Дисертації: "Dynamical system modeling"

1

Kawashima, Hiroaki. "Interval-Based Hybrid Dynamical System for Modeling Dynamic Events and Structures." 京都大学 (Kyoto University), 2007. http://hdl.handle.net/2433/68896.

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FRANCH, Daniel Kudlowiez. "Dynamical system modeling with probabilistic finite state automata." Universidade Federal de Pernambuco, 2017. https://repositorio.ufpe.br/handle/123456789/25448.

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FACEPE
Discrete dynamical systems are widely used in a variety of scientific and engineering applications, such as electrical circuits, machine learning, meteorology and neurobiology. Modeling these systems involves performing statistical analysis of the system output to estimate the parameters of a model so it can behave similarly to the original system. These models can be used for simulation, performance analysis, fault detection, among other applications. The current work presents two new algorithms to model discrete dynamical systems from two categories (synchronizable and non-synchronizable) using Probabilistic Finite State Automata (PFSA) by analyzing discrete symbolic sequences generated by the original system and applying statistical methods and inference, machine learning algorithms and graph minimization techniques to obtain compact, precise and efficient PFSA models. Their performance and time complexity are compared with other algorithms present in literature that aim to achieve the same goal by applying the algorithms to a series of common examples.
Sistemas dinâmicos discretos são amplamente usados em uma variedade de aplicações cientifícas e de engenharia, por exemplo, circuitos elétricos, aprendizado de máquina, meteorologia e neurobiologia. O modelamento destes sistemas envolve realizar uma análise estatística de sequências de saída do sistema para estimar parâmetros de um modelo para que este se comporte de maneira similar ao sistema original. Esses modelos podem ser usados para simulação, referência ou detecção de falhas. Este trabalho apresenta dois novos algoritmos para modelar sistemas dinâmicos discretos de duas categorias (sincronizáveis e não-sincronizáveis) por meio de Autômatos Finitos Probabilísticos (PFSA, Probabilistic Finite State Automata) analisando sequências geradas pelo sistema original e aplicando métodos estatísticos, algoritmos de aprendizado de máquina e técnicas de minimização de grafos para obter modelos PFSA compactos e eficientes. Sua performance e complexidade temporal são comparadas com algoritmos presentes na literatura que buscam atingir o mesmo objetivo aplicando os algoritmos a uma série de exemplos.

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3

Liu, Chunmeni 1970. "Dynamical system modeling of a micro gas turbine engine." Thesis, Massachusetts Institute of Technology, 2000. http://hdl.handle.net/1721.1/9249.

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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2000.
Also available online at the MIT Theses Online homepage .
Includes bibliographical references (p. 123).
Since 1995, MIT has been developing the technology for a micro gas turbine engine capable of producing tens of watts of power in a package less than one cubic centimeter in volume. The demo engine developed for this research has low and diabtic component performance and severe heat transfer from the turbine side to the compressor side. The goals of this thesis are developing a dynamical model and providing a simulation platform for predicting the microengine performance and control design, as well as giving an estimate of the microengine behavior under current design. The thesis first analyzes and models the dynamical components of the microengine. Then a nonlinear model, a linearized model, and corresponding simulators are derived, which are valid for estimating both the steady state and transient behavior. Simulations are also performed to estimate the microengine performance, which include steady states, linear properties, transient behavior, and sensor options. A parameter study and investigation of the startup process are also performed. Analysis and simulations show that there is the possibility of increasing turbine inlet temperature with decreasing fuel flow rate in some regions. Because of the severe heat transfer and this turbine inlet temperature trend, the microengine system behaves like a second-order system with low damping and poor linear properties. This increases the possibility of surge, over-temperature and over-speed. This also implies a potentially complex control system. The surge margin at the design point is large, but accelerating directly from minimum speed to 100% speed still causes surge. Investigation of the sensor options shows that temperature sensors have relatively fast response time but give multiple estimates of the engine state. Pressure sensors have relatively slow response time but they change monotonically with the engine state. So the future choice of sensors may be some combinations of the two. For the purpose of feedback control, the system is observable from speed, temperature, or pressure measurements. Parameter studies show that the engine performance doesn't change significantly with changes in either nozzle area or the coefficient relating heat flux to compressor efficiency. It does depend strongly on the coefficient relating heat flux to compressor pressure ratio. The value of the compressor peak efficiency affects the engine operation only when it is inside the range of the engine operation. Finally, parameter studies indicate that, to obtain improved transient behavior with less possibility of surge, over-temperature and over-speed, and to simplify the system analysis and design as well as the design and implementation of control laws, it is desirable to reduce the ratio of rotor mechanical inertia to thermal inertia, e.g. by slowing the thermal dynamics. This can in some cases decouple the dynamics of rotor acceleration and heat transfer. Several methods were shown to improve the startup process: higher start speed, higher start spool temperature, and higher start fuel flow input. Simulations also show that the efficiency gradient affects the transient behavior of the engine significantly, thereby effecting the startup process. Finally, the analysis and modeling methodologies presented in this thesis can be applied to other engines with severe heat transfer. The estimates of the engine performance can serve as a reference of similar engines as well.
by Chunmei Liu.
S.M.

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4

Hsiao, Yu-Chung Ph D. Massachusetts Institute of Technology. "Automated modeling of nonlinear dynamical subsystems for stable system simulation." Thesis, Massachusetts Institute of Technology, 2015. http://hdl.handle.net/1721.1/99828.

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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2015.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 107-113).
Automated modeling techniques allow fast prototyping from measurement or simulation data and can facilitate many important application scenarios, for instance, shortening the time frame from subsystem design to system integration, calibrating models with higher-order effects, and providing protected models without revealing the intellectual properties of actual designs. Many existing techniques can generate nonlinear dynamical models that are stable when simulated alone. However, such generated models oftentimes result in unstable simulation when interconnected within a physical network. This is because energy-related system properties are not properly enforced, and the generated models erroneously produce numerical energy, which in turn causes instability of the entire physical network. Therefore, when modeling a system that is unable to generate energy, it is essential to enforce passivity in order to ensure stable system simulation. This thesis presents an algorithm that can automatically generate nonlinear passive dynamical models via convex optimization. Convex constraints are proposed to guarantee model passivity and incremental stability. The generated nonlinear models are suited to be interconnected within physical networks in order to enable the hierarchical modeling strategy. Practical examples include circuit networks and arterial networks. It is demonstrated that our generated models, when interconnected within a system, can be simulated in a numerically stable way. The system dynamics of the interconnected models can be faithfully reproduced for a range of operations and show an excellent agreement with a number of system metrics. In addition, it is also shown via these two applications that the proposed modeling technique is applicable to multiple physical domains.
by Yu-Chung Hsiao.
Ph. D.

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5

Mattos, César Lincoln Cavalcante. "Recurrent gaussian processes and robust dynamical modeling." reponame:Repositório Institucional da UFC, 2017. http://www.repositorio.ufc.br/handle/riufc/25604.

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MATTOS, C. L. C. Recurrent gaussian processes and robust dynamical modeling. 2017. 189 f. Tese (Doutorado em Engenharia de Teleinformática)–Centro de Tecnologia, Universidade Federal do Ceará, Fortaleza, 2017.
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The study of dynamical systems is widespread across several areas of knowledge. Sequential data is generated constantly by different phenomena, most of them we cannot explain by equations derived from known physical laws and structures. In such context, this thesis aims to tackle the task of nonlinear system identification, which builds models directly from sequential measurements. More specifically, we approach challenging scenarios, such as learning temporal relations from noisy data, data containing discrepant values (outliers) and large datasets. In the interface between statistics, computer science, data analysis and engineering lies the machine learning community, which brings powerful tools to find patterns from data and make predictions. In that sense, we follow methods based on Gaussian Processes (GP), a principled, practical, probabilistic approach to learning in kernel machines. We aim to exploit recent advances in general GP modeling to bring new contributions to the dynamical modeling exercise. Thus, we propose the novel family of Recurrent Gaussian Processes (RGPs) models and extend their concept to handle outlier-robust requirements and scalable stochastic learning. The hierarchical latent (non-observed) structure of those models impose intractabilities in the form of non-analytical expressions, which are handled with the derivation of new variational algorithms to perform approximate deterministic inference as an optimization problem. The presented solutions enable uncertainty propagation on both training and testing, with focus on free simulation. We comprehensively evaluate the proposed methods with both artificial and real system identification benchmarks, as well as other related dynamical settings. The obtained results indicate that the proposed approaches are competitive when compared to the state of the art in the aforementioned complicated setups and that GP-based dynamical modeling is a promising area of research.
O estudo dos sistemas dinâmicos encontra-se disseminado em várias áreas do conhecimento. Dados sequenciais são gerados constantemente por diversos fenômenos, a maioria deles não passíveis de serem explicados por equações derivadas de leis físicas e estruturas conhecidas. Nesse contexto, esta tese tem como objetivo abordar a tarefa de identificação de sistemas não lineares, por meio da qual são obtidos modelos diretamente a partir de observações sequenciais. Mais especificamente, nós abordamos cenários desafiadores, tais como o aprendizado de relações temporais a partir de dados ruidosos, dados contendo valores discrepantes (outliers) e grandes conjuntos de dados. Na interface entre estatísticas, ciência da computação, análise de dados e engenharia encontra-se a comunidade de aprendizagem de máquina, que fornece ferramentas poderosas para encontrar padrões a partir de dados e fazer previsões. Nesse sentido, seguimos métodos baseados em Processos Gaussianos (PGs), uma abordagem probabilística prática para a aprendizagem de máquinas de kernel. A partir de avanços recentes em modelagem geral baseada em PGs, introduzimos novas contribuições para o exercício de modelagem dinâmica. Desse modo, propomos a nova família de modelos de Processos Gaussianos Recorrentes (RGPs, da sigla em inglês) e estendemos seu conceito para lidar com requisitos de robustez a outliers e aprendizagem estocástica escalável. A estrutura hierárquica e latente (não-observada) desses modelos impõe expressões não- analíticas, que são resolvidas com a derivação de novos algoritmos variacionais para realizar inferência determinista aproximada como um problema de otimização. As soluções apresentadas permitem a propagação da incerteza tanto no treinamento quanto no teste, com foco em realizar simulação livre. Nós avaliamos em detalhe os métodos propostos com benchmarks artificiais e reais da área de identificação de sistemas, assim como outras tarefas envolvendo dados dinâmicos. Os resultados obtidos indicam que nossas propostas são competitivas quando comparadas ao estado da arte, mesmo nos cenários que apresentam as complicações supracitadas, e que a modelagem dinâmica baseada em PGs é uma área de pesquisa promissora.

