821 resultados para 380303 Computer Perception, Memory and Attention


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This paper describes a methodology for deploying flexible dynamic configuration into embedded systems whilst preserving the reliability advantages of static systems. The methodology is based on the concept of decision points (DP) which are strategically placed to achieve fine-grained distribution of self-management logic to meet application-specific requirements. DP logic can be changed easily, and independently of the host component, enabling self-management behavior to be deferred beyond the point of system deployment. A transparent Dynamic Wrapper mechanism (DW) automatically detects and handles problems arising from the evaluation of self-management logic within each DP and ensures that the dynamic aspects of the system collapse down to statically defined default behavior to ensure safety and correctness despite failures. Dynamic context management contributes to flexibility, and removes the need for design-time binding of context providers and consumers, thus facilitating run-time composition and incremental component upgrade.

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This paper describes ways in which emergence engineering principles can be applied to the development of distributed applications. A distributed solution to the graph-colouring problem is used as a vehicle to illustrate some novel techniques. Each node acts autonomously to colour itself based only on its local view of its neighbourhood, and following a simple set of carefully tuned rules. Randomness breaks symmetry and thus enhances stability. The algorithm has been developed to enable self-configuration in wireless sensor networks, and to reflect real-world configurations the algorithm operates with 3 dimensional topologies (reflecting the propagation of radio waves and the placement of sensors in buildings, bridge structures etc.). The algorithm’s performance is evaluated and results presented. It is shown to be simultaneously highly stable and scalable whilst achieving low convergence times. The use of eavesdropping gives rise to low interaction complexity and high efficiency in terms of the communication overheads.

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This paper presents modelling and design optimization of a microfeeder which, as part of a microassembly system, is used for contactless object delivery. The microfeeder consists of an array of microactuators which are controlled by electrostatic actuation and used for maneuvering outcoming air jet for object hovering and delibery. The airflow behaviour in the microactuator is analysed by means of fluid mechanics and Computational Fluid Dynamics (CFD) simulation from three aspects, theoretical analysis, initial design assessment, and design modifications. The focus is put on the basic types of the microfeeder structure and the effects of structural details to the systematic performance. The structural pattern of the microactuator for forming airflow nozzle is identified and two design plans are proposed as basic structure patterns of pneumatic microactuators. The optimized design numerically shows the ability of delivering objects. This paper analyses the flow distribution pattern in microactuators and points out a way for effective design of pneumatic microfeeder systems. The optimization strategy provided by the present paper has close relevance to the design and manufacture of pneumatic microfeeder systems.

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The narrative of transformative crisis appears in both autobiographical and fictional accounts of individual lives; it typically involves a difficult or traumatic episode and a period of self-questioning out of which a person emerges more able and more emotionally mature than before (Booker, 2005; Erikson, 1968; Tedeschi and Calhoun, 1995). The present study used interviews to elicit 22 narratives about crises experienced between the ages of 25 and 40, and about any developmental transformation and change that surrounded these crises. Analysis revealed a common four-phase process to the crisis episodes, common metaphors and recurrent descriptions of identity metamorphosis, ie. of ‘becoming a new person’. Comparison of these findings with theory on fictional plots shows a clear parallel between the four-phase process of crisis found in the current study and the ‘rebirth’ plot described by Booker (2005). The theoretical significance of these findings and interpretations is discussed.

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Product knowledge support needs are compared in two companies with different production volumes and product complexity. Knowledge support requirements identified include: function, performance data, requirements data, common parts, regulatory guidelines and layout data. A process based data driven knowledge reuse method is evaluated in light of the identified product knowledge needs. The evaluation takes place through developing a pilot case with each company. It is found that the method provides more benefit to the high complexity design domain, in which a significant amount of work takes place at the conceptual design stages, relying on a conceptual product representation. There is not such a clear value proposition in a design environment whose main challenge is layout design and the application of standard parts and features. The method supports the requirement for conceptual product representation but does not fully support a standard parts library.

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Over latest decade, Reverse Logistics (RL) has gained more and more attention from both industry and academia. In the past, most research on RL has been focused on automobile, electronic waste, computer, paper, package and package material. There is very little research and practice on drug recycling. Nevertheless, it is vital important to properly dispose expired drug because of hazardous contain which may harm to people and environment. In China, public awareness of the harmfulness of expired drugs is still very low and very few efforts have been made to recycle drugs. Therefore, this research aims to build up a conceptual framework to indentify factors of influencing drug recycling in China, from scratch borrowing from existing literature and industry practices in other recycling areas. This framework helps in designing reverse logistic (RL) network and also can provide a useful reference tool for policymakers at the local and national level. Furthermore, a primary research is planed to validate the framework and RL network.

