ORCID Profile
0000-0002-3925-0256
Current Organisation
CSIRO
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Publisher: Elsevier BV
Date: 04-2008
Publisher: Elsevier BV
Date: 09-2021
Publisher: Elsevier BV
Date: 03-2022
Publisher: American Meteorological Society
Date: 03-2020
Abstract: Seasonal climate forecasts from raw climate models at coarse grids are often biased and statistically unreliable for credible crop prediction at the farm scale. We develop a copula-based postprocessing (CPP) method to overcome this mismatch problem. The CPP forecasts are ensemble based and are generated from the predictive distribution conditioned on raw climate forecasts. CPP performs univariate postprocessing procedures at each station, lead time, and variable separately and then applies the Schaake shuffle to reorder ensemble sequence for a more realistic spatial, temporal, and cross-variable dependence structure. The use of copulas makes CPP free of strong distributional assumptions and flexible enough to describe complex dependence structures. In a case study, we apply CPP to postprocess rainfall, minimum temperature, maximum temperature, and radiation forecasts at a monthly level from the Australian Community Climate and Earth-System Simulator Seasonal model (ACCESS-S) to three representative stations in Australia. We evaluate forecast skill at lead times of 0–5 months on a cross-validation theme in the context of both univariate and multivariate forecast verification. When compared with forecasts that use climatological values as the predictor, the CPP forecast has positive skills, although the skills diminish with increasing lead times and finally become comparable at long lead times. When compared with the bias-corrected forecasts and the quantile-mapped forecasts, the CPP forecast is the overall best, with the smallest bias and greatest univariate forecast skill. As a result of the skill gain from univariate forecasts and the effect of the Schaake shuffle, CPP leads to the most skillful multivariate forecast as well. Further results investigate whether using ensemble mean or additional predictors can enhance forecast skill for CPP.
Publisher: Modelling and Simulation Society of Australia and New Zealand (MSSANZ), Inc.
Date: 12-12-2011
Publisher: Elsevier BV
Date: 07-2021
Publisher: Springer Science and Business Media LLC
Date: 05-2020
Publisher: Springer Berlin Heidelberg
Date: 2007
Publisher: Elsevier BV
Date: 09-2021
Publisher: Oxford University Press (OUP)
Date: 25-11-2020
DOI: 10.1111/RSSA.12526
Abstract: Motivated by the Australian National University poll, we consider a situation where survey data have been collected from respondents for several categorical variables and a primary geographic classification, e.g. postcode. Here, a common and important problem is to obtain estimates for a second target geography that overlaps with the primary geography but has not been collected from the respondents. We examine this problem when areal level census information is available for both geographic classifications. Such a situation is challenging from a small area estimation perspective for several reasons: there is a misalignment between the census and survey information as well as the geographical classifications the geographic areas are potentially small and so prediction can be difficult because of the sparse or spatially missing data issue and there is the possibility of non-stationary spatial dependence. To address these problems we develop a Bayesian model using latent processes, underpinned by a non-stationary spatial basis that combines Moran's I and multiresolution basis functions with a small but representative set of knots. The study results based on simulated data demonstrate that the model can be highly effective and gives more accurate estimates for areas defined by the target geography than several existing models. The model also performs well for the Australian National University poll data to predict on a second geographic classification: statistical area level 2.
