Дисертації з теми "Time-varying network"

The thesis focuses on the social web and on the analysis of social networks with particular emphasis on their temporal aspects. Social networks are represented here by Time Varying Graphs (TVG), a general model for dynamic graphs borrowed from distributed computing. In the first part of the thesis we focus on the temporal aspects of social networks. We develop various temporal centrality measures for TVGs including betweenness, closeness, and eigenvector centralities, which are well known in the context of static graphs. Unfortunately the computational complexities of these temporal centrality metrics are not comparable with their static counterparts. For example, the computation of betweenness becomes intractable in the dynamic setting. For this reason, approximation techniques will also be considered. We apply these temporal measures to two very different datasets, one in the context of knowledge mobilization in a small community of university researchers, the other in the context of Facebook commenting activities among a large number of web users. In both settings, we perform a temporal analysis so to understand the importance of the temporal factors in the dynamics of those networks and to detect nodes that act as “accelerators”. In the second part of the thesis, we focus on a more standard static graph representation. We conduct a propagation study on YouTube datasets to understand and compare the propagation dynamics of two different types of users: subscribers and friends. Finally, we conclude the thesis with the proposal of a general framework to present, in a comprehensive model, the influence of the social web on e-commerce decision making.

Dynamic processes on real-world networks are time-delayed due to finite processing speeds and the need to transmit data over nonzero distances. These time-delays often destabilize the network's dynamics, but are difficult to analyze because they increase the dimension of the network.We present results outlining an alternative means of analyzing these networks, by focusing analysis on the Lipschitz matrix of the relatively low-dimensional undelayed network. The key criteria, intrinsic stability, is computationally efficient to verify by use of the power method. We demonstrate applications from control theory and neural networks.

A payment is the most fundamental aspect of a trade that involves funds. In recent years, the development of new payment services has accelerated significantly as the world has moved further into the digital era. This transition has led to an increased demand of digital payment solutions that can handle trades across the world. As trades today can be agreed at any time wherever the payer and payee are located, the party that mediates payments must at any time to be available in order to mediate an agreed exchange. This requires the payment service provider to always have funds available in the required countries and currencies in order for trades to always be available. This thesis concerns how a payment service provider can reallocate capital in a cost efficient way in order for trades to always be available. Traditionally, the reallocation of capital is done in a rule-based manner, which discard the cost dimension and thereby only focus on the reallocation itself. This thesis concerns methods to optimally reallocate capital focusing on the cost of transferring capital within the network. Where the concerned methods has the potential of transferring capital in a far more cost efficient way. When mathematically formulating the reallocation decisions as an optimization problem, the cost function is formulated as a linear program with both Boolean and real constraints. This impose non-feasibility of locating the optimal solution using traditional methods for linear programs, why developed traditional and more advanced methods were used. The model was evaluated based on a large number of simulations in comparison with the performance of a rule-based reallocation system. The developed model provides a significant cost reduction compared to the rule-based approach and thereby outperforms the traditional reallocation system. Future work should focus on expanding the model by broadening the available transfer options, by increasing the considered uncertainty via a bayesian treatment and finally by considering all cost aspects of the network.
En betalning är den mest fundamentala aspekten av handel som involverar kapital. De senaste åren har utvecklingen av nya betalmedel ökat drastiskt då världen fortsatt att utvecklas genom digitaliseringen. Utvecklingen har lett till en ökad efterfrågan på digitala betalningslösningar som kan hantera handel över hela världen. Då handel idag kan ske när som helst oberoende av var betalaren och betalningsmottagaren befinner sig, måste systemet som genomför betalningen alltid vara tillgängligt för att kunna förmedla handel mellan olika parter. Detta kräver att betalningssystemet alltid måste ha medel tillgängligt i efterfrågade länder och valutor för att handeln ska kunna genomföras. Den här uppsatsen fokuserar på hur kapital kostnadseffektivt kan omallokeras i ett betalsystem för att säkerställa att handel alltid är tillgängligt. Traditionellt har omallokeringen av kapital gjorts på ett regelbaserat sätt, vilket inte tagit hänsyn till kostnadsdimensionen och därigenom enbart fokuserat på själva omallokeringen. Den här uppsatsen använder metoder för att optimalt omallokera kapital baserat på kostnaderna för omallokeringen. Därigenom skapas en möjlighet att flytta kapital på ett avsevärt mer kostnadseffektivt sätt. När omallokeringsbesluten formuleras matematiskt som ett optimeringsproblem är kostnadsfunktionen formulerad som ett linjärt program med både Booleska och reella begränsningar av variablerna. Detta gör att traditionella lösningsmetoder för linjära program inte är användningsbara för att finna den optimala lösningen, varför vidareutveckling av tradtionella metoder tillsammans med mer avancerade metoder använts. Modellen utvärderades baserat på ett stort antal simuleringar som jämförde dess prestanda med det regelbaserade systemet. Den utvecklade modellen presterar en signfikant kostnadsreduktion i jämförelse med det regelbaserade systemet och överträffar därigenom det traditionellt använda systemet. Framtida arbete bör fokusera på att expandera modellen genom att utöka de potentiella överföringsmöjligheterna, att ta ökad hänsyn till osäkerhet genom en bayesiansk hantering, samt slutligen att integrera samtliga kostnadsaspekter i nätverket.

Vascular1Dflow is a program for simulating blood flow in vascular networks. It uses hyperbolic one-dimensional partial differential equations with ordinary differential equations as boundary conditions. The focus of this work has been the development boundary conditions, primarily a time varying elastance model of the left ventricle has been implemented as a boundary condition. The aortic and mitral valves were modeled using a mathematical valve model. The varying elastance model is tested in lumped model and arterial network simulations. An improved solution algorithm for computing pressure and flow at bifurcations is implemented and tested. Also a improved discretization scheme for Windkessel models is presented.

Intelligent Transport Systems (ITS) are becoming a part of our daily lives in various forms of application. Their success depends highly on the accuracy of the digital data they use. In networks where characteristics change by time, time-based network analysis algorithms provide results that are more accurate. However, these analyses require time-based travel speed data to provide accurate results. Conventionally, traffic data are usually obtained using the data provided from loop-detectors. These detectors usually exist on main arteries, freeways and highways
they rarely exist on back roads, secondary roads and streets due to their deployment costs. Today, telematics systems offer fleet operators to track their fleet remotely from a central system. Those systems provide data about the behaviors of vehicles with time information. Therefore, a tracking system can be used as an alternative to detector-based systems on estimating travel speeds on networks. This study aims to provide methods to estimate network characteristics using the data collected directly from fleets consisting of global positioning system (GPS) receiver equipped vehicles. GIS technology is used to process the collected GPS data spatially to match digital road maps. After matching, time-dependent characteristics of roads on which tracked vehicles traveled are estimated. This estimation provides data to perform a time-dependent network analysis. The methods proposed in this study are tested on traffic network of Middle East Technical University campus. The results showed that the proposed methods are capable of measuring time-dependent link-travel times on the network. Peak hours through the network are clearly detected.

Thesis: S.M., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, 2015.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 81-83).
Time-varying graphs are a useful model for networks with dynamic connectivity such as mmWave networks and vehicular networks, yet, despite their great modeling power, many important features of time-varying graphs are still poorly understood. In this thesis, we study the survivability properties of time-varying networks against unpredictable interruptions. We first show that the traditional definition of survivability is not effective in time-varying networks and propose a new survivability framework. To evaluate survivability of time-varying networks under the new framework, we propose two metrics that are analogous to MaxFlow and MinCut in static networks. We show that some fundamental survivability-related results such as Menger's Theorem only conditionally hold in timevarying networks. Then we analyze the complexity of computing the proposed metrics and develop several approximation algorithms. Finally, we conduct trace-driven simulations to demonstrate the application of our survivability framework in the robust design of a real-world bus communication network.
by Qingkai Liang.
S.M.

Community detection is an important aspect of network analysis that has far-reaching consequences, in particular for biological research. In the study of systems biology, it is important to detect communities in biological networks to identify areas that have a heavy correlation between one another or are significant for biological functions. If one were to model networks that evolved over time, a differential network would be a vital part or product of that analysis. One such network could have an edge between two vertices if there is a significant change in the correlation of expression levels between the two genes that the vertices are designed to model. For this particular network, there are no community detection algorithms that suffice. An analysis of the current community detection algorithms shows that most heuristic-based methods are too simple or have too high a cost for detecting communities on such sparse networks. A prototypical algorithm is presented that is preferential to high weight edges when determining community membership. This algorithm, Weighted Sparse Community Finder or WSCF, is an incremental algorithm that develops community structure from highly-weighted community seeds, which are 3-vertex substructures in the network with a high local modularity. A preliminary analysis of this detection algorithm shows that it is functional on data sets consisting of up to 600 genes, with more on a more powerful machine. The communities detected are different than the ones provided by the benchmark algorithms because of the high precedence placed on higher-weight edges. This prototypical algorithm has the potential for refinement and expansion to provide the ability to find significant results for applications in the field of Systems Biology.
Science, Faculty of
Computer Science, Department of
Graduate

Thesis (Ph.D.)--Electrical and Computer Engineering, Georgia Institute of Technology, 2009.
Committee Chair: Yucel Altunbasak; Committee Member: Chuanyi Ji; Committee Member: Ghassan AlRegib; Committee Member: Ozlem Ergun; Committee Member: Russell M. Mersereau.

