Dissertations / Theses on the topic 'Complex system'

The use of terms such as “Engineering Systems”, “System of systems” and others have been coming into greater use over the past decade to denote systems of importance but with implied higher complexity than for the term systems alone. This paper searches for a useful taxonomy or classification scheme for complex Systems. There are two aspects to this problem: 1) distinguishing between Engineering Systems (the term we use) and other Systems, and 2) differentiating among Engineering Systems. Engineering Systems are found to be differentiated from other complex systems by being human-designed and having both significant human complexity as well as significant technical complexity. As far as differentiating among various engineering systems, it is suggested that functional type is the most useful attribute for classification differentiation. Information, energy, value and mass acted upon by various processes are the foundation concepts underlying the technical types.
Engineering Systems Division and Mechanical Engineering, Center for Innovation in Product Development

The mam purpose of this thesis focuses on the investigation of major financial volatility models including the relevant mean model used in the context of volatility estimation, and the development of a systematic nonlinear identification methodology for these problems. Financial volatility is one of the key aspects in financial economics and volatility modelling involves both the mean process modelling, and the volatility process modelling. Although many volatility models have been derived to approximate the volatility process, linear mean models are almost always used and to the best of our knowledge there is no application of fitting the mean process using a nonlinear model with selected structure. Based on the fact that nonlinearity has been observed in many financial market return data sets, the Non linear AutoRegression Moving Average with eXogenous input (NARMAX) modelling methodology with the term selection algorithm Orthogonal Forward Regression (OFR) is proposed to approximate the nonlinear mean process during volatility modelling. However, the assumption of a constant variance is usually violated in financial market return data. A new Weighted OFR algorithm is therefore proposed to correct for the impact of heteroskedastic noise on the term selection of the nonlinear mean model based on the assumption that the variance process is modelled by a Generalized AutoRegressive Conditional Heteroskedastic (GARCH) model. Because the weights to use are unknown, an iterative refined procedure is developed to learn the weights and to simultaneously improve the parameter estimates of both the mean and the volatility models. New validation methods are proposed to validate the nonlinear selected mean model and the volatility model. During the validation, the assumptions associated with the mean model are tested using a correlation method and the assumptions of the volatility model are tested using a Brock-Dechert-Scheinkrnan (80S) independent and identically distributed (i.i.d.) testing method. The prediction performance of the mean and volatility models is evaluated using a hold out Cross Validation (CV)method. A departure in the prediction of the volatility for the linear mean model, when using nonlinear simulated data, is successfully identified by the new validation methods and the nonlinear selected mean model passes the test. Another application of the NARAMX model, in the very new field of modelling mortality rate, is introduced. A quadratic polynomial mortality rate model selected by the OFR algorithm is developed based on the LifeMetrics male deaths and exposures data for England & Wales from the Office of National Statistics. Comparing the long term prediction of the new model with the Cairns-Blake-Dowd (CSO) statistical mortality rate model indicates the better prediction performance of the quadratic polynomial models. A back-testing method is applied to indicate the robustness of the selected NARMAX type mortality rate models. The term selection, parameter estimation, validation methods and new identification procedures proposed in this thesis open a new gateway to apply the NARMAX modelling technique in the financial area, and for mortality rate modelling to provide a new empirical practice of the NARMAX modelling method.

The proposed research is devoted to devising system identification and fault detection approaches and algorithms for a system characterized by nonlinear dynamics. Mathematical models of dynamical systems and fault models are built based on observed data from systems. In particular, we will focus on statistical subspace instrumental variable methods which allow the consideration of an appealing mathematical model in many control applications consisting of a nonlinear feedback system with nonlinearities at both inputs and outputs. Different solutions within the proposed framework are presented to solve the system identification and fault detection problems. Specifically, Augmented Subspace Instrumental Variable Identification (ASIVID) approaches are proposed to identify the closed-loop nonlinear Hammerstein systems. Then fast approaches are presented to determine the system order. Hard-over failures are detected by order determination approaches when failures manifest themselves as rank deficiencies of the dynamical systems. Geometric interpretations of subspace tracking theorems are presented in this dissertation in order to propose a fault tolerance strategy. Possible fields of application considered in this research include manufacturing systems, autonomous vehicle systems, space systems and burgeoning bio-mechanical systems.
Ph.D.
Department of Mechanical, Materials and Aerospace Engineering;
Engineering and Computer Science
Mechanical Engineering

Der Vortrag zeigt die Komplexität der Wechselwirkungen zwischen Infrarotstrahler und Werkstoff beim Infrarotschweißen von Kunststoffen auf. Hierfür werden die Haupteinflüsse auf die Strahler-Werkstoff-Wechselwirkungen beschrieben. Diese sind das Emissionsverhalten des Infrarotstrahlers und das Absorptionsverhalten des Kunststoffs. Der Einfluss der Infrarotstrahlerart (Quarzglasstrahler, Metallfolienstrahler) und von Füllstoffen (Ruß, Glasfasern) im Kunststoff wird näher betrachtet. Zudem enthält der Vortrag eine Empfehlung für die Vorgehensweise beim Infrarotschweißen von Kunststoffen, die Vor- und Nachteile des Fügeverfahrens und einen Einblick in aktuelle Forschungsaktivitäten auf dem Gebiet des Infrarotschweißens von Kunststoffen.

Modern systems are becoming increasingly complex, integrated and distributed, in order to meet the escalating demands for functionality. This has given rise to concepts such as system-of-systems (SoS), which organise a myriad of independent component systems into a collaborative super-system, capable of achieving unmatchable levels of functionality. Despite its advantages, SoS is still an infantile concept with many outstanding security concerns, including the lack of effective behavioural monitoring. This can be largely attributed to its distributed, decentralised and heterogeneous nature, which poses many significant challenges. The uncertainty and dynamics of both the SoS’s structure and function poses further challenges to overcome. Due to the unconventional nature of a SoS, existing behavioural monitoring solutions are often inadequate as they are unable to overcome these challenges. This monitoring deficiency can result in the occurrence of misbehaviour, which is one of the most serious yet underestimated security threats facing SoSs and their components. This thesis presents a novel misbehaviour detection framework specifically developed for operation in a SoS environment. By combining the use of uniquely calculated behavioural threshold profiles and periodic threshold adaptation, the framework is able to cope with monitoring the dynamic behaviour and suddenly occurring changes that affect threshold reliability. The framework improves SoS contribution and monitoring efficiency by controlling monitoring observations using statecharts, which react to the level of behavioural threat perceived by the system. The accuracy of behavioural analysis is improved by using a novel algorithm to quantify detected behavioural abnormalities, in terms of their level of irregularity. The framework utilises collaborative behavioural monitoring to increase the accuracy of the behavioural analysis, and to combat the threat posed by training based attacks to the threshold adaptation process. The validity of the collaborative behavioural monitoring is assured by using the novel behavioural similarity assessment algorithm, which selects the most behaviourally appropriate SoS components to collaborate with. The proposed framework and its subsequent techniques are evaluated via numerous experiments. These examine both the limitations and relative merits when compared to monitoring solutions and techniques from similar research areas. The results of these conclude that the framework is able to offer misbehaviour monitoring in a SoS environment, with increased efficiency and reduced false positive rates, false negative rates, resource usage and run-time requirements.

