Efficient deterministic algorithm for huge-sized noisy sensor localization problems via canonical duality theory
- Authors: Latorre, Vittorio , Gao, David
- Date: 2021
- Type: Text , Journal article
- Relation: IEEE Transactions on Cybernetics Vol. 51, no. 10 (2021), p. 5069-5081
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- Description: This paper presents a new deterministic method and a polynomial-time algorithm for solving general huge-sized sensor network localization problems. The problem is first formulated as a nonconvex minimization, which was considered as an NP-hard based on conventional theories. However, by the canonical duality theory, this challenging problem can be equivalently converted into a convex dual problem. By introducing a new optimality measure, a powerful canonical primal-dual interior (CPDI) point algorithm is developed which can solve efficiently huge-sized problems with hundreds of thousands of sensors. The new method is compared with the popular methods in the literature. Results show that the CPDI algorithm is not only faster than the benchmarks but also much more accurate on networks affected by noise on the distances. © 2013 IEEE.
Intelligent energy prediction techniques for fog computing networks
- Authors: Farooq, Umar , Shabir, Muhammad , Javed, Muhammad , Imran, Muhammad
- Date: 2021
- Type: Text , Journal article
- Relation: Applied Soft Computing Vol. 111, no. (2021), p.
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- Description: Energy Efficiency is a key concern for future fog-enabled Internet of Things (IoT). Since Fog Nodes (FNs) are energy-constrained devices, task offloading techniques must consider the energy consumption of the FNs to maximize the performance of IoT applications. In this context, accurate energy prediction can enable the development of intelligent energy-aware task offloading techniques. In this paper, we present two energy prediction techniques, the first one is based on the Recursive Least Square (RLS) filter and the second one uses the Artificial Neural Network (ANN). Both techniques use inputs such as the number of tasks and size of the tasks to predict the energy consumption at different fog nodes. Simulation results show that both techniques have a root mean square error of less than 3%. However, the ANN-based technique shows up to 20% less root mean square error as compared to the RLS-based technique. © 2021 Elsevier B.V.
Malware detection in edge devices with fuzzy oversampling and dynamic class weighting
- Authors: Khoda, Mahbub , Kamruzzaman, Joarder , Gondal, Iqbal , Imam, Tasadduq , Rahman, Ashfaqur
- Date: 2021
- Type: Text , Journal article
- Relation: Applied Soft Computing Vol. 112, no. (2021), p.
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- Description: In Internet-of-things (IoT) domain, edge devices are used increasingly for data accumulation, preprocessing, and analytics. Intelligent integration of edge devices with Artificial Intelligence (AI) facilitates real-time analysis and decision making. However, these devices simultaneously provide additional attack opportunities for malware developers, potentially leading to information and financial loss. Machine learning approaches can detect such attacks but their performance degrades when benign samples substantially outnumber malware samples in training data. Existing approaches for such imbalanced data assume samples represented as continuous features and thus can generate invalid samples when malware applications are represented by binary features. We propose a novel malware oversampling technique that addresses this issue. Further, we propose two approaches for malware detection. Our first approach uses fuzzy set theory, while the second approach dynamically assigns higher priority to malware samples using a novel loss function. Combining our oversampling technique with these approaches, the proposed approach attains over 9% improvement over competing methods in terms of F1_score. Our approaches can, therefore, result in enhanced privacy and security in edge computing services. © 2021 Elsevier B.V.
Treatment of multiple input uncertainties using the scaled boundary finite element method
- Authors: Dsouza, Shaima , Varghese, Tittu , Ooi, Ean Tat , Natarajan, Sundararajan , Bordas, Stephane
- Date: 2021
- Type: Text , Journal article
- Relation: Applied Mathematical Modelling Vol. 99, no. (2021), p. 538-554
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- Description: This paper presents a non-intrusive scaled boundary finite element method to consider multiple input uncertainties, viz., material and geometry. The types of geometric uncertainties considered include the shape and size of inclusions. The inclusions are implicitly defined, and a robust framework is presented to treat the interfaces, which does not require explicit generation of a conforming mesh or special enrichment techniques. A polynomial chaos expansion is used to represent the input and the output uncertainties. The efficiency and the accuracy of the proposed framework are elucidated in detail with a few problems by comparing the results with the conventional Monte Carlo method. A sensitivity analysis based on Sobol’ indices using the developed framework is presented to identify the critical input parameter that has a higher influence on the output response. © 2021 Elsevier Inc.