This thesis empirically investigates different aspects of time-varying volatility. Chapter 1 estimates a large TVP-FAVAR and recovers a dynamic directed network of connections between European stock volatilities. We propose an ad-hoc estimation methodology that is shown to outperform both standard approaches and competing models. Chapter 2 focuses on tracking dynamic connectedness between US sectoral volatilities using Generalized Forecast Error Variance Decompositions with a Bayesian model. As opposed to estimates obtained with rolling windows, we allow parameters to vary in a more flexible way. We show that there exists a stable relationship between the network structure and the volatility regimes in place at a given time. Chapter 3 estimates the unexpected time-varying volatility component of fiscal budgets in Italy. We show that periods of higher unexpected fiscal volatility are likely to be recessionary. Expansionary policies are effective only when not accompanied by increases in uncertainty.
Aquesta tesi investiga empíricament diferents aspectes de la volatilitat variable. El Capítol 1 estima un TVP-FAVAR i recupera una xarxa de connexions dinàmiques entre les volatilitats de accions europees. Proposem una metodologia d’estimació ad-hoc que es demostri que supera els enfocaments estàndard i els models competidors. El Capítol 2 es centra en el seguiment de la connectivitat dinàmica entre les volatilitats sectorials dels Estats Units mitjançant descomposicions generalitzadas de variància d’errors de previsió amb un model Bayesià. A diferència de les estimacions obtingudes amb finestres enrotllables, permetem que els paràmetres variïn de manera més flexible. Mostrem que existeix una relació estable entre l’estructura de la xarxa i els règims de volatilitat vigents en un moment determinat. El Capítol 3 estima el component variable inesperat de la volatilitat dels pressupostos fiscals a Itàlia. Mostrem que els períodes de major volatilitat fiscal inesperada probablement són recessius. Les polítiques expansives només són efectives quan no s’acompanyen d’increments d’incertesa.

In this thesis we study the impact of time in the analysis of social networks. To do that we represent a knowledge mobilization network, Knowledge-Net, both as a standard static graph and a time-varying graph and study both graphs to see their differences. For our study, we implemented some temporal metrics and added them to Gephi, an open source software for graph and network analysis which already contains some static metrics. Then we used that software to obtain our results. Knowledge-Net is a network built using the knowledge mobilization concept. In social science, knowledge mobilization is defined as the use of knowledge towards the achievement of goals. The networks which are built using the knowledge mobilization concept make more visible the relations among heterogeneous human and non-human individuals, organizational actors and non-human mobilization actors. A time-varying graph is a graph with nodes and edges appearing and disappearing over time. A journey in a time-varying graph is equivalent to a path in a static graph. The notion of shortest path in a static graph has three variations in a time-varying graph: the shortest journey is the journey with the least number of temporal hops, the fastest journey is the journey that takes the least amount of time and the foremost journey is the journey that arrives the soonest. Out of those three, we focus on the foremost journey for our analysis.

Clock synchronization is one of fundamental requirements in distributed networks. However, the imperfection of crystal oscillators is a potential hurdle for network-wide collaboration and degrades the performance of cooperative applications. Since clock discrepancy among nodes is inevitable, many software and hardware attempts have been introduced to meet synchronization requirements. Most of the attempts are built on communication protocols that demand timestamp exchanges to improve synchronization accuracy or resource efficiency. However, link delay and environmental changes sometimes impede these synchronization efforts that achieve in desired accuracy. First, the clock synchronization problem was examined in networks where nodes lack the high accuracy oscillators or programmable network interfaces some previous protocols depend on. Next, a stochastic and practical clock model was developed by using information criteria which followed the principle of Occam's razor. The model was optimized in terms of the number of parameters. Simulation by using real measurements on low-powered micro-controllers validated the derived clock model. Last, based on the model, a clock tracking algorithm was proposed to achieve high synchronization accuracy between unstable clocks. This algorithm employed the Kalman filter to track clock offset and skew. Extensive simulations demonstrated that the proposed synchronization algorithm not only could follow the clock uncertainties shown in real measurements but also was tolerant to corrupted timestamp deliveries. Clock oscillators are vulnerable to noises and environmental changes. As a second approach, clock estimation technique that took circumstances into consideration was proposed. Through experiments on mobile devices, the obstacles were clarified in synchronization over wireless networks. While the causes of clock inaccuracy were focused on, the effect of environmental changes on clock drifting was investigated. The analysis of the observations inspired an M-estimator of clock error that was accurate but under dominant disturbances such as oscillator instability and random network delay. A Kalman filter was designed to compensate with temperature changes and estimate clock offset and skew. The proposed temperature-compensated Kalman filter achieved the better estimates of clock offset and skew by adjusting frequency shifts caused by temperature changes. The proposed Kalman filter-based clock synchronization was implemented in C. A real-time operation was proved by clock tracking between two mobile platforms that the synchronization technique was implemented on. Moreover, the technique was converted to fixed-point algorithm, which might degrade performance, to evaluate the synchronizing operation on fixed-point processors. The fixed-point simulation reported performance degradation caused by limited hardware resources; however, it also corroborated the applicability of the synchronization technique.

We study distributed estimation of dynamic random fields observed by a sparsely connected network of agents/sensors. The sensors are inexpensive, low power, and they communicate locally and perform computation tasks. In the era of large-scale systems and big data, distributed estimators, yielding robust and reliable field estimates, are capable of significantly reducing the large computation and communication load required by centralized estimators, by running local parallel inference algorithms. The distributed estimators have applications in estimation, for example, of temperature, rainfall or wind-speed over a large geographical area; dynamic states of a power grid; location of a group of cooperating vehicles; or beliefs in social networks. The thesis develops distributed estimators where each sensor reconstructs the estimate of the entire field. Since the local estimators have direct access to only local innovations, local observations or a local state, the agents need a consensus-type step to construct locally an estimate of their global versions. This is akin to what we refer to as distributed dynamic averaging. Dynamic averaged quantities, which we call pseudo-quantities, are then used by the distributed local estimators to yield at each sensor an estimate of the whole field. Using terminology from the literature, we refer to the distributed estimators presented in this thesis as Consensus+Innovations-type Kalman filters. We propose three distinct types of distributed estimators according to the quantity that is dynamically averaged: (1) Pseudo-Innovations Kalman Filter (PIKF), (2) Distributed Information Kalman Filter (DIKF), and (3) Consensus+Innovations Kalman Filter (CIKF). The thesis proves that under minimal assumptions the distributed estimators, PIKF, DIKF and CIKF converge to unbiased and bounded mean-squared error (MSE) distributed estimates of the field. These distributed algorithms exhibit a Network Tracking Capacity (NTC) behavior – the MSE is bounded if the degree of instability of the field dynamics is below a threshold. We derive the threshold for each of the filters. The thesis establishes trade-offs between these three distributed estimators. The NTC of the PIKF depends on the network connectivity only, while the NTC of the DIKF and of the CIKF depend also on the observation models. On the other hand, when all the three estimators converge, numerical simulations show that the DIKF improves 2dB over the PIKF. Since the DIKF uses scalar gains, it is simpler to implement than the CIKF. Of the three estimators, the CIKF provides the best MSE performance using optimized gain matrices, yielding an improvement of 3dB over the DIKF. Keywords: Kalman filter, distributed state estimation, multi-agent networks, sensor networks, distributed algorithms, consensus, innovation, asymptotic convergence, mean-squared error, dynamic averaging, Riccati equation, Lyapunov iterations, distributed signal processing, random dynamical systems.

The dissertation thesis investigates the modeling of the neural network activity with a focus on a multilayer forward neural network (MLP – Multi Layer Perceptron). In this often used structure of neural networks, time-varying neurons are used, along with an analogy in modeling hyperstructures of linear differential operators. Using a finite lemma and defined hyperoperation, a hyperstructure composed of neurons is defined for a given transient function. There are examined their properties with an emphasis on structures with a layout.