Publisher: Springer Berlin Heidelberg
Date: 2010
Publisher: Elsevier BV
Date: 09-2018
Publisher: Springer Berlin Heidelberg
Date: 2008
Publisher: Springer Science and Business Media LLC
Date: 10-06-2012
Publisher: Elsevier BV
Date: 2018
Publisher: IEEE
Date: 2009
Publisher: Springer Science and Business Media LLC
Date: 14-04-2023
DOI: 10.1007/S00477-023-02444-X
Abstract: Skilful and localised daily weather forecasts for upcoming seasons are desired by climate-sensitive sectors. Various General circulation models routinely provide such long lead time ensemble forecasts, also known as seasonal climate forecasts (SCF), but require downscaling techniques to enhance their skills from historical observations. Traditional downscaling techniques, like quantile mapping (QM), learn empirical relationships from pre-engineered predictors. Deep-learning-based downscaling techniques automatically generate and select predictors but almost all of them focus on simplified situations where low-resolution images match well with high-resolution ones, which is not the case in ensemble forecasts. To downscale ensemble rainfall forecasts, we take a two-step procedure. We first choose a suitable deep learning model, very deep super-resolution (VDSR), from several outstanding candidates, based on an ensemble forecast skill metric, continuous ranked probability score (CRPS). Secondly, via incorporating other climate variables as extra input, we develop and finalise a very deep statistical downscaling (VDSD) model based on CRPS. Both VDSR and VDSD are tested on downscaling 60 km rainfall forecasts from the Australian Community Climate and Earth-System Simulator Seasonal model version 1 (ACCESS-S1) to 12 km with lead times up to 217 days. Leave-one-year-out testing results illustrate that VDSD has normally higher forecast accuracy and skill, measured by mean absolute error and CRPS respectively, than VDSR and QM. VDSD substantially improves ACCESS-S1 raw forecasts but does not always outperform climatology, a benchmark for SCFs. Many more research efforts are required on downscaling and climate modelling for skilful SCFs.
Publisher: Elsevier BV
Date: 12-2004
Publisher: Springer Science and Business Media LLC
Date: 10-06-2012
Publisher: IEEE
Date: 2010
DOI: 10.1109/WKDD.2010.96
Publisher: Springer US
Date: 12-10-2010
Publisher: Springer Science and Business Media LLC
Date: 21-10-2010
Publisher: Springer Berlin Heidelberg
Date: 2006
DOI: 10.1007/11731139_101
Publisher: Springer Berlin Heidelberg
Date: 2007
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2005
Publisher: Springer Berlin Heidelberg
Date: 2006
DOI: 10.1007/11677437_20
Publisher: Elsevier BV
Date: 08-2002
Publisher: SAGE Publications
Date: 07-2015
DOI: 10.1155/2015/913165
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2010
Publisher: Springer Berlin Heidelberg
Date: 2009
Publisher: Informa UK Limited
Date: 08-05-2016
Publisher: Public Library of Science (PLoS)
Date: 07-03-2019
Publisher: Springer Science and Business Media LLC
Date: 04-04-2018
Publisher: Springer Science and Business Media LLC
Date: 04-06-2020
Publisher: SAGE Publications
Date: 05-2021
DOI: 10.1177/15501477211018623
Abstract: A wireless sensor network is the formation of a temporary network of sensor nodes equipped with limited resources working in an ad hoc environment. Routing protocol is one of the key challenges while designing a wireless sensor network, which requires optimum use of limited resources of a sensor node, such as power and so on. Similarly, data security and integrity is another open issue that has emerged as a flash point in research community in the last decade. This article proposes a secure model for routing data from source to destination named as secure and energy-efficient routing. The proposed secure and energy-efficient routing is inherited from authentication and voice encryption scheme developed for Global System for Mobile Communications. Necessary modifications have been carried out in order to fit the Global System for Mobile Communications technology in a wireless sensor network ad hoc environment. Due to its low complexity, the secure and energy-efficient routing consumes lesser battery power both during encryption/decryption and for routing purposes. It is due to the XoR operation used in the proposed scheme which is considered as the most inexpensive process with respect to time and space complexity. It is observed through simulations that secure and energy-efficient routing can work effectively even in critical power level in a sensor network. The article also presents a simulation-based comparative analysis of the proposed secure and energy-efficient routing with two notable existing secure routing protocols. We proved that the proposed secure and energy-efficient routing helps to achieve the desired performance under dynamically changing network conditions with various numbers of malicious nodes. Moreover, in Global System for Mobile Communications, generally three linear feedback shift registers are used to fragment the key in data encryption mechanism. In this article, a mathematical model is proposed to increase the number of possible combinations of shift register in order to make the data encryption mechanism more secure which has never been done before. Due to its liner complexity, lesser power consumption, and more dynamic route updating, the secure and energy-efficient routing can easily find its use in the emerging Internet-of-Things systems.
