Traceability data provides the knowledge on dependencies and logical relations existing amongst artefacts that are created during software development. In reasoning over traceability data, conclusions can be drawn to increase the quality of software. The paradigm of Model-driven Software Engineering (MDSD) promotes the generation of software out of models. The latter are specified through different modelling languages. In subsequent model transformations, these models are used to generate programming code automatically. Traceability data of the involved artefacts in a MDSD process can be used to increase the software quality in providing the necessary knowledge as described above. Existing traceability solutions in MDSD are based on the integral model mapping of transformation execution to generate traceability data. Yet, these solutions still entail a wide range of open challenges. One challenge is that the collected traceability data does not adhere to a unified formal definition, which leads to poorly integrated traceability data. This aggravates the reasoning over traceability data. Furthermore, these traceability solutions all depend on the existence of a transformation engine. However, not in all cases pertaining to MDSD can a transformation engine be accessed, while taking into account proprietary transformation engines, or manually implemented transformations. In these cases it is not possible to instrument the transformation engine for the sake of generating traceability data, resulting in a lack of traceability data. In this work, we address these shortcomings. In doing so, we propose a generic traceability framework for augmenting arbitrary transformation approaches with a traceability mechanism. To integrate traceability data from different transformation approaches, our approach features a methodology for augmentation possibilities based on a design pattern. The design pattern supplies the engineer with recommendations for designing the traceability mechanism and for modelling traceability data. Additionally, to provide a traceability mechanism for inaccessible transformation engines, we leverage parallel model matching to generate traceability data for arbitrary source and target models. This approach is based on a language-agnostic concept of three similarity measures for matching. To realise the similarity measures, we exploit metamodel matching techniques for graph-based model matching. Finally, we evaluate our approach according to a set of transformations from an SAP business application and the domain of MDSD.

Because of several advantages, e.g. compact structure, high air-gap flux density, and high torque capability, the PM synchronous motor plays an important role in recent years. The basic principle of controlling a PMSM is based on vector control. The control performance is influenced by factors as the plant parameter variations, the external load disturbances, and the unmodeled or nonlinear dynamics. In the thesis, we apply a recently proposed robust 2DOF configuration to designing controllers for PMSM to achieve the robust asymptotical tracking under perturbations in both the motor and the controllers. Two design methods are adopted to implement the desired controllers, i.e. the linear algebraic method and the design method. The effect of the well-known internal model principle is addressed in the former design method. The merit of the latter design method is that both time and frequency domain design specifications can be easily included in the design procedure. Computer simulation results are displayed to illustrate the advantages of our designs.

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Traceability data provides the knowledge on dependencies and logical relations existing amongst artefacts that are created during software development. In reasoning over traceability data, conclusions can be drawn to increase the quality of software. The paradigm of Model-driven Software Engineering (MDSD) promotes the generation of software out of models. The latter are specified through different modelling languages. In subsequent model transformations, these models are used to generate programming code automatically. Traceability data of the involved artefacts in a MDSD process can be used to increase the software quality in providing the necessary knowledge as described above. Existing traceability solutions in MDSD are based on the integral model mapping of transformation execution to generate traceability data. Yet, these solutions still entail a wide range of open challenges. One challenge is that the collected traceability data does not adhere to a unified formal definition, which leads to poorly integrated traceability data. This aggravates the reasoning over traceability data. Furthermore, these traceability solutions all depend on the existence of a transformation engine. However, not in all cases pertaining to MDSD can a transformation engine be accessed, while taking into account proprietary transformation engines, or manually implemented transformations. In these cases it is not possible to instrument the transformation engine for the sake of generating traceability data, resulting in a lack of traceability data. In this work, we address these shortcomings. In doing so, we propose a generic traceability framework for augmenting arbitrary transformation approaches with a traceability mechanism. To integrate traceability data from different transformation approaches, our approach features a methodology for augmentation possibilities based on a design pattern. The design pattern supplies the engineer with recommendations for designing the traceability mechanism and for modelling traceability data. Additionally, to provide a traceability mechanism for inaccessible transformation engines, we leverage parallel model matching to generate traceability data for arbitrary source and target models. This approach is based on a language-agnostic concept of three similarity measures for matching. To realise the similarity measures, we exploit metamodel matching techniques for graph-based model matching. Finally, we evaluate our approach according to a set of transformations from an SAP business application and the domain of MDSD.

Process model matchers automate the detection of activities that represent similar functionality in different models. Thus, they provide support for various tasks related to the management of business processes including model collection management and process design. Yet, prior research primarily demonstrated the matchers’ effectiveness, i.e., the accuracy and the completeness of the results. In this context (i) the size of the empirical data is often small, (ii) all data is used for the matcher development, and (iii) the validity of the design decisions is not studied. As a result, existing matchers yield a varying and typically low effectiveness when applied to different datasets, as among others demonstrated by the process model matching contests in 2013 and 2015. With this in mind, the thesis studies the effectiveness of matchers by separating development from evaluation data and by empirically analyzing the validity and the limitations of design decisions. In particular, the thesis develops matchers that rely on different sources of information. First, the activity labels are considered as natural-language descriptions and the Bag-of-Words Technique is introduced which achieves a high effectiveness in comparison to the state of the art. Second, the Order Preserving Bag-of-Words Technique analyzes temporal dependencies between activities in order to automatically configure the Bag-of-Words Technique and to improve its effectiveness. Third, expert feedback is used to adapt the matchers to the domain characteristics of process model collections. Here, the Adaptive Bag-of-Words Technique is introduced which outperforms the state-of-the-art matchers and the other matchers from this thesis.

