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1
Chabeau, Lucas. "Développement et validation d’un outil multivarié de prédiction dynamique d’un échec de greffe rénale". Electronic Thesis or Diss., Nantes Université, 2024. http://www.theses.fr/2024NANU1033.
Testo completoAbstract (sommario):
Pour de nombreuses pathologies chroniques, la prédiction dynamique d’un événement clinique d’intérêt peut être utile dans une démarche de médecine personnalisée. Dans un tel contexte, les pronostics peuvent être mis à jour tout au long du suivi du patient, à chaque nouvelle information relevée. Ce travail de thèse CIFRE en collaboration avec Sêmeia, consiste à développer et valider un outil de prédiction dynamique de l’échec de greffe rénale. L’outil proposé prédit l’échec de greffe rénale, en compétition avec le décès avec greffon fonctionnel à un horizon de cinq ans. La prédiction est estimée à partir d’informations disponibles à l’inclusion du patient et de trois marqueurs biologiques collectés au cours de son suivi (créatininémie, protéinurie et anticorps antidonneur de type II) permettant d’actualiser le pronostic. Cet outil a été validé pour des temps de prédictions compris entre 1 an et 6 ans posttransplantation. Cette thèse a fait l’objet de trois travaux originaux. Un premier travail, a consisté à développer une procédure d’inférence pour estimer un modèle conjoint pour données longitudinales et données de survie compatible avec l’outil de prédiction. Nous avons mené dans un second travail, une réflexion autours de l’hétérogénéité de la définition de l’horizon de prédiction dans la littérature relative aux prédictions dynamiques. Enfin, nous présentons la construction et la validation du modèle de prédiction dynamique de l’échec de greffe rénale. Le modèle a présenté de bonnes capacités de discrimination et de calibrationFor many chronic diseases, dynamic prediction of a clinical event of interest can be useful in personalised medicine. In this context, prognoses can be updated throughout the patient's follow-up, as new information becomes available. This CIFRE doctoral thesis, in collaboration with Sêmeia, involves developing and validating a dynamic prediction tool for kidney graft failure. The proposed tool predicts kidney graft failure, in competition with death with a functional graft, over a five-year time horizon. The prediction is based on information available at patient inclusion and three biological markers collected during follow-up (serum creatinine, proteinuria and type II donor-specific antibodies). allowing to update the prognosis. This tool has been validated for prediction times of between 1 and 6 years post-transplant. This doctoral thesis was the subject of three original projects. The first involved developing an inference procedure to estimate a joint model for longitudinal and survival data compatible with the prediction tool. Secondly, we examined the heterogeneity in the definition of prediction horizon in the dynamic prediction literature. Finally, we present the construction and validation of a dynamic prediction model for renal transplant failure. The model showed good discrimination and calibration
2
Greco, Antonino. "The role of task relevance in the modulation of brain dynamics during sensory predictions". Doctoral thesis, Università degli studi di Trento, 2021. http://hdl.handle.net/11572/307050.
Testo completoAbstract (sommario):
Associative learning is a fundamental ability biological systems possess in order to adapt to a nonstationary environment. One of the core aspects of associative learning theoretical frameworks is that surprising events drive learning by signalling the need to update the system’s beliefs about the probability structure governing stimuli associations. Specifically, the central neural system generates internal predictions to anticipate the causes of its perceptual experience and compute a prediction error to update its generative model of the environment, an idea generally known as the predictive coding framework. However, it is not clear whether the brain generates these predictions only for goal-oriented behavior or they are more a general characteristic of the brain function. In this thesis, I explored the role of task relevance in modulating brain activity when exposed to sensory associative learning task. In the first study, participants were asked to perform a perceptual detection task while audio-visual stimuli were presented as distractors. These distractors possessed a probability structure that made some of them more paired than others. Results showed that occipital activity triggered by the conditioned stimulus was elicited just before the arrival of the unconditioned visual stimulus. Moreover, occipital activity after the onset of the unconditioned stimulus followed a pattern of precision-weighted prediction errors. In the second study, two more sessions were added to the task in the previous study in which the probability structure for all stimuli associations was identical and the whole experiment was spanned in six days across two weeks. Results showed a difference in the modulation of the beta band induced by the presentation of the unconditioned stimulus preceded by the predictive and unpredictive conditioned auditory stimuli by comparing the pre and post sessions activity. In the third study, participants were exposed to a similar task with respect to the second study with the modification that there was a condition in which the conditioned-unconditioned stimulus association was task-relevant, thus allowing to directly compare task-relevant and task-irrelevant associations. Results showed that both types of associations had similar patterns in terms of activity and functional connectivity when comparing the brain responses to the onset of the unconditioned visual stimulus. Taken together, these findings demonstrate irrelevant associations rely on the same neural mechanisms of relevant ones. Thus, even if task relevance plays a modulatory role on the strength of the neural effects of associative learning, predictive processes take place in sensory associative learning regardless of task relevance.
3
Greco, Antonino. "The role of task relevance in the modulation of brain dynamics during sensory predictions". Doctoral thesis, Università degli studi di Trento, 2021. http://hdl.handle.net/11572/307050.
Testo completoAbstract (sommario):
Associative learning is a fundamental ability biological systems possess in order to adapt to a nonstationary environment. One of the core aspects of associative learning theoretical frameworks is that surprising events drive learning by signalling the need to update the system’s beliefs about the probability structure governing stimuli associations. Specifically, the central neural system generates internal predictions to anticipate the causes of its perceptual experience and compute a prediction error to update its generative model of the environment, an idea generally known as the predictive coding framework. However, it is not clear whether the brain generates these predictions only for goal-oriented behavior or they are more a general characteristic of the brain function. In this thesis, I explored the role of task relevance in modulating brain activity when exposed to sensory associative learning task. In the first study, participants were asked to perform a perceptual detection task while audio-visual stimuli were presented as distractors. These distractors possessed a probability structure that made some of them more paired than others. Results showed that occipital activity triggered by the conditioned stimulus was elicited just before the arrival of the unconditioned visual stimulus. Moreover, occipital activity after the onset of the unconditioned stimulus followed a pattern of precision-weighted prediction errors. In the second study, two more sessions were added to the task in the previous study in which the probability structure for all stimuli associations was identical and the whole experiment was spanned in six days across two weeks. Results showed a difference in the modulation of the beta band induced by the presentation of the unconditioned stimulus preceded by the predictive and unpredictive conditioned auditory stimuli by comparing the pre and post sessions activity. In the third study, participants were exposed to a similar task with respect to the second study with the modification that there was a condition in which the conditioned-unconditioned stimulus association was task-relevant, thus allowing to directly compare task-relevant and task-irrelevant associations. Results showed that both types of associations had similar patterns in terms of activity and functional connectivity when comparing the brain responses to the onset of the unconditioned visual stimulus. Taken together, these findings demonstrate irrelevant associations rely on the same neural mechanisms of relevant ones. Thus, even if task relevance plays a modulatory role on the strength of the neural effects of associative learning, predictive processes take place in sensory associative learning regardless of task relevance.
4
Currier, Patrick Norman. "A Method for Modeling and Prediction of Ground Vehicle Dynamics and Stability in Autonomous Systems". Diss., Virginia Tech, 2011. http://hdl.handle.net/10919/27632.
Testo completoAbstract (sommario):
A future limitation of autonomous ground vehicle technology is the inability of current algorithmic techniques to successfully predict the allowable dynamic operating ranges of unmanned ground vehicles. A further difficulty presented by real vehicles is that the payloads may and probably will change with unpredictably time as will the terrain on which it is expected to operate. To address this limitation, a methodology has been developed to generate real-time estimations of a vehicleâ s instantaneous Maneuvering Manifold. This approach uses force-moment method techniques to create an adaptive, parameterized vehicle model. A technique is developed for estimation of vehicle load state using internal sensors combined with low-magnitude maneuvers. An unscented Kalman filter based estimator is then used to estimate tire forces for use in determining the ground/tire coefficient of friction. Probabilistic techniques are then combined with a combined-slip pneumatic trail based estimator to estimate the coefficient of friction in real-time. This data is then combined to map out the instantaneous maneuvering manifold while applying techniques to account for dynamic rollover and stability limitations. The algorithms are implemented in MATLAB, simulated against TruckSim models, and results are shown to demonstrate the validity of the techniques. The developed methodology is shown to be a novel approach that is capable of addressing the problem of successfully estimating the available maneuvering manifold for autonomous ground vehicles.Ph. D.
5
Chen, Yutao. "Algorithms and Applications for Nonlinear Model Predictive Control with Long Prediction Horizon". Doctoral thesis, Università degli studi di Padova, 2018. http://hdl.handle.net/11577/3421957.
Testo completoAbstract (sommario):
Fast implementations of NMPC are important when addressing real-time control of systems exhibiting features like fast dynamics, large dimension, and long prediction horizon, as in such situations the computational burden of the NMPC may limit the achievable control bandwidth.
For that purpose, this thesis addresses both algorithms and applications.
First, fast NMPC algorithms for controlling continuous-time dynamic systems using a long prediction horizon have been developed.
A bridge between linear and nonlinear MPC is built using partial linearizations or sensitivity update. In order to update the sensitivities only when necessary, a Curvature-like measure of nonlinearity (CMoN) for dynamic systems has been introduced and applied to existing NMPC algorithms. Based on CMoN, intuitive and advanced updating logic have been developed for different numerical and control performance. Thus, the CMoN, together with the updating logic, formulates a partial sensitivity updating scheme for fast NMPC, named CMoN-RTI. Simulation examples are used to demonstrate the effectiveness and efficiency of CMoN-RTI. In addition, a rigorous analysis on the optimality and local convergence of CMoN-RTI is given and illustrated using numerical examples.
Partial condensing algorithms have been developed when using the proposed partial sensitivity update scheme. The computational complexity has been reduced since part of the condensing information are exploited from previous sampling instants. A sensitivity updating logic together with partial condensing is proposed with a complexity linear in prediction length, leading to a speed up by a factor of ten.