In this work. we propose a unified framework called Kalman filter based Radial Basis Functions (KF-RBF) for online functional prediction based on the Radial Basis Functions and the Kalman Filter. The data are nonstationary spatio-ternporal observations irregularly sampled in the spatial domain. We shall assume that a Functional Auto-Regressive (FAR) model is generating the system dynamics. Therefore. to account for the spatial variation. a Radial Basis Function (RBF) network is fitted to the spatial data at every time step. To capture the temporal variation, the regression surfaces arc allowed to change with time. This is achieved by proposing a linear state space model for the RBF weight vectors to evolve temporally. With a fixed functional basis in expressing all regressions. the FAR model call then he re-formulated as a Vector Auto-Regressive (VAR) model embedded in a Kalman Filter. Therefore functional predictions. normally taken place in the Hilbert space. can now be easily implemented 011 a computer. The advantages of our approach are as follows. First it is computationally simple: using the KF. we can obtain the posterior and predictive distributions in closed form. This allows for quick implementation of the model. and provides for full probabilistic inference for the forecasts. Second, the model requires no restrictive assumptions such as stationarity. isotropy or separability of the space/time correlation functions. Third. the method applies to non-lattice data. in which the number and location of sensors can change over time. This framework proposed is further extended by generalizing the real-valued. scalar weights in the functional autoregressive model to operators ill the Reproducing Kernel Hilbert Space (RKHS). This essentially implies that a larger. more intricate class of functions can be represented by this functional autoregressive approach. In other words. the unknown function is expressed as a sum of transformed functions mapped from the past functions in the RKHS. This bigger class of functions can potentially yield a better candidate that is "closer". in the norm sense. to the unknown function. In our research. the KF is used despite the system and observational noise covariance are both unknown. These uncertainties may significantly impact the filter performance. resulting in sub- optimality or divergence. A multiple-model strategy is proposed in view of this. This is motivated by the Interactive Multiple Model (IMM) algorithm in which a collection of filters with different noise characteristics is run in parallel. This strategy avoids the problems associated with the estimation of the noise covariance matrices. Furthermore. it also allows future measurements to be predicted without the assumption of time stationarity of the disturbance terms.

Thesis (M.S.) -- University of Maryland, College Park, 2004.
Thesis research directed by: Dept. of Electrical Engineering and Computer Engineering. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.

The data revolution experienced by social science has revealed complex patterns of interactions in human dynamics, such as the heterogeneity and burstiness of social contacts. The recently uncovered temporal dimension of social interactions calls for a renewed effort in analysis and modeling of empirical time-varying networks. This thesis contributes to pursue this program, through a twofold track: The modeling of dynamical social systems and the study of the impact of temporally evolving substrates on dynamical processes running on top of them. Firstly, we introduce some basic concepts and definition of time-varying networks formalism, and we present and analyze some empirical data of face-to-face interactions, discussing their main statistical properties, such as the bursty dynamics of social interactions. The main body of the exposition is then split into two parts. In the first part we focus on the modeling of social dynamics, with a twofold aim: reproduction of empirical data properties and analytic treatment of the models considered. We present and discuss the behavior of a simple model able to replicate the main statistical properties of empirical face-to-face interactions, at different levels of aggregation, such as individual, group and collective scales. The model considers individuals involved in a social gathering as performing a random walk in space, and it is based on the concept of social "attractiveness": socially attractive people (due to their status or role in the gathering) are more likely to make people stop around them, so they start to interact. We also devote attention to the analytic study of the activitydriven model, a model aimed to capture the relation between the dynamics of time-varying networks and the topological properties of their corresponding aggregated social networks. Through a mapping to the hidden variable model, we obtained analytic expressions for both topological properties of the time-integrated networks and connectivity properties of the evolving network, as a function of the integration time and the form of the activity potential. In the second part of the thesis we study the behavior of diffusive processes taking place on temporal networks, constituted by empirical face-to-face interactions data.We first consider random walks, and thanks to different randomization strategies we introduced, we are able to single out the crucial role of temporal correlations in slowing down the random walk exploration. Then we address spreading dynamics, focusing on the case of a simple SI model taking place on temporal networks, complemented by the study of the impact of different immunization strategies on the infection outbreak. We tackle in particular the effect of the length of the temporal window used to gather information in order to design the immunization strategy, finding that a limited amount of information of the contact patterns is sufficient to identify the individuals to immunize so as to maximize the effect of the vaccination protocol. Our work opens interesting perspectives for further research, in particular regarding the possibility to extend the time-varying networks approach to multiplex systems, composed of several layers of interrelated networks, in which the same individuals interact between them on different layers. Empirical analysis of multiplex networks is still in its infancy, indeed, while the data mining of large, social, multi-layered systems is mature to be exploited, calling for an effort in analysis and modeling. Our understanding of the impact of the temporal dimension of networked structures on the behavior of dynamical processes running on top of them can be applied to more complex multi-layered systems, with particular attention to the effect of temporal correlation between the layers in the diffusion dynamics.
La revolució de dades en ciències socials ha revelat els complexos patrons de les interaccions en la dinàmica humana, com ara l'heterogeneïtat i la burstiness dels contactes socials. La dimensió temporal recentment descoberta en les interaccions socials demana un esforç renovat en l'anàlisi i la modelització de xarxes empíriques de variables en el temps. Aquesta tesi contribueix a aquest programa, a través d'un doble recorregut: la modelització dels sistemes socials dinàmics i l'estudi de l'impacte de substrats temporalment variables en els processos dinàmics que es desenvolupen sobre ells. En primer lloc, hem introduït els conceptes bàsics i definicions del formalisme de les xarxes de variables en el temps, i presentem i analitzem algunes dades empíriques de les interaccions humanes de proximitat, discutint les seves principals propietats estadístiques. El cos principal de l'exposició es divideix llavors en dues parts. A la primera part ens centrem en els models de dinàmica social, amb un doble objectiu: la reproducció de les propietats de dades empíriques i el tractament analític dels models considerats. Hem presentat i discutit el comportament d'un model simple capaç de replicar les principals propietats estadístiques de les interaccions empíriques cara a cara, a diferents nivells d'agregació: individuals, grupals i d'escala col·lectiva. El model considera els individus que participen en un context social com si realitzaran una caminada a l'atzar en l'espai, i es basa en el concepte de "atractivitat social": persones socialment atractives tenen més probabilitat de que la gent que els envolta interactuï amb ells. Ens hem ocupat també de l'estudi analític del model "activity driven", destinat a capturar la relació entre la dinàmica de les xarxes variables en el temps i les propietats de les seves corresponents xarxes socials agregats. A través d'un mapeig amb el formalisme de variables ocultes, hem obtingut expressions analítiques per a les propietats topològiques de les xarxes integrades en el temps i les propietats de connectivitat de la xarxa en evolució, en funció del temps d'integració i de la forma del potencial d'activitat. A la segona part de la tesi hem estudiat el comportament dels processos difusius sobre xarxes temporals constituïdes per les dades empíriques de interaccions humanes. Primer considerem el procés de "random walk", o camí aleatori, i gràcies a les diferents estratègies de randomització que hem introduït, podem destacar el paper crucial de la correlacions temporals en alentir l'exploració del camí aleatori. Després hem dirigit la nostra atenció a la difusió d'epidèmies, centrant-nos en el cas d'un simple model SI que es desenvolupa a les xarxes temporals, complementat amb l'estudi de l'impacte de diferents estratègies d'immunització sobre la difusió de la infecció. Abordem, en particular, l'efecte de la longitud de la finestra temporal utilitzada per reunir informació per tal de dissenyar l'estratègia d'immunització, sobre l'eficàcia de la mateixa vacunació, descobrint que una quantitat limitada d'informació és suficient per maximitzar l'efecte del protocol de vacunació. El nostre treball obre interessants perspectives per a futures investigacions, en particular pel que fa a la possibilitat d'ampliar el formalisme de xarxes de temps variable a sistemes múltiplex, compostos de diverses capes de xarxes interrelacionades, en la qual els mateixos individus interactuen entre ells en diferents capes. L'anàlisi empírica de les xarxes múltiplex és encara en la seva infantesa, de fet, mentre que la mineria de dades de grans sistemes socials, de diverses capes, és madur per ser explotat, demanant un esforç en l'anàlisi i modelització. La nostra comprensió de l'impacte de la dimensió temporal de les xarxes sobre els processos dinàmics que es desenvolupen sobre ells es pot aplicar a sistemes més complexos de múltiples capes, estudiant l'efecte de la correlació temporal entre les capes en la dinàmica de difusió.

