ORCID Profile
0000-0002-8709-5625
Current Organisation
Queensland University of Technology
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In Research Link Australia (RLA), "Research Topics" refer to ANZSRC FOR and SEO codes. These topics are either sourced from ANZSRC FOR and SEO codes listed in researchers' related grants or generated by a large language model (LLM) based on their publications.
Process Control and Simulation | Electrical and Electronic Engineering | Chemical Engineering | Renewable Power and Energy Systems Engineering (excl. Solar Cells) | Software Engineering | Power and Energy Systems Engineering (excl. Renewable Power) | Applied Mathematics | Operations Research | Electrical and Electronic Engineering not elsewhere classified | Process Control And Simulation | Mineral Processing |
Application Tools and System Utilities | Energy Storage, Distribution and Supply not elsewhere classified | Energy Services and Utilities | Alumina production | Application packages | Health not elsewhere classified | Other
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2018
Publisher: Springer Science and Business Media LLC
Date: 08-12-2007
Publisher: Elsevier BV
Date: 08-2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2021
Publisher: IEEE
Date: 2006
Publisher: American Chemical Society (ACS)
Date: 19-06-1999
DOI: 10.1021/IE990033R
Publisher: IEEE
Date: 2006
Publisher: Springer Berlin Heidelberg
Date: 2012
Publisher: Informa UK Limited
Date: 08-2008
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2019
Publisher: Elsevier BV
Date: 05-1998
Publisher: Institute of Navigation
Date: 23-02-2018
DOI: 10.33012/2018.15607
Publisher: Springer International Publishing
Date: 2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2020
Publisher: Elsevier BV
Date: 2012
Publisher: IEEE
Date: 11-2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2020
Publisher: Elsevier BV
Date: 09-2014
Publisher: IOP Publishing
Date: 07-1998
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: Springer International Publishing
Date: 18-07-2017
Publisher: IEEE
Date: 10-2014
Publisher: IEEE
Date: 2008
Publisher: IEEE
Date: 2006
Publisher: Elsevier BV
Date: 08-2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2020
Publisher: Springer Science and Business Media LLC
Date: 29-03-2008
Publisher: IEEE
Date: 09-11-2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2017
Publisher: Elsevier BV
Date: 07-2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2012
Publisher: Elsevier BV
Date: 07-2016
Publisher: Elsevier BV
Date: 02-2007
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2023
Publisher: Institution of Engineering and Technology (IET)
Date: 09-2008
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2024
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2019
Publisher: American Chemical Society (ACS)
Date: 20-06-2003
DOI: 10.1021/IE020763Q
Publisher: Springer Science and Business Media LLC
Date: 16-07-2011
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2019
Publisher: Elsevier BV
Date: 10-2013
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2021
Publisher: FapUNIFESP (SciELO)
Date: 12-2000
Publisher: SAGE Publications
Date: 13-11-2017
Abstract: In chip multiprocessor scheduling, cache memory awareness is critical. However, most existing cache-aware multiprocessor scheduling methods are static and open-loop and thus do not work well in dynamic environments. To address this challenging problem, a control-theoretic approach is presented in this paper for adaptive cache-fair multiprocessor scheduling. The adaptive cache-fair multiprocessor scheduling uses process models to estimate the shared cache size requirements and the instruction count variations of the threads running on multiprocessor cores. Then, it feeds the estimated information back to an adaptive controller. Designed from the process models, the controller decides processor cycle re-allocation through model computation to maintain the instruction counts of the multiple threads at their desired values. The effectiveness of the adaptive cache-fair multiprocessor scheduling is demonstrated through ex les.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2019
Publisher: Elsevier BV
Date: 06-2017
Publisher: Elsevier BV
Date: 10-2006
Publisher: MDPI AG
Date: 09-10-2007
DOI: 10.3390/S7102157
Publisher: Wiley
Date: 08-2008
DOI: 10.1002/RNC.1278
Publisher: IEEE
Date: 06-2014
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2023
Publisher: IEEE
Date: 10-2013
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2016
Publisher: Institution of Engineering and Technology (IET)
Date: 11-2019
Publisher: Springer Science and Business Media LLC
Date: 28-11-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2021
Publisher: Elsevier BV
Date: 02-2017
Publisher: Elsevier BV
Date: 08-2022
Publisher: IEEE
Date: 09-2009
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2023
Publisher: Springer Science and Business Media LLC
Date: 28-02-2023
DOI: 10.1186/S13677-023-00405-X
