Dissertations / Theses on the topic 'Modeling Complex Systems'
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Müller, Thorsten G. "Modeling complex systems with differential equations." [S.l. : s.n.], 2002. http://www.bsz-bw.de/cgi-bin/xvms.cgi?SWB10236319.
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Azevedo, Kyle Kellogg. "Modeling sustainability in complex urban transportation systems." Thesis, Georgia Institute of Technology, 2010. http://hdl.handle.net/1853/37289.
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This thesis proposes a framework to design and analyze sustainability within complex urban transportation systems. Urban transit systems have large variability in temporal and spatial resolution, and are common in lifecycle analyses and sustainability studies. Unlike analyses with smaller scope or broader resolution, these systems are composed of numerous interacting layers, each intricate enough to be a complete system on its own. In addition, detailed interaction with the system environment is often not accounted for in lifecycle studies, despite its strong potential effects on the problem domain. To manage such complexity, this thesis suggests a methodology that focuses on integrating existing modeling constructs in a transparent manner, and capturing structural and functional relationships for efficient model reuse. The Systems Modeling Language (OMG SysML ) is used to formally implement the modeling framework. To demonstrate the method, it is applied to a large scale multi-modal transportation network. Analysis of key network parameters such as emissions output, well-to-wheel energy use, and system capacity are presented in a case study of the Atlanta, Georgia metropolitan area.
Results of the case study highlight several areas that differ from more traditional lifecycle analysis research. External influences such as regional electricity generation are found to have extremely large effects on environmental impact of a regional mobility system. The model is used to evaluate various future scenarios and finds that existing policy measures for curbing energy use and emissions are insufficient for reducing impact in a growing urban region.
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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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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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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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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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Valenzuela, Vega Rene Cristian. "Compact reliability and maintenance modeling of complex repairable systems." Diss., Georgia Institute of Technology, 2014. http://hdl.handle.net/1853/51850.
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Maintenance models are critical for evaluation of the alternative maintenance
policies for modern engineering systems. A poorly selected policy can result in excessive
life-cycle costs as well as unnecessary risks for catastrophic failures of the system. Economic dependence refers to the difference between the cost of combining the maintenance of a number of components and the cost of performing the same maintenance actions individually. Maintenance that takes advantage of this difference is often called opportunistic.
Large number of components and economic inter-dependence are two pervasive characteristics of modern engineering systems that make the modeling of their maintenance processes particularly challenging. Simulation is able to handle both of these characteristics computationally, but the complexity, especially from the model verification perspective, becomes overwhelming as the number of components increases. This research introduces a new procedure for maintenance models of multi-unit repairable systems with economic dependence among its components and under opportunistic maintenance policies. The procedure is based on the stochastic Petri net with aging tokens modeling framework and it makes use of a component-level model approach to overcome the state explosion of the model combined with a novel order-reduction scheme that effectively combines the impact of other components into a single distribution. The justification for
the used scheme is provided, the accuracy is assessed, and applications for the systems of realistic complexity are considered.
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Le, Xuan Tuan. "Understanding complex systems through computational modeling and simulation." Thesis, Paris Sciences et Lettres (ComUE), 2017. http://www.theses.fr/2017PSLEP003.
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Les approches de simulation classiques ne sont en général pas adaptées pour traiter les aspects de complexité que présentent les systèmes complexes tels que l'émergence ou l'adaptation. Dans cette thèse, l'auteur s'appuie sur ses travaux menés dans le cadre d'un projet de simulation sur l’épidémie de grippe en France associée à des interventions sur une population en considérant le phénomène étudié comme un processus diffusif sur un réseau complexe d'individus, l'originalité réside dans le fait que la population y est considérée comme un système réactif. La modélisation de tels systèmes nécessite de spécifier explicitement le comportement des individus et les réactions de ceux-cis tout en produisant un modèle informatique qui doit être à la fois flexible et réutilisable. Les diagrammes d'états sont proposés comme une approche de programmation reposant sur une modélisation validée par l'expertise. Ils correspondent également à une spécification du code informatique désormais disponibles dans les outils logiciels de programmation agent. L'approche agent de type bottom-up permet d'obtenir des simulations de scénario "what-if" où le déroulement des actions peut nécessiter que les agents s'adaptent aux changements de contexte. Cette thèse propose également l'apprentissage pour un agent par l'emploi d'arbre de décision afin d'apporter flexibilité et lisibilité pour la définition du modèle de comportement des agents et une prise de décision adaptée au cours de la simulation. Notre approche de modélisation computationnelle est complémentaire aux approches traditionnelles et peut se révéler indispensable pour garantir une approche pluridisciplinaire validable par l'expertiseTraditional approaches are not sufficient, and sometimes impossible in dealing with complexity issues such as emergence, self-organization, evolution and adaptation of complex systems. As illustrated in this thesis by the practical work of the author in a real-life project, the spreading of infectious disease as well as interventions could be considered as difusion processes on complex networks of heterogeneous individuals in a society which is considered as a reactive system. Modeling of this system requires explicitly specifying of each individual’s behaviors and (re)actions, and transforming them into computational model which has to be flexible, reusable, and ease of coding. Statechart, typical for model-based programming, is a good solution that the thesis proposes. Bottom-up agent based simulation finds emergence episodes in what-if scenarios that change rules governing agent’s behaviors that requires agents to learn to adapt with these changes. Decision tree learning is proposed to bring more flexibility and legibility in modeling of agent’s autonomous decision making during simulation runtime. Our proposition for computational models such as agent based models are complementary to traditional ones, and in some case they are unique solutions due to legal, ethical issues
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Ding, Limei. "On modeling and control of complex dynamic systems." Licentiate thesis, Luleå, 2000. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-26097.
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Nonlinear complex multi-input multi-output process is very troublesome to control. It is usually also ill-modeled. The problem of such process both in control and modeling requires a comprehensive utilization of various techniques. The thesis presents the methods for the modeling and control of complex systems. A typical example of complex processes is mineral flotation that characterized by multivariable, nonlinearities, strong interactions, stochastic disturbances, and large and variable delay times. The dynamic nonlinear model of a inverse continuous flotation process is developed based on the first order kinetics. The complexity of mineral flotation in modeling and control is shown by the analysis of the dynamic model and the relation between the process inputs and the process parameters. As an attractive alternative of conventional control technologies, fuzzy logic control is discussed for the control of an MIMO nonlinear flotation system and the simulation of the control system is carried out. LQG optimal control of the pulp level, a subsystem in a flotation process is studied and the robustness is analyzed based on the structured singular value. The control of infinite dimensional systems is known to all a knotty problem. Orthogonal collocation method, which is one of the methods of weighted residual, is employed to reduce the infinite dimensional model of the systems. In a case study a time delay system is approximated using the orthogonal collocation and a robust controller of the delay system is synthesized based on the method introduced by Glover and McFarlane in 1989. The robustness degradation caused by model reduction is discussed. The results show that the strategies studied in the thesis are available to solve the relevant modeling and control problems of complex systems.Godkänd; 2000; 20070318 (ysko)
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Clark, Kenneth A. "Modeling single-event transients in complex digital systems." Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2002. http://library.nps.navy.mil/uhtbin/hyperion-image/02Jun%5FClark%5FKenneth%5Fphd.pdf.
