The buckling of compressively-loaded members is one of the most important factors limiting the overall strength and stability of a structure. I have developed novel techniques for using active control to wiggle a structural element in such a way that buckling is prevented. I present the results of analysis, simulation, and experimentation to show that buckling can be prevented through computer-controlled adjustment of dynamical behavior.sI have constructed a small-scale railroad-style truss bridge that contains compressive members that actively resist buckling through the use of piezo-electric actuators. I have also constructed a prototype actively controlled column in which the control forces are applied by tendons, as well as a composite steel column that incorporates piezo-ceramic actuators that are used to counteract buckling. Active control of buckling allows this composite column to support 5.6 times more load than would otherwise be possible.sThese techniques promise to lead to intelligent physical structures that are both stronger and lighter than would otherwise be possible.

Thesis (Ph.D.)--University of Delaware, 2006. / Principal faculty advisors: Sandra M. Carberry and Keith S. Decker, Dept. of Computer & Information Sciences. Includes bibliographical references.

Syftet med denna uppsats är att analysera debatten som tog plats på hemsida Newsmill åren 2010och 2012 om intelligent design. Fokusen i själva textanalysen är en argumentationsanalys somtillsammans med kritisk diskursanalys skall utröna och förstå den maktförskjutning som går attfinna inom diskursen. Den kritiska diskursanalysen är byggd på Norman Fairclough. Analysen ärordnad kronologiskt och innefattar tjugotre artiklar allt som allt.Fokus i själva analysen ligger i att utröna och förstå hur debattörerna ställer sig till religion ochvetenskap i deras argumentation kring intelligent design. Här går att finna hur religion ofta förs insom begrepp i relation till personers emotionella agerande och överlag blir religion en belastning fördebattörerna. Vetenskap i kontrast har en övergripande positiv ställning i debatten ochmeningsskiljaktigheten går att finna i var religion skall placeras i relation till just vetenskapen i dessmoderna form.

Using electronic portfolios (e-portfolios) to assist learning is an important component of future educational models. A portfolio is a purposeful collection of student work that exhibits the student's efforts, progress and achievements in one or more areas. An e-portfolio contains a variety of information about a person's learning outcomes, such as artifacts, assertions from others, self-reflective information and presentation for different purposes. E-portfolios become sources of evidence for claims about prior conceptual knowledge or skills. This thesis investigates using the information contained in e-portfolios to initialize the learner model for an intelligent tutoring system. We examine the information model from the e-portfolio standardized specification and present a method that may assist users in initializing learner models using e-portfolios as evidence for claims about prior conceptual knowledge or skills. We developed the EP-LM system for testing how accurately a learner model can be built and how beneficial this approach can be for reflective and personalized learning. Experimental results are presented aiming at testing whether accurate learner models can be created through this approach and whether learners can gain benefits in reflective and personalized learning. Monitoring this process can also help ITS developers and experts identify how an initial learner model can automatically arise from an e-portfolio. Additionally, a well-structured learner model, generated by an intelligent tutoring system also can be attached to an e-portfolio for further use by the owner and others.

This dissertation presents a new type of fuel control algorithm for gaseous fuelled vehicles. Gaseous fuels such as hydrogen and natural gas have been shown to be less polluting than liquid fuels such as gasoline, both at the tailpipe and on a total cycle basis. Unfortunately, it can be expensive to convert vehicles to gaseous fuels, partially due to small production runs for these vehicles. One of major development costs for a new vehicle is the development and calibration of the fuel controller. The research presented here includes a fuel controller which does not require an expensive calibration phase.<p>The controller is based upon a two-part model, separating steady state and dynamic effects. This model is then used to estimate the optimum fuelling for the measured operating condition. The steady state model is calculated using an artificial neural network with an online learning scheme, allowing the model to continually update to improve the controller's performance. This is important during both the initial learning of the characteristics of a new engine, as well as tracking changes due to wear or damage.<p>The dynamic model of the system is concerned with the significant transport delay between the time the fuel is injected and when the exhaust gas oxygen sensor makes the reading. One significant result of this research is the realization that a previous commonly used model for this delay has become significantly less accurate due to the shift from carburettors or central point injection to port injection.<p>In addition to a description of the control scheme used, this dissertation includes a new method of algebraically inverting a neural network, avoiding computationally expensive iterative methods of optimizing the model. This can greatly speed up the control loop (or allow for less expensive, slower hardware).<p>An important feature of a fuel control scheme is that it produces a small, stable limit cycle between rich and lean fuel-air mixtures. This dissertation expands the currently available models for the limit cycle characteristics of a system with a linear controller as well as developing a similar model for the neural network controller by linearizing the learning scheme.<p>One of the most important aspects of this research is an experimental test, in which the controller was installed on a truck fuelled by natural gas. The tailpipe emissions of the truck with the new controller showed better results than the OEM controller on both carbon monoxide and nitrogen oxides, and the controller required no calibration and very little information about the properties of the engine.<p>The significant original contributions resulting from this research include:<br> -collection and summarization of previous work,<br> -development of a method of automatically determining the pure time delay between the fuel injection event and the feedback measurement,<br> -development of a more accurate model for the variability of the transport delay in modern port injection engines,<br> -developing a fuel-air controller requiring minimal knowledge of the engine's parameters,<br> -development of a method of algebraically inverting a neural network which is much faster than previous iterative methods,<br> -demonstrating how to initialize the neural model by taking advantage of some important characteristics of the system,<br> -expansion of the models available for the limit cycle produced by a system with a binary sensor and delay to include integral controllers with asymmetrical gains,<br> -development of a limit cycle model for the new neural controller, and<br> -experimental verification of the controller's tailpipe emissions performance, which compares favourably to the OEM controller.

