Contexte : Les systèmes à logiciels prépondérants sont de plus en plus interopérables formant des alliances nommées « Systèmes-des-Systèmes » (SdS). Les applications des SdS peuvent aller des systèmes de gestion du trafic jusqu’aux systèmes de gestion de crises. Étant donné que les SdS supportent souvent des domaines critiques, ils doivent être fiables en traitant les disfonctionnements ou les défauts et en évitant les défaillances qui pourraient causer des dégâts et pertes importantes aux utilisateurs.Problème : Ajuster les opérations d’un SdS dépend d’une spécification précise et une attestation rigoureuse de sa consistance opérationnelle. Cependant, en plus des limitations des langages pour capturer conjointement la structure et le comportement des SdS, les prédictions de la consistance opérationnelle des SdS reposent sur leurs systèmes constitutifs qui ne sont pas totalement connus au moment de la conception. Par conséquent, les SdS ont été développés et déployés sans évaluation de leurs opérations, puisque les langages actuels ne supportent pas ce type de précision lors de l’évaluation. Objectif : Cette thèse fournit des solutions théoriques et pratiques basées sur un langage formel de description d’architectures pour supporter une évaluation précoce des opérations du SdS par rapport à la structure et le comportement du SdS à travers les simulations. Contributions : Les contributions essentielles de ce projet comprennent (i) une approche de transformation des modèles pour produire automatiquement des modèles de simulation à partir des descriptions des architectures logicielles du SdS, combinant la description structurelle et comportementale du SdS dans la même solution, (ii) une méthode d’évaluation de l’architecture logicielle du SdS pour la prédiction des opérations du SdS tout en considérant les changements inhérents qui peuvent se produire, (iii) la modélisation de l’environnement et la génération automatique des générateurs de stimulus pour soutenir la simulation des SdS, livrant des données pour nourrir tel simulation, et (iv) une méthode pour la synchronisation automatique entre l’architecture descriptive d’exécution (qui change à l’exécution par suite de l’architecture dynamique) et son architecture prescriptive d’origine basée sur des mécanismes de découverte et de récupération de modèles et une transformation de modèle à l'envers.Évaluation : Nous avons conduit des cas d’études pour évaluer nos approches en utilisant le SdS de surveillance des inondations et le SdS d’espace.Résultats : Notre approche montre une précision importante pour (i) produire des simulations des architectures logicielles des SdS sans failles et complètement opérationnelles, (ii) supporte une évaluation et une prédiction fiable des opérations du SdS à la phase de conception, (iii) génère de manière automatique des générateurs de stimuli pour soutenir et nourrir l’exécution de la simulation et (iv) maintien la synchronisation entre les versions descriptives et prescriptives de l’architecture du SdS.Conclusion : Nous avons conclu que les approches proposées font évoluer l’état de l’art de l’évaluation des architectures logicielles des SdS en offrant des solutions pour prédire l’efficacité des opérations du SdS pour maintenir une opération continue malgré les changements architecturaux, fournissant plus de confidence aux utilisateurs qui reposent dans l’avenir sur les services du SdS. / Context: Software-intensive systems have been increasingly interoperated forming alliances termed as “Systems-of-Systems” (SoS). SoS comprises a collection of systems joined to achieve a set of missions that none of the systems can individually accomplish. Each constituent system keeps its own management, goals, and resources while coordinating within the SoS and adapting to meet SoS goals. Applications of SoS range from traffic control to emergency response and crisis management. As SoS often support critical domains, such systems must be trustworthy by dealing with malfunction or defects and avoiding failures that could cause extensive damage and losses to the users.Problem: Correct SoS operations depend on a precise specification of the SoS structure and a rigorous attestation of its behaviors. However, besides limitations on languages to jointly capture SoS structure and behavior, predictions on the SoS emergent behaviors rely on constituent systems not totally known at design-time. Therefore, SoS have been developed and deployed without evaluating their operation, since current languages do not support such precision in evaluation.Objectives: This PhD project provides solutions founded on a formal architectural description language to support an early evaluation of SoS behaviors regarding its inherent SoS structure and dynamics through simulations.Contribution: The main contributions of this project comprise (i) a model transformation approach for automatically producing simulation models from SoS software architecture descriptions, combining SoS structure and behavior description in a same solution, (ii) a SoS software architecture evaluation method for SoS operation prediction considering the inherent changes that can occur, (iii) environment modeling and automatic generation of stimuli generators to sustain the SoS simulation, delivering data to feed such simulation, and (iv) a method for the automatic synchronization between the runtime descriptive architecture (changed at runtime due to dynamic architecture) and its original prescriptive architecture based on model discovery and recovery mechanisms and a backward model transformation.Evaluation: We conducted case studies to assess our approaches using Flood Monitoring SoS and Space SoS.Results: Our approaches show a high accuracy to (i) produce fault-free and operational simulations for SoS software architectures, (ii) support a reliable evaluation and prediction of SoS operation at design-time, (iii) automatically generate stimuli generators to sustain and feed the simulation execution, and (iv) maintain the synchronization between descriptive and prescriptive versions of the SoS architecture.Conclusions: We concluded that the proposed approaches advance the state of the art in SoS software architecture evaluation by offering solutions to predict the SoS operations effectiveness to maintain a continuous operation despite architectural changes, providing more trust for users that in the future shall rely on SoS services.

