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Mukherjee, Prithwiraj. "Modeling complex decision processes." Thesis, Cergy-Pontoise, Ecole supérieure des sciences économiques et commerciales, 2014. http://www.theses.fr/2014ESEC0007.
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Cette thèse contient trois essais traitant de la modélisation des processus de décision complexes dans le marketing. Ces trois essais traitent chacun d'un aspect différent de la prise de décisions complexes, que ce soit au niveau individuel ou au niveau du réseau. Les essais 1 et 2 de cette thèse utilisent des modèles à base d'agents. L’essai 1 est une extension de Goldenberg, Libai, et Muller (2010), qui utilisent un modèle à base d'agents pour démontrer que, contrairement à l'intuition, les produits avec des externalités de réseau ont tendance à se diffuser plus lentement que ceux sans externalités (l'effet dit «chilling»). Dans leur étude, ils utilisent un voisinage de Moore bidimensionnel afin de modéliser le réseau sous-jacent dans le marché des nouveaux produits. En accord avec d'autres études démontrant que la structure du réseau affecte la dynamique de diffusion, j’adapte leurs simulations pour les données de réseau du monde réel. Je trouve que, si les grands réseaux et les réseaux avec degré moyen supérieur ont une plus grande tendance à compenser cet effet de « chilling », le regroupement pourrait l'améliorer. Je démontre également que, pour les mêmes paramètres de haut niveau, un cumul de plusieurs conditions au niveau micro-locales pourrait finir par accélérer la diffusion des externalités de réseau, de fait le rendant plus rapide que sans externalités de réseau. L’essai 2 traite de la controverse entourant la commercialisation de régimes à paliers multiples (MLM) et des questions de la rentabilité de leur force de vente indépendante. M’appuyant sur une littérature rare dans ce domaine, je construis un modèle à base d’agent afin de mesurer la croissance d'un système MLM sur un réseau social. Contrairement au travail existant qui néglige le rôle des dépenses des recrues d'entreprise sur la décision de se joindre, j’inclus les mêmes variables, afin de démontrer qu'il a des effets non négligeables sur la prolifération des régimes MLM. Dans l'essai 3, je construis un nouveau modèle de préférences fondé sur la notion d'ancrage. Ce modèle vectoriel est basé sur le modèle d'utilité (1966) multi-attributs de Lancaster, permettant de faire varier les poids selon le contexte. Les modèles dépendants du contexte sont importants dans l’étude des choix des consommateurs, par exemple, pour expliquer l’adoption de nouveaux produits, le succès d’un produit, et la dynamique du marché. Les modèles dépendants du contexte peuvent être utilisés dans les analyses conjointes, afin de fournir des données d'entrée calibrées pour instancier des modèles à base d'agents qui simulent la croissance de nouveaux produits. Ainsi, l’essai 3 est une petite mais importante pièce du puzzle dans l’ensemble des processus de décision complexes. L'approche de modélisation proposée peut être utilisée pour simuler les processus de décision individuels avec des scénarios de simulation concernant les options disponibles à un seul consommateur, et donc être utilisé pour construire une simulation à base d'agents de l'ensemble du marchéThis thesis contains three essays dealing with the modeling of complex decision processes in marketing. Each of these deals with a different aspect of complex decision making, either at the individual or at the network level. Essays 1 and 2 in this dissertation are studies using agent-based models. Essay 1 is an extension of Goldenberg, Libai, and Muller (2010), who use an agent-based model to demonstrate that contrary to intuition, products with network externalities tend to diffuse slower than those without (the "chilling" effect). In their study, they use a simple 2-dimensional Moore neighborhood as the underlying network substrate depicting the market for new product adoption. In keeping with other studies demonstrating that network structure affects diffusion dynamics, I adapt their simulations for real-world network data and find that while larger networks and networks with higher average degree tend to offset this chilling effect, clustering could enhance it. I also demonstrate that for the same high-level parameters, a cumulation of many local micro-level conditions could end up speeding diffusion with network externalities, actually making it faster than without network externalities. Essay 2 deals with the controversy surrounding multilevel marketing (MLM) schemes and questions of their profitability to their freelance sales force. Building on the sparse literature in this field, I build an agent-based model of the growth of an MLM scheme on a social network. Unlike extant work which neglects the role of recruits' business expenses on the decision to join, I include the same, and show that it has non-trivial effects on the proliferation of MLM schemes. In essay 3, I build a new model of preferences based on the notion of anchoring. This vectorbased model is based on Lancaster's (1966) multiattribute utility model, but allows the weights to be shaped by context. Context-dependent models are important in studying consumer choices, as for example, in explaining new product adoptions, new product takeoff, and market dynamics. Context dependent choice models can be used in conjoint analyses to provide calibrated input data to instantiate agent-based models that simulate new product growth. Thus, Essay 3 is a small but important piece in the overall jigsaw puzzle of complex decision processes. The proposed modeling approach can be used to simulate individual decision processes with what-if scenarios regarding options available to a single consumer, and thus be used to build an agent-based simulation of an entire market
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Nielssen, Johan. "Information modeling of manufacturing processes." Doctoral thesis, KTH, Production Engineering, 2003. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-3628.
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The innovation process is an important process for our primemotor of welfare, manufacturing. During this process, theprerequisites for manufacturing are set. To set the bestpossible prerequisites consideration about products,manufacturing processes, and manufacturing resources must bemade concurrently, which also means involving several differentdisciplines in a collaborative effort.
As a consequence of involving different disciplines, thecommunication of engineering information may be hindered. Thereason is that different disciplines use different terminologyfor the same concept and sometimes have the same terminologyfor different concepts. This may result in difficultiesunderstanding each other, which may, in turn, result inunnecessary loss of quality and productivity.
The main objective of this thesis is to identify informationconcepts (i.e. information requirements) for process planningin a concurrent engineering environment, and to formally definethe corresponding terminology. The work is based on casestudies at Volvo Car Corporation, involving management of weldspot and location system information, and at ABB Body-in-White,involving tender preparation information.
The results are presented in the thesis in terms of aninformation model, the Product-Process-Resource (PPR)information model, and two corroborated hypotheses. The PPRinformation model defines the identified informationrequirements in the scope of the thesis whereas the hypothesesconcern how, e.g., modularization can be used in informationmodeling.
The PPR information model provides the base for aninformation platform in a concurrent engineeringenvironment.
The PPR information model enable model based documentationand, thus, traceability of the evolution of the product,process, and manufacturing resource designs, and theirinterrelations.
Keywords:Information Modeling, Process Planning,Concurrent Engineering, Information Management
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Vedin, Jörgen. "Numerical modeling of auroral processes." Doctoral thesis, Umeå University, Physics, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-1117.