Assessing cohesion of the rocks proposing a new intelligent technique namely group method of data handling
- Authors: Chen, Wusi , Khandelwal, Manoj , Murlidhar, Bhatawdekar , Bui, Dieu , Tahir, Mahmood , Katebi, Javad
- Date: 2020
- Type: Text , Journal article
- Relation: Engineering with Computers Vol. 36, no. 2 (2020), p. 783-793
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- Description: In this study, evaluation and prediction of rock cohesion is assessed using multiple regression as well as group method of data handling (GMDH). It is a well-known fact that cohesion is the most crucial rock shear strength parameter, which is a key parameter for the stability evaluation of some geotechnical structures such as rock slope. To fulfill the aim of this study, a database of three model input parameters, i.e., p wave velocity, uniaxial compressive strength and Brazilian tensile strength and one model output, which is cohesion of limestone samples was prepared and utilized by GMDH. Different GMDH models with neurons and layers and selection pressure were tested and assessed. It was found that GMDH model number 4 (with 8 layers) shows the best performance among all of tested models between the input and output parameters for the prediction and assessment of rock cohesion with coefficient of determination (R2) values of 0.928 and 0.929, root mean square error values of 0.3545 and 0.3154 for training and testing datasets, respectively. Multiple regression analysis was also performed on the same database and R2 values were obtained as 0.8173 and 0.8313 between input and output parameters for the training and testing of the models, respectively. The GMDH technique developed in this study is introduced as a new model in field of rock shear strength parameters. © 2019, Springer-Verlag London Ltd., part of Springer Nature.
Effects of a proper feature selection on prediction and optimization of drilling rate using intelligent techniques
- Authors: Liao, Xiufeng , Khandelwal, Manoj , Yang, Haiqing , Koopialipoor, Mohammadreza , Murlidhar, Bhatawdekar
- Date: 2020
- Type: Text , Journal article
- Relation: Engineering with Computers Vol. 36, no. 2 (Apr 2020), p. 499-510
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- Description: One of the important factors during drilling times is the rate of penetration (ROP), which is controlled based on different variables. Factors affecting different drillings are of paramount importance. In the current research, an attempt was made to better recognize drilling parameters and optimize them based on an optimization algorithm. For this purpose, 618 data sets, including RPM, flushing media, and compressive strength parameters, were measured and collected. After an initial investigation, the compressive strength feature of samples, which is an important parameter from the rocks, was used as a proper criterion for classification. Then using intelligent systems, three different levels of the rock strength and all data were modeled. The results showed that systems which were classified based on compressive strength showed a better performance for ROP assessment due to the proximity of features. Therefore, these three levels were used for classification. A new artificial bee colony algorithm was used to solve this problem. Optimizations were applied to the selected models under different optimization conditions, and optimal states were determined. As determining drilling machine parameters is important, these parameters were determined based on optimal conditions. The obtained results showed that this intelligent system can well improve drilling conditions and increase the ROP value for three strength levels of the rocks. This modeling system can be used in different drilling operations.
The effects of the no-touch gap on the no-touch bipolar radiofrequency ablation treatment of liver cancer : a numerical study using a two compartment model
- Authors: Yap, Shelley , Cheong, Jason , Foo, Ji , Ooi, Ean Tat , Ooi, Ean Hin
- Date: 2020
- Type: Text , Journal article
- Relation: Applied Mathematical Modelling Vol. 78, no. (2020), p. 134-147
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- Description: The no-touch bipolar radiofrequency ablation (RFA) for cancer treatment is advantageous primarily because of its capability to prevent tumour track seeding (TTS). In this technique, the RF probes are placed at a distance (no-touch gap) away from the tumour boundary. Ideally, the RF probes should be placed sufficiently far from the tumour in order to avoid TTS. However, having a gap that is too large can lead to ineffective ablation. This paper investigates how the selection of the no-touch gap can affect the tissue electrical and thermal responses during the no-touch bipolar RFA treatment. Simulations were carried out on a two compartment model using the finite element method. Results obtained indicated that a gap that is too large may lead to incomplete ablation and failure to achieve significant ablation margin. However, keeping the gap to be too small may not be clinically practical. It was suggested that the incomplete ablation and the insufficient ablation margin observed in some of the cases may require the placement of additional probes around the tumour. The present study stresses on the importance of identifying the optimal no-touch gap that can avoid TTS without compromising the treatment outcome. © 2019 Elsevier Inc.