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Sommerer, Christa. "Modeling complex adaptive systems and complexity for interactive art." Thesis, University of South Wales, 2002. https://pure.southwales.ac.uk/en/studentthesis/modeling-complex-adaptive-systems-and-complexity-for-interactive-art(3d7143e3-eb05-49b9-8965-0ffa53767eb9).html.
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Complex System Sciences, as a field of research, has emerged in the past decade. It studies how parts of a system give rise to the collective behaviours of the system and how the system interacts with its environment. It approaches the question of how life on earth could have appeared by searching for inherent structures in living systems and trying to define common patterns within these structures. Complex Systems are also often described as systems where the whole is more complex than the mere sum of its parts, and these systems are also considered to be at the point of maximum computational ability, maximum fitness and maximum evolvability. Several scientific models have simulated Complex Adaptive Systems. These try to model the emergence of complexity within computer-simulated environments inhabited by artificially evolving organisms. My objective in this thesis is to study the application of Complex Systems and Complex Adaptive Systems to Interactive Art and to test how one could construct interactive systems that can create dynamic and open-ended image structures that increase in complexity as users interact with them. Ideally, these interactive artworks should become comparable to Complex Adaptive Systems or even become Complex Systems themselves by satisfying some of the key properties of such systems.
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Ding, Limei. "Modeling control and analysis of complex dynamic chemical systems /." Luleå, 2003. http://epubl.luth.se/1402-1544/2003/28.
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Wall, Anders. "Architectural modeling and analysis of complex real-time systems /." Västerås : Mälardalen University, 2003. http://www.mrtc.mdh.se/publications/0621.pdf.
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Rodriguez, Jesus. "Modeling of complex systems using nonlinear, flexible multibody dynamics." Diss., Georgia Institute of Technology, 2002. http://hdl.handle.net/1853/12344.
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Chow, Fung-kiu, and 鄒鳳嬌. "Modeling the minority-seeking behavior in complex adaptive systems." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2003. http://hub.hku.hk/bib/B29367487.
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Bagdasaryan, A. G. "Modeling and control of complex systems over finite fields." Thesis, Sumy State University, 2013. http://essuir.sumdu.edu.ua/handle/123456789/44326.
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The simulation process can solve several questions as to controllability
(the ability to reach any one state from any other), reachability (the ability
to reach the set of states to which the system can be steered), accessibility
(the ability to reach a subset of the state space from any given initial state).
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Zhang, Tingnan. "Modeling and control of locomotion in complex environments." Diss., Georgia Institute of Technology, 2016. http://hdl.handle.net/1853/54984.
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In this dissertation, we developed predictive models for legged and limbless locomotion on dry, homogeneous granular media. The vertical plane Resistive Force Theory (RFT) for frictional granular fluids accurately predicted the reaction forces on intruders (with complex geometries) translating and rotating at low speeds ( < 0.5 m/s). Using RFT and multibody simulation, we predicted the forward moving speed of legged robots. During the locomotion of lightweight robots and animals where instantaneous limb penetration speed can reach values greater than ~0.5 m/s, a Discrete Element Method (DEM) simulation was developed to capture the limb-ground interaction. We demonstrated that hydrodynamic-like forces generated by accelerated particles can balance the robot weight and inertia, and promote the rapid movement on granular media. Forces from the environment can not only determine locomotion dynamics, but also affect the locomotion strategy. We studied and simulated the limbless locomotion of snakes in a heterogeneous environment and demonstrated how touch sensing was used to adjust the movement pattern. In heterogeneous environments, the long-term locomotion dynamics is also poorly understood. We presented a theory for transport and diffusion in such settings.
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Stealey, John 1962. "Application of a systems-theoretic safety modeling technique for complex system mishap etiology." Thesis, Massachusetts Institute of Technology, 2003. http://hdl.handle.net/1721.1/91775.
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Masai, Pierre. "Modeling the lean organization as a complex system." Thesis, Strasbourg, 2017. http://www.theses.fr/2017STRAD029/document.
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Dans cette thèse, après avoir expliqué l'historique et les concepts principaux de l’organisation lean dans différents contextes, le monde des systèmes complexes est exploré, puis il est montré pourquoi le lean est lui-même un système complexe. Un modèle novateur du lean est proposé sous forme d'ontologie, le Lean Organization Framework (LOF), qui peut être appliqué à toutes les formes d’organisations. Le LOF est testé avec celles qui ont déjà été explorées, proposant ainsi des pistes d’amélioration (lean pour la fabrication, pour l’IT, pour les soins de santé, pour la fonction publique, pour les organisations non gouvernementales, pour les start-ups et pour l’éducation). Il peut également être appliqué à de nouveaux domaines d’activités avec l’aide d’experts dans ces domaines, une approche montrée avec les exemples nouveaux d’une fondation lean et de l’architecture d’entreprise lean (Lean EA) mais aussi en comparant l’organisation lean au système immunitaire, un exemple bien connu de système complexe. Ensuite, un modèle de processus lean est proposé, présentant les propriétés émergentes d’un système complexe, le hoshin kanri (gestion des objectifs de l’organisation), y compris dans sa dimension culturelle. Les résultats de son expérimentation pratique avec l’application eHoshin sont discutés et un premier prototype en open source est présenté, déjà utilisé à ce jour par une centaine d’organisations dans le monde. Une seconde expérimentation plus robuste dans l’industrie (Toyota, dans plusieurs fonctions et entités juridiques) est exposée. Le modèle théorique est enfin amélioré sur base des résultats obtenus. En annexe, les concepts du lean sont expliqués avec leur application à six domaines de connaissance différents et les programmes de simulations sont listésIn this thesis, after explaining the history and main concepts of the lean organization in various contexts, the world of complex systems is explored, then it is shown why the lean organization is itself a Complex System. A novel model of lean is proposed as an ontology, the Lean Organization Framework (LOF), which can be applied to all forms of organizations. The LOF is tested with those already explored (Lean Manufacturing, Lean IT, Lean Healthcare, Lean Government, Lean NGO, Lean Start-Up, Lean Education) and proposes ways to enhance them. It can also be applied to new domains with the help of subject matter experts, an approach that is checked with the novel cases of a Lean Foundation and Lean Enterprise Architecture (Lean EA), but also with the comparison of the lean organization with the immune system, a well-known Complex System example. Then, a model of lean process presenting the emergent properties of a Complex System is proposed: the hoshin kanri, or management of the organization objectives, including in its cultural dimension. The results of its practical implementation with the eHoshin application are discussed and a first open source prototype already used by around one hundred organizations in the world is explained. A second, more robust implementation in the industry is presented (at Toyota, extended to several departments and legal entities). Finally, the theoretical model is improved based on the experimentation results. In the appendices, the lean concepts are explained together with their application to six domains of knowledge and the simulation programs are listed
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Гнатенко, О. С., and О. О. Кальна. "Modeling the interaction of laser radiation with complex biological optical systems." Thesis, Sumy State University, Ukraine, 2018. http://openarchive.nure.ua/handle/document/5784.
