations.
26

Reinforcement Learning with History Lists

Timmer, Stephan 13 March 2009 (has links)
A very general framework for modeling uncertainty in learning environments is given by Partially Observable Markov Decision Processes (POMDPs). In a POMDP setting, the learning agent infers a policy for acting optimally in all possible states of the environment, while receiving only observations of these states. The basic idea for coping with partial observability is to include memory into the representation of the policy. Perfect memory is provided by the belief space, i.e. the space of probability distributions over environmental states. However, computing policies defined on the belief space requires a considerable amount of prior knowledge about the learning problem and is expensive in terms of computation time. In this thesis, we present a reinforcement learning algorithm for solving deterministic POMDPs based on short-term memory. Short-term memory is implemented by sequences of past observations and actions which are called history lists. In contrast to belief states, history lists are not capable of representing optimal policies, but are far more practical and require no prior knowledge about the learning problem. The algorithm presented learns policies consisting of two separate phases. During the first phase, the learning agent collects information by actively establishing a history list identifying the current state. This phase is called the efficient identification strategy. After the current state has been determined, the Q-Learning algorithm is used to learn a near optimal policy. We show that such a procedure can be also used to solve large Markov Decision Processes (MDPs). Solving MDPs with continuous, multi-dimensional state spaces requires some form of abstraction over states. One particular way of establishing such abstraction is to ignore the original state information, only considering features of states. This form of state abstraction is closely related to POMDPs, since features of states can be interpreted as observations of states.
27

The Regulation of Populations Featuring Non-Breeder Pools : A model analysis with implications for management strategy design for the Great Cormorant

Zeibig, Sten 25 January 2010 (has links)
(I) Background. Conflicts emerge when populations of protected species grow to sizes that cause noticeable economic damage - like in the case of the fish consuming Great Cormorant (Phalacrocorax carbo sinensis). One possible approach for reconciliation is to regulate the size of the population in question. In doing so, regulation strategies have to meet multiple targets: 1) population size has to be reduced; 2) the viability of the population has to be maintained; 3) strategies have to adhere to the available budget. This thesis focuses on the regulation of populations that are structured into two groups - breeders and mature non-breeders. The pool of non-breeders provides a reserve for the breeders, whereby they may enable the population to resist regulation attempts. (II) Aims. 1) Development of a modeling framework and a conceptual model to provide an understanding of the functioning and effect of the population structure induced by non-breeders on population dynamics in a fluctuating environment. 2) Uncover the relation between non-breeder characteristics and the performance of regulation strategies. 3) Application of the modeling approach to the regulation of the Cormorant in order to evaluate the results from the conceptual model and find statements to support decisions on management strategies. (III) Methods. A conceptual stochastic time-discrete model, based on the logistic map with overlapping generations, is developed. Different types of threshold regulation strategies are applied. Strategies differed in which part of the model was affected by regulation. Resulting rules from the conceptual model are tested by applying them to a second age-structured model of a cormorant population, parametrized with data gained from a cormorant colony in Denmark. Analyzes of this model focus on the ecological-economic performance of regulation strategies and result in rankings of regulation options. Regulation performance is judged from different economic perspectives.
28

Self-Regulating Neurons. A model for synaptic plasticity in artificial recurrent neural networks

Ghazi-Zahedi, Keyan Mahmoud 04 February 2009 (has links)
Robustness and adaptivity are important behavioural properties observed in biological systems, which are still widely absent in artificial intelligence applications. Such static or non-plastic artificial systems are limited to their very specific problem domain. This work introducesa general model for synaptic plasticity in embedded artificial recurrent neural networks, which is related to short-term plasticity by synaptic scaling in biological systems. The model is general in the sense that is does not require trigger mechanisms or artificial limitations and it operates on recurrent neural networks of arbitrary structure. A Self-Regulation Neuron is defined as a homeostatic unit which regulates its activity against external disturbances towards a target value by modulation of its incoming and outgoing synapses. Embedded and situated in the sensori-motor loop, a network of these neurons is permanently driven by external stimuli andwill generally not settle at its asymptotically stable state. The system´s behaviour is determinedby the local interactions of the Self-Regulating Neurons. The neuron model is analysed as a dynamical system with respect to its attractor landscape and its transient dynamics. The latter is conducted based on different control structures for obstacle avoidance with increasing structural complexity derived from literature. The result isa controller that shows first traces of adaptivity. Next, two controllers for different tasks are evolved and their transient dynamics are fully analysed. The results of this work not only show that the proposed neuron model enhances the behavioural properties, but also points out the limitations of short-term plasticity which does not account for learning and memory.
29

Large deviations and exit time asymptotics for diffusions and stochastic resonance

