Error models in hydrogeology applications

Notre consommation en eau souterraine, en particulier comme eau potable ou pour l'irrigation, a considérablement augmenté au cours des années. De nombreux problèmes font alors leur apparition, allant de la prospection de nouvelles ressources à la remédiation des aquifères pollués. Indépendamment du problème hydrogéologique considéré, le principal défi reste la caractérisation des propriétés du sous-sol. Une approche stochastique est alors nécessaire afin de représenter cette incertitude en considérant de multiples scénarios géologiques et en générant un grand nombre de réalisations géostatistiques. Nous rencontrons alors la principale limitation de ces approches qui est le coût de calcul dû à la simulation des processus d'écoulements complexes pour chacune de ces réalisations. Dans la première partie de la thèse, ce problème est investigué dans le contexte de propagation de l'incertitude, oú un ensemble de réalisations est identifié comme représentant les propriétés du sous-sol. Afin de propager cette incertitude à la quantité d'intérêt tout en limitant le coût de calcul, les méthodes actuelles font appel à des modèles d'écoulement approximés. Cela permet l'identification d'un sous-ensemble de réalisations représentant la variabilité de l'ensemble initial. Le modèle complexe d'écoulement est alors évalué uniquement pour ce sousensemble, et, sur la base de ces réponses complexes, l'inférence est faite. Notre objectif est d'améliorer la performance de cette approche en utilisant toute l'information à disposition. Pour cela, le sous-ensemble de réponses approximées et exactes est utilisé afin de construire un modèle d'erreur, qui sert ensuite à corriger le reste des réponses approximées et prédire la réponse du modèle complexe. Cette méthode permet de maximiser l'utilisation de l'information à disposition sans augmentation perceptible du temps de calcul. La propagation de l'incertitude est alors plus précise et plus robuste. La stratégie explorée dans le premier chapitre consiste à apprendre d'un sous-ensemble de réalisations la relation entre les modèles d'écoulement approximé et complexe. Dans la seconde partie de la thèse, cette méthodologie est formalisée mathématiquement en introduisant un modèle de régression entre les réponses fonctionnelles. Comme ce problème est mal posé, il est nécessaire d'en réduire la dimensionnalité. Dans cette optique, l'innovation du travail présenté provient de l'utilisation de l'analyse en composantes principales fonctionnelles (ACPF), qui non seulement effectue la réduction de dimensionnalités tout en maximisant l'information retenue, mais permet aussi de diagnostiquer la qualité du modèle d'erreur dans cet espace fonctionnel. La méthodologie proposée est appliquée à un problème de pollution par une phase liquide nonaqueuse et les résultats obtenus montrent que le modèle d'erreur permet une forte réduction du temps de calcul tout en estimant correctement l'incertitude. De plus, pour chaque réponse approximée, une prédiction de la réponse complexe est fournie par le modèle d'erreur. Le concept de modèle d'erreur fonctionnel est donc pertinent pour la propagation de l'incertitude, mais aussi pour les problèmes d'inférence bayésienne. Les méthodes de Monte Carlo par chaîne de Markov (MCMC) sont les algorithmes les plus communément utilisés afin de générer des réalisations géostatistiques en accord avec les observations. Cependant, ces méthodes souffrent d'un taux d'acceptation très bas pour les problèmes de grande dimensionnalité, résultant en un grand nombre de simulations d'écoulement gaspillées. Une approche en deux temps, le "MCMC en deux étapes", a été introduite afin d'éviter les simulations du modèle complexe inutiles par une évaluation préliminaire de la réalisation. Dans la troisième partie de la thèse, le modèle d'écoulement approximé couplé à un modèle d'erreur sert d'évaluation préliminaire pour le "MCMC en deux étapes". Nous démontrons une augmentation du taux d'acceptation par un facteur de 1.5 à 3 en comparaison avec une implémentation classique de MCMC. Une question reste sans réponse : comment choisir la taille de l'ensemble d'entrainement et comment identifier les réalisations permettant d'optimiser la construction du modèle d'erreur. Cela requiert une stratégie itérative afin que, à chaque nouvelle simulation d'écoulement, le modèle d'erreur soit amélioré en incorporant les nouvelles informations. Ceci est développé dans la quatrième partie de la thèse, oú cette méthodologie est appliquée à un problème d'intrusion saline dans un aquifère côtier. -- Our consumption of groundwater, in particular as drinking water and for irrigation, has considerably increased over the years and groundwater is becoming an increasingly scarce and endangered resource. Nofadays, we are facing many problems ranging from water prospection to sustainable management and remediation of polluted aquifers. Independently of the hydrogeological problem, the main challenge remains dealing with the incomplete knofledge of the underground properties. Stochastic approaches have been developed to represent this uncertainty by considering multiple geological scenarios and generating a large number of realizations. The main limitation of this approach is the computational cost associated with performing complex of simulations in each realization. In the first part of the thesis, we explore this issue in the context of uncertainty propagation, where an ensemble of geostatistical realizations is identified as representative of the subsurface uncertainty. To propagate this lack of knofledge to the quantity of interest (e.g., the concentration of pollutant in extracted water), it is necessary to evaluate the of response of each realization. Due to computational constraints, state-of-the-art methods make use of approximate of simulation, to identify a subset of realizations that represents the variability of the ensemble. The complex and computationally heavy of model is then run for this subset based on which inference is made. Our objective is to increase the performance of this approach by using all of the available information and not solely the subset of exact responses. Two error models are proposed to correct the approximate responses follofing a machine learning approach. For the subset identified by a classical approach (here the distance kernel method) both the approximate and the exact responses are knofn. This information is used to construct an error model and correct the ensemble of approximate responses to predict the "expected" responses of the exact model. The proposed methodology makes use of all the available information without perceptible additional computational costs and leads to an increase in accuracy and robustness of the uncertainty propagation. The strategy explored in the first chapter consists in learning from a subset of realizations the relationship between proxy and exact curves. In the second part of this thesis, the strategy is formalized in a rigorous mathematical framework by defining a regression model between functions. As this problem is ill-posed, it is necessary to reduce its dimensionality. The novelty of the work comes from the use of functional principal component analysis (FPCA), which not only performs the dimensionality reduction while maximizing the retained information, but also allofs a diagnostic of the quality of the error model in the functional space. The proposed methodology is applied to a pollution problem by a non-aqueous phase-liquid. The error model allofs a strong reduction of the computational cost while providing a good estimate of the uncertainty. The individual correction of the proxy response by the error model leads to an excellent prediction of the exact response, opening the door to many applications. The concept of functional error model is useful not only in the context of uncertainty propagation, but also, and maybe even more so, to perform Bayesian inference. Monte Carlo Markov Chain (MCMC) algorithms are the most common choice to ensure that the generated realizations are sampled in accordance with the observations. Hofever, this approach suffers from lof acceptance rate in high dimensional problems, resulting in a large number of wasted of simulations. This led to the introduction of two-stage MCMC, where the computational cost is decreased by avoiding unnecessary simulation of the exact of thanks to a preliminary evaluation of the proposal. In the third part of the thesis, a proxy is coupled to an error model to provide an approximate response for the two-stage MCMC set-up. We demonstrate an increase in acceptance rate by a factor three with respect to one-stage MCMC results. An open question remains: hof do we choose the size of the learning set and identify the realizations to optimize the construction of the error model. This requires devising an iterative strategy to construct the error model, such that, as new of simulations are performed, the error model is iteratively improved by incorporating the new information. This is discussed in the fourth part of the thesis, in which we apply this methodology to a problem of saline intrusion in a coastal aquifer.

