77 resultados para Transforms,


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Typical inductive power transfer (IPT) systems employ two power conversion stages to generate a high-frequency primary current from low-frequency utility supply. This paper proposes a matrix-converter-based IPT system, which employs high-speed SiC devices to facilitate the generation of high-frequency current through a single power conversion stage. The proposed matrix converter topology transforms a three-phase low-frequency voltage system to a high-frequency single-phase voltage, which, in turn, powers a series compensated IPT system. A comprehensive mathematical model is developed and power losses are evaluated to investigate the efficiency of the proposed converter topology. Theoretical results are presented with simulations, which are performed in MATLAB/Simulink, in comparison to a conventional two-stage converter. Experimental evident of a prototype IPT system is also presented to demonstrate the applicability of the proposed concept.

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Cellular plasticity is fundamental to embryonic development. The importance of cellular transitions in development is first apparent during gastrulation when the process of epithelial to mesenchymal transition transforms polarized epithelial cells into migratory mesenchymal cells that constitute the embryonic and extraembryonic mesoderm. It is now widely accepted that this developmental pathway is exploited in various disease states, including cancer progression. The loss of epithelial characteristics and the acquisition of a mesenchymal-like migratory phenotype are crucial to the development of invasive carcinoma and metastasis. However, given the morphological similarities between primary tumour and metastatic lesions, it is likely that tumour cells re-activate certain epithelial properties through a mesenchymal to epithelial transition (MET) at the secondary site, although this is yet to be proven. MET is also an essential developmental process and has been extensively studied in kidney organogenesis and somitogenesis. In this review we describe the process of MET, highlight important mediators, and discuss their implication in the context of cancer progression.

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In this article we study the azimuthal shear deformations in a compressible Isotropic elastic material. This class of deformations involves an azimuthal displacement as a function of the radial and axial coordinates. The equilibrium equations are formulated in terms of the Cauchy-Green strain tensors, which form an overdetermined system of partial differential equations for which solutions do not exist in general. By means of a Legendre transformation, necessary and sufficient conditions for the material to support this deformation are obtained explicitly, in the sense that every solution to the azimuthal equilibrium equation will satisfy the remaining two equations. Additionally, we show how these conditions are sufficient to support all currently known deformations that locally reduce to simple shear. These conditions are then expressed both in terms of the invariants of the Cauchy-Green strain and stretch tensors. Several classes of strain energy functions for which this deformation can be supported are studied. For certain boundary conditions, exact solutions to the equilibrium equations are obtained. © 2005 Society for Industrial and Applied Mathematics.

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Typical Inductive Power Transfer (IPT) systems employ two power conversion stages to generate a high frequency current from low frequency utility supply. This paper proposes a matrix converter based IPT system that facilitates the generation of high frequency current through a single power conversion stage. The proposed matrix converter topology transforms a 3-phase low frequency voltage system to a high frequency single phase voltage which in turn powers a series compensated IPT system. A comprehensive mathematical model is developed to investigate the behavior of the proposed IPT topology. Theoretical results are presented in comparison to simulations, which are performed in Matlab/ Simulink, to demonstrate the applicability of the proposed concept and the validity of the developed model.

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Taking into consideration of growing energy needs and concern for environmental degradation, clean and inexhaustible energy source, such as solar energy, is receiving greater attention for various applications. The use of solar energy system reduces pollution, waste and has little or no harmful effects on the environment. It is appreciated that this source of energy can be complementary rather than being competitive to conventional energy sources. In order to collect and harness energy from the sun, a solar collector is essential. A solar collector is basically a heat exchanger that transforms solar radiant energy into heat or thermal energy. Improvement of performance is essential for commercial acceptance of their use in such applications. Many studies have been undertaken on the enhancement of thermal performance of solar collectors, using diverse materials of various shapes, dimensions and layouts. In the literature, various collector designs have been proposed and tested with the objective of meeting these requirements [1-8]. Omer et al. [1] found the efficiency of a solar collector of about 70% in a solar assisted heat pump system. Traditional solar collectors are single phase collectors, in which the working fluid is either air or water. Different modifications are suggested and applied to improve the heat transfer between the absorber and working fluid in a collector. These modifications include the use of absorber with fins attached [2,3], corrugated absorber [4,5], matrix type absorber [6], V-groove solar air collector [7]. Karim et al. [8] approached a review of design and construction of three types (flat, vee-grooved, and finned) of air collectors. Two-phase collectors, on the other hand, have significant potential for continuous operation round the clock, when used in conjunction with a compressor, as found in a solar assisted heat-pump cycle.

