This work deals with designing a library of processor models used in embedded systems. Processor architectures are described using the ISAC language. The ISAC language is one of several outcomes of the Lissom project that is taking place at the Faculty of Information Technology, BUT, Brno. The beginning of this work is aimed to provide the introduction to processor architectures used in today's embedded systems. Remaining sections are devoted to presentations of exemplary processor architectures and the description of their implementation. This work is finalized by concluding the gathered experience with emphasis on the suitability of the ISAC language for architecture description and the efficiency of its simulation.

The partitioning of complex processor models on the gate and register-transfer level for parallel functional simulation based on the clock-cycle algorithm is considered. We introduce a hierarchical partitioning scheme combining various partitioning algorithms in the frame of a competing strategy. Melting together the di®erent partitioning results within one level using superpositions we crossover to a mixture of experts one. This approach is improved applying genetic algorithms. We present two new partitioning algorithms (experts), the Backward-Cone-Concentration algorithm (n-BCC) and the Minimum-Overlap Cone-Cluster algorithm (MOCC), both of them taking cones as fundamental units for building partitions.

<p>Examensarbetet syftar till att jämföra nya alternativ till olika system för användning i laborationer i reglerteknikkurser för olika studentkategorier. Det skall utredas vilket av ett antal alternativ till mjukvaruplattformar som är bäst lämpat att användas vid reglerteknikundervisningen i ITN:s reglerlaboratorium vid Linköpings universitet. Tidigare laborationer i berörda kurser skall även modifieras så att dessa kan genomföras i de nya systemen.</p><p>Examensarbetet resulterade i en rad modifierade laborationer och system att tillämpa i dessa. De nya systemen medför att all reglering sköts direkt från en dator istället för tidigare variant med externa apparater som programmeras. På så sätt kan större fokus läggas på regleringen i sig och inte de system som tillämpas för att utföra den.</p> / <p>The purpose of this thesis work is to compare new alternatives for different systems to use in laborations, in courses offered in control system design, for different types of students. The intention is to investigate which of a number of software platform alternatives are best suited for these purposes. Current laborations are to be modified to be viable in the new systems.</p><p>The project resulted in a number of modified laborations and control systems to use in these. The new solutions are made so that all of the control is done by one computer, as opposed to the previous solution where external units had to be programmed and controlled. This way, the laborations can focus more on the control theory and less on the systems used to realize it.</p>

Examensarbetet syftar till att jämföra nya alternativ till olika system för användning i laborationer i reglerteknikkurser för olika studentkategorier. Det skall utredas vilket av ett antal alternativ till mjukvaruplattformar som är bäst lämpat att användas vid reglerteknikundervisningen i ITN:s reglerlaboratorium vid Linköpings universitet. Tidigare laborationer i berörda kurser skall även modifieras så att dessa kan genomföras i de nya systemen. Examensarbetet resulterade i en rad modifierade laborationer och system att tillämpa i dessa. De nya systemen medför att all reglering sköts direkt från en dator istället för tidigare variant med externa apparater som programmeras. På så sätt kan större fokus läggas på regleringen i sig och inte de system som tillämpas för att utföra den. / The purpose of this thesis work is to compare new alternatives for different systems to use in laborations, in courses offered in control system design, for different types of students. The intention is to investigate which of a number of software platform alternatives are best suited for these purposes. Current laborations are to be modified to be viable in the new systems. The project resulted in a number of modified laborations and control systems to use in these. The new solutions are made so that all of the control is done by one computer, as opposed to the previous solution where external units had to be programmed and controlled. This way, the laborations can focus more on the control theory and less on the systems used to realize it.

In this dissertation, we consider analytic and numeric approaches to the solution of probabilistic steady-state problems with specific applications in simulation and queueing theory. Our first objective on steady-state simulations is to develop new estimators for the variance parameter of a selected output process that have better performance than certain existing variance estimators in the literature. To complete our analysis of these new variance estimators, called linear combinations of overlapping variance estimators, we do the following: establish theoretical asymptotic properties of the new estimators; test the theoretical results on a battery of examples to see how the new estimators perform in practice; and use the estimators for confidence interval estimation for both the mean and the variance parameter. Our theoretical and empirical results indicate the new estimators' potential for improvements in accuracy and computational efficiency. Our second objective on steady-state simulations is to derive the expected values of various competing estimators for the variance parameter. In this research, we do the following: formulate the machinery to calculate the exact expected value of a given estimator for the variance parameter; calculate the exact expected values of various variance estimators in the literature; compute these expected values for certain stochastic processes with complicated covariance functions; and derive expressions for the mean squared error of the estimators studied herein. We find that certain standardized time series estimators outperform their competitors as the sample size becomes large. Our research on queueing theory focuses on pricing of the service provided to individual customers in a queueing system. We find sensitivity results that enable efficient computational procedures for dynamic pricing decisions for maximizing the long-run average reward in a queueing facility with the following properties: there are a fixed number of servers, each with the same constant service rate; the system has a fixed finite capacity; the price charged to a customer entering the system depends on the number of customers in the system; and the customer arrival rate depends on the current price of the service. We show that the sensitivity results considered significantly reduce the computational requirements for finding the optimal pricing policies.