Performance benchmarking and incentive regulation – considerations of directing signals for electricity distribution companies

After the restructuring process of the power supply industry, which for instance in Finland took place in the mid-1990s, free competition was introduced for the production and sale of electricity. Nevertheless, natural monopolies are found to be the most efficient form of production in the transmission and distribution of electricity, and therefore such companies remained franchised monopolies. To prevent the misuse of the monopoly position and to guarantee the rights of the customers, regulation of these monopoly companies is required. One of the main objectives of the restructuring process has been to increase the cost efficiency of the industry. Simultaneously, demands for the service quality are increasing. Therefore, many regulatory frameworks are being, or have been, reshaped so that companies are provided with stronger incentives for efficiency and quality improvements. Performance benchmarking has in many cases a central role in the practical implementation of such incentive schemes. Economic regulation with performance benchmarking attached to it provides companies with directing signals that tend to affect their investment and maintenance strategies. Since the asset lifetimes in the electricity distribution are typically many decades, investment decisions have far-reaching technical and economic effects. This doctoral thesis addresses the directing signals of incentive regulation and performance benchmarking in the field of electricity distribution. The theory of efficiency measurement and the most common regulation models are presented. The chief contributions of this work are (1) a new kind of analysis of the regulatory framework, so that the actual directing signals of the regulation and benchmarking for the electricity distribution companies are evaluated, (2) developing the methodology and a software tool for analysing the directing signals of the regulation and benchmarking in the electricity distribution sector, and (3) analysing the real-life regulatory frameworks by the developed methodology and further develop regulation model from the viewpoint of the directing signals. The results of this study have played a key role in the development of the Finnish regulatory model.