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This paper critically examines research on consumer attitudes and behavior towards solar photovoltaic (PV) and renewable energy technology in Australia. The uptake of renewable energy technology by residential consumers in Australia in the past decade has transformed the electricity supply and demand paradigm. Thus, this paper reviews Australian research on consumer behavior, understanding and choices in order to identify gaps in knowledge. As the role of the consumer transforms there is a critical need to understand the ways consumers may respond to future energy policies to mitigate unforeseen negative social and economic consequence of programs designed to achieve positive environmental outcomes.

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Gaining invariance to camera and illumination variations has been a well investigated topic in Active Appearance Model (AAM) fitting literature. The major problem lies in the inability of the appearance parameters of the AAM to generalize to unseen conditions. An attractive approach for gaining invariance is to fit an AAM to a multiple filter response (e.g. Gabor) representation of the input image. Naively applying this concept with a traditional AAM is computationally prohibitive, especially as the number of filter responses increase. In this paper, we present a computationally efficient AAM fitting algorithm based on the Lucas-Kanade (LK) algorithm posed in the Fourier domain that affords invariance to both expression and illumination. We refer to this as a Fourier AAM (FAAM), and show that this method gives substantial improvement in person specific AAM fitting performance over traditional AAM fitting methods.

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In this paper we propose the hybrid use of illuminant invariant and RGB images to perform image classification of urban scenes despite challenging variation in lighting conditions. Coping with lighting change (and the shadows thereby invoked) is a non-negotiable requirement for long term autonomy using vision. One aspect of this is the ability to reliably classify scene components in the presence of marked and often sudden changes in lighting. This is the focus of this paper. Posed with the task of classifying all parts in a scene from a full colour image, we propose that lighting invariant transforms can reduce the variability of the scene, resulting in a more reliable classification. We leverage the ideas of “data transfer” for classification, beginning with full colour images for obtaining candidate scene-level matches using global image descriptors. This is commonly followed by superpixellevel matching with local features. However, we show that if the RGB images are subjected to an illuminant invariant transform before computing the superpixel-level features, classification is significantly more robust to scene illumination effects. The approach is evaluated using three datasets. The first being our own dataset and the second being the KITTI dataset using manually generated ground truth for quantitative analysis. We qualitatively evaluate the method on a third custom dataset over a 750m trajectory.

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Closed WS2 nanoboxes were formed by topotactic sulfidization of a WO3/WO3 center dot 1/3H(2)O intergrowth precursor. Automated diffraction tomography was used to elucidate the growth mechanism of these unconventional hollow structures. By partial conversion and structural analysis of the products, each of them representing a snapshot of the reaction at a given point in time, the overall reaction can be broken down into a cascade of individual steps and each of them identified with a basic mechanism. During the initial step of sulfidization WO3 center dot 1/3H(2)O transforms into hexagonal WO3 whose surface allows for the epitaxial induction of WS2. The initially formed platelets of WS2 exhibit a preferred orientation with respect to the nanorod surface. In the final step individual layers of WS2 coalesce to form closed shells. In essence, a cascade of several topotactic reactions leads to epitactic induction and formation of closed rectangular hollow boxes made up from hexagonal layers.

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This paper presents an efficient algorithm for optimizing the operation of battery storage in a low voltage distribution network with a high penetration of PV generation. A predictive control solution is presented that uses wavelet neural networks to predict the load and PV generation at hourly intervals for twelve hours into the future. The load and generation forecast, and the previous twelve hours of load and generation history, is used to assemble load profile. A diurnal charging profile can be compactly represented by a vector of Fourier coefficients allowing a direct search optimization algorithm to be applied. The optimal profile is updated hourly allowing the state of charge profile to respond to changing forecasts in load.

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Objectives Directly measuring disease incidence in a population is difficult and not feasible to do routinely. We describe the development and application of a new method of estimating at a population level the number of incident genital chlamydia infections, and the corresponding incidence rates, by age and sex using routine surveillance data. Methods A Bayesian statistical approach was developed to calibrate the parameters of a decision-pathway tree against national data on numbers of notifications and tests conducted (2001-2013). Independent beta probability density functions were adopted for priors on the time-independent parameters; the shape parameters of these beta distributions were chosen to match prior estimates sourced from peer-reviewed literature or expert opinion. To best facilitate the calibration, multivariate Gaussian priors on (the logistic transforms of) the time-dependent parameters were adopted, using the Matérn covariance function to favour changes over consecutive years and across adjacent age cohorts. The model outcomes were validated by comparing them with other independent empirical epidemiological measures i.e. prevalence and incidence as reported by other studies. Results Model-based estimates suggest that the total number of people acquiring chlamydia per year in Australia has increased by ~120% over 12 years. Nationally, an estimated 356,000 people acquired chlamydia in 2013, which is 4.3 times the number of reported diagnoses. This corresponded to a chlamydia annual incidence estimate of 1.54% in 2013, increased from 0.81% in 2001 (~90% increase). Conclusions We developed a statistical method which uses routine surveillance (notifications and testing) data to produce estimates of the extent and trends in chlamydia incidence.

