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Institution

Queensland University of Technology

EducationBrisbane, Queensland, Australia
About: Queensland University of Technology is a education organization based out in Brisbane, Queensland, Australia. It is known for research contribution in the topics: Population & Poison control. The organization has 14188 authors who have published 55022 publications receiving 1496237 citations. The organization is also known as: QUT.


Papers
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Journal ArticleDOI
TL;DR: In this paper, the authors reviewed and evaluated contemporary forecasting techniques for photovoltaics into power grids, and concluded that ensembles of artificial neural networks are best for forecasting short-term PV power forecast and online sequential extreme learning machine superb for adaptive networks; while Bootstrap technique optimum for estimating uncertainty.
Abstract: Integration of photovoltaics into power grids is difficult as solar energy is highly dependent on climate and geography; often fluctuating erratically. This causes penetrations and voltage surges, system instability, inefficient utilities planning and financial loss. Forecast models can help; however, time stamp, forecast horizon, input correlation analysis, data pre and post-processing, weather classification, network optimization, uncertainty quantification and performance evaluations need consideration. Thus, contemporary forecasting techniques are reviewed and evaluated. Input correlational analyses reveal that solar irradiance is most correlated with Photovoltaic output, and so, weather classification and cloud motion study are crucial. Moreover, the best data cleansing processes: normalization and wavelet transforms, and augmentation using generative adversarial network are recommended for network training and forecasting. Furthermore, optimization of inputs and network parameters, using genetic algorithm and particle swarm optimization, is emphasized. Next, established performance evaluation metrics MAE, RMSE and MAPE are discussed, with suggestions for including economic utility metrics. Subsequently, modelling approaches are critiqued, objectively compared and categorized into physical, statistical, artificial intelligence, ensemble and hybrid approaches. It is determined that ensembles of artificial neural networks are best for forecasting short term photovoltaic power forecast and online sequential extreme learning machine superb for adaptive networks; while Bootstrap technique optimum for estimating uncertainty. Additionally, convolutional neural network is found to excel in eliciting a model's deep underlying non-linear input-output relationships. The conclusions drawn impart fresh insights in photovoltaic power forecast initiatives, especially in the use of hybrid artificial neural networks and evolutionary algorithms.

446 citations

Journal ArticleDOI
TL;DR: A new method avoids possible speed penalties in R by using the Rcpp extension package in conjunction with the Armadillo C++ matrix library, allowing the automatic pooling of several linear algebra operations into one, which in turn can lead to further speedups.

443 citations

Proceedings ArticleDOI
07 Aug 2002
TL;DR: This paper describes the design and implementation of a system through which existing Web services can be declaratively composed, and the resulting composite Services can be executed following a peer-to-peer paradigm, within a dynamic environment.
Abstract: The development of new services through the integration of existing ones has gained a considerable momentum as a means to create and streamline business-to-business collaborations. Unfortunately, as Web services are often autonomous and heterogeneous entities, connecting and coordinating them in order to build integrated services is a delicate and time-consuming task. In this paper, we describe the design and implementation of a system through which existing Web services can be declaratively composed, and the resulting composite services can be executed following a peer-to-peer paradigm, within a dynamic environment. This system provides tools for specifying composite services through. statecharts, data conversion rules, and provider selection, policies. These specifications are then translated into XML documents that can be interpreted by peer-to-peer inter-connected software components, in order to provision the composite service without requiring a central authority.

443 citations

Journal ArticleDOI
20 Mar 2020-Science
TL;DR: Results support the radial unit hypothesis that different developmental mechanisms promote surface area expansion and increases in thickness and find evidence that brain structure is a key phenotype along the causal pathway that leads from genetic variation to differences in general cognitive function.
Abstract: The cerebral cortex underlies our complex cognitive capabilities, yet little is known about the specific genetic loci that influence human cortical structure. To identify genetic variants that affect cortical structure, we conducted a genome-wide association meta-analysis of brain magnetic resonance imaging data from 51,665 individuals. We analyzed the surface area and average thickness of the whole cortex and 34 regions with known functional specializations. We identified 199 significant loci and found significant enrichment for loci influencing total surface area within regulatory elements that are active during prenatal cortical development, supporting the radial unit hypothesis. Loci that affect regional surface area cluster near genes in Wnt signaling pathways, which influence progenitor expansion and areal identity. Variation in cortical structure is genetically correlated with cognitive function, Parkinson's disease, insomnia, depression, neuroticism, and attention deficit hyperactivity disorder.

436 citations

Journal ArticleDOI
TL;DR: Through monitoring and benchmarking, INFORMAS will strengthen the accountability systems needed to help reduce the burden of obesity, NCDs and their related inequalities.
Abstract: 20 Global Alliance for Improved Nutrition (GAIN), Geneva, Switzerland Summary Non-communicable diseases (NCDs) dominate disease burdens globally and poor nutrition increasingly contributes to this global burden. Compre- hensive monitoring of food environments, and evaluation of the impact of public and private sector policies on food environments is needed to strengthen accountability systems to reduce NCDs. The International Network for Food and Obesity/NCDs Research, Monitoring and Action Support (INFORMAS) is a global network of public-interest organizations and researchers that aims to monitor, benchmark and support public and private sector actions to create healthy food environments and reduce obesity, NCDs and their related inequalities. The INFORMAS framework includes two 'process' modules, that monitor the policies and actions of the public and private sectors, seven 'impact' modules that monitor the key characteristics of food environments and three 'outcome' modules that monitor dietary quality, risk factors and NCD morbidity and mortality. Monitoring frameworks and indicators have been developed for 10 modules to provide consistency, but allowing for stepwise approaches ('minimal', 'expanded', 'optimal') to data collection and analysis. INFORMAS data will enable benchmarking of food environments between countries, and monitoring of progress over time within countries. Through monitoring and benchmarking, INFORMAS will strengthen the account- ability systems needed to help reduce the burden of obesity, NCDs and their related inequalities.

436 citations


Authors

Showing all 14597 results

NameH-indexPapersCitations
Nicholas G. Martin1921770161952
Paul M. Thompson1832271146736
Christopher J. O'Donnell159869126278
Robert G. Parton13645959737
Tim J Cole13682792998
Daniel I. Chasman13448472180
David Smith1292184100917
Dmitri Golberg129102461788
Chao Zhang127311984711
Shi Xue Dou122202874031
Thomas H. Marwick121106358763
Peter J. Anderson12096663635
Bruno S. Frey11990065368
David M. Evans11663274420
Michael Pollak11466357793
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023205
2022641
20214,218
20204,026
20193,623
20183,374