Evidence on the role of ownership structure on firms' innovative performance

Based on the Knowledge Production Function framework given by Griliches -1979-, we slightly modify it so that the innovative output depends upon a set of factors related to the firm internal characteristics and are influenced by the environment. Specifically, regarding the firm internal determinants the effect of the concentration of the ownership, the composition of the boards of directors and the effect of the nature of the ownership (foreign and public) are analyzed. Additionally, in order to capture the determinants of the environment in which the firm operates other variables concerning the internationalization of market, the agglomeration economies and the regional knowledge externalities are also considered. In order to assess the impact of these determinants on the number of patents and models of use awarded by the firm, the discreteness of the latter variable has to be taken into account. We apply Poisson and Negative Binomial models for a more comprehensive evaluation of the hypothesis in a panel of Spanish manufacturing firms. The results show patenting activity is positively favoured by being located in an environment with a high innovative activity, due to the existence of knowledge spillovers and agglomeration economies.

Sustainable Waste-to-Energy Production: Performance Evaluation of Distributed Generation fuelled by Landfill Gas

The environmental impact of landfill is a growing concern in waste management practices. Thus, assessing the effectiveness of the solutions implemented to alter the issue is of importance. The objectives of the study were to provide an insight of landfill advantages, and to consolidate landfill gas importance among others alternative fuels. Finally, a case study examining the performances of energy production from a land disposal at Ylivieska was carried out to ascertain the viability of waste to energy project. Both qualitative and quantitative methods were applied. The study was conducted in two parts; the first was the review of literatures focused on landfill gas developments. Specific considerations were the conception of mechanism governing the variability of gas production and the investigation of mathematical models often used in landfill gas modeling. Furthermore, the analysis of two main distributed generation technologies used to generate energy from landfill was carried out. The review of literature revealed a high influence of waste segregation and high level of moisture content for waste stabilization process. It was found that the enhancement in accuracy for forecasting gas rate generation can be done with both mathematical modeling and field test measurements. The result of the case study mainly indicated the close dependence of the power output with the landfill gas quality and the fuel inlet pressure.

Earnings quality and performance in the banking industry: a profit frontier approach

The analysis of efficiency and productivity in banking has received a great deal of attention for almost three decades now. However, most of the literature to date has not explicitly accounted for risk when measuring efficiency. We propose an analysis of profit efficiency taking into account how the inclusion of a variety of bank risk measures might bias efficiency scores. Our measures of risk are partly inspired by the literature on earnings management and earnings quality, keeping in mind that loan loss provisions, as a generally accepted proxy for risk, can be adjusted to manage earnings and regulatory capital. We also consider some variants of traditional models of profit efficiency where different regimes are stipulated so that financial institutions can be evaluated in different dimensions—i.e., prices, quantities, or prices and quantities simultaneously. We perform this analysis on the Spanish banking industry, whose institutions have been deeply affected by the current international financial crisis, and where re-regulation is taking place. Our results can be explored in multiple dimensions but, in general, they indicate that the impact of earnings management on profit efficiency is of less magnitude than what might a priori be expected, and that on the whole, savings banks have performed less well than commercial banks. However, savings banks are adapting to the new regulatory scenario and rapidly catching up with commercial banks, especially in some dimensions of performance.

Comparative performance of the stable isotope signatures of carbon, nitrogen and oxygen in assessing early vigour and grain yield in durum wheat

