The main goal of the thesis is to build upon the existing research literature and to explore the relationship of sport confidence and sports performance, among which a positive correlation is often found. The theoretical part summarizes the present findings regarding this connection, as well as how sport confidence generally works, what constructs it relates to, or how it is measured. In the empirical part, quantitative research on a sample of Sri Lankan swimmers between 17 and 19, executed using the Sport-Confidence Inventory (SCI; Vealey, Knight, 2002), is then presented. Its results showed that participants scoring high at least in one of the three SCI subscales were performing better, regardless of how well the other components were developed, as opposed to participants whose scores were moderate in all the three subscales. The results also pointed to significant differences between the genders, where it showed that men generally scored higher on the level of sport confidence. Series of recommendations for trainers and psychologists working with athletes, but also for potential follow-up studies, can be drawn from the outcomes. Keywords: sport-confidence, multidimensionality of confidence, performance prediction, competitive swimming, SCI

This work concerns the validation of two 1D Turbine Design Tools, AXIAL by Concepts NREC and TML by GKN Aerospace, and is purely computational. By using the KTH Test Turbine as a reference frame, these two software programs were set up to simulate its performance, and the results consequently validated against existing experimental data from the turbine. The main objective of this work is to investigate the performance prediction abilities of the 1D Design Tools for a variety of turbine parameters such as efficiency, mass flow, power output and degree of reaction, and study the accuracy of these predictions under given boundary conditions, namely turbine stage inlet pressure, temperature and pressure ratio. The main focus of the simulation was to evaluate the impact of the choice of loss model in the 1D Software Tools for estimation of losses. Thus, in order to gain a better understanding of differences and similarities among the scope of available loss models, as well as deviation models, a literature study was performed. Additionally, in order to extend the knowledge of the detailed performance prediction characteristics of these software tools in regard to the loss model implemented, the individual loss coefficients (profile, secondary, trailing edge, tip clearance and incidence) were studied and analysed. The impact of chosen pressure ratio on the 1D simulation accuracy was also investigated. The software tool used and the loss model selected were both found to be of great significance to the accuracy of the simulated performance. The pressure ratio (PR) used for simulation also proved to be of great significance, with simulations performed at an elevated PR providing considerably more accurate results than at the design PR, suggesting that the majority of loss models are more accurate when estimating with higher PR. The KTH Test Turbine stage validated in this work featured a number of special geometrical features of inconvenient nature for 1D simulations. In order to account for this, a number of correction coefficients were developed and implemented and their individual effect on the simulated performance studied. Another special feature of the turbine stage studied was the lean angle of the stator, which impact on the 1D simulations was also investigated. Additionally, a number of different user selectable parameters in AXIAL and their impact on the simulations were investigated. The geometry correction coefficients and stator lean angle were found to be of negligible impact to the overall estimated performance, while the user selectable parameters in AXIAL proved to be of relatively big influence on the simulated results. Lastly, using the TML software tool, the concept of stator-rotor disc cavity flow known as 'purge flow' was simulated and validated against reference data. Purge flow serves to inhibit the inflow of hot air from the main annulus to the inner hub and simultaneously cool the rotor blades. The TML software was found to overestimate the losses associated with the use of purge flow, although providing relatively coherent trends for parameters such as efficiency, mass flow and power, suggesting that a correction coefficient applied to the overall losses from purge flow could potentially provide better overall accuracy in the simulations. / Swedish TURBOPOWER Research Program

The work presented in this thesis targets the analysis and implementation of multi-criteria performance prediction methods for System-on-Chips (SoC).<p>These new SoC architectures offer the opportunity to integrate complete heterogeneous systems into a single chip and can be used to design battery powered handhelds, security critical systems, consumer electronics devices, etc. However, this variety in terms of application usually comes with a lot of different performance objectives like power consumption, yield, design cost, production cost, silicon area and many others. These performance requirements are often very difficult to meet together so that SoC design usually relies on making the right design choices and finding the best performance compromises.<p>In parallel with this architectural paradigm shift, new Very Deep Submicron (VDSM) silicon processes have more and more impact on the performances and deeply modify the way a VLSI system is designed even at the first stages of a design flow.<p>In such a context where many new technological and system related variables enter the game, early exploration of the impact of design choices becomes crucial to estimate the performance of the system to design and reduce its time-to-market.<p>In this context, this thesis presents: <p>- A study of state-of-the-art tools and methods used to estimate the performances of VLSI systems and an original classification based on several features and concepts that they use. Based on this comparison, we highlight their weaknesses and lacks to identify new opportunities in performance prediction.<p>- The definition of new concepts to enable the automatic exploration of large design spaces based on flexible performance criteria and degrees of freedom representing design choices.<p>- The implementation of a couple of two new tools of our own:<p>- Nessie, a tool enabling hierarchical representation of an application along with its platform and automatically performs the mapping and the estimation of their performance.<p>-Yeti, a C++ library enabling the defintion and value estimation of closed-formed expressions and table-based relations. It provides the user with input and model sensitivity analysis capability, simulation scripting, run-time building and automatic plotting of the results. Additionally, Yeti can work in standalone mode to provide the user with an independent framework for model estimation and analysis.<p><p>To demonstrate the use and interest of these tools, we provide in this thesis several case studies whose results are discussed and compared with the literature.<p>Using Yeti, we successfully reproduced the results of a model estimating multi-core computation power and extended them thanks to the representation flexibility of our tool.<p>We also built several models from the ground up to help the dimensioning of interconnect links and clock frequency optimization.<p>Thanks to Nessie, we were able to reproduce the NoC power consumption results of an H.264/AVC decoding application running on a multicore platform. These results were then extended to the case of a 3D die stacked architecture and the performance benefits are then discussed.<p>We end up by highlighting the advantages of our technique and discuss future opportunities for performance prediction tools to explore. / Doctorat en Sciences de l'ingénieur / info:eu-repo/semantics/nonPublished

