This study is focused on grafical modeling of embedded systems using dialects of UML. It provides a brief description of existing profiles. Furthemore it deals with modeling frameworks for the Eclipse platform and describes an implementation of such modeling tool as a part of project Lissom.

The goal of this research is to develop a new paradigm integrating the practices of business process modeling and use case modeling. These two modeling approaches describe the behavior of organizations and systems, and their interactions, but rest on different paradigms and serve different needs. The base of knowledge and information required for each approach is largely common, however, so an integrated approach has advantages in efficiency, consistency and completeness of the overall behavioral model. Both modeling methods are familiar and widely used. Business process modeling is often employed as a precursor to the development of a system to be used in a business organization. Business process modeling teams and stakeholders may spend months or years developing detailed business process models, expecting that these models will provide a useful base of information for system designers. Unfortunately, as the business process model is analyzed by the system designers, it is found that information needed to specify the functionality of the system does not exist in the business process model. System designers may then employ use case modeling to specify the needed system functionality, again spending significant time with stakeholders to gather the needed input. Stakeholders find this two-pass process redundant and wasteful of time and money since the input they provide to both modeling teams is largely identical, with each team capturing only the aspects relevant to their form of modeling. Developing a new paradigm and modeling approach that achieves the objectives of both business process modeling and use case modeling in an integrated form, in one analysis pass, results in time savings, increased accuracy and improved communication among all participants in the systems development process. Analysis of several case studies will show that inefficiency, wasted time and overuse of stakeholder resource time results from the separate application of business process modeling and use case modeling. A review of existing literature on the subject shows that while the problem of modeling both business process and use case information in a coordinated fashion has been recognized before, there are few if any approaches that have been proposed to reconcile and integrate the two methods. Based on both literature review and good modeling practices, a list of goals for the new paradigm and modeling approach forms the basis for the paradigm to be created. A grounded theory study is then conducted to analyze existing modeling approaches for both business processes and use cases and to provide an underlying theory on which to base the new paradigm. The two main innovations developed for the new paradigm are the usage process and the timebox. Usage processes allow system usages (use cases) to be identified as the business process model is developed, and the two to be shown in a combined process flow. Timeboxes allow processes to be positioned in time-relation to each other without the need to combine processes into higher level processes using causal relations that may not exist. The combination of usage processes and timeboxes allows any level of complex behavior to be modeled in one pass, without the redundancy and waste of separate business process and use case modeling work. Several pilot projects are conducted to test the new modeling paradigm in differing modeling situations with participants and subject matter experts asked to compare the traditional models with the new paradigm formulations.

L'intégration de diverses technologies, notamment celles de l'informatique et l'électronique, fait que les systèmes conçus de nos jours sont de plus en plus complexes. Ils ont des comportements plus élaborés et plus difficiles à prévoir, ont un nombre de constituants en interaction plus important et/ou réalisent des fonctions de plus haut niveau. Parallèlement à cette complexification des systèmes, la compétitivité du marché mondial impose aux développeurs de systèmes des contraintes de coût et de délais de plus en plus strictes. La même course s'opère concernant la qualité des systèmes, notamment lorsque ceux-ci mettent en jeu un risque en vies humaines ou un risque financier important. Ainsi, les développeurs sont contraints d'adopter une approche de conception rigoureuse pour répondre aux exigences du système souhaité et satisfaire les diverses contraintes (coût, délais, qualité, sûreté de fonctionnement,...). Plusieurs démarches méthodologiques visant à guider la conception de système sont définies par l'intermédiaire de normes d'Ingénierie Système. Notre travail s'appuie sur la norme EIA-632, qui est largement employée, en particulier dans les domaines aéronautique et militaire. Il consiste à améliorer les processus d'ingénierie système décrits par l'EIA-632, afin d'intégrer une prise en compte globale et explicite de la sûreté de fonctionnement. En effet, jusqu'à présent la sûreté de fonctionnement était obtenue par la réutilisation de modèles génériques après avoir étudié et développé chaque fonction indépendamment. Il n'y avait donc pas de prise en compte spécifique des risques liés à l'intégration de plusieurs technologies. Pour cette raison, nous proposons de nous intéresser aux exigences de Sûreté de Fonctionnement au niveau global et le plus tôt possible dans la phase de développement, pour ensuite les décliner aux niveaux inférieurs, ceci en s'appuyant sur les processus de la norme EIA-632 que nous étoffons. Nous proposons également une méthode original e de déclinaison d'exigences de sûreté de fonctionnement à base d'arbres de défaillances et d'AMDEC, ainsi qu'un modèle d'information basé sur SysML pour appuyer notre approche. Un exemple issu du monde aéronautique permet d'illustrer nos propositions.

