9 resultados para Hull, Isaac, 1773-1843.

em Queensland University of Technology - ePrints Archive


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Short story published in the Queensland University of Technology Student Anthology, Isaac’s Numbers: New Writing from QUT, 2008.

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This article explores the relationship between Georg Calixtus (1586-1656) and Isaac Casaubon (1559-1614). It does this in order to highlight an oversight in the existing literature concerning Calixtus, and to encourage scholars to revisit the work of Early Modern figures who have previously been considered only from modern disciplinary perspectives. By emphasizing the relationship between Calixtus and Casaubon, this article argues that Calixtus was potentially exposed to much broader circles of intellectual debate than has previously been considered, and that a reevaluation of his work in light of these debates is therefore in order.

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Palygorskite (P), goethite (G), and hydrothermally synthesized goethite (HG) were used as supports for Fe and Ni. The catalytic activity of these materials was investigated involving in P, G and HG (supported Fe and Ni) for catalytic decomposition of biomass tar derived from rice hull gasification. The materials were characterized by X-ray diffraction (XRD), X-ray fluorescence (XRF), and transmission electron microscopy (TEM) with an energy dispersive X-ray (EDS). The catalytic activity of P for removal of tar was significantly better than that of G and HG. However, the activity of G with 6 mass% Ni labeled as Ni6/G (tar conversion 94.6%), which was equal to Fe6Ni6/P (94.4%), was better than Ni6/P (64.4%) and Ni6/HG (46.7%). When the loading of Ni (mass%) was 6 mass% on G, tar conversion had the best value (94.6%) and yield of gaseous products reached 486.9, 167.8 and 22.2 mL/(g·tar) for H2, CO, CH4, respectively. The catalytic activity of goethite supported Ni was better in improving tar conversion and improving increased yield of H2, CO, CH4, which was attributed to the existence of Al/Fe substitution of goethite

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The catalytic performance of Fe–Ni/PG (PG: palygorskite) catalysts pre-calcined and reduced at 500 ◦C for catalytic decomposition of tar derived through rice hull gasification was investigated. The materials were characterized by using X-ray diffraction, hydrogen temperature reduction, and transmission electron microscopy. The results showed that ferrites with spinel structure ((Fe, Ni)3O4) were formed during preparation of bimetallic systems during calcination and reduction of the precursors (Fe–Ni/PG catalysts) and NiO metal oxide particles were formed over Fe6–Ni9/PG catalyst. The obtained experimental data showed that Fe–Ni/PG catalysts had greater catalytic activity than natural PG. Tar removal using Fe6–Ni9/PG catalyst was as high as Fe10–Ni6/PG catalyst (99.5%). Fe6–Ni9/PG showed greater catalytic activity with greater H2 yield and showed stronger resistance to carbon deposition, attributed to the presence of NiO nanoparticles. Thus, the addition of nickel and iron oxides played an important role in catalytic cracking of rice hull biomass tar.

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Recently, a convex hull-based human identification protocol was proposed by Sobrado and Birget, whose steps can be performed by humans without additional aid. The main part of the protocol involves the user mentally forming a convex hull of secret icons in a set of graphical icons and then clicking randomly within this convex hull. While some rudimentary security issues of this protocol have been discussed, a comprehensive security analysis has been lacking. In this paper, we analyze the security of this convex hull-based protocol. In particular, we show two probabilistic attacks that reveal the user’s secret after the observation of only a handful of authentication sessions. These attacks can be efficiently implemented as their time and space complexities are considerably less than brute force attack. We show that while the first attack can be mitigated through appropriately chosen values of system parameters, the second attack succeeds with a non-negligible probability even with large system parameter values that cross the threshold of usability.

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Recently a convex hull based human identification protocol was proposed by Sobrado and Birget, whose steps can be performed by humans without additional aid. The main part of the protocol involves the user mentally forming a convex hull of secret icons in a set of graphical icons and then clicking randomly within this convex hull. In this paper we show two efficient probabilistic attacks on this protocol which reveal the user’s secret after the observation of only a handful of authentication sessions. We show that while the first attack can be mitigated through appropriately chosen values of system parameters, the second attack succeeds with a non-negligible probability even with large system parameter values which cross the threshold of usability.

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This paper presents a control design for tracking of attitude and speed of an underactuated slender-hull unmanned underwater vehicle (UUV). The control design is based on Port-Hamiltonian theory. The target dynamics (desired dynamic response) is shaped with particular attention to the target mass matrix so that the influence of the unactuated dynamics on the controlled system is suppressed. This results in achievable dynamics independent of uncontrolled states. Throughout the design, insight of the physical phenomena involved is used to propose the desired target dynamics. The performance of the design is demonstrated through simulation with a high-fidelity model.

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Traditional nearest points methods use all the samples in an image set to construct a single convex or affine hull model for classification. However, strong artificial features and noisy data may be generated from combinations of training samples when significant intra-class variations and/or noise occur in the image set. Existing multi-model approaches extract local models by clustering each image set individually only once, with fixed clusters used for matching with various image sets. This may not be optimal for discrimination, as undesirable environmental conditions (eg. illumination and pose variations) may result in the two closest clusters representing different characteristics of an object (eg. frontal face being compared to non-frontal face). To address the above problem, we propose a novel approach to enhance nearest points based methods by integrating affine/convex hull classification with an adapted multi-model approach. We first extract multiple local convex hulls from a query image set via maximum margin clustering to diminish the artificial variations and constrain the noise in local convex hulls. We then propose adaptive reference clustering (ARC) to constrain the clustering of each gallery image set by forcing the clusters to have resemblance to the clusters in the query image set. By applying ARC, noisy clusters in the query set can be discarded. Experiments on Honda, MoBo and ETH-80 datasets show that the proposed method outperforms single model approaches and other recent techniques, such as Sparse Approximated Nearest Points, Mutual Subspace Method and Manifold Discriminant Analysis.

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This paper presents a motion control system for tracking of attitude and speed of an underactuated slender-hull unmanned underwater vehicle. The feedback control strategy is developed using the Port-Hamiltonian theory. By shaping of the target dynamics (desired dynamic response in closed loop) with particular attention to the target mass matrix, the influence of the unactuated dynamics on the controlled system is suppressed. This results in achievable dynamics independent of stable uncontrolled states. Throughout the design, the insight of the physical phenomena involved is used to propose the desired target dynamics. Integral action is added to the system for robustness and to reject steady disturbances. This is achieved via a change of coordinates that result in input-to-state stable (ISS) target dynamics. As a final step in the design, an anti-windup scheme is implemented to account for limited actuator capacity, namely saturation. The performance of the design is demonstrated through simulation with a high-fidelity model.