<p></p><p>One-sided ultrasonic non-destructive evaluation (UNDE) is extensively used to characterize structures that need to be inspected and maintained from defects and flaws that could affect the performance of power plants, such as nuclear power plants. Most UNDE systems send acoustic pulses into the structure of interest, measure the received waveform and use an algorithm to reconstruct the quantity of interest. The most widely used algorithm in UNDE systems is the synthetic aperture focusing technique (SAFT) because it produces acceptable results in real time. A few regularized inversion techniques with linear models have been proposed which can improve on SAFT, but they tend to make simplifying assumptions that show artifacts and do not address how to obtain reconstructions from large real data sets. In this thesis, we present two studies. The first study covers the model-based iterative reconstruction (MBIR) technique which is used to resolve some of the issues in SAFT and the current linear regularized inversion techniques, and the second study covers the direct deep learning (DDL) technique which is used to further resolve issues related to non-linear interactions between the ultrasound signal and the specimen.</p> <p>In the first study, we propose a model-based iterative reconstruction (MBIR) algorithm designed for scanning UNDE systems. MBIR reconstructs the image by optimizing a cost function that contains two terms: the forward model that models the measurements and the prior model that models the object. To further reduce some of the artifacts in the results, we enhance the forward model of MBIR to account for the direct arrival artifacts and the isotropic artifacts. The direct arrival signals are the signals received directly from the transmitter without being reflected. These signals contain no useful information about the specimen and produce high amplitude artifacts in regions close to the transducers. We resolve this issue by modeling these direct arrival signals in the forward model to reduce their artifacts while maintaining information from reflections of other objects. Next, the isotropic artifacts appear when the transmitted signal is assumed to propagate in all directions equally. Therefore, we modify our forward model to resolve this issue by modeling the anisotropic propagation. Next, because of the significant attenuation of the transmitted signal as it propagates through deeper regions, the reconstruction of deeper regions tends to be much dimmer than closer regions. Therefore, we combine the forward model with a spatially variant prior model to account for the attenuation by reducing the regularization as the pixel gets deeper. Next, for scanning large structures, multiple scans are required to cover the whole field of view. Typically, these scans are performed in raster order which makes adjacent scans share some useful correlations. Reconstructing each scan individually and performing a conventional stitching method is not an efficient way because this could produce stitching artifacts and ignore extra information from adjacent scans. We present an algorithm to jointly reconstruct measurements from large data sets that reduces the stitching artifacts and exploits useful information from adjacent scans. Next, using simulated and extensive experimental data, we show MBIR results and demonstrate how we can improve over SAFT as well as existing regularized inversion techniques. However, even with this improvement, MBIR still results in some artifacts caused by the inherent non-linearity of the interaction between the ultrasound signal and the specimen.</p> <p>In the second study, we propose DDL, a non-iterative model-based reconstruction method for inverting measurements that are based on non-linear forward models for ultrasound imaging. Our approach involves obtaining an approximate estimate of the reconstruction using a simple linear back-projection and training a deep neural network to refine this to the actual reconstruction. While the technique we are proposing can show significant enhancement compared to the current techniques with simulated data, one issue appears with the performance of this technique when applied to experimental data. The issue is a modeling mismatch between the simulated training data and the real data. We propose an effective solution that can reduce the effect of this modeling mismatch by adding noise to the simulation input of the training set before simulation. This solution trains the neural network on the general features of the system rather than specific features of the simulator and can act as a regularization to the neural network. Another issue appears similar to the issue in MBIR caused by the attenuation of deeper reflections. Therefore, we propose a spatially variant amplification technique applied to the back-projection to amplify deeper regions. Next, to reconstruct from a large field of view that requires multiple scans, we propose a joint deep neural network technique to jointly reconstruct an image from these multiple scans. Finally, we apply DDL to simulated and experimental ultrasound data to demonstrate significant improvements in image quality compared to the delay-and-sum approach and the linear model-based reconstruction approach.</p><br><p></p>

