A new technique to measure interfacial tension of transformer oil using UV-Vis spectroscopy
- Authors: Abu Bakar, Norazhar , Abu-Siada, Ahmed , Islam, Syed , El-Naggar, Mohammed
- Date: 2015
- Type: Text , Journal article
- Relation: IEEE Transactions on Dielectrics and Electrical Insulation Vol. 22, no. 2 (2015), p. 1275-1282
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- Description: Interfacial tension (IFT) and acid numbers of insulating oil are correlated with the number of years that a transformer has been in service and are used as a signal for transformer oil reclamation. Oil sampling for IFT measurement calls for extra precautions due to its high sensitivity to various oil parameters and environmental conditions. The current used technique to measure IFT of transformer oil is relatively expensive, requires an expert to conduct the test and it takes long time since the extraction of oil sample, sending it to external laboratory and getting the results back. This paper introduces a new technique to estimate the IFT of transformer oil using ultraviolet-to-visible (UV-Vis) spectroscopy. UV-Vis spectral response of transformer oil can be measured instantly with relatively cheap equipment, does not need an expert person to conduct the test and has the potential to be implemented online. Results show that there is a good correlation between oil spectral response and its IFT value. Artificial neural network (ANN) approach is proposed to model this correlation.
A review of dissolved gas analysis measurement and interpretation techniques
- Authors: Abu Bakar, Norazhar , Abu-Siada, Ahmed , Islam, Syed
- Date: 2014
- Type: Text , Journal article
- Relation: IEEE Electrical Insulation Magazine Vol. 30, no. 3 (2014), p. 39-49
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- Description: Dissolved gas analysis (DGA) is used to assess the condition of power transformers. It uses the concentrations of various gases dissolved in the transformer oil due to decomposition of the oil and paper insulation. DGA has gained worldwide acceptance as a method for the detection of incipient faults in transformers.
Understanding power transformer frequency response analysis signatures
- Authors: Abu-Siada, Ahmed , Hashemnia, Naser , Islam, Syed , Masoum, Mohammad
- Date: 2013
- Type: Text , Journal article
- Relation: IEEE Electrical Insulation Magazine Vol. 29, no. 3 (2013), p. 48-56
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- Description: This paper presents a comprehensive analysis of the effects of various faults on the FRA signatures of a transformer simulated by a high-frequency model. The faults were simulated through changes in the values of some of the electrical components in the model. It was found that radial displacement of a winding alters the FRA signature over the entire frequency range (10 Hz-1 MHz), whereas changes due to axial displacement occur only at frequencies above 200 kHz. A Table listing various transformer faults and the associated changes in the FRA signature was compiled and could be used in the formulation of standard codes for power transformer FRA signature interpretation.
A combined virtual element method and the scaled boundary finite element method for linear elastic fracture mechanics
- Authors: Adak, Dibyendu , Pramod, ALN , Ooi, Ean Tat , Natarajan, Sundararajan
- Date: 2020
- Type: Text , Journal article
- Relation: Engineering Analysis with Boundary Elements Vol. 113, no. (2020), p. 9-16
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- Description: In this paper, we propose a framework that combines the recently introduced virtual element method (VEM) and the scaled boundary finite element method (SBFEM) to evaluate the fracture parameters. The domain is discretized with arbitrary polygons and the element that contains the crack tip is treated within the framework of the SBFEM. This facilitates a semi-analytical treatment of the crack tip singularity allowing the fracture parameters are estimated directly from the definition. The VEM is employed for the rest of the domain. The salient feature of the VEM is that the terms in the stiffness matrix are computed without requiring higher order quadrature schemes. As both the methods satisfy partition of unity and the compatibility condition, the matrices are assembled as in the conventional FEM. The accuracy of the proposed formulation is demonstrated with two standard benchmark examples. The proposed VEM-SBFEM framework yields accurate results. © 2019 Elsevier Ltd
Machine learning for 5G security : architecture, recent advances, and challenges
- Authors: Afaq, Amir , Haider, Noman , Baig, Muhammad , Khan, Komal , Imran, Muhammad , Razzak, Imran
- Date: 2021
- Type: Text , Journal article
- Relation: Ad Hoc Networks Vol. 123, no. (2021), p.