Thesis (M.S. in Systems Engineering)--Naval Postgraduate School, September 2008.
Thesis Advisor(s): Paulo, Eugene. "September 2008." Description based on title screen as viewed on October 31, 2008. Includes bibliographical references (p. 63-64). Also available in print.

L'architecture système cherche à se distinguer de son domaine d'origine, l'ingénierie système, en devenant un domaine émergent. Loin d'être reconnue en tant que science ou discipline à proprement parler, sa pratique est de plus en plus répandue de nos jours. Cependant, cette pratique reste encore peu formalisée et peu enseignée, faute d'un corpus de connaissances, de techniques ou de démarches établi et accessible.Notre thèse contribue à combler ce manque en proposant un paradigme de la conception architecturale des systèmes artificiels complexes. Ce dernier est construit en se basant sur des paradigmes existants, en les combinant, puis en les complétant. Il vise à doter l'architecte de systèmes artificiels complexes d'un cadre opérant, voire performatif. Il se traduit par une structuration de la démarche de conception en quatre niveaux.Un niveau dit archétypal condense les grands principes de toute démarche de conception architecturale de systèmes artificiels complexes. Ces principes sont dérivés de diverses démarches déjà appliquées, principalement à la conception de systèmes ou de produits, mais également à la conception architecturale de bâtiments.Un niveau dit général repose sur le principe d'une partition présent-futur, se différenciant en cela des approches d'ingénierie qui s'appuient traditionnellement sur une dichotomie problème-solution. L'idée prépondérante tient dans l'assentiment que lorsqu'un architecte conçoit, il ne résout pas de problèmes, mais il imagine des futurs possibles et plausibles, nécessitant qu'il perçoive le présent. Cette vision impacte directement la nature des artéfacts sur lesquels il travaille. Nous proposons ensuite d'agréger ces artéfacts en des modèles, reflétant soit sa perception du présent, soit son élaboration des futurs, évoluant suivant des processus identifiés.Un niveau dit particulier a pour objectif de permettre la narration d'une conception particulière. Nous proposons pour cela une notation de la conception. Elle s'appuie sur un certain nombre de mécanismes élémentaires, dont celui de l'enchaînement divergence-convergence, que nous nommons mécanisme de respiration de la conception architecturale.Un niveau dit de boîte à outils n'est pas traité dans le cadre de cette thèse. Il comprendrait les différentes opérations cognitives nécessaires à l'architecte pour accomplir sa tâche de conception (abstraction, questionnement, jugement, comparaison, décision, etc.)L'approche proposée est illustrée par un exemple de conception architecturale d'un système complexe : « rendre une ville plus sûre » (connu dans la littérature anglo-saxonne comme Safe City)
Architecting seeks now to be distinct from its original domain, systems engineering, becoming an emergent domain. Far from being recognized as a science or a discipline, its practice is nowadays more and more widespread. However, this practice is still poorly formalized, and insufficiently being taught, lacking a well-established and accessible corpus of knowledge, techniques or approaches.This thesis contributes filling that gap by proposing a paradigm of the architectural design of artificial complex systems. The latter is built based on existing paradigms that are combined, then completed. It aims at providing architects with an effective, even performative framework. It results in an approach of the architectural design structured in four levels.A so-called archetypal level grasps the core principles of any approach of architectural design of artificial complex systems. These principles are derived from various approaches already applied, mainly in the field of system or product design, but also of architectural design of buildings.A so-called general level lies on the principle of a present-future division. In that sense, it differs from engineering approaches that usually rely on a problem-solution dichotomy. It is based on the following main idea: we should consent that when an architect designs, he does not solve problems, but he devises possible and plausible futures, requiring to perceive the present. This vision of the architect's way of working directly impacts the kind of artefacts he handles. We sho how to aggregate these artefacts into models, reflecting either his perception of the present, or his development of futures while progressing through some identified processes.A so-called particular level aims at allowing the storytelling of a given design. To achieve this goal, a notation of the design process is suggested. It lies on some basic mechanisms, one of them being the combination divergence-convergence, called the breathing of the architectural design.At last, a so-called toolbox level, not addressed in this thesis, would consist of various cognitive operations required for the architect in order to accomplish his task of designing (abstraction, questioning, judgement, comparison, decision, etc.)The whole approach is illustrated by an example of an architectural design of a complex system: a Safe City

Thesis (S.M. in Engineering and Management)--Massachusetts Institute of Technology, Engineering Systems Division, System Design and Management Program, 2012.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 104-105).
Over recent years, the global marketplace and its consumers have developed a mutual recognition for the rising prominence of design that delivers high quality user experience. For the provider of products and services, such design has emerged as a critical differentiator that creates new opportunities for revenue and drives customer loyalty. For the consumer, design can often surpass commoditized technical specifications as a basis for making purchase decisions. Nevertheless, despite their best intentions, many organizations still fail to successfully integrate design strategies into their existing processes and culture. This research examines the critical factors that enable successful implementation of design strategies under current market conditions in a way that produces sustained customer loyalty and revenue. Methods utilized in this research include one-on-one interviews, online surveys, and comparative case studies to ensure proper balance and perspective. The interviews were conducted with design leaders at organizations such as Philips and Frog Design. They covered the role that design plays at organizations, including the conditions necessary for successful design strategies and the barriers that they generally face. Surveys conducted for this research examined the personal experiences of 120 managers, engineers, and designers on the development of consumer-facing products, services, and systems. The result of these methods was a series of findings that supports the notion that a holistic "systems-based" approach offers competitive advantages to organizations looking to successfully implement a design-oriented strategy. It is this perspective that allows organizations to realize solutions that balance business objectives, technical capabilities, and design principles to meet customer intentions. It is proposed by this thesis that a systems-based approach has become necessary due to the ever-converging nature of today's networked products and services, particularly in the consumer marketplace. This thesis concludes with a framework of methods and principles for conceiving and designing user experiences for a complex market of convergent products and services. The framework borrows methods from Systems Thinking, Design Thinking, and User Experience Design to create a singular process that provides the clarity and simplicity necessary for a user experience amidst the complexity of a system design process.
by Todd R. Reily.
S.M.in Engineering and Management

Thesis (M.S. in Operations Research)--Naval Postgraduate School, June 2007.
Thesis Advisor(s): Thomas W. Lucas. "June 2007." Includes bibliographical references (p. 65-66). Also available in print.