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Erdogan, Ezgi. "A Complex Dynamical Systems Model Of Education, Research, Employment, And Sustainable Human Development." Master's thesis, METU, 2010. http://etd.lib.metu.edu.tr/upload/3/12612138/index.pdf.

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Economic events of this era reflect the fact that the value of information and technology has surpassed the value of physical production. This motivates countries to focus on increasing the education levels of citizens. However, policy making about education system and its returns requires dynamical analyses in order to be sustainable. The study aims to investigate the dynamic characteristics of a country-wide education system, in particular, that of Turkey. System Dynamics modeling, which is one of the most commonly referred tools for understanding the complex social structures, is used. Our model introduces dynamic relationships among different classes of labor forces with varying education levels, university admissions, research quality, and the investments made in education, research and other sectors. Model experimentation provides new insights into the investment and capacity-related aspects of the education system environment.

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7

Wang, Chiying. "Contributions to Collective Dynamical Clustering-Modeling of Discrete Time Series." Digital WPI, 2016. https://digitalcommons.wpi.edu/etd-dissertations/198.

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The analysis of sequential data is important in business, science, and engineering, for tasks such as signal processing, user behavior mining, and commercial transactions analysis. In this dissertation, we build upon the Collective Dynamical Modeling and Clustering (CDMC) framework for discrete time series modeling, by making contributions to clustering initialization, dynamical modeling, and scaling. We first propose a modified Dynamic Time Warping (DTW) approach for clustering initialization within CDMC. The proposed approach provides DTW metrics that penalize deviations of the warping path from the path of constant slope. This reduces over-warping, while retaining the efficiency advantages of global constraint approaches, and without relying on domain dependent constraints. Second, we investigate the use of semi-Markov chains as dynamical models of temporal sequences in which state changes occur infrequently. Semi-Markov chains allow explicitly specifying the distribution of state visit durations. This makes them superior to traditional Markov chains, which implicitly assume an exponential state duration distribution. Third, we consider convergence properties of the CDMC framework. We establish convergence by viewing CDMC from an Expectation Maximization (EM) perspective. We investigate the effect on the time to convergence of our efficient DTW-based initialization technique and selected dynamical models. We also explore the convergence implications of various stopping criteria. Fourth, we consider scaling up CDMC to process big data, using Storm, an open source distributed real-time computation system that supports batch and distributed data processing. We performed experimental evaluation on human sleep data and on user web navigation data. Our results demonstrate the superiority of the strategies introduced in this dissertation over state-of-the-art techniques in terms of modeling quality and efficiency.

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8

Anderson, James David. "Dynamical system decomposition and analysis using convex optimization." Thesis, University of Oxford, 2012. http://ora.ox.ac.uk/objects/uuid:624001be-28d5-4837-a7d8-2222e270e658.

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This thesis is concerned with investigating new methods for the analysis of large-scale dynamical systems using convex optimization. The proposed methodology is based on composite Lyapunov theory and is computationally implemented using polynomial programming techniques. The main result of this work is the development of a system decomposition framework that makes it possible to analyze systems that are of such a scale that traditional methods cannot cope with. We begin by addressing the problem of model invalidation. A barrier certificate method for invalidating models in the presence of uncertain data is presented for both continuous and discrete time models. It is shown how a re-parameterization of the time dependent variables can improve the numerical conditioning of the underlying optimization problem. The main contribution of this thesis is the development of an automated dynamical system decomposition framework that permits us to verify the stability of systems that typically have a state dimension large enough to render traditional computational methods intractable. The underlying idea is to decompose a system into a set of lower order subsystems connected in feedback in such a manner that composite methods for stability verification may be employed. What is unique about the algorithm presented is that it takes into account both dynamics and the topology of the interconnection graph. In the first instance we illustrate the methodology with an ecological network and primal Internet congestion control scheme. The versatility of the decomposition framework is also highlighted when it is shown that when applied to a model of the EGF-MAPK signaling pathway it is capable of identifying biologically relevant subsystems in addition to stability verification. Finally we introduce stability metrics for interconnected dynamical systems based on the theory of dissipativity. We conclude by outlining a clustering based decomposition algorithm that explicitly takes into account the input and output dynamics when determining the system decomposition.

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9

Xie, Junfei. "Data-Driven Decision-Making Framework for Large-Scale Dynamical Systems under Uncertainty." Thesis, University of North Texas, 2016. https://digital.library.unt.edu/ark:/67531/metadc862845/.

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Managing large-scale dynamical systems (e.g., transportation systems, complex information systems, and power networks, etc.) in real-time is very challenging considering their complicated system dynamics, intricate network interactions, large scale, and especially the existence of various uncertainties. To address this issue, intelligent techniques which can quickly design decision-making strategies that are robust to uncertainties are needed. This dissertation aims to conquer these challenges by exploring a data-driven decision-making framework, which leverages big-data techniques and scalable uncertainty evaluation approaches to quickly solve optimal control problems. In particular, following techniques have been developed along this direction: 1) system modeling approaches to simplify the system analysis and design procedures for multiple applications; 2) effective simulation and analytical based approaches to efficiently evaluate system performance and design control strategies under uncertainty; and 3) big-data techniques that allow some computations of control strategies to be completed offline. These techniques and tools for analysis, design and control contribute to a wide range of applications including air traffic flow management, complex information systems, and airborne networks.

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10

Yin, Yuan. "Physics-Aware Deep Learning and Dynamical Systems : Hybrid Modeling and Generalization." Electronic Thesis or Diss., Sorbonne université, 2023. http://www.theses.fr/2023SORUS161.

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L'apprentissage profond a fait des progrès dans divers domaines et est devenu un outil prometteur pour modéliser les phénomènes dynamiques physiques présentant des relations hautement non linéaires. Cependant, les approches existantes sont limitées dans leur capacité à faire des prédictions physiquement fiables en raison du manque de connaissances préalables et à gérer les scénarios du monde réel où les données proviennent de dynamiques multiples ou sont irrégulièrement distribuées dans le temps et l'espace. Cette thèse vise à surmonter ces limitations dans les directions suivantes: améliorer la modélisation de la dynamique basée sur les réseaux neuronaux en exploitant des modèles physiques grâce à la modélisation hybride ; étendre le pouvoir de généralisation des modèles de dynamique en apprenant les similitudes à partir de données de différentes dynamiques pour extrapoler vers des systèmes invisibles ; et gérer les données de forme libre et prédire continuellement les phénomènes dans le temps et l'espace grâce à la modélisation continue. Nous soulignons la polyvalence des techniques d'apprentissage profond, et les directions proposées montrent des promesses pour améliorer leur précision et leur puissance de généralisation, ouvrant la voie à des recherches futures dans de nouvelles applications
Deep learning has made significant progress in various fields and has emerged as a promising tool for modeling physical dynamical phenomena that exhibit highly nonlinear relationships. However, existing approaches are limited in their ability to make physically sound predictions due to the lack of prior knowledge and to handle real-world scenarios where data comes from multiple dynamics or is irregularly distributed in time and space. This thesis aims to overcome these limitations in the following directions: improving neural network-based dynamics modeling by leveraging physical models through hybrid modeling; extending the generalization power of dynamics models by learning commonalities from data of different dynamics to extrapolate to unseen systems; and handling free-form data and continuously predicting phenomena in time and space through continuous modeling. We highlight the versatility of deep learning techniques, and the proposed directions show promise for improving their accuracy and generalization power, paving the way for future research in new applications

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JÃnior, Amauri Holanda de Souza. "Regional Models and Minimal Learning Machines for Nonlinear Dynamical System Identification." Universidade Federal do CearÃ, 2014. http://www.teses.ufc.br/tde_busca/arquivo.php?codArquivo=14269.