Partial matrix factorization algorithms are also proposed to exploit partial sensitivity update. By applying splitting methods to multi-stage problems, only part of the resulting KKT system need to be updated, which is computationally dominant in on-line optimization. Significant improvement has been proved by giving floating point operations (flops).
Second, efficient implementations of NMPC have been achieved by developing a Matlab based package named MATMPC. MATMPC has two working modes: the one completely relies on Matlab and the other employs the MATLAB C language API. The advantages of MATMPC are that algorithms are easy to develop and debug thanks to Matlab, and libraries and toolboxes from Matlab can be directly used. When working in the second mode, the computational efficiency of MATMPC is comparable with those software using optimized code generation. Real-time implementations are achieved for a nine degree of freedom dynamic driving simulator and for multi-sensory motion cueing with active seat.Implementazioni rapide di NMPC sono importanti quando si affronta il controllo in tempo reale di sistemi che presentano caratteristiche come dinamica veloce, ampie dimensioni e orizzonte di predizione lungo, poiché in tali situazioni il carico di calcolo dell'MNPC può limitare la larghezza di banda di controllo ottenibile. A tale scopo, questa tesi riguarda sia gli algoritmi che le applicazioni. In primo luogo, sono stati sviluppati algoritmi veloci NMPC per il controllo di sistemi dinamici a tempo continuo che utilizzano un orizzonte di previsione lungo. Un ponte tra MPC lineare e non lineare viene costruito utilizzando linearizzazioni parziali o aggiornamento della sensibilità. Al fine di aggiornare la sensibilità solo quando necessario, è stata introdotta una misura simile alla curva di non linearità (CMoN) per i sistemi dinamici e applicata agli algoritmi NMPC esistenti. Basato su CMoN, sono state sviluppate logiche di aggiornamento intuitive e avanzate per diverse prestazioni numeriche e di controllo. Pertanto, il CMoN, insieme alla logica di aggiornamento, formula uno schema di aggiornamento della sensibilità parziale per NMPC veloce, denominato CMoN-RTI. Gli esempi di simulazione sono utilizzati per dimostrare l'efficacia e l'efficienza di CMoN-RTI. Inoltre, un'analisi rigorosa sull'ottimalità e sulla convergenza locale di CMoN-RTI viene fornita ed illustrata utilizzando esempi numerici. Algoritmi di condensazione parziale sono stati sviluppati quando si utilizza lo schema di aggiornamento della sensibilità parziale proposto. La complessità computazionale è stata ridotta poiché parte delle informazioni di condensazione sono sfruttate da precedenti istanti di campionamento. Una logica di aggiornamento della sensibilità insieme alla condensazione parziale viene proposta con una complessità lineare nella lunghezza della previsione, che porta a una velocità di un fattore dieci. Sono anche proposti algoritmi di fattorizzazione parziale della matrice per sfruttare l'aggiornamento della sensibilità parziale. Applicando metodi di suddivisione a problemi a più stadi, è necessario aggiornare solo parte del sistema KKT risultante, che è computazionalmente dominante nell'ottimizzazione online. Un miglioramento significativo è stato dimostrato dando operazioni in virgola mobile (flop). In secondo luogo, sono state realizzate implementazioni efficienti di NMPC sviluppando un pacchetto basato su Matlab chiamato MATMPC. MATMPC ha due modalità operative: quella si basa completamente su Matlab e l'altra utilizza l'API del linguaggio MATLAB C. I vantaggi di MATMPC sono che gli algoritmi sono facili da sviluppare e eseguire il debug grazie a Matlab e le librerie e le toolbox di Matlab possono essere utilizzate direttamente. Quando si lavora nella seconda modalità, l'efficienza computazionale di MATMPC è paragonabile a quella del software che utilizza la generazione di codice ottimizzata. Le realizzazioni in tempo reale sono ottenute per un simulatore di guida dinamica di nove gradi di libertà e per il movimento multisensoriale con sedile attivo.
6
Garside, Simon. "Dynamic prediction of road traffic networks". Thesis, Lancaster University, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.387431.
Testo completo
7
Choudhury, Nazim Ahmed. "Mining Time-aware Actor-level Evolution Similarity for Link Prediction in Dynamic Network". Thesis, The University of Sydney, 2018. http://hdl.handle.net/2123/18640.
Testo completoAbstract (sommario):
Topological evolution over time in a dynamic network triggers both the addition and deletion of actors and the links among them. A dynamic network can be represented as a time series of network snapshots where each snapshot represents the state of the network over an interval of time (for example, a minute, hour or day). The duration of each snapshot denotes the temporal scale/sliding window of the dynamic network and all the links within the duration of the window are aggregated together irrespective of their order in time. The inherent trade-off in selecting the timescale in analysing dynamic networks is that choosing a short temporal window may lead to chaotic changes in network topology and measures (for example, the actors’ centrality measures and the average path length); however, choosing a long window may compromise the study and the investigation of network dynamics. Therefore, to facilitate the analysis and understand different patterns of actor-oriented evolutionary aspects, it is necessary to define an optimal window length (temporal duration) with which to sample a dynamic network. In addition to determining the optical temporal duration, another key task for understanding the dynamics of evolving networks is being able to predict the likelihood of future links among pairs of actors given the existing states of link structure at present time. This phenomenon is known as the link prediction problem in network science. Instead of considering a static state of a network where the associated topology does not change, dynamic link prediction attempts to predict emerging links by considering different types of historical/temporal information, for example the different types of temporal evolutions experienced by the actors in a dynamic network due to the topological evolution over time, known as actor dynamicities. Although there has been some success in developing various methodologies and metrics for the purpose of dynamic link prediction, mining actor-oriented evolutions to address this problem has received little attention from the research community. In addition to this, the existing methodologies were developed without considering the sampling window size of the dynamic network, even though the sampling duration has a large impact on mining the network dynamics of an evolutionary network. Therefore, although the principal focus of this thesis is link prediction in dynamic networks, the optimal sampling window determination was also considered.
8
Piccinini, Federico. "Dynamic load balancing based on latency prediction". Thesis, KTH, Skolan för informations- och kommunikationsteknik (ICT), 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-143333.
Testo completoAbstract (sommario):
Spotify is a music streaming service that offers access to a vast music catalogue; it counts more than 24 million active users in 28 different countries. Spotify's backend is made up by a constellation of independent loosely-coupled services; each service consists of a set of replicas, running on a set of servers in multiple data centers: each request to a service needs to be routed to an appropriate replica. Balancing the load across replicas is crucial to exploit available resources in the best possible way, and to provide optimal performances to clients. The main aim of this project is exploring the possibility of developing a load balancing algorithm that exploits request-reply latencies as its only load index. There are two reasons why latency is an appealing load index: in the first place it has a significant impact on the experience of Spotify users; in the second place, identifying a good load index in a distributed system presents significant challenges due to phenomena that might arise from the interaction of the different system components such as multi-bottlenecks. The use of latency as load index is even more attractive under this light, because it allows for a simple black box model where it is not necessary to model resource usage patterns and bottlenecks of every single service individually: modeling each system would be an impractical task, due both to the number of services and to the speed at which these services evolve. In this work, we justify the choice of request-reply latency as a load indicator, by presenting empirical evidence that it correlates well with known reliable load index obtained through a white box approach. In order to assess the correlation between latency and a known load index obtained through a white box approach, we present measurements from the production environment and from an ad-hoc test environment. We present the design of a novel load balancing algorithm based on a modified ' accrual failure detector that exploits request-reply latency as an indirect measure of the load on individual backends; we analyze the algorithm in detail, providing an overview of potential pitfalls and caveats; we also provide an empirical evaluation of our algorithm, compare its performances to a pure round-robin scheduling discipline and discuss which parameters can be tuned and how they affect the overall behavior of the load balancer.
9
Alkindi, Ahmed Bin Masoud Bin Ali. "Performance optimisation through modelling and dynamic prediction". Thesis, University of Warwick, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.399475.
Testo completo
10
Somoye, Adesina Eniari. "A computer prediction of robot dynamic performance". Thesis, University of Surrey, 1985. http://epubs.surrey.ac.uk/848049/.
Testo completoAbstract (sommario):
A survey of existing literature on robot research indicated that little work had been done in the area of general dynamic simulation of Industrial robots which possessed the facility for quick evaluation of different robot structures and drive configurations. The Robot Arm Dynamic Simulation Package ( RADSP ) presented in this thesis has been developed with these features in mind to provide rapid assessment of Industrial robots using modular computation. The simulation package is written in FORTRAN 77 and can be executed on a PRIME or other suitable microcomputers. The freebody method is employed for the generation of the kinematic and dynamic motion algorithms because of its simplicity and short procedures which are found to be compatible with the modular concept. The freebody method is employed in the modelling of serial structural robots on the assumption that the Coriolis and Gyroscopic torque terms are insignificant in the robot torque equations at robot velocities. The RADSP consists of simulation program modules which represent the four main parts of an Industrial robot; these are the controller, the power actuator, the joint and the link. Also, the RADSP includes the inverse kinematic, trajectory planning, result, geometrical modelling and graph plotting package modules. The validation of the RADSP was undertaken on the Asea IRb6 robot for the three upper links and on a robot belt - drive. The results of the validation works were found to be acceptable.
11
Ebert, Anthony C. "Dynamic queueing networks: Simulation, estimation and prediction". Thesis, Queensland University of Technology, 2020. https://eprints.qut.edu.au/180771/1/Anthony_Ebert_Thesis.pdf.
Testo completoAbstract (sommario):
Inspired by the problem of managing passenger flow in airport terminals, novel statistical approaches to simulation, estimation and prediction of these systems were developed. A simulation algorithm was developed with computational speed-ups of more than one hundred-fold. The computational improvement was leveraged to infer parameters governing a dynamic queueing system for the first time. Motivated by the original application, contributions to both functional data analysis as well as combined parameter and state inference were made.
