Can supervise: YES
Nguyen, LV, Hu, G & Spanos, CJ 2019, 'Efficient Sensor Deployments for Spatio-Temporal Environmental Monitoring', IEEE Transactions on Systems Man and Cybernetics: Systems.View/Download from: UTS OPUS or Publisher's site
Le, A, Le, H, Nguyen, L & Phan, M 2019, 'An efficient adaptive hierarchical sliding mode control strategy using neural networks for 3D overhead cranes', International Journal of Automation and Computing.View/Download from: UTS OPUS
This paper proposes a new approach to robustly control a 2D under-actuated overhead crane system, where a payload is effectively transported to a destination in real time with small sway angles, given its inherent uncertainties such as actuator nonlinearities and external disturbances. The control law is proposed to be developed by the use of the robust hierarchical sliding mode control (HSMC) structure in which a second-level sliding surface is formulated by two first-level sliding surfaces drawn on both actuated and under-actuated outputs of the crane. The unknown and uncertain parameters of the proposed control scheme are then adaptively estimated by the fuzzy observer, where the adaptation mechanism is derived from the Lyapunov theory. More importantly, stability of the proposed strategy is theoretically proved. Effectiveness of the proposed adaptive fuzzy observer based HSMC (AFHSMC) approach was extensively validated by implementing the algorithm in both synthetic simulations and real-life experiments, where the results obtained by our method are highly promising.
Thiyagarajan, K, Kodagoda, S, Nguyen, LV & Ranasinghe, R 2018, 'Sensor Failure Detection and Faulty Data Accommodation Approach for Instrumented Wastewater Infrastructures', IEEE Access, vol. 6, no. 1, pp. 56562-56562.View/Download from: Publisher's site
Nguyen, LV, Nguyen, HT & Le, HX 2017, 'Efficient Approach for Maximizing Lifespan in Wireless Sensor Networks by Using Mobile Sinks', ETRI Journal, vol. 39, no. 3, pp. 353-363.View/Download from: UTS OPUS or Publisher's site
Recently, sink mobility has been shown to be highly beneficial in improving network lifetime in wireless sensor networks (WSNs). Numerous studies have exploited mobile sinks (MSs) to collect sensed data in order to improve energy efficiency and reduce WSN operational costs. However, there have been few studies on the effectiveness of MS operation on WSN closed operating cycles. Therefore, it is important to investigate how data is collected and how to plan the trajectory of the MS in order to gather data in time, reduce energy consumption, and improve WSN network lifetime. In this study, we combine two methods, the clusterhead election algorithm and the MS trajectory optimization algorithm, to propose the optimal MS movement strategy. This study aims to provide a closed operating cycle for WSNs, by which the energy consumption and running time of a WSN is minimized during the cluster election and data gathering periods. Furthermore, our flexible MS movement scenarios achieve both a long network lifetime and an optimal MS schedule. The simulation results demonstrate that our proposed algorithm achieves better performance than other wellknown algorithms.
Nguyen, LV, Kodagoda, S, Ranasinghe, R & Dissanayake, G 2017, 'Adaptive Placement for Mobile Sensors in Spatial Prediction under Locational Errors', IEEE Sensors Journal, vol. 17, no. 3, pp. 794-802.View/Download from: UTS OPUS or Publisher's site
Nguyen, LV, Kodagoda, S & Ranasinghe, R 2016, 'Spatial Sensor Selection via Gaussian Markov Random Fields', IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS, vol. 46, no. 9, pp. 1226-1239.View/Download from: UTS OPUS or Publisher's site
Nguyen, LV, Kodagoda, S, Ranasinghe, R & Dissanayake, G 2016, 'Information-Driven Adaptive Sampling Strategy for Mobile Robotic Wireless Sensor Network', IEEE Transactions on Control Systems Technology, vol. 24, no. 1, pp. 372-379.View/Download from: UTS OPUS or Publisher's site
This brief addresses the issue of monitoring physical spatial phenomena of interest using information collected by a resource-constrained network of mobile, wireless, and noisy sensors that can take discrete measurements as they navigate through the environment. We first propose an efficient novel optimality criterion for designing a sampling strategy to find the most informative locations in taking future observations to minimize the uncertainty at all unobserved locations of interest. This solution is proven to be within bounds. The computational complexity of this proposition is shown to be practically feasible. We then prove that under a certain condition of monotonicity property, the approximate entropy at resulting locations obtained by our proposed algorithm is within 1-(1/e) of the optimum, which is then utilized as a stopping criterion for the sampling algorithm. The criterion enables the prediction results to be within user-defined accuracies by controlling the number of mobile sensors. The effectiveness of the proposed method is illustrated using a prepublished data set.