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Kleiner, Matthias. "Thermodynamic Modeling of Complex Systems: Polar and Associating Fluids and Mixtures /." München : Verl. Dr. Hut, 2009. http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&doc_number=017156328&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA.
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Asthorsson, Axel. "Simulation meta-modeling of complex industrial production systems using neural networks." Thesis, University of Skövde, School of Humanities and Informatics, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-1036.
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Simulations are widely used for analysis and design of complex systems. Real-world complex systems are often too complex to be expressed with tractable mathematical formulations. Therefore simulations are often used instead of mathematical formulations because of their flexibility and ability to model real-world complex systems in some detail. Simulation models can often be complex and slow which lead to the development of simulation meta-models that are simpler and faster models of complex simulation models. Artificial neural networks (ANNs) have been studied for use as simulation meta-models with different results. This final year project further studies the use of ANNs as simulation meta-models by comparing the predictability of five different neural network architectures: feed-forward-, generalized feed-forward-, modular-, radial basis- and Elman artificial neural networks where the underlying simulation is of complex production system. The results where that all architectures gave acceptable results even though it can be said that Elman- and feed-forward ANNs performed the best of the tests conducted here. The difference in accuracy and generalization was considerably small.
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Fischer, Jan [Verfasser]. "Molecular Modeling of Complex Systems for Applications in Thermodynamics / Jan Fischer." München : Verlag Dr. Hut, 2010. http://d-nb.info/1009973002/34.
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Brena, Barbara. "First principles modeling of soft X-ray spectroscopy of complex systems." Doctoral thesis, Stockholm, 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-403.
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Ashcraft, Robert Wilson. "Ab initio modeling of complex aqueous and gaseous systems containing nitrogen." Thesis, Massachusetts Institute of Technology, 2008. http://hdl.handle.net/1721.1/45918.
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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Chemical Engineering, 2008.Includes bibliographical references (p. 305-312).
Nitrogen chemistry is ubiquitous in everyday life, from biological processes at ambient conditions to atmospheric chemistry at low pressures and temperatures to high-temperature combustion. Understanding the chemical behavior of nitrogen-containing species under a variety of conditions and in multiple phases is critical to accurately modeling system behavior. The further ability to model system behavior based solely on a first principles approach would be a boon to researchers attempting to design and understand technologies utilizing complex systems. This work attempts to further these abilities for both solution-phase and gas-phase predictions from ab initio calculations. An overview of solvation thermodynamics is given that relates computational chemistry to phenomenological thermodynamics for common equilibrium expressions. Special attention is paid to fully understanding the role of activity coefficients, standard states, and reference states and how these affect the subsequent expressions. A procedure is outlined for estimating the thermochemical properties of small molecules in aqueous solution based on computational chemistry calculations utilizing continuum solvation models. The partitioning of the entropic and enthalpic contributions is of the utmost importance if one is to accurately estimate the enthalpy of formation and entropy in solution. Procedures for rate coefficient estimation via solution-phase transition state theory, simple electron transfer theory, and dissociative isomerizations within a solvent cage are also discussed. The oxidation of hydroxylamine in aqueous nitric acid was chosen as a test system. A detailed chemical mechanism was constructed and thermochemical and rate parameters from computational chemistry calculations were used to model the behavior of the system. Using current continuum solvation models, it does not appear possible to build reliable predictive models of complex aqueous systems, particular those with a high ionic strength. However, the present semi-quantitative models may be helpful in focusing attention on the key unknowns.
(cont.) Group additivity values were estimated for more than 50 new functional groups containing nitrogen based on high-level computational chemistry estimates of the thermochemical parameters of 105 non-cyclic C/H/N/O species. The thermochemical and kinetics databases of the group's Reaction Mechanism Generator software were restructured to be more extensible and to explicitly include nitrogen chemistry. This allows new chemistry to be added to the software more easily and will allow predictions for gas-phase nitrogencontaining systems in the very near future.
by Robert Wilson Ashcraft.
Ph.D.
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Kleiner, Matthias. "Thermodynamic modeling of complex systems polar and associating fluids and mixtures." München Verl. Dr. Hut, 2008. http://d-nb.info/992644917/04.
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Wiltshire, Serge William. "On The Application Of Computational Modeling To Complex Food Systems Issues." ScholarWorks @ UVM, 2019. https://scholarworks.uvm.edu/graddis/1077.
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Transdisciplinary food systems research aims to merge insights from multiple fields, often revealing confounding, complex interactions. Computational modeling offers a means to discover patterns and formulate novel solutions to such systems-level problems. The best models serve as hubs—or boundary objects—which ground and unify a collaborative, iterative, and transdisciplinary process of stakeholder engagement. This dissertation demonstrates the application of agent-based modeling, network analytics, and evolutionary computational optimization to the pressing food systems problem areas of livestock epidemiology and global food security. It is comprised of a methodological introduction, an executive summary, three journal-article formatted chapters, and an overarching discussion section.
Chapter One employs an agent-based computer model (RUSH-PNBM v.1.1) developed to study the potential impact of the trend toward increased producer specialization on resilience to catastrophic epidemics within livestock production chains. In each run, an infection is introduced and may spread according to probabilities associated with the various modes of contact between hog producer, feed mill, and slaughter plant agents. Experimental data reveal that more-specialized systems are vulnerable to outbreaks at lower spatial densities, have more abrupt percolation transitions, and are characterized by less-predictable outcomes; suggesting that reworking network structures may represent a viable means to increase biosecurity.
Chapter Two uses a calibrated, spatially-explicit version of RUSH-PNBM (v.1.2) to model the hog production chains within three U.S. states. Key metrics are calculated after each run, some of which pertain to overall network structures, while others describe each actor’s positionality within the network. A genetic programming algorithm is then employed to search for mathematical relationships between multiple individual indicators that effectively predict each node’s vulnerability. This “meta-metric” approach could be applied to aid livestock epidemiologists in the targeting of biosecurity interventions and may also be useful to study a wide range of complex network phenomena.
Chapter Three focuses on food insecurity resulting from the projected gap between global food supply and demand over the coming decades. While no single solution has been identified, scholars suggest that investments into multiple interventions may stack together to solve the problem. However, formulating an effective plan of action requires knowledge about the level of change resulting from a given investment into each wedge, the time before that effect unfolds, the expected baseline change, and the maximum possible level of change. This chapter details an evolutionary-computational algorithm to optimize investment schedules according to the twin goals of maximizing global food security and minimizing cost. Future work will involve parameterizing the model through an expert informant advisory process to develop the existing framework into a practicable food policy decision-support tool.
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Bui, Quang Vu. "Pretopology and Topic Modeling for Complex Systems Analysis : Application on Document Classification and Complex Network Analysis." Thesis, Paris Sciences et Lettres (ComUE), 2018. http://www.theses.fr/2018PSLEP034/document.