La réalisation de systèmes complexes de mesures, de contrôle et de surveillance nécessite une méthodologie de conception rigoureuse et des outils adaptés pour aboutir à un système sans faute. La démarche méthodologique proposée vise à aider à la conception d'un système complexe dédié à la gestion intelligente du confort et à la sécurité des personnes au sein de l'habitat. Cette démarche s'intègre dans un processus d'Ingénierie Système et s'appuie sur les standards UML et SYSML, mais également sur le formalisme HiLes, développé par le LAAS et adapté à la conception de modèle commande, la vérification formelle et la simulation numérique. Ces travaux de thèse présentent les méthodes et les outils de conception préconisés pour un habitat intelligent intégrant les objectifs de confort et de sécurité. Deux expérimentations sont décrites utilisant une commande auto-adaptative basée sur l'apprentissage des habitudes des utilisateurs dans leur environnement de vie et implémentés sur site réel.

The objective of this dissertation is to introduce a unified framework for modeling and simulating discrete event logistics systems (DELS) by using a formal language, the System Modeling Language (SysML), for conceptual modeling and a corresponding methodology for translating the conceptual model into a simulation model. There are three parts in this research: plant modeling, control modeling, and simulation generation. Part 1:Plant Modeling of Discrete Event Logistics Systems. Contemporary DELS are complex and challenging to design. One challenge is to describe the system in a formal language. We propose a unified framework for modeling DELS using SysML. A SysML subset for plant modeling is identified in this research. We show that any system can be described by using the proposed subset if the system can be modeled using finite state machines or finite state automata. Furthermore, the system modeled by the proposed subset can avoid the state explosion problem, i.e., the number of the system states grows exponentially when the number of the components increases. We also compare this approach to other existing modeling languages. Part 2:Control Modeling of Discrete Event Logistics Systems. The development of contemporary manufacturing control systems is an extremely complex process. One approach for modeling control systems uses activity diagrams from SysML, providing a standard object-oriented graphical notation and enhancing reusability. However, SysML activity diagrams do not directly support the kind of analysis needed to verify the control model, such as might be available with a Petri net (PN) model. We show that a control model represented by UML/SysML activity diagrams can be transformed into an equivalent PN, so the analysis capability of PN can be used and the results applied back in the activity diagram model. We define a formal mathematical notation for activity diagrams, show the mapping rules between PN and activity diagrams, and propose a formal transformation algorithm. Part 3:Discrete Event Simulation Generation. The challenge of cost-effectively creating discrete event simulation models is well-known. One approach to alleviate this issue is to describe a system using a descriptive modeling language and then transform the system model to a simulation model. Some researchers have tried to realize this idea using a transformation script. However, most of the transformation approaches depend on a domain specific language, so extending the domain specific language may require modifying the transformation script. We propose a transformation approach from SysML to a simulation language. We show that a transformation script can be independent of the associated domain specific language if the domain specific language is implemented as domain libraries using a proposed SysML subset. In this case, both the domain library and the system model can be transformed to a target simulation language. We demonstrate a proof-of-concept example using AnyLogic as the target simulation language.

