645 resultados para Artificial lift method


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This monograph provides an overview of recruitment learning approaches from a computational perspective. Recruitment learning is a unique machine learning technique that: (1) explains the physical or functional acquisition of new neurons in sparsely connected networks as a biologically plausible neural network method; (2) facilitates the acquisition of new knowledge to build and extend knowledge bases and ontologies as an artificial intelligence technique; (3) allows learning by use of background knowledge and a limited number of observations, consistent with psychological theory.

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The need to attract and retain a high calibre cadre of public servants today has resulted in a renaissance of interest in public service motivation (PSM) within public management literature. This article outlines a study of PSM with graduate employees within an Australian public sector. The study extends our understanding of PSM by adopting a longitudinal, mixed method design, including surveys and individual interviews, to consider the effects of socialisation on levels of PSM. Results show an organisation's mission and values do not affect individual PSM while work type and communication style is vital and organisational socialisation can provide a negative influence.

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We recorded echolocation calls from 14 sympatric species of bat in Britain. Once digitised, one temporal and four spectral features were measured from each call. The frequency-time course of each call was approximated by fitting eight mathematical functions, and the goodness of fit, represented by the mean-squared error, was calculated. Measurements were taken using an automated process that extracted a single call from background noise and measured all variables without intervention. Two species of Rhinolophus were easily identified from call duration and spectral measurements. For the remaining 12 species, discriminant function analysis and multilayer back-propagation perceptrons were used to classify calls to species level. Analyses were carried out with and without the inclusion of curve-fitting data to evaluate its usefulness in distinguishing among species. Discriminant function analysis achieved an overall correct classification rate of 79% with curve-fitting data included, while an artificial neural network achieved 87%. The removal of curve-fitting data improved the performance of the discriminant function analysis by 2 %, while the performance of a perceptron decreased by 2 %. However, an increase in correct identification rates when curve-fitting information was included was not found for all species. The use of a hierarchical classification system, whereby calls were first classified to genus level and then to species level, had little effect on correct classification rates by discriminant function analysis but did improve rates achieved by perceptrons. This is the first published study to use artificial neural networks to classify the echolocation calls of bats to species level. Our findings are discussed in terms of recent advances in recording and analysis technologies, and are related to factors causing convergence and divergence of echolocation call design in bats.

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There is much for non-Indigenous researchers to consider when researching in Indigenous contexts. This paper is a story of discovery for two researchers working on a project with the Indigenous Sports Program section of the Australian Sports Commission. It documents the slow, meticulous and sometimes clumsy steps taken to gain access to communities and conduct research guided by a social justice ethic. The research was successful in that eventually it was possible to develop the trust of individuals and some of the Indigenous communities more broadly, so that information could be gathered and given within the context of shared understandings and mutual interest. However, it is the turbulent journey, filled as it is, with latent tendencies, privileged assumptions and eventually reflexive readings of the data, which remains the focus of this paper. Tentative recommendations are offered to those wishing to advance this politically and epistemologically challenging approach to culturally based research

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Due to their unobtrusive nature, vision-based approaches to tracking sports players have been preferred over wearable sensors as they do not require the players to be instrumented for each match. Unfortunately however, due to the heavy occlusion between players, variation in resolution and pose, in addition to fluctuating illumination conditions, tracking players continuously is still an unsolved vision problem. For tasks like clustering and retrieval, having noisy data (i.e. missing and false player detections) is problematic as it generates discontinuities in the input data stream. One method of circumventing this issue is to use an occupancy map, where the field is discretised into a series of zones and a count of player detections in each zone is obtained. A series of frames can then be concatenated to represent a set-play or example of team behaviour. A problem with this approach though is that the compressibility is low (i.e. the variability in the feature space is incredibly high). In this paper, we propose the use of a bilinear spatiotemporal basis model using a role representation to clean-up the noisy detections which operates in a low-dimensional space. To evaluate our approach, we used a fully instrumented field-hockey pitch with 8 fixed high-definition (HD) cameras and evaluated our approach on approximately 200,000 frames of data from a state-of-the-art real-time player detector and compare it to manually labeled data.

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In this chapter, I consider the efficacy of creative practice as a research method, concentrating specifically on its applications in the performing arts, using one of my own recent projects, The Ex/centric Fixations Project (2009), as an example.