Abstract: Over-committing computing resources is a widely adopted strategy for increased cluster utilization in Infrastructure as a Service (IaaS) cloud data centers. A potential consequence of over-committing computing resources is memory overload of physical machines (PMs). Memory overload occurs if memory usage exceeds a defined alarm threshold, exposing running computation tasks at a risk of being terminated by the operating system. A prevailing measure to handle memory overload of a PM is live migration of virtual machines (VMs). However, this not only consumes network bandwidth, CPU, and other resources, but also compels a temporary unavailability of the VMs being migrated. To handle memory overload, we present a memory sharing system in this paper for PMs in cloud data centers. With memory sharing, a PM automatically borrows memory from a remote PM when necessary, and releases the borrowed memory when memory overload disappears. This is implemented through swapping inactive memory pages to remote memory resource. Experimental studies conducted on InfiniBand-networked PMs show that the memory sharing system is fully functional. The measured throughput and latency are around 929 Mbps and 1.3 $$\\mu$$ μ s, respectively, on average for remote memory access. They are similar to those from accessing a local-volatile memory express solid-state drive, and thus are promising in real applications.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2021
Publisher: Springer International Publishing
Date: 2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Publisher: Informa UK Limited
Date: 2004
DOI: 10.1252/JCEJ.37.210
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2019
Publisher: Elsevier BV
Date: 09-1997
Publisher: Elsevier BV
Date: 10-2002
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2018
Publisher: American Chemical Society (ACS)
Date: 15-12-2000
DOI: 10.1021/IE000205G
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2020
Publisher: Informa UK Limited
Date: 05-11-2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2018
Publisher: IEEE
Date: 08-2020
Publisher: Springer Berlin Heidelberg
Date: 2012
Publisher: Elsevier BV
Date: 10-2014
Publisher: Springer Science and Business Media LLC
Date: 06-03-2017
Publisher: IEEE
Date: 03-2008
Publisher: Elsevier BV
Date: 09-2021
Publisher: Elsevier BV
Date: 10-2023
Publisher: Elsevier BV
Date: 2007
Publisher: Elsevier BV
Date: 06-1998
Publisher: Elsevier BV
Date: 2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2009
Publisher: Elsevier BV
Date: 04-2019
Publisher: Elsevier BV
Date: 03-2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2019
Publisher: Elsevier BV
Date: 06-2021
Publisher: IEEE
Date: 07-2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2018
Publisher: Elsevier BV
Date: 06-2000
Publisher: Bentham Science Publishers Ltd.
Date: 15-05-2014
Publisher: Elsevier BV
Date: 05-2008
Publisher: Springer International Publishing
Date: 2015
Publisher: Elsevier BV
Date: 08-2009
Publisher: IEEE
Date: 09-2010
DOI: 10.1109/HPCC.2010.51
Publisher: Edith Cowan University
Date: 2010
Publisher: IEEE
Date: 04-2013
Publisher: Elsevier BV
Date: 08-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2022
Publisher: IEEE
Date: 07-2018
Publisher: Elsevier BV
Date: 08-2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2022
Publisher: American Chemical Society (ACS)
Date: 14-08-1999
DOI: 10.1021/IE980535P
Publisher: IEEE
Date: 09-2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2022
Publisher: Elsevier BV
Date: 2018
Publisher: American Physical Society (APS)
Date: 06-04-2021
Publisher: Elsevier BV
Date: 04-2020
Publisher: MDPI AG
Date: 06-12-2007
DOI: 10.3390/S7123179
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2023
Publisher: Elsevier BV
Date: 05-2021
Publisher: IEEE
Date: 10-2010
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2022
Publisher: Springer Science and Business Media LLC
Date: 04-11-2023
DOI: 10.1007/S00521-022-07941-8
Abstract: Energy efficiency is a critical issue in the management and operation of cloud data centers, which form the backbone of cloud computing. Virtual machine (VM) placement has a significant impact on energy-efficiency improvement for virtualized data centers. Among various methods to solve the VM-placement problem, the genetic algorithm (GA) has been well accepted for the quality of its solution. However, GA is also computationally demanding, particularly in the computation of its fitness function. This limits its application in large-scale systems or specific scenarios where a fast VM-placement solution of good quality is required. Our analysis in this paper reveals that the execution time of the standard GA is mostly consumed in the computation of its fitness function. Therefore, this paper designs a data structure extended from a previous study to reduce the complexity of the fitness computation from quadratic to linear one with respect to the input size of the VM-placement problem. Incorporating with this data structure, an alternative fitness function is proposed to reduce the number of instructions significantly, further improving the execution-time performance of GA. Experimental studies show that our approach achieves 11 times acceleration of GA computation for energy-efficient VM placement in large-scale data centers with about 1500 physical machines in size.