Thesis (Ph. D.) -- University of Maryland, College Park, 2004.
Thesis research directed by: Civil Engineering. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2012.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 247-258).
We consider the control of possibly time-varying wireless networks under reconfiguration delays. Reconfiguration delay is the time it takes to switch network resources from one subset of nodes to another and it is a widespread phenomenon observed in many practical systems. Optimal control of networks has been studied to a great extent in the literature, however, the significant effects of reconfiguration delays received limited attention. Moreover, simultaneous presence of time-varying channels and reconfiguration delays has never been considered and we show that it impacts the system fundamentally. We first consider a Delay Tolerant Network model where data messages arriving randomly in time and space are collected by mobile collectors. In this setting reconfiguration delays correspond to travel times of collectors. We utilize a combination of wireless transmission and controlled mobility to improve the system delay scaling with load [rho] from [theta](1/(1-[rho])²) to [theta](1/1-[rho]), where the former is the delay for the corresponding system without wireless transmission. We propose control algorithms that stabilize the system whenever possible and have optimal delay scaling. Next, we consider a general queuing network model under reconfiguration delays and interference constraints which includes wireless, satellite and optical networks as special cases. We characterize the impacts of reconfiguration delays on system stability and delay, and propose scheduling algorithms that persist with service schedules for durations of time based on queue lengths to minimize negative impacts of reconfiguration delays. These algorithms provide throughput-optimality without requiring knowledge of arrival rates since they dynamically adapt inter-switching durations to stochastic arrivals. Finally, we present optimal scheduling under time-varying channels and reconfiguration delays, which is the main contribution of this thesis. We show that under the simultaneous presence of these two phenomenon network stability region shrinks, previously suggested policies are unstable, and new algorithmic approaches are necessary. We propose techniques based on state-action frequencies of Markov Decision Process theory to characterize the network stability region and propose throughput-optimal algorithms. The state-action frequency technique is applicable to a broad class of systems with or without reconfiguration delays, and provides a new framework for characterizing network stability region and developing throughput-optimal scheduling policies.
by Güner Dinc̦er C̦elik.
Ph.D.

This dissertation is focused on the development of generative audio systems - a term used describe generative music systems that generate both formal structure and synthesized audio content from the same audio-rate computational process. In other words, a system wherein the synthesis and organizational processes are inseparable and operate at the sample level.

First, a series of generative software systems are described. These systems each employ a different method to create generativity and, though they are not strictly generative audio systems, they lay important groundwork for the rest of the discussion as ideas from and contributions to the fields of generative algorithmic composition, computational aesthetics, music information dynamics, and digital signal processing are introduced.

Second, the dissertation investigates the use of a novel signal processing technique in which time-varying allpass filters are placed into feedback networks, producing synthesis structures capable of yielding interesting emergent sonic behaviors. Ideas from the field of computational aesthetics are employed to allow a large system built from these synthesis structures to become “aesthetically aware”. Many theories about computational aesthetics center around a favorable balance between order and complexity in a stimulus - a successful artistic work is neither too orderly nor too complex. Using a model of human perception based on the “mere exposure” effect, which describes how listener appreciation and boredom change as they experience repeated exposure to a stimulus, the AAS-4 system autonomously determines when and how to modify its own parameters to avoid repetitions that may lead to boredom in listeners.

The dissertation concludes with objective analysis of the generative system by considering the complexity of its output from an information-theoretic perspective. It was found that the generative audio system described here is capable of producing output with equivalent complexity to that of real-world musical examples. It is also shown that the level of complexity in the generated audio and real-world examples falls in-between the low complexity of silence and sinusoids and the maximal complexity of white noise, corresponding with the theories from computational aesthetics. Future directions of this work are also described. Two appendices describing related topics that would disrupt the flow of the dissertation are included.

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, February 2004.
Includes bibliographical references (p. 283-295).
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Satellite and wireless networks operate over time varying channels that depend on attenuation conditions, power allocation decisions, and inter-channel interference. In order to reliably integrate these systems into a high speed data network and meet the increasing demand for high throughput and low delay, it is necessary to develop efficient network layer strategies that fully utilize the physical layer capabilities of each network element. In this thesis, we develop the notion of network layer capacity and describe capacity achieving power allocation and routing algorithms for general networks with wireless links and adaptive transmission rates. Fundamental issues of delay, throughput optimality, fairness, implementation complexity, and robustness to time varying channel conditions and changing user demands are discussed. Analysis is performed at the packet level and fully considers the queueing dynamics in systems with arbitrary, potentially bursty, arrival processes. Applications of this research are examined for the specific cases of satellite networks and ad-hoc wireless networks. Indeed, in Chapter 3 we consider a multi-beam satellite downlink and develop a dynamic power allocation algorithm that allocates power to each link in reaction to queue backlog and current channel conditions. The algorithm operates without knowledge of the arriving traffic or channel statistics, and is shown to achieve maximum throughput while maintaining average delay guarantees. At the end of Chapter 4, a crosslinked collection of such satellites is considered and a satellite separation principle is developed, demonstrating that joint optimal control can be implemented with separate algorithms for the downlinks and crosslinks.
(cont.) Ad-hoc wireless networks are given special attention in Chapter 6. A simple cell- partitioned model for a mobile ad-hoc network with N users is constructed, and exact expressions for capacity and delay are derived. End-to-end delay is shown to be O(N), and hence grows large as the size of the network is increased. To reduce delay, a transmission protocol which sends redundant packet information over multiple paths is developed and shown to provide O(vN) delay at the cost of reducing throughput. A fundamental rate- delay tradeoff curve is established, and the given protocols for achieving O(N) and O(vN) delay are shown to operate on distinct boundary points of this curve. In Chapters 4 and 5 we consider optimal control for a general time-varying network. A cross-layer strategy is developed that stabilizes the network whenever possible, and makes fair decisions about which data to serve when inputs exceed capacity. The strategy is decoupled into separate algorithms for dynamic flow control, power allocation, and routing, and allows for each user to make greedy decisions independent of the actions of others. The combined strategy is shown to yield data rates that are arbitrarily close to the optimally fair operating point that is achieved when all network controllers are coordinated and have perfect knowledge of future events. The cost of approaching this fair operating point is an end-to-end delay increase for data that is served by the network.
by Michael J. Neely.
Ph.D.

In the thesis there are described the implementations of various types of filters to remove disturbing signals, which often degrade the ECG signal. In particular, it is a zero isoline fluctuations and power network interference. It is used a principle of the Lynn’s linear filters. The individual filters are designed in a recursive and non-recursive implementation. Then there is described a time-varying linear Lynn's filter for removing drift of zero isoline signal. The thesis also includes filters with minimized calculating time of response, by sampling rate conversion method for both interference types. In conclusion there is an experimental study of the filter implementation for ECG signal with false and real interferences.

In this dissertation, neural networks are designed to effectively model static non-linear aeroelastic problems in adaptive structures and linear dynamic aeroelastic systems with time varying stiffness. The use of adaptive materials in aircraft wings allows for the change of the contour or the configuration of a wing (morphing) in flight. The use of smart materials, to accomplish these deformations, can imply that the stiffness of the wing with a morphing contour changes as the contour changes. For a rapidly oscillating body in a fluid field, continuously adapting structural parameters may render the wing to behave as a time variant system. Even the internal spars/ribs of the aircraft wing which define the wing stiffness can be made adaptive, that is, their stiffness can be made to vary with time. The immediate effect on the structural dynamics of the wing, is that, the wing motion is governed by a differential equation with time varying coefficients. The study of this concept of a time varying torsional stiffness, made possible by the use of active materials and adaptive spars, in the dynamic aeroelastic behavior of an adaptable airfoil is performed here. A time marching technique is developed for solving linear structural dynamic problems with time-varying parameters. This time-marching technique borrows from the concept of Time-Finite Elements in the sense that for each time interval considered in the time-marching, an analytical solution is obtained. The analytical solution for each time interval is in the form of a matrix exponential and hence this technique is termed as Matrix Exponential time marching. Using this time marching technique, Artificial Neural Networks can be trained to represent the dynamic behavior of any linearly time varying system. In order to extend this methodology to dynamic aeroelasticity, it is also necessary to model the unsteady aerodynamic loads over an airfoil. Accordingly, an unsteady aerodynamic panel method is developed using a distributed set of doublet panels over the surface of the airfoil and along its wake. When the aerodynamic loads predicted by this panel method are made available to the Matrix Exponential time marching scheme for every time interval, a dynamic aeroelastic solver for a time varying aeroelastic system is obtained. This solver is now used to train an array of neural networks to represent the response of this two dimensional aeroelastic system with a time varying torsional stiffness. These neural networks are developed into a control system for flutter suppression. Another type of aeroelastic problem of an adaptive structure that is investigated here is the shape control of an adaptive bump situated on the leading edge of an airfoil. Such a bump is useful in achieving flow separation control for lateral directional maneuverability of the aircraft. Since actuators are being used to create this bump on the wing surface, the energy required to do so needs to be minimized. The adverse pressure drag as a result of this bump needs to be controlled so that the loss in lift over the wing is made minimal. The design of such a "spoiler bump" on the surface of the airfoil is an optimization problem of maximizing pressure drag due to flow separation while minimizing the loss in lift and energy required to deform the bump. One neural network is trained using the CFD code FLUENT to represent the aerodynamic loading over the bump. A second neural network is trained for calculating the actuator loads, bump displacement and lift, drag forces over the airfoil using the finite element solver, ANSYS and the previously trained neural network. This non-linear aeroelastic model of the deforming bump on an airfoil surface using neural networks can serve as a fore-runner for other non-linear aeroelastic problems. This work enhances the traditional aeroelastic modeling by introducing time varying parameters in the differential equations of motion. It investigates the calculation of non-conservative aerodynamic loads on morphing contours and the resulting structural deformation for non-linear aeroelastic problems through the use of neural networks. Geometric modeling of morphing contours is also addressed.
Ph. D.