This thesis develops a methodology for designing resilient system-of-systems (SoS) networks. This methodology includes a capability-based resilience assessment framework, used to quantify SoS resilience. A complex networks approach is used to generate potential SoS network designs, focusing on scale-free and random network topologies, degree-based and random rewiring adaptation, and targeted and random node removal threats. Statistical design methods, specifically response surface methodology, are used to evaluate SoS networks and provide an understanding of the advantages and disadvantages of potential designs. Linear regression is used to model a continuous representation of the network design space, and determine optimally resilient networks for particular threat types. The methodology is applied to an information exchange (IE) network model (i.e., a message passing network model) and military command and control (C2) model. Results show that optimally resilient IE network topologies are random for networks with adaptation, regardless of the threat type. However, the optimally resilient adaptation method sharply transitions from being fully random to fully degree-based as threat randomness increases. These findings suggest that intermediately defined networks should not be considered when designing for resilience. Cost-benefit analysis of C2 networks suggests that resilient C2 networks are more cost-effective than robust ones, as long as the cost of rewiring network links is less than three-fourths the cost of creating new links. This result identifies a threshold for which a resilient network design approach is more cost-effective than a robust one.This thesis develops a methodology for designing resilient system-of-systems (SoS) networks. This methodology includes a capability-based resilience assessment framework, used to quantify SoS resilience. A complex networks approach is used to generate potential SoS network designs, focusing on scale-free and random network topologies, degree-based and random rewiring adaptation, and targeted and random node removal threats. Statistical design methods, specifically response surface methodology, are used to evaluate SoS networks and provide an understanding of the advantages and disadvantages of potential designs. Linear regression is used to model a continuous representation of the network design space, and determine optimally resilient networks for particular threat types. The methodology is applied to an information exchange (IE) network model (i.e., a message passing network model) and military command and control (C2) model. Results show that optimally resilient IE network topologies are random for networks with adaptation, regardless of the threat type. However, the optimally resilient adaptation method sharply transitions from being fully random to fully degree-based as threat randomness increases. These findings suggest that intermediately defined networks should not be considered when designing for resilience. Cost-benefit analysis of C2 networks suggests that resilient C2 networks are more cost-effective than robust ones, as long as the cost of rewiring network links is less than three-fourths the cost of creating new links. This result identifies a threshold for which a resilient network design approach is more cost-effective than a robust one.

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

Complex network theory provides an elegant and powerful framework to statistically investigate the topology of local and long range dynamical interrelationships, i.e., teleconnections, in the climate system. Employing a refined methodology relying on linear and nonlinear measures of time series analysis, the intricate correlation structure within a multivariate climatological data set is cast into network form. Within this graph theoretical framework, vertices are identified with grid points taken from the data set representing a region on the the Earth's surface, and edges correspond to strong statistical interrelationships between the dynamics on pairs of grid points. The resulting climate networks are neither perfectly regular nor completely random, but display the intriguing and nontrivial characteristics of complexity commonly found in real world networks such as the internet, citation and acquaintance networks, food webs and cortical networks in the mammalian brain. Among other interesting properties, climate networks exhibit the "small-world" effect and possess a broad degree distribution with dominating super-nodes as well as a pronounced community structure. We have performed an extensive and detailed graph theoretical analysis of climate networks on the global topological scale focussing on the flow and centrality measure betweenness which is locally defined at each vertex, but includes global topological information by relying on the distribution of shortest paths between all pairs of vertices in the network. The betweenness centrality field reveals a rich internal structure in complex climate networks constructed from reanalysis and atmosphere-ocean coupled general circulation model (AOGCM) surface air temperature data. Our novel approach uncovers an elaborately woven meta-network of highly localized channels of strong dynamical information flow, that we relate to global surface ocean currents and dub the backbone of the climate network in analogy to the homonymous data highways of the internet. This finding points to a major role of the oceanic surface circulation in coupling and stabilizing the global temperature field in the long term mean (140 years for the model run and 60 years for reanalysis data). Carefully comparing the backbone structures detected in climate networks constructed using linear Pearson correlation and nonlinear mutual information, we argue that the high sensitivity of betweenness with respect to small changes in network structure may allow to detect the footprints of strongly nonlinear physical interactions in the climate system. The results presented in this thesis are thoroughly founded and substantiated using a hierarchy of statistical significance tests on the level of time series and networks, i.e., by tests based on time series surrogates as well as network surrogates. This is particularly relevant when working with real world data. Specifically, we developed new types of network surrogates to include the additional constraints imposed by the spatial embedding of vertices in a climate network. Our methodology is of potential interest for a broad audience within the physics community and various applied fields, because it is universal in the sense of being valid for any spatially extended dynamical system. It can help to understand the localized flow of dynamical information in any such system by combining multivariate time series analysis, a complex network approach and the information flow measure betweenness centrality. Possible fields of application include fluid dynamics (turbulence), plasma physics and biological physics (population models, neural networks, cell models). Furthermore, the climate network approach is equally relevant for experimental data as well as model simulations and hence introduces a novel perspective on model evaluation and data driven model building. Our work is timely in the context of the current debate on climate change within the scientific community, since it allows to assess from a new perspective the regional vulnerability and stability of the climate system while relying on global and not only on regional knowledge. The methodology developed in this thesis hence has the potential to substantially contribute to the understanding of the local effect of extreme events and tipping points in the earth system within a holistic global framework.
Die Theorie komplexer Netzwerke bietet einen eleganten Rahmen zur statistischen Untersuchung der Topologie lokaler und langreichweitiger dynamischer Zusammenhänge (Telekonnektionen) im Klimasystem. Unter Verwendung einer verfeinerten, auf linearen und nichtlinearen Korrelationsmaßen der Zeitreihenanalyse beruhenden Netzwerkkonstruktionsmethode, bilden wir die komplexe Korrelationsstruktur eines multivariaten klimatologischen Datensatzes auf ein Netzwerk ab. Dabei identifizieren wir die Knoten des Netzwerkes mit den Gitterpunkten des zugrundeliegenden Datensatzes, während wir Paare von besonders stark korrelierten Knoten als Kanten auffassen. Die resultierenden Klimanetzwerke zeigen weder die perfekte Regularität eines Kristallgitters, noch eine vollkommen zufällige Topologie. Vielmehr weisen sie faszinierende und nichttriviale Eigenschaften auf, die charakteristisch für natürlich gewachsene Netzwerke wie z.B. das Internet, Zitations- und Bekanntschaftsnetzwerke, Nahrungsnetze und kortikale Netzwerke im Säugetiergehirn sind. Besonders erwähnenswert ist, dass in Klimanetzwerken das Kleine-Welt-Phänomen auftritt. Desweiteren besitzen sie eine breite Gradverteilung, werden von Superknoten mit sehr vielen Nachbarn dominiert, und bilden schließlich regional wohldefinierte Untergruppen von intern dicht vernetzten Knoten aus. Im Rahmen dieser Arbeit wurde eine detaillierte, graphentheoretische Analyse von Klimanetzwerken auf der globalen topologischen Skala durchgeführt, wobei wir uns auf das Netzwerkfluss- und Zentralitätsmaß Betweenness konzentrierten. Betweenness ist zwar lokal an jedem Knoten definiert, enthält aber trotzdem Informationen über die globale Netzwerktopologie. Dies beruht darauf, dass die Verteilung kürzester Pfade zwischen allen möglichen Paaren von Knoten in die Berechnung des Maßes eingeht. Das Betweennessfeld zeigt reichhaltige und zuvor verborgene Strukturen in aus Reanalyse- und Modelldaten der erdoberflächennahen Lufttemperatur gewonnenen Klimanetzen. Das durch unseren neuartigen Ansatz enthüllte Metanetzwerk, bestehend aus hochlokalisierten Kanälen stark gebündelten Informationsflusses, bringen wir mit der Oberflächenzirkulation des Weltozeans in Verbindung. In Analogie mit den gleichnamigen Datenautobahnen des Internets nennen wir dieses Metanetzwerk den Backbone des Klimanetzwerks. Unsere Ergebnisse deuten insgesamt darauf hin, dass Meeresoberflächenströmungen einen wichtigen Beitrag zur Kopplung und Stabilisierung des globalen Oberflächenlufttemperaturfeldes leisten. Wir zeigen weiterhin, dass die hohe Sensitivität des Betweennessmaßes hinsichtlich kleiner Änderungen der Netzwerktopologie die Detektion stark nichtlinearer physikalischer Wechselwirkungen im Klimasystem ermöglichen könnte. Die in dieser Arbeit vorgestellten Ergebnisse wurden mithilfe statistischer Signifikanztests auf der Zeitreihen- und Netzwerkebene gründlich auf ihre Robustheit geprüft. In Anbetracht fehlerbehafteter Daten und komplexer statistischer Zusammenhänge zwischen verschiedenen Netzwerkmaßen ist diese Vorgehensweise besonders wichtig. Weiterhin ist die Entwicklung neuer, allgemein anwendbarer Surrogate für räumlich eingebettete Netzwerke hervorzuheben, die die Berücksichtigung spezieller Klimanetzwerkeigenschaften wie z.B. der Wahrscheinlichkeitsverteilung der Kantenlängen erlauben. Unsere Methode ist universell, weil sie zum Verständnis des lokalisierten Informationsflusses in allen räumlich ausgedehnten, dynamischen Systemen beitragen kann. Deshalb ist sie innerhalb der Physik und anderer angewandter Wissenschaften von potentiell breitem Interesse. Mögliche Anwendungen könnten sich z.B. in der Fluiddynamik (Turbulenz), der Plasmaphysik und der Biophysik (Populationsmodelle, neuronale Netzwerke und Zellmodelle) finden. Darüber hinaus ist der Netzwerkansatz für experimentelle Daten sowie Modellsimulationen gültig, und eröffnet folglich neue Perspektiven für Modellevaluation und datengetriebene Modellierung. Im Rahmen der aktuellen Klimawandeldebatte stellen Klimanetzwerke einen neuartigen Satz von Analysemethoden zur Verfügung, der die Evaluation der lokalen Vulnerabilität und Stabilität des Klimasystems unter Berücksichtigung globaler Randbedingungen ermöglicht. Die in dieser Arbeit entwickelten und untersuchten Methoden könnten folglich in der Zukunft, innerhalb eines holistisch-globalen Ansatzes, zum Verständnis der lokalen Auswirkungen von Extremereignissen und Kipppunkten im Erdsystem beitragen.

