ORCID Profile
0000-0002-1758-6824
Current Organisation
Curtin University
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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.
Civil Engineering | Structural Engineering | Pattern Recognition and Data Mining |
Expanding Knowledge in Engineering | Expanding Knowledge in Technology | Civil Building Management and Services
Publisher: Springer Berlin Heidelberg
Date: 2008
Publisher: IEEE
Date: 09-2012
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2018
Publisher: IEEE
Date: 2001
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2003
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2004
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2000
DOI: 10.1109/9.839961
Publisher: World Scientific Pub Co Pte Lt
Date: 05-2014
DOI: 10.1142/S0218001414560047
Abstract: In this paper, we consider the problem of robust face recognition using color information. In this context, sparse representation-based algorithms are the state-of-the-art solutions for gray facial images. We will integrate the existing sparse representation-based algorithms with color information and this integration can improve the previous performances significantly. Furthermore, we propose a new performance metric, namely the discriminativeness (DIS) to describe the recognition effectiveness for sparse representation algorithms. We find out that the richer information in color space can be used to increase the DIS, i.e. enhancing the robustness in face recognition. Extensive experiments have been conducted under different conditions, including various feature extractors, random pixel corruptions and occlusions on AR and GT databases, to demonstrate the advantages of using color information in robust face recognition. Detailed analysis is also included for each experiment to explain why and how color improve the robustness of different sparse representation-based methods.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2016
Publisher: IEEE
Date: 2005
Publisher: Springer Berlin Heidelberg
Date: 2008
Publisher: Springer International Publishing
Date: 2015
Publisher: IEEE
Date: 2003
Publisher: IEEE
Date: 06-2014
Publisher: IEEE
Date: 2006
Publisher: Springer Science and Business Media LLC
Date: 03-02-2013
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2021
Publisher: Springer International Publishing
Date: 2014
Publisher: IEEE
Date: 06-2007
Publisher: Springer International Publishing
Date: 2019
Publisher: IEEE
Date: 05-2002
Publisher: MDPI AG
Date: 13-01-2020
DOI: 10.3390/S20020447
Abstract: Across the globe, remote image data is rapidly being collected for the assessment of benthic communities from shallow to extremely deep waters on continental slopes to the abyssal seas. Exploiting this data is presently limited by the time it takes for experts to identify organisms found in these images. With this limitation in mind, a large effort has been made globally to introduce automation and machine learning algorithms to accelerate both classification and assessment of marine benthic biota. One major issue lies with organisms that move with swell and currents, such as kelps. This paper presents an automatic hierarchical classification method local binary classification as opposed to the conventional flat classification to classify kelps in images collected by autonomous underwater vehicles. The proposed kelp classification approach exploits learned feature representations extracted from deep residual networks. We show that these generic features outperform the traditional off-the-shelf CNN features and the conventional hand-crafted features. Experiments also demonstrate that the hierarchical classification method outperforms the traditional parallel multi-class classifications by a significant margin (90.0% vs. 57.6% and 77.2% vs. 59.0%) on Benthoz15 and Rottnest datasets respectively. Furthermore, we compare different hierarchical classification approaches and experimentally show that the sibling hierarchical training approach outperforms the inclusive hierarchical approach by a significant margin. We also report an application of our proposed method to study the change in kelp cover over time for annually repeated AUV surveys.
Publisher: Oxford University Press (OUP)
Date: 22-11-2019
Abstract: Underwater imaging is being extensively used for monitoring the abundance of lobster species and their bio ersity in their local habitats. However, manual assessment of these images requires a huge amount of human effort. In this article, we propose to automate the process of lobster detection using a deep learning technique. A major obstacle in deploying such an automatic framework for the localization of lobsters in erse environments is the lack of large annotated training datasets. Generating synthetic datasets to train these object detection models has become a popular approach. However, the current synthetic data generation frameworks rely on automatic segmentation of objects of interest, which becomes difficult when the objects have a complex shape, such as lobster. To overcome this limitation, we propose an approach to synthetically generate parts of the lobster. To handle the variability of real-world images, these parts were inserted into a set of erse background marine images to generate a large synthetic dataset. A state-of-the-art object detector was trained using this synthetic parts dataset and tested on the challenging task of Western rock lobster detection in West Australian seas. To the best of our knowledge, this is the first automatic lobster detection technique for partially visible and occluded lobsters.
Publisher: IEEE
Date: 06-2008
Publisher: Elsevier
Date: 2017
Publisher: SAGE Publications
Date: 26-05-2020
Abstract: Convolutional neural networks have been widely employed for structural health monitoring and damage identification. The convolutional neural network is currently considered as the state-of-the-art method for structural damage identification due to its capabilities of efficient and robust feature learning in a hierarchical manner. It is a tendency to develop a convolutional neural network with a deeper architecture to gain a better performance. However, when the depth of the network increases to a certain level, the performance will degrade due to the gradient vanishing issue. Residual neural networks can avoid the problem of vanishing gradients by utilizing skip connections, which allows the information flowing to the next layer through identity mappings. In this article, a deep residual network framework is proposed for structural health monitoring of civil engineering structures. This framework is composed of purely residual blocks which operate as feature extractors and a fully connected layer as a regressor. It learns the damage-related features from the vibration characteristics such as mode shapes and maps them into the damage index labels, for ex le, stiffness reductions of structures. To evaluate the efficacy and robustness of the proposed framework, an intensive evaluation is conducted with both numerical and experimental studies. The comparison between the proposed approach and the state-of-the-art models, including a sparse autoencoder neural network, a shallow convolutional neural network and a convolutional neural network with the same structure but without skip connections, is conducted. In the numerical studies, a 7-storey steel frame is investigated. Four scenarios with considering measurement noise and finite element modelling errors in the data sets are studied. The proposed framework consistently outperforms the state-of-the-art models in all the scenarios, especially for the most challenging scenario, which includes both measurement noise and uncertainties. Experimental studies on a prestressed concrete bridge in the laboratory are conducted. The proposed framework demonstrates consistent damage prediction results on this beam with the state-of-the-art models.