O escopo desta dissertação é o desenvolvimento de um método para detectar os modelos da matriz dinâmica que estejam degradando o desempenho de controladores preditivos baseados em modelos. O método proposto se baseia na análise de correlação cruzada entre o erro nominal do controlador em malha fechada e a uma estimativa da contribuição de cada canal para o cálculo da saída, filtrada pela função de sensibilidade do controlador. Esse método pode ser empregado na auditoria de controladores com variáveis controladas em setpoints e/ou com variáveis que operem entre faixas, como é usual de se encontrar na indústria. Esta dissertação apresenta os resultados da aplicação bem sucedida do método no sistema de quatro tanques (JOHANSSON, 2000), para o qual três cenários foram avaliados. No primeiro cenário, o método localizou corretamente discrepâncias de ganho e de dinâmica de modelos de um controlador preditivo baseado em modelos (Model-based Predictive Controller, ou controlador MPC). No segundo, o método foi utilizado para avaliar a influência de uma variável externa para melhorar o desempenho de um controlador afetado por distúrbios não medidos. No terceiro cenário, o método localizou canais com modelos nulos que deveriam ser incluídos na matriz de controle de um controlador MPC de estrutura descentralizada. Os resultados deste estudo de caso foram comparados com aqueles obtidos pelo método proposto por BADWE, GUDI e PATWARDHAN (2009), constatando-se que o método proposto é mais robusto que o método usado na comparação, não demandando ajustes de parâmetros por parte do usuário para fornecer bons resultados. A dissertação inclui também um estudo de caso da aplicação industrial do método na auditoria de desempenho de um controlador preditivo linear de estrutura descentralizada, com doze variáveis controladas, oito manipuladas e quatro distúrbios não medidos, aplicado a um sistema de fracionamento de propeno e propano em uma indústria petroquímica. A auditoria permitiu reduzir o escopo de revisão do controlador a dezenove canais da matriz, sendo que quatorze destes correspondiam a canais com modelos nulos que deveriam ser incluídos na matriz. A eficácia do método foi comprovada repetindo-se a avaliação da qualidade de modelo para todas as variáveis controladas. / The scope of this dissertation is the development of a method to detect the models of the dynamic matrix that are affecting the performance of model-based predictive controllers. The proposed method is based on the cross correlation analysis between the nominal controller error and an estimate of the contribution of each channel to the controller output, filtered by the controller nominal sensitivity function. The method can be used in the performance assessment of controllers employing variables controlled at the setpoint and/or those controlled within ranges. This dissertation presents the results of the successful application of the method to the quadruple-tank process (JOHANSSON, 2000), for which three scenarios were evaluated. In the first scenario, the method correctly located gain and dynamic mismatches on a model-based predictive controller (MPC controller). In the second one, the method was used to evaluate the influence of an external variable to improve the performance of a controller affected by unmeasured disturbances. In the third scenario, the method located null models that should be included in the dynamic matrix of a decentralized MPC controller. The results of the three scenarios were compared with the ones obtained through the method proposed by BADWE, GUDI e PATWARDHAN (2009). The proposed method was considered more robust than the reference one for not requiring parameters estimation performed by the user to provide good results. This dissertation also includes a case study about the application of the method on the performance assessment of an industrial linear predictive controller of decentralized structure. The controller has twelve controlled variables, eight manipulated variables, and four unmeasured disturbances and is applied to a propylene-propane fractionation system of a petrochemical industry. The performance assessment allowed reducing the scope of the controller revision to nineteen channels of the models matrix, fourteen of which were null models that should be included in the controller. The efficacy of the proposed method was confirmed by repeating the model quality evaluation for all the controlled variables.