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This thesis addresses the problem of identifying nonlinear dynamic systems from a machine learning perspective. In this context, very little is assumed to be known about the system under investigation, and the only source of information comes from input/output measurements on the system. It corresponds to the black-box modeling approach. Numerous strategies and models have been proposed over the last decades in the machine learning field and applied to modeling tasks in a straightforward way. Despite of this variety, the methods can be roughly categorized into global and local modeling approaches. Global modeling consists in fitting a single regression model to the available data, using the whole set of input and output observations. On the other side of the spectrum stands the local modeling approach, in which the input space is segmented into several small partitions and a specialized regression model is fit to each partition. The first contribution of the thesis is a novel supervised global learning model, the Minimal Learning Machine (MLM). Learning in MLM consists in building a linear mapping between input and output distance matrices and then estimating the nonlinear response from the geometrical configuration of the output points. Given its general formulation, the Minimal Learning Machine is inherently capable of operating on nonlinear regression problems as well as on multidimensional response spaces. Naturally, its characteristics make the MLM able to tackle the system modeling problem. The second significant contribution of the thesis represents a different modeling paradigm, called Regional Modeling (RM), and it is motivated by the parsimonious principle. Regional models stand between the global and local modeling approaches. The proposal consists of a two-level clustering approach in which we first partition the input space using the Self-Organizing Map (SOM), and then perform clustering over the prototypes of the trained SOM. After that, regression models are built over the clusters of SOM prototypes, or regions in the input space. Even though the proposals of the thesis can be thought as quite general regression or supervised learning models, the performance assessment is carried out in the context of system identification. Comprehensive performance evaluation of the proposed models on synthetic and real-world datasets is carried out and the results compared to those achieved by standard global and local models. The experiments illustrate that the proposed methods achieve accuracies that are comparable to, and even better than, more traditional machine learning methods thus offering a valid alternative to such approaches.
This thesis addresses the problem of identifying nonlinear dynamic systems from a machine learning perspective. In this context, very little is assumed to be known about the system under investigation, and the only source of information comes from input/output measurements on the system. It corresponds to the black-box modeling approach. Numerous strategies and models have been proposed over the last decades in the machine learning field and applied to modeling tasks in a straightforward way. Despite of this variety, the methods can be roughly categorized into global and local modeling approaches. Global modeling consists in fitting a single regression model to the available data, using the whole set of input and output observations. On the other side of the spectrum stands the local modeling approach, in which the input space is segmented into several small partitions and a specialized regression model is fit to each partition. The first contribution of the thesis is a novel supervised global learning model, the Minimal Learning Machine (MLM). Learning in MLM consists in building a linear mapping between input and output distance matrices and then estimating the nonlinear response from the geometrical configuration of the output points. Given its general formulation, the Minimal Learning Machine is inherently capable of operating on nonlinear regression problems as well as on multidimensional response spaces. Naturally, its characteristics make the MLM able to tackle the system modeling problem. The second significant contribution of the thesis represents a different modeling paradigm, called Regional Modeling (RM), and it is motivated by the parsimonious principle. Regional models stand between the global and local modeling approaches. The proposal consists of a two-level clustering approach in which we first partition the input space using the Self-Organizing Map (SOM), and then perform clustering over the prototypes of the trained SOM. After that, regression models are built over the clusters of SOM prototypes, or regions in the input space. Even though the proposals of the thesis can be thought as quite general regression or supervised learning models, the performance assessment is carried out in the context of system identification. Comprehensive performance evaluation of the proposed models on synthetic and real-world datasets is carried out and the results compared to those achieved by standard global and local models. The experiments illustrate that the proposed methods achieve accuracies that are comparable to, and even better than, more traditional machine learning methods thus offering a valid alternative to such approaches.

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12

Liu, Qingfang. "Extensions of Multivariate Dynamical Systems for Simultaneous Explanations of Neural and Behavioral Data." The Ohio State University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=osu153123061730762.

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13

Giraldo, Trujillo Luis Felipe. "Modeling and Analysis of Human Group Dynamics." The Ohio State University, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=osu1462450933.

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14

Ikeda, Takuya. "Sparse Optimal Control for Continuous-Time Dynamical Systems." Kyoto University, 2019. http://hdl.handle.net/2433/242441.

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15

Durmaz, Oguz. "Dynamical Modeling Of The Flow Over Flapping Wing By Applying Proper Orthogonal Decomposition And System Identification." Master's thesis, METU, 2011. http://etd.lib.metu.edu.tr/upload/12613549/index.pdf.

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In this study the dynamical modeling of the unsteady flow over a flapping wing is considered. The technique is based on collecting instantaneous velocity field data of the flow using Particle Image Velocimetry (PIV), applying image processing to these snapshots to locate the airfoil, filling the airfoil and its surface with proper velocity data, applying Proper Orthogonal Decomposition (POD) to these post-processed images to compute the POD modes and time coefficients, and finally fitting a discrete time state space dynamical model to the trajectories of the time coefficients using subspace system identification (N4SID). The procedure is applied using MATLAB for the data obtained from NACA 0012, SD 7003, elliptic airfoil and flat plate, and the results show that the dynamical model obtained can represent the flow dynamics with acceptable accuracy.

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16

Singla, Puneet. "Multi-resolution methods for high fidelity modeling and control allocation in large-scale dynamical systems." Texas A&M University, 2005. http://hdl.handle.net/1969.1/3785.

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This dissertation introduces novel methods for solving highly challenging model- ing and control problems, motivated by advanced aerospace systems. Adaptable, ro- bust and computationally effcient, multi-resolution approximation algorithms based on Radial Basis Function Network and Global-Local Orthogonal Mapping approaches are developed to address various problems associated with the design of large scale dynamical systems. The main feature of the Radial Basis Function Network approach is the unique direction dependent scaling and rotation of the radial basis function via a novel Directed Connectivity Graph approach. The learning of shaping and rota- tion parameters for the Radial Basis Functions led to a broadly useful approximation approach that leads to global approximations capable of good local approximation for many moderate dimensioned applications. However, even with these refinements, many applications with many high frequency local input/output variations and a high dimensional input space remain a challenge and motivate us to investigate an entirely new approach. The Global-Local Orthogonal Mapping method is based upon a novel averaging process that allows construction of a piecewise continuous global family of local least-squares approximations, while retaining the freedom to vary in a general way the resolution (e.g., degrees of freedom) of the local approximations. These approximation methodologies are compatible with a wide variety of disciplines such as continuous function approximation, dynamic system modeling, nonlinear sig-nal processing and time series prediction. Further, related methods are developed for the modeling of dynamical systems nominally described by nonlinear differential equations and to solve for static and dynamic response of Distributed Parameter Sys- tems in an effcient manner. Finally, a hierarchical control allocation algorithm is presented to solve the control allocation problem for highly over-actuated systems that might arise with the development of embedded systems. The control allocation algorithm makes use of the concept of distribution functions to keep in check the "curse of dimensionality". The studies in the dissertation focus on demonstrating, through analysis, simulation, and design, the applicability and feasibility of these ap- proximation algorithms to a variety of examples. The results from these studies are of direct utility in addressing the "curse of dimensionality" and frequent redundancy of neural network approximation.

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17

North, Ben. "Learning dynamical models for visual tracking." Thesis, University of Oxford, 1998. http://ora.ox.ac.uk/objects/uuid:6ed12552-4c30-4d80-88ef-7245be2d8fb8.

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Using some form of dynamical model in a visual tracking system is a well-known method for increasing robustness and indeed performance in general. Often, quite simple models are used and can be effective, but prior knowledge of the likely motion of the tracking target can often be exploited by using a specially-tailored model. Specifying such a model by hand, while possible, is a time-consuming and error-prone process. Much more desirable is for an automated system to learn a model from training data. A dynamical model learnt in this manner can also be a source of useful information in its own right, and a set of dynamical models can provide discriminatory power for use in classification problems. Methods exist to perform such learning, but are limited in that they assume the availability of 'ground truth' data. In a visual tracking system, this is rarely the case. A learning system must work from visual data alone, and this thesis develops methods for learning dynamical models while explicitly taking account of the nature of the training data --- they are noisy measurements. The algorithms are developed within two tracking frameworks. The Kalman filter is a simple and fast approach, applicable where the visual clutter is limited. The recently-developed Condensation algorithm is capable of tracking in more demanding situations, and can also employ a wider range of dynamical models than the Kalman filter, for instance multi-mode models. The success of the learning algorithms is demonstrated experimentally. When using a Kalman filter, the dynamical models learnt using the algorithms presented here produce better tracking when compared with those learnt using current methods. Learning directly from training data gathered using Condensation is an entirely new technique, and experiments show that many aspects of a multi-mode system can be successfully identified using very little prior information. Significant computational effort is required by the implementation of the methods, and there is scope for improvement in this regard. Other possibilities for future work include investigation of the strong links this work has with learning problems in other areas. Most notable is the study of the 'graphical models' commonly used in expert systems, where the ideas presented here promise to give insight and perhaps lead to new techniques.

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18

Basharat, Arslan. "MODELING SCENES AND HUMAN ACTIVITIES IN VIDEOS." Doctoral diss., University of Central Florida, 2009. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/3830.