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One of the most conspicuous problems in space physics for the last decades has been to theoretically describe how the large parallel electric fields on auroral field lines can be generated. There is strong observational evidence of such electric fields, and stationary theory supports the need for electric fields accelerating electrons to the ionosphere where they generate auroras. However, dynamic models have not been able to reproduce these electric fields. This thesis sheds some light on this incompatibility and shows that the missing ingredient in previous dynamic models is a correct description of the electron temperature. As the electrons accelerate towards the ionosphere, their velocity along the magnetic field line will increase. In the converging magnetic field lines, the mirror force will convert much of the parallel velocity into perpendicular velocity. The result of the acceleration and mirroring will be a velocity distribution with a significantly higher temperature in the auroral acceleration region than above. The enhanced temperature corresponds to strong electron pressure gradients that balance the parallel electric fields. Thus, in regions with electron acceleration along converging magnetic field lines, the electron temperature increase is a fundamental process and must be included in any model that aims to describe the build up of parallel electric fields. The development of such a model has been hampered by the difficulty to describe the temperature variation. This thesis shows that a local equation of state cannot be used, but the electron temperature variations must be descibed as a nonlocal response to the state of the auroral flux tube. The nonlocal response can be accomplished by the particle-fluid model presented in this thesis. This new dynamic model is a combination of a fluid model and a Particle-In-Cell (PIC) model and results in large parallel electric fields consistent with in-situ observations.
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Vedin, Jörgen. "Numerical modeling of auroral processes /." Umeå : Dept. of Physics, Umeå Univ, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-1117.
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Hubler, David K. "Modeling Electrochemical Water Treatment Processes." Diss., The University of Arizona, 2012. http://hdl.handle.net/10150/265367.
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Several electrochemical processes are modeled at process levels and atomic scales. Processes are presented for acid generation and ion exchange media regeneration, along with corresponding process models. Transport and reaction processes in individual ion exchange beads are also modeled. Acids of mild strength (pH = ~1-2) are generated from electrolyte solutions and their strength is effectively modeled as a function of time. The regeneration of ion exchange media is also modeled, to close agreement with measurements, and the process model is reconciled with a model for solute flux from an individual ion exchange bead. Together, the models show that the "gentle" regeneration process is controlled by the plating rate. Processes interior to the particle are controlled by diffusion, but all processes are faster than the characteristic time for plating. In a separate process, an electrochemical method is used to produce hypochlorite for disinfection. The process generates perchlorate as a toxic byproduct. Density function theory is used to construct an atomic-scale model of the mechanism for producing perchlorate, as well as the aging of the boron-doped diamond anode used in the process. The mechanism shows that the boron-doped diamond surface plays an important role in chemisorbing and stabilizing radicals of oxychlorine anions, allowing the radicals to live long enough to react and form higher ions like perchlorate. Wear mechanisms that occur on the anode are shown to oxidize and etch the surface, changing its chemical functionality over time. As the surface ages, the overpotential for water oxidation is decreased, decreasing the efficiency of the electrode.
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Szymkiewicz, Paul M. "Towards modeling of retrofit processes." Thesis, Georgia Institute of Technology, 2015. http://hdl.handle.net/1853/53891.
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Energy retrofits can be executed by a building owner with or without the supervision of a third-party agent. We define process models to capture third-party energy retrofit inspection activities, and refine, augment, and generalize those models to then examine the impact of third-party retrofit inspections. Buildings included in the study vary considerably in type, and so do retrofit programs applied to those buildings.
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Sharma, Sandeep Ph D. Massachusetts Institute of Technology. "Predictive modeling of combustion processes." Thesis, Massachusetts Institute of Technology, 2009. http://hdl.handle.net/1721.1/54583.
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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Chemical Engineering, 2009.Cataloged from PDF version of thesis.
Includes bibliographical references (p. 161-169).
Recently, there has been an increasing interest in improving the efficiency and lowering the emissions from operating combustors, e.g. internal combustion (IC) engines and gas turbines. Different fuels, additives etc. are used in these combustors to try to find the optimal operating conditions and fuel combination which gives the best results. This process is ad-hoc and costly, and the expertise gained on one system cannot easily be transfered to other situations. To improve this process a more fundamental understanding of chemistry and physical processes is required. The fundamental constants like rate coefficients of elementary reactions are readily transferable enabling us to use results from one set of experiments or calculations in a different situation. In our group we have taken this approach and developed the software Reaction Mechanism Generator (RMG), which generates chemical mechanism for oxidation and pyrolysis of a given fuel under a set of user-defined physical conditions. RMG uses group additivity values to generate thermochemistry of molecules and has a database of rate coefficients of elementary reactions. These two sets of data are used to generate chemical kinetic mechanism in a systematic manner. The reaction mechanisms generated by RMG are purely predictive and elementary rate coefficient from any reliable source can be added to RMG database to improve the quality of its predictions. The goal of my thesis was two fold, first to extend the capabilities and database of RMG and to release it as an open source software for the chemical kinetic community to use.
(cont.) The second was to take a practical system of interest and use RMG to generate the chemical mechanism and thereby demonstrate the utility of RMG in generating predictive chemical mechanisms for practical situations. As a part of the second step our hope was to generate new chemical insights into soot formation processes which are of great interest. The three most important contributions of the thesis are listed below. 1. My work with RMG has resulted in order of magnitude improvements in the cpu and memory usage of RMG and it has added many useful features to RMG like ac- curate sensitivity analysis for better interpreting the final mechanism. I have also worked on extending the database of RMG, by adding thermochemistry of ringed species that cannot be treated adequately by group additivity. Also kinetic rate rules for intramolecular-H-migration reactions in OOQOOH molecules were added to RMG database, which are important in predicting the low temperature oxidation of alkanes. 2. Recently there have been considerable advances in the methodology for rate coefficient calculations for loose transition states, i.e transition states that are not saddle points. These type of transition states are encountered often in radical-radical reactions. In addition to these advances there has been significant progress in accurate calculation of the pressure dependent rate coefficients for complicated potential energy surfaces with multiple wells and multiple product channels. The method is based on the master equation formulation of the problem. These detailed equations are then appropriately coarse-grained to calculate the phenomenological rate coefficients.
(cont.) I have used these state of the art techniques to calculate the rate coefficients for the formation of various aromatic species like benzene and styrene. The rate coefficients predicted by these methods were tested under certain conditions and are in good agreement with experimental data. 3. Finally to model a two-dimensional diffusion flame we have developed a solver that is able to solve a complicated set of highly coupled differential equations in an efficient manner to give accurate results. The solver in conjunction with chemistry that is developed using techniques mentioned in the last two points is used to solve the mole fraction profiles in the diffusion flame. The results of the simulations are compared to the experimental measurements and this process gives us insight into soot formation in diffusion flames.
by Sandeep Sharma.
Ph.D.
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Andrade, Restrepo Martín. "Mathematical modeling and evolutionary processes." Thesis, Sorbonne Paris Cité, 2019. http://www.theses.fr/2019USPCC021.