Over the last two decades there has been an increase in the research of artificial neural networks (ANNs) to forecasting problems. Both in theoretical and empirical works, ANNs have shown evidence of good performance, in many cases outperforming established statistical benchmarks. This thesis starts by reviewing the advances in ANNs for time series forecasting, assessing their performance in the literature, analysing the current state of the art, the modelling issues that have been solved and which are still critical for forecasting with ANNs, thereby indicating future research directions. The specification of the input vector is identified as the most crucial unresolved modelling issue for ANNs’ accuracy. Notably, there is no rigorous empirical evaluation of the multiple published input variable selection methodologies. This problem is addressed from four different perspectives. A rigorous evaluation of several published methodologies, along with new proposed variations, is performed on low frequency data, exploring which input variable selection methodologies perform best. This analysis concludes that regression based methodologies outperformed other linear and nonlinear ones. The best way to code deterministic seasonality in the inputs of the ANNs is explored, a topic overlooked in the ANN literature, and a parsimonious encoding based on seasonal indices is proposed. The effect of the frequency of the time series on specifying the inputs for ANNs for forecasting is evaluated, revealing several challenges in modelling high frequency time series and providing evidence that the performance of several input variable specification methodologies is not consistent for different data frequencies. This leads to an evaluation of methodologies to select input variables for ANNs solely for high frequency data. Regression based methodologies are found to perform best, in agreement with the evaluation on low frequency dataset, while the ranking of the remaining methodologies is found to be inconsistent for different data frequencies.

On considère la synthèse de la commande basée sur un asservissement affecté par des retards. L’approche utilisée repose sur des méthodes ensemblistes. Une partie de cette thèse est consacrée à une conception de commande active pour la compensation des retards qui apparaissent dans des canaux de communication entre le capteur et correcteur. Ce problème est considéré dans une perspective générale du cadre de commande tolérante aux défauts où des retards variés sont vus comme un mode particulier de dégradation du capteur. Le cas avec transmission de mesure retardée pour des systèmes avec des capteurs redondants est également examiné. Par conséquent, un cadre unifié est proposé afin de régler le problème de commande basé sur la transmission des mesures avec retard qui peuvent également être fournies par des capteurs qui sont affectés par des défauts soudains.Dans la deuxième partie le concept d’invariance positive pour des systèmes linéaires à retard à temps discret est exposé. En ce qui concerne l’invariance pour cette classe des systèmes dynamiques, il existe deux idées principales. La première approche repose sur la réécriture d’un tel système dans l’espace d’état augmenté et de le considérer comme un système linéaire. D’autre part, la seconde approche considère l’invariance dans l’espace d’état initial. Cependant, la caractérisation d’un tel ensemble invariant est encore une question ouverte, même pour le cas linéaire. Par conséquent, l’objectif de cette thèse est d’introduire une notion générale d’invariance positive pour des systèmes linéaires à retard à temps discret. Également, certains nouveaux éclairages sur l’existence et la construction pour les ensembles invariants positifs robustes sont détaillés. En outre, les nouveaux concepts d’invariance alternatives sont décrits
We considered the process regulation which is based on feedback affected by varying delays. Proposed approach relies on set-based control methods. One part of the thesis examines active control design for compensation of delays in sensor-to controller communication channel. This problem is regarded in a general perspective of the fault tolerant control where delays are considered as a particular degradation mode of the sensor. Obtained results are also adapted to the systems with redundant sensing elements that are prone to abrupt faults. In this sense, an unified framework is proposed in order to address the control design with outdated measurements provided by unreliable sensors.Positive invariance for linear discrete-time systems with delays is outlined in the second part of the thesis. Concerning this class of dynamics, there are two main approaches which define positive invariance. The first one relies on rewriting a delay-difference equation in the augmented state-space and applying standard analysis and control design tools for the linear systems. The second approach considers invariance in the initial state-space. However, the initial state-space characterization is still an open problem even for the linear case and it represents our main subject of interest. As a contribution, we provide new insights on the existence of the positively invariant sets in the initial state-space. Moreover, a construction algorithm for the minimal robust D-invariant set is outlined. Additionally, alternative invariance concepts are discussed

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Complex network analysis is a powerful tool into research of complex systems like brain networks. This work aims to describe the topological changes in neural functional connectivity networks of neocortex and hippocampus during slow-wave sleep (SWS) in animals submited to a novel experience exposure. Slow-wave sleep is an important sleep stage where occurs reverberations of electrical activities patterns of wakeness, playing a fundamental role in memory consolidation. Although its importance there s a lack of studies that characterize the topological dynamical of functional connectivity networks during that sleep stage. There s no studies that describe the topological modifications that novel exposure leads to this networks. We have observed that several topological properties have been modified after novel exposure and this modification remains for a long time. Major part of this changes in topological properties by novel exposure are related to fault tolerance
A an?lise da topologia de redes ? uma poderosa ferramenta no estudo de sistemas complexos tal como as redes cerebrais. Este trabalho procura descrever as mudan?as na topologia de redes de conex?o funcional em neur?nios do c?rtex sensorial e do hipocampo durante o sono de ondas lentas (SWS) em animais expostos ? novidade. O sono de ondas lentas ? um importante estado do sono onde h? reverbera??o de padr?es de atividade el?trica ocorridos na vig?lia, tendo com isso papel fundamental na consolida??o de mem?ria. Apesar de sua import?ncia ainda n?o h? estudos que caracterizam a din?mica da topologia de redes de conex?o funcional durante este estado. Tampouco h? estudos que descrevem as modifica??es topol?gicas que a exposi??o ? novidade traz a essas redes. Observamos que v?rias propriedades topol?gicas s?o modificadas ap?s a exposi??o ? novidade e que tais modifica??es se mant?m por um longo per?odo de tempo. A maior parte das propriedades modificadas pela exposi??o ? novidade est? relacionada ? toler?ncia ? falha

Parmi les évolutions majeures de l'informatique, nous distinguons l'émergence des technologies mobiles sans fil. Le développement actuel de ces technologies permet de réaliser des communications ad-hoc directes entre de nombreux types d'entités mobiles, comme des véhicules, des robots terrestres ou des drones. Dans un réseau de tels équipements, l'ensemble des liens de communication qui existe à un instant donné dépend des distances entre les entités et la topologie du réseau change continuellement lorsque les entités se déplacent. Les hypothèses habituelles sur la connexité du réseau n'ont pas leur place ici, néanmoins, une autre forme de connexité appelée connexité temporelle est souvent disponible à travers le temps et l'espace. L'objectif de cette thèse a été de développer des algorithmes pour les flottes d'appareils dans le cas des réseaux tolérant aux délais (DTN). De manière simplifiée, les réseaux tolérants aux délais sont des réseaux pour lesquels certaines parties peuvent se retrouver isolées pendant un moment sans que cela pose problème. Nous nous intéressons, en particulier, au cas où ces appareils sont organisés sous la forme de groupes, et où la notion de groupe elle même survit à ces déconnexions transitoires. Ainsi, une grande partie de la thèse s'articule autour de la notion des groupes tolérant aux délais (groupe DTN). Dans notre cas cet éloignement est limité dans le temps et nous parlons alors de "diamètre temporel borné" au sein du groupe. Le fait de borner le diamètre temporel du groupe lui permet de distinguer entre l'éloignement temporaire d'un noeud et sa perte définitive (crash ou autre)
Among the major developments in computer science, we distinguish the emergence of mobile wireless technologies. The current development of these technologies allows for direct ad-hoc communications between many types of mobile entities, such as vehicles, land robots or drones. In a network of such devices, the set of communication links that exists at a given instant depends upon the distances between the entities. As a result, the topology of the network changes continuously as the entities move. The common assumption on connectivity may not be relevant in this case, but another kind of connectivity called temporal connectivity is often alvailable over time and space. The goal of this thesis has been the development of algorithms for fleets of mobile devices in the case of delay-tolerant networks. In a simpler way, the delay-tolerant networks are networks where some parts can be isolated during a certain time without problems. We are interested, in particular, in the case where the devices are organised as groups, and where the notion of group itself survives to these deconnections. Hence, a big part of this thesis relates to the notion of delay-tolerant groups (DTN groups). In our case, these deconnections are limited in time and we speak of a "bounded temporal diameter" within the group. The fact of limiting the temporal diameter of the group enables it to distinguish between temporary deconnections and final loss (crash or other) of some nodes