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In this dissertation, we address the problem of understanding human activities in videos by developing a two-pronged approach: coarse level modeling of scene activities and fine level modeling of individual activities. At the coarse level, where the resolution of the video is low, we rely on person tracks. At the fine level, richer features are available to identify different parts of the human body, therefore we rely on the body joint tracks. There are three main goals of this dissertation: (1) identify unusual activities at the coarse level, (2) recognize different activities at the fine level, and (3) predict the behavior for synthesizing and tracking activities at the fine level. The first goal is addressed by modeling activities at the coarse level through two novel and complementing approaches. The first approach learns the behavior of individuals by capturing the patterns of motion and size of objects in a compact model. Probability density function (pdf) at each pixel is modeled as a multivariate Gaussian Mixture Model (GMM), which is learnt using unsupervised expectation maximization (EM). In contrast, the second approach learns the interaction of object pairs concurrently present in the scene. This can be useful in detecting more complex activities than those modeled by the first approach. We use a 14-dimensional Kernel Density Estimation (KDE) that captures motion and size of concurrently tracked objects. The proposed models have been successfully used to automatically detect activities like unusual person drop-off and pickup, jaywalking, etc. The second and third goals of modeling human activities at the fine level are addressed by employing concepts from theory of chaos and non-linear dynamical systems. We show that the proposed model is useful for recognition and prediction of the underlying dynamics of human activities. We treat the trajectories of human body joints as the observed time series generated from an underlying dynamical system. The observed data is used to reconstruct a phase (or state) space of appropriate dimension by employing the delay-embedding technique. This transformation is performed without assuming an exact model of the underlying dynamics and provides a characteristic representation that will prove to be vital for recognition and prediction tasks. For recognition, properties of phase space are captured in terms of dynamical and metric invariants, which include the Lyapunov exponent, correlation integral, and correlation dimension. A composite feature vector containing these invariants represents the action and will be used for classification. For prediction, kernel regression is used in the phase space to compute predictions with a specified initial condition. This approach has the advantage of modeling dynamics without making any assumptions about the exact form (polynomial, radial basis, etc.) of the mapping function. We demonstrate the utility of these predictions for human activity synthesis and tracking.
Ph.D.
School of Electrical Engineering and Computer Science
Engineering and Computer Science
Computer Science PhD

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19

Arat, Seda. "A Systems Biology Approach to Microbiology and Cancer." Diss., Virginia Tech, 2015. http://hdl.handle.net/10919/75149.

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Systems biology is an interdisciplinary field that focuses on elucidating complex biological processes (systems) by investigating the interactions among its components through an iterative cycle composed of data generation, data analysis and mathematical modeling. Our contributions to systems biology revolve around the following two axes: - Data analysis: Two data analysis projects, which were initiated when I was a co-op at GlaxoSmithKline, are discussed in this thesis. First, next generation sequencing data generated for a phase I clinical trial is analyzed to determine the altered microbial community in human gut before and after antibiotic usage (Chapter 2). To our knowledge, there have not been similar comparative studies in humans on the impacts on the gut microbiome of an antibiotic when administered by different modes. Second, publicly available gene expression data is analyzed to investigate human immune response to tuberculosis (TB) infection (Chapter 3). The novel feature of this study is systematic drug repositioning for the prevention, control and treatment of TB using the Connectivity map. - Mathematical modeling: Polynomial dynamical systems, a state- and time- discrete logical modeling framework, is used to model two biological processes. First, a denitrification pathway in Pseudomonas aeruginosa is modeled to shed light on the reason of greenhouse gas nitrous oxide accumulation (Chapter 4). It is the first mathematical model of denitrification that can predict the effect of phosphate on the denitrification performance of this bacterium. Second, an iron homeostasis pathway linked to iron utilization, oxidative stress response and oncogenic pathways is constructed to investigate how normal breast cells become cancerous (Chapter 5). To date, our intracellular model is the only expanded core iron model that can capture a breast cancer phenotype by overexpression and knockout simulations.
Ph. D.

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20

Mannari, Toko. "Mass Transport and Discharging Dynamics of Redox Flow Battery for Power Supply." Kyoto University, 2020. http://hdl.handle.net/2433/259738.

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21

Bider, Ilia. "State-Oriented Business Process Modeling : Principles, Theory and Practice." Doctoral thesis, KTH, Computer and Systems Sciences, DSV, 2002. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-3375.

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In the last 50 years, a considerable amount of research workhas been completed in the mathematical system theory and theoryof control. Implementation of the results from this researchinto practice has drastically decreased the production costs.Most production processes are highly automated, and the use ofrobots in industry is growing. As far as office, or businessprocesses are concerned, the situation is quite different.Though the office workers and sales personnel have obtainedmuch help from the modern computers, the office and salesprocesses are far behind the production processes on the levelof automation. The computers are used in the office mainly tohelp in performing various activities, e.g., to write a letter,to print an invoice, to complete a transaction, etc. Thecontrol of the business processes in the office remains, to alarge extent, manual. There is a lot to gain if the controlover business processes could be automated, at leastpartially.

The material presented in this thesis is aimed to supportthe following hypothesis: "The ideas worked out in the Mathematical system theory formodeling and controlling physical processes can be successfullyused for modeling and controlling business processes." Oneof the main ideas of mathematical system theory is to considera process as a set of valid trajectories in a state space, andthis idea is the keystone for the thesis. The thesis startswith reformulating the state-oriented approach for the domainof business processes to show what kind of sate space can beused in this domain. First, the approach is introducedinformally by means of an example. Next, a possibleformalization adjusted to the properties of business processesis discussed. Then, experimental evidences that the methodsuggested in the thesis can be used in practice are presented.The suggested method is also compared with other methods ofbusiness process modeling to find out the areas where it hasadvantages over the other methods. In the conclusion, theresults are summarized, and plansfor the future are drawn.

Most of the material included in the thesis has beenpublished and presented at international conferences. Thecontribution of this thesis consists in organizing the materialin support of the main hypothesis.

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22

Tastan, Mesut. "Analysis And Prediction Of Gene Expression Patterns By Dynamical Systems, And By A Combinatorial Algorithm." Master's thesis, METU, 2005. http://etd.lib.metu.edu.tr/upload/12606672/index.pdf.

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Modeling and prediction of gene-expression patterns has an important place in computational biology and bioinformatics. The measure of gene expression is determined from the genomic analysis at the mRNA level by means of microarray technologies. Thus, mRNA analysis informs us not only about genetic viewpoints of an organism but also about the dynamic changes in environment of that organism. Different mathematical methods have been developed for analyzing experimental data. In this study, we discuss the modeling approaches and the reasons why we concentrate on models derived from differential equations and improve the pioneering works in this field by including affine terms on the right-hand side of the nonlinear differential equations and by using Runge- Kutta instead of Euler discretization, especially, with Heun&rsquo
s method. Herewith, for stability analysis we apply modified Brayton and Tong algorithm to time-discrete dynamics in an extended space.

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23

Yang, Xige. "MATHEMATICAL MODELS OF PATTERN FORMATION IN CELL BIOLOGY." The Ohio State University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=osu1542236214346341.

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24

Chombart, Anne. "Commande supervisée de systèmes hybrides." Grenoble INPG, 1997. http://www.theses.fr/1997INPG0170.

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Le travail que nous presentons dans cette these a pour but de construire un superviseur discret pour un systeme a dynamique continue dans l'optique de la contraindre a respecter des specifications de fonctionnement. Remarquant que la nature discrete du superviseur d'une part et la nature continue du systeme a controler d'autre part posent la question de la compatibilite des informations mutuellement echangees entre ces deux elements, nous avons choisi de traiter le probleme comme celui de la modelisation et de l'analyse d'un systeme dynamique hybride. D'une facon generale, un systeme dynamique hybride est un systeme qui comporte plusieurs dynamiques de natures differentes. Le terme dynamique signifie qu'il y a une evolution dans le temps du systeme. Le terme hybride specifie que ces evolutions sont du type continu et evenementiel. Apres l'etude des methodes de modelisation des systemes dynamiques hybrides, proposees dans la litterature, utilisant les automates a etats finis pour representer les systemes a evenements discrets, nous nous sommes apercus, que quelle que soit la methodologie adoptee, evenementielle, continue ou combinee, le probleme de modelisation se ramene a la determination de regions de l'espace d'etat continu delimitees par des frontieres susceptibles de caracteriser le comportement dynamique du procede, afin que les evenements qui y sont associes portent l'information necessaire a l'analyse de l'automate correspondant. Nous avons remarque que ce qui differencie les approches presentees est la maniere de determiner les transitions entre les etats du graphe correspondant a ce que nous appelons le squelette de la structure commune. Le modele d'automate representant la partie continue du systeme etudie est construit sur la base d'une partition de l'espace d'etat continu obtenue par la construction de fonctions de lyapunov et de domaines de stabilite associes, permettant ainsi de proposer une structure equivalente a celle du squelette de la structure mise en evidence. Ce squelette correspond a l'armature de la structure du controleur hybride ou superviseur discret que nous cherchons a construire.

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25

Gilson, Jean-Louis. "Structures singulières et intermittence dans les modèles en couches de la turbulence." Université Joseph Fourier (Grenoble), 1996. http://www.theses.fr/1996GRE10171.

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Cette these vise a decrire l'intermittence des modeles en couches, a partir d'ingredients de leur dynamique elementaire. Nous etudions en parallele deux modeles, l'un du a obukhov et novikov (on), l'autre plus populaire recemment du a gledzer ohkitani et yamada (goy). Le premier chapitre est introductif ; on y decrit en particulier, les origines de la modelisation adoptee. Le second presente les proprietes generales de ces systemes (bifurcations, statistique. . . ). Une methode d'integration adaptative, exposee au troisieme chapitre, nous permet de decrire la formation generique de solutions, singulieres et autosimilaires, aux equations inviscides des deux modeles. Les non-linearites selectionnent une valeur unique de l'exposant d'echelle. Lorsqu'elles sont suffisament singulieres (cas du modele on), de telles solutions decrivent correctement les fluctuations les plus intenses de la dynamique en presence de forcage et de dissipation. En revanche, dans le cas du modele goy, une physique plus riche apparait. Le quatrieme chapitre tente alors de decrire la multi-fractalite observee a partir des interactions entre un milieu turbulent moyen et les objets coherents introduits plus haut. Des simulations directes, nous permettent de decrire d'une part les consequences de collisions entre un objet ideal et un defaut, et d'autre part la formation des fluctuations les plus singulieres du vrai signal. Nous modelisons egalement le milieu turbulent par un forcage aleatoire gaussien decorrele en temps avec pour la variance differentes hypotheses physiques. Par un calcul analytique d'instantons, nous predisons la disparition de la multifractalite dans la limite d'un nombre de reynolds infini

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26

Goutierre, Emmanuel. "Machine learning-based particle accelerator modeling." Electronic Thesis or Diss., université Paris-Saclay, 2024. http://www.theses.fr/2024UPASG106.

