412 resultados para Growing neural gas


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Nowadays, demand for automated Gas metal arc welding (GMAW) is growing and consequently need for intelligent systems is increased to ensure the accuracy of the procedure. To date, welding pool geometry has been the most used factor in quality assessment of intelligent welding systems. But, it has recently been found that Mahalanobis Distance (MD) not only can be used for this purpose but also is more efficient. In the present paper, Artificial Neural Networks (ANN) has been used for prediction of MD parameter. However, advantages and disadvantages of other methods have been discussed. The Levenberg–Marquardt algorithm was found to be the most effective algorithm for GMAW process. It is known that the number of neurons plays an important role in optimal network design. In this work, using trial and error method, it has been found that 30 is the optimal number of neurons. The model has been investigated with different number of layers in Multilayer Perceptron (MLP) architecture and has been shown that for the aim of this work the optimal result is obtained when using MLP with one layer. Robustness of the system has been evaluated by adding noise into the input data and studying the effect of the noise in prediction capability of the network. The experiments for this study were conducted in an automated GMAW setup that was integrated with data acquisition system and prepared in a laboratory for welding of steel plate with 12 mm in thickness. The accuracy of the network was evaluated by Root Mean Squared (RMS) error between the measured and the estimated values. The low error value (about 0.008) reflects the good accuracy of the model. Also the comparison of the predicted results by ANN and the test data set showed very good agreement that reveals the predictive power of the model. Therefore, the ANN model offered in here for GMA welding process can be used effectively for prediction goals.

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In this paper, a recently introduced model-based method for precedent-free fault detection and isolation (FDI) is modified to deal with multiple input, multiple output (MIMO) systems and is applied to an automotive engine with exhaust gas recirculation (EGR) system. Using normal behavior data generated by a high fidelity engine simulation, the growing structure multiple model system (GSMMS) approach is used to construct dynamic models of normal behavior for the EGR system and its constituent subsystems. Using the GSMMS models as a foundation, anomalous behavior is detected whenever statistically significant departures of the most recent modeling residuals away from the modeling residuals displayed during normal behavior are observed. By reconnecting the anomaly detectors (ADs) to the constituent subsystems, EGR valve, cooler, and valve controller faults are isolated without the need for prior training using data corresponding to particular faulty system behaviors.

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The topic of “the cloud” has attracted significant attention throughout the past few years (Cherry 2009; Sterling and Stark 2009) and, as a result, academics and trade journals have created several competing definitions of “cloud computing” (e.g., Motahari-Nezhad et al. 2009). Underpinning this article is the definition put forward by the US National Institute of Standards and Technology, which describes cloud computing as “a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources that can be rapidly provisioned and released with minimal management effort or service provider interaction” (Garfinkel 2011, p. 3). Despite the lack of consensus about definitions, however, there is broad agreement on the growing demand for cloud computing. Some estimates suggest that spending on cloudrelated technologies and services in the next few years may climb as high as USD 42 billion/year (Buyya et al. 2009).

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According to a study conducted by the International Maritime organisation (IMO) shipping sector is responsible for 3.3% of the global Greenhouse Gas (GHG) emissions. The 1997 Kyoto Protocol calls upon states to pursue limitation or reduction of emissions of GHG from marine bunker fuels working through the IMO. In 2011, 14 years after the adoption of the Kyoto Protocol, the Marine Environment Protection Committee (MEPC) of the IMO has adopted mandatory energy efficiency measures for international shipping which can be treated as the first ever mandatory global GHG reduction instrument for an international industry. The MEPC approved an amendment of Annex VI of the 1973 International Convention for the Prevention of Pollution from Ships (MARPOL 73/78) to introduce a mandatory Energy Efficiency Design Index (EEDI) for new ships and the Ship Energy Efficiency Management Plan (SEEMP) for all ships. Considering the growth projections of human population and world trade the technical and operational measures may not be able to reduce the amount of GHG emissions from international shipping in a satisfactory level. Therefore, the IMO is considering to introduce market-based mechanisms that may serve two purposes including providing a fiscal incentive for the maritime industry to invest in more energy efficient manner and off-setting of growing ship emissions. Some leading developing countries already voiced their serious reservations on the newly adopted IMO regulations stating that by imposing the same obligation on all countries, irrespective of their economic status, this amendment has rejected the Principle of Common but Differentiated Responsibility (the CBDR Principle), which has always been the cornerstone of international climate change law discourses. They also claimed that negotiation for a market based mechanism should not be continued without a clear commitment from the developed counters for promotion of technical co-operation and transfer of technology relating to the improvement of energy efficiency of ships. Against this backdrop, this article explores the challenges for the developing counters in the implementation of already adopted technical and operational measures.