Les réseaux dynamiques sont constitués d’entités établissant des contacts les unes avec les autres dans le temps. Un défi majeur dans les réseaux dynamiques est de prédire les modèles de mobilité et de décider si l’évolution de la topologie satisfait aux exigences du succès d’un algorithme donné. Les types de dynamique résultant de ces réseaux sont variés en échelle et en nature. Par exemple,certains de ces réseaux restent connexes tout le temps; d’autres sont toujours déconnectés mais offrent toujours une sorte de connexité dans le temps et dans l’espace(connexité temporelle); d’autres sont connexes de manière récurrente, périodique,etc. Tous ces contextes peuvent être représentés sous forme de classes de graphes dynamiques correspondant à des conditions nécessaires et/ou suffisantes pour des problèmes ou algorithmes distribués donnés. Étant donné un graphe dynamique,une question naturelle est de savoir à quelles classes appartient ce graphe. Dans ce travail, nous apportons une contribution à l’automatisation de la classification de graphes dynamiques. Nous proposons des stratégies pour tester l’appartenance d’un graphe dynamique à une classe donnée et nous définissons un cadre générique pour le test de propriétés dans les graphes dynamiques. Nous explorons également le cas où aucune propriété sur le graphe n’est garantie, à travers l’étude du problème de maintien d’une forêt d’arbres couvrants dans un graphe dynamique
Dynamic networks consist of entities making contact over time with one another. A major challenge in dynamic networks is to predict mobility patterns and decide whether the evolution of the topology satisfies requirements for the successof a given algorithm. The types of dynamics resulting from these networks are varied in scale and nature. For instance, some of these networks remain connected at all times; others are always disconnected but still offer some kind of connectivity over time and space (temporal connectivity); others are recurrently connected,periodic, etc. All of these contexts can be represented as dynamic graph classes corresponding to necessary or sufficient conditions for given distributed problems or algorithms. Given a dynamic graph, a natural question to ask is to which of the classes this graph belongs. In this work we provide a contribution to the automation of dynamic graphs classification. We provide strategies for testing membership of a dynamic graph to a given class and a generic framework to test properties in dynamic graphs. We also attempt to understand what can still be done in a context where no property on the graph is guaranteed through the distributed problem of maintaining a spanning forest in highly dynamic graphs