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Les accélérateurs de particules reposent sur des simulations de haute précision pour optimiser la dynamique du faisceau. Ces simulations sont coûteuses en ressources de calcul, rendant leur analyse en temps réel difficilement réalisable. Cette thèse propose de surmonter cette limitation en explorant le potentiel de l'apprentissage automatique pour développer des modèles de substitution des simulations d'accélérateurs de particules. Ce travail se concentre sur ThomX, une source Compton compacte, et introduit deux modèles de substitution : LinacNet et Implicit Neural ODE (INODE). Ces modèles sont entraînés sur une base de données développée dans le cadre de cette thèse, couvrant une grande variété de conditions opérationnelles afin d'assurer leur robustesse et leur capacité de généralisation. LinacNet offre une représentation complète du nuage de particules en prédisant les coordonnées de toutes les macro-particules du faisceau plutôt que de se limiter à ses observables. Cette modélisation détaillée, couplée à une approche séquentielle prenant en compte la dynamique cumulative des particules tout au long de l'accélérateur, garantit la cohérence des prédictions et améliore l'interprétabilité du modèle. INODE, basé sur le cadre des Neural Ordinary Differential Equations (NODE), vise à apprendre les dynamiques implicites régissant les systèmes de particules sans avoir à résoudre explicitement les équations différentielles pendant l'entraînement. Contrairement aux méthodes basées sur NODE, qui peinent à gérer les discontinuités, INODE est conçu théoriquement pour les traiter plus efficacement. Ensemble, LinacNet et INODE servent de modèles de substitution pour ThomX, démontrant leur capacité à approximer la dynamique des particules. Ce travail pose les bases pour développer et améliorer la fiabilité des modèles basés sur l'apprentissage automatique en physique des accélérateurs
Particle accelerators rely on high-precision simulations to optimize beam dynamics. These simulations are computationally expensive, making real-time analysis impractical. This thesis seeks to address this limitation by exploring the potential of machine learning to develop surrogate models for particle accelerator simulations. The focus is on ThomX, a compact Compton source, where two surrogate models are introduced: LinacNet and Implicit Neural ODE (INODE). These models are trained on a comprehensive database developed in this thesis that captures a wide range of operating conditions to ensure robustness and generalizability. LinacNet provides a comprehensive representation of the particle cloud by predicting all coordinates of the macro-particles, rather than focusing solely on beam observables. This detailed modeling, coupled with a sequential approach that accounts for cumulative particle dynamics throughout the accelerator, ensures consistency and enhances model interpretability. INODE, based on the Neural Ordinary Differential Equation (NODE) framework, seeks to learn the implicit governing dynamics of particle systems without the need for explicit ODE solving during training. Unlike traditional NODEs, which struggle with discontinuities, INODE is theoretically designed to handle them more effectively. Together, LinacNet and INODE serve as surrogate models for ThomX, demonstrating their ability to approximate particle dynamics. This work lays the groundwork for developing and improving the reliability of machine learning-based models in accelerator physics

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27

Buchin, Anatoly. "Modeling of single cell and network phenomena of the nervous system : ion dynamics during epileptic oscillations and inverse stochastic resonance." Thesis, Paris, Ecole normale supérieure, 2015. http://www.theses.fr/2015ENSU0041/document.

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Dans cette thèse nous avons utilisé des méthodes de systèmes dynamiques et des simulations numériques pour étudier les mécanismes d'oscillations d'épilepsie associés à des concentrations d’ions dynamiques et au comportement bimodal des cellules Purkinje du cervelet. Le propos général de ce travail est l'interaction entre les propriétés intrinsèques des neurones simple et la structure d'entrée synaptique contrôlant l'excitabilité neuronale. Dans la première partie de la thèse nous avons développé un modèle de transition de crise épileptique dans le lobe temporal du cerveau. Plus précisément nous nous sommes concentrés sur le rôle du cotransporteur KCC2, qui est responsable de la maintenance du potassium extracellulaire et du chlorure intracellulaire dans les neurones. Des données expérimentales récentes ont montré que cette molécule est absente dans un groupe significatif de cellules pyramidales dans le tissue neuronal de patients épileptiques suggérant son rôle épileptogène. Nous avons trouvé que l'addition d’une quantité critique de cellules pyramidale KCC2 déficient au réseau de subiculum, avec une connectivité réaliste, peut provoquer la génération d’oscillations pathologiques, similaire aux oscillations enregistrées dans des tranches de cerveau épileptogène humaines. Dans la seconde partie de la thèse, nous avons étudié le rôle du bruit synaptique dans les cellules de Purkinje. Nous avons étudié l'effet de l'inhibition de la génération du potentiel d’action provoquée par injection de courant de bruit, un phénomène connu comme résonance stochastique inverse (RSI). Cet effet a déjà été trouvé dans des modèles neuronaux, et nous avons fournis sa première validation expérimentale. Nous avons trouvé que les cellules de Purkinje dans des tranches de cerveau peuvent être efficacement inhibées par des injectionsde bruit de courant. Cet effet est bien reproduit par le modèle phénoménologique adapté pour différentes cellules. En utilisant des méthodes de la théorie de l'information, nous avons montré que RSI prend en charge une transmission efficace de l'information des cellules de Purkinje simples suggérant son rôle pour les calculs du cervelet
In this thesis we used dynamical systems methods and numericalsimulations to study the mechanisms of epileptic oscillations associated with ionconcentration changes and cerebellar Purkinje cell bimodal behavior. The general issue in this work is the interplay between single neuron intrinsicproperties and synaptic input structure controlling the neuronal excitability. In the first part of this thesis we focused on the role of the cellular intrinsicproperties, their control over the cellular excitability and their response to thesynaptic inputs. Specifically we asked the question how the cellular changes ininhibitory synaptic function might lead to the pathological neural activity. We developed a model of seizure initiation in temporal lobe epilepsy. Specifically we focused on the role of KCC2 cotransporter that is responsible for maintaining the baseline extracellular potassium and intracellular chloride levels in neurons. Recent experimental data has shown that this cotransporter is absent in the significant group of pyramidal cells in epileptic patients suggesting its epileptogenic role. We found that addition of the critical amount of KCC2-deficient pyramidal cells to the realistic subiculum network can switch the neural activity from normal to epileptic oscillations qualitatively reproducing the activity recorded in human epileptogenic brain slices. In the second part of this thesis we studied how synaptic noise might control the Purkinje cell excitability. We investigated the effect of spike inhibition caused by noise current injection, so-called inverse stochastic resonance (ISR). This effect has been previously found in single neuron models while we provided its first experimental evidence. We found that Purkinje cells in brain slices could be efficiently inhibited by current noise injections. This effect is well reproduced by the phenomenological model fitted for different cells. Using methods of information theory we showed that ISR supports an efficient information transmission of single Purkinje cells suggesting its role for cerebellar computations

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28

Monga, Pavinder. "A System Dynamics Model of the Development of New Technologies for Ship Systems." Thesis, Virginia Tech, 2001. http://hdl.handle.net/10919/35258.

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System Dynamics has been applied to various fields in the natural and social sciences. There still remain countless problems and issues where understanding is lacking and the dominant theories are event-oriented rather than dynamic in nature. One such research area is the application of the traditional systems engineering process in new technology development. The Navy has been experiencing large cost overruns in projects dealing with the implementation of new technologies on complex ship systems. We believe that there is a lack of understanding of the dynamic nature of the technology development process undertaken by aircraft-carrier builders and planners. Our research effort is to better understand the dynamics prevalent in the new technology development process and we use a dynamic modeling technique, namely, System Dynamics in our study.

We provide a comprehensive knowledge elicitation process in which members from the Newport News Shipbuilding, the Naval Sea Command Cost Estimating Group, and the Virginia Tech System Performance Laboratory take part in a group model building exercise. We build a System Dynamics model based on the information and data obtained from the experts. Our investigation of the dynamics yields two dominant behaviors that characterize the technology development process. These two dynamic behaviors are damped oscillation and goal seeking. Furthermore, we propose and investigate four dynamic hypotheses in the system. For the current structure of the model, we see that an increase in the complexity of new technologies leads to an increase in the total costs, whereas a increase in the technology maturity leads to a decrease in the total costs in the technology development process. Another interesting insight is that an increase in training leads to a decrease in total costs.
Master of Science

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29

Hayden, Kevin. "Modeling of dynamical systems /." abstract and full text PDF (UNR users only), 2007. http://0-gateway.proquest.com.innopac.library.unr.edu/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:1446796.

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Thesis (M.S.)--University of Nevada, Reno, 2007.
"May, 2007." Includes bibliographical references (leaves 128-129). Library also has microfilm. Ann Arbor, Mich. : ProQuest Information and Learning Company, [2008]. 1 microfilm reel ; 35 mm. Online version available on the World Wide Web.