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La recherche présentée dans cette thèse concerne différents sujets dans le domaine de la biomathématique. J’aborde diverses questions en biologie (et liées aux systèmes complexes) avec des méthodes mathématiques et numériques. Ces questions sont les suivantes: (i) Les processus passifs sont-ils suffisants pour justifier la distribution asymétrique des protéines endommagées pendant et après la cytokinèse de la levure? (ii) Quels processus sont à l’origine des schémas complexes d’expansion de l’amyloïde bêta dans le cerveau des patients atteints de la maladie d’Alzheimer? (iii) Qu’y a-t-il derrière la dichotomie de ‘clusters’ vs. ‘cline-like’ dans les modèles d’évolution le long de gradients environnementaux? (iv) Comment cette dichotomie affecte-t-elle la dynamique spatiale des invasions? (v) Comment la multi-stabilité se manifeste-t-elle dans ces modèles? Ces questions sont abordées (à différentes échelles, certaines totalement et certaines partiellement) avec différentes méthodes théoriques. Les résultats devraient permettre de mieux comprendre les processus biologiques analysés et de motiver la poursuite des travaux expérimentaux et empiriques susceptibles de contribuer à résoudre les incertitudes persistantesThe research presented in this thesis concerns different topics in the field of Biomathematics. I address diverse questions arising in biology (and related to complex systems) with mathematical and numerical methods. These questions are: (i) Are passive-processes enough to justify the asymmetric distribution of damaged proteins during and after yeast cytokinesis? (ii) What processes are behind the complex patterns of expansion of Amyloid beta in the brains of patients with Alzheimer’s disease? (iii) What is behind the clustering and cline-like dichotomy in models of evolution along environmental gradients? (iv) How does this dichotomy affect the spatial dynamics of invasions and range expansions? (v) How does multi-stability manifest in these models? These questions are approached (at different scales, some fully and some partially) with different theoretical methods. Results are expected to shed light on the biological processes analyzed and to motivate further experimental and empirical work which can help solve lingering uncertainties
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Sharma, Chetan M. Eng Massachusetts Institute of Technology. "Automatic modeling of machining processes." Thesis, Massachusetts Institute of Technology, 2021. https://hdl.handle.net/1721.1/130833.
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Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, February, 2021Cataloged from the official PDF of thesis.
Includes bibliographical references (pages 47-48).
3 axis CNC milling is a ubiquitous manufacturing method in industry due to its versatility and precision. The fundamental parameters that dictate cutting performance ("speeds, feeds, and engagement") must be manually set by the machine programmer; proper operation therefore relies heavily on operator skill. In this thesis, an intelligent CNC controller is presented that uses low-cost sensors to fit an analytical model of cutting forces. The analytical nature of this model allows for favorable convergence characteristics and low computational costs. This is used to optimize cutting feeds with respect to process constraints for future movements; as more data is collected, the model continuously reinforced. This intelligent controller therefore abstracts out some of the complexities of machining and makes the process more approachable.
by Chetan Sharma.
M. Eng.
M.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science
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Su, Jiann-Cherng. "Residual stress modeling in machining processes." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2006. http://hdl.handle.net/1853/14030.
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Thesis (Ph.D)--Mechanical Engineering, Georgia Institute of Technology, 2007.Committee Chair: Liang, Steven Y.; Committee Member: Garmestani, Hamid; Committee Member: Huang, Yong; Committee Member: Melkote, Shreyes N.; Committee Member: Neu, Richard W. Part of the SMARTech Electronic Thesis and Dissertation Collection.
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Welf, Erik Steven. "Integrative modeling of cell adhesion processes." Access to citation, abstract and download form provided by ProQuest Information and Learning Company; downloadable PDF file, 285 p, 2009. http://proquest.umi.com/pqdweb?did=1833641671&sid=4&Fmt=2&clientId=8331&RQT=309&VName=PQD.
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Thesis (Ph.D.)--University of Delaware, 2009.Principal faculty advisors: Babatunde Ogunnaike, Dept. of Chemical Engineering, and Ulhas P. Naik, Dept. of Biological Sciences. Includes bibliographical references.
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Dong, Wen S. M. Massachusetts Institute of Technology. "Influence modeling of complex stochastic processes." Thesis, Massachusetts Institute of Technology, 2006. http://hdl.handle.net/1721.1/37386.
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Thesis (S.M.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2006.Includes bibliographical references (leaves 75-76).
A complex stochastic process involving human behaviors or human group behaviors is computationally hard to model with a hidden Markov process. This is because the state space of such behaviors is often a Cartesian product of a large number of constituent probability spaces, and is exponentially large. A sample for those stochastic processes is normally composed of a large collection of heterogeneous constituent samples. How to combine those heterogeneous constituent samples in a consistent and stable way is another difficulty for the hidden Markov process modeling. A latent structure influence process models human behaviors and human group behaviors by emulating the work of a team of experts. In such a team, each expert concentrates on one constituent probability space, investigates one type of constituent samples, and/or employ one type of technique. An expert improves his work by considering the results from the other experts, instead of the raw data for them. Compared with the hidden Markov process, the latent structure influence process is more expressive, more stable to outliers, and less likely to overfit. It can be used to study the interaction of over 100 persons and get good results.
(cont.) This thesis is organized in the following way. Chapter 0 reviews the notation and the background concepts necessary to develop this thesis. Chapter 1 describes the intuition behind the latent structure influence process and the situations where it outperforms the other dynamic models. In Chapter 2, we give inference algorithms based on two different interpretations of the influence model. Chapter 3 applies the influence algorithms to various toy data sets and real-world data sets. We hope our demonstrations of the influence modeling could serve as templates for the readers to develop other applications. In Chapter 4, we conclude with the rationale and other considerations for influence modeling.
by Wen Dong.
S.M.
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Mehrabi, M. Reza. "Modeling transport processes in directional solidification." Thesis, Massachusetts Institute of Technology, 1994. http://hdl.handle.net/1721.1/11999.
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Arruda, Guilherme Ferraz de. "Modeling spreading processes in complex networks." Universidade de São Paulo, 2017. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-20072018-160836/.
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Mathematical modeling of spreading processes have been largely studied in the literature, and its presented a boom in the past few years. This is a fundamental task on the understanding and prediction of real spreading processes on top of a population and are subject to many structural and dynamical constraints. Aiming at a better understanding of this processes, we focused in two task: the modeling and the analysis of both dynamical and structural aspects of these processes. Initially, we proposed a new and general model that unifies epidemic and rumor spreading. Besides, regarding the analysis of these processes, we extended the classical formalism to multilayer networks, in which the theory was lacking. Interestingly, this study opened up new challenges concerning the understanding of multilayer networks. More specifically, regarding their spectral properties. In this thesis, we analyzed such processes on top of single and multilayer networks. Thus, throughout our analysis, we followed three complementary approaches: (i) analytical, (ii) numerical and (iii) simulations, mainly Monte Carlo simulations. Our main results are: (i) a new unifying model, enabling us to model and understand spreading processes on large systems, (ii) characterization of new phenomena on multilayer networks, such as layer-wise localization and the barrier effect and (iii) an spectral analysis of multilayer systems, suggesting a universal parameter and proposing a new analytical tool for its analysis. Our contributions enable further research on modeling of spreading processes, also emphasizing the importance of considering the complete multilayer structure instead of any coarse-graining. Additionally, it can be directly applied on the prediction and modeling real processes. Thus, aside from the theoretical interest and its mathematical implications, it also presents important social impact.A modelagem matemática dos processos de disseminação tem sido amplamente estudada na literatura, sendo que o seu estudo apresentou um boom nos últimos anos. Esta é uma tarefa fundamental na compreensão e previsão de epidemias reais e propagação de rumores numa população, ademais, estas estão sujeitas a muitas restrições estruturais e dinâmicas. Com o objetivo de entender melhor esses processos, nos concentramos em duas tarefas: a de modelagem e a de análise de aspectos dinâmicos e estruturais. No primeiro, propomos um modelo novo e geral que une a epidemia e propagação de rumores. Também, no que diz respeito à análise desses processos, estendemos o formalismo clássico às redes multicamadas, onde tal teoria era inexistente. Curiosamente, este estudo abriu novos desafios relacionados à compreensão de redes multicamadas, mais especificamente em relação às suas propriedades espectrais. Nessa tese, analisamos esses processos em redes de uma e múltiplas camadas. Ao longo de nossas análises seguimos três abordagens complementares: (i) análises analíticas, (ii) experimentos numéricos e (iii) simulações de Monte Carlo. Assim, nossos principais resultados são: (i) um novo modelo que unifica as dinâmicas de rumor e epidemias, nos permitindo modelar e entender tais processos em grandes sistemas, (ii) caracterização de novos fenômenos em redes multicamadas, como a localização em camadas e o efeito barreira e (iii) uma análise espectral de sistemas multicamadas, sugerindo um parâmetro de escala universal e propondo uma nova ferramenta analítica para sua análise. Nossas contribuições permitem que novas pesquisas sobre modelagem de processos de propagação, enfatizando também a importância de se considerar a estrutura multicamada. Dessa forma, as nossas contribuições podem ser diretamente aplicadas à predição e modelagem de processos reais. Além do interesse teórico e matemático, nosso trabalho também apresenta implicações sociais importantes.
