263 resultados para COMMUTATIVE AUTOMORPHIC LOOP


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We conducted an in-situ X-ray micro-computed tomography heating experiment at the Advanced Photon Source (USA) to dehydrate an unconfined 2.3 mm diameter cylinder of Volterra Gypsum. We used a purpose-built X-ray transparent furnace to heat the sample to 388 K for a total of 310 min to acquire a three-dimensional time-series tomography dataset comprising nine time steps. The voxel size of 2.2 μm3 proved sufficient to pinpoint reaction initiation and the organization of drainage architecture in space and time. We observed that dehydration commences across a narrow front, which propagates from the margins to the centre of the sample in more than four hours. The advance of this front can be fitted with a square-root function, implying that the initiation of the reaction in the sample can be described as a diffusion process. Novel parallelized computer codes allow quantifying the geometry of the porosity and the drainage architecture from the very large tomographic datasets (20483 voxels) in unprecedented detail. We determined position, volume, shape and orientation of each resolvable pore and tracked these properties over the duration of the experiment. We found that the pore-size distribution follows a power law. Pores tend to be anisotropic but rarely crack-shaped and have a preferred orientation, likely controlled by a pre-existing fabric in the sample. With on-going dehydration, pores coalesce into a single interconnected pore cluster that is connected to the surface of the sample cylinder and provides an effective drainage pathway. Our observations can be summarized in a model in which gypsum is stabilized by thermal expansion stresses and locally increased pore fluid pressures until the dehydration front approaches to within about 100 μm. Then, the internal stresses are released and dehydration happens efficiently, resulting in new pore space. Pressure release, the production of pores and the advance of the front are coupled in a feedback loop.

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The ability to accurately predict the remaining useful life of machine components is critical for machine continuous operation, and can also improve productivity and enhance system safety. In condition-based maintenance (CBM), maintenance is performed based on information collected through condition monitoring and an assessment of the machine health. Effective diagnostics and prognostics are important aspects of CBM for maintenance engineers to schedule a repair and to acquire replacement components before the components actually fail. All machine components are subjected to degradation processes in real environments and they have certain failure characteristics which can be related to the operating conditions. This paper describes a technique for accurate assessment of the remnant life of machines based on health state probability estimation and involving historical knowledge embedded in the closed loop diagnostics and prognostics systems. The technique uses a Support Vector Machine (SVM) classifier as a tool for estimating health state probability of machine degradation, which can affect the accuracy of prediction. To validate the feasibility of the proposed model, real life historical data from bearings of High Pressure Liquefied Natural Gas (HP-LNG) pumps were analysed and used to obtain the optimal prediction of remaining useful life. The results obtained were very encouraging and showed that the proposed prognostic system based on health state probability estimation has the potential to be used as an estimation tool for remnant life prediction in industrial machinery.

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Elaborated Intrusion theory (EI theory; Kavanagh, Andrade, & May, 2005) posits two main cognitive components in craving: associative processes that lead to intrusive thoughts about the craved substance or activity, and elaborative processes supporting mental imagery of the substance or activity. We used a novel visuospatial task to test the hypothesis that visual imagery plays a key role in craving. Experiment 1 showed that spending 10 min constructing shapes from modeling clay (plasticine) reduced participants' craving for chocolate compared with spending 10 min 'letting your mind wander'. Increasing the load on verbal working memory using a mental arithmetic task (counting backwards by threes) did not reduce craving further. Experiment 2 compared effects on craving of a simpler verbal task (counting by ones) and clay modeling. Clay modeling reduced overall craving strength and strength of craving imagery, and reduced the frequency of thoughts about chocolate. The results are consistent with EI theory, showing that craving is reduced by loading the visuospatial sketchpad of working memory but not by loading the phonological loop. Clay modeling might be a useful self-help tool to help manage craving for chocolate, snacks and other foods.