Nguyen, L & Valls Miro, J 2019, 'Acoustic Sensor Networks and Mobile Robotics for Sound Source Localization', IEEE International Conference on Control & Automation, Edinburgh, UK.View/Download from: UTS OPUS
Thiyagarajan, K, Kodagoda, S, Nguyen, LV & Wickramanayake, S 2018, 'Gaussian Markov Random Fields for Localizing Reinforcing Bars in Concrete Infrastructure', International Symposium on Automation and Robotics in Construction, Germany, pp. 1052-1052.View/Download from: UTS OPUS or Publisher's site
Nguyen, LV, Ulapane, N & Valls Miro, J 2018, 'Adaptive Sampling for Spatial Prediction in Environmental Monitoring using Wireless Sensor Networks: A Review', IEEE Conference on Industrial Electronics and Applications, Wuhan, China.View/Download from: UTS OPUS
Ulapane, N, Nguyen, LV, Valls Miro, J & Dissanayake, G 2018, 'A Solution to the Inverse Pulsed Eddy Current Problem Enabling 3D Profiling', IEEE Conference on Industrial Electronics and Applications, Wuhan, China.View/Download from: UTS OPUS
Nguyen, LV, Hu, G & Spanos, CJ 2017, 'Spatio-temporal environmental monitoring for smart buildings', Control & Automation (ICCA), 2017 13th IEEE International Conference on, IEEE, Ohrid, Macedonia, pp. 277-282.View/Download from: UTS OPUS or Publisher's site
The paper addresses the problem of efficiently monitoring environmental fields in a smart building by the use of a network of wireless noisy sensors that take discretely-predefined measurements at their locations through time. It is proposed that the indoor environmental fields are statistically modeled by spatio-temporal non-parametric Gaussian processes. The proposed models are able to effectively predict and estimate the indoor climate parameters at any time and at any locations of interest, which can be utilized to create timely maps of indoor environments. More importantly, the monitoring results are practically crucial for building management systems to efficiently control energy consumption and maximally improve human comfort in the building. The proposed approach was implemented in a real tested space in a university building, where the obtained results are highly promising.
Nguyen, LV, Hu, G & Spanos, CJ 2017, 'Efficient spatio-temporal sensor deployments: A smart building application', Control & Automation (ICCA), 2017 13th IEEE International Conference on, IEEE, Ohrid, Macedonia, pp. 612-617.View/Download from: UTS OPUS or Publisher's site
The paper addresses the problem of efficiently deploying sensors in spatial environments, e.g. smart buildings, for the purpose of monitoring environmental phenomena. By modelling the environmental fields using spatio-temporal Gaussian processes, a new and efficient optimality criterion of minimizing prediction uncertainties is proposed to find the best sensor locations. Though the environmental processes spatially and temporally vary, the proposed approach of choosing sensor positions is not affected by time variations, which significantly reduces computational complexity of the optimization problem. The sensor deployment problem is then solved by a practically and feasibly polynomial algorithm, where its solutions are guaranteed. The proposed approaches were implemented in a real tested space in a university building, where the obtained results are highly promising.
Thiyagarajan, K, Kodagoda, S & Nguyen, LV 2017, 'Predictive Analytics for Detecting Sensor Failure Using Autoregressive Integrated Moving Average Model', Proceedings of the 12th IEEE Conference on Industrial Electronics and Applications, IEEE Conference on Industrial Electronics and Applications, IEEE, Siem Reap, Cambodia, pp. 1926-1931.View/Download from: UTS OPUS
Sensors play a vital role in monitoring the important parameters of critical infrastructure. Failure of such sensors causes destabilization to the entire system. In this regard, this paper proposes a predictive analytics solution for detecting the failure of a sensor that measures surface temperature from an urban sewer. The proposed approach incorporates a forecasting technique based on the past time series of sparse data using an autoregressive integrated moving average (ARIMA) model. Based on the 95% forecast interval and continuity of faulty data, a criterion was set to detect anomalies and to issue a warning for sensor failure. The forecasted and faulty data were assumed Gaussian distributed. By using the probability density of the distribution, the mean and variance were computed for faulty data to examine the abnormality in the variance value of each day to detect the sensor failure. The experimental results on
the sewer temperature data are appealing.