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ABU, HAMMAD AYMAN ABDALLAH. "SIMULATION MODELING OF MANUFACTURED HOUSING PROCESSES." University of Cincinnati / OhioLINK, 2002. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1015617645.
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Shi, Ruijie. "Subspace identification methods for process dynamic modeling /." *McMaster only, 2001.
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Meyer, Andreas, Sergey Smirnov, and Mathias Weske. "Data in business processes." Universität Potsdam, 2011. http://opus.kobv.de/ubp/volltexte/2011/5304/.
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Process and data are equally important for business process management. Process data is especially relevant in the context of automated business processes, process controlling, and representation of organizations' core assets. One can discover many process modeling languages, each having a specific set of data modeling capabilities and the level of data awareness. The level of data awareness and data modeling capabilities vary significantly from one language to another.
This paper evaluates several process modeling languages with respect to the role of data. To find a common ground for comparison, we develop a framework, which systematically organizes process- and data-related aspects of the modeling languages elaborating on the data aspects. Once the framework is in place, we compare twelve process modeling languages against it. We generalize the results of the comparison and identify clusters of similar languages with respect to data awareness.Prozesse und Daten sind gleichermaßen wichtig für das Geschäftsprozessmanagement. Prozessdaten sind dabei insbesondere im Kontext der Automatisierung von Geschäftsprozessen, dem Prozesscontrolling und der Repräsentation der Vermögensgegenstände von Organisationen relevant. Es existieren viele Prozessmodellierungssprachen, von denen jede die Darstellung von Daten durch eine fest spezifizierte Menge an Modellierungskonstrukten ermöglicht. Allerdings unterscheiden sich diese Darstellungenund damit der Grad der Datenmodellierung stark untereinander. Dieser Report evaluiert verschiedene Prozessmodellierungssprachen bezüglich der Unterstützung von Datenmodellierung. Als einheitliche Grundlage entwickeln wir ein Framework, welches prozess- und datenrelevante Aspekte systematisch organisiert. Die Kriterien legen dabei das Hauptaugenmerk auf die datenrelevanten Aspekte. Nach Einführung des Frameworks vergleichen wir zwölf Prozessmodellierungssprachen gegen dieses. Wir generalisieren die Erkenntnisse aus den Vergleichen und identifizieren Cluster bezüglich des Grades der Datenmodellierung, in welche die einzelnen Sprachen eingeordnet werden.
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Dávila-Felipe, Miraine. "Pathwise decompositions of Lévy processes : applications to epidemiological modeling." Thesis, Paris 6, 2016. http://www.theses.fr/2016PA066651.
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Cette thèse est consacrée à l'étude de décompositions trajectorielles de processus de Lévy spectralement positifs et des relations de dualité pour des processus de ramification, motivée par l'utilisation de ces derniers comme modèles probabilistes d'une dynamique épidémiologique. Nous modélisons l'arbre de transmission d'une maladie comme un arbre de ramification, où les individus évoluent indépendamment les uns des autres, ont des durées de vie i.i.d. (périodes d'infectiosité) et donnent naissance (infections secondaires) à un taux constant durant leur vie. Le processus d'incidence dans ce modèle est un processus de Crump-Mode-Jagers (CMJ) et le but principal des deux premiers chapitres est d'en caractériser la loi conjointement avec l'arbre de transmission partiellement observé, inferé à partir des données de séquences. Dans le Chapitre I, nous obtenons une description en termes de fonctions génératrices de la loi du nombre d'individus infectieux, conditionnellement à l'arbre de transmission reliant les individus actuellement infectés. Une version plus élégante de cette caractérisation est donnée dans le Chapitre II, en passant par un résultat général d'invariance par retournement du temps pour une classe de processus de ramification. Finallement, dans le Chapitre III nous nous intéressons à la loi d'un processus de ramification (sous)critique vu depuis son temps d'extinction. Nous obtenons un résultat de dualité qui implique en particulier l'invariance par retournement du temps depuis leur temps d'extinction des processus CMJ (sous)critiques et de l'excursion hors de 0 de la diffusion de Feller critique (le processus de largeur de l'arbre aléatoire de continuum)This dissertation is devoted to the study of some pathwise decompositions of spectrally positive Lévy processes, and duality relationships for certain (possibly non-Markovian) branching processes, driven by the use of the latter as probabilistic models of epidemiological dynamics. More precisely, we model the transmission tree of a disease as a splitting tree, i.e. individuals evolve independently from one another, have i.i.d. lifetimes (periods of infectiousness) that are not necessarily exponential, and give birth (secondary infections) at a constant rate during their lifetime. The incidence of the disease under this model is a Crump-Mode-Jagers process (CMJ); the overarching goal of the two first chapters is to characterize the law of this incidence process through time, jointly with the partially observed (inferred from sequence data) transmission tree. In Chapter I we obtain a description, in terms of probability generating functions, of the conditional likelihood of the number of infectious individuals at multiple times, given the transmission network linking individuals that are currently infected. In the second chapter, a more elegant version of this characterization is given, passing by a general result of invariance under time reversal for a class of branching processes. Finally, in Chapter III we are interested in the law of the (sub)critical branching process seen from its extinction time. We obtain a duality result that implies in particular the invariance under time reversal from their extinction time of the (sub)critical CMJ processes and the excursion away from 0 of the critical Feller diffusion (the width process of the continuum random tree)
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Wanduku, Divine. "Stochastic Modeling of Network-Centric Epidemiological Processes." Scholar Commons, 2012. http://scholarcommons.usf.edu/etd/4252.
Full textAbstract:
The technological changes and educational expansion have created the heterogeneity in the human species. Clearly, this heterogeneity generates a structure in the population
dynamics, namely: citizen, permanent resident, visitor, and etc. Furthermore, as the heterogeneity in the population increases, the human mobility between meta-populations patches
also increases. Depending on spatial scales, a meta-population patch can be decomposed into sub-patches, for examples: homes, neighborhoods, towns, etc. The dynamics of human
mobility in a heterogeneous and scaled structured population is still its infancy level. We develop and investigate (1) an algorithmic two scale human mobility dynamic model for a meta-population. Moreover,the two scale human mobility dynamic model can be extended to multi-scales by applying the algorithm. The subregions and regions are interlinked via intra-and inter regional transport network systems. Under various types of growth order assumptions on the intra and interregional residence times of the residents of a sub region, different patterns of static behavior of the mobility process are studied. Furthermore, the human mobility dynamic model is applied to a two-scale population dynamic exhibiting a special real life human transportation network pattern. The static evolution of all categories of residents of a given site ( homesite, visiting sites within the region, and visiting sites in other regions) over continuous changes in the intra and inter-regional visiting times is also analyzed.