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Background Failure to convey time-critical information to team members during surgery diminishes members’ perception of the dynamic information relevant to their task, and compromises shared situational awareness. This research reports the dialog around clinical decisions made by team members in the time-pressured and high-risk context of surgery, and the impact of these communications on shared situational awareness. Methods Fieldwork methods were used to capture the dynamic integration of individual and situational elements in surgery that provided the backdrop for clinical decisions. Nineteen semi structured interviews were performed with 24 participants from anaesthesia, surgery, and nursing in the operating rooms of a large metropolitan hospital in Queensland, Australia. Thematic analysis was used. Results: The domain “coordinating decisions in surgery” was generated from textual data. Within this domain, three themes illustrated the dialog of clinical decisions, i.e., synchronizing and strategizing actions, sharing local knowledge, and planning contingency decisions based on priority. Conclusion Strategies used to convey decisions that enhanced shared situational awareness included the use of “self-talk”, closed-loop communications, and “overhearing” conversations that occurred at the operating table. Behaviours’ that compromised a team’s shared situational awareness included tunnelling and fixating on one aspect of the situation.

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The challenge of persistent appearance-based navigation and mapping is to develop an autonomous robotic vision system that can simultaneously localize, map and navigate over the lifetime of the robot. However, the computation time and memory requirements of current appearance-based methods typically scale not only with the size of the environment but also with the operation time of the platform; also, repeated revisits to locations will develop multiple competing representations which reduce recall performance. In this paper we present a solution to the persistent localization, mapping and global path planning problem in the context of a delivery robot in an office environment over a one-week period. Using a graphical appearance-based SLAM algorithm, CAT-Graph, we demonstrate constant time and memory loop closure detection with minimal degradation during repeated revisits to locations, along with topological path planning that improves over time without using a global metric representation. We compare the localization performance of CAT-Graph to openFABMAP, an appearance-only SLAM algorithm, and the path planning performance to occupancy-grid based metric SLAM. We discuss the limitations of the algorithm with regard to environment change over time and illustrate how the topological graph representation can be coupled with local movement behaviors for persistent autonomous robot navigation.

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A new control method for battery storage to maintain acceptable voltage profile in autonomous microgrids is proposed in this article. The proposed battery control ensures that the bus voltages in the microgrid are maintained during disturbances such as load change, loss of micro-sources, or distributed generations hitting power limit. Unlike the conventional storage control based on local measurements, the proposed method is based on an advanced control technique, where the reference power is determined based on the voltage drop profile at the battery bus. An artificial neural network based controller is used to determine the reference power needed for the battery to hold the microgrid voltage within regulation limits. The pattern of drop in the local bus voltage during power imbalance is used to train the controller off-line. During normal operation, the battery floats with the local bus voltage without any power injection. The battery is charged or discharged during the transients with a high gain feedback loop. Depending on the rate of voltage fall, it is switched to power control mode to inject the reference power determined by the proposed controller. After a defined time period, the battery power injection is reduced to zero using slow reverse-droop characteristics, ensuring a slow rate of increase in power demand from the other distributed generations. The proposed control method is simulated for various operating conditions in a microgrid with both inertial and converter interfaced sources. The proposed battery control provides a quick load pick up and smooth load sharing with the other micro-sources in a disturbance. With various disturbances, maximum voltage drop over 8% with conventional energy storage is reduced within 2.5% with the proposed control method.

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The INEX 2011 Relevance Feedback track offered a refined approach to the evaluation of Focused Relevance Feedback algorithms through simulated exhaustive user feedback. Run in largely identical fashion to the Relevance Feedback track in INEX 2010[2], we simulated a user-in-the loop by re-using the assessments of ad-hoc retrieval obtained from real users who assess focused ad-hoc retrieval submissions. We present the evaluation methodology, its implementation, and experimental results obtained for four submissions from two participating organisations. As the task and evaluation methods did not change between INEX 2010 and now, explanations of these details from the INEX 2010 version of the track have been repeated verbatim where appropriate.

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Changing environments present a number of challenges to mobile robots, one of the most significant being mapping and localisation. This problem is particularly significant in vision-based systems where illumination and weather changes can cause feature-based techniques to fail. In many applications only sections of an environment undergo extreme perceptual change. Some range-based sensor mapping approaches exploit this property by combining occasional place recognition with the assumption that odometry is accurate over short periods of time. In this paper, we develop this idea in the visual domain, by using occasional vision-driven loop closures to infer loop closures in nearby locations where visual recognition is difficult due to extreme change. We demonstrate successful map creation in an environment in which change is significant but constrained to one area, where both the vanilla CAT-Graph and a Sum of Absolute Differences matcher fails, use the described techniques to link dissimilar images from matching locations, and test the robustness of the system against false inferences.