Publisher: Elsevier BV
Date: 10-2008
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2022
Publisher: Elsevier BV
Date: 06-2000
Publisher: Elsevier BV
Date: 07-2022
Publisher: Elsevier BV
Date: 12-2007
Publisher: Elsevier BV
Date: 10-2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2021
Publisher: Elsevier BV
Date: 17-05-2004
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2022
Publisher: IEEE
Date: 1997
Publisher: WORLD SCIENTIFIC
Date: 08-2014
DOI: 10.1142/8214
Publisher: Elsevier BV
Date: 05-2003
Publisher: IEEE
Date: 10-2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2021
Publisher: IEEE
Date: 1996
Publisher: Hindawi Limited
Date: 08-11-2018
DOI: 10.1155/2018/4283087
Abstract: This paper proposes novel randomized gossip-consensus-based sync (RGCS) algorithms to realize efficient time calibration in dynamic wireless sensor networks (WSNs). First, the unreliable links are described by stochastic connections, reflecting the characteristic of changing connectivity gleaned from dynamic WSNs. Secondly, based on the mutual drift estimation, each pair of activated nodes fully adjusts clock rate and offset to achieve network-wide time synchronization by drawing upon the gossip consensus approach. The converge-to-max criterion is introduced to achieve a much faster convergence speed. The theoretical results on the probabilistic synchronization performance of the RGCS are presented. Thirdly, a Revised-RGCS is developed to counteract the negative impact of bounded delays, because the uncertain delays are always present in practice and would lead to a large deterioration of algorithm performances. Finally, extensive simulations are performed on the MATLAB and OMNeT++ platform for performance evaluation. Simulation results demonstrate that the proposed algorithms are not only efficient for synchronization issues required for dynamic topology changes but also give a better performance in terms of converging speed, collision rate, and the robustness of resisting delay, and outperform other existing protocols.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2020
Publisher: IEEE
Date: 09-2019
Publisher: Public Library of Science (PLoS)
Date: 27-02-2013
Publisher: IEEE
Date: 10-2011
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2019
Publisher: Springer International Publishing
Date: 2017
Publisher: IEEE
Date: 11-2015
Publisher: IEEE
Date: 10-2014
Publisher: Springer Science and Business Media LLC
Date: 25-08-2007
Publisher: IEEE
Date: 06-2006
Publisher: Wiley
Date: 2000
Abstract: A state space model discrete in time and space was developed for on‐line and realtime computation and prediction of strip temperature in a continuous galvanizing annealing furnace. The model development was based on a combination of physical modeling and system identification. An equivalent heat transfer coefficient was introduced to simplify the physical relations of the process. Compact model expressions suitable for on‐line and real‐time computation were obtained using the finite difference technique. A stability analysis of the model was conducted to guide the selections of time and space steps. System identification aspects were also discussed. The model was successfully applied to industry with good performance.
Publisher: Elsevier BV
Date: 11-2019
Publisher: Springer Science and Business Media LLC
Date: 12-08-2016
Publisher: IEEE
Date: 11-2012
Publisher: SAGE Publications
Date: 15-03-2017
Abstract: The Internet of Things generates rich information either from different sources or the same source via different measurement methods. This demands data fusion for decision making. Despite the progress in data fusion, existing data fusion techniques, such as the classic Dempster–Shafer evidence Theory, face challenges when dealing with highly conflicting sources of evidence. To address this problem, an Adaptive and Robust evidence Theory (ART) is presented in this paper through a robust combination of conjunctive and disjunctive rules. It is capable of handling both conflicting and reliable sources of evidence. When the sources of evidence are reliable, the conjunctive rule plays a predominant role, whereas if the sources of evidence are in high conflict the disjunctive rule is critical. Our ART approach was compared with existing representative evidence theory methods through two ex les, and was further applied in the prediction of floor water inrush in coal mines. The ART approach presented in this paper was demonstrated to behave better than the existing methods.
Publisher: Elsevier BV
Date: 07-2017
Publisher: IEEE
Date: 11-2012
Publisher: Wiley
Date: 22-10-2008
Publisher: Wiley
Date: 28-10-2009
DOI: 10.1002/ASJC.156
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2021
Publisher: Wiley
Date: 03-03-2004
DOI: 10.1002/AIC.10058
Publisher: Springer Berlin Heidelberg
Date: 2012
Publisher: IEEE
Date: 12-2016
Publisher: IEEE
Date: 12-2018
Publisher: SAGE Publications
Date: 26-07-2017
Abstract: H ∞ control for networked control systems with exogenous disturbances and norm-bounded parameter uncertainties has been extensively investigated. However, how to better use the limited network capacity and computation resources while reducing the conservativeness of the H ∞ control is still not fully understood. This paper presents a new dynamic discrete event-triggered scheme with improved modelling and control design to tackle this problem. The event-triggering is designed with periodic data s ling, and consequently the closed-loop system is formulated as a unified time-delayed model with the s led data. From this model, an augmented Lyapunov–Krasovskii functional is constructed with triple-integral terms. A new free-weight matrix technique and the Wirtinger-based inequality are utilized over the Lyapunov–Krasovskii functional to derive a less conservative controller. This leads to two delay-range-dependent stability criteria in terms of linear matrix inequalities. Integrating all these components forms a co-design method for the minimum H ∞ performance index and our event-triggered scheme. Simulation experiments are conducted to demonstrate the approach presented in this paper.