12
Gámez, López Antonio Juan. "Application of nonlinear dimensionality reduction to climate data for prediction". Phd thesis, Universität Potsdam, 2006. http://opus.kobv.de/ubp/volltexte/2006/1095/.
Testo completoAbstract (sommario):
This Thesis was devoted to the study of the coupled system composed by El Niño/Southern Oscillation and the Annual Cycle. More precisely, the work was focused on two main problems:
1. How to separate both oscillations into an affordable model for understanding the behaviour of the whole system.
2. How to model the system in order to achieve a better understanding of the interaction, as well as to predict future states of the system.
We focused our efforts in the Sea Surface Temperature equations, considering that atmospheric effects were secondary to the ocean dynamics. The results found may be summarised as follows:
1. Linear methods are not suitable for characterising the dimensionality of the sea surface temperature in the tropical Pacific Ocean. Therefore they do not help to separate the oscillations by themselves. Instead, nonlinear methods of dimensionality reduction are proven to be better in defining a lower limit for the dimensionality of the system as well as in explaining the statistical results in a more physical way [1]. In particular, Isomap, a nonlinear modification of Multidimensional Scaling methods, provides a physically appealing method of decomposing the data, as it substitutes the euclidean distances in the manifold by an approximation of the geodesic distances. We expect that this method could be successfully applied to other oscillatory extended systems and, in particular, to meteorological systems.
2. A three dimensional dynamical system could be modeled, using a backfitting algorithm, for describing the dynamics of the sea surface temperature in the tropical Pacific Ocean. We observed that, although there were few data points available, we could predict future behaviours of the coupled ENSO-Annual Cycle system with an accuracy of less than six months, although the constructed system presented several drawbacks: few data points to input in the backfitting algorithm, untrained model, lack of forcing with external data and simplification using a close system. Anyway, ensemble prediction techniques showed that the prediction skills of the three dimensional time series were as good as those found in much more complex models. This suggests that the climatological system in the tropics is mainly explained by ocean dynamics, while the atmosphere plays a secondary role in the physics of the process. Relevant predictions for short lead times can be made using a low dimensional system, despite its simplicity. The analysis of the SST data suggests that nonlinear interaction between the oscillations is small, and that noise plays a secondary role in the fundamental dynamics of the oscillations [2].
A global view of the work shows a general procedure to face modeling of climatological systems. First, we should find a suitable method of either linear or nonlinear dimensionality reduction. Then, low dimensional time series could be extracted out of the method applied. Finally, a low dimensional model could be found using a backfitting algorithm in order to predict future states of the system.Das Ziel dieser Arbeit ist es das Verhalten der Temperatur des Meers im tropischen Pazifischen Ozean vorherzusagen. In diesem Gebiet der Welt finden zwei wichtige Phänomene gleichzeitig statt: der jährliche Zyklus und El Niño. Der jährliche Zyklus kann als Oszillation physikalischer Variablen (z.B. Temperatur, Windgeschwindigkeit, Höhe des Meeresspiegels), welche eine Periode von einem Jahr zeigen, definiert werden. Das bedeutet, dass das Verhalten des Meers und der Atmosphäre alle zwölf Monate ähnlich sind (alle Sommer sind ähnlicher jedes Jahr als Sommer und Winter des selben Jahres). El Niño ist eine irreguläre Oszillation weil sie abwechselnd hohe und tiefe Werte erreicht, aber nicht zu einer festen Zeit, wie der jährliche Zyklus. Stattdessen, kann el Niño in einem Jahr hohe Werte erreichen und dann vier, fünf oder gar sieben Jahre benötigen, um wieder aufzutreten. Es ist dabei zu beachten, dass zwei Phänomene, die im selben Raum stattfinden, sich gegenseitig beeinflussen. Dennoch weiß man sehr wenig darüber, wie genau el Niño den jährlichen Zyklus beeinflusst, und umgekehrt. Das Ziel dieser Arbeit ist es, erstens, sich auf die Temperatur des Meers zu fokussieren, um das gesamte System zu analysieren; zweitens, alle Temperaturzeitreihen im tropischen Pazifischen Ozean auf die geringst mögliche Anzahl zu reduzieren, um das System einerseits zu vereinfachen, ohne aber andererseits wesentliche Information zu verlieren. Dieses Vorgehen ähnelt der Analyse einer langen schwingenden Feder, die sich leicht um die Ruhelage bewegt. Obwohl die Feder lang ist, können wir näherungsweise die ganze Feder zeichnen wenn wir die höchsten Punkte zur einen bestimmten Zeitpunkt kennen. Daher, brauchen wir nur einige Punkte der Feder um ihren Zustand zu charakterisieren. Das Hauptproblem in unserem Fall ist die Mindestanzahl von Punkten zu finden, die ausreicht, um beide Phänomene zu beschreiben. Man hat gefunden, dass diese Anzahl drei ist. Nach diesem Teil, war das Ziel vorherzusagen, wie die Temperaturen sich in der Zeit entwickeln werden, wenn man die aktuellen und vergangenen Temperaturen kennt. Man hat beobachtet, dass eine genaue Vorhersage bis zu sechs oder weniger Monate gemacht werden kann, und dass die Temperatur für ein Jahr nicht vorhersagbar ist. Ein wichtiges Resultat ist, dass die Vorhersagen auf kurzen Zeitskalen genauso gut sind, wie die Vorhersagen, welche andere Autoren mit deutlich komplizierteren Methoden erhalten haben. Deswegen ist meine Aussage, dass das gesamte System von jährlichem Zyklus und El Niño mittels einfacherer Methoden als der heute angewandten vorhergesagt werden kann.
13
Chung, Hyun-Joon Arora Jasbir S. Abdel-Malek Karim. "Optimization-based dynamic prediction of 3D human running". [Iowa City, Iowa] : University of Iowa, 2009. http://ir.uiowa.edu/etd/348.
Testo completo
14
Egan, Colin. "Dynamic branch prediction in high performance superscalar processors". Thesis, University of Hertfordshire, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.340035.
Testo completo
15
Broberg, Felix. "Prediction Assisted Fully Dynamic All-Pairs Effective Resistance". Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-281341.
Testo completoAbstract (sommario):
We study the approximate fully dynamic online all-pairs effective resistance problem in a prediction assisted setting. We introduce a prediction model for predicting which vertex pairs will be involved in future edge insertions, deletions, or effective resistance queries. The model is general enough to be compatible with some link predictors and can be used by other prediction assisted algorithms. Our main contribution is a data structure that utilizes the devised prediction model to maintain the effective resistance between all vertex pairs. The running time matches the performance of the best known offline algorithm if the predictions are good and the best known online data structure if the predictions are bad. A minor contribution is that we give the best known algorithm for the offline problem.Vi undersöker den approximativa och fullkomligt dynamiska effektiva resistensen för samtliga noder i en förutsägelseassisterad miljö. Vi skapar en förutsägelsemodell för att förutspå vilka nodpar som är del av framtida kanttillägg, kantborttagningar och förfrågningar om den effektiva resistensen. Modellen är generell nog at vara kompatibel med forskning inom länkförutsägelse inom grafer och kan användas av andra förutsägelseassisterade algoritmer. Vårt största bidrag är i formen av en datastruktur som använder sig av den skapade förutsägelsemodellen för att bibehålla den effektiva resistensen mellan samtliga noder. Körtiden matchar den bästa algoritmen för offline-varianten av problemet om förutsägelserna är bra, medan den matchar den bästa datastrukturen för online-varianten om förutsägelserna är dåliga. Ett mindre bidrag är att vi skapar den hittills bästa algoritmen för offline-varianten av problemet.
16
Chung, Hyun-Joon. "Optimization-based dynamic prediction of 3D human running". Diss., University of Iowa, 2009. https://ir.uiowa.edu/etd/348.
Testo completoAbstract (sommario):
Mathematical modeling of human running is a challenging problem from analytical and computational points of view. Purpose of the present research is to develop and study formulations and computational procedures for simulation of natural human running. The human skeletal structure is modeled as a mechanical system that includes link lengths, mass moments of inertia, joint torques, and external forces. The model has 55 degrees of freedom, 49 for revolute joints and 6 for global translation and rotation. Denavit-Hartenberg method is used for kinematics analysis and recursive Lagrangian formulation is used for the equations of motion. The dynamic stability is achieved by satisfying the zero moment point (ZMP) condition during the ground contact phase. B-spline interpolation is used for discretization of the joint angle profiles. The joint torque square, impulse at the foot strike, and yawing moment are included in the performance measure. A minimal set of constraints is imposed in the formulation of the problem to simulate natural running motion. Normal running with arm fixed, slow jog along curves, and running with upper body motion are formulated. Simulation results are obtained for various cases and discussed. The cases are running with different foot locations, running with backpack, and running with different running speeds. Also, extreme cases are performed. Each case gives reasonable cause and effect results. Furthermore, sparsity of the formulation is studied. The results obtained with the formulation are validated with the experimental data. The proposed formulation is robust and can predict natural motion of human running.
17
Pittaras, Athanasios. "Adaptive signal prediction with application to ship motions". Thesis, University College London (University of London), 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.288852.
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Silva, Andre Espozel Pinheiro da. "Testing dynamic agency predictions to corporate finance". reponame:Repositório Institucional do FGV, 2017. http://hdl.handle.net/10438/18243.
Testo completoAbstract (sommario):
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This papers tests theoretical predictions concerning to agent compensation, debt structure and investment in the models of dynamic agency in DeMarzo and Fishman (2007), DeMarzo and Sannikov (2006) and DeMarzo, Fishman, He and Wang (2012). The results related to agent compensation are consistent with the patterns predicted in the models, indicating that the firm-years that the models would have as more likely to pay dividends are indeed the ones more likely to pay; also, among firms that pay dividends, more profits generate higher dividend payments and higher executive compensation, as predicted in the models. The prediction that firms that go well and reach a payment threshold present marginal q equal to average q, and thus after controlling for average q cash flows would not explain investment is also supported by the tests in here. On the other hand, predictions related to the role of the credit line and to the debt structure are not compatible with the results in here. The credit line doesn’t seem to be the provider of financial slack that protects the firm from low cash flows and also doesn’t seem to have the dynamics of being paid when profits are high and being more used when profits are low.