The development of the two scale human mobility dynamic model provides a suitable approach to undertake the study of the non-uniform global spread of emergent infectious
diseases of humans in a systematic and unified way. In view of this, we derive (2) a SIRS stochastic epidemic dynamic process in a two scale structured population. By defining a positively self invariant set for the dynamic model the stochastic asymptotic stability results of the disease free equilibrium are developed(2). Furthermore, the significance of the stability results are illustrated in a simple real life scenario that is under controlled quarantine disease strategy. In addition, the epidemic dynamic model (2) is applied to a
SIR influenza epidemic in a two scale population that is under the influence of a special real life human mobility pattern. The simulated trajectories for the different states (susceptible, Infective, Removal) with respect to current location in the two-scale population structure are presented. The simulated findings reveal comparative evolution patterns for the different states and current locations over time.
The SIRS stochastic epidemic dynamic model (2) is extended to a SIR delayed stochastic epidemic dynamic model(3). The delay effects in the dynamic model (3) is temporary
and account for natural or infection acquired immunity conferred by the disease after disease recovery. Again, we justify the model validation as a prerequisite for the dynamic modeling. Moreover, we also exhibit the real life scenario under controlled quarantine disease strategy.In addition, the developed delayed SIR dynamic model is also applied to SIR influenza epidemic with temporary immunity to an influenza disease strain. The simulated results reveal an oscillatory effect in the trajectory of the naturally immune population. Moreover, the oscillations are more significant at the homesite.
We further extended the stochastic temporary delayed epidemic dynamic model (3) into a stochastic delayed epidemic dynamic model with varying immunity period(4). The varying immunity period accounts for the varying time lengths of natural immunity against the infectious agent exhibited within the naturally immune population. Obviously, the stochastic dynamic model with varying immunity period generalizes the SIR temporary delayed dynamic.
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Witzig, Andreas. "Modeling the optical processes in semiconductor lasers /." [S.l.] : [s.n.], 2002. http://e-collection.ethbib.ethz.ch/show?type=diss&nr=14694.
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Ströhlein, Guido. "Modeling of reactive- and bio-chromatographic processes /." Zürich : ETH, 2007. http://e-collection.ethbib.ethz.ch/show?type=diss&nr=16950.
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Isangulov, Rustam. "Mathematical modeling applied to oil field processes." Thesis, Imperial College London, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.511882.
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Rahmandad, Hazhir. "Three essays on modeling dynamic organizational processes." Thesis, Massachusetts Institute of Technology, 2005. http://hdl.handle.net/1721.1/33658.
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Thesis (Ph. D.)--Massachusetts Institute of Technology, Sloan School of Management, 2005.Includes bibliographical references.
Essay 1- Effects of Feedback Delay on Learning. Learning figures prominently in many theories of organizations. Understanding barriers to learning is therefore central to understanding firms' performance. This essay investigates the role of time delays between taking an action and observing the results in impeding learning. These delays ubiquitous in real world settings can introduce important tradeoffs between the long-term and the short-term performance. In this essay, four learning algorithms, with different levels of complexity and rationality, are built and their performances in a simple resource allocation task are analyzed. The study focuses on understanding the effect of time delays on learning. Simulation analysis shows that regardless of the level of rationality of the organization, misperceived delays can impede learning significantly. Essay 2- Heterogeneity and Network Structure in the Dynamics of Diffusion: Comparing Agent-Based and Differential Equation Models When is it better to use agent-based (AB) models, and when should differential equation (DE) models be used?
(cont.) Where DE models assume homogeneity and perfect mixing within compartments, AB models can capture heterogeneity in agent attributes and in the network of interactions among them. The costs and benefits of such disaggregation should guide the choice of model type. AB models may enhance realism but entail computational and cognitive costs that may limit sensitivity analysis and model scope. Using contagious disease as an example, we contrast the dynamics of AB models with those of the analogous mean-field DE model. We examine agent heterogeneity and the impact of different network topologies, including fully connected, random, Watts-Strogatz small world, scale-free, and lattice networks. Surprisingly, in many conditions differences between the DE and AB dynamics are not statistically significant for key metrics relevant to public health, including diffusion speed, peak load on health services infrastructure and total disease burden. We discuss implications for the choice between AB and DE models, level of aggregation, and model boundary. The results apply beyond epidemiology: from innovation adoption to financial panics, many important social phenomena involve analogous processes of diffusion and social contagion.
(cont.) Essay 3- Dynamics of Multiple-release Product Development Product development (PD) is a crucial capability for firms in competitive markets. Building on case studies of software development at a large firm, this essay explores the interaction among the different stages of the PD process, the underlying architecture of the product, and the products in the field. We introduce the concept of the "adaptation trap," where intendedly functional adaptation of workload can overwhelm the PD organization and force it into firefighting (Repenning 2001) as a result of the delay in seeing the additional resource need from the field and underlying code-base. Moreover, the study highlights the importance of architecture and underlying product-base in multiple-release product development, through their impact on the quality of new models under development, as well as through resource requirements for bug-fixing. Finally, this study corroborates the dynamics of tipping into firefighting that follows quality-productivity tradeoffs under pressure. Put together, these dynamics elucidate some of the reasons why PD capability is hard to build and why it easily erodes.
(cont.) Consequently, we offer hypotheses on the characteristics of the PD process that increase its strategic significance and discuss some practical challenges in the face of these dynamics.
by Hazhir Rahmandad.
Ph.D.
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Kim, Gwang-Soo 1975. "Multiscale modeling of thin film deposition processes." Thesis, Massachusetts Institute of Technology, 2002. http://hdl.handle.net/1721.1/29277.
Full textAbstract:
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Chemical Engineering, 2002.Includes bibliographical references.
Ionized physical vapor deposition (IPVD) and electrochemical deposition (ECD) are two major thin film deposition processes in the microelectronics industry. The ion fluxes with high kinetic energies in IPVD process involve complex surface interactions that affect overall topology of the microscale features. Copper ECD process involves complex surface reactions and transport phenomena that ranges over different length scales. In this work, predictive simulation tools for these two processes have been developed by investigating the surface reaction and the transport phenomena in IPVD and ECD processes. In the IPVD process, molecular dynamics (MD) techniques with embedded-atom potentials are used to study the surface reactions for atoms with high impinging energies (30 - 50 eV). The surface reaction rates are combined with ballistic transport and level set methods. The resulting tool demonstrates the effect of the kinetic energy driven surface diffusion on the feature profile evolution. For the ECD process of copper, detailed surface kinetic mechanisms are developed based on the competitive adsorption/desorption model in the presence of three representative additives, poly ethylene glycol (PEG) and bis-(sodium sulfoprophyl) (SPS) and chloride. The proposed kinetic mechanism is capable of describing the synergistic effect of different additives on the copper deposition. Statistically designed experiments were performed with the rotating disk electrode (RDE) apparatus. A hydrodynamic model was developed for RDE and is used to fit the kinetic parameters that are independent of the transport effect.