Munoz, F, Valls Miro, J, Dissanayake, G, Ulapane, N & Nguyen, LV 2017, 'Design of a Lock-in Amplifier Integrated with a Coil System for Eddy-Current Non-Destructive Inspection', Proceedings of the 12th IEEE Conference on Industrial Electronics and Applications, 12th IEEE Conference on Industrial Electronics and Applications, IEEE, Siem Reap, Cambodia, pp. 1948-1953.View/Download from: UTS OPUS
Eddy-current non-destructive inspections of conductive
components are of great interest in several industries
including civil infrastructure and the mining industry. In this
work, we have used a driver-pickup coil system as the probe
to carry out inspection of ferromagnetic plates. The specific
geometric configuration of the probe generates weak electric
signals that are buried in a noisy environment. In order to detect
these weak signals, we have designed and implemented a lock-in
amplifier as part of the signal processing technique to increase
the signal-to-noise ratio and also improve the sensitivity of the
probe. We have used Comsol as a finite element method (FEM)
to design the probe and conducted experiments with the probe
and the lock-in amplifier. The experimental results, which are
in agreement with the FEM results, indicate that the designed
probe along with a lock-in amplifier can potentially be used to
estimate the thickness of thin plates
Ulapane, N, Nguyen, LV, Valls Miro, J, Alempijevic, A & Dissanayake, G 2017, 'Designing A Pulsed Eddy Current Sensing Set-up for Cast Iron Thickness Assessment', Proceedings of the 12th IEEE Conference on Industrial Electronics and Applications, IEEE Conference on Industrial Electronics and Applications, IEEE, Siem Reap, Cambodia, pp. 901-906.View/Download from: UTS OPUS
Pulsed Eddy Current (PEC) sensors possess proven functionality in measuring ferromagnetic material thickness. However, most commercial PEC service providers as well as researchers have investigated and claim functionality of sensors on homogeneous structural steels (steel grade Q235 for example). In this paper, we present design steps for a PEC sensing set-up to measure thickness of cast iron, which is unlike steel, is a highly inhomogeneous and non-linear ferromagnetic material. The setup
includes a PEC sensor, sensor excitation and reception circuits, and a unique signal processing method. The signal processing method yields a signal feature which behaves as a function of thickness. The signal feature has a desirable characteristic of being lowly influenced by lift-off. Experimental results show that the set-up is usable for Non-destructive Evaluation (NDE) applications such as cast iron water pipe assessment.
Nguyen, LV, Ulapane, N, Valls Miro, J, Dissanayake, G & Munoz, F 2017, 'Improved Signal Interpretation for Cast Iron Thickness Assessment based on Pulsed Eddy Current Sensing', Proceedings of the 12th IEEE Conference on Industrial Electronics and Applications, IEEE Conference on Industrial Electronics and Applications, IEEE, Siem Reap, Cambodia, pp. 2005-2010.View/Download from: UTS OPUS
This paper presents a novel signal processing approach for computing thickness of ferromagnetic cast iron material, widely employed in older infrastructure such as water mains or bridges. Measurements are gathered from a Pulsed Eddy Current (PEC) based sensor placed on top of the material, with unknown lift-off, as commonly used during non-destructive testing (NDT). The approach takes advantage of an analytical
logarithmic model proposed in the literature for the decaying voltage induced at the PEC sensor pick-up coil. An increasingly more accurate and robust algorithm is proven here by means of an Adaptive Least Square Fitting Line (ALSFL) recursive strategy, suitable to recognize the most linear part of the sensor's logarithmic output voltage for subsequent gradient computation, from which thickness is then derived. Moreover, efficiency is also gained as processing can be carried out on only one decaying voltage signal, unlike averaging over multiple measurements as
is usually done in the literature. Importantly, the new signal processing methodology demonstrates highest accuracy at the lower thicknesses, a circumstance most relevant to NDT evaluation. Experiments that verify the proposed method in real-world thickness assessment of cast iron material are presented and compared with current practices, showing promising results.