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Bratt, Mattias. "Teleoperation with significant dynamics". Licentiate thesis, Stockholm : Skolan för datavetenskap och kommunikation, Kungliga Tekniska högskolan, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-11389.
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Randle, Andrew Martin. "Dynamic radio channel effects from L-band foliage scatter". Thesis, University of York, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.341630.
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Gillis, John M. Martin W. Gary. "An investigation of student conjectures in static and dynamic geometry environments". Auburn, Ala., 2005. http://repo.lib.auburn.edu/EtdRoot/2005/SPRING/Curriculum_and_Teaching/Dissertation/GILLIS_JOHN_2.pdf.
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Qi, Xiaojiang. "Adaptive prediction for systems subject to abrupt dynamic changes /". [S.l.] : [s.n.], 1989. http://library.epfl.ch/theses/?nr=801.
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Aleen, Farhana Afroz. "Dynamic execution prediction and pipeline balancing of streaming applications". Thesis, Georgia Institute of Technology, 2010. http://hdl.handle.net/1853/37192.
Testo completoAbstract (sommario):
The number and scope of data driven streaming applications is growing. Such streaming applications are promising targets for
effectively utilizing multi-cores because of their inherent amenability to pipelined parallelism. While existing methods of
orchestrating streaming programs on multi-cores have mostly been static, real-world applications show ample variations in execution time that may cause the achieved speedup and throughput to be sub-optimal. One of the principle challenges for moving towards dynamic pipeline balancing has been the lack of approaches that can predict upcoming dynamic variations in execution efficiently, well before they occur. In this thesis, we propose an automated dynamic execution behavior prediction approach based on compiler analysis that can be used to efficiently estimate the time to be spent in different pipeline stages for
upcoming inputs. Our approach first uses dynamic taint analysis to automatically generate an input-based execution characterization of the streaming program, which identifies the key control points where variation in execution might occur with respect to the associated input elements. We then automatically generate a light-weight emulator from the
program using this characterization that can predict the execution paths taken for new streaming inputs and provide execution time estimates and possible dynamic variations. The main challenge in devising such an approach is the essential trade-off
between accuracy and overhead of dynamic analysis. We present experimental evidence that our technique can accurately and
efficiently estimate dynamic execution behaviors for several benchmarks with a small error rate. We also showed that the error rate could be lowered with the trade-off of execution overhead by implementing a selective symbolic expression generation for each of the complex conditions of control-flow operations. Our experiments show that dynamic pipeline balancing using our predicted execution behavior can achieve considerably higher speedup and throughput along with more effective utilization of multi-cores than static balancing approaches.
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Rogers, Frederick. "Prediction of dynamic bending stresses of ships at sea". Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape8/PQDD_0001/MQ42438.pdf.
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Sebastian, James W. "Parametric prediction of the transverse dynamic stability of ships". Monterey, California. Naval Postgraduate School, 1997. http://hdl.handle.net/10945/8800.
Testo completoAbstract (sommario):
There currently exists no direct method for predicting the righting energy of a ship based on key geometric hull properties. Consequently, naval architects traditionally select hull parameters based on other constraints and merely check the dynamic stability indicators after designing the preliminary body plan. Quantifying these relationships would allow such indicators to be used as design variables in optimizing a hull form. Additionally, the hull form has a considerable impact on ship motion theory and dynamic stability criteria. This thesis suggests possible functional relationships, to predict the residuary stability of a design using basic hull parameters
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Turner, James David. "A dynamic prediction and monitoring framework for distributed applications". Thesis, University of Warwick, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.396960.
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Schonfeld, Daniel (Daniel Ryan). "Dynamic prediction of terminal-area severe convective weather penetration". Thesis, Massachusetts Institute of Technology, 2015. http://hdl.handle.net/1721.1/98561.
Testo completoAbstract (sommario):
Thesis: S.M., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2015.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages [110]-112).
Despite groundbreaking technology and revised operating procedures designed to improve the safety of air travel, numerous aviation accidents still occur every year. According to a recent report by the FAA's Aviation Weather Research Program, over 23% of these accidents are weather-related, typically taking place during the takeoff and landing phases. When pilots fly through severe convective weather, regardless of whether or not an accident occurs, they cause damage to the aircraft, increasing maintenance cost for airlines. These concerns, coupled with the growing demand for air transportation, put an enormous amount of pressure on the existing air traffic control system. Moreover, the degree to which weather impacts airspace capacity, defined as the number of aircraft that can simultaneously y within the terminal area, is not well understood. Understanding how weather impacts terminal area air traffic flows will be important for quantifying the effect that uncertainty in weather forecasting has on flows, and developing an optimal strategy to mitigate this effect. In this thesis, we formulate semi-dynamic models and employ Multinomial Logistic Regression, Classification and Regression Trees (CART), and Random Forests to accurately predict the severity of convective weather penetration by flights in several U.S. airport terminal areas. Our models perform consistently well when re-trained on each individual airport rather than using common models across airports. Random Forests achieve the lowest prediction error with accuracies as high as 99%, false negative rates as low as 1%, and false positive rates as low as 3%. CART is the least sensitive to differences across airports, exhibiting very steady performance. We also identify weather-based features, particularly those describing the presence of fast-moving, severe convective weather within the projected trajectory of the flight, as the best predictors of future penetration.
by Daniel Schonfeld.
S.M.
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Yusop, M. Y. Mohd. "Energy saving for pneumatic actuation using dynamic model prediction". Thesis, Cardiff University, 2006. http://orca.cf.ac.uk/56066/.
Testo completoAbstract (sommario):
This research investigates a novel method for energy saving in a point-to-point actuation of a pneumatic system. The method predicts the system's actuation using the Gas Law and the actuator model, and commits air supply cut-off at the time when the energy in the actuator is sufficient to complete the actuation task. Experimental implementation is compared with simulation. The effect of the method is compared with conventional no cut-off and end-stroke cut-off actuations. Lumped and finite difference methods are used for transmission line and system actuation modelling. The transmission line, actuator and control valves are modelled and integrated for the system actuation simulation and comparisons between simulation and measured data are performed. Pressure oscillation in the transmission line exists and is captured by stimulating the air dynamics using a new transmission line experimental method. The friction model of the pneumatic actuator is determined by experiment and applied to the energy saving control system as well as the computer modelling algorithm. The determination of pneumatic transmission line bulk modulus is performed through experiment due to the confidentiality of Young's modulus value needed for its calculation. Significant effect has been achieved in implementation and it is found that cut off at end-stroke and cut-off using model prediction can reduce the amount of air potential energy wasted in conventional actuation by up to 43.5% and 80.2% respectively. However the actuation time of predicted cut-off increases by up to 25%.
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Li, Rui M. Eng Massachusetts Institute of Technology. "G-Network for outcome prediction under dynamic intervention regimes". Thesis, Massachusetts Institute of Technology, 2020. https://hdl.handle.net/1721.1/129838.
Testo completoAbstract (sommario):
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, February, 2020Cataloged from student-submitted PDF of thesis.
Includes bibliographical references (pages 56-57).
Counterfactual prediction is useful in settings where one would like to know what would have happened had an alternative regime been followed, but one only knows the outcomes under the observational regime. Typically, the regimes are dynamic and time-varying. In these scenarios, G-computation can be used for counterfactual prediction. This work explores a novel recurrent neural network approach to G-computation, dubbed G-Net. Many implementations of G-Net were explored and compared to the baseline, linear regression. Two independent datasets were used to evaluate the performance of G-Net: one from a physiological simulator, CVSim, and another from the real-world MIMIC database. Results from the CVSim experiments suggest that G-Net outperforms the traditional linear regression approach to G-computation. The best G-Net model found from the CVSim experiments was then evaluated using the MIMIC dataset. The outcomes under a few different counterfactual strategies on the MIMIC cohort were explored and evaluated for clinical plausibility.
by Rui Li.
M. Eng.
M.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science
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Saigiridharan, Lakshidaa. "Dynamic prediction of repair costs in heavy-duty trucks". Thesis, Linköpings universitet, Statistik och maskininlärning, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-166133.
Testo completoAbstract (sommario):
Pricing of repair and maintenance (R&M) contracts is one among the most important processes carried out at Scania. Predictions of repair costs at Scania are carried out using experience-based prediction methods which do not involve statistical methods for the computation of average repair costs for contracts terminated in the recent past. This method is difficult to apply for a reference population of rigid Scania trucks. Hence, the purpose of this study is to perform suitable statistical modelling to predict repair costs of four variants of rigid Scania trucks. The study gathers repair data from multiple sources and performs feature selection using the Akaike Information Criterion (AIC) to extract the most significant features that influence repair costs corresponding to each truck variant. The study proved to show that the inclusion of operational features as a factor could further influence the pricing of contracts. The hurdle Gamma model, which is widely used to handle zero inflations in Generalized Linear Models (GLMs), is used to train the data which consists of numerous zero and non-zero values. Due to the inherent hierarchical structure within the data expressed by individual chassis, a hierarchical hurdle Gamma model is also implemented. These two statistical models are found to perform much better than the experience-based prediction method. This evaluation is done using the mean absolute error (MAE) and root mean square error (RMSE) statistics. A final model comparison is conducted using the AIC to draw conclusions based on the goodness of fit and predictive performance of the two statistical models. On assessing the models using these statistics, the hierarchical hurdle Gamma model was found to perform predictions the best
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Hong, Yili. "Reliability prediction based on complicated data and dynamic data". [Ames, Iowa : Iowa State University], 2009.