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- Description: The granularization of crucial network functions implementation using software-centric, and virtualized approaches in 5G networks have brought forth unprecedented security challenges in general and privacy concerns. Moreover, these software components’ premature deployment and compromised supply chain put the individual network components at risk and have a ripple effect for the rest of the network. Some of the novel threats to 5G assets include tampering in identity and access management, supply-chain poisoning, masquerade and bot attacks, loop-holes in source codes. Machine learning (ML) in this context can help to provide heavily dynamic and robust security mechanisms for the software-centric architecture of 5G Networks. ML models’ development and implementation also rely on programmable environments; hence, they can play a vital role in designing, modelling, and automating efficient security protocols. This article presents the threat landscape across 5G networks and discusses the feasibility and architecture of different ML-based models to counter these threats. Also, we present the architecture for automated threat intelligence using cooperative and coordinated ML to secure 5G assets and infrastructure. We also present the summary of closely related existing works along with future research challenges. © 2021 Elsevier B.V.
Improving voltage of remote connection using wind-solar farms equipped with new voltage control strategy based on virtual impedance monitoring enabled by IEC 61850 communication
- Authors: Aghanoori, Navid , Masoum, Mohammad , Islam, Syed , Abu-Siada, Ahmed , Nethery, Steven
- Date: 2019
- Type: Text , Journal article
- Relation: IET Generation, Transmission and Distribution Vol. 13, no. 11 (2019), p. 2112-2122
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- Description: This study explores how the voltage control of a remote part of the utility gird can be improved using more sophisticated voltage control on wind-solar farms equipped with fast communication platforms. The idea is to make renewable plant the master voltage controller during large disturbance events in the grid. This is done by proposing an enhanced voltage droop control strategy based on instantaneous reactive power consumption by monitoring the virtual impedance of the point of connection using a new customised data class model of IEC 61850 communication protocol. The conventional centralised voltage droop control strategy and the proposed instantaneous direct voltage control method are both implemented on the White Rock Solar Wind Farm in NSW, Australia and their performances are compared using both MATLAB Simulink simulations under 5% voltage step disturbances, single-phase-to-ground and three-phase-to-ground faults as well as some tests conducted in the field.
Enhancement of microgrid operation by considering the cascaded impact of communication delay on system stability and power management
- Authors: Aghanoori, Navid , Masoum, Mohammad , Abu-Siada, Ahmed , Islam, Syed
- Date: 2020
- Type: Text , Journal article
- Relation: International Journal of Electrical Power and Energy Systems Vol. 120, no. (2020), p.
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- Description: Power management, system stability and communication structure are three key aspects of microgrids (MGs) that have been explored in many research studies. However, the cascaded effect of communication structure on system stability followed by the impact of stability on the power management has not been fully explored in the literature yet and needs more attention. This paper not only explores this cascaded impact, but also provides a comprehensive platform to optimally consider three layers of MG design and operation from this perspective. For generation cost minimization and stability assessment, the proposed platform uses an adaptive particle swarm optimization (PSO) while a new class of data exchange scheme based on IEC 61850 protocol is proposed to reduce the communication time delays among the inverters of distributed generations and the MG control center. This paper also considers the system stability using small-signal model of a MG in a real-time manner as an embedded function in the PSO. In this context investigations have been conducted by modeling an isolated MG with solar farm, fuel cell generator and micro-turbine in MATLAB Simulink. Detailed simulation results indicate the proposed power and stability management method effectively reduces the MG generation cost through maximizing the utilization of the available renewable generations while considering system stability. © 2020 Elsevier Ltd
Pattern recognition in bioinformatics : Girls lose out
- Authors: Ahmad, Shandar , Chetty, Madhu , Schmidt, Bertil
- Date: 2010
- Type: Text , Journal article
- Relation: Pattern Recognition Letter Vol. 31, no. 14 (2010), p. 2071-2072