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30

Boldt, Frank. "A Framework for Modeling Irreversible Processes Based on the Casimir Companion." Doctoral thesis, Universitätsbibliothek Chemnitz, 2014. http://nbn-resolving.de/urn:nbn:de:bsz:ch1-qucosa-145179.

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Анотація:

Thermodynamic processes in finite time are in general irreversible. But there are chances to avoid irreversibility. For instance, there are canonical ensembles of special quantum systems with a given probability distribution describing the likelihood to find the system at time t=0 in a particular state with energy E_i(0), which can be controlled in a specific way, such that the initial probability distribution is recovered at the end of the process (t=T), but the state energies did change, hence E_i(0) is not equal to E_i(T). This allows to change thermodynamic quantities (expectation values) adiabatically, reversibly and in finite time. Such special processes are called Shortcuts to Adiabaticity. The presented thesis analyzes the origin of these shortcuts utilizing special Hamiltonian systems with dynamical algebra. Their main feature is to provide canonical invariance, which means a canonical ensemble stays canonical under Hamiltonian dynamics. This invariance carried by the dynamical algebra will be discussed using Lie group theory. In addition, the persistence of the dynamical algebra with respect to calculating expectation values will be deduced. This allows to benefit from all intrinsic symmetries within the discussion of ensemble trajectories. In consequence, these trajectories will evolve under Hamiltonian dynamics on a specific manifold given by the so-called Casimir companion. In addition, the deformation of this manifold due to non-Hamiltonian (dissipative) dynamics will be discussed, which allows to present a framework for modeling irreversible processes based on Hamiltonian systems with dynamical algebra. An application of this framework based on the parametric harmonic oscillator will be presented by determining time-optimal controls for transitions between two equilibrium as well as between non-equilibrium and equilibrium states. The latter one will lead to time-optimal equilibration strategies for a statistical ensemble of parametric harmonic oscillators
Thermodynamische Prozesse in endlicher Zeit sind im Allgemeinen irreversibel. Es gibt jedoch Möglichkeiten, diese Irreversibilität zu umgehen. Ein kanonisches Ensemble eines speziellen quantenmechanischen Systems kann zum Beispiel auf eine ganz spezielle Art und Weise gesteuert werden, sodass nach endlicher Zeit T wieder eine kanonische Besetzungverteilung hergestellt ist, sich aber dennoch die Energie des Systems geändert hat (E(0) ungleich E(T)). Solche Prozesse erlauben das Ändern thermodynamischer Größen (Ensemblemittelwerte) der erwähnten speziellen Systeme in endlicher Zeit und auf eine adiabatische und reversible Art. Man nennt diese Art von speziellen Prozessen Shortcuts to Adiabaticity und die speziellen Systeme hamiltonsche Systeme mit dynamischer Algebra. Die vorliegende Dissertation hat zum Ziel den Ursprung dieser Shortcuts to Adiabaticity zu analysieren und eine Methodik zu entwickeln, die es erlaubt irreversible thermodynamische Prozesse adequat mittels dieser speziellen Systeme zu modellieren. Dazu wird deren besondere Eigenschaft ausgenutzt, die kanonische Invarianz, d.h. ein kanonisches Ensemble bleibt kanonisch bezüglich hamiltonscher Dynamik. Der Ursprung dieser Invarianz liegt in der dynamischen Algebra, die mit Hilfe der Theorie der Lie-Gruppen näher betrachtet wird. Dies erlaubt, eine weitere besondere Eigenschaft abzuleiten: Die Ensemblemittelwerte unterliegen ebenfalls den Symmetrien, die die dynamische Algebra widerspiegelt. Bei näherer Betrachtung befinden sich alle Trajektorien der Ensemblemittelwerte auf einer Mannigfaltigkeit, die durch den sogenannten Casimir Companion beschrieben wird. Darüber hinaus wird nicht-hamiltonsche/dissipative Dynamik betrachtet, welche zu einer Deformation der Mannigfaltigkeit führt. Abschließend wird eine Zusammenfassung der grundlegenden Methodik zur Modellierung irreversibler Prozesse mittels hamiltonscher Systeme mit dynamischer Algebra gegeben. Zum besseren Verständnis wird ein ausführliches Anwendungsbeispiel dieser Methodik präsentiert, in dem die zeitoptimale Steuerung eines Ensembles des harmonischen Oszillators zwischen zwei Gleichgewichtszuständen sowie zwischen Gleichgewichts- und Nichtgleichgewichtszuständen abgeleitet wird

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31

Cao, Lan. "Modeling Dynamics in Agile Software Development." Digital Archive @ GSU, 2005. http://digitalarchive.gsu.edu/cis_diss/4.

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Анотація:

Agile software development challenges the traditional way of software development and project management. In rapidly changing environments, changing requirements and tight schedule constraints require software developers to take a different approach toward the process of software development. However, beyond a few case studies, surveys and studies focused on specific practices such as pair programming, the effectiveness and applicability of agile methods have not been established adequately. The objective of my research is to improve the understanding of and gain insights into these issues. For this purpose, I develop a system dynamic simulation model that considers the complex interdependencies among the variety of practices used in agile development. The model is developed on the basis of an extensive review of the literature as well as quantitative and qualitative data collected from real projects in seven organizations. The development of the model was guided by dynamic hypotheses on customer involvement, refactoring and quality of design. The model was refined and validated using data from independent projects. The model helps in answering important questions on the impact of customer behavior, cost of making changes and economics of pair programming. Experimentation with the model suggests that the cost of change is not constant; instead, its value changes cyclically and increases towards the later phase of development. Also, the results of simulation show that with no pair programming, fewer tasks are delivered and it costs more to deliver a task when compared to development with pair programming. Further, customer behavior has a major impact on project performance. The quality of customer feedback is found to be very critical to the successful of an agile software development project. The primary contribution of this research is the simulation model of agile software development that can be used a tool to examine the impact of agile practices and management policies on critical project variables including project scope, schedule, and cost. This research provides a mechanism to study agile development as a dynamic system of practices rather than using a static view and in isolation. The results from this study are expected to be of significant interest to practitioners of agile methods by providing them a simulation environment to examine the impact of their practices, procedures and management policies.

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32

Damle, Pushkar Hari. "A system dynamics model of the integration of new technologies for ship systems." Thesis, Virginia Tech, 2003. http://hdl.handle.net/10919/35216.

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Анотація:

System dynamics has been used to better understand the dynamics within complex natural and social systems. This understanding enables us to make decisions and define strategies that help to resolve the problematic behaviors associated within these systems. For example within an operating environment such as the US Navy, decisions taken today can have long lasting impact on system performance. The Navy has experienced large cost overruns during the new technology implementation process on ship systems that can also have an impact on total life cycle performance. The integration phase of the implementation process represents most of the cost overruns experienced in the overall new technology life cycle (development, integration, and operation/support/disposal). We have observed a general concern that there is a lack of understanding for the dynamic behavior of those processes which comprise the integration phase, among ship-builders and planners. One of the goals of our research effort has been to better understand the dynamic behavior of the new technology integration processes, using a dynamic modeling technique known as System Dynamics. Our approach has also been to provide a comprehensive knowledge elicitation process in which members from the shipbuilding industry, the US Navy, and the Virginia Tech System Performance Laboratory take part in group model building exercises. The system dynamics model that is developed in this manner is based on data obtained from the experts. An investigation of these dynamics yields a dominant cost behavior that characterizes the technology integration processes. This behavior is S-shaped growth. The following two dynamic hypotheses relative to lifecycle cost and performance of the inserted new technology were confirmed: (1) For the current structure of the model we observe the more the complexity of the new technology, the less affordable a technology becomes; (2) Integration of immature (less developed) technologies is associated with higher costs. Another interesting insight is that cost is very sensitive to the material procurement. Future research can be addressed to a more detailed level of abstraction for various activities included in the technology integration phase, such as testing and evaluation, cost of rework and risks associated with inadequate testing etc. This will add to our evolving understanding of the behavior of individual activities in the technology integration process.
Master of Science

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33

Hughes, Jonathan L. "Applications of Stability Analysis to Nonlinear Discrete Dynamical Systems Modeling Interactions." VCU Scholars Compass, 2015. http://scholarscompass.vcu.edu/etd/3819.

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Many of the phenomena studied in the natural and social sciences are governed by processes which are discrete and nonlinear in nature, while the most highly developed and commonly used mathematical models are linear and continuous. There are significant differences between the discrete and the continuous, the nonlinear and the linear cases, and the development of mathematical models which exhibit the discrete, nonlinear properties occurring in nature and society is critical to future scientific progress. This thesis presents the basic theory of discrete dynamical systems and stability analysis and explores several applications of this theory to nonlinear systems which model interactions involving economic agents and biological populations. In particular we will explore the stability properties of equilibria associated with inter-species and intergenerational population dynamics in biology and market price and agent composition dynamics in economics.

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34

Schröder, Jochen. "Modelling, state observation and diagnosis of quantised systems /." Berlin [u.a.] : Springer, 2003. http://www.loc.gov/catdir/enhancements/fy0813/2002030222-d.html.

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35

KUO, FENG-YANG. "AN ARCHITECTURE FOR DIALOGUE MANAGEMENT SUPPORT IN INFORMATION SYSTEMS (FRAMEWORK, MODELING DYNAMIC, METHODOLOGY)." Diss., The University of Arizona, 1985. http://hdl.handle.net/10150/187932.