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Thomas, Jason Christopher. "Prediction of Fluid Viscosity Through Transient Molecular Dynamic Simulations". Diss., CLICK HERE for online access, 2009. http://contentdm.lib.byu.edu/ETD/image/etd3312.pdf.
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Lindberg, Jonas, e Källman Isak Wolfert. "Vehicle Collision Risk Prediction Using a Dynamic Bayesian Network". Thesis, KTH, Matematisk statistik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-273629.
Testo completoAbstract (sommario):
This thesis tackles the problem of predicting the collision risk for vehicles driving in complex traffic scenes for a few seconds into the future. The method is based on previous research using dynamic Bayesian networks to represent the state of the system. Common risk prediction methods are often categorized into three different groups depending on their abstraction level. The most complex of these are interaction-aware models which take driver interactions into account. These models often suffer from high computational complexity which is a key limitation in practical use. The model studied in this work takes interactions between drivers into account by considering driver intentions and the traffic rules in the scene. The state of the traffic scene used in the model contains the physical state of vehicles, the intentions of drivers and the expected behaviour of drivers according to the traffic rules. To allow for real-time risk assessment, an approximate inference of the state given the noisy sensor measurements is done using sequential importance resampling. Two different measures of risk are studied. The first is based on driver intentions not matching the expected maneuver, which in turn could lead to a dangerous situation. The second measure is based on a trajectory prediction step and uses the two measures time to collision (TTC) and time to critical collision probability (TTCCP). The implemented model can be applied in complex traffic scenarios with numerous participants. In this work, we focus on intersection and roundabout scenarios. The model is tested on simulated and real data from these scenarios. %Simulations of these scenarios is used to test the model. In these qualitative tests, the model was able to correctly identify collisions a few seconds before they occur and is also able to avoid false positives by detecting the vehicles that will give way.Detta arbete behandlar problemet att förutsäga kollisionsrisken för fordon som kör i komplexa trafikscenarier för några sekunder i framtiden. Metoden är baserad på tidigare forskning där dynamiska Bayesianska nätverk används för att representera systemets tillstånd. Vanliga riskprognosmetoder kategoriseras ofta i tre olika grupper beroende på deras abstraktionsnivå. De mest komplexa av dessa är interaktionsmedvetna modeller som tar hänsyn till förarnas interaktioner. Dessa modeller lider ofta av hög beräkningskomplexitet, vilket är en svår begränsning när det kommer till praktisk användning. Modellen som studeras i detta arbete tar hänsyn till interaktioner mellan förare genom att beakta förarnas avsikter och trafikreglerna i scenen. Tillståndet i trafikscenen som används i modellen innehåller fordonets fysiska tillstånd, förarnas avsikter och förarnas förväntade beteende enligt trafikreglerna. För att möjliggöra riskbedömning i realtid görs en approximativ inferens av tillståndet givet den brusiga sensordatan med hjälp av sekventiell vägd simulering. Två olika mått på risk studeras. Det första är baserat på förarnas avsikter, närmare bestämt att ta reda på om de inte överensstämmer med den förväntade manövern, vilket då skulle kunna leda till en farlig situation. Det andra riskmåttet är baserat på ett prediktionssteg som använder sig av time to collision (TTC) och time to critical collision probability (TTCCP). Den implementerade modellen kan tillämpas i komplexa trafikscenarier med många fordon. I detta arbete fokuserar vi på scerarier i korsningar och rondeller. Modellen testas på simulerad och verklig data från dessa scenarier. I dessa kvalitativa tester kunde modellen korrekt identifiera kollisioner några få sekunder innan de inträffade. Den kunde också undvika falsklarm genom att lista ut vilka fordon som kommer att lämna företräde.
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Aldokhayel, Abdulaziz. "A Kalman Filter-based Dynamic Model for Bus Travel Time Prediction". Thesis, Université d'Ottawa / University of Ottawa, 2018. http://hdl.handle.net/10393/38060.
Testo completoAbstract (sommario):
Urban areas are currently facing challenges in terms of traffic congestion due to city expansion and population increase. In some cases, physical solutions are limited. For example, in certain areas it is not possible to expand roads or build a new bridge. Therefore, making public transpiration (PT) affordable, more attractive and intelligent could be a potential solution for these challenges. Accuracy in bus running time and bus arrival time is a key component of making PT attractive to ridership. In this thesis, a dynamic model based on Kalman filter (KF) has been developed to predict bus running time and dwell time while taking into account real-time road incidents. The model uses historical data collected by Automatic Vehicle Location system (AVL) and Automatic Passenger Counters (APC) system. To predict the bus travel time, the model has two components of running time prediction (long and short distance prediction) and dwell time prediction. When the bus closes its doors before leaving a bus stop, the model predicts the travel time to all downstream bus stops. This is long distance prediction. The model will then update the prediction between the bus’s current position and the upcoming bus stop based on real-time data from AVL. This is short distance prediction. Also, the model predicts the dwell time at each coming bus stop. As a result, the model reduces the difference between the predicted arrival time and the actual arrival time and provides a better understanding for the transit network which allows lead to have a good traffic management.
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Sepp, Löfgren Nicholas. "Accelerating bulk material property prediction using machine learning potentials for molecular dynamics : predicting physical properties of bulk Aluminium and Silicon". Thesis, Linköpings universitet, Teoretisk Fysik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-179894.
Testo completoAbstract (sommario):
In this project machine learning (ML) interatomic potentials are trained and used in molecular dynamics (MD) simulations to predict the physical properties of total energy, mean squared displacement (MSD) and specific heat capacity for systems of bulk Aluminium and Silicon. The interatomic potentials investigated are potentials trained using the ML models kernel ridge regression (KRR) and moment tensor potentials (MTPs). The simulations using these ML potentials are then compared with results obtained from ab-initio simulations using the gold standard method of density functional theory (DFT), as implemented in the Vienna ab-intio simulation package (VASP). The results show that the MTP simulations reach comparable accuracy compared to the DFT simulations for the properties total energy and MSD for Aluminium, with errors in the orders of magnitudes of meV and 10-5 Å2. Specific heat capacity is not reasonably replicated for Aluminium. The MTP simulations do not reasonably replicate the studied properties for the system of Silicon. The KRR models are implemented in the most direct way, and do not yield reasonably low errors even when trained on all available 10000 time steps of DFT training data. On the other hand, the MTPs require only to be trained on approximately 100 time steps to replicate the physical properties of Aluminium with accuracy comparable to DFT. After being trained on 100 time steps, the trained MTPs achieve mean absolute errors in the orders of magnitudes for the energy per atom and force magnitude predictions of 10-3 and 10-1 respectively for Aluminium, and 10-3 and 10-2 respectively for Silicon. At the same time, the MTP simulations require less core hours to simulate the same amount of time steps as the DFT simulations. In conclusion, MTPs could very likely play a role in accelerating both materials simulations themselves and subsequently the emergence of the data-driven materials design and informatics paradigm.
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Fairley, Thomas Euan. "Predicting the dynamic performance of seats". Thesis, University of Southampton, 1986. https://eprints.soton.ac.uk/52370/.
Testo completoAbstract (sommario):
The transmission of vibration to people through seats was studied with the final objective of being able to predict the dynamic performance of seats. It was necessary to investigate both the dynamic response and the subjective response of a person sitting on a seat, in addition to the dynamic response of the seat itself. Initial experiments were conducted to investigate the effect of variables, such as the magnitude of the vibration and the posture of the body, on the transmission of vibration through a seat. Seat transmissibilities measured in a laboratory with vertical-only vibration were shown to be similar to those measured in a vehicle with multiple vibration. Frequency response functions were determined for the transmission of multiple axis translational vehicle vibration to the cushion and the backrest of a seat. The apparent mass of the seated body was measured as a pre-requisite for the prediction of seat transmissibilities. It was found that the resonance frequency of the body decreased when the magnitude of the vibration was increased. Increased muscle tension tended to have the opposite effect. The apparent masses of sixty people - men, women and children - were very similar once they were normalised to remove the effect of the different static weights on the seat. Some correlations with the body characteristics were significant - such as that between the normalised apparent mass at resonance and total body mass. A method of measuring the dynamic stiffness of a seat using a rigid indenter instead of a person to load the seat was developed. The results, in conjunction with measurements of the apparent mass of the body, were used to predict seat transmissibilities. Predicted seat transmissibilities were shown to be similar to transmissibilities that were measured with a person sitting on the seat. It was investigated how the weight of a person and the magnitude of the vibration could affect predicted seat transmissibilities. The discomfort caused by vibration transmitted to a seated person was measured using a novel method of adjustment. It was investigated whether the discomfort caused by simultaneous vertical and fore-and-aft vibration could be predicted from the discomfort that would have been caused if the vibration in each axis had occurred separately. A root-sum-square summation procedure was found to be the best.
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Chin, See Loong. "Incomplete gene structure prediction with almost 100% specificity". Thesis, Texas A&M University, 2003. http://hdl.handle.net/1969.1/258.
Testo completoAbstract (sommario):
The goals of gene prediction using computational approaches are to determine gene location and the corresponding functionality of the coding region. A subset of gene prediction is the gene structure prediction problem, which is to define the exon-intron boundaries of a gene. Gene prediction follows two general approaches: statistical patterns identification and sequence similarity comparison. Similarity based approaches have gained increasing popularity with the recent vast increase in genomic data in GenBank. The proposed gene prediction algorithm is a similarity based algorithm which capitalizes on the fact that similar sequences bear similar functions. The proposed algorithm, like most other similarity based algorithms, is based on dynamic programming. Given a genomic DNA, X = x1 xn and a closely related cDNA, Y = y1 yn, these sequences are aligned with matching pairs stored in a data set. These indexes of matching sets contain a large jumble of all matching pairs, with a lot of cross over indexes. Dynamic programming alignment is again used to retrieve the longest common non-crossing subsequence from the collection of matching fragments in the data set. This algorithm was implemented in Java on the Unix platform. Statistical comparisons were made against other software programs in the field. Statistical evaluation at both the DNA and exonic level were made against Est2genome, Sim4, Spidey, and Fgenesh-C. The proposed gene structure prediction algorithm, by far, has the best performance in the specificity category. The resulting specificity was greater than 98%. The proposed algorithm also has on par results in terms of sensitivity and correlation coeffcient. The goal of developing an algorithm to predict exonic regions with a very high level of correctness was achieved.