Nguyen, L & Kodagoda, S 2016, 'Soil Organic Matter Estimation in Precision Agriculture using Wireless Sensor Networks', IEEE International Conference on Control, Automation, Robotics and Vision, International Conference on Control, Automation, Robotics and Vision, IEEE, Thailand.View/Download from: UTS OPUS or Publisher's site
Nguyen, LV, Kodagoda, S, Ranasinghe, R & Dissanayake, G 2014, 'Mobile Robotic Wireless Sensor Networks for Efficient Spatial Prediction', 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE/RSJ International Conference on Intelligent Robots and Systems, IEEE, Chicago, IL, USA, pp. 1176-1181.View/Download from: UTS OPUS or Publisher's site
This paper addresses the issue of monitoring physical spatial phenomena of interest utilizing the information collected by a network of mobile, wireless and noisy sensors that can take discrete measurements as they navigate through the environment. The spatial phenomenon is statistically modelled by a Gaussian Markov Random Field (GMRF) with hyperparameters that are learnt as the measurements accumulate over
time. In this context, the GMRF approximately represents the spatial field on an irregular lattice of triangulation by exploiting a stochastic partial differential equation (SPDE) approach, which benefits remarkably in computation due to the sparsity
of the precision matrix. A technique of the one-step-ahead forecast is employed to predict the future measurements that are required to find the optimal sampling locations. It is shown that optimizing the sampling path problem with the logarithm
of the determinant either of a covariance matrix using a GP model or of a precision matrix using a GMRF model for mobile robotic wireless sensor networks (MRWSNs) even by a greedy algorithm is impractical. This paper proposes an efficient novel
optimality criterion for the adaptive sampling strategy to find the most informative locations in taking future observations that minimize the uncertainty at unobserved locations. The computational complexity of our proposed method is linear, which makes the MRWSN scalable and practically feasible. The effectiveness of the proposed approach is compared and demonstrated using a pre-published data set with appealing results.
Nguyen, LV, Kodagoda, S, Ranasinghe, R & Dissanayake, G 2014, 'Spatially-Distributed Prediction with Mobile Robotic Wireless Sensor Networks', 2014 13th International Conference on Control, Automation, Robotics & Vision, International Conference on Control, Automation, Robotics and Vision, Institute of Electrical and Electronics Engineers Inc., Marina Bay Sands, Singapore, pp. 1153-1158.View/Download from: UTS OPUS or Publisher's site
This paper presents a distributed spatial estimation and prediction approach to address the centrally-computed scheme of Gaussian Process regression at each robotic sensor in resource-constrained networks of mobile, wireless and noisy agents monitoring physical phenomena of interest. A mobile sensor independently estimate its own parameters using collective measurements from itself and local neighboring agents as they navigate through the environment. A spatially-distributed prediction algorithm is designed utilizing methods of Jacobi overrelaxation and discrete-time average consensus to enable a robotic sensor to update its estimation of obtaining the global model parameters and recursively compute the global goal of inference. A distributed navigation strategy is also considered to drive sensors to the most uncertain locations enhancing the quality of prediction and learning parameters. Experimental results in a real-world data set illustrate the effectiveness of the proposed approach and is highly comparable to those of the centralized scheme.
Nguyen, LV, Kodagoda, S, Ranasinghe, R, Dissanayake, G, Bustamante, H, Vitanage, D & Nguyen, T 2014, 'Spatial Prediction of Hydrogen Sulfide in Sewers with a Modified Gaussian Process Combined Mutual Information', 2014 13th International Conference on Control, Automation, Robotics & Vision, International Conference on Control, Automation, Robotics and Vision, Institute of Electrical and Electronics Engineers Inc., Marina Bay Sands, Singapore, pp. 1130-1135.View/Download from: UTS OPUS or Publisher's site
This paper proposes a data driven machine learning model for spatial prediction of hydrogen sulfide (H2S) in a gravity sewer system. The gaseous H2S in the overhead of the gravity sewer is modelled using a Gaussian Process with a new covariance function due to constraints of sewer boundaries. The covariance function is proposed based on the distance between two locations computed along the lengths of the sewer network. A mutual information based strategy is used to choose the best k sensor measurements and their locations from among n potential sensor observations and their locations. This provably NP-hard combinatorial sensor selection problem is addressed by maximizing the mutual information between the selected locations and the locations that are not selected or do not have any sensor deployments. A proof-of-concept study was carried out comparing the spatial prediction of H2S with a complex model currently used by Sydney Water. The proposed approach is shown to be effective in both modelling and predicting the H2S spatial concentrations in sewers as well as identifying optimal number of H2S sensors and their locations for a required level of prediction accuracy.