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- Description: Editorial- With the advent of high speed computers, in-silico studies on biological patterns in recent years have been significantly impacted by the pattern recognition techniques. In this special issue, ‘Pattern Recognition in Bioinformatics’, we present various sophisticated algorithms for a wide range of pattern recognition problems from the world of complex biological systems, whether these are specific sequence signatures – motifs that stand out in discovering its partner – or substructures in an interaction network that determines an organisms’ response to external stimuli. The 12 high-quality articles included in this special issue are essentially based on significant extensions of the selected papers presented at the Third International Conference on Pattern Recognition in Bioinformatics (PRIB 2008) held in Melbourne, Australia. All these selected papers for special issue have again undergone a thorough review by at least three reviewers who are experts in the field. The fresh review process was followed to ensure that the papers met the high standards of scientific and technical merit of the Pattern Recognition Letters journal. The issue is broadly divided into three sections of four papers each, namely (1) Section 1: Interaction Networks and Feature-based Predictions (2) Section 2: Microarray and Transcription Data Analysis (3) Section 3: Sequence Analysis and Motif Discovery
Texture as pixel feature for video object segmentation
- Authors: Ahmed, Rakib , Karmakar, Gour , Dooley, Laurence
- Date: 2008
- Type: Text , Journal article
- Relation: Electronics Letters Vol. 44, no. 19 (2008), p. 1126-1127
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Prefix coding of integers with real-valued predictions using cosets
- Authors: Ali, Mortuza , Murshed, Manzur
- Date: 2007
- Type: Text , Journal article
- Relation: IEEE Communications Letters, vol. 11, no. 10, IEEE Communications Society, p. 814-816
- Full Text: false
- Description: In predictive coding of integers real-valued residuals are mapped to integers before encoding, leaving room for improvement by reducing the loss due to rounding. In this paper, we propose a new prefix coding scheme where actual integer values, instead of the residuals, are encoded using cosets with real domain predictions as the side information. This novel coding scheme outperforms Golomb-based coding by reducing the rounding loss with similar computational and memory complexity.
Symbol coding of Laplacian distributed prediction residuals
- Authors: Ali, Mortuza , Murshed, Manzur
- Date: 2015
- Type: Text , Journal article
- Relation: Digital Signal Processing: A Review Journal Vol. 44, no. 1 (2015), p. 76-87
- Relation: http://purl.org/au-research/grants/arc/DP130103670
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- Description: Predictive coding schemes, proposed in the literature, essentially model the residuals with discrete distributions. However, real-valued residuals can arise in predictive coding, for example, from the usage of an r order linear predictor specified by r real-valued coefficients. In this paper, we propose a symbol-by-symbol coding scheme for the Laplace distribution, which closely models the distribution of real-valued residuals in practice. To efficiently exploit the real-valued predictions at a given precision, the proposed scheme essentially combines the process of residual computation and coding, in contrast to conventional schemes that separate these two processes. In the context of adaptive predictive coding framework, where the source statistics must be learnt from the data, the proposed scheme has the advantage of lower 'model cost' as it involves learning only one parameter. In this paper, we also analyze the proposed parametric coding scheme to establish the relationship between the optimal value of the coding parameter and the scale parameter of the Laplace distribution. Our experimental results demonstrated the compression efficiency and computational simplicity of the proposed scheme in adaptive coding of residuals against the widely used arithmetic coding, Rice-Golomb coding, and the Merhav-Seroussi-Weinberger scheme adopted in JPEG-LS.
- Description: Predictive coding schemes, proposed in the literature, essentially model the residuals with discrete distributions. However, real-valued residuals can arise in predictive coding, for example, from the usage of an r order linear predictor specified by r real-valued coefficients. In this paper, we propose a symbol-by-symbol coding scheme for the Laplace distribution, which closely models the distribution of real-valued residuals in practice. To efficiently exploit the real-valued predictions at a given precision, the proposed scheme essentially combines the process of residual computation and coding, in contrast to conventional schemes that separate these two processes. In the context of adaptive predictive coding framework, where the source statistics must be learnt from the data, the proposed scheme has the advantage of lower 'model cost' as it involves learning only one parameter. In this paper, we also analyze the proposed parametric coding scheme to establish the relationship between the optimal value of the coding parameter and the scale parameter of the Laplace distribution. Our experimental results demonstrated the compression efficiency and computational simplicity of the proposed scheme in adaptive coding of residuals against the widely used arithmetic coding, Rice-Golomb coding, and the Merhav-Seroussi-Weinberger scheme adopted in JPEG-LS. © 2015 Elsevier Inc. All rights reserved.