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Tino, Peter, Christian Schittenkopf, Georg Dorffner e Engelbert J. Dockner. "A symbolic dynamics approach to volatility prediction". SFB Adaptive Information Systems and Modelling in Economics and Management Science, WU Vienna University of Economics and Business, 1998. http://epub.wu.ac.at/1142/1/document.pdf.
Testo completoAbstract (sommario):
We consider the problem of predicting the direction of daily volatility changes in the Dow Jones Industrial Average (DJIA). This is accomplished by quantizing a series of historic volatility changes into a symbolic stream over 2 or 4 symbols. We compare predictive performance of the classical fixed-order Markov models with that of a novel approach to variable memory length prediction (called prediction fractal machine, or PFM) which is able to select very specific deep prediction contexts (whenever there is a sufficient support for such contexts in the training data). We learn that daily volatility changes of the DJIA only exhibit rather shallow finite memory structure. On the other hand, a careful selection of quantization cut values can strongly enhance predictive power of symbolic schemes. Results on 12 non-overlapping epochs of the DJIA strongly suggest that PFMs can outperform both traditional Markov models and (continuous-valued) GARCH models in the task of predicting volatility one time-step ahead. (author's abstract)Series: Working Papers SFB "Adaptive Information Systems and Modelling in Economics and Management Science"
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Barber, IV John Letherman. "Application of optimal prediction to molecular dynamics". Berkeley, Calif. : Oak Ridge, Tenn. : Lawrence Berkeley National Laboratory ; distributed by the Office of Scientific and Technical Information, U.S. Dept. of Energy, 2004. http://www.osti.gov/servlets/purl/838987-xpCsPP/native/.
Testo completoAbstract (sommario):
Thesis (Ph.D.); Submitted to the University of California at Berkeley, Berkeley, CA 94720 (US); 1 Dec 2004.Published through the Information Bridge: DOE Scientific and Technical Information. "LBNL--56842" Barber IV, John Letherman. USDOE Director. Office of Science. Advanced Scientific Computing Research (US) 12/01/2004. Report is also available in paper and microfiche from NTIS.
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Alkhatib, Ahmad. "Prediction and control of uncertain system dynamics". Diss., The University of Arizona, 2000. http://hdl.handle.net/10150/289175.
Testo completoAbstract (sommario):
I investigate the importance of determining the exact dimensionality of a nonlinear system in time series prediction by comparing the effects of varying the embedding vector dimension of linear and nonlinear prediction algorithms. I use the logistic and Henon maps to demonstrate that when the embedding vector dimension of a prediction algorithm is less than the actual dimension of the underlying time series, then the prediction algorithm is unable to accurately capture the dynamics of the time series. On the other hand, when the embedding vector dimension is overestimated, then the prediction horizon collapses quickly, but systematically, with the predicted values filling a manifold bounded by the true dimensional time series attractor. I conclude that the prediction horizon and reconstruction of attractors is extremely sensitive to the accuracy of the estimation of the embedding vector dimension. This will be illustrated for a time series associated with a physical system that was part of the Santa Fe prediction competition [7]. I apply nonlinear chaos theory in modeling and forecasting variable-bitrate (VBR) video sequences. Nonlinear chaos modeling offers an alternative approach to stochastic (typically linear) approaches, with the advantages of lower dimensionality and more determinism. However, the goodness of its predictions strongly depends on the accuracy with which the dimensionality of a chaotic model is estimated from empirical data. The contributions of this study are twofold. First, I present a novel approach for estimating the embedding vector dimension of any chaotic time series that satisfies the functional relationship of Farmer and Sidorowich [12]. The proposed approach is applied to VBR video data and is used to show the existence of chaos in packetized video traffic. Second, I develop a chaos-theory-based model for VBR intra-coded video, which can be used to generate a rich set of synthetic traces that exhibit similar statistical structure to the original data. These traces are useful in performance evaluation and resource allocation in integrated computer networks. Chaotic systems and their control appear in many diverse situations and present challenging problems for control engineers. In this study, chaotic behavior is initiated in a nonlinear beam-balancing system by moving a mass back-and-forth in a particular move sequence. This mass movement could be considered a disturbance to the system. A predictive control strategy is created to counteract this chaotic disturbance and move the beam to a desired angular location. The control strategy does not depend on prior knowledge of the system dynamics. Instead, time series values from the system are used to predict future behavior of the necessary system variable(s).
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Rivadeneira, Juan Carlos. "Predictions versus measurements of turbocharger nonlinear dynamic response". Texas A&M University, 2005. http://hdl.handle.net/1969.1/3723.
Testo completoAbstract (sommario):
The present work advances progress on the validation against measurements of
linear and nonlinear rotordynamic models for predicting the dynamic shaft response of
automotive turbochargers (TCs) supported on floating ring bearings. Waterfall spectra of
measured shaft motions at the compressor and turbine ends of a test TC rotor evidences a
complex response, showing synchronous (1X) and multiple subsynchronous frequencies
along the entire operating speed range (maximum shaft speed ~ 65 krpm). Postprocessing
of the raw test data by mathematical software allows filtering the
synchronous and subsynchronous vibration components for later comparisons to
predicted shaft motions. The static performance of the floating ring bearings is analyzed
with in-house software (XLSFRBThermal®), which considers thermal expansion of the
shaft and bearing components as well as static loading on the bearing due to lubricant
feed pressure. In addition, the program calculates rotordynamic force coefficients for the
inner and outer films of the floating ring bearing. The turbocharger Finite Element (FE)
structural model for the linear and nonlinear analyses includes lumped masses for the
compressor and turbine wheels and the thrust collar. The mass imbalance distribution on
the TC rotor is estimated from the test data using a procedure derived from the two-plane
balancing method with influence coefficients. The linear model yields predictions of
rotor synchronous (1X) response to imbalance and damped eigenvalues. The analysis
evidences that the rotor cylindrical-bending mode is unstable at all shaft speeds while the
rotor conical model becomes more unstable as lubricant feed pressure decreases. The
predicted synchronous (1X) motions agree well with the test data, showing a critical
speed at approximately 20 krpm. The linear stability results indicate the existence of three critical speeds occurring at 4, 20 and 50 krpm. The second critical speed
corresponds to the rotor cylindrical-bending mode, showing larger amplitudes of motion
at the compressor nose than at the turbine end. The third critical speed associated to the
rotor first bending modes is well damped. In the nonlinear transient analysis, the
nonlinear equations of motion of the system (rotor-FRB) are integrated, and the bearing
reaction forces are calculated at each time step in a numerical integration procedure. The
model then yields predictions of total motion which is decomposed into synchronous
(1X) and subsynchronous motions, amplitudes and frequencies. The nonlinear analysis
predicts synchronous (1X) amplitudes that correlate well with the test data at high shaft
speeds (> 30 krpm) but underpredicts the imbalance response at low shaft speeds (<30
krpm). The time transient simulations predict multiple frequency subsynchronous
motions for shaft speeds ranging from 10 krpm to 55 krpm, with amplitudes and
frequencies that are in good agreement with the measurements. Finally, the shaft motion
measurements and predictions demonstrate that the turbocharger dynamic response does
not depend greatly on the lubricant feed pressure and inlet temperature for the conditions
tested.
42
Lyon, Scott Marvin. "The Pseudo-Rigid-Body Model for Dynamic Predictions of Macro and Micro Compliant Mechanisms". BYU ScholarsArchive, 2003. https://scholarsarchive.byu.edu/etd/82.
Testo completoAbstract (sommario):
This work discusses the dynamic predictions of compliant mechanisms using the Pseudo-Rigid-Body model (PRBM). In order to improve the number of mechanisms that can be modeled, this research develops and identifies several key concepts in the behavior of beam segments where both ends are fixed to a rigid body (fixed-fixed flexible segments). A model is presented, and several examples are discussed. The dynamic behavior of several compliant segments is predicted using the PRBM and the results are compared to finite element analysis and experimental results. Details are presented as to the transient behavior of a typical uniform rectangular cross section beam. The results of this study are extended and applied to compliant planar mechanisms. It is shown by comparison with finite element analysis and experimental results that the PRBM is a good model of the physical system's dynamic behavior. The method is also demonstrated for use with compliant microelectromechanical (MEMS) systems.
43
Chierichetti, Maria. "Combined analytical and experimental approaches to dynamic component stress prediction". Diss., Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/44850.
Testo completoAbstract (sommario):
In modern times, the ability to investigate the aeroelastic behavior of dynamic components on rotorcraft has become essential for the prediction of their useful fatigue life. At the same time, the aeroelastic modeling of a rotorcraft is particularly complex and costly.
Inaccuracies in numerical predictions are mostly due to imprecisions in the structural modeling, to the presence of structural degradation or to the limited information on aerodynamic loads.
The integration of experimental measurements on dynamic components such as rotor blades has the potential to improve fatigue estimation, augment the knowledge of the dynamic behavior and inform numerical models.
The objective of this research is the development of a combined numerical and experimental approach, named Confluence Algorithm, that accurately predicts the response of dynamic components with a limited set of experimental data.
The integration of experimental measurements into a numerical algorithm enables the continuous and accurate tracking of the dynamic strain and stress fields.
The Confluence Algorithm systematically updates the numerical model of the external loads, and mass and stiffness distributions to improve the representation and extrapolation of the experimental data, and to extract information on the response of the system at non-measured locations.
The capabilities of this algorithm are first verified in a numerical framework and with well-controlled lab experiments.