A difference of convex optimization algorithm for piecewise linear regression
- Authors: Bagirov, Adil , Taheri, Sona , Asadi, Soodabeh
- Date: 2019
- Type: Text , Journal article
- Relation: Journal of Industrial and Management Optimization Vol. 15, no. 2 (2019), p. 909-932
- Relation: http://purl.org/au-research/grants/arc/DP140103213
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- Description: The problem of finding a continuous piecewise linear function approximating a regression function is considered. This problem is formulated as a nonconvex nonsmooth optimization problem where the objective function is represented as a difference of convex (DC) functions. Subdifferentials of DC components are computed and an algorithm is designed based on these subdifferentials to find piecewise linear functions. The algorithm is tested using some synthetic and real world data sets and compared with other regression algorithms.
Implementing an ANN model optimized by genetic algorithm for estimating cohesion of limestone samples
- Authors: Khandelwal, Manoj , Marto, Aminaton , Fatemi, Seyed , Ghoroqi, Mahyar , Armaghani, Danial , Singh, Trilok , Tabrizi, Omid
- Date: 2018
- Type: Text , Journal article
- Relation: Engineering with Computers Vol. 34, no. 2 (2018), p. 307-317
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- Description: Shear strength parameters such as cohesion are the most significant rock parameters which can be utilized for initial design of some geotechnical engineering applications. In this study, evaluation and prediction of rock material cohesion is presented using different approaches i.e., simple and multiple regression, artificial neural network (ANN) and genetic algorithm (GA)-ANN. For this purpose, a database including three model inputs i.e., p-wave velocity, uniaxial compressive strength and Brazilian tensile strength and one output which is cohesion of limestone samples was prepared. A meaningful relationship was found for all of the model inputs with suitable performance capacity for prediction of rock cohesion. Additionally, a high level of accuracy (coefficient of determination, R2 of 0.925) was observed developing multiple regression equation. To obtain higher performance capacity, a series of ANN and GA-ANN models were built. As a result, hybrid GA-ANN network provides higher performance for prediction of rock cohesion compared to ANN technique. GA-ANN model results (R2 = 0.976 and 0.967 for train and test) were better compared to ANN model results (R2 = 0.949 and 0.948 for train and test). Therefore, this technique is introduced as a new one in estimating cohesion of limestone samples. © 2017, Springer-Verlag London Ltd., part of Springer Nature.
A comparison of bidding strategies for online auctions using fuzzy reasoning and negotiation decision functions
- Authors: Kaur, Preetinder , Goyal, Madhu , Lu, Jie
- Date: 2017
- Type: Text , Journal article
- Relation: IEEE Transactions on Fuzzy Systems Vol. 25, no. 2 (2017), p. 425-438
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- Description: Bidders often feel challenged when looking for the best bidding strategies to excel in the competitive environment of multiple and simultaneous online auctions for same or similar items. Bidders face complicated issues for deciding which auction to participate in, whether to bid early or late, and how much to bid. In this paper, we present the design of bidding strategies, which aim to forecast the bid amounts for buyers at a particular moment in time based on their bidding behavior and their valuation of an auctioned item. The agent develops a comprehensive methodology for final price estimation, which designs bidding strategies to address buyers' different bidding behaviors using two approaches: Mamdani method with regression analysis and negotiation decision functions. The experimental results show that the agents who follow fuzzy reasoning with a regression approach outperform other existing agents in most settings in terms of their success rate and expected utility.
ZERO++ : Harnessing the power of zero appearances to detect anomalies in large-scale data sets
- Authors: Pang, Guansong , Ting, Kaiming , Albrecht, David , Jin, Huidong
- Date: 2016
- Type: Text , Journal article
- Relation: Journal of Artificial Intelligence Research Vol. 57, no. (2016), p. 593-620
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- Description: 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.
