Univ., Diss. u.d.T.: Getty, Michael: A constraint-based approach to the meter of Beowulf--Stanford, 1998.

This dissertation introduces the Explicit Reference Governor: a simple and systematic add-on control unit that provides constraint handling capabilities to any pre-stabilized nonlinear system by suitably manipulating its applied reference. The main innovation of the proposed framework is that constraint satisfaction is ensured without having to solve implicit equations. As a result, the Explicit Reference Governor is particularly well suited for applications with limited computational capabilities. The basic idea behind the scheme consists in manipulating the derivative of the applied reference so that, at any given time instant, the currently applied reference will not cause a violation of constraints anytime in the future. The theory behind the proposed framework is presented in general terms and is then detailed to provide specific design strategies. Possible extensions to ensure robustness are also proposed. In addition to introducing the general theory of the Explicit Reference Governor, the dissertation illustrates its step-by-step implementation on Unmanned Aerial Vehicles. / Doctorat en Sciences de l'ingénieur et technologie / info:eu-repo/semantics/nonPublished

We consider the problem of finding a bounded-curvature path in the plane from one configuration αs to another configuration αt that avoids the interior of a set of polygonal obstacles Ε. We call any such path from αs to αt a feasible path. In this thesis, we develop algorithms to find feasible paths that have explicit guarantees on when they will return a feasible path. We phrase our guarantees and run time analysis in terms of the complexity of the desired solution (see k and λ below). In a sense, our algorithms are output sensitive, which is particularly desirable because there are no known bounds on the solution complexity amid arbitrary polygonal environments. Our first major result is an algorithm that given Ε, αs, αt, and a positive integer k either (i) verifies that every feasible path has a descriptive complexity greater than k or (ii) outputs a feasible path. The run time of this algorithm is bounded by a polynomial in n (the total number of obstacle vertices in Ε), m (the bit precision of the input), and k. This result complements earlier work by Fortune and Wilfong: their algorithm considers paths of arbitrary descriptive complexity (it has no dependence on k), but it never outputs a path, just whether or not a feasible path exists. Our second major result is an algorithm that given E, αs, αt, a length λ, and an approximation factor Ε, either (i) verifies that every feasible path has length greater than λ or (ii) constructs a feasible path that is at most (1+ Ε) times longer than the shortest feasible path. The run time of this algorithm is bounded by a polynomial in n, m, Ε-1, and λ. This algorithm is the result of applying the techniques developed earlier in our thesis to the previous approximation approaches. A shortcoming of these prior approximation algorithms is that they only search a special class of feasible paths. This restriction implies that the path that they return may be arbitrarily longer than the shortest path. Our algorithm returns a true approximation because we search for arbitrary shortest paths. / Science, Faculty of / Computer Science, Department of / Graduate

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<p>The purpose of this study was to investigate leaning behaviours when completing tasks with constrained reaches. A logistic regression was developed, with the input of individual subject anthropometry and specific task characteristics, and the resulting model was able to provide a very accurate prediction of when an individual would lean. The inputs to this model give insight into what factors are important in the decision making process when a worker chooses whether lean. The task hand locations with the longest reaches resulted in the most frequent choice to lean. Leaning appears to be particularly common, and important, with long reaching and pulling tasks that can reduce task hand shoulder and trunk loads and improve balance, while allowing the worker to get closer to the task. Leaning hand forces were highest during pulling tasks. These findings are very important to document, as current ergonomic tools neglect to consider that different task characteristics may change how, and when, a worker leans. Even when only the direction of the task hand force was changed, leaning hand forces differed significantly. In this study, leaning hand height was slightly higher for the shoulder height, when compared to the umbilical height, task hand locations. The average height of the leaning hand did not vary considerably and ranged between 106.6cm to 116.3cm, depending on the condition. The leaning hand force magnitude changed as task hand location, force direction and force level changed. Leaning hand forces increased with increasing task hand load. Task hand forces in the push direction were higher compared to push and down exertions, regardless of task hand location or task hand load. The findings from this study are of particular use to industry as ergonomists now have representative forces and heights, to help guide leaning estimates during proactive risk assessments.</p> / Master of Science (MSc)

This thesis presents a new approach to blind deconvolution algorithms. The proposed method is a combination of a classical blind deconvolution subspace method and a marginalized particle filter. It is shown that the new method provides better performance than just a marginalized particle filter, and better robustness than the classical subspace method. The properties of the new method make it a candidate for further exploration of its potential application in acoustic blind dereverberation. / Thesis / Master of Applied Science (MASc)