Numerical results from a comprehensive UH-60A multibody model are then compared with available experimental data. These analyses demonstrate that the integration of the Confluence Algorithm improves the accuracy of the numerical prediction of the dynamic response of systems characterized by a periodic behavior, even in presence of non-linearities. The algorithm enables the use of simplified models that are corrected through experimental data to achieve accurate tracking of the system.
44
Bataineh, Mohammad Hindi. "New neural network for real-time human dynamic motion prediction". Thesis, The University of Iowa, 2015. http://pqdtopen.proquest.com/#viewpdf?dispub=3711174.
Testo completoAbstract (sommario):
Artificial neural networks (ANNs) have been used successfully in various practical problems. Though extensive improvements on different types of ANNs have been made to improve their performance, each ANN design still experiences its own limitations. The existing digital human models are mature enough to provide accurate and useful results for different tasks and scenarios under various conditions. There is, however, a critical need for these models to run in real time, especially those with large-scale problems like motion prediction which can be computationally demanding. For even small changes to the task conditions, the motion simulation needs to run for a relatively long time (minutes to tens of minutes). Thus, there can be a limited number of training cases due to the computational time and cost associated with collecting training data. In addition, the motion problem is relatively large with respect to the number of outputs, where there are hundreds of outputs (between 500-700 outputs) to predict for a single problem. Therefore, the aforementioned necessities in motion problems lead to the use of tools like the ANN in this work.
This work introduces new algorithms for the design of the radial-basis network (RBN) for problems with minimal available training data. The new RBN design incorporates new training stages with approaches to facilitate proper setting of necessary network parameters. The use of training algorithms with minimal heuristics allows the new RBN design to produce results with quality that none of the competing methods have achieved. The new RBN design, called Opt_RBN, is tested on experimental and practical problems, and the results outperform those produced from standard regression and ANN models. In general, the Opt_RBN shows stable and robust performance for a given set of training cases.
When the Opt_RBN is applied on the large-scale motion prediction application, the network experiences a CPU memory issue when performing the optimization step in the training process. Therefore, new algorithms are introduced to modify some steps of the new Opt_RBN training process to address the memory issue. The modified steps should only be used for large-scale applications similar to the motion problem. The new RBN design proposes an ANN that is capable of improved learning without needing more training data. Although the new design is driven by its use with motion prediction problems, the consequent ANN design can be used with a broad range of large-scale problems in various engineering and industrial fields that experience delay issues when running computational tools that require a massive number of procedures and a great deal of CPU memory.
The results of evaluating the modified Opt_RBN design on two motion problems are promising, with relatively small errors obtained when predicting approximately 500-700 outputs. In addition, new methods for constraint implementation within the new RBN design are introduced. Moreover, the new RBN design and its associated parameters are used as a tool for simulated task analysis. This work initiates the idea that output weights (W) can be used to determine the most critical basis functions that cause the greatest reduction in the network test error. Then, the critical basis functions can specify the most significant training cases that are responsible for the proper performance achieved by the network. The inputs with the most change in value can be extracted from the basis function centers (U) in order to determine the dominant inputs. The outputs with the most change in value and their corresponding key body degrees-of-freedom for a motion task can also be specified using the training cases that are used to create the network's basis functions.
45
Bernhardsson, Viktor, e Rasmus Ringdahl. "Real time highway traffic prediction based on dynamic demand modeling". Thesis, Linköpings universitet, Kommunikations- och transportsystem, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-112094.
Testo completoAbstract (sommario):
Traffic problems caused by congestion are increasing in cities all over the world. As a traffic management tool traffic predictions can be used in order to make prevention actions against traffic congestion. There is one software for traffic state estimations called Mobile Millennium Stockholm (MMS) that are a part of a project for estimate real-time traffic information.In this thesis a framework for running traffic predictions in the MMS software have been implemented and tested on a stretch north of Stockholm. The thesis is focusing on the implementation and evaluation of traffic prediction by running a cell transmission model (CTM) forward in time.This method gives reliable predictions for a prediction horizon of up to 5 minutes. In order to improve the results for traffic predictions, a framework for dynamic inputs of demand and sink capacity has been implemented in the MMS system. The third part of the master thesis presents a model which adjusts the split ratios in a macroscopic traffic model based on driver behavior during congestion.
46
Shen, Luou. "Freeway Travel Time Estimation and Prediction Using Dynamic Neural Networks". FIU Digital Commons, 2008. http://digitalcommons.fiu.edu/etd/17.
Testo completoAbstract (sommario):
Providing transportation system operators and travelers with accurate travel time information allows them to make more informed decisions, yielding benefits for individual travelers and for the entire transportation system. Most existing advanced traveler information systems (ATIS) and advanced traffic management systems (ATMS) use instantaneous travel time values estimated based on the current measurements, assuming that traffic conditions remain constant in the near future. For more effective applications, it has been proposed that ATIS and ATMS should use travel times predicted for short-term future conditions rather than instantaneous travel times measured or estimated for current conditions. This dissertation research investigates short-term freeway travel time prediction using Dynamic Neural Networks (DNN) based on traffic detector data collected by radar traffic detectors installed along a freeway corridor. DNN comprises a class of neural networks that are particularly suitable for predicting variables like travel time, but has not been adequately investigated for this purpose. Before this investigation, it was necessary to identifying methods for data imputation to account for missing data usually encountered when collecting data using traffic detectors. It was also necessary to identify a method to estimate the travel time on the freeway corridor based on data collected using point traffic detectors. A new travel time estimation method referred to as the Piecewise Constant Acceleration Based (PCAB) method was developed and compared with other methods reported in the literatures. The results show that one of the simple travel time estimation methods (the average speed method) can work as well as the PCAB method, and both of them out-perform other methods. This study also compared the travel time prediction performance of three different DNN topologies with different memory setups. The results show that one DNN topology (the time-delay neural networks) out-performs the other two DNN topologies for the investigated prediction problem. This topology also performs slightly better than the simple multilayer perceptron (MLP) neural network topology that has been used in a number of previous studies for travel time prediction.
47
Kerfs, Jeremy N. "Models for Pedestrian Trajectory Prediction and Navigation in Dynamic Environments". DigitalCommons@CalPoly, 2017. https://digitalcommons.calpoly.edu/theses/1716.
Testo completoAbstract (sommario):
Robots are no longer constrained to cages in factories and are increasingly taking on roles alongside humans. Before robots can accomplish their tasks in these dynamic environments, they must be able to navigate while avoiding collisions with pedestrians or other robots. Humans are able to move through crowds by anticipating the movements of other pedestrians and how their actions will influence others; developing a method for predicting pedestrian trajectories is a critical component of a robust robot navigation system. A current state-of-the-art approach for predicting pedestrian trajectories is Social-LSTM, which is a recurrent neural network that incorporates information about neighboring pedestrians to learn how people move cooperatively around each other. This thesis extends and modifies that model to output parameters for a multimodal distribution, which better captures the uncertainty inherent in pedestrian movements. Additionally, four novel architectures for representing neighboring pedestrians are proposed; these models are more general than current trajectory prediction systems and have fewer hyper-parameters. In both simulations and real-world datasets, the multimodal extension significantly increases the accuracy of trajectory prediction. One of the new neighbor representation architectures achieves state-of-the-art results while reducing the number of both parameters and hyper-parameters compared to existing solutions. Two techniques for incorporating the trajectory predictions into a planning system are also developed and evaluated on a real-world dataset. Both techniques plan routes that include fewer near-collisions than algorithms that do not use trajectory predictions. Finally, a Python library for Agent-Based-Modeling and crowd simulation is presented to aid in future research.
48
Tong, Xianqiao. "Real-time Prediction of Dynamic Systems Based on Computer Modeling". Diss., Virginia Tech, 2014. http://hdl.handle.net/10919/47361.
Testo completoAbstract (sommario):
This dissertation proposes a novel computer modeling (DTFLOP modeling) technique to predict the real-time behavior of dynamic systems. The proposed DTFLOP modeling classifies the computation into the sequential computation, which is conducted on the CPU, and the parallel computation, which is performed on the GPU and formulates the data transmission between the CPU and the GPU using the parameters of the memory access speed and the floating point operations to be carried out on the CPU and the GPU by relating the calculation rate respectively. With the help of the proposed DTFLOP modeling it is possible to estimate the time cost for computing the model that represents a dynamic system given a certain computer. The proposed DTFLOP modeling can be utilized as a general method to analyze the computation of a model related to a dynamic system and two real life systems are selected to demonstrate its performance, the cooperative autonomous vehicle system and the full-field measurement system.
For the cooperative autonomous vehicle system a novel parallel grid-based RBE technique is firstly proposed. The formulations are derived by identifying the parallel computation in the prediction and correction processes of the RBE. A belief fusion technique, which fuses not only the observation information but also the target motion information, has hen been proposed. The proposed DTFLOP modeling is validated using the proposed parallel grid-based RBE technique with the GPU implementation by comparing the estimated time cost with the actual time cost of the parallel grid-based RBE. The superiority of the proposed parallel grid-based RBE technique is investigated by a number of numerical examples in comparison with the conventional grid-based RBE technique. The belief fusion technique is examined by a simulated target search and rescue test and it is observed to maintain more information of the target compared with the conventional observation fusion technique and eventually leads to the better performance of the target search and rescue.
For the full-field measurement system a novel parallel DCT full-field measurement technique for measuring the displacement and strain field on the deformed surface of a structure is proposed. The proposed parallel DCT full-field measurement technique measures the displacement and strain field by tracking the centroids of the marked dots on the deformed surface. It identifies and develops the parallel computation in the image analysis and the field estimation processes and then is implemented into the GPU to accelerate the conventional full-field measurement techniques. The detail strategy of the GPU implementation is also developed and presented. The corresponding software package, which also includes a graphic user interface, and the hardware system consist of two digital cameras, LED lights and adjustable support legs to accommodate indoor or outdoor experimental environments are proposed. The proposed DTFLOP modeling is applied to the proposed parallel DCT full-field measurement technique to estimate its performance and the well match with the actual performance demonstrates the DTFLOP modeling. A number of both simulated and real experiments, including the tensile, compressive and bending experiments in the laboratory and outdoor environments, are performed to validate and demonstrate the proposed parallel DCT full-field measurement technique.Ph. D.