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Les travaux de cette thèse présentent le développement d'algorithmes de classification de documents d'une part, ou d'analyse de réseaux complexes d'autre part, en s'appuyant sur la prétopologie, une théorie qui modélise le concept de proximité. Le premier travail développe un cadre pour la classification de documents en combinant une approche de topicmodeling et la prétopologie. Notre contribution propose d'utiliser des distributions de sujets extraites à partir d'un traitement topic-modeling comme entrées pour des méthodes de classification. Dans cette approche, nous avons étudié deux aspects : déterminer une distance adaptée entre documents en étudiant la pertinence des mesures probabilistes et des mesures vectorielles, et effet réaliser des regroupements selon plusieurs critères en utilisant une pseudo-distance définie à partir de la prétopologie. Le deuxième travail introduit un cadre général de modélisation des Réseaux Complexes en développant une reformulation de la prétopologie stochastique, il propose également un modèle prétopologique de cascade d'informations comme modèle général de diffusion. De plus, nous avons proposé un modèle agent, Textual-ABM, pour analyser des réseaux complexes dynamiques associés à des informations textuelles en utilisant un modèle auteur-sujet et nous avons introduit le Textual-Homo-IC, un modèle de cascade indépendant de la ressemblance, dans lequel l'homophilie est fondée sur du contenu textuel obtenu par un topic-modelThe work of this thesis presents the development of algorithms for document classification on the one hand, or complex network analysis on the other hand, based on pretopology, a theory that models the concept of proximity. The first work develops a framework for document clustering by combining Topic Modeling and Pretopology. Our contribution proposes using topic distributions extracted from topic modeling treatment as input for classification methods. In this approach, we investigated two aspects: determine an appropriate distance between documents by studying the relevance of Probabilistic-Based and Vector-Based Measurements and effect groupings according to several criteria using a pseudo-distance defined from pretopology. The second work introduces a general framework for modeling Complex Networks by developing a reformulation of stochastic pretopology and proposes Pretopology Cascade Model as a general model for information diffusion. In addition, we proposed an agent-based model, Textual-ABM, to analyze complex dynamic networks associated with textual information using author-topic model and introduced Textual-Homo-IC, an independent cascade model of the resemblance, in which homophily is measured based on textual content obtained by utilizing Topic Modeling
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Gramatica, Ruggero. "Quantitative semantics and graph theory as a framework for complex systems modeling." Thesis, King's College London (University of London), 2015. http://kclpure.kcl.ac.uk/portal/en/theses/quantitative-semantics-and-graph-theory-as-a-framework-for-complex-systems-modeling(54bb7023-2f85-4128-898f-cc7039c45ff9).html.
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The study of Complex Systems focuses on how interactions of constituents within a system, individually or grouped into clusters, produce behavioral patterns locally or globally and how these interact with the external environment. Over the last few decades the study of Complex Systems has gone through a growing rate of interest and today, given a sufficiently big set of data, we are able to construct comprehensive models describing emerging characteristics and properties of complex phenomena transcending the different domains of physical, biological and social sciences. The use of network theory has shown, amongst others, a particular t in describing statical and dynamical correlations of complex data sets because its ability to deal not only with deterministic quantities but also with probabilistic methods. A complex system is generally an open system flexible in adapting to variable external conditions in the way that it exchanges information with environment and adjusts its internal structure in the process of self-organization. Moreover, it has been shown how real world phenomena that are represented by complex systems display interesting statistical properties such as power-law distributions, long-range interactions, scale invariance, criticality, multifractality and hierarchical structure. In the era of big data where effort is largely put to collect large data sets carrying relevant information about given phenomena to be studied and analysed, the interesting field of quantitative semantics, e.g. dealing with information expressed in natural language, is becoming more and more relevant particularly in the social sciences. However, recent studies are expanding these techniques to become a tool for structuring and organising information across a number of disparate disciplines. In this Thesis I propose a methodology that (i) extracts a structured complex data set from large corpora of descriptive language sources and efficiently exploits the power of quantitative semantics techniques to map the essence of a complex phenomena into a network representation, and (ii) combines such induced knowledge network with a graph theoretical framework utilising a number of graph theory tools to study the emerging properties of complex systems. Thus, leveraging on developments in Computational Linguistics and Network Theory, the proposed approach builds a graph representation of knowledge, which is analyzed with the aim of observing correlations between any two nodes or across clusters of nodes and highlights emerging properties by means of both topological structure analysis and dynamic evolution, i.e. the change in connectivity. Under this framework I will provide two real-world applications: - The fist application deals with the creation of a structured network of biomedical concepts starting from an unstructured corpus of biological text-based data set (peer reviewed articles) and next it retrieves known pathophysiological Mode of Actions by applying a stochastic random-walk measure and finds new ones by meaningfully selecting and aggregating contributions from known bio-molecular interactions. By exploiting the proposed graph-theoretic model, this approach has proven to be an innovative way to find emergent mechanism of actions aimed at drug repurposing where existing biologic compounds originally intended to deal with certain pathophysiologic actions are redirected for treating other type of clinical indications. - The second application consists of a representation of a finnancial and economic system through a network of interacting entities and to devise a novel semantic index influenced by the topological properties of agglomerated information in a semantic graph. I have shown how it is possible to fully capture the dynamical aspects of the phenomena under investigation by identifying clusters carrying in uflential information and tracking them over time. By computing graph-based statistics over such clusters I turn the evolution of textual information into a mathematically well-defined, multivariate time series, where each time series encodes the evolution of particular structural, topological and semantic properties of the set of concepts previously extracted and filtered. Eventually an autoregressive model with vectorial exogenous inputs is defined, which linearly mixes previous values of an index with the evolution of other time series induced by the semantic information in the graph. The methodology brie y described above concludes the contribution of my research work in the field of Complex Systems and it has been instrumental in successfully defining a graph-theoretical model for the study of drug repurposing [1] and the construction of a framework for the analysis of financial and economic unstructured data (see chapter 6).
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Diergardt, Martin. "Modeling scenarios for analyzing the risks of complex computer based informatin systems /." Zürich : ETH, 2006. http://e-collection.ethbib.ethz.ch/show?type=diss&nr=16712.
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Andersson, Johan. "Modeling the temporal behavior of complex embedded systems : a reverse engineering approach." Licentiate thesis, Mälardalen University, Department of Computer Science and Electronics, 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-125.
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Diergardt, Martin. "Modeling scenarios for analyzing the risks of complex computer based information systems." Berlin dissertation.de, 2006. http://www.dissertation.de/buch.php3?buch=5105.
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SABELLA, SABRINA. "Complex Modeling and Analysis of the Energy Systems in Afghanistan with OSeMOSYS." Thesis, KTH, Skolan för industriell teknik och management (ITM), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-292933.
