936 resultados para Computer-Aided Magnetic Levitation


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Auxetic materials (or metamaterials) are those with a negative Poisson ratio (NPR) and display the unexpected property of lateral expansion when stretched, as well as an equal and opposing densification when compressed. Such geometries are being progressively employed in the development of novel products, especially in the fields of intelligent expandable actuators, shape morphing structures and minimally invasive implantable devices. Although several auxetic and potentially auxetic geometries have been summarized in previous reviews and research, precise information regarding relevant properties for design tasks is not always provided. In this study we present a comparative study of two-dimensional and three-dimensional auxetic geometries carried out by means of computer-aided design and engineering tools (from now on CAD–CAE). The first part of the study is focused on the development of a CAD library of auxetics. Once the library is developed we simulate the behavior of the different auxetic geometries and elaborate a systematic comparison, considering relevant properties of these geometries, such as Poisson ratio(s), maximum volume or area reductions attainable and equivalent Young's modulus, hoping it may provide useful information for future designs of devices based on these interesting structures.

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The subject of this thesis is the real-time implementation of algebraic derivative estimators as observers in nonlinear control of magnetic levitation systems. These estimators are based on operational calculus and implemented as FIR filters, resulting on a feasible real-time implementation. The algebraic method provide a fast, non-asymptotic state estimation. For the magnetic levitation systems, the algebraic estimators may replace the standard asymptotic observers assuring very good performance and robustness. To validate the estimators as observers in closed-loop control, several nonlinear controllers are proposed and implemented in a experimental magnetic levitation prototype. The results show an excellent performance of the proposed control laws together with the algebraic estimators.

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Indiana Department of Transportation, Indianapolis

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Thesis (M.S.)--University of Illinois.

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Cover title.

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This paper describes a formal component language, used to support automated component-based program development. The components, referred to as templates, are machine processable, meaning that appropriate tool support, such as retrieval support, can be developed. The templates are highly adaptable, meaning that they can be applied to a wide range of problems. Some of the main features of the language are described, including: higher-order parameters; state variable declarations; specification statements and conditionals; applicability conditions and theories; meta-level place holders; and abstract data structures.

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Immunoinformatics is an emergent branch of informatics science that long ago pullulated from the tree of knowledge that is bioinformatics. It is a discipline which applies informatic techniques to problems of the immune system. To a great extent, immunoinformatics is typified by epitope prediction methods. It has found disappointingly limited use in the design and discovery of new vaccines, which is an area where proper computational support is generally lacking. Most extant vaccines are not based around isolated epitopes but rather correspond to chemically-treated or attenuated whole pathogens or correspond to individual proteins extract from whole pathogens or correspond to complex carbohydrate. In this chapter we attempt to review what progress there has been in an as-yet-underexplored area of immunoinformatics: the computational discovery of whole protein antigens. The effective development of antigen prediction methods would significantly reduce the laboratory resource required to identify pathogenic proteins as candidate subunit vaccines. We begin our review by placing antigen prediction firmly into context, exploring the role of reverse vaccinology in the design and discovery of vaccines. We also highlight several competing yet ultimately complementary methodological approaches: sub-cellular location prediction, identifying antigens using sequence similarity, and the use of sophisticated statistical approaches for predicting the probability of antigen characteristics. We end by exploring how a systems immunomics approach to the prediction of immunogenicity would prove helpful in the prediction of antigens.