49
Zayani, Mohamed-Haykel. "Link prediction in dynamic and human-centered mobile wireless networks". Phd thesis, Institut National des Télécommunications, 2012. http://tel.archives-ouvertes.fr/tel-00787564.
Testo completoAbstract (sommario):
During the last years, we have observed a progressive and continuous expansion of human-centered mobile wireless networks. The advent of these networks has encouraged the researchers to think about new solutions in order to ensure efficient evaluation and design of communication protocols. In fact, these networks are faced to several constraints as the lack of infrastructure, the dynamic topology, the limited resources and the deficient quality of service and security. We have been interested in the dynamicity of the network and in particular in human mobility. The human mobility has been widely studied in order to extract its intrinsic properties and to harness them to propose more accurate approaches. Among the prominent properties depicted in the literature, we have been specially attracted by the impact of the social interactions on the human mobility and consequently on the structure of the network. To grasp structural information of such networks, many metrics and techniques have been borrowed from the Social Network Analysis (SNA). The SNA can be seen as another network measurement task which extracts structural information of the network and provides useful feedback for communication protocols. In this context, the SNA has been extensively used to perform link prediction in social networks relying on their structural properties. Motivated by the importance of social ties in human-centered mobile wireless networks and by the possibilities that are brought by SNA to perform link prediction, we are interested by designing the first link prediction framework adapted for mobile wireless networks as Mobile Ad-hoc Networks (MANETs) and Delay/Disruption Tolerant Networks (DTN). Our proposal tracks the evolution of the network through a third-order tensor over T periods and computes the sociometric Katz measure for each pair of nodes to quantify the strength of the social ties between the network entities. Such quantification gives insights about the links that are expected to occur in the period T+1 and the new links that are created in the future without being observed during the tracking time. To attest the efficiency of our framework, we apply our link prediction technique on three real traces and we compare its performance to the ones of other well-known link prediction approaches. The results prove that our method reaches the highest level of accuracy and outperforms the other techniques. One of the major contributions behind our proposal highlights that the link prediction in such networks can be made in a distributed way. In other words, the nodes can predict their future links relying on the local information (one-hop and two-hop neighbors) instead of a full knowledge about the topology of the network. Furthermore, we are keen to improve the link prediction performance of our tensor-based framework. To quantify the social closeness between the users, we take into consideration two aspects of the relationships: the recentness of the interactions and their frequency. From this perspective, we wonder if we can consider a third criterion to improve the link prediction precision. Asserting the heuristic that stipulates that persistent links are highly predictable, we take into account the stability of the relationships (link and proximity stabilities). To measure it, we opt for the entropy estimation of a time series proposed in the Lempel-Ziv data compression algorithm. As we think that our framework measurements and the stability estimations complement each other, we combine them in order to provide new link prediction metrics. The simulation results emphasize the pertinence of our intuition. Providing a tensor-based link prediction framework and proposing relative enhancements tied to stability considerations represent the main contributions of this thesis. Along the thesis, our concern was also focused on mechanisms and metrics that contribute towards improving communication protocols in these mobile networks [...]
50
Zayani, Mohamed-Haykel. "Link prediction in dynamic and human-centered mobile wireless networks". Electronic Thesis or Diss., Evry, Institut national des télécommunications, 2012. http://www.theses.fr/2012TELE0031.
Testo completoAbstract (sommario):
Durant ces dernières années, nous avons observe une expansion progressive et continue des réseaux mobile sans-fil centres sur l’être humain. L’apparition de ces réseaux a encouragé les chercheurs à réfléchir à de nouvelles solutions pour assurer une évaluation efficace et une conception adéquate des protocoles de communication. En effet, ces réseaux sont sujets à de multiples contraintes telles que le manque d’infrastructure, la topologie dynamique, les ressources limitées ainsi que la qualité de service et la sécurité des informations précaires. Nous nous sommes spécialement intéressés à l’aspect dynamique du réseau et en particulier à la mobilité humaine. La mobilité humaine a été largement étudiée pour pouvoir extraire ses propriétés intrinsèques et les exploiter pour des approches plus adaptées à cette mobilité. Parmi les propriétés les plus intéressantes soulevées dans la littérature, nous nous sommes focalisés sur l’impact des interactions sociales entre les entités du réseau sur la mobilité humaine et en conséquence sur la structure du réseau. Pour recueillir des informations structurelles sur le réseau, plusieurs métriques et techniques ont été empruntées de l’analyse des réseaux sociaux (SNA). Cet outil peut être assimilé à une autre alternative pour mesurer des indicateurs de performance du réseau. Plus précisément, il extrait des informations structurelles du réseau et permet aux protocoles de communication de bénéficier d’indications utiles telles que la robustesse du réseau, les nœuds centraux ou encore les communautés émergentes. Dans ce contexte, la SNA a été largement utilisée pour prédire les liens dans les réseaux sociaux en se basant notamment sur les informations structurelles. Motivés par l’importance des liens sociaux dans les réseaux mobiles sans-fil centres sur l’être humain et par les possibilités offertes par la SNA pour prédire les liens, nous nous proposons de concevoir la première méthode capable de prédire les liens dans les réseaux sans-fil mobiles tels que les réseaux ad-hoc mobiles (MANETs) et les réseaux tolérants aux délais (DTNs). Notre proposition suit l’évolution de la topologie du réseau sur T périodes à travers un tenseur (en ensemble de matrices d’adjacence et chacune des matrices correspond aux contacts observés durant une période bien spécifique). Ensuite, elle s’appuie sur le calcul de la mesure sociométrique de Katz pour chaque paire de nœuds pour mesurer l’étendue des relations sociales entre les différentes entités du réseau. Une telle quantification donne un aperçu sur les liens dont l’occurrence est fortement pressentie à la période T+1 et les nouveaux liens qui se créent dans le futur sans pour autant avoir été observés durant le temps de suivi. Pour attester l’efficacité de notre proposition, nous l’appliquons sur trois traces réelles et nous comparons sa performance à celles d’autres techniques de prédiction de liens présentées dans la littérature. Les résultats prouvent que notre méthode est capable d’atteindre le meilleur niveau d’efficacité et sa performance surpasse celles des autres techniques. L’une des majeures contributions apportées par cette proposition met en exergue la possibilité de prédire les liens d’une manière décentralisée. En d’autres termes, les nœuds sont capables de prédire leurs propres liens dans le futur en se basant seulement sur la connaissance du voisinage immédiat (voisins à un et deux sauts). En outre, nous sommes désireux d’améliorer encore plus la performance de notre méthode de prédiction de liens. Pour quantifier la force des relations sociales entre les entités du réseau, nous considérons deux aspects dans les relations : la récence des interactions et leur fréquence. À partir de là, nous nous demandons s’il est possible de prendre en compte un troisième critère pour améliorer la précision des prédictions […]During the last years, we have observed a progressive and continuous expansion of human-centered mobile wireless networks. The advent of these networks has encouraged the researchers to think about new solutions in order to ensure efficient evaluation and design of communication protocols. In fact, these networks are faced to several constraints as the lack of infrastructure, the dynamic topology, the limited resources and the deficient quality of service and security. We have been interested in the dynamicity of the network and in particular in human mobility. The human mobility has been widely studied in order to extract its intrinsic properties and to harness them to propose more accurate approaches. Among the prominent properties depicted in the literature, we have been specially attracted by the impact of the social interactions on the human mobility and consequently on the structure of the network. To grasp structural information of such networks, many metrics and techniques have been borrowed from the Social Network Analysis (SNA). The SNA can be seen as another network measurement task which extracts structural information of the network and provides useful feedback for communication protocols. In this context, the SNA has been extensively used to perform link prediction in social networks relying on their structural properties. Motivated by the importance of social ties in human-centered mobile wireless networks and by the possibilities that are brought by SNA to perform link prediction, we are interested by designing the first link prediction framework adapted for mobile wireless networks as Mobile Ad-hoc Networks (MANETs) and Delay/Disruption Tolerant Networks (DTN). Our proposal tracks the evolution of the network through a third-order tensor over T periods and computes the sociometric Katz measure for each pair of nodes to quantify the strength of the social ties between the network entities. Such quantification gives insights about the links that are expected to occur in the period T+1 and the new links that are created in the future without being observed during the tracking time. To attest the efficiency of our framework, we apply our link prediction technique on three real traces and we compare its performance to the ones of other well-known link prediction approaches. The results prove that our method reaches the highest level of accuracy and outperforms the other techniques. One of the major contributions behind our proposal highlights that the link prediction in such networks can be made in a distributed way. In other words, the nodes can predict their future links relying on the local information (one-hop and two-hop neighbors) instead of a full knowledge about the topology of the network. Furthermore, we are keen to improve the link prediction performance of our tensor-based framework. To quantify the social closeness between the users, we take into consideration two aspects of the relationships: the recentness of the interactions and their frequency. From this perspective, we wonder if we can consider a third criterion to improve the link prediction precision. Asserting the heuristic that stipulates that persistent links are highly predictable, we take into account the stability of the relationships (link and proximity stabilities). To measure it, we opt for the entropy estimation of a time series proposed in the Lempel-Ziv data compression algorithm. As we think that our framework measurements and the stability estimations complement each other, we combine them in order to provide new link prediction metrics. The simulation results emphasize the pertinence of our intuition. Providing a tensor-based link prediction framework and proposing relative enhancements tied to stability considerations represent the main contributions of this thesis. Along the thesis, our concern was also focused on mechanisms and metrics that contribute towards improving communication protocols in these mobile networks […]