Peithmann, Dierk 10 December 2007 (has links)
Diese Arbeit behandelt die Asymptotik von Austritts- und Übergangszeiten für gewisse schwach zeitinhomogene Diffusionsprozesse. Darauf basierend wird ein probabilistischer Begriff der stochastischen Resonanz (SR) studiert. Techniken der großen Abweichungen spielen eine zentrale Rolle. Im ersten Teil der Arbeit (Kapitel 1-3) werden Resultate aus der Theorie der großen Abweichungen für zeithomogene Diffusionen rekapituliert. Es werden die klassischen Resultate von Freidlin und Wentzell und Erweiterungen dieser Theorie präsentiert, und es wird an das Kramers''sche Austrittszeitengesetz erinnert. Teil II befasst sich mit dem Phänomen der SR, d.h. mit Periodizitätseigenschaften von Diffusionen. In Kapitel 4 werden physikalische Maße zur Messung der Periodizität diskutiert. Deren Nachteile legen es nahe, einem alternativen, probabilistischen Ansatz zu folgen, der hier behandelt wird. Das 5. Kapitel dient der Herleitung eines gleichmäßigen Prinzips der großen Abweichungen für Diffusionen mit schwach zeitabhängigem, periodischem Drift. Die Gleichmäßigkeit des Prinzips ermöglicht die exakte Bestimmung exponentieller Übergangsraten in Kapitel 6, das die zentralen Ergebnisse des 2. Teils beinhaltet. Hierdurch wird die Maximierung gewisser Übergangswahrscheinlichkeiten ermöglicht, was zum in Kapitel 7 studierten Resonanzbegriff führt. Teil III der Arbeit setzt sich mit der Asymptotik von Austrittszeiten sogenannter selbststabilisierender Diffusionen auseinander. In Kapitel 8 wird der Zusammenhang zwischen interagierenden Teilchensystemen und selbststabilisierenden Diffusionen erläutert und die Existenz- und Eindeutigkeitsfrage behandelt. Das 9. Kapitel dient dem Studium der großen Abweichungen dieser Klasse von Diffusionen. In Kapitel 10 wird das Kramers''sche Austrittszeitengesetz auf selbststabilisierende Diffusionen übertragen, und in Kapitel 11 wird der Einfluß der selbststabilisierenden Komponente auf das Austrittszeitengesetz illustriert. / In this thesis, we study the asymptotic behavior of exit and transition times of certain weakly time inhomogeneous diffusion processes. Based on these asymptotics, a probabilistic notion of stochastic resonance (SR) is investigated. Large deviations techniques play the key role throughout this work. In the first part (Chapters 1-3) we recall the large deviations theory for time homogeneous diffusions. We present the classical results due to Freidlin and Wentzell and extensions thereof, and we remind of Kramers'' exit time law. Part II deals with the phenomenon of stochastic resonance. That is, we study periodicity properties of diffusion processes. In Chapter 4 we explain the paradigm of stochastic resonance and discuss physical notions of measuring periodicity of diffusions. Their drawbacks suggest to follow an alternative probabilistic approach, which is treated in this work. In Chapter 5 we derive a large deviations principle for diffusions subject to a weakly time dependent periodic drift term. The uniformity of the obtained large deviations bounds w.r.t. the system''s parameters plays a key role for the treatment of transition time asymptotics in Chapter 6, which contains the main result of the second part. The exact exponential transition rates obtained here allow for maximizing transition probabilities, which finally leads to the announced probabilistic notion of resonance studied in Chapter 7. In the third part we investigate the exit time asymptotics of a certain class of so-called self-stabilizing diffusions. In Chapter 8 we explain the connection between interacting particle systems and self-stabilizing diffusions, and we address the question of existence. The following Chapter 9 is devoted to the study of the large deviations behavior of these diffusions. In Chapter 10 Kramers'' exit law is carried over to our class of self-stabilizing diffusions. Finally, the influence of self-stabilization is illustrated in Chapter 11.
30

The exponent of Hölder calmness for polynomial systems

Heerda, Jan 27 April 2012 (has links)
Diese Arbeit befasst sich mit Untersuchung der Hölder Calmness, eines Stabilitätskonzeptes das man als Verallgemeinerung des Begriffs der Calmness erhält. Ausgehend von Charakterisierungen dieser Eigenschaft für Niveaumengen von Funktionen, werden, unter der Voraussetzung der Hölder Calmness, Prozeduren zur Bestimmung von Elementen dieser Mengen analysiert. Ebenso werden hinreichende Bedingungen für Hölder Calmness studiert. Da Hölder Calmness (nichtleerer) Lösungsmengen endlicher Ungleichungssysteme mittels (lokaler) Fehlerabschätzungen beschrieben werden kann, werden auch Erweiterungen der lokalen zu globalen Ergebnissen diskutiert. Als Anwendung betrachten wir speziell den Fall von Niveaumengen von Polynomen bzw. allgemeine Lösungsmengen polynomialer Gleichungen und Ungleichungen. Eine konkrete Frage, die wir beantworten wollen, ist die nach dem Zusammenhang zwischen dem größten Grad der beteiligten Polynome sowie dem Typ, d.h. dem auftretenden Exponenten, der Hölder Calmness des entsprechenden Systems. / This thesis is concerned with an analysis of Hölder calmness, a stability property derived from the concept of calmness. On the basis of its characterization for (sub)level sets, we will cogitate about procedures to determine points in such sets under a Hölder calmness assumption. Also sufficient conditions for Hölder calmness of (sub)level sets and of inequality systems will be given and examined. Further, since Hölder calmness of (nonempty) solution sets of finite inequality systems may be described in terms of (local) error bounds, we will as well amplify the local propositions to global ones. As an application we investigate the case of (sub)level sets of polynomials and of general solution sets of polynomial equations and inequalities. A concrete question we want to answer here is, in which way the maximal degree of the involved polynomials is connected to the exponent of Hölder calmness or of the error bound for the system in question.

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