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The problem of efficient parallelization of 2D Ising spin systems requires realistic algorithmic design and implementation based on an understanding of issues from computer science and statistical physics. In this work, we not only consider fundamental parallel computing issues but also ensure that the major constraints and criteria of 2D Ising spin systems are incorporated into our study. This realism in both parallel computation and statistical physics has rarely been reflected in previous research for this problem. In this thesis,we designed and implemented a variety of parallel algorithms for both sweep spin selection and random spin selection. We analyzed our parallel algorithms on a portable and general parallel machine model, namely the LogP model. We were able to obtain rigorous theoretical run-times on LogP for all the parallel algorithms. Moreover, a guiding equation was derived for choosing data layouts (blocked vs. stripped) for sweep spin selection. In regards to random spin selection, we were able to develop parallel algorithms with efficient communication schemes. We analyzed randomness of our schemes using statistical methods and provided comparisons between the different schemes. Furthermore, algorithms were implemented and performance data gathered and analyzed in order to determine further design issues and validate theoretical analysis. / Master of Science

Doctor of Philosophy / Department of Civil Engineering / Mustaque Hossain / A Superpave asphalt mixture with 4.75-mm nominal maximum aggregate size (NMAS) is a promising, low-cost pavement preservation treatment for agencies such as the Kansas Department of Transportation (KDOT). The objective of this research study is to develop an optimized 4.75-mm NMAS Superpave mixture in Kansas. In addition, the study evaluated the residual tack coat application rate for the 4.75-mm NMAS mix overlay. Two, hot-in-place recycling (HIPR) projects in Kansas, on US-160 and K-25, were overlaid with a 15- to 19-mm thick layer of 4.75-mm NMAS Superpave mixture in 2007. The field tack coat application rate was measured during construction. Cores were collected from each test section for Hamburg wheel tracking device (HWTD) and laboratory bond tests performed after construction and after one year in service. Test results showed no significant effect of the tack coat application rate on the rutting performance of rehabilitated pavements. The number of wheel passes to rutting failure observed during the HWTD test was dependent on the aggregate source as well as on in-place density of the cores. Laboratory pull-off tests showed that most cores were fully bonded at the interface of the 4.75-mm NMAS overlay and the HIPR layer, regardless of the tack application rate. The failure mode during pull-off tests at the HMA interface was highly dependent on the aggregate source and mix design of the existing layer material. This study also confirmed that overlay construction with a high tack coat application rate may result in bond failure at the HMA interface. Twelve different 4.75-mm NMAS mix designs were developed using materials from the aforementioned but two binder grades and three different percentages of natural (river) sand. Laboratory performance tests were conducted to assess mixture performance. Results show that rutting and moisture damage potential in the laboratory depend on aggregate type irrespective of binder grade. Anti-stripping agent affects moisture sensitivity test results. Fatigue performance is significantly influenced by river sand content and binder grade. Finally, an optimized 4.75-mm NMAS mixture design was developed and verified based on statistical analysis of performance data.