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This Master's Thesis aimed to use quantitative analysis to explore dierent pathways for the sustainable development of Afghanistan. The Open Source Energy Modeling System (OSeMOSYS) was adopted to build an energy model of the country. Electricity demand projections for residential, industrial and commercial sectors were created using both a bottom-up and a top-down approach. These were then used as input data for the optimisation model. Starting from the Reference scenario, three additional scenarios were elaborated: Limit Import scenario, Renewable scenario and National Policies scenario. These showed different options of the least-cost energy mix and explored fundamental aspects to be considered for sustainable development, such as grid access, energy reliability, efficiency and costs, potential of renewable energy. In detail, the Limit Import scenario restricted electricity import up to 60% by 2050. The Renewable scenario applied the following constraints of minimum RE penetration: 20% by 2020, 30% by 2030, 40% by 2040. The National Policies scenario implemented the capacity of power plants that were already planned and commissioned by the country's future plans. The results highlighted a strong dependency on import as well as a consistent fossil-fuel baseload across all scenarios. Even if the investment costs were decreasing over time, renewables would enter the mix only if strict targets were applied. Hydro power represented the only green technology to play a bigger role in the mix. Overall, the results of this study could be used as an informative source for the national policy makers.Denna masteruppsats syftade till att använda kvantitativ analys för att utforska olika vägar för en hållbar utveckling i Afghanistan. Open Source Energy Modeling System (OSeMOSYS) antogs för att bygga en energimodell av landet. Prognoser för efterfrågan på el för bostads-, industri- och kommersiella sektorer skapades med både en bottom-up och top-down-metod. Dessa användes sedan som indata för optimeringsmodellen. Från och med referensscenariot utarbetades ytterligare tre scenarier: Limit Import-scenario, Förnyelsebar scenario och Nationella Policies-scenariot. Dessa visade olika alternativ för den billigaste energimixen och undersökte grundläggande aspekter som ska beaktas för hållbar utveckling, såsom nätåtkomst, energisäkerhet, effektivitet och kostnader, potential för förnybar energi. I detalj, begränsade scenariot Limit Import elimporten upp till 60 % fram till 2050. I det förnybara scenariot tillämpades följande begränsningar av minsta möjliga REpenetration: 20 % till 2020, 30 % till 2030, 40 % till 2040. National Policies-scenariot implementerade kapaciteten hos kraftverk som redan planerades och beställdes av landets framtida planer. Resultaten visade pa ett starkt beroende av import såväl som en konsekvent basbelastning för fossila bränslen i alla scenarier. Även om investeringskostnaderna minskade över tiden skulle förnybara energikällor komma in i mixen endast om strikta mål tillämpades. Vattenkraft representerade den enda gröna tekniken som spelade en större roll i mixen. Sammantaget kan resultaten av denna studie användas som en informativ källa för de nationella beslutsfattarna.
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Siddiqui, Jalal K. "Modeling the response of troponin C to calcium in increasingly complex systems." The Ohio State University, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=osu1480258715871156.
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Dougherty, Francis Laverne. "A Complex Adaptive Systems Analysis of Productive Efficiency." Diss., Virginia Tech, 2014. http://hdl.handle.net/10919/65146.
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Linkages between Complex Adaptive Systems (CAS) thinking and efficiency
analysis remain in their infancy. This research associates the basic building blocks
of the CAS 'flocking' metaphor with the essential building block concepts of Data
Envelopment Analysis (DEA). Within a proposed framework DEA"decisionmaking
units" (DMUs) are represented as agents in the agent-based modeling
(ABM) paradigm. Guided by simple rules, agent DMUs representing business units
of a larger management system, 'align' with one another to achieve mutual
protection/risk reduction and 'cohere' with the most efficient DMUs among them
to achieve the greatest possible efficiency in the least possible time. Analysis of the
resulting patterns of behavior can provide policy insights that are both evidencebased
and intuitive. This research introduces a consistent methodology that will be
called here the Complex Adaptive Productive Efficiency Method (CAPEM) and
employs it to bridge these domains. This research formalizes CAPEM
mathematically and graphically. It then conducts experimentation employing using
the resulting CAPEM simulation using data of a sample of electric power plants
obtained from Rungsuriyawiboon and Stefanou (2003). Guided by rules, individual
agent DMUs (power plants) representing business units of a larger management
system,'align' with one another to achieve mutual protection/risk reduction and
'cohere' with the most efficient DMUs among them to achieve the greatest possible
efficiency in the least possible time. Using a CAS ABM simulation, it is found that
the flocking rules (alignment, cohesion and separation), taken individually and in selected combinations, increased the mean technical efficiency of the power plant
population and conversely decreased the time to reach the frontier. It is found
however that these effects were limited to a smaller than expected sub-set of these
combinations of the flocking factors. Having been successful in finding even a
limited sub-set of flocking rules that increased efficiency was sufficient to support
the hypotheses and conclude that employing the flocking metaphor offers useful
options to decision-makers for increasing the efficiency of management systems.Ph. D.
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Kamapantula, Bhanu K. "In-silico Models for Capturing the Static and Dynamic Characteristics of Robustness within Complex Networks." VCU Scholars Compass, 2015. http://scholarscompass.vcu.edu/etd/4049.
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Understanding the role of structural patterns within complex networks is essential to establish the governing principles of such networks. Social networks, biological networks, technological networks etc. can be considered as complex networks where information processing and transport plays a central role. Complexity in these net works can be due to abstraction, scale, functionality and structure. Depending on the abstraction each of these can be categorized further. Gene regulatory networks are one such category of biological networks. Gene regulatory networks (GRNs) are assumed to be robust under internal and external perturbations. Network motifs such as feed-forward loop motif and bifan motif are believed to play a central role functionally in retaining GRN behavior under lossy conditions. While the role of static characteristics like average shortest path, density, degree centrality among other topological features is well documented by the research community, the structural role of motifs and their dynamic characteristics are not xiii well understood. Wireless sensor networks in the last decade were intensively studied using network simulators. Can we use in-silico experiments to understand biological network topologies better? Does the structure of these motifs have any role to play in ensuring robust information transport in such networks? How do their static and dynamic roles differ? To understand these questions, we use in-silico network models to capture the dynamic characteristics of complex network topologies. Developing these models involve network mapping, sink selection strategies and identifying metrics to capture robust system behavior. Further, I studied the dynamic aspect of network characteristics using variation in network information flow under perturbations defined by lossy conditions and channel capacity. We use machine learning techniques to identify significant features that contribute to robust network performance. Our work demonstrates that although the structural role of feed-forward loop motif in signal transduction within GRNs is minimal, these motifs stand out under heavy perturbations.
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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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Wikberg, Per. "Eliciting Knowledge from Experts in Modeling of Complex Systems : Managing Variation and Interactions." Doctoral thesis, Linköping : Department of Computer and Information Science, Linköping University, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-10111.
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Dulac, Nicolas 1978. "A framework for dynamic safety and risk management modeling in complex engineering systems." Thesis, Massachusetts Institute of Technology, 2007. http://hdl.handle.net/1721.1/42175.
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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, June 2007."February 2007."
Includes bibliographical references (p. 328-338).
Almost all traditional hazard analysis or risk assessment techniques, such as failure modes and effect analysis (FMEA), fault tree analysis (FTA), and probabilistic risk analysis (PRA) rely on a chain-of-event paradigm of accident causation. Event-based techniques have some limitations for the study of modem engineering systems. Specifically, they are not suited to handle complex software-intensive systems, complex human-machine interactions, and systems-of-systems with distributed decision-making that cut across both physical and organizational boundaries. STAMP (System-Theoretic Accident Model and Processes) is a comprehensive accident model created by Nancy Leveson that is based on systems theory. It draws on concepts from engineering, mathematics, cognitive and social psychology, organizational theory, political science, and economics. The general notion in STAMP is that accidents result from inadequate enforcement of safety constraints in design, development, and operation. STAMP includes traditional failure-based models as a subset, but goes beyond physical failures to include causal factors involving dysfunctional interactions among non-failing components; software and logic design errors; errors in complex human decision-making; various organizational characteristics such as workforce, safety processes and standards, contracting; and other managerial, social, organizational, and cultural factors. The main contribution of this thesis is the augmentation of STAMP with a dynamic executable modeling framework in order to further improve safety in the development and operation of complex engineering systems. This executable modeling framework: 1) enables the dynamic analysis of safety-related decision-making in complex systems, 2) assists with the design and testing of non-intuitive policies and processes to better mitigate risks and prevent time-dependent risk increase, and 3) enables the identification of technical and organizational factors to detect and monitor states of increasing risk before an accident occurs.