The goal of this thesis is to develop an intuitive, adaptive, and flexible architecture for controlling intelligent mobile robots. This architecture is a hybrid architecture that combines deliberative planning, reactive control, finite state automata, behaviour trees and uses competition for behaviour selection. This behaviour selection is based on a task manager, which selects behaviours based on approximations of their applicability to the current situation and the expected reward value for performing that behaviour. One important feature of this architecture is that it makes important behavioural information explicit using Extensible Markup Language (XML). This explicit representation is an important part in making the architecture easy to debug and extend. The utility, intuitiveness and flexibility of this architecture is shown in an evaluation of this architecture against older control programs that lack such explicit behavioural representation. This evaluation was carried out by developing behaviours for several common robotic tasks and demonstrating common problems that arose during the course of this development. / February 2006

Les systèmes tutoriels intelligents (STI) sont un grand pas vers une réforme dans l'éducation. Ces systèmes offrent une souplesse d'enseignement que les autres aides pédagogiques informatiques n'ont pas. De ce fait, ils pourraient, s'ils sont bien intégrés dans un programme éducatif, décharger les professeurs pour qu'ils consacrent une attention particulière aux étudiants plus faibles. Les systèmes tutoriels intelligents atteignent cette souplesse grâce à la combinaison de sous-systèmes; l'un d'entre eux est la communication. Plusieurs recherches ont été effectuées dans ce sens notamment pour la communication en langage naturel. Cette communication peut être divisée en trois parties soit la compréhension du langage naturel, la génération de texte en langage naturel et la planification des dialogues. Cette dernière représente la base de ce type de communication. CTS (Cognitive Tutoring System) est un moteur de système tutoriel intelligent basé sur la conscience d'accès développée par le GDAC. CTS a été intégré à Canadarm Tutor pour son développement. Ce mémoire traite de l'ajout d'un système de planification du dialogue basé sur les travaux effectués sur Beetle. Dans un premier temps, plusieurs correctifs seront apportés au fonctionnement du Réseau des Actes pour tenter de stabiliser son comportement; d'autres amèneront le système plus près de ses fondements notamment l'ajout de la délibération. L'ajout du planificateur tel que décrit dans le STI de Beetle s'effectuera dans un second temps et utilisera l'architecture unique de CTS pour le faire. Cette combinaison d'architecture apportera plusieurs avantages et donnera un système de planification de dialogue générique et augmentable. ______________________________________________________________________________

Using electronic portfolios (e-portfolios) to assist learning is an important component of future educational models. A portfolio is a purposeful collection of student work that exhibits the student's efforts, progress and achievements in one or more areas. An e-portfolio contains a variety of information about a person's learning outcomes, such as artifacts, assertions from others, self-reflective information and presentation for different purposes. E-portfolios become sources of evidence for claims about prior conceptual knowledge or skills. This thesis investigates using the information contained in e-portfolios to initialize the learner model for an intelligent tutoring system. We examine the information model from the e-portfolio standardized specification and present a method that may assist users in initializing learner models using e-portfolios as evidence for claims about prior conceptual knowledge or skills. We developed the EP-LM system for testing how accurately a learner model can be built and how beneficial this approach can be for reflective and personalized learning. Experimental results are presented aiming at testing whether accurate learner models can be created through this approach and whether learners can gain benefits in reflective and personalized learning. Monitoring this process can also help ITS developers and experts identify how an initial learner model can automatically arise from an e-portfolio. Additionally, a well-structured learner model, generated by an intelligent tutoring system also can be attached to an e-portfolio for further use by the owner and others.