Nesta tese, com a ambição de desenvolver um pacote de controle que poderia ser implementado a baixo custo nas indústrias brasileiras, é estudado o controle preditivo (MPC) de sistemas estáveis e instáveis com modelos incertos, baseado em um modelo de realinhamento e o uso de técnicas de desigualdades matriciais lineares (LMI), para resolução de problemas de controle robusto. Na primeira parte da tese, a aplicabilidade de um controlador baseado em um modelo de realinhamento é considerada. Como o controlador preditivo baseado em um modelo de realinhamento não requer o uso de um observador de estados, espera-se que seja mais eficiente e mais robusto a distúrbios não medidos que um controlador que precise de um observador de estados. Essa hipótese é testada comparando-se o desempenho e a robustez a distúrbios não medidos deste controlador com um controlador que requer o uso de um observador de estados, através da simulação do controle de um separador polipropileno/propano (PP) industrial. Por outro lado, a desvantagem de um controlador baseado em um modelo de realinhamento é a sensibilidade da construção do modelo e das matrizes do controlador a erros numéricos que aumentam com o tamanho do sistema. As etapas do algoritmo de controle mais sensíveis a erros numéricos são então destacadas com o objetivo de discutir a aplicabilidade de um controlador baseado em um modelo de realinhamento a sistemas de grande porte. Além disso, sempre que possível, os métodos utilizados para reduzir a sensibilidade das etapas problemáticas a erros numéricos são apresentados. A segunda parte da tese trata da solução do problema de controle robusto preditivo baseado em um modelo de sistema incerto. Este problema é usualmente abordado através da inclusão de restrições não lineares sobre os custos associados aos modelos da planta no problema de controle, de tal modo que a ação de controle é obtida a cada instante de tempo através da resolução de um problema de otimização não linear, cujo custo computacional pode ser proibitivo para sistemas de grande porte. Nesta parte da tese, o problema de MPC robusto é então reformulado como um problema baseado em LMIs, que pode ser resolvido com uma fração do esforço computacional. A abordagem proposta é comparada com o MPC robusto convencional e testada através da simulação do controle de um reator e de um separador C3/C4 industriais. Finalmente, na terceira parte da tese, o MPC de sistemas estáveis e instáveis com incertezas no modelo que permite a saturação das entradas manipuladas é abordado. Um subconjunto das entradas manipuladas é alocado ao controle das saídas instáveis através de uma lei de realimentação de estados, enquanto as outras entradas são deixadas livres para controlar as saídas estáveis restantes. Assume-se que as saídas são controladas dentro de faixas e que os setpoints das saídas são tratados como entradas manipuladas adicionais. Os controladores desenvolvidos nesta parte da tese permitem a saturação das entradas associadas às saídas instáveis através da manipulação das entradas livres e dos setpoints das saídas instáveis. A viabilidade recursiva dos controladores desenvolvidos é garantida, permitindo-se que os setpoints das saídas instáveis deixem temporariamente suas faixas. O desempenho dos controladores desenvolvidos é testado através da simulação do controle de dois processos integradores da indústria química. / In this thesis, with the ambition of developing a homemade control package that could be implemented at a low cost in Brazilian industries, the model predictive control (MPC) of uncertain stable and unstable systems based on a realigned state space representation and on the use of the Linear Matrix Inequality (LMI) techniques is addressed. In the first part of this thesis, the practical implementability of a controller based on a realigned model is considered. Since a Model Predictive Controller based on a realigned model does not require the use of a state observer, it is expected to be more efficient and more robust to unmeasured disturbances than a controller requiring the use of a state observer. This assumption is tested by comparing the performance and the robustness to unmeasured disturbances of a controller based on a realigned model with a controller requiring the use of a state observer through the simulation of the control of a nonlinear industrial propylene/propane (PP) splitter. On the other hand, a disadvantage of a controller based on a realigned model is that the construction of the model and controller matrices is very sensitive to numerical errors. The steps of the control algorithm that are more sensitive to numerical errors are then highlighted in order to discuss the practical applicability of an MPC based on the realigned model to large-scale systems. Also, wherever possible, some ways of decreasing the sensitiveness of the problematic steps to numerical errors are presented. The second part of this thesis addresses the solution to the problem of robust MPC of systems with model uncertainty. The usual approach of dealing with this kind of problem is through the inclusion of nonlinear cost constraints in the control problem so that the control action is obtained at each sampling time as the solution to a nonlinear programming (NLP) problem that, for high order systems, can be computationally expensive. In this part of the thesis, the robust MPC problem is then recast as an LMI problem that can be solved in real time with a fraction of the computer effort. The proposed approach is compared with the conventional robust MPC and tested through the simulation of the control of a reactor system and a C3/C4 splitter of the process industry. Finally, in the third part of the thesis, the model predictive control of uncertain process systems with stable and unstable outputs that allows input saturation is addressed. A subset of the manipulated inputs is allocated to the control of the unstable outputs through a state feedback control law while the other inputs are left free to control the remaining stable outputs. It is assumed that the outputs are controlled inside zones and that the output setpoints are treated as additional free manipulated inputs. The proposed controllers allow the saturation of the inputs related to the unstable outputs by manipulating the free inputs and the setpoints of the unstable outputs. The recursive feasibility of the controllers is guaranteed by allowing the setpoints of the unstable outputs to temporarily leave their bounds. The performance of the proposed approach is tested through the simulation of the control of two integrating chemical process systems.