(cont.) The modeling framework is created by combining STAMP safety control structures with system dynamic modeling principles. A component-based model-building methodology is proposed to facilitate the building of customized STAMP-based dynamic risk management models and make them accessible to managers and engineers with limited simulation experience. A library of generic executable components is provided as a basis for model creation, refinement, and validation. A toolset is assembled to identify risk increase patterns, analyze time-dependent risks, assist engineers and managers in safety-related decision-making, create and test risk mitigation actions and policies, and monitor the system for states of increasing risk. The usefulness of the new framework is demonstrated in two independent projects: 1) A risk analysis of the NASA Independent Technical Authority (ITA), an organization mandated by the Columbia Accident Investigation Board (CAIB) to provide independent safety oversight of space shuttle operations, and 2) A risk management study for the Exploration Systems Mission Directorate (ESMD) at NASA. For these two projects, model refinement, validation and analysis required extensive data collection and interactions with NASA workforce. Over 45 interviews were conducted at five NASA centers (HQ, MSFC, KSC, JSC, and LaRC). Interviewees included representatives from the Office of the Administrator, the Office of the Chief Engineer, the Office of Safety and Mission Assurance, ESMD Directorate Offices, Program/Project Offices, and many others. Among other data sources, 200 pages of interview transcripts were compiled and used for model creation and validation activities. Specific risks analyzed include: 1) NASA workforce and knowledge management issues, 2) the impact of various levels of outsourcing, 3) the impact of safety priority on design, and 4) the impact of requirements change on safety and schedule during development.
by Nicolas Dulac.
Ph.D.
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Tao, Li. "Understanding the performance of healthcare services: a data-driven complex systems modeling approach." HKBU Institutional Repository, 2014. https://repository.hkbu.edu.hk/etd_oa/89.
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Healthcare is of critical importance in maintaining people’s health and wellness. It has attracted policy makers, researchers, and practitioners around the world to .nd better ways to improve the performance of healthcare services. One of the key indicators for assessing that performance is to show how accessible and timely the services will be to speci.c groups of people in distinct geographic locations and in di.erent seasons, which is commonly re.ected in the so-called wait times of services. Wait times involve multiple related impact factors, called predictors, such as demographic characteristics, service capacities, and human behaviors. Some impact factors, especially individuals’ behaviors, may have mutual interactions, which can lead to tempo-spatial patterns in wait times at a systems level. The goal of this thesis is to gain a systematic understanding of healthcare services by investigating the causes and corresponding dynamics of wait times. This thesis presents a data-driven complex systems modeling approach to investigating the causes of tempo-spatial patterns in wait times from a self-organizing perspective. As the predictors of wait times may have direct, indirect, and/or moderating e.ects, referred to as complex e.ects, a Structural Equation Modeling (SEM)-based analysis method is proposed to discover the complex e.ects from aggregated data. Existing regression-based analysis techniques are only able to reveal pairwise relationships between observed variables, whereas this method allows us to explore the complex e.ects of observed and/or unobserved(latent) predictors on waittimes simultaneously. This thesis then considers how to estimate the variations in wait times with respect to changes in speci.c predictors and their revealed complex e.ects. An integrated projection method using the SEM-based analysis, projection, and a queuing model analysis is developed. Unlike existing studies that either make projections based primarily on pairwise relationships between variables, or queuing model-based discrete event simulations, the proposed method enables us to make a more comprehensive estimate by taking into account the complex e.ects exerted by multiple observed and latent predictors, and thus gain insights into the variations in the estimated wait times over time. This thesis further presents a method for designing and evaluating service management strategies to improve wait times, which are determined by service management behaviors. Our proposed strategy for allocating time blocks in operating rooms (ORs) incorporates historical feedback information about ORs and can adapt to the unpredictable changes in patient arrivals and hence shorten wait times. Existing time block allocations are somewhat ad hoc and are based primarily on the allocations in previous years, and thus result in ine.cient use of service resources. Finally, this thesis proposes a behavior-based autonomy-oriented modeling method for modeling and characterizing the emergent tempo-spatial patterns at a systems level by taking into account the underlying individuals’ behaviors with respect to various impact factors. This method uses multi-agent Autonomy-Oriented Computing (AOC), a computational modeling and problem-solving paradigm with a special focus on addressing the issues of self-organization and interactivity, to model heterogeneous individuals (entities), autonomous behaviors, and the mutual interactions between entities and certain impact factors. The proposed method therefore eliminates to a large extent the strong assumptions that are used to de.ne the stochastic properties of patient arrivalsand servicesinstochasticmodeling methods(e.g.,thequeuing model and discrete event simulation), and those of .xed relationships between entities that are held by system dynamics methods. The method is also more practical than agent-based modeling (ABM) for discovering the underlying mechanisms for emergent patterns, as AOC provides a general principle for explicitly stating what fundamental behaviors of and interactions between entities should be modeled. To demonstrate the e.ectiveness of the proposed systematic approach to understanding the dynamics and relevant patterns of wait times in speci.c healthcare service systems, we conduct a series of studies focusing on the cardiac care services in Ontario, Canada. Based on aggregated data that describe the services from 2004 to 2007, we use the SEM-based analysis method to (1) investigate the direct and moderating e.ects that speci.c demand factors, in terms of certaingeodemographicpro.les, exert onpatient arrivals, whichindirectly a.ect wait times; and (2) examine the e.ects of these factors (e.g., patient arrivals, physician supply, OR capacity, and wait times) on the wait times in subsequent units in a hospital. We present the e.ectiveness of integrated projection in estimating the regional changes in service utilization and wait times in cardiac surgery services in 2010-2011. We propose an adaptive OR time block allocation strategy and evaluate its performance based on a queuing model derived from the general perioperative practice. Finally, we demonstrate how to use the behavior-based autonomy-oriented modeling method to model and simulate the cardiac care system. We .nd that patients’ hospital selection behavior, hospitals’ service adjusting behavior, and their interactions via wait times may account for the emergent tempo-spatial patterns that are observed in the real-world cardiac care system. In summary, this thesis emphasizes the development of a data-driven complex systems modeling approach for understanding wait time dynamics in a healthcare service system. This approach will provide policy makers, researchers, and practitioners with a practically useful method for estimating the changes in wait times in various “what-if” scenarios, and will support the design and evaluation of resource allocation strategies for better wait times management. By addressing the problem of characterizing emergenttempo-spatial waittimepatternsinthe cardiac care system from a self-organizing perspective, we have provided a potentially e.ective means for investigating various self-organized patterns in complex healthcare systems. Keywords: Complex Healthcare Service Systems, Wait Times, Data-Driven Complex Systems Modeling, Autonomy-Oriented Computing(AOC), Cardiac Care
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Cao, Libo. "Nonlinear Wavelet Compression Methods for Ion Analyses and Dynamic Modeling of Complex Systems." Ohio University / OhioLINK, 2004. http://www.ohiolink.edu/etd/view.cgi?ohiou1107790393.