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In this paper we clearly demonstrate that changes in oceanic nutrients are a first order factor in determining changes in the primary production of the northwest European continental shelf on time scales of 5–10 yr. We present a series of coupled hydrodynamic ecosystem modelling simulations, using the POLCOMS-ERSEM system. These are forced by both reanalysis data and a single example of a coupled ocean-atmosphere general circulation model (OA-GCM) representative of possible conditions in 2080–2100 under an SRES A1B emissions scenario, along with the corresponding present day control. The OA-GCM forced simulations show a substantial reduction in surface nutrients in the open-ocean regions of the model domain, comparing future and present day time-slices. This arises from a large increase in oceanic stratification. Tracer transport experiments identify a substantial fraction of on-shelf water originates from the open-ocean region to the south of the domain, where this increase is largest, and indeed the on-shelf nutrient and primary production are reduced as this water is transported on-shelf. This relationship is confirmed quantitatively by comparing changes in winter nitrate with total annual nitrate uptake. The reduction in primary production by the reduced nutrient transport is mitigated by on-shelf processes relating to temperature, stratification (length of growing season) and recycling. Regions less exposed to ocean-shelf exchange in this model (Celtic Sea, Irish Sea, English Channel, and Southern North Sea) show a modest increase in primary production (of 5–10%) compared with a decrease of 0–20% in the outer shelf, Central and Northern North Sea. These findings are backed up by a boundary condition perturbation experiment and a simple mixing model.

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The European Skynet Radiometers network (EuroSkyRad or ESR) has been recently established as a research network of European PREDE sun-sky radiometers. Moreover, ESR is federated with SKYNET, an international network of PREDE sun-sky radiometers mostly present in East Asia. In contrast to SKYNET, the European network also integrates users of the CIMEL CE318 sky–sun photometer. Keeping instrumental duality in mind, a set of open source algorithms has been developed consisting of two modules for (1) the retrieval of direct sun products (aerosol optical depth, wavelength exponent and water vapor) from the sun extinction measurements; and (2) the inversion of the sky radiance to derive other aerosol optical properties such as size distribution, single scattering albedo or refractive index. In this study we evaluate the ESR direct sun products in comparison with the AERosol RObotic NETwork (AERONET) products. Specifically, we have applied the ESR algorithm to a CIMEL CE318 and PREDE POM simultaneously for a 4-yr database measured at the Burjassot site (Valencia, Spain), and compared the resultant products with the AERONET direct sun measurements obtained with the same CIMEL CE318 sky–sun photometer. The comparison shows that aerosol optical depth differences are mostly within the nominal uncertainty of 0.003 for a standard calibration instrument, and fall within the nominal AERONET uncertainty of 0.01–0.02 for a field instrument in the spectral range 340 to 1020 nm. In the cases of the Ångström exponent and the columnar water vapor, the differences are lower than 0.02 and 0.15 cm, respectively. Therefore, we present an open source code program that can be used with both CIMEL and PREDE sky radiometers and whose results are equivalent to AERONET and SKYNET retrievals.

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The ocean plays an important role in regulating the climate, acting as a sink for carbon dioxide, perturbing the carbonate system and resulting in a slow decrease of seawater pH. Understanding the dynamics of the carbonate system in shelf sea regions is necessary to evaluate the impact of Ocean Acidification (OA) in these societally important ecosystems. Complex hydrodynamic and ecosystem coupled models provide a method of capturing the significant heterogeneity of these areas. However rigorous validation is essential to properly assess the reliability of such models. The coupled model POLCOMS–ERSEM has been implemented in the North Western European shelf with a new parameterization for alkalinity explicitly accounting for riverine inputs and the influence of biological processes. The model has been validated in a like with like comparison with North Sea data from the CANOBA dataset. The model shows good to reasonable agreement for the principal variables, physical (temperature and salinity), biogeochemical (nutrients) and carbonate system (dissolved inorganic carbon and total alkalinity), but simulation of the derived variables, pH and pCO2, are not yet fully satisfactory. This high uncertainty is attributed mostly to riverine forcing and primary production. This study suggests that the model is a useful tool to provide information on Ocean Acidification scenarios, but uncertainty on pH and pCO2 needs to be reduced, particularly when impacts of OA on ecosystem functions are included in the model systems.

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Front detection and aggregation techniques were applied to 300m resolution MERIS satellite ocean colour data for the first time, to describe frequently occurring shelf-sea fronts near to the Scottish coast. Medium resolution (1km) thermal and colour data have previously been used to analyse the distribution of surface fronts, though these cannot capture smaller frontal zones or those in close proximity to the coast, particularly where the coastline is convoluted. Seasonal frequent front maps, derived from both chlorophyll and SST data, revealed a number of key frontal zones, a subset of which were based on new insights into the sediment and plankton dynamics provided exclusively by the higher-resolution chlorophyll fronts. The methodology is described for applying colour and thermal front data to the task of identifying zones of ecological importance that could assist the process of defining marine protected areas. Each key frontal zone is analysed to describe its spatial and temporal extent and variability, and possible mechanisms. It is hoped that these tools can provide guidance on the dynamic habitats of marine fauna towards aspects of marine spatial planning and conservation.