Engine Management Systems (EMS) is getting more complicated each year with new functions being introduced due to tighter emission regulations of both air quality and CO2. This directly a ects the calibration process of a powertrain because the number of vehicle parameters has increased about 10 times in the last 10 years. Self-calibrating feature such as proposed in this thesis has the potential to increase the e ciency of calibrating a complex EMS. The feature is intended to adapt itself to the engine behaviour and performance by continuously updating its calibration maps as the engine is being operated. This process will reduce the needs for new calibration data and additional ne-tuning when an EMS is being carried over to a di erent vehicle. The self-calibrating feature automatically adjusts the air path calibration maps of an engine. It adjusts the air path setpoint maps in real-time for steady state operating conditions.

Osteoarthritis is a common degenerative disorder that affects joints. Despite recent therapeutic advances, osteoarthritis continues to be a challenging health problem, and elderly population is particularly at risk. Pain is the most unbearable symptom experienced by osteoarthritic patients. Currently, several pharmacological medications are available to manage osteoarthritic pain. Opioids, potent analgesics, have shown extraordinary ability to reduce intense pain in many osteoarthritic clinical trials. Although many clinical trials have investigated the efficacy and safety of opioids in osteoarthritic patients, there is an increased need for a study to integrate the reported outcomes and utilize them to achieve a better understanding of efficacy and safety profiles of opioids. Therefore, in our present study, efficacy, safety, and tolerability profiles of opioid compounds used to manage osteoarthritic pain were assessed and compared using a model-based meta-analysis (MBMA). To achieve our goal, a comprehensive database consisting of pain relief compounds with information on summary-level of efficacy over time, adverse events and dropout rates was compiled from multiple sources. MBMA was conducted using nonlinear mixed-effects modeling approach. The results showed that the selected models successfully captured the observed data, and primary efficacy endpoint estimations indicated that the ED50 of oxycodone, oxymorphone, and tramadol were 47, 84, and 247 mg per day, respectively. Efficacy-time course analysis showed that opioids had rapid time to efficacy onset, suggesting potential powerful pain relief effects. Also, it was found that gastrointestinal adverse events were the most opioid-associated and dose-dependent adverse effects. In addition, the analysis revealed that opioids are well-tolerable at low to moderate doses. The results presented here provided clinically meaningful insights into the efficacy and safety of oxycodone, oxymorphone, and tramadol. In addition, the presented framework analysis has a clinical impact on drug development where it can help in optimizing the dose of opioids to manage osteoarthritic pain, making precise key decisions for positioning of new drugs, and designing more efficient clinical trials.