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Seer, Qiu Han. "Complex Dynamics in Fed-Batch Systems: Modeling, Analysis and Control of Alcoholic Fermentations." Thesis, Curtin University, 2017. http://hdl.handle.net/20.500.11937/56546.
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Modeling and control of fed-batch fermentation processes has been a subject of great interest to realize high productivity and yields from the fermentation technique. The goal of this dissertation was to gain insights into how the complex dynamic behaviors exhibited in fed-batch fermentation systems affect the stability of standard single-loop as well as non-standard feedback control structures. Novel PID stability theorems were established to help construct the controller stabilizing regions.
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Rogers, Bradley W. (Bradley Warren). "Understanding, modeling and improving the development of complex products : method and study." Thesis, Massachusetts Institute of Technology, 2009. http://hdl.handle.net/1721.1/49780.
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Thesis (M.B.A.)--Massachusetts Institute of Technology, Sloan School of Management; and, (S.M.)--Massachusetts Institute of Technology, Engineering Systems Division; in conjunction with the Leaders for Manufacturing Program at MIT, 2009.Includes bibliographical references (p. 90).
Development of new aerostructure designs frequently occurs through a complex process that is difficult to understand and control. Tight requirements for weight, cost, strength, and aerodynamic behavior create many interdependencies in the product design, which translate through to the design process. An increasing fragmentation of the commercial aerospace industry has also added a dimension of complexity to the process - outsourced component designs are often interdependent with in-house component designs, resulting in frequently changing requirements for supplier components during the design process. This thesis offers an analysis of the product development processes of a first-tier aerostructures supplier, Spirit AeroSystems. Although this host company provides the context for analysis, the method is meant to be generally applicable to the development of any complex product. The Design Structure Matrix (DSM) methodology is used to capture the required interaction between tasks of the development of a propulsion structure for commercial aircraft. The task times, time variations, work loads, interdependencies, likelihoods of rework, and learning curves are then quantified and applied to a discrete-event Monte Carlo simulation model which outputs probabilistic completion time and workload of the project. The model is then used to show how changing the customer requirements at different points in the development cycle affect the cost and schedule of development.
(cont.) The failure modes and effects analysis (FMEA) is applied to quantify risks and ensure proper control of their likelihoods and consequences A holistic industry-level analysis provides insight into the complexities of developing an interdependent product across multiple organizations. Potential recommendations to improve the development process are outlined. Finally, the "Three Lens" methodology is applied to identify implementation obstacles. This paper builds upon product development process simulation theory by introducing process independent externalities into the model to show how changing customer requirements may impact the cost and schedule of development. It also proposes a new framework for optimal staffing based upon the maturity of the customer requirements. Finally this paper shows that a disintegrated, sections-based design process architecture, like that used for the Boeing 787, is sub-optimal for product development, and it proposes a new architecture for developing aircraft.
by Bradley W. Rogers.
S.M.
M.B.A.
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Bian, Linkan. "Stochastic modeling and prognostic analysis of complex systems using condition-based real-time sensor signals." Diss., Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/51753.
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This dissertation presents a stochastic framework for modeling the degradation processes of components in complex engineering systems using sensor based signals. Chapters 1 and 2 discuses the challenges and the existing literature in monitoring and predicting the performance of complex engineering systems. Chapter 3 presents the degradation model with the absorbing failure threshold for a single unit and the RLD estimation using the first-passage-time approach. Subsequently, we develop the estimate of the RLD using the first-passage-time approach for two cases: information prior distributions and non-informative prior distributions. A case study is presented using real-world data from rolling elements bearing applications. Chapter 4 presents a stochastic methodology for modeling degradation signals from components functioning under dynamically evolving environmental conditions. We utilize in-situ sensor signals related to the degradation process, as well as the environmental conditions, to predict and continuously update, in real-time, the distribution of a component’s residual lifetime. Two distinct models are presented. The first considers future environmental profiles that evolve in a deterministic manner while the second assumes the environment evolves as a continuous-time Markov chain. Chapters 5 and 6 generalize the failure-dependent models and develop a general model that examines the interactions among the degradation processes of interconnected components/subsystems. In particular, we model how the degradation level of one component affects the degradation rates of other components in the system. Hereafter, we refer to this type of component-to-component interaction caused by their stochastic dependence as degradation-rate-interaction (DRI). Chapter 5 focuses on the scenario in which these changes occur in a discrete manner, whereas, Chapter 6 focuses on the scenario, in which DRIs occur in a continuous manner. We demonstrate that incorporating the effects of component interactions significantly improves the prediction accuracy of RLDs. Finally, we outline the conclusion remarks and a future work plan in Chapter 7.
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Prabhala, Sasanka V. "Designing Computer Agents with Personality to Improve Human-Machine Collaboration in Complex Systems." Wright State University / OhioLINK, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=wright1173299872.
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Jha, Sumit Kumar. "Model Validation and Discovery for Complex Stochastic Systems." Research Showcase @ CMU, 2010. http://repository.cmu.edu/dissertations/10.
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In this thesis, we study two fundamental problems that arise in the modeling of stochastic systems: (i) Validation of stochastic models against behavioral specifications such as temporal logics, and (ii) Discovery of kinetic parameters of stochastic biochemical models from behavioral specifications.
We present a new Bayesian algorithm for Statistical Model Checking of stochastic systems based on a sequential version of Jeffreys’ Bayes Factor test. We argue that the Bayesian approach is more suited for application do- mains like systems biology modeling, where distributions on nuisance parameters and priors may be known. We prove that our Bayesian Statistical Model Checking algorithm terminates for a large subclass of prior probabilities. We also characterize the Type I/II errors associated with our algorithm. We experimentally demonstrate that this algorithm is suitable for the analysis of complex biochemical models like those written in the BioNetGen language. We then argue that i.i.d. sampling based Statistical Model Checking algorithms are not an effective way to study rare behaviors of stochastic models and present another Bayesian Statistical Model Checking algorithm that can incorporate non-i.i.d. sampling strategies.
We also present algorithms for synthesis of chemical kinetic parameters of stochastic biochemical models from high level behavioral specifications. We consider the setting where a modeler knows facts that must hold on the stochastic model but is not confident about some of the kinetic parameters in her model. We suggest algorithms for discovering these kinetic parameters from facts stated in appropriate formal probabilistic specification languages. Our algorithms are based on our theoretical results characterizing the probability of a specification being true on a stochastic biochemical model. We have applied this algorithm to discover kinetic parameters for biochemical models with as many as six unknown parameters.
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Grabowicz, Przemyslaw Adam. "Complex networks approach to modeling online social systems. The emergence of computational social science." Doctoral thesis, Universitat de les Illes Balears, 2014. http://hdl.handle.net/10803/131220.