Thesis (S.M.)--Massachusetts Institute of Technology, Engineering Systems Division, 2005.
Includes bibliographical references (leaves 96-98).
Air Force sustainment operations are the focus of an intensive internal effort to improve performance and reduce costs. Past improvement initiatives have often failed to produce the intended results, and have caused performance to decline in some cases. Exploratory research was conducted at an Air Logistics Center to study how improvements are executed. Two conclusions are drawn from this research. The first is that changing sustainment operations is a problem of high dynamic and behavioral complexity. The second conclusion is that system models are well suited to coordinating change at the ALC because they provide insight into how a complicated system can be managed and improved. Three key findings support these conclusions. First, there is significant correlation between categories of unavailable F-16 aircraft such that reductions in one category are associated with increases in another. Second, an analysis of change efforts in two parts of the ALC shows that systemic influences, such as the inability to reinvest in improvements, are hindering change initiatives in one part of the ALC.
(cont.) The third finding is that a model of sustainment operations suggests that independent improvement initiatives are outperformed by coordinated efforts driven with an understanding of systemic interactions. Leaders throughout the sustainment community have expressed their desire to understand how sustainment operations function as a system. A hybrid approach to change is offered as a method for understanding and improving sustainment operations. System models are used to quantify and model system interactions; then policies and recommendations are drawn from the models. Recommendations may include process-level improvements utilizing change methods already in use at the ALC.
by Justin M. Hemann.
S.M.

Condition-based maintenance is an emerging paradigm of modern health monitoring, where maintenance operations are based upon diagnostics and prognostics. Prognostics promises to optimise maintenance scheduling, resources and supply chain management, leading to reductions in operational disruption, spares inventory, maintenance labour cost and hazardous conditions. The main objective of this research is to develop generic datadriven prognostic approaches to address several challenges associated with complex system prognostics, where in this particular work, the developed techniques are applied to the degradation data obtained from civil aerospace gas turbine engines. This thesis contains four key contributions. Firstly, deterministic Bayesian prognostics is used to deal with large uncertainty in degradation data. The novelty and value in the presented formulation lies in a fuller Bayesian treatment of observation error than prior art while retaining the closed-form solution desirable for real-time, deterministic computation. Secondly, the Bayesian hierarchical model (BHM) is introduced to optimise the use of fleet data from multiple assets. This formulation allows Bayesian updates of an individual predictive model to be made, based upon data received from a fleet of assets with different in-service lives. The results obtained demonstrate BHM capability in dealing with some extreme scenarios, occurring in complex system prognostics. The next contribution lies in developing variational inference for the existing BHM to overcome the computational and convergence concerns that are raised by sampling methods needed for the inference of the original formulation. This technique delivers an approximate but deterministic solution, where the quality of approximation is found to be satisfactory with respect to prediction performance, computational speed and ease of use. In the final contribution, an integration concept is proposed, combining the Bayesian data modelling technique with an information theoretic change-point detection algorithm to solve a wide class of prognostic problems, such as information arising from irregular events occurring during the life-cycle of an asset. This integration concept has a great potential to be implemented in complex system prognostics as it demonstrates several advantages of the deterministic BHM in combination with change-point detection to utilise, optimally, all available multiple unit data as well as data available at various levels of the system hierarchy.

Innovation has long been considered crucial for companies to gain a competitive edge in the global marketplace. Unfortunately, a solid understanding of the system of innovation does not exist. The literature lacks formal definitions and methodologies for the system of innovation. Many surrogates for innovation metrics have been posited in past research but none have solidified the overall concept of an innovation system or science. It has been speculated that innovation as a system is complex. Additionally, some researchers have suggested that this innovation system is adaptive. In these instances, of the literature, surrogates were again utilized in place of solid modeling and hypothesis that is benchmarked against real world case studies. Surrogates, such as patent citation, do serve a useful purpose to assist in the understanding of the historic nature of the innovation system but they fall short of defining the system completely. This paper seeks to aid in the solidification of a hypothesis of the system of innovation as a complex adaptive system. Initial consideration is directed towards the historic interactions that have taken place in the system of innovation. These interactions are viewed through the surrogate of patent citation as there is little other record of innovation. The novelty of this paper is that patent citations form not the core but rather a starting point for the definition of innovation as a complex adaptive system. Various models are built using techniques of cellular automata as well as agent-based modeling to assist in the understanding of the principles at work in the innovation system. These models present startling evidence that there exists an upper bound on the number of interactions any one invention should utilize in its course towards being deemed an innovation. Additionally, the models describe the benefits of partnership between innovating entities in a rapidly changing marketplace such as the current technological markets. This paper asserts specific conclusions, from the models, that assist in understanding that the system of innovation is truly a complex adaptive system. The models are further supported through real world examples.