This research is focused on helping engineers design better systems by supporting their decision making. When engineers design a system, they have an almost unlimited number of possible system alternatives to consider. Modern systems are difficult to design because of a need to satisfy many different stakeholder concerns from a number of domains which requires a large amount of expert knowledge. Current systems engineering practices try to simplify the design process by providing practical approaches to managing the large amount of knowledge and information needed during the process. Although these methods make designing a system more practical, they do not support a structured decision making process, especially at early stages when designers are selecting the appropriate system architecture, and instead rely on designers using ad hoc frameworks that are often self-contradictory. In this dissertation, a framework for performing architecture exploration at early stages of the design process is presented. The goal is to support more rational and self-consistent decision making by allowing designers to explicitly represent their architecture exploration problem and then use computational tools to perform this exploration. To represent the architecture exploration problem, a modeling language is presented which explicitly models the problem as an architecture selection decision. This language is based on the principles of decision-based design and decision theory, where decisions are made by picking the alternative that results in the most preferred expected outcome. The language is designed to capture potential alternatives in a compact form, analysis knowledge used to predict the quality of a particular alternative, and evaluation criteria to differentiate and rank outcomes. This language is based on the Object Management Group's System Modeling Language (SysML). Where possible, existing SysML constructs are used; when additional constructs are needed, SysML's profile mechanism is used to extend the language. Simply modeling the selection decision explicitly is not sufficient, computational tools are also needed to explore the space of possible solutions and inform designers about the selection of the appropriate alternative. In this investigation, computational tools from the mathematical programming domain are considered for this purpose. A framework for modeling an architecture selection decision in mixed-integer linear programming (MIP) is presented. MIP solvers can then solve the MIP problem to identify promising candidate architectures at early stages of the design process. Mathematical programming is a common optimization domain, but it is rarely used in this context because of the difficulty of manually formulating an architecture selection or exploration problem as a mathematical programming optimization problem. The formulation is presented in a modular fashion; this enables the definition of a model transformation that can be applied to transform the more compact SysML representation into the mathematical programming problem, which is also presented. A modular superstructure representation is used to model the design space; in a superstructure a union of all potential architectures is represented as a set of discrete and continuous variables. Algebraic constraints are added to describe both acceptable variable combinations and system behavior to allow the solver to eliminate clearly poor alternatives and identify promising alternatives. The overall framework is demonstrated on the selection of an actuation subsystem for a hydraulic excavator. This example is chosen because of the variety of potential architecture embodiments and also a plethora of well-known configurations which can be used to verify the results.

In this research, the focus is on improving a designer's capability to determine near-optimal sizes of components for a given system architecture. Component sizing is a hard problem to solve because of the presence of competing objectives, requirements from multiple disciplines, and the need for finding a solution quickly for the architecture being considered. In current approaches, designers rely on heuristics and iterate over the multiple objectives and requirements until a satisfactory solution is found. To improve on this state of practice, this research introduces advances in the following two areas: a.) Formulating a component sizing problem in a manner that is convenient to designers and b.) Solving the component sizing problem in an efficient manner so that all of the imposed requirements are satisfied simultaneously and the solution obtained is mathematically optimal. In particular, an acausal, algebraic, equation-based, declarative modeling approach is taken to solve component sizing problems efficiently. This is because global optimization algorithms exist for algebraic models and the computation time is considerably less as compared to the optimization of dynamic simulations. In this thesis, the mathematical programming language known as GAMS (General Algebraic Modeling System) and its associated global optimization solvers are used to solve component sizing problems efficiently. Mathematical programming languages such as GAMS are not convenient for formulating component sizing problems and therefore the Systems Modeling Language developed by the Object Management Group (OMG SysML ) is used to formally capture and organize models related to component sizing into libraries that can be reused to compose new models quickly by connecting them together. Model-transformations are then used to generate low-level mathematical programming models in GAMS that can be solved using commercial off-the-shelf solvers such as BARON (Branch and Reduce Optimization Navigator) to determine the component sizes that satisfy the requirements and objectives imposed on the system. This framework is illustrated by applying it to an example application for sizing a hydraulic log splitter.