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Background Directed cell migration is essential for normal development. In most of the migratory cell populations that have been analysed in detail to date, all of the cells migrate as a collective from one location to another. However, there are also migratory cell populations that must populate the areas through which they migrate, and thus some cells get left behind while others advance. Very little is known about how individual cells behave to achieve concomitant directional migration and population of the migratory route. We examined the behavior of enteric neural crest-derived cells (ENCCs), which must both advance caudally to reach the anal end and populate each gut region. Results The behaviour of individual ENCCs was examined using live imaging and mice in which ENCCs express a photoconvertible protein. We show that individual ENCCs exhibit very variable directionalities and speed; as the migratory wavefront of ENCCs advances caudally, each gut region is populated primarily by some ENCCs migrating non-directionally. After populating each region, ENCCs remain migratory for at least 24 hours. Endothelin receptor type B (EDNRB) signaling is known to be essential for the normal advance of the ENCC population. We now show that perturbation of EDNRB principally affects individual ENCC speed rather than directionality. The trajectories of solitary ENCCs, which occur transiently at the wavefront, were consistent with an unbiased random walk and so cell-cell contact is essential for directional migration. ENCCs migrate in close association with neurites. We showed that although ENCCs often use neurites as substrates, ENCCs lead the way, neurites are not required for chain formation and neurite growth is more directional than the migration of ENCCs as a whole. Conclusions Each gut region is initially populated by sub-populations of ENCCs migrating non-directionally, rather than stopping. This might provide a mechanism for ensuring a uniform density of ENCCs along the growing gut.

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Voluntary and compliance markets for forest carbon and other (emission avoidance and biosequestration) activities are growing internationally and across Australia. Queensland and its Natural Resource Management (NRM) regions have an opportunity to take a variety of actions to help guide these markets to secure multiple landscape benefits and to build landscape resilience in the face of climate change. As the national arrangements for offsets within Australia’s Clean Energy Package (CEP) and emissions trading environment emerge, Queensland’s regions can prepare themselves and their landholding communities to take advantage of these opportunities to deliver improved climate resilience in their regional landscapes.

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Agriculture is responsible for a significant proportion of total anthropogenic greenhouse gas emissions (perhaps 18% globally), and therefore has the potential to contribute to efforts to reduce emissions as a means of minimising the risk of dangerous climate change. The largest contributions to emissions are attributed to ruminant methane production and nitrous oxide from animal waste and fertilised soils. Further, livestock, including ruminants, are an important component of global and Australian food production and there is a growing demand for animal protein sources. At the same time as governments and the community strengthen objectives to reduce greenhouse gas emissions, there are growing concerns about global food security. This paper provides an overview of a number of options for reducing methane and nitrous oxide emissions from ruminant production systems in Australia, while maintaining productivity to contribute to both objectives. Options include strategies for feed modification, animal breeding and herd management, rumen manipulation and animal waste and fertiliser management. Using currently available strategies, some reductions in emissions can be achieved, but practical commercially available techniques for significant reductions in methane emissions, particularly from extensive livestock production systems, will require greater time and resource investment. Decreases in the levels of emissions from these ruminant systems (i.e., the amount of emissions per unit of product such as meat) have already been achieved. However, the technology has not yet been developed for eliminating production of methane from the rumen of cattle and sheep digesting the cellulose and lignin-rich grasses that make up a large part of the diet of animals grazing natural pastures, particularly in arid and semi-arid grazing lands. Nevertheless, the abatement that can be achieved will contribute significantly towards reaching greenhouse gas emissions reduction targets and research will achieve further advances.

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Coal seam gas (CSG) is a growing industry in Queensland and represents a potential major employer and deliverer of financial prosperity for years to come. CSG is a natural gas composed primarily of methane and is found trapped underground in coal beds. During the gas extraction process, significant volumes of associated water are also produced. This associated water could be a valuable resource, however, the associated water comprises of various salt constituents that make it problematic for beneficial use. Consequently, there is a need to implement various water treatment strategies to purify the associated water to comply with Queensland’s strict guidelines and to mitigate environmental risks. The resultant brine is also of importance as ultimately it also has to be dealt with in an economical manner. In some ways it can be considered that the CSG industry does not face a water problem, as this has inherent value to society, but rather has a “salt issue” to solve. This study analyzes the options involved in both the water treatment and salt recovery processes. A brief overview of the constituents present in Queensland CS water is made to illustrate the challenges involved and a range of treatment technologies discussed. Water treatment technologies examined include clarification (ballasted flocculation, dissolved air flotation, electrocoagulation), membrane filtration (ultrafiltration), ion exchange softening and desalination (ion exchange, reverse osmosis desalination and capacitance deionization). In terms of brine management we highlighted reinjection, brine concentration ponds, membrane techniques (membrane distillation, forward osmosis), thermal methods, electrodialysis, electrodialysis reversal, bipolar membrane electrodialysis, wind assisted intensive evaporation, membrane crystallization, eutectic freeze crystallization and vapor compression. As an entirety this investigation is designed to be an important tool in developing CS water treatment management strategies for effective management in Queensland and worldwide.