Understanding the behavior of complex, large-scale, interconnected systems in a rigorous and structured manner is one of the most pressing scientific and technological challenges of current times. These systems include, among many others, transportation and communications systems, smart grids and power grids, financial markets etc. Failures of these systems have potentially enormous social, environmental and financial costs. In this work, we investigate the failure mechanisms of load-sharing complex systems. The systems are composed of multiple nodes or components whose failures are determined based on the interaction of their respective strengths and loads (or capacity and demand respectively) as well as the ability of a component to share its load with its neighbors when needed. Each component possesses a specific strength (capacity) and can be in one of three states: failed, damaged or functioning normally. The states are determined based on the load (demand) on the component. We focus on two distinct mechanisms to model the interaction between components strengths and loads. The first, a Loss of Strength (LOS) model and the second, a Customer Service (CS) model. We implement both models on lattice and scale-free graph network topologies. The failure mechanisms of these two models demonstrate temporal scaling phenomena, phase transitions and multiple distinct failure modes excited by extremal dynamics. We find that the resiliency of these models is sensitive to the underlying network topology. For critical ranges of parameters the models demonstrate power law and exponential failure patterns. We find that the failure mechanisms of these models have parallels to failure mechanisms of critical infrastructure systems such as congestion in transportation networks, cascading failure in electrical power grids, creep-rupture in composite structures, and draw-downs in financial markets. Based on the different variants of failure, strategies for mitigating and postponing failure in these critical infrastructure systems can be formulated.

In the presence of operational uncertainty, one of the greatest challenges in systems engineering is to ensure system effectiveness, mission capability and survivability. Safety management is shifting from passive, reactive and diagnosis-based approaches to autonomous architectures that will manage safety and survivability through active, proactive and prognosis-based solutions. Resilience engineering is an emerging discipline, with alternative recommendations on safer and more survivable system architectures. A resilient system can "absorb" the impact of change due to unexpected disturbances, while it "adapts" to change, in order to maintain its physical integrity and mission capability. A framework of proposed resilience estimations is the basis for a scenario-based assessment technique, driven by modeling and simulation-based (M&S) analysis, for obtaining system performance, health monitoring, damage propagation and overall mission capability responses. For the technique development and testing, a small-scale canonical problem has been formulated, involving a reconfigurable spring-mass-damper system, in a multi-spring configuration. Operational uncertainty is introduced through disturbance factors, such as external forces with varying magnitude, input frequency, event duration and occurrence time. Case studies with varying levels of damping and alternative reconfiguration strategies return the effects of operational uncertainty on system performance, mission capability, and survivability, as well as on the "restore", "absorb", and "adapt" resilience capacities. The Topological Investigation for Resilient and Effective Systems, through Increased Architecture Survivability (TIRESIAS) technique is demonstrated for a reduced scale, reconfigurable naval cooling network application. With uncertainty effects modeled through network leak combinations, TIRESIAS provides insight on leak effects to survival times, mission capability degradations, and on resilience function capacities, for the baseline configuration. Comparative case studies were conducted for different architecture configurations, which have been generated for different total number of control valves and valve locations on the topology.

Thesis (Ph. D.)--Ohio State University, 2003.
Title from first page of PDF file. Document formatted into pages; contains xiii, 102 p.: ill. (some col.). Includes abstract and vita. Advisor: Roger A. Crawfis, Dept. of Computer and Information Science. Includes bibliographical references (p. 98-102).

Quantifying the behavior of complex systems arguably presents the common ¡°hard¡±problem across the physical, biological, social, economic sciences. Individual-based or agent-based models have proved useful in a variety of different real world systems: from the physical, biological, medical domains through to social and even financial domains. There are many different models in each of these fields, each with their own particular assumptions, strengths and weaknesses for particular application areas. However, there is a lack of minimal model analysis in which both numerical and analytic results can be obtained, and hence allowing different application domains to be analyzed on a common footing. This thesis focuses on a few simple, yet highly non-trivial, minimal models of a population of interacting objects (so-called agents) featuring internal dynamical grouping. In addition to analyzing these models, I apply them to a number of distinct real world systems. Both the numerical and analytical results suggest that these simple models could be key factors in explaining the overall collective behavior and emergent properties in a wide range of real world complex systems. In particular, I study variants of a particular model (called the EZ model) in order to explain the attrition time in modern conflicts, and the evolution of contagion phenomena in such a dynamically evolving population. I also study and explain the empirical data obtained for online guilds and offline gangs, leading to a team-based model which captures the common quantitative features of the data. I then move on to develop a resource competition model (i.e. the so-called El Farol model) and apply it to the carbon emissions market, mapping the different market factors into model parameters which enable me to explore the potential market behaviors under a variety of scenarios.

This thesis is concerned with the application of system identification techniques to the analysis of complex physiological systems. The techniques are applied to neuronal spike-train data obtained from elements of the neuromuscular system. A brief description of the neuromuscular system is given in chapter 1, along with a more detailed discussion of the muscle spindle, which is the component of the neuromuscular system which this study deals with. In addition, some possibilities for system identification studies of the muscle spindle are discussed. The identification procedure is based on statistical methods for the treatment of point-process data. The point-process representation of a spike-train is introduced in chapter 2 with definitions of time and frequency domain point-process parameters. Estimates for these parameters are given, along with expressions for their asymptotic distributions. The linear point-process system identification model is introduced and estimates are described for the model parameters in terms of the previously defined point-process parameters. These point-process and linear parameter estimates are applied to muscle spindle spike-train data. In the analysis of a single spike-train certain important features only show up in the frequency domain, and for input and output spike-trains a linear transfer function type description is constructed in the frequency domain. The mathematical model of this transfer function is used as the basis for an analogue computer simulation of a subsystem of the muscle spindle. This consists of a linear first order filter followed by an encoder which generates output spikes. Data logged from the simulation is processed in the same manner as experimental data, and the effect of varying the simulation parameters on the linear model estimates is looked at. It is shown that in general the linear model description reflects the properties of the linear filter in the simulation, and varying the simulation parameters can be used to accurately match results from simulated data with those obtained from real data. Chapter 3 compares the point-process approach with a more conventional filtering and sampled data approach to estimate power spectra. The filtering of spike-trains with broad band spectra is investigated, and this shows up a pitfall in the choice of filter cut-off frequency. It is concluded that the point-process approach is preferable due to shorter computational times, and the well documented statistical propeties of the point-process estimates. The application of the point-process techniques described in chapter 2 to the analysis of more general spike-train data is considered in chapter 4. Three techniques for measuring the degree of coupling between two spike-trains are compared, and the point-process frequency domain measure is found to be the most sensitive. This measure is also applied to a data set containing a strong single periodicity, and the ability to detect coupling at a single harmonic is demonstrated. The analysis of coupling between spike-trains in the frequency domain is extended to deal with multiple spike-trains, and the ability to distinguish genuine coupling from the effect of a common input is shown to be a powerful tool which can be used to investigate communications pathways in neural systems. Finally, one special feature of the muscle spindle response to a spike-train input is analysed using the simulation. It is demonstrated that the point-process approach can produce results about a particular phenomenon from a single experiment much more rapidly than using a repetitive trial and error approach. Chapter 5 considers the extension of the linear point-process identification model introduced in chapter 2. Higher order time and frequency domain point-process parameters are defined and estimates given. In the time domain, a new technique for rapidly generating higher order time domain parameters is developed. The quadratic point-process model is introduced and solutions for its parameters given. These estimates are applied to muscle