(cont.) A reactor scale model is developed based on the Galerkin finite element method. The model includes momentum transport, transient mass transport, potential distribution and detailed surface kinetic mechanisms. The experimental film thickness uniformity on the blank wafer with commercial electrochemical deposition cell is compared with the simulation result. The reactor scale model is used to investigate the various effects on the film thickness uniformity including terminal effects and mass transport effects. The analysis shows the qualitative difference between two effects and how they can be eliminated. Also, the reactor scale simulation tool is used to model the pulse plating process. Improved performance of the pulse plating over the constant current operation suggests that the relaxation period is the critical parameter that determines the film thickness uniformity. A computationally efficient feature scale model is developed. Mass transport, potential distribution and detailed surface reactions are included in the model ...
by Gwang-Soo Kim.
Ph.D.
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Busch, John Victor 1956. "Technical cost modeling of plastics fabrication processes." Thesis, Massachusetts Institute of Technology, 1987. http://hdl.handle.net/1721.1/14837.
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Kohn, Alexander Wolfe. "Modeling non-radiative processes in solar materials." Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/115806.
Full textAbstract:
Thesis: Ph. D., Massachusetts Institute of Technology, Department of Chemistry, 2018.Cataloged from PDF version of thesis.
Includes bibliographical references (pages 89-102).
In this thesis, we investigate methods and systems for understanding the electronic properties of a variety of systems relevant to organic photovoltaics. The second chapter examines how to predict the radiative and non-radiative decay rates of a large family of naphthalene derivatives. Naphthalene is a common building block in many organic electronic devices and possesses complex photophysics that are difficult to capture. Principally using time-dependent density functional theory, we are able to reproduce the experimental rates and, moreover, the fluorescence quantum yield, quite accurately. The next chapter then goes into extensions of the methodology discussed and analyzed in the prior chapter. Anthracene derivatives used for transferring triplet energy between a quantum dot and rubrene phase are found to have varying impacts on the total transfer efficiency based on the triplet lifetime of the anthracene derivative. Most potently, significant spin-orbit coupling in some of the derivatives causes substantial deactivation. An additional family, BODIPY dyes, is also investigated. They are found to undergo internal conversion gated by an excited-state conformational change, suggesting this may be a common motif. The fourth and fifth chapters investigate different interfacial effects and their impacts on the energy levels of electrons and holes in disordered organic devices. They look at specific systems: the interface between three different donors, PPV, P3HT, PTB7, and PCBM. They find that the interface can both reduce and induce disorder in different systems and that full treatment of the electronic environment is important for capturing accurate results. The final chapter investigates the use of neural networks to predict optimal range-separation parameters for density functionals.
by Alexander Wolfe Kohn.
Ph. D.
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Tarud, Joan. "Simulations and modeling of biomass gasification processes." Thesis, Massachusetts Institute of Technology, 2004. http://hdl.handle.net/1721.1/31178.
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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, June 2005.Includes bibliographical references (leaves 80-81).
Available, low-cost, energy supplies are vital for the world's economy and stability. The current sources of energy harm our environment and are not renewable. Therefore, technology must accommodate new sustainable sources of energy to provide for the high-energy consumption. Biomass is a sustainable energy source that could ease the current reliance on fossil fuels. Gasification of biomass is a promising technology being researched by the National Renewable Energy Laboratory. An Aspen Plus® model was developed for the Thermochemical Process Development Unit (gasification unit) at the National Renewable Energy Laboratory. The model was designed for a feed of poultry litter and was also run with a feed of wood. The Aspen Plus® model is capable of trying various test conditions for the solids removal and scrubbing (condensation) systems. The model as it is currently formulated is not capable of predicting gasification output mixture compositions. It is desirable to decrease the amount of carbon dioxide and tars (defined for this study as compounds with a molecular weight equal to or greater than benzene) in the product gas of the TCPDU. Therefore, the model was run at temperatures for the scrubbing fluid varying from 15⁰C to 60⁰C (for wood) and from 10⁰C to 50⁰C (for poultry litter) and found that the total mole fraction of tars in the product gas for poultry litter feed to decrease by 4% by increasing the scrubbing fluid temperature from 40⁰C to 50⁰C and to increase by 4% by decreasing the temperature to 10⁰C and for wood feed to decrease by 7% by decreasing the temperature from 26⁰C to 15⁰C and to decrease by 10% by increasing the temperature from 26⁰C to 60⁰C.
(cont.) The model was run for mole fractions of tars between 0 and 1, in increments of approximately 0.2, in the scrubbing fluid (with water as the remaining fluid). When the amount of tars in the scrubbing fluid increases to approximately 0.2, the amount of tars in the exit stream increases 58-fold for wood and 50-fold for poultry litter. As a secondary effect, by increasing the tar mole fraction from 0 to 1 in the scrubbing fluid, the model predicts a decrease in mole fractions of carbon dioxide in the product gas of 66% and 36% for poultry litter and wood feeds respectively.
by Joan Tisdale.
S.M.
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Eleftheriadis, Stefanos. "Gaussian processes for modeling of facial expressions." Thesis, Imperial College London, 2016. http://hdl.handle.net/10044/1/44106.
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Automated analysis of facial expressions has been gaining significant attention over the past years. This stems from the fact that it constitutes the primal step toward developing some of the next-generation computer technologies that can make an impact in many domains, ranging from medical imaging and health assessment to marketing and education. No matter the target application, the need to deploy systems under demanding, real-world conditions that can generalize well across the population is urgent. Hence, careful consideration of numerous factors has to be taken prior to designing such a system. The work presented in this thesis focuses on tackling two important problems in automated analysis of facial expressions: (i) view-invariant facial expression analysis; (ii) modeling of the structural patterns in the face, in terms of well coordinated facial muscle movements. Driven by the necessity for efficient and accurate inference mechanisms we explore machine learning techniques based on the probabilistic framework of Gaussian processes (GPs). Our ultimate goal is to design powerful models that can efficiently handle imagery with spontaneously displayed facial expressions, and explain in detail the complex configurations behind the human face in real-world situations. To effectively decouple the head pose and expression in the presence of large out-of-plane head rotations we introduce a manifold learning approach based on multi-view learning strategies. Contrary to the majority of existing methods that typically treat the numerous poses as individual problems, in this model we first learn a discriminative manifold shared by multiple views of a facial expression. Subsequently, we perform facial expression classification in the expression manifold. Hence, the pose normalization problem is solved by aligning the facial expressions from different poses in a common latent space. We demonstrate that the recovered manifold can efficiently generalize to various poses and expressions even from a small amount of training data, while also being largely robust to corrupted image features due to illumination variations. State-of-the-art performance is achieved in the task of facial expression classification of basic emotions. The methods that we propose for learning the structure in the configuration of the muscle movements represent some of the first attempts in the field of analysis and intensity estimation of facial expressions. In these models, we extend our multi-view approach to exploit relationships not only in the input features but also in the multi-output labels. The structure of the outputs is imposed into the recovered manifold either from heuristically defined hard constraints, or in an auto-encoded manner, where the structure is learned automatically from the input data. The resulting models are proven to be robust to data with imbalanced expression categories, due to our proposed Bayesian learning of the target manifold. We also propose a novel regression approach based on product of GP experts where we take into account people's individual expressiveness in order to adapt the learned models on each subject. We demonstrate the superior performance of our proposed models on the task of facial expression recognition and intensity estimation.