A count data model for heart rate variability forecasting and premature ventricular contraction detection
- Authors: Allami, Ragheed , Stranieri, Andrew , Balasubramanian, Venki , Jelinek, Herbert
- Date: 2017
- Type: Text , Journal article
- Relation: Signal Image and Video Processing Vol. 11, no. 8 (2017), p. 1427-1435
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- Description: Heart rate variability (HRV) measures including the standard deviation of inter-beat variations (SDNN) require at least 5 min of ECG recordings to accurately measure HRV. In this paper, we predict, using counts data derived from a 3-min ECG recording, the 5-min SDNN and also detect premature ventricular contraction (PVC) beats with a high degree of accuracy. The approach uses counts data combined with a Poisson-generated function that requires minimal computational resources and is well suited to remote patient monitoring with wearable sensors that have limited power, storage and processing capacity. The ease of use and accuracy of the algorithm provide opportunity for accurate assessment of HRV and reduce the time taken to review patients in real time. The PVC beat detection is implemented using the same count data model together with knowledge-based rules derived from clinical knowledge.
Dynamic voltage signature of large scale PV enriched streesed power system
- Authors: Alzahrani, Saeed , Shah, Rakibuzzaman , Mithulananthan, Nadarajah , Sode-Yome, Arthit
- Date: 2020
- Type: Text , Conference proceedings
- Relation: 2nd International Conference on Smart Power and Internet Energy Systems, SPIES 2020; Bangkok, Thailand; 15th-18th September 2020 p. 275-280
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- Description: Renewable power generations including flexible demand and energy storage systems leverage significant changes in network operation. Thereby, power systems with high renewable penetration manifest deteriorated resilience to disturbances. Hence, the stable operation of the system could be affected. With a paradigm shift, dynamic voltage stability becomes one of the major concerns for the transmission system operators (TSOs). Predicting the dynamic voltage signature for the transmission system with high penetration of renewables is essential to assist in selecting appropriate corrective control. This paper utilized a comprehensive assessment framework to identify the dynamic voltage signature of the power system with PV and various loads. The voltage recovery index has been chosen as the quantifiable index to extricate the dynamic voltage signature. The applicability of the proposed framework is discussed using simulation studies on the IEEE-39 bus test system. © 2020 IEEE.
Fuzzy logic approach in power transformers management and decision making
- Authors: Arshad, Muhammad , Islam, Syed , Khaliq, Abdul
- Date: 2014
- Type: Text , Journal article
- Relation: IEEE Transactions on Dielectrics and Electrical Insulation Vol. 21, no. 5 (2014), p. 2343-2354
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- Description: The degradation of insulation systems is a complex physical process, many parameters act at the same time thus making the interpretation extremely difficult. The insulation is very much responsive in transformer serving closer to design life. Strategic maintenance and operational procedures are best formulated where the condition of existing unit has been accurately assessed. To facilitate asset management and decision making, asset's condition assessment is vital using reliable, non-intrusive diagnostics and monitoring tools together with expert system. Transformer assessment can be carried out effectively by identifying and integrating its criticalities using fuzzy logic technique. In this research, asset management and decision making model has been developed using diagnostics and data interpretation techniques based on fuzzy logic approach. Enhance reliability could be achieved by integrating real time condition monitoring, maintenance, management activities and cost effective optimization techniques. This model facilitates effectively to address criticalities and allow better planning, maintenance approach as well as to predict the remnant life of the asset within a practical accuracy.
Robust image corner detection based on the chord-to-point distance accumulation technique
- Authors: Awrangjeb, Mohammad , Lu, Guojun
- Date: 2008
- Type: Text , Journal article
- Relation: IEEE Transactions on Multimedia, vol. 10, no. 6, IEEE, p. 1059-1072
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Performance comparisons of contour-based corner detectors
- Authors: Awrangjeb, Mohammad , Lu, Guojun , Fraser, Clive
- Date: 2012
- Type: Text , Journal article
- Relation: IEEE Transactions on Image Processing Vol. 21, no. 9 (2012), p. 4167-4179
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- Description: Abstract— Corner detectors have many applications in computer vision and image identification and retrieval. Contour-based corner detectors directly or indirectly estimate a significance measure (e.g., curvature) on the points of a planar curve, and select the curvature extrema points as corners. While an extensive number of contour-based corner detectors have been proposed over the last four decades, there is no comparative study of recently proposed detectors. This paper is an attempt to fill this gap. The general framework of contour-based corner detection is presented, and two major issues – curve smoothing and curvature estimation, which have major impacts on the corner detection performance, are discussed. A number of promising detectors are compared using both automatic and manual evaluation systems on two large datasets. It is observed that while the detectors using indirect curvature estimation techniques are more robust, the detectors using direct curvature estimation techniques are faster.