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Анотація:

The management of man-computer dialogues involves policies, procedures, and methodologies that enable users and designers to control, monitor, and enhance the user-computer interface. Effective dialogue management can be facilitated by a computer-aided work-bench of dialogue management tools that integrate pertinent environmental attributes into executable dialogue forms. Consequently, a methodology for generating dialogue designs is required. This research presents a framework for modeling user-computer interactions, or dialogues. The approach taken herein focuses on analysis of task, user, and information technology attributes. This analytical framework isolates dialogue entities and entity groupings. Together, these entities and their groupings suggest a language for information presentation and elicitation in the user-computer dialogue process. As a result, alternative dialogue models can be specified independent of hardware and software technologies. Furthermore, these models can be evaluated to ensure completeness, consistency, and integrity. Under this framework, various dialogue management functions can be integrated into a generalized dialogue management environment. Such an environment facilitates the transformation of task, user, and information technology attributes into executable dialogue definitions. The architecture of this environment is characterized by functionally layered and modularized software tools for dialogue management. The implementation of the proposed methodologies and the dialogue management architecture results in a set of dialogue management design facilities. These facilities foster effective management of dialogues within organizations and lead to a better understanding of the dialogue process.

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36

Saad, Fady M. (Fady Malak). "Modeling and comparing a startup dynamics in the US and Egypt." Thesis, Massachusetts Institute of Technology, 2013. http://hdl.handle.net/1721.1/90701.

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Анотація:

Thesis: S.M. in Engineering and Management, Massachusetts Institute of Technology, Engineering Systems Division, System Design and Management Program, 2013.
"June 2013." Cataloged from PDF version of thesis.
Includes bibliographical references (page 76).
In today's world startups are playing a key role to stimulate the economy, solve pressing problems and create fulfilling employment opportunities. However, the failure rate of startups in the US, one of the most prominent countries for encouraging startups, has been eight out of ten, a very high proportion. In this thesis, I explore this topic further with a hypothesis that company's sustained success depends not only on its financial growth, but also its dynamic ability to continuously fulfill its key stakeholders' needs and aspirations, and its ability to adapt to the specific conditions of its evolving ecosystem. This thesis provides a new holistic, system-driven conceptualization of a startup and its internal dynamics from human resources, product development, customers, and financials. I develop a System Dynamics model to represent these internal dynamics and simulate it over a period of five years to gain more insight about a startup behavior. In addition, I bring in the impact of exogenous factors from the entrepreneurial ecosystem as a "second layer" of variables in the entrepreneurial model. Through a process of validating and comparing the model to the literature, I identify five key internal leverage points for the sustained success of the modeled startup. Moreover, after performing a sensitivity analysis to the model, I identify the key exogenous leverage points in studied entrepreneurship ecosystems. I then compare and contrast the US and Egyptian case by embedding the modeled startup in the Egyptian ecosystem. A significant change of the behavior of the modeled startup with a much lower final Firm Valuation and Job Attractiveness is observed. I conclude with a discussion of the high leverage points in the Egyptian ecosystem based on this analysis, and recommendations for entrepreneurs and policy makers.
by Fady M. Saad.
S.M. in Engineering and Management

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37

Xue, Wenbo. "System dynamics based traffic system modelling." Thesis, Brunel University, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.422213.

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38

Hockenberry, James Richard. "Power system dynamic load modeling." Thesis, Massachusetts Institute of Technology, 1997. http://hdl.handle.net/1721.1/42594.

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39

Lindau, Jules Washington. "Multidimensional dynamic compression system modeling." Diss., This resource online, 1995. http://scholar.lib.vt.edu/theses/available/etd-02132009-171914/.

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40

Guan, Jinyan. "Bayesian generative modeling for complex dynamical systems." Thesis, The University of Arizona, 2016. http://pqdtopen.proquest.com/#viewpdf?dispub=10109036.

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Анотація:

This dissertation presents a Bayesian generative modeling approach for complex dynamical systems for emotion-interaction patterns within multivariate data collected in social psychology studies. While dynamical models have been used by social psychologists to study complex psychological and behavior patterns in recent years, most of these studies have been limited by using regression methods to fit the model parameters from noisy observations. These regression methods mostly rely on the estimates of the derivatives from the noisy observation, thus easily result in overfitting and fail to predict future outcomes. A Bayesian generative model solves the problem by integrating the prior knowledge of where the data comes from with the observed data through posterior distributions. It allows the development of theoretical ideas and mathematical models to be independent of the inference concerns. Besides, Bayesian generative statistical modeling allows evaluation of the model based on its predictive power instead of the model residual error reduction in regression methods to prevent overfitting in social psychology data analysis.

In the proposed Bayesian generative modeling approach, this dissertation uses the State Space Model (SSM) to model the dynamics of emotion interactions. Specifically, it tests the approach in a class of psychological models aimed at explaining the emotional dynamics of interacting couples in committed relationships. The latent states of the SSM are composed of continuous real numbers that represent the level of the true emotional states of both partners. One can obtain the latent states at all subsequent time points by evolving a differential equation (typically a coupled linear oscillator (CLO)) forward in time with some known initial state at the starting time. The multivariate observed states include self-reported emotional experiences and physiological measurements of both partners during the interactions. To test whether well-being factors, such as body weight, can help to predict emotion-interaction patterns, We construct functions that determine the prior distributions of the CLO parameters of individual couples based on existing emotion theories. Besides, we allow a single latent state to generate multivariate observations and learn the group-shared coefficients that specify the relationship between the latent states and the multivariate observations.

Furthermore, we model the nonlinearity of the emotional interaction by allowing smooth changes (drift) in the model parameters. By restricting the stochasticity to the parameter level, the proposed approach models the dynamics in longer periods of social interactions assuming that the interaction dynamics slowly and smoothly vary over time. The proposed approach achieves this by applying Gaussian Process (GP) priors with smooth covariance functions to the CLO parameters. Also, we propose to model the emotion regulation patterns as clusters of the dynamical parameters. To infer the parameters of the proposed Bayesian generative model from noisy experimental data, we develop a Gibbs sampler to learn the parameters of the patterns using a set of training couples.

To evaluate the fitted model, we develop a multi-level cross-validation procedure for learning the group-shared parameters and distributions from training data and testing the learned models on held-out testing data. During testing, we use the learned shared model parameters to fit the individual CLO parameters to the first 80% of the time points of the testing data by Monte Carlo sampling and then predict the states of the last 20% of the time points. By evaluating models with cross-validation, one can estimate whether complex models are overfitted to noisy observations and fail to generalize to unseen data. I test our approach on both synthetic data that was generated by the generative model and real data that was collected in multiple social psychology experiments. The proposed approach has the potential to model other complex behavior since the generative model is not restricted to the forms of the underlying dynamics.

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41

Guan, Jinyan. "Bayesian Generative Modeling of Complex Dynamical Systems." Diss., The University of Arizona, 2016. http://hdl.handle.net/10150/612950.

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Анотація:

This dissertation presents a Bayesian generative modeling approach for complex dynamical systems for emotion-interaction patterns within multivariate data collected in social psychology studies. While dynamical models have been used by social psychologists to study complex psychological and behavior patterns in recent years, most of these studies have been limited by using regression methods to fit the model parameters from noisy observations. These regression methods mostly rely on the estimates of the derivatives from the noisy observation, thus easily result in overfitting and fail to predict future outcomes. A Bayesian generative model solves the problem by integrating the prior knowledge of where the data comes from with the observed data through posterior distributions. It allows the development of theoretical ideas and mathematical models to be independent of the inference concerns. Besides, Bayesian generative statistical modeling allows evaluation of the model based on its predictive power instead of the model residual error reduction in regression methods to prevent overfitting in social psychology data analysis. In the proposed Bayesian generative modeling approach, this dissertation uses the State Space Model (SSM) to model the dynamics of emotion interactions. Specifically, it tests the approach in a class of psychological models aimed at explaining the emotional dynamics of interacting couples in committed relationships. The latent states of the SSM are composed of continuous real numbers that represent the level of the true emotional states of both partners. One can obtain the latent states at all subsequent time points by evolving a differential equation (typically a coupled linear oscillator (CLO)) forward in time with some known initial state at the starting time. The multivariate observed states include self-reported emotional experiences and physiological measurements of both partners during the interactions. To test whether well-being factors, such as body weight, can help to predict emotion-interaction patterns, we construct functions that determine the prior distributions of the CLO parameters of individual couples based on existing emotion theories. Besides, we allow a single latent state to generate multivariate observations and learn the group-shared coefficients that specify the relationship between the latent states and the multivariate observations. Furthermore, we model the nonlinearity of the emotional interaction by allowing smooth changes (drift) in the model parameters. By restricting the stochasticity to the parameter level, the proposed approach models the dynamics in longer periods of social interactions assuming that the interaction dynamics slowly and smoothly vary over time. The proposed approach achieves this by applying Gaussian Process (GP) priors with smooth covariance functions to the CLO parameters. Also, we propose to model the emotion regulation patterns as clusters of the dynamical parameters. To infer the parameters of the proposed Bayesian generative model from noisy experimental data, we develop a Gibbs sampler to learn the parameters of the patterns using a set of training couples. To evaluate the fitted model, we develop a multi-level cross-validation procedure for learning the group-shared parameters and distributions from training data and testing the learned models on held-out testing data. During testing, we use the learned shared model parameters to fit the individual CLO parameters to the first 80% of the time points of the testing data by Monte Carlo sampling and then predict the states of the last 20% of the time points. By evaluating models with cross-validation, one can estimate whether complex models are overfitted to noisy observations and fail to generalize to unseen data. I test our approach on both synthetic data that was generated by the generative model and real data that was collected in multiple social psychology experiments. The proposed approach has the potential to model other complex behavior since the generative model is not restricted to the forms of the underlying dynamics.