An improved simplex-based adaptive evolutionary digital filter and its application for fault detection of rolling element bearings
- Authors: Xiao, Huifang , Shao, Yimin , Zhou, Xiaojun , Wilcox, Steven
- Date: 2014
- Type: Text , Journal article
- Relation: Measurement: Journal of the International Measurement Confederation Vol. 55, no. (2014), p. 25-32
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- Description: The de-noising performance and convergence behavior of the adaptive evolutionary digital filter (EDF) are restricted by the factors of constant evolutionary coefficients and taking the reciprocal of average energy of residual signal as the fitness function. In this paper, an improved adaptive evolutionary digital filter based on the simplex method (EDF-SM) is proposed to overcome the shortcomings of the original EDF. A new evolutionary rule was constructed by introducing the simplex-based mutating method and by then combining this with the original cloning and mating methods. The reciprocal of sample entropy was taken as the fitness function and variable evolutionary coefficients were employed. Numerical examples show that the proposed EDF-SM exhibits a higher convergence rate and a better de-noising behavior than the other EDFs. The effectiveness of the proposed method in discovering fault characteristics and detecting faults of rolling element bearings is supported using an experimental test. © 2014 Elsevier Ltd. All rights reserved.
Optimality conditions and optimization methods for quartic polynomial optimization
- Authors: Wu, Zhiyou , Tian, Jing , Quan, Jing , Ugon, Julien
- Date: 2014
- Type: Text , Journal article
- Relation: Applied Mathematics and Computation Vol. 232, no. (2014), p. 968-982
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- Description: In this paper multivariate quartic polynomial optimization program (QPOP) is considered. Quartic optimization problems arise in various practical applications and are proved to be NP hard. We discuss necessary global optimality conditions for quartic problem (QPOP). And then we present a new (strongly or ε-strongly) local optimization method according to necessary global optimality conditions, which may escape and improve some KKT points. Finally we design a global optimization method for problem (QPOP) by combining the new (strongly or ε-strongly) local optimization method and an auxiliary function. Numerical examples show that our algorithms are efficient and stable.
Using meta-regression data mining to improve predictions of performance based on heart rate dynamics for Australian football
- Authors: Jelinek, Herbert , Kelarev, Andrei , Robinson, Dean , Stranieri, Andrew , Cornforth, David
- Date: 2014
- Type: Text , Journal article
- Relation: Applied Soft Computing Vol. 14, no. PART A (2014), p. 81-87
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- Description: This work investigates the effectiveness of using computer-based machine learning regression algorithms and meta-regression methods to predict performance data for Australian football players based on parameters collected during daily physiological tests. Three experiments are described. The first uses all available data with a variety of regression techniques. The second uses a subset of features selected from the available data using the Random Forest method. The third used meta-regression with the selected feature subset. Our experiments demonstrate that feature selection and meta-regression methods improve the accuracy of predictions for match performance of Australian football players based on daily data of medical tests, compared to regression methods alone. Meta-regression methods and feature selection were able to obtain performance prediction outcomes with significant correlation coefficients. The best results were obtained by the additive regression based on isotonic regression for a set of most influential features selected by Random Forest. This model was able to predict athlete performance data with a correlation coefficient of 0.86 (p < 0.05). © 2013 Published by Elsevier B.V. All rights reserved.
- Description: C1
Extraction and processing of real time strain of embedded FBG sensors using a fixed filter FBG circuit and an artificial neural network
- Authors: Kahandawa, Gayan , Epaarachchi, Jayantha , Wang, Hao , Canning, John , Lau, Alan
- Date: 2013
- Type: Text , Journal article
- Relation: Measurement: Journal of the International Measurement Confederation Vol. 46, no. 10 (2013), p. 4045-4051
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- Description: Fibre Bragg Grating (FBG) sensors have been used in the development of structural health monitoring (SHM) and damage detection systems for advanced composite structures over several decades. Unfortunately, to date only a handful of appropriate configurations and algorithm sare available for using in SHM systems have been developed. This paper reveals a novel configuration of FBG sensors to acquire strain reading and an integrated statistical approach to analyse data in real time. The proposed configuration has proven its capability to overcome practical constraints and the engineering challenges associated with FBG-based SHM systems. A fixed filter decoding system and an integrated artificial neural network algorithm for extracting strain from embedded FBG sensor were proposed and experimentally proved. Furthermore, the laboratory level experimental data was used to verify the accuracy of the system and it was found that the error levels were less than 0.3% in predictions. The developed SMH system using this technology has been submitted to US patent office and will be available for use of aerospace applications in due course. © 2013 Elsevier Ltd. All rights reserved.