An improved curvature scale-space corner detector and a robust corner matching approach for transformed image identification
- Authors: Awrangjeb, Mohammad , Lu, Guojun
- Date: 2008
- Type: Text , Journal article
- Relation: Image Processing, IEEE Transactions Vol. 17, no. 12 (2008), p. 2425-2441
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- Description: There are many applications, such as image copyright protection, where transformed images of a given test image need to be identified. The solution to this identification problem consists of two main stages. In stage one, certain representative features, such as corners, are detected in all images. In stage two, the representative features of the test image and the stored images are compared to identify the transformed images for the test image. Curvature scale-space (CSS) corner detectors look for curvature maxima or inflection points on planar curves. However, the arc-length used to parameterize the planar curves by the existing CSS detectors is not invariant to geometric transformations such as scaling. As a solution to stage one, this paper presents an improved CSS corner detector using the affine-length parameterization which is relatively invariant to affine transformations. We then present an improved corner matching technique as a solution to the stage two. Finally, we apply the proposed corner detection and matching techniques to identify the transformed images for a given image and report the promising results.
Energy-balanced transmission policies for wireless sensor networks
- Authors: Azad, Arman , Kamruzzaman, Joarder
- Date: 2011
- Type: Text , Journal article
- Relation: IEEE Transactions on Mobile Computing Vol. 10, no. 7 (2011), p. 927-940
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- Description: Transmission policy, in addition to topology control, routing, and MAC protocols, can play a vital role in extending network lifetime. Existing transmission policies, however, cause an extremely unbalanced energy usage that contributes to early demise of some sensors reducing overall network's lifetime drastically. Considering cocentric rings around the sink, we decompose the transmission distance of traditional multihop scheme into two parts: ring thickness and hop size, analyze the traffic and energy usage distribution among sensors and determine how energy usage varies and critical ring shifts with hop size. Based on above observations, we propose a transmission scheme and determine the optimal ring thickness and hop size by formulating network lifetime as an optimization problem. Numerical results show substantial improvements in terms of network lifetime and energy usage distribution over existing policies. Two other variations of this policy are also presented by redefining the optimization problem considering: 1) concomitant hop size variation by sensors over lifetime along with optimal duty cycles, and 2) a distinct set of hop sizes for sensors in each ring. Both variations bring increasingly uniform energy usage with lower critical energy and further improves lifetime. A heuristic for distributed implementation of each policy is also presented.
Incremental DC optimization algorithm for large-scale clusterwise linear regression
- Authors: Bagirov, Adil , Taheri, Sona , Cimen, Emre
- Date: 2021
- Type: Text , Journal article
- Relation: Journal of Computational and Applied Mathematics Vol. 389, no. (2021), p. 1-17
- Relation: https://purl.org/au-research/grants/arc/DP190100580
- Full Text: false
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- Description: The objective function in the nonsmooth optimization model of the clusterwise linear regression (CLR) problem with the squared regression error is represented as a difference of two convex functions. Then using the difference of convex algorithm (DCA) approach the CLR problem is replaced by the sequence of smooth unconstrained optimization subproblems. A new algorithm based on the DCA and the incremental approach is designed to solve the CLR problem. We apply the Quasi-Newton method to solve the subproblems. The proposed algorithm is evaluated using several synthetic and real-world data sets for regression and compared with other algorithms for CLR. Results demonstrate that the DCA based algorithm is efficient for solving CLR problems with the large number of data points and in particular, outperforms other algorithms when the number of input variables is small. © 2020 Elsevier B.V.
An L-2-Boosting Algorithm for Estimation of a Regression Function
- Authors: Bagirov, Adil , Clausen, Conny , Kohler, Michael
- Date: 2010
- Type: Text , Journal article
- Relation: IEEE Transactions on Information Theory Vol. 56, no. 3 (2010), p. 1417-1429
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- Description: An L-2-boosting algorithm for estimation of a regression function from random design is presented, which consists of fitting repeatedly a function from a fixed nonlinear function space to the residuals of the data by least squares and by defining the estimate as a linear combination of the resulting least squares estimates. Splitting of the sample is used to decide after how many iterations of smoothing of the residuals the algorithm terminates. The rate of convergence of the algorithm is analyzed in case of an unbounded response variable. The method is used to fit a sum of maxima of minima of linear functions to a given data set, and is compared with other nonparametric regression estimates using simulated data.