The present paper studied the performance of the stable isotope signatures of carbon (δ13C), nitrogen (δ15N) and oxygen (δ18O) in plants when used to assess early vigour and grain yield (GY) in durum wheat growing under mild and moderate Mediterranean stress conditions. A collection of 114 recombinant inbred lines was grown under rainfed (RF) and supplementary irrigation (IR) conditions. Broad sense heritabilities (H2) for GY and harvest index (HI) were higher under RF conditions than under IR. Broad sense heritabilities for δ13C were always above 0·60, regardless of the plant part studied, with similar values for IR and RF trials. Some of the largest genetic correlations with GY were those shown by the δ13C content of the flag leaf blade and mature grains. Under both water treatments, mature grains showed the highest negative correlations between δ13C and GY across genotypes. Flag leaf δ13C was negatively correlated with GY only under RF conditions. The δ13C in seedlings was negatively correlated, under IR conditions only, with GY but also with early vigour. The sources of variation in early vigour were studied by stepwise analysis using the stable isotope signatures measured in seedlings. The δ13C was able to explain almost 0·20 of this variation under RF, but up to 0·30 under IR. In addition, nitrogen concentration in seedlings accounted for another 0·05 of variation, increasing the amount explained to 0·35. The sources of variation in GY were also studied through stable isotope signatures and biomass of different plant parts: δ13C was always the first parameter to appear in the models for both water conditions, explaining c. 0·20 of the variation. The second parameter (δ15N or N concentration of grain, or biomass at maturity) depended on the water conditions and the plant tissue being analysed. Oxygen isotope composition (δ18O) was only able to explain a small amount of the variation in GY. In this regard, despite the known and previously described value of δ13C as a tool in breeding, δ15N is confirmed as an additional tool in the present study. Oxygen isotope composition does not seem to offer any potential, at least under the conditions of the present study.

Bowing the violin : a case study for auditory-motor pattern modelling in the context of music performance

This project addresses methodological and technological challenges in the development of multi-modal data acquisition and analysis methods for the representation of instrumental playing technique in music performance through auditory-motor patterning models. The case study is violin playing: a multi-modal database of violin performances has been constructed by recording different musicians while playing short exercises on different violins. The exercise set and recording protocol have been designed to sample the space defined by dynamics (from piano to forte) and tone (from sul tasto to sul ponticello), for each bow stroke type being played on each of the four strings (three different pitches per string) at two different tempi. The data, containing audio, video, and motion capture streams, has been processed and segmented to facilitate upcoming analyses. From the acquired motion data, the positions of the instrument string ends and the bow hair ribbon ends are tracked and processed to obtain a number of bowing descriptors suited for a detailed description and analysis of the bow motion patterns taking place during performance. Likewise, a number of sound perceptual attributes are computed from the audio streams. Besides the methodology and the implementation of a number of data acquisition tools, this project introduces preliminary results from analyzing bowing technique on a multi-modal violin performance database that is unique in its class. A further contribution of this project is the data itself, which will be made available to the scientific community through the repovizz platform.

Key performance indicators for supplier and assortement evaluation

Tutkimuksen tavoite oli selvittää suorituskyvyn mittaamista, mittareita ja niiden suunnittelua tukku- ja jakeluliiketoiminnassa. Kriittisten menestystekijöiden mittarit auttavat yritystä kohti yhteistä päämäärää. Kriittisten menestystekijöiden mittarit ovat usein yhdistetty strategiseen suunnitteluun ja implementointiin ja niillä on yhtäläisyyksiä monien strategisten työkalujen kun Balanced scorecardin kanssa. Tutkimus ongelma voidaan esittää kysymyksen muodossa. •Mitkä ovat Oriola KD:n pitkänaikavälin tavoitteita tukevat kriittisten menestystekijöiden mittarit (KPIs) toimittajan ja tuotevalikoiman mittaamisessa? Tutkimus on jaettu kirjalliseen ja empiiriseen osaan. Kirjallisuus katsaus käsittelee aikaisempaa tutkimusta strategian, toimitusketjun hallinnan, toimittajan arvioinnin ja erilaisten suorituskyvyn mittaamisjärjestelmien osalta. Empiirinen osuus etenee nykytila-analyysista ehdotettuihin kriittisten menestystekijöiden mittareihin, jotka ovat kehitetty kirjallisuudesta löydetyn mallin avulla. Tutkimuksen lopputuloksena ovat case yrityksen tarpeisiin kehitetyt kriittisten menestystekijöiden mittarit toimittajan ja tuotevalikoiman arvioinnissa.