Publisher: Springer Science and Business Media LLC
Date: 13-03-2010
Publisher: Springer Berlin Heidelberg
Date: 2013
Publisher: IEEE
Date: 06-2008
Publisher: Springer Science and Business Media LLC
Date: 03-07-2015
Publisher: Elsevier BV
Date: 11-2014
Publisher: IEEE
Date: 06-2008
Publisher: Springer Science and Business Media LLC
Date: 27-03-2018
Publisher: MDPI AG
Date: 19-07-2021
DOI: 10.3390/RS13142827
Abstract: Aboveground dry weight (AGDW) and leaf area index (LAI) are indicators of crop growth status and grain yield as affected by interactions of genotype, environment, and management. Unmanned aerial vehicle (UAV) based remote sensing provides cost-effective and non-destructive methods for the high-throughput phenotyping of crop traits (e.g., AGDW and LAI) through the integration of UAV-derived vegetation indexes (VIs) with statistical models. However, the effects of different modelling strategies that use different dataset compositions of explanatory variables (i.e., combinations of sources and temporal combinations of the VI datasets) on estimates of AGDW and LAI have rarely been evaluated. In this study, we evaluated the effects of three sources of VIs (visible, spectral, and combined) and three types of temporal combinations of the VI datasets (mono-, multi-, and full-temporal) on estimates of AGDW and LAI. The VIs were derived from visible (RGB) and multi-spectral imageries, which were acquired by a UAV-based platform over a wheat trial at five s ling dates before flowering. Partial least squares regression models were built with different modelling strategies to estimate AGDW and LAI at each prediction date. The results showed that models built with the three sources of mono-temporal VIs obtained similar performances for estimating AGDW (RRMSE = 11.86% to 15.80% for visible, 10.25% to 16.70% for spectral, and 10.25% to 16.70% for combined VIs) and LAI (RRMSE = 13.30% to 22.56% for visible, 12.04% to 22.85% for spectral, and 13.45% to 22.85% for combined VIs) across prediction dates. Mono-temporal models built with visible VIs outperformed the other two sources of VIs in general. Models built with mono-temporal VIs generally obtained better estimates than models with multi- and full-temporal VIs. The results suggested that the use of UAV-derived visible VIs can be an alternative to multi-spectral VIs for high-throughput and in-season estimates of AGDW and LAI. The combination of modelling strategies that used mono-temporal datasets and a self-calibration method demonstrated the potential for in-season estimates of AGDW and LAI (RRMSE normally less than 15%) in breeding or agronomy trials.
Publisher: Modelling and Simulation Society of Australia and New Zealand (MSSANZ), Inc.
Date: 12-2013
Publisher: IEEE
Date: 12-2010
DOI: 10.1109/ICDM.2010.51
Publisher: Informa UK Limited
Date: 18-12-2020
Publisher: Elsevier BV
Date: 06-2021
Publisher: Springer Science and Business Media LLC
Date: 23-07-2010
Publisher: Springer Berlin Heidelberg
Date: 2007
Publisher: Springer Berlin Heidelberg
Date: 2007
Publisher: Elsevier BV
Date: 05-2017
Publisher: AI Access Foundation
Date: 29-12-2016
DOI: 10.1613/JAIR.5228
Abstract: This paper introduces a new unsupervised anomaly detector called ZERO++ which employs the number of zero appearances in subspaces to detect anomalies in categorical data. It is unique in that it works in regions of subspaces that are not occupied by data whereas existing methods work in regions occupied by data. ZERO++ examines only a small number of low dimensional subspaces to successfully identify anomalies. Unlike existing frequency-based algorithms, ZERO++ does not involve subspace pattern searching. We show that ZERO++ is better than or comparable with the state-of-the-art anomaly detection methods over a wide range of real-world categorical and numeric data sets and it is efficient with linear time complexity and constant space complexity which make it a suitable candidate for large-scale data sets.
