Thèses sur le sujet « DES modeling »

Real-time systems are systems which must produce a result within a given time frame. A result given outside of this time frame is as useless as not delivering any result at all. It is therefore essential to verify that real-time systems fulfill their timing requirements. A model of the system can facilitate the verification process. This thesis investigates two possible methods for modeling a real-time system with respect to CPU-utilization and latency of the different components in the system. The two methods are evaluated and one method is chosen for implementation.The studied system is the decoder of a WCDMA system which utilizes a real-time operating called system OSEck. The methodology of analyzing the system and different ways of obtaining measurements to base the model upon will be described. The model was implemented using the simulation library VirtualTime, which contains a model of the previously mentioned operating system. Much work was spent acquiring input for the model, since the quality of the model depends largely on the quality of the analysis work. The model created contains two of the studied systems main components.This thesis identifies thorough system knowledge and efficient profiling methods as the key success factors when creating models of real-time systems.

This research project studies the feasibility of developing and applying an integrated field simulator to simulate the production performance of an entire oil or gas field. It integrates the performance of the reservoir, the wells, the chokes, the gathering system, the surface processing facilities and, whenever applicable, gas and water injection systems.

The approach adopted for developing the integrated simulator is to couple existing commercial reservoir and process simulators using available linking technologies. The simulators are dynamically linked and customized into a single hybrid application that benefits from the concept of open software architecture. The integrated field simulator is linked to an optimization routine developed based on the genetic algorithm search strategies. This enables optimization of the system at field level, from the reservoir to the process. Modeling the wells and the gathering network is achieved by customizing the process simulator.

This study demonstrates that the integrated simulation improves currentcapabilities to simulate the performance of an entire field and optimize its design. This is achieved by evaluating design options including spread and layout of the wells and gathering system, processing alternatives, reservoir development schemes, and production strategies.

Effectiveness of the integrated simulator is demonstrated and tested through several field-level case studies that discuss and investigate technical problems relevant to offshore field development. The case studies cover topics such as process optimization, optimum tie-in of satellite wells into existing process facilities, optimal well location, and field layout assessment of a high pressure high temperature deepwater oil field.

Case study results confirm the viability of the total field simulator by demonstrating that the field performance simulation and optimal design were obtained in an automated process with reasonable computation time. No significant simplifying assumptions were required to solve the system and tedious manual data transfer between simulators, as conventionally practiced, was avoided.

Cross-organizational business processes are common in today’s economy. Of necessity, enterprises conduct their business in cooperation to create products and services for the marketplace. Thus business processes inherently involve autonomous partners with heterogeneous software designs and implementations. The existing business modeling approaches that employ high-level abstractions are difficult to operationalize, and the approaches that employ low-level abstractions lead to highly rigid processes that lack business semantics. We propose a novel business model based on multiagent abstractions. Unlike existing approaches, our model gives primacy to the contractual relationships among the business partners, thus providing a notion of business-level correctness, and offers flexibility to the participants. Our approach employs reusable patterns as building blocks to model recurring business scenarios. A step-by-step methodology guides a modeler in constructing a business model. Our approach employs temporal logic to formalize the correctness properties of a business model, and model checking to verify if a given operationalization satisfies those properties. Developer studies found that our approach yields improved model quality compared to the traditional approaches from the supply chain and healthcare domains.

Commitments capture how an agent relates with another agent, whereas goals describe states of the world that an agent is motivated to bring about. It makes intuitive sense that goals and commitments be understood as being complementary to each other. More importantly, an agent’s goals and commitments ought to be coherent, in the sense that an agent’s goals would lead it to adopt or modify relevant commitments and an agent’s commitments would lead it to adopt or modify relevant goals. However, despite the intuitive naturalness of the above connections, they have not yet been studied in a formal framework. This dissertation provides a combined operational semantics for goals and commitments. Our semantics yields important desirable properties, including convergence of the configurations of cooperating agents, thereby delineating some theoretically well-founded yet practical modes of cooperation in a multiagent system.

We formalize the combined operational semantics of achievement commitments and goals in terms of hierarchical task networks (HTNs) and show how HTN planning provides a natural representation and reasoning framework for them. Our approach combines a domain-independent theory capturing the lifecycles of goals and commitments, generic patterns of reasoning, and domain models. We go beyond existing approaches by proposing a first-order representation that accommodates settings where the commitments and goals are templatic and may be applied repeatedly with differing bindings for domain objects. Doing so not only leads to a more perspicuous modeling, it also enables us to support a variety of practical patterns.

Traditionally, models of a robot's kinematics and sensors have been provided by designers through manual processes. Such models are used for sensorimotor tasks, such as manipulation and stereo vision. However, these techniques often yield static models based on one-time calibrations or ideal engineering drawings; models that often fail to represent the actual hardware, or in which individual unimodal models, such as those describing kinematics and vision, may disagree with each other.

Humans, on the other hand, are not so limited. One of the earliest forms of self-knowledge learned during infancy is knowledge of the body and senses. Infants learn about their bodies and senses through the experience of using them in conjunction with each other. Inspired by this early form of self-awareness, the research presented in this thesis attempts to enable robots to learn unified models of themselves through data sampled during operation. In the presented experiments, an upper torso humanoid robot, Nico, creates a highly-accurate self-representation through data sampled by its sensors while it operates. The power of this model is demonstrated through a novel robot vision task in which the robot infers the visual perspective representing reflections in a mirror by watching its own motion reflected therein.

In order to construct this self-model, the robot first infers the kinematic parameters describing its arm. This is first demonstrated using an external motion capture system, then implemented in the robot's stereo vision system. In a process inspired by infant development, the robot then mutually refines its kinematic and stereo vision calibrations, using its kinematic structure as the invariant against which the system is calibrated. The product of this procedure is a very precise mutual calibration between these two, traditionally separate, models, producing a single, unified self-model.

The robot then uses this self-model to perform a unique vision task. Knowledge of its body and senses enable the robot to infer the position of a mirror placed in its environment. From this, an estimate of the visual perspective describing reflections in the mirror is computed, which is subsequently refined over the expected position of images of the robot's end-effector as reflected in the mirror, and their real-world, imaged counterparts. The computed visual perspective enables the robot to use the mirror as an instrument for spacial reasoning, by viewing the world from its perspective. This test utilizes knowledge that the robot has inferred about itself through experience, and approximates tests of mirror use that are used as a benchmark of self-awareness in human infants and animals.