En français : Un des défis majeurs de la science de complexité se situe à l'investigation de l'émergence, où les interactions entre les composants microscopiques d'un système produisent la propriété globale, et réciproquement, la dynamique globale influence le bas niveau. Cette thèse a comme ambition de 1) élucider le mécanisme sous-jacent des systèmes complexes par la modélisation concrète des systèmes réels, et aussi 2) comparer entre les différents modèles proposés pour détecter la condition universelle de l'émergence. Pour cela, nous développons la nouvelle méthodologie basé sur l'interaction entre la théorie de système dynamique et la géométrie informationnelle, afin d'avoir la dialectique entre la modélisation constructive/déterministe et l'analyse des interactions sous la formalisation stochastique. La thèse se compose de 7 Parties, parmi lesquelles la Partie 2 à 6 correspondent au premier objectif, et la Partie 7 au seconde. Dans la Partie 1, nous allons réviser l'histoire de la science de la complexité et proposer la stratégie dialectique entre les méthodologies constructive et interactions-analytique, basé sur la théorie de système dynamique et la géométrie informationnelle. En Partie 2, nous traitons un modèle de réseau neuronal avec le comportement chaotique nommé ``l'itinérance chaotique" comme un candidat de la dynamique du cortex cérébral, et analysons l'effet de l'apprentissage autonome sans superviseur comme une source de créativité qui est la propriété émergente du système neuronal. La théorie de la mesure intérieure est étendue afin de interpréter l'émergence des nouveaux attracteurs par ``le chaos comme le catalyseur d'apprentissage. " En Partie 3, nous avons appliqué la dynamique du réseau neuronal chaotique aux robots qui manifestent la dynamique de recherche collective de manière émergente, au défi de la détection optimale des informations sporadiques. L'efficacité de la recherche collective est évaluée avec un simulateur virtuel. En Partie 4, nous développons les nouvelles mesures de la complexité du point de vue de la géométrie informationnelle, et analysons les données des réseaux sociaux. Les mesures de la complexité jouera un rôle principal dans la Partie 7. En Partie 5, nous appliquons la stratégie dialectique entre le système dynamique et la géométrie informationnelle vers la compréhension de la morphogenèse lors de l'embryogenèse chez le poisson zèbre. Quelques propositions théoriques sont établies et testées avec les données tentatives dérivées des projets européens Embryomics et BioEMERGENCES. En Partie 6, nous analysons les systèmes complexes liés au linguistique. Nous avons découvert les nouveaux invariants et la composition géométrique entre les voyelles japonaises, qui sont les propriétés émergentes au niveau du système. Nous développons aussi la modélisation écologique de l'environnement multilingue dans un contexte de la dialectique entre la théorie linguistique et la modélisation mathématique. En Partie 7, nous révisons les résultats obtenus dans les Parties précédentes sous une perspective comparative, en vue de détecter la structure universelle de l'émergence comme l'organisation des interactions qui ne dépende pas explicitement sur la propriété des composants. Surtout la comparaison entre les Parties 2 et 4, ainsi 5 et 6, nous indique la typologie et la stratégie de détection de la dynamique de l'émergence comme la relation et le contraint entre les foncteurs et méta-foncteurs. D'autre possibilité d'application de la stratégie établie est mise en discussion
En anglais : Recently emerging complex systems sciences tackle the systems where complex in- teractions between components lead to the manifestation of emergent property linking different levels of organization. This thesis aims to reveal the mechanism of emergent property in complex systems, both in concrete modeling as well as comparative analysis between different systems. We tackle various sub jects in complex systems science with newly proposed unified theoretical framework, based on the dialectic between dynam- ical system theory and information geometry. The thesis has therefore two levels of ob jectives: 1) Modeling and understanding of concrete complex systems with the use of constructive and interaction-analytical methodologies, and 2) comparison between different complex systems to characterize universal structure of emergence. The thesis consists of 7 Parts, in which Part 2 to 6 correspond to the first ob jective, and the Part 7 to the second one: In Part 1, we review the historical context of complex systems science and propose a dialectical strategy between the constructive and interaction-analytical methodology, based on the dynamical system theory and information geometry, respectively. In Part 2, we treat a candidate model of brain cortex dynamics known as “chaotic itinerancy”, and incorporate the effect of autonomous learning seeking for the creativity of intelligence as emergent property of neural system. The interpretation of emergence in terms of the internal measurement theory is extended to derive the concept of “chaotic itinerancy as catalyst of learning”. In Part 3, the dynamics of chaotic neural network is applied to emergent collective behavior of robots, so that to realize optimal intermittent search of sporadic informa- tion. The effectiveness of the collective infotaxis is analyzed on a simulator basis. In Part 4, we define novel complexity measures from information geometrical point of view and apply to the analysis of social network data. The established complexity measures play a key role in comparative analysis between different systems in Part 7. In Part 5, we apply the dialectical strategy between dynamical system and infor- mation geometry toward the understanding of morphogenesis during zebrafish embryo- genesis. Theoretical propositions are tested with tentative experimental data from two european pro jects, Embryomics and BioEMERGENCES. In Part 6, complex systems related to linguistics are investigated. We discovered novel invariants and geometrical relation between japanese vowels, as a system-level emergent property. Ecological modeling approach to multilingual environment is also proposed along the dialectical strategy between linguistic theory and mathematical modeling. In Part 7, we review the obtained results in previous Parts with comparative per- spective, seeking for a characterization of universal structure of emergence in terms of the organization of interactions that does not explicitly depend on the property of components. Comparison between Part 2 and 4, as well as 5 and 6, derived candi- date qualitative dynamics of emergence and its detection strategy as the dynamics and constraint between functors and meta-functors. Further possibility of the proposed strategy is discussed

Developing and testing complex information technology (IT) systems is a difficult task. This is even more difficult if parts of the system, no matter if hard- or software, are not available when needed due to delays or other reasons. The architecture and design cannot be evaluated and existing parts cannot be reliably tested. Thus the whole concept of the system cannot be validated before all parts are in place. To solve this problem in an industrial project, where the development of the server-side should be finished and tested (clear for production) while the hardware sensors where designed but not implemented, we developed an emulator (software) for the hardware sensors meeting the exact specification as well as parts of the server solution. This allowed proceeding with the server-side development, testing, and system validation without the hardware sensors in place. Following the exact specification should allow replacing the emulator with the real sensors without complications, once they are available. In the end, being able to develop hard- and software in parallel the project can be in production much earlier than performing the development in sequence.