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There is a considerable scope for improving the temperature control of exothermic processes. In this thesis, a sub-optimal control strategy is developed through utilizing the dynamic, simulation tool. This scheme is built around easily obtained knowledge of the system and still retains flexibility. It can be applied to both exothermic batch and fed-batch processes. It consists of servo and regulatory modes, where a Generalized Predictive Controller (GPC) was used to provide self-tuning facilities. The methods outlined allow for limited thermal runaway whilst keeping some spare cooling capacity to ensure that operation at constraints are not violated. A special feature of the method proposed is that switching temperatures and temperature profiles can be readily found from plant trials whilst the addition rate profile Is capable of fairly straightforward computation. The work shows that It is unnecessary to demand stability for the whole of the exothermic reaction cycle, permitting a small runaway has resulted in a fast temperature response within the given safety margin. The Idea was employed for an exothermic single Irreversible reaction and also to a set of complex reactions. Both are carried out in a vessel with a heating/cooling coil. Two constraints are Imposed; (1) limited heat transfer area, and (11) a maximum allowable reaction temperature Tmax. The non-minimum phase problem can be considered as one of the difficulties in managing exothermic fed-batch process when cold reactant Is added to vessel at the maximum operating temperature. The control system coped with this within limits, a not unexpected result. In all cases, the new strategy out-performed the conventional controller and produced smoother variations in the manipulated variable. The simulation results showed that batch to batch variations and disturbances In cooling were successfully handled. GPC worked well but can be susceptible to measurement noise.

The performance of product development processes is important to the commercial success of new products. The improvement of these processes is thus a strategic imperative for many engineering companies - the aero-engine is one example of a complex product for which market pressures necessitate ever-shorter development times. This thesis argues that process modelling and simulation can support the improvement of complex product development processes. A literature review identified that design process modelling is a well-establishedresearch area encompassing a diverse range of approaches. However, most existing tools and methods are not widely applied in industry. An extended case study was therefore conducted to explore the pragmatic utility of process modelling and simulation. It is argued that iteration is a key driver of design process behaviour which cannot be fully reflected in a mechanistic model. Understanding iteration can help select an appropriate representation for a given process domain and modelling objective. A model-based approach to improve the management of iterative design processes was developed. This approach shows that design process simulation models can support practice despite their limited fidelity. The modelling and simulation framework resulting from this work was enhanced for application to a wider range of process improvement activities. A robust and extensible software platform was also developed. The framework and software tool have made significant contribution to research projects investigating process redesign, process robustness and process optimisation. These projects are discussed to validate the framework and tool and to highlight their applicability beyond the original approach. The research results were disseminated in academia and industry - 72 copies of the software were distributed following requests in the first three months of its release.

The partitioning of complex processor models on the gate and register-transfer level for parallel functional simulation based on the clock-cycle algorithm is considered. We introduce a hierarchical partitioning scheme combining various partitioning algorithms in the frame of a competing strategy. Melting together different partitioning results within one level using superpositions we crossover to a mixture of experts one. This approach is improved applying genetic algorithms. In addition we present two new partitioning algorithms both of them taking cones as fundamental units for building partitions.

Thesis (D.Tech.: Mechanical Engineering)-Dept. of Mechanical Engineering, Durban Institute of Technology, 2005 xxi, 265 leaves / The aim of this study is to develop, using neural networks, a model to aid the performance monitoring of operational diesel engines in industrial settings. Feed-forward and modular neural network-based models are created for the prediction of the specific fuel consumption on any normally aspirated direct injection four-stroke diesel engine. The predictive capability of each model is compared to that of a published quadratic method. Since engine performance maps are difficult and time consuming to develop, there is a general scarcity of these maps, thereby limiting the effectiveness of any engine monitoring program that aims to manage the fuel consumption of an operational engine. Current methods applied for engine consumption prediction are either too complex or fail to account for specific engine characteristics that could make engine fuel consumption monitoring simple and general in application. This study addresses these issues by providing a neural network-based predictive model that requires two measured operational parameters: the engine speed and torque, and five known engine parameters. The five parameters are: rated power, rated and minimum specific fuel consumption bore and stroke. The neural networks are trained using the performance maps of eight commercially available diesel engines, with one entire map being held out of sample for assessment of model generalisation performance and application validation. The model inputs are defined using the domain expertise approach to neural network input specification. This approach requires a thorough review of the operational and design parameters affecting engine fuel consumption performance and the development of specific parameters that both scale and normalize engine performance for comparative purposes. Network architecture and learning rate parameters are optimized using a genetic algorithm-based global search method together with a locally adaptive learning algorithm for weight optimization. Network training errors are statistically verified and the neural network test responses are validation tested using both white and black box validation principles. The validation tests are constructed to enable assessment of the confidence that can be associated with the model for its intended purpose. Comparison of the modular network with the feed-forward network indicates that they learn the underlying function differently, with the modular network displaying improved generalisation on the test data set. Both networks demonstrate improved predictive performance over the published quadratic method. The modular network is the only model accepted as verified and validated for application implementation. The significance of this work is that fuel consumption monitoring can be effectively applied to operational diesel engines using a neural network-based model, the consequence of which is improved long term energy efficiency. Further, a methodology is demonstrated for the development and validation testing of modular neural networks for diesel engine performance prediction.