Traditionally, environmental impacts of man made products have been determined by performing a life cycle assessment (LCA) on the product. As the name implies, LCA is usually covers the entire life of the product in a so-called "cradle-to-grave" assessment. In determining environmental impacts over the whole product life, LCA's are reasonably adequate. However, in providing detailed impacts on a particular phase of life, LCA's are lacking. Detailed assessments are important because very few stakeholders have influence over a product during all phases of life. Stakeholders need detailed impact assessments in their particular phase of life. More detailed assessments give stakeholders more information that can be used for better environmental management (EM) and more environmentally benign operations. In many LCA's, the manufacturing phase of life has been over-generalized and over-simplified because of its relatively small environmental impact, as compared to other phases of life. Nevertheless, certain stakeholders, such as manufacturing companies, need detailed impact information for the manufacturing phase of life so that they can create a more sustainable manufacturing process. Most traditional LCA's use a case-based approach, which was deemed to be inadequate. For these LCA's, the information provided for each case is often quite detailed and specific. However, this makes the assessment less flexible, limiting the quality of the assessment to the degree that the current scenario matches the existing cases. In order to make a more user-specific assessment, a model-based approach was used. To give the model flexibility, a parametric model was created based on mathematical equations that represent various parts of the manufacturing process. To give the model structure, an activity-based costing (ABC) approach was used. Using the ABC structure, the manufacturing process was broken down into activities, each of which was characterized by mathematical models. Large models would be difficult to construct and simulate by hand, so a model was built with the aid of a computer. The modeling language SysML (Systems Modeling Language) was used to create an object-oriented model of the manufacturing process, using the ABC structure. SysML defines overall properties and behaviors of the various elements in the model, while the plug-in tool ParaMagic was used to execute the model via a Mathematica Solver. The model computes carbon dioxide emissions, energy consumption, and waste mass generation for a particular manufacturing scenario. The goal of the model was to quantify environmental impact factors in order to aid manufacturing stakeholders in EM. The overall goal of the research was to determine whether an activity-based, object-oriented model was a valid approach, and whether the computer-aided tools adequately implemented this approach. Findings show that SysML is capable of modeling large and complex systems. However, due to some limitations of Paramagic, only some of SysML's capabilities were utilized. Nevertheless, Paramagic is capable of extracting information out of a manufacturing model built in SysML, and solving parametric relations in Mathematica in a timely manner. Timely solutions of complex models are critical for stakeholders keeping a competitive edge.

This dissertation supports human decision-making with a Model-Based Systems Engineering methodology enabling engineering analysis, and in particular Operations Research analysis of discrete-event logistics systems, to be more widely used in a cost-effective and correct manner. A methodology is a collection of related processes, methods, and tools, and the process of interest is posing a question about a system model and then identifying and building answering analysis models. Methods and tools are the novelty of this dissertation, which when applied to the process will enable the dissertation's goal. One method which directly enables the goal is adding automation to analysis model-building. Another method is abstraction, to make explicit a frequently-used bridge to analysis and also expose analysis model-building repetition to justify automation. A third method is formalization, to capture knowledge for reuse and also enable automation without human interpreters. The methodology, which is itself a contribution, also includes two supporting tool contributions. A tool to support the abstraction method is a definition of a token-flow network, an abstract concept which generalizes many aspects of discrete-event logistics systems and underlies many analyses of them. Another tool to support the formalization method is a definition of a well-formed question, the result of an initial study of semantics, categories, and patterns in questions about models which induce engineering analysis. This is more general than queries about models in any specific modeling language, and also more general than queries answerable by navigating through a model and retrieving recorded information. A final contribution follows from investigating tools for the automation method. Analysis model-building is a model-to-model transformation, and languages and tools for model-to-model transformation already exist in Model-Driven Architecture of software. The contribution considers if and how these tools can be re-purposed by contrasting software object-oriented code generation and engineering analysis model-building. It is argued that both use cases share a common transformation paradigm but executed at different relative levels of abstraction, and the argument is supported by showing how several Operations Research analyses can be defined in an object-oriented way across multiple layered instance-of abstraction levels. Enabling Operations Research analysis of discrete-event logistics systems to be more widely used in a cost-effective and correct manner requires considering fundamental questions about what knowledge is required to answer a question about a system, how to formally capture that knowledge, and what that capture enables. Developments here are promising, but provide only limited answers and leave much room for future work.