Non-centralized control schemes for large-scale systems, including energy networks, are more flexible, scalable, and reliable than the centralized counterpart. These benefrts are obtained by having a set of local control!ers, each of which is responsible for a partition of the system, instead of one central entity that controls the whole system. Furthermore,in sorne cases, employing a non­ centralized control structure might be necessary due to the intractability problem of the centralized method.Thus, this thesis is devoted to the study of non-centralized optimization-based control approaches for large-scale energy systems. Mainly,this thesis focuses on the communication and cooperation processes of local controllers, which are integral parts of such schemes. Throughout this thesis,the model predictíve control framework is applied to solve the economic dispatch problem of large-scale energy systems. In a non-centralized architecture, local controllers must cooperatively solve the economic dispatch problem, which is formulated as a convex optimization problem with edge-based coupling constraints, at each time step.Therefore, first, the augmented Lagrangian approach is deployed to decompose the problem and to design two distributed optimization methods, which are iterative and require the local controllers to exchange information with each other at each iteration. lt is then shown that the sequence produced by these methods converges to an optima!solution when sorne cond tions, which include how the controllers must communicate and cooperate, are satisfied. However, in practice, the communication process might not always be perfect,i.e.,the required communication assumption does not hold. In the case of communication link failures, the distributed methods might not be able to compute a solution.Therefore,an information exchange protocol that is based on consensus is designed to overcome this problem. Furthermore, the proposed distributed methods are also further·extended such that they work over random communication networks and asynchronous updates, i.e.,when not all controllers always perform the updates . Under this setup, the convergence and the convergence rate of the algorithms are shown. Additionally, the implementation of these distributed methods to an MPC-based economic dispatch is also presented. The discussion includes the techniques that can be used to reduce the number of iterat ions and the performance of the methods in a numerical study. Considering that the aforementioned methods are comrnunication-intensive, an alternative non-centralized scheme, which provides a trade-off between comrnunication intensity and suboptirnality,is proposed.The scheme consists of repartitioning the network online with the aim of obtaining self-sufficient subsystems, forming coalitions for subsystems that are not self-sufficient,and decomposing the economic dispatch problem of the system into coalition-based subproblems. In this scheme, each subsystem only communicates to the others that belong to the sarne coalition;thus, reducing communication. Especially when all subsystems are self-sufficient, exchanging information is not needed. Finally,a cooperation problem during the implementation of the decisions is discussed. Specifically, sorne subsystems do not cornply with the computed decisions to gain better performance at the cost of deteriorating the performance of the other subsystems.A resilient scheme that can cope with this problem is formulated.lt consists of a stochastic method to robustify the decisions against such adversaria! behavior and an identification and mitigation method that is based on hypothesis testing using Bayesian inference.The proposed scheme, in general,can mitigate the effect of non-
Los esquemas de control no centralizados aplicados a sistemas a gran escala, entre los que se incluyen las redes energéticas, son más flexibles, escalables y fiables que sus equivalentes centralizados. Dichos beneficios pueden obtenerse empleando un conjunto de controladores locales, donde cada uno de ellos es responsable de una parte del sistema, en lugar de una entidad central que controle la totalidad del sistema.Asimismo,el uso de una estructura de control no centralizada podría ser, en algunos casos, necesario, dado el problema de intratabilidad del método centralizado. Por consiguiente, la presente tesis trata sobre el estudio de enfoques de control no centralizados basados en optimización para redes energéticas a gran escala. Principalmente, esta tesis se centra en los procesos de comunicación y cooperación llevados a cabo por los controladores locales , que constituyen partes esenciales de dichos esquemas . A lo largo de esta tesis, el control predictivo basado en modelos se usa para resolver el problema de expedir energia en redes energéticas a gran escala desde un punto de vista económico. En arquitecturas no centralizadas, los controladores locales deben resolver dicho problema de forma cooperativa, el cual se formula como un problema de optimización convexo con restricciones de acoplamiento en los enlaces entre nodos, que debe ser resuelto en cada instante de tiempo. Para ello, el método de Lagrangiano aumentado se utiliza inicialmente para descomponer el problema y diseñar dos métodos de optimización distribuidos , que son iterativos y requieren que los controladores locales intercambien información entre ellos en cada iteración . A continuación, se muestra que la secuencia generada por estos métodos converge a la solución óptima a condición de que se cumplan ciertas condiciones,incluyendo cómo los controladores deben comunicarse y cooperar. Sin embargo, en la práctica,la comunicación no siempre es perfecta, es decir,el supuesto de comunicación requerido no se cumple. En el caso de fallos en los enlaces de comunicación, los métodos distribuidos podrían no ser capaces de proporcionar una solución. Para paliar este problema, se diseña un protocolo de información basado en consenso.l'v1ás aún, los métodos de optimización distribuidos se extienden a fin de que sean capaces de trabajar en redes con comunicaciones aleatorias y actualizaciones asíncronas, es decir,redes en que no todos los controladores realicen las actualizaciones . En esta configuración se muestran la convergencia y el orden de convergencia de dichos algoritmos. Se muestra, además, la implementación de estos métodos en el control predictivo económico basado en modelos para redes energéticas. La discusión incluye las técnicas que pueden usarse para reducir el número de iteraciones, así como el desempeño de los métodos, a través de un estudio numérico. Teniendo en cuenta que los métodos anteriormente mencionados requieren una comunicación intensa,se propone otro esquema no centralizado que proporciona un compromiso entre intensidad de comunicación y suboptimalidad . Dicha estrategia consiste en volver a particionar en línea el sistema con el objetivo de obtener subsistemas autosuficientes,formando coaliciones de subsistemas que no lo sean por separado,y descomponiendo el problema económico de expedición de energía en subproblemas de tipo coalicional. En este esquema ,cada subsistema se comunica únicamente con aquellos otros subsistemas que pertenezcan a la misma coalición, reduciendo asi el tráfico de comunicación. En particular, cuando todos los subsistemas son autosuficientes, el intercambio de información ya no es necesario. Finalmente,se considera el problema de la cooperación durante la implementación de las decisiones Específicamente, algunos subsistemas no acatan las decisiones tomadas con el fin de lograr un desempeño propio superior a expensas de empeorar el desempeño de otros subsistemas. Es por esto que, con el fin de lidiar con este problema, se propone un esquema resiliente, el cual consiste en un método estocástico para hacer las decisiones más robustas frente a tal comportamiento adverso, y un método de identificación y mitigación basado en evaluación de hipótesis usando inferencia bayesiana. En general, el esquema propuesto logra mitigar el efecto de los subsistemas incumplidores sobre el resto, y en un caso concreto, también permite identificar los subsistemas adversos.
Els esquemes de control no centralitzats aplicats a sistemes a gran escala, entre els quals s’inclouen les xarxes energètiques, són més flexibles, escalables i fiables que els seus equivalents centralitzats. Aquests beneficis es poden obtenir fent servir un conjunt de controladors locals, en què cadascun d’ells és responsable d’una part del sistema, en lloc d’una entitat central que controli la totalitat del sistema. Així mateix, l’ús d’una estructura de control no centralitzada podria ser, en alguns casos, necessari, donat el problema d’intractabilitat del mètode centralitzat. Per tant, la present tesi tracta sobre l’estudi d’enfocaments de control no centralitzats basats en optimització per a xarxes energètiques a gran escala. Principalment, aquesta tesi se centra en els processos de comunicació i cooperació duts a terme pels controladors locals, que constitueixen parts essencials d’aquests esquemes. Al llarg d’aquesta tesi, el control predictiu basat en models s’utilitza per a resoldre el problema d’expedició d’energia en xarxes energètiques a gran escala des d’un punt de vista econòmic. En arquitectures no centralitzades, els controladors locals han de resoldre aquest problema de forma cooperativa, formulat com un problema d’optimització convex amb restriccions d’acoblament en els enllaços entre nodes i que ha de ser resolt a cada instant de temps. A tal efecte, el mètode de Lagrangià augmentat s’utilitza inicialment per a descomposar el problema i dissenyar dos mètodes d’optimització distribuïts, que són iteratius i requereixen que els controladors locals intercanviïn informació entre ells a cada iteració. A continuació, es mostra que la seqüència generada per aquests mètodes convergeix a la solució òptima si es compleixen certes condicions, incloent la manera en què els controladors s’han de comunicar i cooperar. No obstant això, a la pràctica, la comunicació no és sempre perfecta, és a dir, el supòsit de comunicació perfecta no es compleix. En el cas de fallades en els enllaços de comunicació, els mètodes distribuïts podrien no ser capaços de proporcionar una solució. Per a resoldre aquest problema, es dissenya un protocol d’informació basat en consens. A més, els mètodes d’optimització distribuïts s’amplien per tal que siguin capaços de treballar en xarxes amb comunicacions aleatòries i actualitzacions asíncrones, és a dir, xarxes en què no tots els controladors realitzin les actualitzacions. En aquestes configuracions es mostren la convergència i l’ordre de convergència d’aquests algoritmes. A més, es mostra també la implementació d’aquests mètodes en el control predictiu econòmic basat en models per a xarxes energètiques. La discussió inclou les tècniques que es poden emprar per a reduir el nombre d’iteracions, així com el rendiment dels mètodes, fent servir un estudi numèric. Tenint en compte que els mètodes anteriorment esmentats requereixen una comunicació intensa, es proposa un altre esquema no centralitzat que proporciona un compromís entre intensitat de comunicació i suboptimalitat. Aquesta estratègia consisteix en tornar a particionar el sistema en línia amb l’objectiu d’obtenir subsistemes autosuficients, formant coalicions de subsistemes que no ho siguin per separat, i descomposant el problema econòmic d’expedició d’energia en subproblemes de tipus coalicional. En aquest esquema, cada subsistema es comunica únicament amb aquells altre subsistemes que pertanyin a la mateixa coalició, reduint així el trànsit de comunicació. En particular, quan tots els sistemes són autosuficients, l’intercanvi d’informació deixa de ser necessari. Finalment, es considera el problema de la cooperació durant la implementació de les decisions. Específicament, alguns subsistemes no acaten les decisions preses amb la finalitat de millorar el propi rendiment a costa de disminuir el d’altres subsistemes. És per això que, a fi de solucionar aquest problema, es proposa un esquema resilient, el qual consisteix en un mètode estocàstic per fer les decisions més robustes davant d’aquest comportament advers, i un mètode d’identificació i mitigació basat en evaluar hipòtesis utilitzant inferència bayesiana. En general, l’esquema proposat aconsegueix mitigar l’efecte que els subsistemes no obedients exerceixen sobre la resta, i en un cas concert, també permet identificar els subsistemes adversos.
ABSTRAKSI (Indfonesian) Skema kendali yang tidak tersentralisasi untuk sistem berskala besar, seperti sistem aringan energi, lebih fleksibel, skalabel, dan reliabel dibandingkan dengan skema tersentralisasi. Keuntungan ini diperoleh dari terdapatnya satu set pengendali lokal, yang hanya bertanggung jawab terhadap satu partisi dari sistem tersebut, daripada jika hanya terdapat satu entitas yang mengendalikan seluruh sistem. Bahkan dalam beberapa sistem, penerapan struktur kendali yang tidak tersentralisasi menjadi keharusan karena adanya permasalahan intraktabilitas dari metode tersentralisasi. Oleh karena itu, disertasi ini bertujuan untuk melakukan studi pada metode kendali berdasarkan optimisasi dengan struktur yang tidak tersentralisasi untuk sistem energi berskala besar. Khususnya, disertasi ini memfokuskan pada proses komunikasi dan kooperasi pengendali‐pengendali lokal, yang merupakan bagian integral dalam skema yang dimaksud. Pada disertasi ini, sistem kontrol prediktif (model predictive control (MPC)) diterapkan untuk menyelesaikan optimisasi economic dispatch pada sistem energi berskala besar. Dalam arsitektur yang tidak tersentralisasi, pengendali‐pengendali lokal harus menyelesaikan permasalahan economic dispatch secara kooperatif. Permasalahan economic dispatch ini diformulasikan sebagai optimisasi yang konveks dan memiliki konstrain terkopling. Oleh karena itu, pendekatan Lagrange yang teraugmentasi diterapkan untuk mendekomposisi permasalahan optimisasi terkait. Pendekatan ini juga digunakan untuk merancang dua metode optimisasi terdistribusi, yang iteratif dan mengharuskan pengendali‐pengendali lokal bertukar informasi satu sama lain pada setiap iterasi. Sekuensi yang dihasilkan dari kedua metode tersebut akan terkonvergensi pada suatu solusi yang optimal apabila beberapa kondisi, yang meliputi bagaimana pengendali harus berkomunikasi dan berkooperasi, terpenuhi. Namun, pada praktiknya, proses komunikasi yang terjadi mungkin tidak selalu sempurna, dalam hal ini asumsi pada proses komunikasi yang dibutuhkan tidak terpenuhi. Pada kasus kegagalan jaringan komunikasi, metode terdistribusi yang dirancang mungkin tidak dapat menemukan solusinya. Oleh karena itu, suatu protokol untuk pertukaran informasi yang berdasarkan pada konsensus dirancang untuk mengatasi permasalahan ini. Selanjutnya, dua metode terdistribusi yang telah dirancang juga dikembangkan lebih jauh sehingga metode‐metode tersebut dapat bekerja pada jaringan komunikasi stokastik dengan proses yang asinkron, yaitu proses dimana tidak semua pengendali selalu melakukan pembaruan. Dalam hal ini, konvergensi dan laju konvergensi dari metode yang dirancang dipertunjukkan. Selain itu, implementasi dari metode terdistribusi pada sistem economic dispatch berbasis MPC juga dibahas. Diskusi pada bagian ini mencakup beberapa teknik yang dapat digunakan untuk mengurangi jumlah iterasi dan performa dari metode‐metode yang dirancang pada suatu studi numerik. Dengan pertimbangan bahwa metode‐metode yang disebut sebelumnya membutuhkan komunikasi yang intensif, maka sebuah skema alternatif, yang memberikan trade‐off antara intensitas komunikasi dan suboptimalitas, juga dirancang. Skema ini terdiri dari repartisi sistem online yang bertujuan untuk mendapatkan subsistemsubsistem yang swasembada, pembentukan koalisi untuk subsistem‐subsistem yang tidak swasembada, dan dekomposisi permasalahan economic dispatch menjadi subproblem berbasis koalisi. Dalam skema ini, tiap subsistem hanya perlu berkomunikasi dengan subsistem‐subsistem lain yang berada pada koalisi yang sama; sehingga mengurangi aliran komunikasi. Jika semua subsistem yang terbentuk swasembada, maka pertukaran informasi tidak dibutuhkan sama sekali. Pada akhirnya, disertasi ini juga membahas mengenai suatu permasalahan koperasi dalam masa implementasi keputusan (solusi). Pada permasalahan kooperasi ini, terdapat beberapa subsistem yang tidak menuruti keputusan (solusi), misalnya dengan tujuan untuk mendapatkan kinerja yang lebih baik dan di saat yang bersamaan memperburuk kinerja subsistem lainnya. Maka, sebuah skema resilien yang dapat mengatasi permasalahan ini dirumuskan. Skema tersebut terdiri dari sebuah metode stokastik untuk merobustifikasi keputusan terhadap perilaku adversari dan sebuah metode identifikasi dan mitigasi yang berdasarkan pada pengujian hipotesis dengan menggunakan inferensi Bayes. Skema yang diusulkan, secara umum, dapat memitigasi pengaruh subsistem yang tidak patuh pada subsistem reguler, dan pada kasus tertentu, juga dapat mengidentifikasi subsistem yang menjadi adversari.