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A firm, as a dynamic, evolving, and quasi-autonomous system of knowledge production and application, develops knowledge management capability (KMC) through strategic learning in order to sustain competitive advantages in a dynamic environment. Knowledge governance mechanisms and knowledge processes connect and interact with each other forming learning mechanisms, which carry out double loop learning that drives genesis and evolution of KMC to modify operating routines that effect desired performance. This paper reports a study that was carried out within a context of construction contractors, a type of project-based firms, operating within the dynamic Hong Kong construction market. A multiple-case design was used to incorporate evidence from the literature and interviews, with the help of system dynamics modeling, to visualize the evolution of KMC. The study demonstrates the feasibility to visualize how a firm's KMC matches its operating environment over time. The findings imply that knowledge management (KM) applications can be better planned and controlled through evaluation of KM performance over time from a capability perspective.

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This tutorial is designed to assist users who wish to use the LCD screen on the Spartan-3E board. In this tutorial, the PicoBlaze microcontroller is used to control the LCD. The tutorial is organised into three Parts. In Part A, code is written to display the message "Hello World" on the LCD. Part B demonstrates how to define and display custom characters. Finally, Part C shows how the display can be shifted and flashed. Shifting is done by using a delay in the main PicoBlaze program loop, while flashing is done using the PicoBlaze interrupt. The slider switches can be used to select the shifting direction, and to turn shifting and flashing on and off.

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The UN Decade of Action outlines five pillars of activity within a safe system framework to achieve the goal of slowing and then reversing the global growth in road traffic fatalities, especially in low-income and middle-income countries. The first four pillars - road safety management, safer roads and mobility, safer vehicles, and safer road users – have a strong focus on prevention of road traffic crashes and mitigation of energy exchange when a crash occurs. The fifth pillar – post-crash response – is far more specific, focusing only on crash victims in the event of a safe system failure. The victims appear to be relevant to the first four pillars only insofar as their numbers can be used to evaluate the success of road safety programs and identify the target groups and contributing factors. This paper argues that a better understanding of the lived experience of long term disability from traffic crashes has the potential to provide a feedback loop from the fifth pillar to the first. Research conducted in Thailand with male crash victims with spinal injury demonstrates that patterns of attribution and social and cultural factors have important implications for road safety management and for interventions aimed at influencing behaviour. In addition, the mobility constraints experienced by people with long term disability can point to systemic issues that might otherwise go unnoticed. The UN Decade of Action can benefit from a more thorough exploration of the experiences and circumstances of people with long term disability as the result of a road traffic crash. Rather than being evidence of the failure of the safe system, they can inform the development of more effective road safety management on low-income and middle-income countries.

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Australia has many isolated communities that require human services provided by qualified professionals. Maintaining a viable and equitable spread of such educational capital across space as a public good is a challenge. Reports investigating this problem repeatedly point to ‘family issues’ such as limited options for children’s education, and limited access to ongoing professional development, as deterrents for rural/remote employment despite lucrative incentive schemes. This paper draws on semi-structured interviews with 30 parents of school-aged children, who work as doctors, nurses, teachers and police in six rural/remote towns in Queensland. We asked them how their family units reconcile career opportunities with educational strategy for family members over time and space. This paper considers these issues as a sociology of education problem in a context of educational marketisation and spiralling credentialism. This paper offers the concept of ‘mobius markets’ to capture the cyclical and intergenerational process underway in middle class professional families of investing in educational capitals, maintaining or maximising their value and profiting from them. A mobius strip is the topological anomaly of a single loop with one twist in it, whereby the loop becomes one continuous surface, not the double-sided shape it appears to be. This project is interested in how the middle class professional family is similarly on a constant circuit, investing in educational capitals, upgrading their currency/value, and profiting from them. This elaborated sense of educational markets extends the more usual sociological focus on school choice.

