Thesis (Ph.D.)--Kent State University, 2008. / Title from PDF t.p. (viewed Jan. 26, 2010). Advisor: Joseph Diestel. Keywords: absolute continiuty, range of a vector measure. Includes bibliographical references (p. 40-41).

Thesis (Ph. D.)--University of Wisconsin--Madison, 1970. / Typescript. Vita. eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references.

Thesis (Ph.D.) in Oceanography--University of Maine, 2008. / Includes vita. Includes bibliographical references (leaves 156-166).

Thesis (M. Soc. Sc.)--University of Hong Kong, 2003. / Also available in print.

Thesis (doctoral)--Katholieke Universiteit te Leuven. / Includes bibliographical references (p. 141-152).

No description available.

A thorough review of the literature on the model reduction of linear, time-invariant, dynamical systems in both the frequency and time domains is presented. Particular attention is paid to the least-squares extension of the classical method of Padé approximation. An account is given of the development of apparently different approaches of least-squares parameter- matching Padé model reduction applied to continuous-time and discrete-time systems. These approaches are shown to be related via a unifying theory. From the formulation it is possible to show several interesting features of the least-squares approach which lead to a fuller understanding of exactly how the reduced model approximates the full system. An error index is derived in the general continuous-time case and it is shown that a range of system parameter preservation options are available. Using the theory developed in the continuous-time case a non-uniqueness property of the method is proven. An ‘optimal’ least-squares method based on the approach and the introduction of weighting for the system parameters are both investigated. The unifying theory is extended to the discrete-time case where an important new stability preservation property is proved and is shown to provide the basis for a new least- squares Padé method. This method uses transformations between the z- and 5-planes to guarantee stable reduced order models approximating stable high order continuous-time systems. The application of least-squares Padé approximation is further extended to the multivariable case with particular attention given to the factors affecting the levels of order reduction achieved. Appropriate numerical examples are used to illustrate the main points of the thesis and graphs of the impulse and step responses are used throughout to visually highlight the accuracy of approximation.

Underground mines are the source of 33% of US coal production and 60% of worldwide coal production. Room-and-pillar mining with continuous miners has been the most common production system used in these mines since the 1960s. The introduction of continuous miners mechanized the underground coal mining industry triggering a period of sustained growth in mine productivity; however, productivity peaked at the turn of the century and has been in decline for a decade. Research on productivity in underground coal mines began at Southern Illinois University Carbondale in 2000 and led to development of a deterministic spreadsheet model for evaluating continuous miner production systems. As with other production models, this model uses a heuristic approach to define the fundamental input parameter known as a cut sequence. This dissertation presents a dynamic programming algorithm to supplant that trial-and-error practice of determining and evaluating room-and-pillar mining sequences. Dynamic programming has been used in mining to optimize multi-stage processes where production parameters are stage-specific; however, this is the inaugural attempt at considering parameters that are specific to paths between stages. The objective of the algorithm is to maximize continuous miner utilization for true production when coal is actually being loaded into haulage units. This is accomplished with an optimal value function designed to minimize cut-cycle time. In addition to loading time, cut-cycle time also includes change-out and place change times. The reasonableness of the methodology was validated by modeling an actual mining sequence and comparing results with time study and production report data collected from a cooperating mine over a two-week time period in which more than 300 cuts were mined. The validation effort also inspired some fine-tuning adjustments to the algorithm. In a case study application of the dynamic programming algorithm, a seven-day "optimal mining sequence" was identified for three crosscuts of advance on an eleven-entry super-section developing a main entry system for a new mine in southern Illinois. Productivity improvements attributable to the optimal sequence were marginal but the case study application reconfirmed the reasonableness of the methodology.

Determining the minimum distance between two objects is a problem that has been solved using many different approaches. Most methods proposed so far are, in essence, limited to solve the problem amongst convex polyhedra. Thus, to deal with concave objects, these methods partition concave objects into convex sub-objects and solve the convex problem between all possible sub-object combinations. This adds a large computational expense, especially when the concave objects in the scene are complicated, or when concave quadratically bound objects are to be linearized. In this work, two optimization-based formulations are proposed to solve the minimum distance problem without the need for partitioning concave objects into convex sub-objects. The first one, referred to as the continuous approach, uses concepts of computational solid geometry in order to represent objects with concavities. On the other hand, in the second formulation, referred to as the combinatorial approach, the geometries of the objects are replaced by large sets of points arranged in surface meshes. Since the optimization problem is not unimodal (i.e., has more than one local minimum point), global optimization techniques are used. Simulated Annealing and Genetic Algorithms, with constraint handling techniques such as penalty and repair strategies are used in the continuous approach. In order to eliminate the computational expense of determining the feasibility of every trial point, the combinatorial approach replaces the objects' geometry by a set of points on the surface of each object. This reduces the minimum distance problem to an unconstrained combinatorial optimization problem where the combination of points (one on each object) that minimizes the distance between objects is the solution. Additionally, Genetic Algorithms with niche formation techniques were developed in order to allow the distance algorithm to track multiple minima. In a series of numerical examples, a preliminary implementation of the proposed algorithms has proven to be robust and equivalent, in terms of computational efficiency, to some conventional approaches. / Graduate

Lean techniques are tools that reduce waste in the process and create value for the end-customer. Initially, the concept of lean thinking started in manufacturing, but with the tremendous advantages it offers in terms of value creation for the customer, defect reduction, increase of profits for corporations, it has been recognized as an important tool across a wide spectrum of industries. Although Healthcare industry has started applying these techniques, there is very little work published on how to apply these techniques to this particular industry. In this study, a framework for applying lean thinking to healthcare industries is presented. The framework depicts a systematic methodology for identifying value streams. The framework was developed specifically for the healthcare industry, but it can be applied to service industry in general. A case study is presented on how to apply this framework. Value stream mapping has been conducted at a clinic to identify areas of improvement. The components of the developed framework have been used to define a future state of process based on input from process owners, nurses, physicians, and patient surveys. The study has identified factors that influence the success of implementation of lean techniques in healthcare. Also the potential for future work has been identified.