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42

Kilminster, Devin. "Modelling dynamical systems via behaviour criteria." University of Western Australia. Dept. of Mathematics and Statistics, 2002. http://theses.library.uwa.edu.au/adt-WU2003.0029.

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Анотація:

An important part of the study of dynamical systems is the fitting of models to time-series data. That is, given the data, a series of observations taken from a (not fully understood) system of interest, we would like to specify a model, a mathematical system which generates a sequence of “simulated” observations. Our aim is to obtain a “good” model — one that is in agreement with the data. We would like this agreement to be quantitative — not merely qualitative. The major subject of this thesis is the question of what good quantitative agreement means. Most approaches to this question could be described as “predictionist”. In the predictionist approach one builds models by attempting to answer the question, “given that the system is now here, where will it be next?” The quality of the model is judged by the degree to which the states of the model and the original system agree in the near future, conditioned on the present state of the model agreeing with that of the original system. Equivalently, the model is judged on its ability to make good short-term predictions on the original system. The main claim of this thesis is that prediction is often not the most appropriate criterion to apply when fitting models. We show, for example, that one can have models that, while able to make good predictions, have long term (or free-running) behaviour bearing little resemblance to that exhibited in the original time-series. We would hope to be able to use our models for a wide range of purposes other than just prediction — certainly we would like our models to exhibit good free-running behaviour. This thesis advocates a “behaviourist” approach, in which the criterion for a good model is that its long-term behaviour matches that exhibited by the data. We suggest that the behaviourist approach enjoys a certain robustness over the predictionist approaches. We show that good predictors can often be very poorly behaved, and suggest that well behaved models cannot perform too badly at the task of prediction. The thesis begins by comparing the predictionist and behaviourist approaches in the context of a number of simplified model-building problems. It then presents a simple theory for the understanding of the differences between the two approaches. Effective methods for the construction of well-behaved models are presented. Finally, these methods are applied to two real-world problems — modelling of the response of a voltage-clamped squid “giant” axon, and modelling of the “yearly sunspot number”.

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43

Al-Nahwi, Ammar Adnan. "Modeling of industrial pumping system dynamics." Thesis, Massachusetts Institute of Technology, 1996. http://hdl.handle.net/1721.1/38116.

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44

Currier, Patrick Norman. "A Method for Modeling and Prediction of Ground Vehicle Dynamics and Stability in Autonomous Systems." Diss., Virginia Tech, 2011. http://hdl.handle.net/10919/27632.

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Анотація:

A future limitation of autonomous ground vehicle technology is the inability of current algorithmic techniques to successfully predict the allowable dynamic operating ranges of unmanned ground vehicles. A further difficulty presented by real vehicles is that the payloads may and probably will change with unpredictably time as will the terrain on which it is expected to operate. To address this limitation, a methodology has been developed to generate real-time estimations of a vehicleâ s instantaneous Maneuvering Manifold. This approach uses force-moment method techniques to create an adaptive, parameterized vehicle model. A technique is developed for estimation of vehicle load state using internal sensors combined with low-magnitude maneuvers. An unscented Kalman filter based estimator is then used to estimate tire forces for use in determining the ground/tire coefficient of friction. Probabilistic techniques are then combined with a combined-slip pneumatic trail based estimator to estimate the coefficient of friction in real-time. This data is then combined to map out the instantaneous maneuvering manifold while applying techniques to account for dynamic rollover and stability limitations. The algorithms are implemented in MATLAB, simulated against TruckSim models, and results are shown to demonstrate the validity of the techniques. The developed methodology is shown to be a novel approach that is capable of addressing the problem of successfully estimating the available maneuvering manifold for autonomous ground vehicles.
Ph. D.

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45

COCCO, LUISANNA. "Complex system simulation: agent-based modeling and system dynamics." Doctoral thesis, Università degli Studi di Cagliari, 2013. http://hdl.handle.net/11584/266241.

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Анотація:

This thesis deals with some simulation based approaches used to study software market and software development. Nowadays, the term Software as a Service is everywhere and is described as the future of software. SaaS, also called On-Demand Software, is a software application delivery model that together with Commercial Open Source Software another pricing approach is slowly gaining ground. Indeed, in recent years, traditional software also called On-Premise software appears overpriced, user's willingness to buy it is decreased and therefore the purchase preferences are moving from traditional pricing models to new pricing approaches. To study these new pricing tendencies, different models have been realized by using two of the most common numerical techniques: Agent based Modeling and System Dynamics. With agent based modeling two business models have been realized: a model to study the competition among CRM On-Premise and On Demand vendors and another model to study the competition among CRM On-Demand vendors offering CRM products, with and without source code availability. Our goals are to propose business models to analyze and study the CRM software market, and to propose a useful tool to forecast business winning strategy and investment and pricing business policies. Instead, with system dynamics a tool for highlighting how a Global Software Development environment on the Cloud Platform may facilitate GSD with respect to an environment set up On Premise has been realized. All these models are based on many insights from literature and market analysis. However, concerning the business models, this is the first time that the software market has been modeled using heterogeneous agent model and detailing investment and pricing policies of firms and purchase preferences of customers, and consequently building the model on existing scientific knowledge has not been simple. In addition, lack of experimental data to initialize or validate the models clearly limits the validity of the models, and for this reason the future main objective will be to validate the model using real enterprise data.

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46

Scheda, Riccardo. "Modeling cell differentiation using dynamical systems on graphs." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020. http://amslaurea.unibo.it/21721/.

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Анотація:

La cellula vivente è un sistema complesso governato da molti processi che non sono ancora stati compresi: il processo di differenziazione cellulare è uno di questi. La differenziazione cellulare è il processo in cui le cellule di un tipo specifico si riproducono e danno origine a diversi tipi di cellule. La differenziazione cellulare è regolata dai cosiddetti Gene Regulatory Networks (GRN). Un GRN è una raccolta di regolatori molecolari che interagiscono tra loro e con altre sostanze nella cellula per governare i livelli di espressione genica di mRNA e proteine. Kauffman propose per la prima volta nel 1969 di modellare GRN attraverso le cosiddette Random Boolean Networks (RBN). I RBN sono reti in cui ogni nodo può avere solo due possibili valori: 0 o 1, dove ogni nodo rappresenta un gene in GRN che può essere ”on” oppure ”off”. Queste reti possono modellizzare i GRN perchè l’attività di un nodo rappresenta il livello di espressione di un gene nell’intera regolazione. In questo lavoro di tesi ci avvaliamo di un modello matematico per sviluppare e riprodurre una possibile rete di regolazione genica per il processo di differenziazione cellulare.

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47

Ionescu, Cezar [Verfasser]. "Vulnerability modeling and monadic dynamical systems / Cezar Ionescu." Berlin : Freie Universität Berlin, 2009. http://d-nb.info/1023491036/34.

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48

Cerrato, Dean Edward. "Modeling unknown dynamical systems using adaptive structure networks." Thesis, Massachusetts Institute of Technology, 1993. http://hdl.handle.net/1721.1/46440.

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Анотація:

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1993.
Includes bibliographical references (leaves 177-179).
Dean Edward Cerrato.
M.S.

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49

Follen, Kenneth. "A System Dynamics Modeling Methodology for Compressible Fluid Systems with Applications to Internal Combustion Engines." The Ohio State University, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=osu1281971505.

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50

Wang, Zhongwei. "Dynamic modelling of planetary gear systems for gear tooth fault." Thesis, Curtin University, 2010. http://hdl.handle.net/20.500.11937/1284.

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Анотація:

Geared systems have been widely used in mechanical applications for more than a hundred years. A large range of literature has been published especially for spur/helical gear systems and the investigations into technical areas of spur/helical gears have been very well developed, including understanding of condition monitoring systems, diagnostic and prognostic methods. However, there is a lack of understanding on the general dynamic behavior of planetary gear systems with tooth faults. Planetary gears are normally used as effective power transmission elements with high power to weight/volume ratios, large speed reductions in compact volume, and high reliability. They tend to have high efficiency and are used in many applications, such as automotive, heavy truck/tractor, helicopter, wind turbines and bucket wheel reclaimer gearboxes.The purpose of this research is to develop a vibration analysis system that simulates dynamic behavior of large low speed, high torque planetary spur gear systems such as used in bucket wheel reclaimer and wind turbine gearboxes, with and without gear element faults. This thesis investigates lumped mass modelling methods for planetary gearbox dynamic behavior based on previous gearbox modelling research including the use of the coupled torsional-transverse behavior of the gear body. The dynamic model of the planetary spur gear system includes effects such as: variable tooth mesh stiffness, dynamic transmission error effects, and pitch and profile excitation for gear fault detection purposes. Different tooth faults are simulated using the concept of combined torsional mesh stiffness. The dynamics of spur planetary gear systems with and without tooth faults are compared and analyzed to improve the understanding of fault detection in the present gear systems.Dynamic modelling of gear systems, such as outlined in this thesis can assist in understanding the consequence of large transient events, including the fluctuations in tooth loads which can reduce gear fatigue life and lead to further tooth damage. Early detection of faults on gear teeth can be used to initiate maintenance actions in order to reduce repair work and avoid catastrophic breakdown.

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