Guillermo A. Narsilio

Bio: Guillermo A. Narsilio is an academic researcher from University of Melbourne. The author has contributed to research in topics: Geothermal energy & Heat pump. The author has an hindex of 20, co-authored 126 publications receiving 1645 citations. Previous affiliations of Guillermo A. Narsilio include Commonwealth Scientific and Industrial Research Organisation & Australian Research Council.

Emerging geothermal energy technologies

Abstract: Geothermal energy, whether as a source of electricity or to heat or cool buildings, has an enormous potential as a renewable energy source. This paper presents a broad overview of geothermal energy, with a focus on the emerging technologies of Enhanced Geothermal Systems (EGS) and Ground Source Heat Pumps (GSHPs). EGS and GSHP systems are important because of their potential for widespread use, as opposed to traditional geothermal energy systems which are generally limited to tectonically active regions. Commercial application of EGS technology is some years off but its enormous potential demands investment for further research. GSHP heat pump technology is available now — policy makers need to be educated and the potential of GSHPs demonstrated. Research should focus on optimising the design of the in-ground component of GSHP systems to maximise their economic, as well as environmental, benefits.

American Society for Testing and Materials

Abstract: The American Society for Testing and Materials (ASTM) is an independent organization devoted to the development of standards.

IEEE transactions on pattern analysis and machine intelligence

Abstract: In the real world, a realistic setting for computer vision or multimedia recognition problems is that we have some classes containing lots of training data and many classes contain a small amount of training data. Therefore, how to use frequent classes to help learning rare classes for which it is harder to collect the training data is an open question. Learning with Shared Information is an emerging topic in machine learning, computer vision and multimedia analysis. There are different level of components that can be shared during concept modeling and machine learning stages, such as sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, etc. Regarding the specific methods, multi-task learning, transfer learning and deep learning can be seen as using different strategies to share information. These learning with shared information methods are very effective in solving real-world large-scale problems. This special issue aims at gathering the recent advances in learning with shared information methods and their applications in computer vision and multimedia analysis. Both state-of-the-art works, as well as literature reviews, are welcome for submission. Papers addressing interesting real-world computer vision and multimedia applications are especially encouraged. Topics of interest include, but are not limited to: • Multi-task learning or transfer learning for large-scale computer vision and multimedia analysis • Deep learning for large-scale computer vision and multimedia analysis • Multi-modal approach for large-scale computer vision and multimedia analysis • Different sharing strategies, e.g., sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, • Real-world computer vision and multimedia applications based on learning with shared information, e.g., event detection, object recognition, object detection, action recognition, human head pose estimation, object tracking, location-based services, semantic indexing. • New datasets and metrics to evaluate the benefit of the proposed sharing ability for the specific computer vision or multimedia problem. • Survey papers regarding the topic of learning with shared information. Authors who are unsure whether their planned submission is in scope may contact the guest editors prior to the submission deadline with an abstract, in order to receive feedback.

Physical properties of hydrate-bearing sediments

Abstract: [1] Methane gas hydrates, crystalline inclusion compounds formed from methane and water, are found in marine continental margin and permafrost sediments worldwide. This article reviews the current understanding of phenomena involved in gas hydrate formation and the physical properties of hydrate-bearing sediments. Formation phenomena include pore-scale habit, solubility, spatial variability, and host sediment aggregate properties. Physical properties include thermal properties, permeability, electrical conductivity and permittivity, small-strain elastic P and S wave velocities, shear strength, and volume changes resulting from hydrate dissociation. The magnitudes and interdependencies of these properties are critically important for predicting and quantifying macroscale responses of hydrate-bearing sediments to changes in mechanical, thermal, or chemical boundary conditions. These predictions are vital for mitigating borehole, local, and regional slope stability hazards; optimizing recovery techniques for extracting methane from hydrate-bearing sediments or sequestering carbon dioxide in gas hydrate; and evaluating the role of gas hydrate in the global carbon cycle.

Enhanced geothermal systems (EGS): A review

Abstract: Geothermal energy is a renewable energy source that can be found in abundance on our planet. Only a small fraction of it is currently converted to electrical power, though in recent years installed geothermal capacity has increased considerably all over the world. This review focuses on Enhanced Geothermal Systems (EGS), which represent a path for turning the enormous resources provided by geothermal energy into electricity for human consumption efficiently and on a large scale. The paper presents a general overview of this ever-expanding technology from its origins to the current state of the art. The Geodynamics plant in Habanero (Australia), which started up on 2 May 2013, is the first privately-run commercial EGS plant to produce electricity on a large scale. Thanks to the technological development of EGS in recent years, the future looks bright for such plants in the decades to come.

Evaluation of remotely sensed and modelled soil moisture products using global ground-based in situ observations

Abstract: In situ soil moisture data from more than 200 stations located in Africa, Australia, Europe and the United States are used to determine the reliability of three soil moisture products, one analysis from the ECMWF (European Centre for Medium-Range Weather Forecasts) numerical weather prediction system (SM-DAS-2) and two remotely sensed soil moisture products, namely ASCAT (Advanced scatterometer) and SMOS (Soil Moisture Ocean Salinity). SM-DAS-2 is produced offline at ECMWF and relies on an advanced surface data assimilation system (Extended Kalman Filter) used to optimally combine conventional observations with satellite measurements. ASCAT remotely sensed surface soil moisture is provided in near real time by EUMETSAT. At ECMWF, ASCAT is used for soil moisture analyses in SM-DAS-2, also. Finally the SMOS remotely sensed soil moisture data level two product developed at CESBIO is used. Evaluation of the times series as well as of the anomaly values, shows good performances of the three products to capture surface soil moisture annual cycle and short term variability. Correlations with in situ data are very satisfactory over most of the investigated sites located in contrasted biomes and climate conditions with averaged values of 0.70 for SM-DAS-2, 0.53 for ASCAT and 0.54 for SMOS. Although radio frequency interference disturbs the natural microwave emission of the Earth observed by SMOS in several parts of the world, hence the soil moisture retrieval, performances of SMOS over Australia are very encouraging.