Publisher: Elsevier BV
Date: 2006
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2011
Publisher: Wiley
Date: 2007
DOI: 10.1002/NME.2022
Publisher: Elsevier BV
Date: 02-2017
Publisher: Wiley
Date: 11-2008
DOI: 10.1002/APJ.230
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2019
Publisher: IEEE
Date: 12-2008
Publisher: Elsevier BV
Date: 06-2018
Publisher: IEEE
Date: 12-2013
Publisher: Institution of Engineering and Technology (IET)
Date: 2008
Publisher: Elsevier BV
Date: 03-2008
Publisher: Springer Berlin Heidelberg
Date: 2006
DOI: 10.1007/11901914_33
Publisher: World Scientific Pub Co Pte Lt
Date: 09-2007
DOI: 10.1142/S0218127407019032
Abstract: Addressing the weakened Hilbert's 16th problem or the Hilbert–Arnold problem, this paper gives an upper bound B(n) ≤ 7n + 5 for the number of zeros of the Abelian integrals for a class of Liénard systems. We proved the main result using the Picard–Fuchs equations and the algebraic structure of the integrals.
Publisher: IEEE
Date: 2009
Publisher: IEEE
Date: 10-2018
Publisher: Elsevier BV
Date: 10-2008
Publisher: Springer Science and Business Media LLC
Date: 03-08-2020
DOI: 10.1186/S13677-020-00193-8
Abstract: With the development of cloud computing, edge computing has been proposed to provide real-time and low-delay services to users. Current research usually integrates cloud computing and edge computing as cloud-edge fusion computing for more personalized services. However, both cloud computing and edge computing suffer from high network consumption, which remains a key problem yet to be solved in cloud-edge fusion computing environments. The cost of network consumption can be ided into two parts: migration costs and communication costs. To solve the high network consumption problem, some virtual machines can be migrated from overloaded physical machines to others with the help of virtualization technology. Current network perception migration strategies focus more on the communication cost by optimizing the communication topology. Considering both communication and migration costs, this paper addresses the high network consumption problem in terms of the communication correlations of virtual machines and the network traffic of the migration process. It proposes three heuristic virtual machine migration algorithms, LM, mCaM and mCaM2, to balance communication costs and migration costs. The performance of these algorithms is compared with those of existing virtual machine migration algorithms through experiments. The experimental results show that our virtual machine migration algorithms clearly optimize the communication cost and migration cost. These three algorithms have a lower network cost than AppAware, an existing algorithm, by 20% on average. This means that these three algorithms improve the network performance and reduce the network consumption in cloud-edge fusion computing environments. They also outperform existing algorithms in terms of operation time by 70% on average.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2019
Publisher: American Chemical Society (ACS)
Date: 24-01-1998
DOI: 10.1021/IE970507R
Publisher: Elsevier BV
Date: 03-2009
Publisher: Springer International Publishing
Date: 2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: Elsevier BV
Date: 02-2003
Publisher: Springer International Publishing
Date: 2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2019
Publisher: Elsevier BV
Date: 04-2012
Publisher: IEEE
Date: 12-2013
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2019
Publisher: IEEE
Date: 2004
Publisher: Elsevier BV
Date: 04-2002
Publisher: Springer Science and Business Media LLC
Date: 21-09-2020
Publisher: Elsevier BV
Date: 11-2016
Start Date: 09-2014
End Date: 12-2018
Amount: $320,000.00
Funder: Australian Research Council
View Funded ActivityStart Date: 2005
End Date: 12-2008
Amount: $223,000.00
Funder: Australian Research Council
View Funded ActivityStart Date: 06-2022
End Date: 05-2025
Amount: $490,000.00
Funder: Australian Research Council
View Funded ActivityStart Date: 2017
End Date: 06-2021
Amount: $285,000.00
Funder: Australian Research Council
View Funded ActivityStart Date: 08-2016
End Date: 12-2019
Amount: $435,000.00
Funder: Australian Research Council
View Funded Activity