Thesis (M.B.A.)--Massachusetts Institute of Technology, Sloan School of Management; and, (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering; in conjunction with the Leaders for Manufacturing Program at MIT, 2004.
Includes bibliographical references (leaf 63).
E-commerce is generally thought of as a world without walls. Although a computer monitor may replace a storefront window, the products that are purchased online have to be distributed from a brick and mortar warehouse. Amazon.com now makes it possible to instantly order and quickly receive everything from CDs and books to large toys and home furnishings. Amazon's success not only depends on their e-business capabilities, but also on their distribution and warehouse management systems that support them. Fulfillment center management therefore has become an important component of Amazon's unique set of system activities that serve as a corporate strategy. In an attempt to improve current and future warehouse management practices, the engineering group at Amazon has recently invested in Discrete Event Simulation technology. This thesis focuses on the creation of a discrete event simulation of the Fernley, NV semi-automated distribution center's outbound flow process. More specifically, the business subjects investigated include: picker variability, tote diversion, item-per-tote reduction, and conveyor merge logic. The model presented is Amazon's first attempt at simulating this environment and serves as an initial step towards a more detailed simulation of this facility. Preliminary findings from the simulation are presented and the report is concluded with a cultural evaluation of the present engineering directive. It should be noted that this published version of the thesis has been approved by Amazon.com and does not contain any proprietary data. A fully detailed version of this thesis was only submitted to Amazon.com and to the advisors listed below.
by Anthony G. Faranca.
S.M.
M.B.A.

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics; and, (M.B.A.)--Massachusetts Institute of Technology, Sloan School of Management, 2000.
Also available online at the MIT Theses Online homepage .
Includes bibliographical references (p. 187).
The process by which complex, technically integrated products are designed and introduced can be a source of competitive advantage, especially when marketplaces are sensitive to product quality levels. An extension of this competitive advantage is the process by which ongoing production programs are supported. The ongoing support of complex products involves periodic evaluation of critical systems that effect overall customer satisfaction with the product. Such evaluation is performed to identify systems whose performance has dropped to levels that require full system redesign. In this study, a methodology is proposed that can be utilized in the redesign process of systems embedded within complex products. The overall methodology involves identification of critical target systems, a failure mode analysis of the identified system(s), use of benchmarking and/or statistical tools as necessary to characterize features that can be utilized to improve system performance, and finally, conceptual design activity to implement such features. Specifics of each of the aforementioned process steps within the overall methodology are illustrated through work performed on a project sponsored by Ford Motor Company to address poor performance of the water seal system on the SN-95 Mustang convertible. In addition to the technical issues encountered during execution of the proposed system redesign methodology, organizational issues significantly impact the overall effectiveness of ongoing production program support. Organizational structures can raise barriers to efficient organizational knowledge transfer, thus introducing inefficiencies into the overall product development process. This study examines the relationship between organizational structure and knowledge flow amongst the various stakeholders of ongoing production programs. This relationship is used to characterize mechanisms that promote effective transfer, management, and growth of the product development knowledge base within an organization's overall product development community. Examples of mechanisms that were characterized as promoting effective knowledge transfer, management, and growth include the use of aligned organizational metrics against which the different stakeholders responsible for support of a specific program are judged, and rigorous use of a formal process for documentation of experience-based lessons-learned.
by Robert Giammatteo.
M.B.A.
S.M.

This collected volume gives a concise account of the most rel-evant scientific results of the COST Action IS1104 "The EU in the new complex geography of economic systems: models, tools and policy evaluation", a four-year project supported by COST (European Cooperation in Science and Technology). It is divided into three parts reflecting the different perspectives under which complex spatial economic systems have been studied: (i) the Macro perspective looks at the interactions among international or regional trading partners; (ii) the Meso perspective considers the functioning of (financial, labour) markets as social network structures; and, finally, (iii) the Micro perspective focuses on the strategic choices of single firms and households. This Volume points also at open issues to be addressed in future research.

Cette thèse s'inscrit dans le cadre de systèmes complexes appliqués aux systèmes économiques. Dans cette thèse, un modèle multi-agent a été proposé, qui modélise le cycle de production. Il est consitué d'entreprises, ouvirers/foyers, et une banque, et repecte la conservation de la monnaie. Son hypothèse centrale est que les entreprises se basent sur une marge espérée pour déterminer leur production. Un scénario simple de ce modèle, ou les marges espérées sont homogènes, a été analysé dans le cadre de models de croissance stochastique. Les résultats sont la distribution de tailles d'entreprises rassemblant des lois de puissance, et leur distribution du taux de croissance de forme 'tente', ainsi qu'une dépendence de taille de la variance de la croissance. Ces résultats sont proches aux faits stylisés issus d'études empiriques. Dans un scénario plus complet, le modèle contient des caractéristiques supplémentaires: des marges espérées hétérogèges, ainsi que des paiements d'intérêts, la possibilité de faire faillite. Cela ramène le modèle aux modèles macro-économiques multi-agents. Les extensions sont décrites de façon théorique par des équations de replicateur. Les résultats nouveaux sont la distribution d'age d'entreprises actives, la distribution de leur taux de profit, la distribution de dette, des statistiques sur les faillites, et des cycles de vie caractéristiques. Tout ces résultats sont qualitativement en accord avec des résultats d'études empiriques de plusieurs pays.Le modèle proposé génère des résultats prometteurs, en respectant le principe que des résultats qui apparaissent simultanément peuvent soit etre générés par un même processus, soit par plusieurs aui qui sont compatibles
The thesis is in the field of complex systems, applied to an economic system. In this thesis, an agent-based model has been proposed to model the production cycle. It comprises firms, workers, and a bank, and respects stock-flow consistency. Its central assumption is that firms plan their production based on an expected profit margin. A simple scenario of the model, where the expected profit margin is the same for all firms, has been analyzed in the context of simple stochastic growth models. Results are a firms' size distribution close to a power law, and tent-shaped growth rate distribution, and a growth rate variance scaling with firm size. These results are close to empirically found stylized facts. In a more comprehensive version, the model contains additional features: heterogeneous profits margins, as well as interest payments and the possibility of bankruptcy. This relates the model to agent-based macroeconomic models. The extensions are described theoretically theoretically with replicator dynamics. New results are the age distribution of active firms, their profit rate distribution, debt distribution, bankruptcy statistics, as well as typical life cycles of firms, which are all qualitatively in agreement with studies of firms databases of various countries.The proposed model yields promising results by respecting the principle that jointly found results may be generated by the same process, or by several ones which are compatible