La transformation de modèles est une opération fondamentale dans l'ingénierie dirigée par les modèles. Elle peut être manuelle ou automatisée, mais dans ce dernier cas elle nécessite de la part du développeur qui la conçoit la maîtrise des méta-modèles impliqués dans la transformation. La génération de transformations de modèles à partir d'exemples permet la création d'une transformation de modèle en se basant sur des exemples de modèles sources et cibles. Le fait de travailler au niveau modèle permet d'utiliser les syntaxes concrètes définies pour les méta-modèles et ne nécessite plus une maîtrise parfaite de ces derniers.Nous proposons une méthode de génération de transformations de modèles à partir d'exemples basée sur l'Analyse Relationnelle de Concepts (ARC) permettant d'obtenir un ensemble de règles de transformations ordonnées sous forme de treillis. L'ARC est une méthode de classification qui se base sur des liens de correspondances entre les modèles pour faire émerger des règles. Ces liens étant un problème commun à toute les méthodes de génération de transformation de modèles à partir d'exemples, nous proposons une méthode basée sur des méthodes d'alignement d'ontologie permettant de les générer. / Model transformation is a fundamental operation for Model Driven Engineering. It can be performed manually or automatically, but in the later cas the developper needs to master all the meta-models involved. Model Transformation generation from examples allows to create a model transformation based on source models examples and target models exemples. Working at the model level allows the use of concrete syntaxes defined for the meta-models so there is no more need for the developper to perfectly know them.We propose a method to generate model transformations from examples using Relational Concept Analysis (RCA) which provides a set of transformation rules ordered under the structure of a lattice. RCA is a classification method based on matching links between models to extract rules. Those matching are a common feature between the model transformation generation from examples methods, so we propose a method based on an ontology matching approach to generate them.

Cette thèse étudie le problème du pilotage d'un système de direction découplé dans un véhicule. L'idée est de contrôler les deux sous-systèmes (système de restitution et système de braquage) constituant la direction de manière à : fournir au conducteur, par l'intermédiaire du volant, des sensations de conduite lui permettant d'appréhender le comportement dynamique de son véhicule ; procurer au conducteur une direction répondant à certains critères de confort ; permettre l'amélioration du comportement dynamique du véhicule en jouant sur la dynamique du système de direction. L'étude présente un modèle de référence décrivant le comportement souhaité d'un système de direction une fois implanté dans un véhicule. Ce modèle est construit de manière à prendre en compte l'influence des efforts extérieurs issus du contact pneus/sol, ceux-ci étant représentatifs du comportement dynamique du véhicule<br />Deux méthodes de commande sont proposées afin de reproduire le comportement du modèle de référence sur un système de direction découplée. Des essais sur prototype ont été réalisés et des résultats expérimentaux sont proposés.

One unresolved issue in early word learning research is the relationship between word learning, categorization, and attention. Two distinct cognitive processes, attentional preferences related to categorical processing and inter-modal matching are involved in this relationship. Keeping the effects of these processes separate and controlled can be a difficult task. Not doing so can potentially confound the interpretation of research in this area. In a series of four preferential looking studies, the effects of referential assignment and novelty seeking in infancy were teased apart. In Study 1, 13-month olds preferred to look toward a monitor on which the stimuli changed category on every trial, and away from a monitor on which the stimuli were drawn from a single category. This preference developed in conditions in which infants listened to labels, non-language sound, or participated in silence. In Study 2, 18-month-olds developed the same preference when listening to non-language sounds or when participating in silence, but developed no preference when listening to labels. Results of studies 3 and 4 suggest that the lack of preference by 18-month-olds in the label condition result from competing behaviors of novelty seeking and referential assignment. / text

Process model matchers automate the detection of activities that represent similar functionality in different models. Thus, they provide support for various tasks related to the management of business processes including model collection management and process design. Yet, prior research primarily demonstrated the matchers’ effectiveness, i.e., the accuracy and the completeness of the results. In this context (i) the size of the empirical data is often small, (ii) all data is used for the matcher development, and (iii) the validity of the design decisions is not studied. As a result, existing matchers yield a varying and typically low effectiveness when applied to different datasets, as among others demonstrated by the process model matching contests in 2013 and 2015. With this in mind, the thesis studies the effectiveness of matchers by separating development from evaluation data and by empirically analyzing the validity and the limitations of design decisions. In particular, the thesis develops matchers that rely on different sources of information. First, the activity labels are considered as natural-language descriptions and the Bag-of-Words Technique is introduced which achieves a high effectiveness in comparison to the state of the art. Second, the Order Preserving Bag-of-Words Technique analyzes temporal dependencies between activities in order to automatically configure the Bag-of-Words Technique and to improve its effectiveness. Third, expert feedback is used to adapt the matchers to the domain characteristics of process model collections. Here, the Adaptive Bag-of-Words Technique is introduced which outperforms the state-of-the-art matchers and the other matchers from this thesis.

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