Plusieurs applications modernes nécessitent un traitement de flux massifs de données XML, cela crée de défis techniques. Parmi ces derniers, il y a la conception et la mise en ouvre d'outils pour optimiser le traitement des requêtes XPath et fournir une estimation précise des coûts de ces requêtes traitées sur un flux massif de données XML. Dans cette thèse, nous proposons un nouveau modèle de prédiction de performance qui estime a priori le coût (en termes d'espace utilisé et de temps écoulé) pour les requêtes structurelles de Forward XPath. Ce faisant, nous réalisons une étude expérimentale pour confirmer la relation linéaire entre le traitement de flux, et les ressources d'accès aux données. Par conséquent, nous présentons un modèle mathématique (fonctions de régression linéaire) pour prévoir le coût d'une requête XPath donnée. En outre, nous présentons une technique nouvelle d'estimation de sélectivité. Elle se compose de deux éléments. Le premier est le résumé path tree: une présentation concise et précise de la structure d'un document XML. Le second est l'algorithme d'estimation de sélectivité: un algorithme efficace de flux pour traverser le synopsis path tree pour estimer les valeurs des paramètres de coût. Ces paramètres sont utilisés par le modèle mathématique pour déterminer le coût d'une requête XPath donnée. Nous comparons les performances de notre modèle avec les approches existantes. De plus, nous présentons un cas d'utilisation d'un système en ligne appelé "online stream-querying system". Le système utilise notre modèle de prédiction de performance pour estimer le coût (en termes de temps / mémoire) d'une requête XPath donnée. En outre, il fournit une réponse précise à l'auteur de la requête. Ce cas d'utilisation illustre les avantages pratiques de gestion de performance avec nos techniques / Many modern applications require processing of massive streams of XML data, creating difficult technical challenges. Among these, there is the design and implementation of applications to optimize the processing of XPath queries and to provide an accurate cost estimation for these queries processed on a massive steam of XML data. In this thesis, we propose a novel performance prediction model which a priori estimates the cost (in terms of space used and time spent) for any structural query belonging to Forward XPath. In doing so, we perform an experimental study to confirm the linear relationship between stream-processing and data-access resources. Therefore, we introduce a mathematical model (linear regression functions) to predict the cost for a given XPath query. Moreover, we introduce a new selectivity estimation technique. It consists of two elements. The first one is the path tree structure synopsis: a concise, accurate, and convenient summary of the structure of an XML document. The second one is the selectivity estimation algorithm: an efficient stream-querying algorithm to traverse the path tree synopsis for estimating the values of cost-parameters. Those parameters are used by the mathematical model to determine the cost of a given XPath query. We compare the performance of our model with existing approaches. Furthermore, we present a use case for an online stream-querying system. The system uses our performance predicate model to estimate the cost for a given XPath query in terms of time/memory. Moreover, it provides an accurate answer for the query's sender. This use case illustrates the practical advantages of performance management with our techniques

Nous abordons dans cette thèse la tâche de prédiction de performances des systèmes de reconnaissance automatique de la parole (SRAP).Il s'agit d'une tâche utile pour mesurer la fiabilité d'hypothèses de transcription issues d'une nouvelle collection de données, lorsque la transcription de référence est indisponible et que le SRAP utilisé est inconnu (boîte noire).Notre contribution porte sur plusieurs axes:d'abord, nous proposons un corpus français hétérogène pour apprendre et évaluer des systèmes de prédiction de performances ainsi que des systèmes de RAP.Nous comparons par la suite deux approches de prédiction: une approche à l'état de l'art basée sur l'extraction explicite de traitset une nouvelle approche basée sur des caractéristiques entraînées implicitement à l'aide des réseaux neuronaux convolutifs (CNN).L'utilisation jointe de traits textuels et acoustiques n'apporte pas de gains avec de l'approche état de l'art,tandis qu'elle permet d'obtenir de meilleures prédictions en utilisant les CNNs. Nous montrons également que les CNNs prédisent clairement la distribution des taux d'erreurs sur une collection d'enregistrements, contrairement à l'approche état de l'art qui génère une distribution éloignée de la réalité.Ensuite, nous analysons des facteurs impactant les deux approches de prédiction. Nous évaluons également l'impact de la quantité d'apprentissage des systèmes de prédiction ainsi que la robustesse des systèmes appris avec les sorties d'un système de RAP particulier et utilisés pour prédire la performance sur une nouvelle collection de données.Nos résultats expérimentaux montrent que les deux approches de prédiction sont robustes et que la tâche de prédiction est plus difficile sur des tours de parole courts ainsi que sur les tours de parole ayant un style de parole spontané.Enfin, nous essayons de comprendre quelles informations sont capturées par notre modèle neuronal et leurs liens avec différents facteurs.Nos expériences montrent que les représentations intermédiaires dans le réseau encodent implicitementdes informations sur le style de la parole, l'accent du locuteur ainsi que le type d'émission.Pour tirer profit de cette analyse, nous proposons un système multi-tâche qui se montre légèrement plus efficace sur la tâche de prédiction de performance. / In this thesis, we focus on performance prediction of automatic speech recognition (ASR) systems.This is a very useful task to measure the reliability of transcription hypotheses for a new data collection, when the reference transcription is unavailable and the ASR system used is unknown (black box).Our contribution focuses on several areas: first, we propose a heterogeneous French corpus to learn and evaluate ASR prediction systems.We then compare two prediction approaches: a state-of-the-art (SOTA) performance prediction based on engineered features and a new strategy based on learnt features using convolutional neural networks (CNNs).While the joint use of textual and signal features did not work for the SOTA system, the combination of inputs for CNNs leads to the best WER prediction performance. We also show that our CNN prediction remarkably predicts the shape of the WER distribution on a collection of speech recordings.Then, we analyze factors impacting both prediction approaches. We also assess the impact of the training size of prediction systems as well as the robustness of systems learned with the outputs of a particular ASR system and used to predict performance on a new data collection.Our experimental results show that both prediction approaches are robust and that the prediction task is more difficult on short speech turns as well as spontaneous speech style.Finally, we try to understand which information is captured by our neural model and its relation with different factors.Our experiences show that intermediate representations in the network automatically encode information on the speech style, the speaker's accent as well as the broadcast program type.To take advantage of this analysis, we propose a multi-task system that is slightly more effective on the performance prediction task.