An ideal drinking water distribution system (DWDS) must supply safe drinking water with free chlorine residual (FCR) in the form of HOCI and OCIֿ at a required concentration level. Meanwhile the FCR is consumed in the bulk liquid phase and at the DWDS pipes wall as the result of chemical reactions. Because of these, an optimized chlorine dosing for the DWDS using model-based predictive control (MBPC) is developed through the steps of modelling the FCR transport along the main pipes of the DWDS, designing chlorine dosing and implementing a multiple-input multiple-output system control scheme in Matlab 7.0.1 software. Discrete time-space models (DTSM) that can be used to predict free chlorine residual (FCR) concentration along the pipes of the DWDS over time is developed using explicit finite difference method (EFDM). Simulations of the DTSM using step and rectangular pulse input show that the effect of water flow rate velocity is much stronger than the effect of chlorine effective diffusivity coefficient on the FCR distribution and decay process in the DWDS main pipes. Therefore, the FCR axial diffusion in single pipes of the DWDS can be neglected. Investigating the effect of injection time, initial chlorine distribution, and overall chlorine decay rate constant involved in the process have provided a thorough understanding of chlorination and the effectiveness of all the parameters. This study proposed a model-based chlorine dosing design (MBCDD) based on a conventional-optimum design process (CODP) (Aurora, 2004), which is created for uncertain water demand based on the DTSM simulation. / In the MBCDD, the constraints must be met by designing distances between chlorine boosters and optimal value of the initial chlorine distribution in order to maintain the controlled variable (CV), i.e. FCR concentration with a certain degree of robustness to the variations of water flow rate. The MBCDD can cope with the simulated DWDS (SDWDS) with the conditions; the main pipe is 12 inch diameter size with the pipe length of 8.5 km, the first consumers taking the water from the point of 0.83 km, the assumed pipe wall chlorine decay rate constant of 0.45 m/day, and the value of chlorine overall decay rate constants follow Rosman's model (1994), by proposing a set of rules for selecting the locations for additional chlorine dosing boosters, and setting the optimal chlorine dosing concentrations for each booster in order to maintain a relatively even FCR distribution along the DWDS, which is robust against volumetric water supply velocity (VWS) variations. An example shows that by implementing this strategy, MBCDD can control the FCR along the 8.5 km main pipe of 12 inch diameter size with the VWS velocity from 0.2457 to 2.457 km/hr and with the assumed wall and bulk decay constants of 0.45 and 0.55 m/day, respectively. An adaptive chlorine dosing design (ACDD) as another CODP of chlorine dosing which has the same concept with the MBCDD without the rule of critical velocity is also proposed in this study. The ACDD objective is to obtain the optimum value of initial chlorine distribution for every single change in the VWS. Simulation of the ACDD on the SDWDS shows that the ACDD can maintain the FCR concentration within the required limit of 0.2-0.6 mg/1. / To enable water quality modelling for studying the effectiveness of chlorine dosing and injection in the form of mass flow rate of pure gaseous chlorine as manipulated variable (MV), a multiple-input multiple-output (MIMO) system is developed in Simulink for Matlab 7.0.1 software by considering the disturbances of temperature and circuiting flow. The MIMO system can be used to design booster locations and distribution along a main pipe of the DWDS, to monitor the FCR concentration at the point just before injection (mixing) and between two boosters, and to implement feedback and open-loop control. This study also proposed a decentralized model-based control (DMBC) based on the MBCDD-ACDD and centralized model predictive control (CMPC) in order to optimize MV to control the CV along the main pipe of the DWDS in the MIMO system from the FCR concentration at just after the chlorine injection (CVin) to the FCR concentration (CVo) before the next chlorine injection with the constraints of 0.2-0.6 ppm for both the CVin and CVo. A comparison of the performances of decentralized PI (DPI) control, DMBC and CMPC, shows that the performances of the DMBC and CMPC in controlling the MIMO system are almost the same, and they both are significantly better than the DPI control performance. In brief, model-based predictive control (MBPC), in this case a decentralized model-based control (DMBC) and a centralized predictive control (CMPC), enable optimization of chlorine dosing for the DWDS.