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We used coincident Envisat RA2 and AATSR temperature and wind speed data from 2008/2009 to calculate the global net sea-air flux of dimethyl sulfide (DMS), which we estimate to be 19.6 Tg S a21. Our monthly flux calculations are compared to open ocean eddy correlation measurements of DMS flux from 10 recent cruises, with a root mean square difference of 3.1 lmol m22 day21. In a sensitivity analysis, we varied temperature, salinity, surface wind speed, and aqueous DMS concentration, using fixed global changes as well as CMIP5 model output. The range of DMS flux in future climate scenarios is discussed. The CMIP5 model predicts a reduction in surface wind speed and we estimate that this will decrease the global annual sea-air flux of DMS by 22% over 25 years. Concurrent changes in temperature, salinity, and DMS concentration increase the global flux by much smaller amounts. The net effect of all CMIP5 modelled 25 year predictions was a 19% reduction in global DMS flux. 25 year DMS concentration changes had significant regional effects, some positive (Southern Ocean, North Atlantic, Northwest Pacific) and some negative (isolated regions along the Equator and in the Indian Ocean). Using satellite-detected coverage of coccolithophore blooms, our estimate of their contribution to North Atlantic DMS emissions suggests that the coccolithophores contribute only a small percentage of the North Atlantic annual flux estimate, but may be more important in the summertime and in the northeast Atlantic.

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It has long been recognised that there are strong interactions and feedbacks between climate, upper ocean biogeochemistry and marine food webs, and also that food web structure and phytoplankton community distribution are important determinants of variability in carbon production and export from the euphotic zone. Numerical models provide a vital tool to explore these interactions, given their capability to investigate multiple connected components of the system and the sensitivity to multiple drivers, including potential future conditions. A major driver for ecosystem model development is the demand for quantitative tools to support ecosystem-based management initiatives. The purpose of this paper is to review approaches to the modelling of marine ecosystems with a focus on the North Atlantic Ocean and its adjacent shelf seas, and to highlight the challenges they face and suggest ways forward. We consider the state of the art in simulating oceans and shelf sea physics, planktonic and higher trophic level ecosystems, and look towards building an integrative approach with these existing tools. We note how the different approaches have evolved historically and that many of the previous obstacles to harmonisation may no longer be present. We illustrate this with examples from the on-going and planned modelling effort in the Integrative Modelling Work Package of the EURO-BASIN programme.

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Front detection and aggregation techniques were applied to 300m resolution MERIS satellite ocean colour data for the first time, to describe frequently occurring shelf-sea fronts near to the Scottish coast. Medium resolution (1km) thermal and colour data have previously been used to analyse the distribution of surface fronts, though these cannot capture smaller frontal zones or those in close proximity to the coast, particularly where the coastline is convoluted. Seasonal frequent front maps, derived from both chlorophyll and SST data, revealed a number of key frontal zones, a subset of which were based on new insights into the sediment and plankton dynamics provided exclusively by the higher-resolution chlorophyll fronts. The methodology is described for applying colour and thermal front data to the task of identifying zones of ecological importance that could assist the process of defining marine protected areas. Each key frontal zone is analysed to describe its spatial and temporal extent and variability, and possible mechanisms. It is hoped that these tools can provide guidance on the dynamic habitats of marine fauna towards aspects of marine spatial planning and conservation.

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Ecosystems consist of complex dynamic interactions among species and the environment, the understanding of which has implications for predicting the environmental response to changes in climate and biodiversity. However, with the recent adoption of more explorative tools, like Bayesian networks, in predictive ecology, few assumptions can be made about the data and complex, spatially varying interactions can be recovered from collected field data. In this study, we compare Bayesian network modelling approaches accounting for latent effects to reveal species dynamics for 7 geographically and temporally varied areas within the North Sea. We also apply structure learning techniques to identify functional relationships such as prey–predator between trophic groups of species that vary across space and time. We examine if the use of a general hidden variable can reflect overall changes in the trophic dynamics of each spatial system and whether the inclusion of a specific hidden variable can model unmeasured group of species. The general hidden variable appears to capture changes in the variance of different groups of species biomass. Models that include both general and specific hidden variables resulted in identifying similarity with the underlying food web dynamics and modelling spatial unmeasured effect. We predict the biomass of the trophic groups and find that predictive accuracy varies with the models' features and across the different spatial areas thus proposing a model that allows for spatial autocorrelation and two hidden variables. Our proposed model was able to produce novel insights on this ecosystem's dynamics and ecological interactions mainly because we account for the heterogeneous nature of the driving factors within each area and their changes over time. Our findings demonstrate that accounting for additional sources of variation, by combining structure learning from data and experts' knowledge in the model architecture, has the potential for gaining deeper insights into the structure and stability of ecosystems. Finally, we were able to discover meaningful functional networks that were spatially and temporally differentiated with the particular mechanisms varying from trophic associations through interactions with climate and commercial fisheries.