Performance of high power fibre laser cutting of thick-section steel and mediumsection aluminium

Cutting of thick section stainless steel and mild steel, and medium section aluminium using the high power ytterbium fibre laser has been experimentally investigated in this study. Theoretical models of the laser power requirement for cutting of a metal workpiece and the melt removal rate were also developed. The calculated laser power requirement was correlated to the laser power used for the cutting of 10 mm stainless steel workpiece and 15 mm mild steel workpiece using the ytterbium fibre laser and the CO2 laser. Nitrogen assist gas was used for cutting of stainless steel and oxygen was used for mild steel cutting. It was found that the incident laser power required for cutting at a given cutting speed was lower for fibre laser cutting than for CO2 laser cutting indicating a higher absorptivity of the fibre laser beam by the workpiece and higher melting efficiency for the fibre laser beam than for the CO2 laser beam. The difficulty in achieving an efficient melt removal during high speed cutting of the 15 mmmild steel workpiece with oxygen assist gas using the ytterbium fibre laser can be attributed to the high melting efficiency of the ytterbium fibre laser. The calculated melt flow velocity and melt film thickness correlated well with the location of the boundary layer separation point on the 10 mm stainless steel cut edges. An increase in the melt film thickness caused by deceleration of the melt particles in the boundary layer by the viscous shear forces results in the flow separation. The melt flow velocity increases with an increase in assist gas pressure and cut kerf width resulting in a reduction in the melt film thickness and the boundary layer separation point moves closer to the bottom cut edge. The cut edge quality was examined by visual inspection of the cut samples and measurement of the cut kerf width, boundary layer separation point, cut edge squareness (perpendicularity) deviation, and cut edge surface roughness as output quality factors. Different regions of cut edge quality in 10 mm stainless steel and 4 mm aluminium workpieces were defined for different combinations of cutting speed and laserpower.Optimization of processing parameters for a high cut edge quality in 10 mmstainless steel was demonstrated

Financier’s credit risk: A Performance-Based Classification Model

The purpose of this study is to view credit risk from the financier’s point of view in a theoretical framework. Results and aspects of the previous studies regarding measuring credit risk with accounting based scoring models are also examined. The theoretical framework and previous studies are then used to support the empirical analysis which aims to develop a credit risk measure for a bank’s internal use or a risk management tool for a company to indicate its credit risk to the financier. The study covers a sample of Finnish companies from 12 different industries and four different company categories and employs their accounting information from 2004 to 2008. The empirical analysis consists of six stage methodology process which uses measures of profitability, liquidity, capital structure and cash flow to determine financier’s credit risk, define five significant risk classes and produce risk classification model. The study is confidential until 15.10.2012.

Financial performance of segments in the intelligent home network

The goal of this study is to examine the intelligent home business network in order to determine which part of the network has the best financial abilities to produce new business models and products/services by using financial statement analysis. A group of 377 studied limited companies is divided into four examined segments based on their offering in producing intelligent homes. The segments are customer service providers, system integrators, subsystem suppliers and component suppliers. Eight different key figures are calculated from each of the companies to get a comprehensive view of their financial performances, after which each of the segments is studied statistically to determine the performances of the whole segments. The actual performance differences between the segments are calculated by using the multi-criteria decision analysis method in which the performances of the key figures are graded and each key figure is weighted according to its importance for the goal of the study. The results of this analysis showed that subsystem suppliers have the best financial performance. Second best are system integrators, third are customer service providers and fourth component suppliers. None of the segments were strikingly poor, but even component suppliers were rather reasonable in their performance; so, it can be said that no part of the intelligent home business network has remarkably inadequate financial abilities to develop new business models and products/services.

Kernel-Based Ranking. Methods for Learning and Performance Estimation

Machine learning provides tools for automated construction of predictive models in data intensive areas of engineering and science. The family of regularized kernel methods have in the recent years become one of the mainstream approaches to machine learning, due to a number of advantages the methods share. The approach provides theoretically well-founded solutions to the problems of under- and overfitting, allows learning from structured data, and has been empirically demonstrated to yield high predictive performance on a wide range of application domains. Historically, the problems of classification and regression have gained the majority of attention in the field. In this thesis we focus on another type of learning problem, that of learning to rank. In learning to rank, the aim is from a set of past observations to learn a ranking function that can order new objects according to how well they match some underlying criterion of goodness. As an important special case of the setting, we can recover the bipartite ranking problem, corresponding to maximizing the area under the ROC curve (AUC) in binary classification. Ranking applications appear in a large variety of settings, examples encountered in this thesis include document retrieval in web search, recommender systems, information extraction and automated parsing of natural language. We consider the pairwise approach to learning to rank, where ranking models are learned by minimizing the expected probability of ranking any two randomly drawn test examples incorrectly. The development of computationally efficient kernel methods, based on this approach, has in the past proven to be challenging. Moreover, it is not clear what techniques for estimating the predictive performance of learned models are the most reliable in the ranking setting, and how the techniques can be implemented efficiently. The contributions of this thesis are as follows. First, we develop RankRLS, a computationally efficient kernel method for learning to rank, that is based on minimizing a regularized pairwise least-squares loss. In addition to training methods, we introduce a variety of algorithms for tasks such as model selection, multi-output learning, and cross-validation, based on computational shortcuts from matrix algebra. Second, we improve the fastest known training method for the linear version of the RankSVM algorithm, which is one of the most well established methods for learning to rank. Third, we study the combination of the empirical kernel map and reduced set approximation, which allows the large-scale training of kernel machines using linear solvers, and propose computationally efficient solutions to cross-validation when using the approach. Next, we explore the problem of reliable cross-validation when using AUC as a performance criterion, through an extensive simulation study. We demonstrate that the proposed leave-pair-out cross-validation approach leads to more reliable performance estimation than commonly used alternative approaches. Finally, we present a case study on applying machine learning to information extraction from biomedical literature, which combines several of the approaches considered in the thesis. The thesis is divided into two parts. Part I provides the background for the research work and summarizes the most central results, Part II consists of the five original research articles that are the main contribution of this thesis.