L'exploration de l'espace de conception au niveau système est effectuée tôt dans le flot de conception des systèmes embarqués et des systèmes sur puce. L'objectif est d'identifier un partitionnement matériel / logiciel approprié qui réponde à un ensemble de contraintes concernant la fonctionnalité, la performance, la surface de silicium, la consommation d'énergie, etc. Lors des étapes de conception précoces, des modèles de système précis, tels que des modèles RTL, peuvent être encore indisponibles. Par ailleurs, la complexité de ces modèles présente l'inconvénient d'être exigeant et lent dans la vérification. Il est communément admis que le seul remède à ce problème est l'abstraction, ce qui a engendré l'apparition de plates-formes virtuelles basées sur des techniques telles que la modélisation au niveau transactionnel. Étant non fonctionnels, les modèles \textit{approximately timed} vont encore plus loin en faisant l'abstraction de données simplement selon leur présence ou absence et en introduisant des instructions symboliques. La méthodologie DIPLODOCUS et son profil UML correspondant réalisent les abstractions susmentionnées. La méthodologie s'appuie sur l'approche en Y, qui traite des fonctionnalités (appelées application) et leur réalisation (appelée architecture) de manière orthogonale. La sémantique formelle de DIPLODOCUS ouvre conjointement la voie à la simulation et à la vérification formelle, ce qui a été démontré préalablement a ce travail. Cette thèse propose des améliorations à la méthodologie qui permettent la vérification des propriétés fonctionnelles et non fonctionnelles. Au début, nous nous concentrons sur la façon dont les propriétés fonctionnelles sont exprimées. Puisque la vérification des modèles de haut niveau est habituellement réalisée avec la logique temporelle, nous suggérons une façon plus intuitive qui correspond au niveau d'abstraction du modèle qui doit être vérifié. Le langage graphique, mais formel nommé TEPE est la première contribution de ce travail. Pour atteindre un niveau élevé de confiance en vérification dans un délai raisonnable, le modèle doit être exécuté efficacement. La deuxième contribution vise donc une sémantique d'exécution pour les modèles DIPLODOCUS et une stratégie de simulation qui s'appuie sur l'abstraction. L'avantage est qu'une granularité grossière du modèle d'application se traduit directement par une augmentation de la vitesse de simulation. Comme troisième contribution, nous présentons un compromis entre la couverture limitée de la simulation et l'exhaustivité des techniques formelles. Lorsqu'il s'agit de modèles complexes, l'exhaustivité peut être entravée par le problème d'explosion combinatoire. En raison de l'abstraction de données, les modèles d'application DIPLODOCUS comportent des opérateurs non-déterministes. La simulation à couverture élargie vise à exploiter un sous-ensemble, ou bien l'intégralité, des valeurs des variables aléatoires. Par conséquent, une analyse statique des modèles DIPLODOCUS est effectuée et les informations caractérisant la partie significative de l'espace d'état de l'application sont propagées au simulateur. Enfin, nous fournissons des preuves de l'applicabilité des contributions par le biais d'une étude de cas dans le domaine du traitement du signal. Il sera démontré que les propriétés courantes se traduisent aisément en TEPE. Par ailleurs, la simulation rapide et sa couverture élargie fournissent des indications pertinentes qui sont susceptibles d'aider le développeur à configurer une plate-forme radio logicielle.