Application of soft computing to predict blast-induced ground vibration
- Authors: Khandelwal, Manoj , Kumar, Lalit , Yellishetty, Mohan
- Date: 2011
- Type: Text , Journal article
- Relation: Engineering with Computers Vol. 27, no. 2 (2011), p. 117-125
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- Description: In this study, an attempt has been made to evaluate and predict the blast-induced ground vibration by incorporating explosive charge per delay and distance from the blast face to the monitoring point using artificial neural network (ANN) technique. A three-layer feed-forward back-propagation neural network with 2-5-1 architecture was trained and tested using 130 experimental and monitored blast records from the surface coal mines of Singareni Collieries Company Limited, Kothagudem, Andhra Pradesh, India. Twenty new blast data sets were used for the validation and comparison of the peak particle velocity (PPV) by ANN and conventional vibration predictors. Results were compared based on coefficient of determination and mean absolute error between monitored and predicted values of PPV. © 2009 Springer-Verlag London Limited.
Blast-induced ground vibration prediction using support vector machine
- Authors: Khandelwal, Manoj
- Date: 2011
- Type: Text , Journal article
- Relation: Engineering with Computers Vol. 27, no. 3 (2011), p. 193-200
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- Description: Ground vibrations induced by blasting are one of the fundamental problems in the mining industry and may cause severe damage to structures and plants nearby. Therefore, a vibration control study plays an important role in the minimization of environmental effects of blasting in mines. In this paper, an attempt has been made to predict the peak particle velocity using support vector machine (SVM) by taking into consideration of maximum charge per delay and distance between blast face to monitoring point. To investigate the suitability of this approach, the predictions by SVM have been compared with conventional vibration predictor equations. Coefficient of determination (CoD) and mean absolute error were taken as a performance measure. © 2010 Springer-Verlag London Limited.
Classification through incremental max-min separability
- Authors: Bagirov, Adil , Ugon, Julien , Webb, Dean , Karasozen, Bulent
- Date: 2011
- Type: Text , Journal article
- Relation: Pattern Analysis and Applications Vol. 14, no. 2 (2011), p. 165-174
- Relation: http://purl.org/au-research/grants/arc/DP0666061
- Full Text: false
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- Description: Piecewise linear functions can be used to approximate non-linear decision boundaries between pattern classes. Piecewise linear boundaries are known to provide efficient real-time classifiers. However, they require a long training time. Finding piecewise linear boundaries between sets is a difficult optimization problem. Most approaches use heuristics to avoid solving this problem, which may lead to suboptimal piecewise linear boundaries. In this paper, we propose an algorithm for globally training hyperplanes using an incremental approach. Such an approach allows one to find a near global minimizer of the classification error function and to compute as few hyperplanes as needed for separating sets. We apply this algorithm for solving supervised data classification problems and report the results of numerical experiments on real-world data sets. These results demonstrate that the new algorithm requires a reasonable training time and its test set accuracy is consistently good on most data sets compared with mainstream classifiers. © 2010 Springer-Verlag London Limited.
Spectrum of Variable-Random trees
- Authors: Liu, Fei , Ting, Kaiming , Yu, Yang , Zhou, Zhi-Hua
- Date: 2008
- Type: Text , Journal article
- Relation: The Journal of Artificial Intelligence Research Vol. 32, no. (2008), p. 355-384
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- Description: In this paper, we show that a continuous spectrum of randomisation exists, in which most existing tree randomisations are only operating around the two ends of the spectrum. That leaves a huge part of the spectrum largely unexplored. We propose a base learner VR-Tree which generates trees with variable-randomness. VR-Trees are able to span from the conventional deterministic trees to the complete-random trees using a probabilistic parameter. Using VR-Trees as the base models, we explore the entire spectrum of randomised ensembles, together with Bagging and Random Subspace. We discover that the two halves of the spectrum have their distinct characteristics; and the understanding of which allows us to propose a new approach in building better decision tree ensembles. We name this approach Coalescence, which coalesces a number of points in the random-half of the spectrum. Coalescence acts as a committee of "experts" to cater for unforeseeable conditions presented in training data. Coalescence is found to perform better than any single operating point in the spectrum, without the need to tune to a specific level of randomness. In our empirical study, Coalescence ranks top among the benchmarking ensemble methods including Random Forests, Random Subspace and C5 Boosting; and only Coalescence is significantly better than Bagging and Max-Diverse Ensemble among all the methods in the comparison. Although Coalescence is not significantly better than Random Forests, we have identified conditions under which one will perform better than the other.