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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.Submitted by Renato Vasconcelos (ppgeti@ufc.br) on 2017-09-09T02:26:38Z No. of bitstreams: 1 2017_tes_clcmattos.pdf: 5961013 bytes, checksum: fc44d8b852e28fa0e1ebe0c87389c0da (MD5)
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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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Khare, Neeraj Prasad. "Predictive Modeling of Metal-Catalyzed Polyolefin Processes." Diss., Virginia Tech, 2003. http://hdl.handle.net/10919/11065.
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This dissertation describes the essential modeling components and techniques for building comprehensive polymer process models for metal-catalyzed polyolefin processes. The significance of this work is that it presents a comprehensive approach to polymer process modeling applied to large-scale commercial processes. Most researchers focus only on polymerization mechanisms and reaction kinetics, and neglect physical properties and phase equilibrium. Both physical properties and phase equilibrium play key roles in the accuracy and robustness of a model.
This work presents the fundamental principles and practical guidelines used to develop and validate both steady-state and dynamic simulation models for two large-scale commercial processes involving the Ziegler-Natta polymerization to produce high-density polyethylene (HDPE) and polypropylene (PP). It also provides a model for the solution polymerization of ethylene using a metallocene catalyst. Existing modeling efforts do not include physical properties or phase equilibrium in their calculations. These omissions undermine the accuracy and predictive power of the models.
The forward chapters of the dissertation discuss the fundamental concepts we consider in polymer process modeling. These include physical and thermodynamic properties, phase equilibrium, and polymerization kinetics. The later chapters provide the modeling applications described above.Ph. D.
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Zhang, Mingchuan. "Advanced spatial information processes: modeling and application." Diss., Virginia Polytechnic Institute and State University, 1985. http://hdl.handle.net/10919/76087.
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Making full use of spatial information is an important problem in information-processing and decision making. In this dissertation, two Bayesian decision theoretic frameworks for context classification are developed which make full use of spatial information. The first framework is a new multispectral image context classification technique which is based on a recursive algorithm for optimal estimation of the state of a two-dimensional discrete Markov Random Field (MRF). The implementation of the recursive algorithm is a form of dynamic programming. The second framework is based on a stochastic relaxation algorithm and Markov-Gibbs Random Fields. The relaxation algorithm constitutes an optimization using annealing. We also discuss how to estimate the Markov Random Field Model parameters, which is a key problem in using MRF in image processing and pattern recognition. The estimation of transition probabilities in a 2-D MRF is converted into two 1-D estimation problems. Then a Space-varying estimation method for transition probabilities is discussed.Ph. D.
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Keefe, Matthew James. "Statistical Monitoring and Modeling for Spatial Processes." Diss., Virginia Tech, 2017. http://hdl.handle.net/10919/76664.
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Statistical process monitoring and hierarchical Bayesian modeling are two ways to learn more about processes of interest. In this work, we consider two main components: risk-adjusted monitoring and Bayesian hierarchical models for spatial data. Usually, if prior information about a process is known, it is important to incorporate this into the monitoring scheme. For example, when monitoring 30-day mortality rates after surgery, the pre-operative risk of patients based on health characteristics is often an indicator of how likely the surgery is to succeed. In these cases, risk-adjusted monitoring techniques are used. In this work, the practical limitations of the traditional implementation of risk-adjusted monitoring methods are discussed and an improved implementation is proposed. A method to perform spatial risk-adjustment based on exact locations of concurrent observations to account for spatial dependence is also described. Furthermore, the development of objective priors for fully Bayesian hierarchical models for areal data is explored for Gaussian responses. Collectively, these statistical methods serve as analytic tools to better monitor and model spatial processes.Ph. D.
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Gupta, Shailesh. "Mathematical Modeling of Thin Strip Casting Processes." The Ohio State University, 1997. http://rave.ohiolink.edu/etdc/view?acc_num=osu1391679731.
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P, Snehalatha Suma. "Logical Modeling of ETL Processes Using XML." University of Cincinnati / OhioLINK, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1276948742.
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Ganguly, Shreyan. "Modeling Nonstationarity Using Locally Stationary Basis Processes." The Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1563408374215259.
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Popescu, Catalin Nicolae. "Modeling and control of extrusion coating." Diss., Georgia Institute of Technology, 2001. http://hdl.handle.net/1853/13700.
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Asp, Grönhagen Klara. "Phase-field modeling of surface-energy driven processes." Doctoral thesis, KTH, Metallografi, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-11036.
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Surface energy plays a major role in many phenomena that are important in technological and industrial processes, for example in wetting, grain growth and sintering. In this thesis, such surface-energy driven processes are studied by means of the phase-field method. The phase-field method is often used to model mesoscale microstructural evolution in materials. It is a diffuse interface method, i.e., it considers the surface or phase boundary between two bulk phases to have a non-zero width with a gradual variation in physical properties such as energy density, composition and crystalline structure. Neck formation and coarsening are two important diffusion-controlled features in solid-state sintering and are studied using our multiphase phase-field method. Inclusion of Navier-Stokes equation with surface-tension forces and convective phase-field equations into the model, enables simulation of reactive wetting and liquid-phase sintering. Analysis of a spreading liquid on a surface is investigated and is shown to follow the dynamics of a known hydrodynamic theory. Analysis of important capillary phenomena with wetting and motion of two particles connected by a liquid bridge are studied in view of important parameters such as contact angles and volume ratios between the liquid and solid particles. The interaction between solute atoms and migrating grain boundaries affects the rate of recrystallization and grain growth. The phenomena is studied using a phase-field method with a concentration dependent double-well potential over the phase boundary. We will show that with a simple phase-field model it is possible to model the dynamics of grain-boundary segregation to a stationary boundary as well as solute drag on a moving boundary. Another important issue in phase-field modeling has been to develop an effective coupling of the phase-field and CALPHAD methods. Such coulping makes use of CALPHAD's thermodynamic information with Gibbs energy function in the phase-field method. With the appropriate thermodynamic and kinetic information from CALPHAD databases, the phase-field method can predict mictrostructural evolution in multicomponent multiphase alloys. A phase-field model coupled with a TQ-interface available from Thermo-Calc is developed to study spinodal decomposition in FeCr, FeCrNi and TiC-ZrC alloys.QC 20100622
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Kadkhodabeigi, Mehdi. "Modeling of Tapping Processes in Submerged Arc Furnaces." Doctoral thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for materialteknologi, 2011. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-13240.
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Ammar, Mohammed E. "Cross-directional processes : modeling, identification and robust control." Thesis, University of British Columbia, 2009. http://hdl.handle.net/2429/14843.
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The aim of this work is to improve the identification of cross-directional (CD)
models as well as to simplify the robust control design problem. Providing
computationally efficient techniques for identification and robust control will
facilitate the implementation of an autonomous CD control system. These are challenging problems as CD processes are large scale distributed parameter systems. The conventional model for a CD process is a spatial interaction matrix cascaded by a low order transfer function in the temporal domain. This representation results in a large dimensional multi-variable model.
Model uncertainties in the process are inevitable and rise from several sources. As the CD process models are usually identified by input-output data from bump tests, there is a demand for better identification techniques that minimizes the uncertainties in CD mapping and response shape models.