Publisher: Elsevier BV
Date: 12-2010
Publisher: Elsevier BV
Date: 2010
Publisher: IEEE
Date: 2003
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2008
Publisher: Wiley
Date: 05-08-2014
DOI: 10.1002/JOC.4109
Publisher: Elsevier BV
Date: 02-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2003
Publisher: MDPI AG
Date: 16-05-2022
DOI: 10.3390/SU14106031
Abstract: Within the context of globalization, the development of renewable energy is critical for attaining sustainable development, and the digital economy is also a critical driver for achieving it. This article incorporates globalization, renewable energy development, and the digital economy into its research framework, investigates the relationship between globalization and renewable energy development, and explores the moderating effect of the digital economy, using panel data from countries along the Belt and Road (B& R) from 2001 to 2018. It is found that globalization facilitates the development of renewable energy. The 1% increase in globalization results in a 1.06% increase in renewable energy development the level of globalization has a significant effect on renewable energy development in high-income countries, upper-middle-income countries, and low-income countries, but not in lower-middle-income countries the digital economy has a moderating effect on the impact of globalization on renewable energy development in countries along the B& R. Simultaneously, the effect of globalization on renewable energy development in B& R countries is influenced by the digital economy’s single threshold effect, and the effect of globalization on renewable energy development is more pronounced when the level of digital economy development is less than the threshold of 0.061. The conclusions of this article have significant implications for the B& R countries’ sustainable development in the contexts of globalization and the digital economy.
Publisher: Atlantis Press
Date: 2006
Publisher: Springer Berlin Heidelberg
Date: 2007
Publisher: Elsevier BV
Date: 06-2004
Publisher: Springer Berlin Heidelberg
Date: 2005
DOI: 10.1007/11553939_170
Publisher: Elsevier BV
Date: 03-2022
Publisher: Springer Berlin Heidelberg
Date: 2005
DOI: 10.1007/11589990_108
Publisher: Springer Science and Business Media LLC
Date: 05-05-2013
Publisher: IEEE Comput. Soc
Date: 2003
Publisher: Springer Science and Business Media LLC
Date: 22-08-2018
Publisher: Elsevier BV
Date: 05-2023
Publisher: Springer Science and Business Media LLC
Date: 31-12-2018
Publisher: ACM
Date: 04-08-2023
Publisher: Modelling and Simulation Society of Australia and New Zealand
Date: 29-11-2015
Publisher: Springer Berlin Heidelberg
Date: 2004
Publisher: Springer International Publishing
Date: 2019
Publisher: Oxford University Press (OUP)
Date: 29-04-2015
DOI: 10.1111/RSSC.12103
Abstract: Previous climate research concluded that causal influences which have contributed to changes in frost risk in south-eastern Australia include greenhouse gas concentration, El-Niño southern oscillation and other effects. Some of the climatic indices representing these effects have spatiotemporal misalignment and may have a spatially and temporally varying effect on observed data. Other indices are constructed from grid-referenced physical models, which creates a point-to-area problem. To address these issues we use a spatiodynamic model, which comprises a blending of spatially varying and temporally dynamic parameters. For the data that we examine the model proposed performs well in out-of-s le validation compared with a spatiotemporal model.
Publisher: Elsevier BV
Date: 05-2005
Publisher: Elsevier BV
Date: 05-2021
Publisher: IEEE Comput. Soc
Date: 2002
Publisher: Springer Berlin Heidelberg
Date: 2002
Publisher: Elsevier BV
Date: 05-2019
Publisher: Springer Berlin Heidelberg
Date: 2007
Publisher: Water Science Policy gUG
Date: 15-12-2021
DOI: 10.53014/HDDJ2412
Abstract: Looking back on COP26, we argue that there is power in telling stories about adaptation to water-related climate change impacts in Australia and the Pacific.
Publisher: Public Library of Science (PLoS)
Date: 25-06-2020
Publisher: Elsevier BV
Date: 12-2021
Publisher: Elsevier BV
Date: 04-2020
Publisher: Modelling and Simulation Society of Australia and New Zealand (MSSANZ), Inc.
Date: 12-12-2011
No related grants have been discovered for Huidong jin.