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Structural damage detection using measured dynamic data for pattern recognition is a promising approach. These pattern recognition techniques utilize artificial neural networks and genetic algorithm to match pattern features. In this study, an artificial neural network–based damage detection method using frequency response functions is presented, which can effectively detect nonlinear damages for a given level of excitation. The main objective of this article is to present a feasible method for structural vibration–based health monitoring, which reduces the dimension of the initial frequency response function data and transforms it into new damage indices and employs artificial neural network method for detecting different levels of nonlinearity using recognized damage patterns from the proposed algorithm. Experimental data of the three-story bookshelf structure at Los Alamos National Laboratory are used to validate the proposed method. Results showed that the levels of nonlinear damages can be identified precisely by the developed artificial neural networks. Moreover, it is identified that artificial neural networks trained with summation frequency response functions give higher precise damage detection results compared to the accuracy of artificial neural networks trained with individual frequency response functions. The proposed method is therefore a promising tool for structural assessment in a real structure because it shows reliable results with experimental data for nonlinear damage detection which renders the frequency response function–based method convenient for structural health monitoring.

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It is not uncommon to hear a person of interest described by their height, build, and clothing (i.e. type and colour). These semantic descriptions are commonly used by people to describe others, as they are quick to relate and easy to understand. However such queries are not easily utilised within intelligent surveillance systems as they are difficult to transform into a representation that can be searched for automatically in large camera networks. In this paper we propose a novel approach that transforms such a semantic query into an avatar that is searchable within a video stream, and demonstrate state-of-the-art performance for locating a subject in video based on a description.

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It is not uncommon to hear a person of interest described by their height, build, and clothing (i.e. type and colour). These semantic descriptions are commonly used by people to describe others, as they are quick to communicate and easy to understand. However such queries are not easily utilised within intelligent video surveillance systems, as they are difficult to transform into a representation that can be utilised by computer vision algorithms. In this paper we propose a novel approach that transforms such a semantic query into an avatar in the form of a channel representation that is searchable within a video stream. We show how spatial, colour and prior information (person shape) can be incorporated into the channel representation to locate a target using a particle-filter like approach. We demonstrate state-of-the-art performance for locating a subject in video based on a description, achieving a relative performance improvement of 46.7% over the baseline. We also apply this approach to person re-detection, and show that the approach can be used to re-detect a person in a video steam without the use of person detection.

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The last three decades have been difficult for companies and industry. In an increasingly competitive international business climate with shifting national environmental regulations, higher standards are being demanded by the consumer and community groups, not-to-mention the escalating cost of primary resources such as water, steel and minerals. The cause of these pressures is the traditional notion held by business executives and engineers that there is an inherent trade off between eco-efficiency and improving the economic bottom line. However there is significant evidence and examples of best practice to show that there is in fact no trade-off between the environment and the economy if sustainable development through continual improvement is adopted. It is highly possible therefore for companies to make a profitable transition towards sustainable business practice, where along the transition significant business opportunities can be taken advantage of. Companies are by their very nature dynamic, influential and highly capable of adapting to change. Making an organisational transformation to a sustainable business is not outside the capacity of the typical company, who know much of what is needed already to change their activities to satisfy current market demands while achieving competitiveness. However in order to make the transition towards sustainable business practice companies require some key mechanisms such as accurate information on methodologies and opportunities, understanding of the financial and non-financial incentives, permission from stakeholders and shareholders, understanding of the emerging market opportunities, a critical mass of leaders in their sector and demonstrated case studies, and awarding appropriate risk-taking activities undertaken by engineers and CEOs. Satisfying these requirements will adopt an innovative culture within the company that strives for continual improvement and successfully transforms itself to achieve competitiveness in the 21st Century. This paper will summarise the experiences of The Natural Edge Project (TNEP) and its partners in assisting organisations to make a profitable transition towards sustainable business practice through several initiatives. The Natural Advantage of Nations publication provides the critical information required by business leaders and engineers to set the context of sustainable business practice. The Profiting in a Carbon Constrained World report, developed with Natural Capitalism Inc led by Hunter Lovins, summarises the opportunities available to companies to take advantage of the carbon trading market mechanisms such as the Chicago Climate Exchange and European Climate Exchange. The Sustainability Helix then guides the company through the transition by identifying the key tools and methodologies required by companies to reduce environmental loading while dramatically improving resource productivity and achieving competitiveness. Finally, the Engineering Sustainable Solutions Program delivers the key engineering information required by companies and university departments to deliver sustainable engineering solutions. The initiatives are of varying complexity and level of application, however all are designed to provide key staff the critical information required to make a profitable transition towards sustainable business practice. It is then their responsibility to apply and teach their knowledge to the rest of the organisation.