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This thesis is devoted to quantitative description, analysis, and modeling of complex social systems in the form of online social networks. Statistical patterns of the systems under study are unveiled and interpreted using concepts and methods of network science, social network analysis, and data mining. A long-term promise of this research is that predicting the behavior of complex techno-social systems will be possible in a way similar to contemporary weather forecasting, using statistical inference and computational modeling based on the advancements in understanding and knowledge of techno-social systems. Although the subject of this study are humans, as opposed to atoms or molecules in statistical physics, the availability of extremely large datasets on human behavior permits the use of tools and techniques of statistical physics. This dissertation deals with large datasets from online social networks, measures statistical patterns of social behavior, and develops quantitative methods, models, and metrics for complex techno-social systems.La presente tesis está dedicada a la descripción, análisis y modelado cuantitativo de sistemas complejos sociales en forma de redes sociales en internet. Mediante el uso de métodos y conceptos provenientes de ciencia de redes, análisis de redes sociales y minería de datos se descubren diferentes patrones estadísticos de los sistemas estudiados. Uno de los objetivos a largo plazo de esta línea de investigación consiste en hacer posible la predicción del comportamiento de sistemas complejos tecnológico-sociales, de un modo similar a la predicción meteorológica, usando inferencia estadística y modelado computacional basado en avances en el conocimiento de los sistemas tecnológico-sociales. A pesar de que el objeto del presente estudio son seres humanos, en lugar de los átomos o moléculas estudiados tradicionalmente en la física estadística, la disponibilidad de grandes bases de datos sobre comportamiento humano hace posible el uso de técnicas y métodos de física estadística. En el presente trabajo se utilizan grandes bases de datos provenientes de redes sociales en internet, se miden patrones estadísticos de comportamiento social, y se desarrollan métodos cuantitativos, modelos y métricas para el estudio de sistemas complejos tecnológico-sociales.
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Koch, Patrick N. "Hierarchical modeling and robust synthesis for the preliminary design of large scale complex systems." Diss., Georgia Institute of Technology, 1997. http://hdl.handle.net/1853/16651.
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Luo, Weiqi. "Atomistic Materials Modeling of Complex Systems: Carbynes, Carbon Nanotube Devices and Bulk Metallic Glasses." Columbus, Ohio : Ohio State University, 2008. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1218567734.
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Ganapathy, Subhashini. "HUMAN-CENTERED TIME-PRESSURED DECISION MAKING IN DYNAMIC COMPLEX SYSTEMS." Wright State University / OhioLINK, 2006. http://rave.ohiolink.edu/etdc/view?acc_num=wright1152229142.
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Huchak, Bogdan, and Богдан Олексійович Гучак. "Application of the theory of complex systems in studies of the functioning of air transport systems." Thesis, National Aviation University, 2021. https://er.nau.edu.ua/handle/NAU/51117.
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1. Jay Forrester's theory of complex systems. URL: https://vikent.ru/enc/1264/
2. Pisarenko V.N. A modern presentation of air transportation system. Samara National Research University named after academician S.P. Korolova.2017
3. T. I. Aliev. Research of complex systems based on combined approach. Theory of simulation modeling.A complex system is a system that consists of elements of different types and has heterogeneous connections between them, so the air transport system consists of a set of jointly operating aircraft, a complex of ground facilities for flight preparation and support, personnel engaged in flight operation, maintenance, and repair of aircraft and ground facilities, as well as subsystems for controlling the process of flight and technical operation. Structurally, ATS includes the following elements: crew, aircraft, flight and technical operation system, flight support system, ATS. Further applying a systematic approach to considering the problem of flight safety, individual elements of the ATC or their combination, in turn, can be considered as an independent system, for example, "Crew - Aircraft".
Складна система - це система, що складається з елементів різного типу і має неоднорідні зв’язки між собою, тому система повітряного транспорту складається з набору літаків, що діють спільно, комплексу наземних споруд для підготовки та забезпечення польоту, персоналу, задіяного в польоті , технічне обслуговування та ремонт літальних та наземних споруд, а також підсистем управління процесом польоту та технічної експлуатації. Структурно ОПР включає такі елементи: екіпаж, літальний апарат, систему льотно-технічної експлуатації, систему забезпечення польотів, ОПР. Надалі застосовуючи системний підхід до розгляду проблеми безпеки польотів, окремі елементи УВД або їх поєднання, в свою чергу, можна розглядати як самостійну систему, наприклад, "Екіпаж - Літак".
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Chaves, Café Daniel. "Multi-level modeling for verification and synthesis of complex systems in a multi-physics context." Thesis, CentraleSupélec, 2015. http://www.theses.fr/2015SUPL0019/document.
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À l'ère de systèmes électroniques intégrés, les ingénieurs font face au défi de concevoir et de tester des systèmes hétérogènes contenant des parties analogiques, numériques, mécaniques et même du logiciel embarqué. Cela reste très difficile car il n'y a pas d'outil unifiant ces différents domaines de l’ingénierie. Ces systèmes, dits hétérogènes, ont leur comportement exprimées et spécifiés par plusieurs formalismes, chacun particulier à son domaine d'expertise (diagramme de machines à état pour les circuits de contrôle numérique, équations différentielles pour les modèles mécaniques, ou bien des réseaux de composants pour les circuits analogiques). Les outils de conception existants sont destinés à traiter des systèmes homogènes en utilisant un seul formalisme à la fois. Dans l'état actuel, l'industrie se bat avec des problèmes d'intégration à chaque étape de la conception, à savoir la spécification, la simulation, la validation et le déploiement. L'absence d'une approche qui comprend les spécifications des interfaces inter-domaines est souvent la cause des problèmes d'intégration de différentes parties d'un système hétérogène. Cette thèse propose une approche pour faire face à l'hétérogénéité en utilisant SysML comme outil fédérateur. Notre proposition repose sur la définition d'une sémantique explicite pour les diagrammes SysML ainsi que des éléments d'adaptation sémantiques capables d'enlever les ambiguïtés dans les interfaces multi-domaines. Pour démontrer l'efficacité de ce concept, un ensemble d'outils basés sur l'ingénierie dirigé par les modèles a été construit pour générer du code exécutable automatiquement à partir des spécificationsIn the era of highly integrated electronics systems, engineers face the challenge of designing and testing multi-faceted systems with single-domain tools. This is difficult and error-prone. These so called heterogeneous systems have their operation and specifications expressed by several formalisms, each one particular to specific domains or engineering fields (software, digital hardware, analog, etc.). Existing design tools are meant to deal with homogeneous designs using one formalism at a time. In the current state, industry is forced to battle with integration issues at every design step, i.e. specification, simulation, validation and deployment. Common divide-to-conquer approaches do not include cross-domain interface specification from the beginning of the project. This lack is often the cause of issues and rework while trying to connect parts of the system that were not designed with the same formalism. This thesis proposes an approach to deal with heterogeneity by embracing it from the beginning of the project using SysML as the unifying tool. Our proposal hinges on the assignment of well-defined semantics to SysML diagrams, together with semantic adaptation elements. To demonstrate the effectiveness of this concept, a toolchain is built and used to generate systems simulation executable code automatically from SysML specifications for different target languages using model driven engineering techniques