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This invention concerns the control of rotating excavation machinery, for instance to avoid collisions with obstacles. In a first aspect the invention is a control system for autonomous path planning in excavation machinery, comprising: A map generation subsystem to receive data from an array of disparate and complementary sensors to generate a 3-Dimensional digital terrain and obstacle map referenced to a coordinate frame related to the machine's geometry, during normal operation of the machine. An obstacle detection subsystem to find and identify obstacles in the digital terrain and obstacle map, and then to refine the map by identifying exclusion zones that are within reach of the machine during operation. A collision detection subsystem that uses knowledge of the machine's position and movements, as well as the digital terrain and obstacle map, to identify and predict possible collisions with itself or other obstacles, and then uses a forward motion planner to predict collisions in a planned path. And, a path planning subsystem that uses information from the other subsystems to vary planned paths to avoid obstacles and collisions. In other aspects the invention is excavation machinery including the control system; a method for control of excavation machinery; and firmware and software versions of the control system.

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Porosity is one of the key parameters of the macroscopic structure of porous media, generally defined as the ratio of the free spaces occupied (by the volume of air) within the material to the total volume of the material. Porosity is determined by measuring skeletal volume and the envelope volume. Solid displacement method is one of the inexpensive and easy methods to determine the envelope volume of a sample with an irregular shape. In this method, generally glass beads are used as a solid due to their uniform size, compactness and fluidity properties. The smaller size of the glass beads means that they enter into the open pores which have a larger diameter than the glass beads. Although extensive research has been carried out on porosity determination using displacement method, no study exists which adequately reports micro-level observation of the sample during measurement. This study set out with the aim of assessing the accuracy of solid displacement method of bulk density measurement of dried foods by micro-level observation. Solid displacement method of porosity determination was conducted using a cylindrical vial (cylindrical plastic container) and 57 µm glass beads in order to measure the bulk density of apple slices at different moisture contents. A scanning electron microscope (SEM), a profilometer and ImageJ software were used to investigate the penetration of glass beads into the surface pores during the determination of the porosity of dried food. A helium pycnometer was used to measure the particle density of the sample. Results show that a significant number of pores were large enough to allow the glass beads to enter into the pores, thereby causing some erroneous results. It was also found that coating the dried sample with appropriate coating material prior to measurement can resolve this problem.

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In the health care industry, Job Satisfaction (JS) is linked with work performance, psychological well-being and employee turnover. Although research into JS among health professionals has a long history worldwide, there has been very little analysis in Vietnam. No study has addressed JS of preventive medicine workers in Vietnam, and there is no reliable and valid instrument in Vietnamese language and context for evaluation of JS in this group. This project was conducted to fill these gaps. The findings contribute evidence regarding factors that influence JS in this sector of the health industry that should be applied to personnel management policies and practices in Vietnam.

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This paper relates to the importance of impact of the chosen bottle-point method when conducting ion exchange equilibria experiments. As an illustration, potassium ion exchange with strong acid cation resin was investigated due to its relevance to the treatment of various industrial effluents and groundwater. The “constant mass” bottle-point method was shown to be problematic in that depending upon the resin mass used the equilibrium isotherm profiles were different. Indeed, application of common equilibrium isotherm models revealed that the optimal fit could be with either the Freundlich or Temkin equations, depending upon the conditions employed. It could be inferred that the resin surface was heterogeneous in character, but precise conclusions regarding the variation in the heat of sorption were not possible. Estimation of the maximum potassium loading was also inconsistent when employing the “constant mass” method. The “constant concentration” bottle-point method illustrated that the Freundlich model was a good representation of the exchange process. The isotherms recorded were relatively consistent when compared to the “constant mass” approach. Unification of all the equilibrium isotherm data acquired was achieved by use of the Langmuir Vageler expression. The maximum loading of potassium ions was predicted to be at least 116.5 g/kg resin.