Mutual Fund Performance and the Market Timing Ability - Finnish Evidence

The purpose of this thesis is to examine the performance of Finnish equity funds and their market timing ability. Fund performance is evaluated by using annual returns and various risk-adjusted measures, including Sharpe ratio, DDSR, SKASR, Treynor ratio and Jensen’s alpha, whereas portfolio manager’s timing ability is examined with Treynor-Mazuy model and Henriksson-Merton model. The data is collected from the Finnish fund market during the sample period from January 1997 to February 2010. Results show that Finnish equity funds have been able to outperform the market return on a risk-adjusted basis, but these results are influenced heavily by the exceptionally good performance during the IT-bubble. Market timing models show that fund managers have been, to some degree, able to time the market but not a single fund have been able to possess security selection ability and market timing ability simultaneously.

Scheduling of guarded command based models

Formal methods provide a means of reasoning about computer programs in order to prove correctness criteria. One subtype of formal methods is based on the weakest precondition predicate transformer semantics and uses guarded commands as the basic modelling construct. Examples of such formalisms are Action Systems and Event-B. Guarded commands can intuitively be understood as actions that may be triggered when an associated guard condition holds. Guarded commands whose guards hold are nondeterministically chosen for execution, but no further control flow is present by default. Such a modelling approach is convenient for proving correctness, and the Refinement Calculus allows for a stepwise development method. It also has a parallel interpretation facilitating development of concurrent software, and it is suitable for describing event-driven scenarios. However, for many application areas, the execution paradigm traditionally used comprises more explicit control flow, which constitutes an obstacle for using the above mentioned formal methods. In this thesis, we study how guarded command based modelling approaches can be conveniently and efficiently scheduled in different scenarios. We first focus on the modelling of trust for transactions in a social networking setting. Due to the event-based nature of the scenario, the use of guarded commands turns out to be relatively straightforward. We continue by studying modelling of concurrent software, with particular focus on compute-intensive scenarios. We go from theoretical considerations to the feasibility of implementation by evaluating the performance and scalability of executing a case study model in parallel using automatic scheduling performed by a dedicated scheduler. Finally, we propose a more explicit and non-centralised approach in which the flow of each task is controlled by a schedule of its own. The schedules are expressed in a dedicated scheduling language, and patterns assist the developer in proving correctness of the scheduled model with respect to the original one.

Analysis of emergency braking performance with particular consideration of temperature effects on brakes

During vehicle deceleration due to braking there is friction between the lining surface and the brake drum or disc. In this process the kinetic energy of vehicle is turned into thermal energy that raises temperature of the components. The heating of the brake system in the course of braking is a great problem, because besides damaging the system, it may also affect the wheel and tire, which can cause accidents. In search of the best configuration that considers the true conditions of use, without passing the safety limits, models and formulations are presented with respect to the brake system, considering different braking conditions and kinds of brakes. Some modeling was analyzed using well-known methods. The flat plate model considering energy conservation was applied to a bus, using for this a computer program. The vehicle is simulated to undergo an emergency braking, considering the change of temperature on the lining-drum. The results include deceleration, braking efficiency, wheel resistance, normal reaction on the tires and the coefficient of adhesion. Some of the results were compared with dynamometer tests made by FRAS-LE and others were compared with track tests made by Mercedes-Benz. The convergence between the results and the tests is sufficient to validate the mathematical model. The computer program makes it possible to simulate the brake system performance in the vehicle. It assists the designer during the development phase and reduces track tests.