Consider the context of selecting an optimal system from among a finite set of competing systems, based on a "stochastic" objective function and subject to multiple "stochastic" constraints. In this context, we characterize the asymptotically optimal sample allocation that maximizes the rate at which the probability of false selection tends to zero in two scenarios: first in the context of general light-tailed distributions, and second in the specific context in which the objective function and constraints may be observed together as multivariate normal random variates. In the context of general light-tailed distributions, we present the optimal allocation as the result of a concave maximization problem for which the optimal solution is the result of solving one of two nonlinear systems of equations. The first result of its kind, the optimal allocation is particularly easy to obtain in contexts where the underlying distributions are known or can be assumed, e.g., normal, Bernoulli. A consistent estimator for the optimal allocation and a corresponding sequential algorithm for implementation are provided. Various numerical examples demonstrate where and to what extent the proposed allocation differs from competing algorithms. In the context of multivariate normal distributions, we present an exact, asymptotically optimal allocation. This allocation is the result of a concave maximization problem in which there are at least as many constraints as there are suboptimal systems. Each constraint corresponding to a suboptimal system is a convex optimization problem. Thus the optimal allocation may easily be obtained in the context of a "small" number of systems, where the quantifier "small" depends on the available computing resources. A consistent estimator for the optimal allocation and a fully sequential algorithm, fit for implementation, are provided. The sequential algorithm performs significantly better than equal allocation in finite time across a variety of randomly generated problems. The results presented in the general and multivariate normal context provide the first foundation of exact asymptotically optimal sampling methods in the context of "stochastically" constrained simulation optimization on finite sets. Particularly, the general optimal allocation model is likely to be most useful when correlation between the objective and constraint estimators is low, but the data are non-normal. The multivariate normal optimal allocation model is likely to be useful when the multivariate normal assumption is reasonable or the correlation is high. / Ph. D.

The visual display terminal (VDT) is an integral part of the modern office. An issue of concern associated with the use of the VDT is maintaining privacy of on-screen materials. Privacy filters are products designed to restrict the viewing angle to documents displayed on a VDT, so that the on-screen material is not visible to persons other than the VDT operator. Privacy filters restrict the viewing angle either by diffraction or diffusion of the light emitted from the VDT. Constrained posture is a human factors engineering problem that has been associated with VDT use. The purpose of this research was to evaluate whether the use of privacy filters affected: 1) the restriction of postures associated with VDT use, 2) operator performance, and 3) subjective ratings of display issues, posture, and performance. Nine participants performed three types of tasks: word processing, data entry, and Web browsing. Each task was performed under three filter conditions: no filter, diffraction filter, and diffusion filter. Participants were videotaped during the tasks using a camera mounted above the VDT workstation. The videotape was analyzed and horizontal head deviation was measured at 50 randomly selected points during each task. Horizontal head deviation was measured as the angle between an absolute reference line, which bisects the center of the VDT screen, and a reference point located at the center of the participant's head. Standard deviation of head deviation were evaluated across filter type and task type. Accuracy- and/or time-based measures were used to evaluate performance within each task. Participants used a seven-point scale to rate the following: readability, image quality, brightness, glare, posture restriction, performance, and discomfort. The results indicated that the interaction between task and filter type affected the standard deviation of horizontal head deviation (a measure of the average range of horizontal deviation). The standard deviation of horizontal deviation was significantly larger within the Web browsing task under the no filter and diffusion filter conditions as compared to the diffraction filter condition. Filter type affected subjective ratings of the following: readability, image quality, brightness, posture restriction, and discomfort. The diffraction filter resulted in lower readability, image quality, and brightness ratings than the diffusion and no filter conditions. Participants reported that the ability to change postures was significantly decreased by the use of the diffraction filter as compared to the no filter and diffraction filter conditions. The diffraction filter resulted in an increase in reported discomfort as compared to the no filter condition. The interaction between filter and task type affected subjective ratings of performance. Participants reported a decrease in the rating of perceived performance under the diffraction filter / Web browsing condition as compared to the no filter / word processing, diffusion filter / Web browsing, and diffusion filter / data entry conditions. A decrease in the rating of perceived performance was reported in the diffraction filter / data entry condition as compared to the no filter / word processing and diffusion filter / Web browsing conditions. Neither diffraction nor diffusion filter affected performance within any of the tasks, based on the objective performance measures used in the experiment. / Master of Science

This dissertation provides a complete mathematical framework to compute the sensitivities with respect to system parameters for any second order hybrid Ordinary Differential Equation (ODE) and rank 1 and 3 Differential Algebraic Equation (DAE) systems. The hybrid system is characterized by discontinuities in the velocity state variables due to an impulsive forces at the time of event. At the time of event, such system may also exhibit a change in the equations of motion or in the kinematic constraints. The analytical methodology that solves the sensitivities for hybrid systems is structured based on jumping conditions for both, the velocity state variables and the sensitivities matrix. The proposed analytical approach is then benchmarked against a known numerical method. The mathematical framework is extended to compute sensitivities of the states of the model and of the general cost functionals with respect to model parameters for both, unconstrained and constrained, hybrid mechanical systems. This dissertation emphasizes the penalty formulation for modeling constrained mechanical systems since this formalism has the advantage that it incorporates the kinematic constraints inside the equation of motion, thus easing the numerical integration, works well with redundant constraints, and avoids kinematic bifurcations. In addition, this dissertation provides a unified mathematical framework for performing the direct and the adjoint sensitivity analysis for general hybrid systems associated with general cost functions. The mathematical framework computes the jump sensitivity matrix of the direct sensitivities which is found by computing the Jacobian of the jump conditions with respect to sensitivities right before the event. The main idea is then to obtain the transpose of the jump sensitivity matrix to compute the jump conditions for the adjoint sensitivities. Finally, the methodology developed obtains the sensitivity matrix of cost functions with respect to parameters for general hybrid ODE systems. Such matrix is a key result for design analysis as it provides the parameters that affect the given cost functions the most. Such results could be applied to gradient based algorithms, control optimization, implicit time integration methods, deep learning, etc. / Ph. D.