The transistor count for todays VLSI technology reaches 40 million transistors on one chip. In order to successfully design a system with such complexity, new computer-aided design (CAD) tools are needed. This dissertation shows approaches for coping with the problem of increasing complexity of VLSI design in three aspects: 1) capturing a higher level of abstraction, 2) using a new target architecture, and 3) using a new optimization technique. The advantage of working at a higher level of abstraction is that the number of objects that designers have to manipulate is reduced so that more complex systems can be delivered in shorter periods of time. The functions that can be used to capture higher levels of abstraction are surveyed and categorized into an is-a hierarchy. A partitioned-bus architecture that consists of complex functional units used to realize complex functions is proposed. The issues of synthesizing the complex functions to the partitioned-bus architecture are addressed. These issues are focused on the functional partitioning problem which is a known NP-complete problem. Algorithms used to optimize several metrics that affect the solution qualities of functional partitioning are presented. The metrics include communication buffer size, register file size, system delay, the number of buses, the number of links, and the number of multiplexers. These metrics are used to form a cost function, which is utilized by the Problem Space Genetic Partitioning algorithm (PSGP) to search for a good solution. Test cases with known optimal solutions are used to evaluate the solution qualities that PSGP can attain under run time and memory space constraints. The experimental results show that PSGP can reach an average about 87% of the optima for two-way partitioning. Another study also shows that PSGP outperforms the widely used Simulated Annealing algorithm.
Ph. D.

Thesis (Ph.D.)--Indiana University, Dept. of Psychology and Cognitive Science, 2004.
Title from PDF t.p. (viewed Dec. 8, 2008). Source: Dissertation Abstracts International, Volume: 66-05, Section: B, page: 2859. Chair: Kelly S. Mix.

The solubility of anthracene was measured in binary and ternary co-solvent-water systems. The binary systems consisted of water and a completely miscible organic solvent (CMOS); while the ternary system incorporated a partially miscible organic solvent (PMOS) into the binary systems. The data were used to test the following model:(UNFORMATTED TABLE OR EQUATION FOLLOWS) log Sᵃ(c,p,w) = log Sᵃ(w) + f(c) σᵃ(c) + [(Sᴾ(w) 10 (f(c) σᴾ(c))/D(p)] σᵃ(p). (TABLE/EQUATION ENDS) The terms on the right of the equality sign are the aqueous solubility of anthracene, the solubility of anthracene in CMOS-water, and the solubility of anthracene due to the incorporation of the PMOS, respectively. This model predicts that the incorporation of a PMOS, as a solubilized solute, in CMOS-water mixtures can lead to an increase in the solubility of anthracene due to the cosolvency effect of the PMOS. The results indicate a good correlation between the observed vs. predicted increase in solubility. The deviations observed may be explained by the interactions between the solvent components.

Thesis (S.M.)--Massachusetts Institute of Technology, System Design and Management Program, 2007.
Vita.
Includes bibliographical references (p. 121-128).
Robust design methodologies are frequently utilized by organizations to develop robust and reliable complex systems. The intent of robust design is to create systems that are insensitive to variations from production, the environment, and time and use. While this process is effective, it can also be very time consuming and resource intensive for an engineering team. In addition, most robust design activity takes place at the detailed design phase, when the majority of the product life cycle cost has already been committed. Addressing robustness and the "ilities" at the architecture level may be more effective because it is the earliest and highest leverage point in the product development process. Furthermore, some system architectures are inherently more robust than others. In this thesis, a framework based on principles is proposed to architect complex systems for type I and II robustness. The principles are obtained by tracing the architectural evolution of the jet engine, which is an extremely complex system that has evolved to high reliability. This framework complements existing robust design methods, while simultaneously incorporating the robustness focus earlier in the product development process.
by Jason C. Slagle.
S.M.

Under de senaste åren har produkterna blivit mer invecklade beträffande anslutningen, prestanda och funktionalitet. Därför är syftet av denna studie att undersöka hur komplexa system utvecklas och leds genom att genomföra fallstudie på olika svenska företag som utvecklar mekatroniska och cyber-fysiska system. Resultatet av denna studie har lett till identifieringen av många utmaningar som de undersökta företagen har och som i sin tur har lett till framställningen av ett analytiskt ramverk som diskuterar hur och vad man bör göra för att utveckla komplexa produkter på ett effektivt sätt, så att onödig komplexitet i produktutvecklingen kan reduceras.
In recent years, products have become more complex in terms of connectivity, performance and functionality. Therefore, this study aims at studying how complex products are developed and managed through conducting multiple case studies at different Swedish companies that develop mechatronic or cyberphysical systems. The results of this study is the identification of many challenges that the investigated companies have, which have led to a presentation of an analytical framework that discusses how complex product development projects can and should be managed in order to be efficient, in order to reduce unnecessary complexity in the way companies develop these complex products.

The complexity of technical systems has increased significantly in order to address evolving customer needs and environmental concerns. From a product development process viewpoint, the pervasive nature of multi-disciplinary systems (i.e. mechanical, electrical, electronic, control, software) has brought some important integration challenges to overcome conventional disciplinary boundaries imposed by discipline specific approaches. This research focuses on functional reasoning, aiming to develop a structured framework based on the System State Flow Diagram (SSFD) for function modelling of complex multidisciplinary systems on a practical and straightforward basis. The framework is developed at two stages. 1) The development of a prototype for the SSFD framework. The proposed SSFD framework are tested and validated through application to selected desktop case studies. 2) Further development and extension of the SSFD framework for the analysis of complex multidisciplinary systems with multiple operation modes and functional requirements. The developed framework is validated on real world case studies collaborated with industrial partners. The main conclusion of this research is that the SSFD framework offers a rigorous and coherent function modelling methodology for the analysis of complex multidisciplinary systems. Further advantages of the SSFD framework is that 1) the effectiveness of the Failure Mode Avoidance (FMA) process can be enhanced by integrating the SSFD framework with relevant tools of the FMA process, and 2) the integration of the SSFD with the SysML systems engineering diagrams is doable, which can promote the take-up of the approach in industry.

Thesis (S.M. in System Design and Management)--Massachusetts Institute of Technology, Engineering Systems Division, Technology and Policy Program, 2010.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 79-81).
Obsolescence mitigation is an increasingly important aspect of large systems development & maintenance that has often only been considered once obsolescence is imminent. For long lifecycle systems, this has become a major concern as the lifecycles of the components that are encompassed within these systems are often far shorter - up to ten times shorter - than the overall system lifecycle. Many defense systems can be characterized in this manner and therefore require obsolescence mitigation approaches to ensure the continuing ability for the system to perform and evolve. Current system-level obsolescence mitigation practices make recommendations for designing new systems to slow the onset of obsolescence and make the system more flexible when change for obsolescence is required. However, currently fielded systems were often not designed with this in mind. Other obsolescence mitigation techniques focus only on the approach to mitigating component-level obsolescence locally without examining the impact of the change on the system as a whole. This thesis combines the recommended approaches for obsolescence mitigation, the experience and lessons learned for obsolescence mitigation on a real-world case study system gained from interviews with key subject matter experts, along with systems engineering techniques for dealing with engineering change in systems to develop a robust systems engineering and management approach for obsolescence in large complex systems. The thesis provides the reader with a flow chart and a clustered DSM of the tasks along with a checklist that could be used with this obsolescence engineering and management approach.
by Jaime E. Devereaux.
S.M.in System Design and Management