445 resultados para ENERGY-CONSTRAINED ASSIMILATION

em Queensland University of Technology - ePrints Archive


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This paper is concerned with how a localised and energy-constrained robot can maximise its time in the field by taking paths and tours that minimise its energy expenditure. A significant component of a robot's energy is expended on mobility and is a function of terrain traversability. We estimate traversability online from data sensed by the robot as it moves, and use this to generate maps, explore and ultimately converge on minimum energy tours of the environment. We provide results of detailed simulations and parameter studies that show the efficacy of this approach for a robot moving over terrain with unknown traversability as well as a number of a priori unknown hard obstacles.

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This paper investigates communication protocols for relaying sensor data from animal tracking applications back to base stations. While Delay Tolerant Networks (DTNs) are well suited to such challenging environments, most existing protocols do not consider the available energy that is particularly important when tracking devices can harvest energy. This limits both the network lifetime and delivery probability in energy-constrained applications to the point when routing performance becomes worse than using no routing at all. Our work shows that substantial improvement in data yields can be achieved through simple yet efficient energy-aware strategies. Conceptually, there is need for balancing the energy spent on sensing, data mulling, and delivery of direct packets to destination. We use empirical traces collected in a flying fox (fruit bat) tracking project and show that simple threshold-based energy-aware strategies yield up to 20% higher delivery rates. Furthermore, these results generalize well for a wide range of operating conditions.

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This research addresses efficient use of the available energy in resource constrained mobile sensor nodes to prevent early depletion of the battery and maximize the packet delivery rate. This research contributes two energy-aware enhancement strategies to improve the network lifetime and delivery probability for energy constrained applications in the delay-tolerant networking environment.

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The available wind power is stochastic and requires appropriate tools in the OPF model for economic and reliable power system operation. This paper exhibit the OPF formulation with factors involved in the intermittency of wind power. Weibull distribution is adopted to find the stochastic wind speed and power distribution. The reserve requirement is evaluated based on the wind distribution and risk of under/over estimation of the wind power. In addition, the Wind Energy Conversion System (WECS) is represented by Doubly Fed Induction Generator (DFIG) based wind farms. The reactive power capability for DFIG based wind farm is also analyzed. The study is performed on IEEE-30 bus system with wind farm located at different buses and with different wind profiles. Also the reactive power capacity to be installed in the wind farm to maintain a satisfactory voltage profile under the various wind flow scenario is demonstrated.

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The aims of the project were to scope and develop sustainable energy curriculum frameworks for Australian higher education Institutions that meet the needs of Australian and international student graduates and employers, both now and into the near future. The focus was on student centred learning and outcomes and to support graduates with the knowledge, skills and generic attributes required to work in the rapidly expanding sustainable energy industry in Australia and globally. The outputs of the project are designed to be relevant to specialist Sustainable Engineering and Energy Studies programs, as well as conventional engineering, science and humanities and social science programs that have a sustainable energy focus or major.

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In most visual mapping applications suited to Autonomous Underwater Vehicles (AUVs), stereo visual odometry (VO) is rarely utilised as a pose estimator as imagery is typically of very low framerate due to energy conservation and data storage requirements. This adversely affects the robustness of a vision-based pose estimator and its ability to generate a smooth trajectory. This paper presents a novel VO pipeline for low-overlap imagery from an AUV that utilises constrained motion and integrates magnetometer data in a bi-objective bundle adjustment stage to achieve low-drift pose estimates over large trajectories. We analyse the performance of a standard stereo VO algorithm and compare the results to the modified vo algorithm. Results are demonstrated in a virtual environment in addition to low-overlap imagery gathered from an AUV. The modified VO algorithm shows significantly improved pose accuracy and performance over trajectories of more than 300m. In addition, dense 3D meshes generated from the visual odometry pipeline are presented as a qualitative output of the solution.

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This guide is to support institutions in developing and teaching tertiary level programmes for sustainable energy professionals. Ongoing curriculum renewal is more difficult but vital for multidisciplinary courses preparing graduates to work in a specialised rapidly changing field. After more than 15 years of offering tertiary level “sustainable energy” qualifications in Australian Universities there was a clear need to assess how these courses are taught and develop curriculum frameworks to guide Universities designing/redesigning programs and courses to provide graduates with the relevant skills, knowledge and attributes (capabilities) seen by graduates and employers as required to work in this rapidly changing field. This guide presents the sustainable energy curriculum frameworks developed by the “Renewing the sustainable energy curriculum – providing internationally relevant skills for a carbon constrained economy” project, which was conducted over a two-and-a-quarter year period.

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OBJECTIVE: To compare, in patients with cancer and in healthy subjects, measured resting energy expenditure (REE) from traditional indirect calorimetry to a new portable device (MedGem) and predicted REE. DESIGN: Cross-sectional clinical validation study. SETTING: Private radiation oncology centre, Brisbane, Australia. SUBJECTS: Cancer patients (n = 18) and healthy subjects (n = 17) aged 37-86 y, with body mass indices ranging from 18 to 42 kg/m(2). INTERVENTIONS: Oxygen consumption (VO(2)) and REE were measured by VMax229 (VM) and MedGem (MG) indirect calorimeters in random order after a 12-h fast and 30-min rest. REE was also calculated from the MG without adjustment for nitrogen excretion (MGN) and estimated from Harris-Benedict prediction equations. Data were analysed using the Bland and Altman approach, based on a clinically acceptable difference between methods of 5%. RESULTS: The mean bias (MGN-VM) was 10% and limits of agreement were -42 to 21% for cancer patients; mean bias -5% with limits of -45 to 35% for healthy subjects. Less than half of the cancer patients (n = 7, 46.7%) and only a third (n = 5, 33.3%) of healthy subjects had measured REE by MGN within clinically acceptable limits of VM. Predicted REE showed a mean bias (HB-VM) of -5% for cancer patients and 4% for healthy subjects, with limits of agreement of -30 to 20% and -27 to 34%, respectively. CONCLUSIONS: Limits of agreement for the MG and Harris Benedict equations compared to traditional indirect calorimetry were similar but wide, indicating poor clinical accuracy for determining the REE of individual cancer patients and healthy subjects.