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Any government deciding to invoke widespread change in its higher education sector through implementation of new policies impacts on every institution and all staff and students, often in both the time taken up and the heightened emotions caused. The central phenomenon that this study addresses is the process and consequences of policy changes in higher education in Australia. The aim of this article is to record the research design through the perspective (evaluation research), theoretical framework (program evaluation) and methods (content analysis, descriptive statistical analysis and bibliometric analysis) applied to the investigation of the 2003 federal government higher education reform package. This approach allows both the intended and unintended consequences arising from the policy implementation of three national initiatives focused on learning and teaching in higher education in Australia to surface. As a result, this program evaluation, also known in some disciplines as policy implementation analysis, will demonstrate the applicability of illuminative evaluation as a methodology and reinforce how program evaluation will assist and advise future government reform and policy implementation, and will serve as a legacy for future evaluative research.Any government deciding to invoke widespread change in its higher education sector through implementation of new policies impacts on every institution and all staff and students, often in both the time taken up and the heightened emotions caused. The central phenomenon that this study addresses is the process and consequences of policy changes in higher education in Australia. The aim of this article is to record the research design through the perspective (evaluation research), theoretical framework (program evaluation) and methods (content analysis, descriptive statistical analysis and bibliometric analysis) applied to the investigation of the 2003 federal government higher education reform package. This approach allows both the intended and unintended consequences arising from the policy implementation of three national initiatives focused on learning and teaching in higher education in Australia to surface. As a result, this program evaluation, also known in some disciplines as policy implementation analysis, will demonstrate the applicability of illuminative evaluation as a methodology and reinforce how program evaluation will assist and advise future government reform and policy implementation, and will serve as a legacy for future evaluative research.

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This paper presents a new metric, which we call the lighting variance ratio, for quantifying descriptors in terms of their variance to illumination changes. In many applications it is desirable to have descriptors that are robust to changes in illumination, especially in outdoor environments. The lighting variance ratio is useful for comparing descriptors and determining if a descriptor is lighting invariant enough for a given environment. The metric is analysed across a number of datasets, cameras and descriptors. The results show that the upright SIFT descriptor is typically the most lighting invariant descriptor.

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Exogenous adenosine causes a monophasic dilation of the coronary vessels in paced, perfused rat heart preparations. Because levels of endogenous adenosine in paced hearts may mask the presence of high potency adenosine receptors, we have developed a method to measure coronary vascular responses in a potassium-arrested heart. Hearts from adult male, Wistar rats were perfused at a constant flow rate of 10 mL/min in the nonrecirculating, Langendorff mode, using Krebs-Henseleit buffer. After 30 min, coronary perfusion pressure was 44 +/- 1 mmHg (mean +/- SEM). Hearts were then perfused with a modified Krebs-Henseleit buffer containing 35 mM potassium. Coronary perfusion pressure increased by 84 +/- 3 mmHg. Adenosine-induced reductions in coronary perfusion pressure were expressed as a percentage of the maximal increase in pressure produced by modified Krebs-Henseleit buffer from the equilibration level. A concentration-response curve for adenosine (n = 6) was biphasic and best described by the presence of two adenosine receptors, with negative log EC50 values of 8.8 +/- 0.3 and 4.3 +/- 0.1, representing 29 +/- 3 and 71 +/- 3%, respectively, of the observed response. Interstitial adenosine sampled by microdialysis during potassium arrest was 25% of the concentration found in paced hearts. Endogenous adenosine in nonarrested hearts may obscure the biphasic response of the coronary vessels to adenosine.

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This paper aims to develop a meshless approach based on the Point Interpolation Method (PIM) for numerical simulation of a space fractional diffusion equation. Two fully-discrete schemes for the one-dimensional space fractional diffusion equation are obtained by using the PIM and the strong-forms of the space diffusion equation. Numerical examples with different nodal distributions are studied to validate and investigate the accuracy and efficiency of the newly developed meshless approach.

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This thesis develops a novel approach to robot control that learns to account for a robot's dynamic complexities while executing various control tasks using inspiration from biological sensorimotor control and machine learning. A robot that can learn its own control system can account for complex situations and adapt to changes in control conditions to maximise its performance and reliability in the real world. This research has developed two novel learning methods, with the aim of solving issues with learning control of non-rigid robots that incorporate additional dynamic complexities. The new learning control system was evaluated on a real three degree-of-freedom elastic joint robot arm with a number of experiments: initially validating the learning method and testing its ability to generalise to new tasks, then evaluating the system during a learning control task requiring continuous online model adaptation.