En este trabajo se aborda el problema de controlar un proceso cuya salida se muestrea de forma irregular. Para ello se propone utilizar un predictor que estima las salidas del proceso en instantes regulares de tiempo más un controlador convencional que calcula la acción de control a partir de las estimaciones del predictor (técnica conocida como control inferencial). La predicción consiste en estimar las variables de salida que se desean controlar a partir de las mediciones realizadas con diversos sensores utilizando para ello un modelo matemático del proceso. El filtro de Kalman permite hacer la predicción de forma óptima si las perturbaciones tienen una distribución gaussiana de media cero, pero con el inconveniente de requerir un elevado coste computacional cuando se utilizan diferentes sensores con retardos temporales variantes. En este trabajo se propone una estrategia de predicción alternativa de bajo coste computacional cuyo diseño se basa en el conocimiento de la disponibilidad de mediciones y de los retardos (del proceso, del sistema de medición o del sistema de transmisión de datos) y de la naturaleza de las perturbaciones. Los predictores propuestos minimizan el error de predicción frente al muestreo aleatorio con retardos variantes, perturbaciones, ruido de medida, error de modelado, retardos en la acción de control e incertidumbre en los tiempos de medición. Las diferentes estrategias de diseño que se proponen se clasifican según el tipo de información que se dispone de las perturbaciones y del coste computacional requerido. Se han planteado los diseños para sistemas monovariables, multivariables, lineales y no lineales. Asimismo, también se ha elaborado una forma más eficiente de incluir mediciones escasas con retardo en el filtro de Kalman, con el objetivo de reducir el coste computacional de la predicción. En este trabajo se demuestra que los sistemas de control inferencial que utilizan los predictores propuestos cumplen con el principio de sep
Peñarrocha Alós, I. (2006). Sensores virtuales para procesos con medidas escasas y retardos temporales [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/3882
Palancia

Cette thèse est dédiée à l'analyse de stabilité des systèmes à pas d'échantillonnage variable et à la commande dynamique de l'échantillonnage. L'objectif est de concevoir des lois d'échantillonnage permettant de réduire la fréquence d'actualisation de la commande par retour d'état, tout en garantissant la stabilité du système.Tout d'abord, un aperçu des récents défis et axes de recherche sur les systèmes échantillonnés est présenté. Ensuite, une nouvelle approche de contrôle dynamique de l'échantillonnage, "échantillonnage dépendant de l'état", est proposée. Elle permet de concevoir hors-ligne un échantillonnage maximal dépendant de l'état défini sur des régions coniques de l'espace d'état, grâce à des LMIs.Plusieurs types de systèmes sont étudiés. Tout d'abord, le cas de système LTI idéal est considéré. La fonction d'échantillonnage est construite au moyen de polytopes convexes et de conditions de stabilité exponentielle de type Lyapunov-Razumikhin. Ensuite, la robustesse vis-à-vis des perturbations est incluse. Plusieurs applications sont proposées: analyse de stabilité robuste vis-à-vis des variations du pas d'échantillonnage, contrôles event-triggered et self-triggered, et échantillonnage dépendant de l'état. Enfin, le cas de système LTI perturbé à retard est traité. La construction de la fonction d'échantillonnage est basée sur des conditions de stabilité L2 et sur un nouveau type de fonctionnelles de Lyapunov-Krasovskii avec des matrices dépendant de l'état. Pour finir, le problème de stabilisation est traité, avec un nouveau contrôleur dont les gains commutent en fonction de l'état du système. Un co-design contrôleur/fonction d'échantillonnage est alors proposé

Dan Sha.
Thesis (Ph.D.)--Chinese University of Hong Kong, 1999.
Includes bibliographical references (p. 193-206).
Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web.
Mode of access: World Wide Web.
Abstracts in English and Chinese.

碩士
龍華科技大學
機械工程系碩士班
107
In traditional adaptive control, the gradient descent method or least square method is used to estimate the parameters of a time-varying system. The singularity problem in numerical analysis is easily caused due to the errors of numerical rounding and external environmental disturbances in computer calculation, which will affect the response and stability of the closed-loop control system. In this paper, a neural network adaptive controller is proposed. The parameters of a time-varying system are estimated online by using the neural network controller. The control parameters of the self-tuning regulator are calculated by using the pole assignment method. Finally, the experimental results show that the neural network adaptive controller has great stability and fault-tolerance in numerical analysis when estimating the parameters of a highly uncertain time-varying system. Moreover, the stability time, overshoot and mean square error are better than the traditional self-tuning regulator, which proves that the proposed neural network adaptive controller has excellent response characteristics and robustness.

Many biological processes are effectively modeled as networks, but a frequent assumption is that these networks do not change during data collection. However, that assumption does not hold for many phenomena, such as neural growth during learning or changes in genetic regulation during cell differentiation. Approaches are needed that explicitly model networks as they change in time and that characterize the nature of those changes.

In this work, we develop a new class of graphical models in which the conditional dependence structure of the underlying data-generation process is permitted to change over time. We first present the model, explain how to derive it from Bayesian networks, and develop an efficient MCMC sampling algorithm that easily generalizes under varying levels of uncertainty about the data generation process. We then characterize the nature of evolving networks in several biological datasets.

We initially focus on learning how neural information flow networks change in songbirds with implanted electrodes. We characterize how they change in response to different sound stimuli and during the process of habituation. We continue to explore the neurobiology of songbirds by identifying changes in neural information flow in another habituation experiment using fMRI data. Finally, we briefly examine evolving genetic regulatory networks involved in Drosophila muscle differentiation during development.

We conclude by suggesting new experimental directions and statistical extensions to the model for predicting novel neural flow results.

Dissertation

碩士
國立臺灣大學
土木工程學研究所
89
The road users not only concern travel time but also care about the probability of reach destination. The study considers stochastic, time-varying transportation network, where the travel time on the network should be represented as random variables. The probability distribution functions vary with time. The paper first identifies a set of relationships between the mean and variance of the travel time that is dynamic and stochastic in network. Upon these relationships, compute the reliability index of arriving destination in time, and use the index as link weight to generate routing decisions. Lastly, analysis the trade-off relation between travel time and reliability, provide different routing paths for users to make decisions.

碩士
國立交通大學
土木工程系所
96
Time varying systems find many applications in various fields. In mechanical and civil engineering, a system with active control devices of modifying stiffness or damping of the system is a time varying system. When a structure is damaged under dynamic loading, the structure normally displays changes in stiffness and damping with time. The changes with time in stiffness and damping of a system result in time varying instantaneous model parameters is an important issue in damage assessment of a structure. The present work develops a novel procedure of establishing BP neural network of a time varying system and estimating instantaneous model parameters of the system from established neural network. The connective weights and thresholds in a neural network are assumed as functions of time and are expanded by polynomials. A weighted least-squares approach is applied to determine the coefficients of the polynomials. Because of using the weighted least-squares approach, the coefficients of the polynomials also depend on time. Consequently, only low orders of polynomials are needed to expand the connective weights and thresholds. The feasibility of the proposed procedure is demonstrated by processing numerically simulated dynamic responses of a nonlinear system and a time-varying linear system. It is also performed to investigate the effects of weighting function in the weighted least-square approach, polynomial order, and noise on establishing a suitable neural network and determining instantaneous model parameters. Finally, the proposed procedure is applied to process measured dynamics responses of a RC structure under shaking table tests. The experimental structure has been shaken to perform nonlinear behaviors. When dramatic changes are observed in the slope of the measured relationship between force and displacement for the experimental structure, the identified instantaneous model parameters also show significant changes.

碩士
國立中興大學
電機工程學系所
94
It is usually difficult to build a real-time (RT) control network using the standard TCP/IP and 10 BASE-T Ethernet, because the medium access control (MAC) protocol of 10 BASE-T Ethernet, the 1-persistent carrier sense multiple access with collision detection (CSMA/CD) protocol, has unpredictable time delay characteristics, and congestion effect caused queueing delay time. When RT packets are transported over an ordinary 10 BASE-T Ethernet, RT packets from a node may experience a large time delay. This may sometimes cause closed-loop instability and performance degradation. To improve the influence of time delays while maintaining performance, we propose a compensating scheme which consists of two compensators- a fuzzy-PID controller and a wavelet neuron network compensator. The proposed design is experimentally verified to show its effectiveness and superiority.