Nguyen, LV, Kodagoda, S, Ranasinghe, R & Dissanayake, G 2013, 'Locational Optimization based Sensor Placement for Monitoring Gaussian Processes Modeled Spatial Phenomena', Proc. 2013 IEEE 8th Conference on Industrial Electronics and Applications, IEEE Conference on Industrial Electronics and Applications, IEEE, Melbourne, Australia, pp. 1-6.View/Download from: UTS OPUS or Publisher's site
This paper addresses the sensor placement problem associated with monitoring spatial phenomena, where mobile sensors are located on the optimal sampling paths yielding a lower prediction error. It is proposed that the spatial phenomenon to be monitored is modeled using a Gaussian Process and a variance based density function is employed to develop an expected-value function. A locational optimization based effective algorithm is employed to solve the resulting minimization of the expectedvalue function. We designed a mutual information based strategy to select the most informative subset of measurements effectively with low computational time. Our experimental results on realworld datasets have verified the superiority of the proposed approach.
Zainudin, Z, Kodagoda, S & Nguyen, LV 2012, 'Mutual Information Based Data Selection in Gaussian Processes for People Tracking', Australasian Conference on Robotics and Automation 2012, Australasian Conference on Robotics and Automation, The ACRA 2012 Organising Committee, Wellington, New Zealand, pp. 1-6.View/Download from: UTS OPUS
It is the general perception that models describing human motion patterns enhance tracking even with long term occlusions. One effective way of learning such patterns is to use Gaussian Processes (GP). However, with the increase of the amount of training data with time, the GP becomes computationally intractable. In this work, we have proposed a Mutual Information (MI) based technique along with the Mahalanobis Distance (MD) measure to keep the most informative data while discarding the least informative data. The algorithm is tested with data collected in an office environment with a Segway robot equipped with a laser range finder. It leads to more than 80% data reduction while keeping the rms errors within the required bounds. We have also implemented a GP based Particle filter tracker for long term people tracking with occlusions. The comparison results with Extended Kalman Filter based tracker shows the superiority of the proposed approach.
Nguyen, LV, Kodagoda, S, Ranasinghe, R & Dissanayake, G 2012, 'Simulated Annealing Based Approach for Near-Optimal Sensor Selection in Gaussian Processes', Proc. 2012 IEEE International Conference on Control, Automation and Information Sciences, International Conference on Control, Automation and Information Sciences, IEEE, Ho Chi Minh City, Vietnam, pp. 142-147.View/Download from: UTS OPUS or Publisher's site
This paper addresses the sensor selection problem associated with monitoring spatial phenomena, where a subset of k sensor measurements from among a set of n potential sensor measurements is to be chosen such that the root mean square prediction error is minimised. It is proposed that the spatial phenomena to be monitored is modelled using a Gaussian Process and a simulated annealing based approximately heuristic algorithm is used to solve the resulting minimisation problem. The algorithm is shown to be computationally efficient and is illustrated using both indoor and outdoor environment monitoring scenarios. It is shown that, although the proposed algorithm is not guaranteed to find the optimum, it always provides accurate solutions for broad range real-world and computer generated datasets.
Nguyen, LV, Ranasinghe, R, Kodagoda, S & Dissanayake, G 2012, 'Sensor Selection Based Routing for Monitoring Gaussian Processes Modeled Spatial Phenomena', Australasian Conference on Robotics and Automation 2012, Australasian Conference on Robotics and Automation, The ACRA 2012 Organising Committee, Wellington, New Zealand, pp. 1-7.View/Download from: UTS OPUS
This paper addresses the trade-off between the sensing quality and the energy consumption in the wireless sensor network associated with monitoring spatial phenomena. We use a non-parametric Gaussian Process to model the spatial phenomena to be monitored and simulated annealing based approximately heuristic algorithm for sensor selection. Our novel Sensor Selection based Routing (SSR) algorithm uses this model to identify the most informative nodes, which gives the root mean square prediction error less than a specified threshold, to construct the minimal energy expended routing tree rooted at the sink. Our experiments have verified that the proposed computationally efficient SSR algorithm has significant advantages over conventional techniques.