Mapping and misalignment detection are problems that are specific to CD processes due to the configuration of the paper machine and its unconventional scanning method. These problems are of great practical importance for industrial implementations. This work proposes modeling the CD process as a two-dimensional (2D)
system that is spatially noncausal. The spatial noncausal transfer functions facilitate input design and identification in the spatial domain. Both noncausal finite impulse response (FIR) models and rational transfer functions are used to model the CD response. The FIR model representation is
convenient for input design and identification in the CD while the spatial
noncausal rational transfer function is more suitable for robust control design. The 2D representation for the plant and the controller is convenient for implementation in an autonomous control scheme with iterative feedback tuning or adaptive control. Robust stability criteria are developed to investigate the stability of the
process under feedback. Employing the 2D representation results in criteria
that are computationally efficient as the 2D stability conditions are replaced
by a set of simple 1D problems. A robust stability criterion that is based on the v-gap stability criterion provides bounds for robust stability against perturbations in the plant or the controller. This feature permits designing a two-dimensional controller in an adaptive control scheme or a simple re-tuning of an existing controller through iterative feedback tuning. This
property is convenient when switching between different grades of paper.
Another stability criterion based on the concept of phase margins is developed to determine the closed-loop’s tolerance to misalignment uncertainties. Finally, a simple loop-shaping technique using spatial transfer functions is proposed to shape the closed-loop’s two-dimensional frequency response. The noncausal spatial transfer function provides a convenient tool for improving the closed-loop’s performance at low temporal frequencies. An adaptive control scheme is implemented to retune the system after a grade change. The developed techniques for identification and robust control can be used as a part of a more sophisticated autonomous control system.
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Song, Zhili Jack. "Modeling of Gas Flows in Steelmaking Decarburization Processes." Doctoral thesis, KTH, Tillämpad processmetallurgi, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-128541.
Full textAbstract:
The purpose of the current study is to increase the understanding of different steelmaking processes at the decarburization stages by use of mathematical modeling. More specifically, two De-Laval nozzles from a VOD (Vaccum Oxygen Decarburization) process, which is used for producing stainless steels with ultra-low carbon grades, was investigated for different vessel pressures. Moreover, the post combustion phenomena in a BOF or LD (Linz-Donawitz) process as well as an AOD (Argon Oxygen Decarburization) process were studied focusing on the decarburization stage. Two industrial VOD nozzles were numerically studied and compared at different temperatures and ambient pressures. Flow patterns of the oxygen jet under different ambient pressures were predicted and the flow information at different positions from the nozzle was analyzed. In addition, the effects of different ambient temperatures on the jet velocity and the dynamic pressure were compared. The predictions revealed that a little under-expansion is somewhat helpful to improve the dynamic pressure. The jet dynamic pressure and its width for the specific nozzle geometry were also studied. It was observed that a variation in the ambient pressure can influence the jet momentum and its width. In addition, a high ambient temperature was found to have a positive effect on the improvement of the jet dynamic pressure. Furthermore, it was found that a change in ambient pressure has a stronger effect on the jet force than a change in the ambient temperature. In addition, it was proved that the profiles of the dynamic pressure at a certain blowing distance fit well to Multi-Gaussian distribution. Post combustion in a BOF/LD and an AOD process during decarburization was also studied. Two mathematical models were created to show the post combustion phenomenon inside the converters, respectively. For the CFD modeling of the two processes, the realizable k-ɛ model, the species transport model and the discrete ordinate were adopted to calculate the turbulence, gas reaction and radiation present in the gas phase in the converter. For the BOF/LD modeling, a series of plant tests were conducted to collect data, which were used in the current model. These include the off-gas information, emissivity data, oxygen blowing parameters and the chemical composition of steel. After the simulation, the predicted flow pattern and detailed information of the gases taking part in the post combustion were compared to plant data. Specifically, the off-gas data from the plant was used for the model verification. The measured CO2 concentration was 15-20 wt% and the predicted value from the modeling was 16.7 wt%. For the AOD converter of interest in the current work, a fan is installed in the end of the AOD flue to help extract the off-gas from the converter. The influence of different fan gauge pressures as well as temperatures of the gas mixture, containing the generated CO and argon, on the post combustion in the whole AOD system was studied. It was indicated from the modeling results that the post combustion was only present in the flue for the present modeling conditions. Moreover, a critical fan gauge pressure (approx.. -550 Pa) was found which could yield a maximum post combustion in the flue gas. For both two models (BOF/LD and AOD), simulations indicated that a change of the converter temperature from 1500 to 1700 °C did not influence the post combustion reaction to a large degree. In addition, these two models can be regarded as the first step for a future more in-depth modeling work of the post combustion.QC 20130913
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Kethers, Stefanie. "Multi-perspective modeling and analysis of cooperation processes." [S.l.] : [s.n.], 2000. http://deposit.ddb.de/cgi-bin/dokserv?idn=962847658.
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Maurstad, Ola. "Population Balance Modeling of Agglomeration in Granulation Processes." Doctoral thesis, Norwegian University of Science and Technology, Faculty of Engineering Science and Technology, 2002. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-123.
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Agglomeration (the sticking together of particles) is often the major growth mechanism in granulation processes. The population balance equation (PBE) is a mathematical framework that is often applied to systems to describe how the particle size distribution changes as a function of time. Different kinetic terms are included in the PBE to describe the different particle growth mechanisms. In this work, a new kinetic model framework is proposed for the growth mechanism binary agglomeration. Binary agglomeration means that only two particles are involved in an agglomeration event. The generality of the new model framework is an advantage over the previous coalescence kernel framework. It is shown that an existing coalescence kernel model can be expressed by means of the new framework.
The new model framework is then adapted to the special case of fluidized bed granulation (FBG) by proposing/choosing expressions for the three submodels in the model framework. An advantage of the new FBG model is that a maximum number of agglomeration events per unit time can be estimated. This means that the model is one step closer to being used predictively. At the moment, no population balance models can predict granulation processes where agglomeration is the dominant growth mechanism. It is shown that both the new FBG model and an existing model could fit experimental data well, however, the new model reflects the situation that the presence of surface liquid is rate limiting for the agglomeratio process.
Experiments in a laboratory batch fluidized bed granulator were carried out. Samples of the particle size distribution were taken at intervals during an experiment. These data were used to fit the model parameters of the FBG model. The dissertation includes a discussion of the effect of certain operating conditions such as bed temperature and liquid spray rate on a model parameter.
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Liu, Huamin. "Modeling and optimal control of deteriorating production processes." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp02/NQ27995.pdf.
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Zhou, Liang. "Modeling interface advection in the die casting processes." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp01/MQ36914.pdf.
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Xie, Nan. "Computational analyses for modeling fluidized bed gasification processes." [Ames, Iowa : Iowa State University], 2007.
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Li, Zhichao. "Modeling, analysis, and experimental investigations of grinding processes." Diss., Manhattan, Kan. : Kansas State University, 2006. http://hdl.handle.net/2097/198.
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Schwab, Julian [Verfasser]. "Modeling regulatory processes with qualitative networks / Julian Schwab." Ulm : Universität Ulm, 2020. http://d-nb.info/121575826X/34.
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Horne, Simon James. "A novel hybrid approach to modeling chemical processes." Thesis, University of Newcastle Upon Tyne, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.531756.
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El-Bachir, Naoufel. "Stochastic default intensity modeling with dependent jump processes." Thesis, University of Reading, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.515698.
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Hebing, Lukas [Verfasser]. "Modeling and Control of Fermentation Processes / Lukas Hebing." Düren : Shaker, 2020. http://d-nb.info/1229779477/34.
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