<p>Model Based Testing (MBT) is a new-age test automation technique traditionally used for Functional Black-Box Testing. Its capability of generating test cases by using model developed from the analysis of the abstract behavior of the System under Test is gaining popularity. Many commercial and open source MBT tools are available currently in market. But each one has its own specific way of modeling and test case generation mechanism that is suitable for varied types of systems. Ericsson, a telecommunication equipment provider company, is currently adapting Model Based Testing in some of its divisions for functional testing. Those divisions haven’t yet attempted adapting Model Based Testing for non-functional testing in a full-pledged manner. A comparative study between various MBT tools will help one of the Ericsson’s testing divisions to select the best tool for adapting to its existing test environment. This also helps in improving the quality of testing while reducing cost, time and effort. This thesis work helps Ericsson testing division to select such an effective MBT tool. Based on aspects such as functionality, flexibility, adaptability, performance etc., a comparative study is carried out on various available MBT tools and a few were selected among them: Qtronic, ModelJUnit and Elvior Motes.This thesis also helps to understand the usability of the selected tools for modeling of non-functional requirements using a new method. A brief idea of modeling the non-functional requirements is suggested in this thesis. A System under Test was identified and its functional behavior was modeled along with the non functional requirements in Qtronic and ModelJUnit. An experimental analysis, backed by observations of using the new proposed method indicates that the method is efficient enough to carry out modeling non-functional requirements along with modeling of functional requirements by identifying the appropriate approach.Model Based Testing (MBT) is a new-age test automation technique traditionally used for Functional Black-Box Testing. Its capability of generating test cases by using model developed from the analysis of the abstract behavior of the System under Test is gaining popularity. Many commercial and open source MBT tools are available currently in market. But each one has its own specific way of modeling and test case generation mechanism that is suitable for varied types of systems. Ericsson, a telecommunication equipment provider company, is currently adapting Model Based Testing in some of its divisions for functional testing. Those divisions haven’t yet attempted adapting Model Based Testing for non-functional testing in a full-pledged manner. A comparative study between various MBT tools will help one of the Ericsson’s testing divisions to select the best tool for adapting to its existing test environment. This also helps in improving the quality of testing while reducing cost, time and effort. This thesis work helps Ericsson testing division to select such an effective MBT tool. Based on aspects such as functionality, flexibility, adaptability, performance etc., a comparative study is carried out on various available MBT tools and a few were selected among them: Qtronic, ModelJUnit and Elvior Motes.</p><p>This thesis also helps to understand the usability of the selected tools for modeling of non-functional requirements using a new method. A brief idea of modeling the non-functional requirements is suggested in this thesis. A System under Test was identified and its functional behavior was modeled along with the non functional requirements in Qtronic and ModelJUnit. An experimental analysis, backed by observations of using the new proposed method indicates that the method is efficient enough to carry out modeling non-functional requirements along with modeling of functional requirements by identifying the appropriate approach.</p>

<p>This is a final thesis done at BT, considering one of their forklift truck models called Reflex. The first part of this report is about a preliminary investigation investigating what kind of diagnostic systems BTwants to use, and also which demands there are to meet all expectations on such system. Secondly a diagnostic system, which will show if the drive wheel is worn out, will be presented. </p><p>In the preliminary investigation, two kinds of diagnostic systems were mentioned. These were Model based diagnosis and Predictive analysis. Model based diagnosis is based on measurements made by sensors at the truck, while predictive analysis is based more on statistics and retrieved data about the lifetime of a truck in specific environments. </p><p>The diagnosis system for the drive wheel is based on a model made in Matlab's Simulink. Due to poor documentation, rough simplifications in the model have been made. However, one can still see the differences of principle. </p><p>The main thought was detecting a difference in the lowest torque level from the engine, varying the diameter of the drive wheel. By measurements made directly at the truck, different torques could be observed with varying diameter of the drive wheel, varying load on the truck and varying friction in the gearbox. Using hypothesis tests, it is possible to say whether the drive wheel is worn out or not. </p><p>Results show that if the drive wheel diameter is reduced by 25 mm, torque is reduced by 7% and if the drive wheel diameter is reduced as much as 50 mm, a torque reduction of 11% would be achieved.</p>

<p>To simplify troubleshooting and reliability of a process, a diagnosis system can supervise the process and alarm if any faults are detected. A diagnosis system can also identify one, or several faults, i.e. isolate faults, that may have caused the alarm. If model-based diagnosis is used, tests based on observations from the process are compared to a model of the process to diagnose the process. It can be a hard task to find which tests to be used for maximal fault detection and fault isolation. Structural Methods require not very detailed knowledge of the process to be diagnosed and can be used to find such tests early in the design of new processes. Sensors are used to get observations of a process. Therefore, sensors placed on different positions in the process gives different possibilities for observations. A specific set of sensors are in this work called a sensor configuration. </p><p>This thesis contributes with a method to predict and examine the fault detection and fault isolation possibility. By using these two diagnosis properties, a suitable sensor configuration is computed and tests to be used in a future diagnosis system are suggested. For this task an algorithm which can be used in the design phase of diagnosis systems, and a Matlab implementation of this algorithm are described. </p><p>In one part of this work the Matlab implementation and the algorithm are used to study how a model-based diagnosis-system can be used to supervise the fuel system in an Unmanned Aerial Vehicle (UAV).</p>