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The primary objective of this study is to develop a robust queue estimation algorithm for motorway on-ramps. Real-time queue information is the most vital input for a dynamic queue management that can treat long queues on metered on-ramps more sophistically. The proposed algorithm is developed based on the Kalman filter framework. The fundamental conservation model is used to estimate the system state (queue size) with the flow-in and flow-out measurements. This projection results are updated with the measurement equation using the time occupancies from mid-link and link-entrance loop detectors. This study also proposes a novel single point correction method. This method resets the estimated system state to eliminate the counting errors that accumulate over time. In the performance evaluation, the proposed algorithm demonstrated accurate and reliable performances and consistently outperformed the benchmarked Single Occupancy Kalman filter (SOKF) method. The improvements over SOKF are 62% and 63% in average in terms of the estimation accuracy (MAE) and reliability (RMSE), respectively. The benefit of the innovative concepts of the algorithm is well justified by the improved estimation performance in the congested ramp traffic conditions where long queues may significantly compromise the benchmark algorithm’s performance.

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In condition-based maintenance (CBM), effective diagnostic and prognostic tools are essential for maintenance engineers to identify imminent fault and predict the remaining useful life before the components finally fail. This enables remedial actions to be taken in advance and reschedule of production if necessary. All machine components are subjected to degradation processes in real environments and they have certain failure characteristics which can be related to the operating conditions. This paper describes a technique for accurate assessment of the remnant life of bearings based on health state probability estimation and historical knowledge embedded in the closed loop diagnostics and prognostics system. The technique uses the Support Vector Machine (SVM) classifier as a tool for estimating health state probability of machine degradation process to provide long term prediction. To validate the feasibility of the proposed model, real life fault historical data from bearings of High Pressure-Liquefied Natural Gas (HP-LNG) pumps were analysed and used to obtain the optimal prediction of remaining useful life (RUL). The results obtained were very encouraging and showed that the proposed prognosis system based on health state probability estimation has the potential to be used as an estimation tool for remnant life prediction in industrial machinery.

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The feral pig, Sus scrofa, is a widespread and abundant invasive species in Australia. Feral pigs pose a significant threat to the environment, agricultural industry, and human health, and in far north Queensland they endanger World Heritage values of the Wet Tropics. Historical records document the first introduction of domestic pigs into Australia via European settlers in 1788 and subsequent introductions from Asia from 1827 onwards. Since this time, domestic pigs have been accidentally and deliberately released into the wild and significant feral pig populations have become established, resulting in the declaration of this species as a class 2 pest in Queensland. The overall objective of this study was to assess the population genetic structure of feral pigs in far north Queensland, in particular to enable delineation of demographically independent management units. The identification of ecologically meaningful management units using molecular techniques can assist in targeting feral pig control to bring about effective long-term management. Molecular genetic analysis was undertaken on 434 feral pigs from 35 localities between Tully and Innisfail. Seven polymorphic and unlinked microsatellite loci were screened and fixation indices (FST and analogues) and Bayesian clustering methods were used to identify population structure and management units in the study area. Sequencing of the hyper-variable mitochondrial control region (D-loop) of 35 feral pigs was also examined to identify pig ancestry. Three management units were identified in the study at a scale of 25 to 35 km. Even with the strong pattern of genetic structure identified in the study area, some evidence of long distance dispersal and/or translocation was found as a small number of individuals exhibited ancestry from a management unit outside of which they were sampled. Overall, gene flow in the study area was found to be influenced by environmental features such as topography and land use, but no distinct or obvious natural or anthropogenic geographic barriers were identified. Furthermore, strong evidence was found for non-random mating between pigs of European and Asian breeds indicating that feral pig ancestry influences their population genetic structure. Phylogenetic analysis revealed two distinct mitochondrial DNA clades, representing Asian domestic pig breeds and European breeds. A significant finding was that pigs of Asian origin living in Innisfail and south Tully were not mating randomly with European breed pigs populating the nearby Mission Beach area. Feral pig control should be implemented in each of the management units identified in this study. The control should be coordinated across properties within each management unit to prevent re-colonisation from adjacent localities. The adjacent rainforest and National Park Estates, as well as the rainforest-crop boundary should be included in a simultaneous control operation for greater success.