Publisher: Springer Berlin Heidelberg
Date: 2009
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Publisher: IEEE
Date: 07-2019
Publisher: IEEE
Date: 07-2017
Publisher: Springer Berlin Heidelberg
Date: 2011
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2018
Publisher: IEEE
Date: 06-2010
Publisher: IEEE
Date: 09-2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2018
Publisher: Elsevier BV
Date: 10-2012
Publisher: Elsevier
Date: 2018
Publisher: IEEE
Date: 12-2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2005
Publisher: American Institute of Mathematical Sciences (AIMS)
Date: 2021
DOI: 10.3934/MFC.2020024
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2017
Publisher: Elsevier BV
Date: 06-2020
Publisher: IEEE
Date: 09-2018
Publisher: IEEE
Date: 04-2018
Publisher: IEEE
Date: 08-2011
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2014
Publisher: Springer International Publishing
Date: 2019
Publisher: IEEE
Date: 09-2016
Publisher: Springer International Publishing
Date: 2019
Publisher: Elsevier BV
Date: 10-2021
Publisher: Elsevier BV
Date: 2020
Publisher: Elsevier BV
Date: 11-2021
Publisher: Elsevier BV
Date: 09-2018
DOI: 10.1016/J.NEUNET.2018.06.005
Abstract: By introducing sign constraints on the weights, this paper proposes sign constrained rectifier networks (SCRNs), whose training can be solved efficiently by the well known majorization-minimization (MM) algorithms. We prove that the proposed two-hidden-layer SCRNs, which exhibit negative weights in the second hidden layer and negative weights in the output layer, are capable of separating any number of disjoint pattern sets. Furthermore, the proposed two-hidden-layer SCRNs can decompose the patterns of each class into several clusters so that each cluster is convexly separable from all the patterns from the other classes. This provides a means to learn the pattern structures and analyse the discriminant factors between different classes of patterns. Experimental results are provided to show the benefits of sign constraints in improving classification performance and the efficiency of the proposed MM algorithm.
Publisher: Springer Science and Business Media LLC
Date: 15-09-2021
DOI: 10.1038/S41598-021-97643-3
Abstract: Our aim was to investigate the usefulness of machine learning approaches on linked administrative health data at the population level in predicting older patients’ one-year risk of acute coronary syndrome and death following the use of non-steroidal anti-inflammatory drugs (NSAIDs). Patients from a Western Australian cardiovascular population who were supplied with NSAIDs between 1 Jan 2003 and 31 Dec 2004 were identified from Pharmaceutical Benefits Scheme data. Comorbidities from linked hospital admissions data and medication history were inputs. Admissions for acute coronary syndrome or death within one year from the first supply date were outputs. Machine learning classification methods were used to build models to predict ACS and death. Model performance was measured by the area under the receiver operating characteristic curve (AUC-ROC), sensitivity and specificity. There were 68,889 patients in the NSAIDs cohort with mean age 76 years and 54% were female. 1882 patients were admitted for acute coronary syndrome and 5405 patients died within one year after their first supply of NSAIDs. The multi-layer neural network, gradient boosting machine and support vector machine were applied to build various classification models. The gradient boosting machine achieved the best performance with an average AUC-ROC of 0.72 predicting ACS and 0.84 predicting death. Machine learning models applied to linked administrative data can potentially improve adverse outcome risk prediction. Further investigation of additional data and approaches are required to improve the performance for adverse outcome risk prediction.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2015
Publisher: IEEE
Date: 2001
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2005
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2018
Publisher: Springer International Publishing
Date: 2016
Publisher: Elsevier BV
Date: 12-2008
Publisher: Springer Science and Business Media LLC
Date: 20-09-2006
Publisher: IEEE
Date: 05-2019
Publisher: IEEE
Date: 2001
Publisher: Springer Science and Business Media LLC
Date: 02-10-2011
Publisher: Elsevier BV
Date: 02-2011
Publisher: Springer International Publishing
Date: 2019
Publisher: Springer Berlin Heidelberg
Date: 2011
Publisher: Springer International Publishing
Date: 2019
Publisher: Institution of Engineering and Technology (IET)
Date: 10-2017
Publisher: Springer Berlin Heidelberg
Date: 2011
Publisher: IEEE
Date: 06-2011
Publisher: Elsevier BV
Date: 08-2007
Publisher: Elsevier BV
Date: 04-2000
Start Date: 2021
End Date: 12-2023
Amount: $355,331.00
Funder: Australian Research Council
View Funded ActivityStart Date: 07-2021
End Date: 06-2024
Amount: $290,831.00
Funder: Australian Research Council
View Funded Activity