This dissertation presents a new type of fuel control algorithm for gaseous fuelled vehicles. Gaseous fuels such as hydrogen and natural gas have been shown to be less polluting than liquid fuels such as gasoline, both at the tailpipe and on a total cycle basis. Unfortunately, it can be expensive to convert vehicles to gaseous fuels, partially due to small production runs for these vehicles. One of major development costs for a new vehicle is the development and calibration of the fuel controller. The research presented here includes a fuel controller which does not require an expensive calibration phase.<p>The controller is based upon a two-part model, separating steady state and dynamic effects. This model is then used to estimate the optimum fuelling for the measured operating condition. The steady state model is calculated using an artificial neural network with an online learning scheme, allowing the model to continually update to improve the controller's performance. This is important during both the initial learning of the characteristics of a new engine, as well as tracking changes due to wear or damage.<p>The dynamic model of the system is concerned with the significant transport delay between the time the fuel is injected and when the exhaust gas oxygen sensor makes the reading. One significant result of this research is the realization that a previous commonly used model for this delay has become significantly less accurate due to the shift from carburettors or central point injection to port injection.<p>In addition to a description of the control scheme used, this dissertation includes a new method of algebraically inverting a neural network, avoiding computationally expensive iterative methods of optimizing the model. This can greatly speed up the control loop (or allow for less expensive, slower hardware).<p>An important feature of a fuel control scheme is that it produces a small, stable limit cycle between rich and lean fuel-air mixtures. This dissertation expands the currently available models for the limit cycle characteristics of a system with a linear controller as well as developing a similar model for the neural network controller by linearizing the learning scheme.<p>One of the most important aspects of this research is an experimental test, in which the controller was installed on a truck fuelled by natural gas. The tailpipe emissions of the truck with the new controller showed better results than the OEM controller on both carbon monoxide and nitrogen oxides, and the controller required no calibration and very little information about the properties of the engine.<p>The significant original contributions resulting from this research include:<br> -collection and summarization of previous work,<br> -development of a method of automatically determining the pure time delay between the fuel injection event and the feedback measurement,<br> -development of a more accurate model for the variability of the transport delay in modern port injection engines,<br> -developing a fuel-air controller requiring minimal knowledge of the engine's parameters,<br> -development of a method of algebraically inverting a neural network which is much faster than previous iterative methods,<br> -demonstrating how to initialize the neural model by taking advantage of some important characteristics of the system,<br> -expansion of the models available for the limit cycle produced by a system with a binary sensor and delay to include integral controllers with asymmetrical gains,<br> -development of a limit cycle model for the new neural controller, and<br> -experimental verification of the controller's tailpipe emissions performance, which compares favourably to the OEM controller.

A Multiagent Framework for a Diagnostic and Prognostic System Irtaza Barlas 124 Pages Directed By: Dr. George Vactsevanos The shortcomings of the current diagnostic and prognostic systems stem from the limitations of their frameworks. The framework is typically designed on the passive, open loop, static, and isolated notions of diagnostics, in that the framework does not observe its diagnostic results (open-looped), hence can not improve its performance (static). Its passivity is attributed to the fact that an external event triggers the diagnostic or prognostic action. There is also no effort in place to team-up the diagnostic systems for a collective learning, hence the implementation is isolated. In this research we extend the current approaches of the design and implementation of diagnostic and prognostic systems by presenting a framework based upon Multiagent systems. This research created novel architectures by providing such unique features to the framework, as learning, reasoning, and coordination. As the primary focus of the research the concept of Case-Based Reasoning was exploited to reason in the temporal domain to generate better prognosis, and improve the accuracy of detection as well as prediction. It was shown that the dynamic behavior of the intelligent agent helps it to learn over time, resulting in improved performance. An analysis is presented to show that a coordinated effort to diagnose also makes sense in uncertain situations when there are certain number of systems attempting to communicate certain number of failures, since there can be high probability of finding a shareable experience.