Can supervise: YES
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 or Publisher's site
In this paper, a new adaptive hierarchical sliding mode control scheme for a 3D overhead crane system is proposed. A controller is first designed by the use of a hierarchical structure of two first-order sliding surfaces represented by two actuated and un-actuated subsystems in the bridge crane. Parameters of the controller are then intelligently estimated, where uncertain parameters due to disturbances in the 3D overhead crane dynamic model are proposed to be represented by radial basis function networks whose weights are derived from a Lyapunov function. The proposed approach allows the crane system to be robust under uncertainty conditions in which some uncertain and unknown parameters are highly difficult to determine. Moreover, stability of the sliding surfaces is proved to be guaranteed. Effectiveness of the proposed approach is then demonstrated by implementing the algorithm in both synthetic and real-life systems, where the results obtained by our method are highly promising.
Le, HX, Le, AV & Nguyen, L 2019, 'Adaptive Fuzzy Observer based Hierarchical Sliding Mode Control for Uncertain 2D Overhead Cranes', Cyber-Physical Systems, vol. 5, no. 3, pp. 191-208.View/Download from: UTS OPUS or Publisher's site
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.
Nguyen, L, Valls Miro, J & Qiu, X 2019, 'Multilevel B-Splines based Learning Approach for Sound Source Localization', IEEE Sensors Journal, vol. 10, no. 10, pp. 3871-3881.View/Download from: UTS OPUS or Publisher's site
In this paper, a new learning approach for sound source localization is presented using ad hoc either synchronous or asynchronous distributed microphone networks based on the time differences of arrival (TDOA) estimation. It is first to propose a new concept in which the coordinates of a sound source location are defined as the functions of TDOAs, computing for each pair of microphone signals in the network. Then, given a set of pre-recorded sound measurements and their corresponding source locations, the multilevel B-splines-based learning model is proposed to be trained by the input of the known TDOAs and the output of the known coordinates of the sound source locations. For a new acoustic source, if its sound signals are recorded, the correspondingly computed TDOAs can be fed into the learned model to predict the location of the new source. Superiorities of the proposed method are to incorporate the acoustic characteristics of a targeted environment and even remaining uncertainty of TDOA estimations into the learning model before conducting its prediction and to be applicable for both synchronous or asynchronous distributed microphone sensor networks. The effectiveness of the proposed algorithm in terms of localization accuracy and computational cost in comparisons with the state-of-the-art methods was extensively validated on both synthetic simulation experiments as well as in three real-life environments.
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
This paper addresses the problem of efficiently deploying sensors in spatial environments, e.g., buildings, for the purposes of monitoring spatio-temporal environmental phenomena. By modeling the environmental fields using spatio-temporal Gaussian processes, a new and efficient optimality-cost function 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 proven not to be affected by time variations, which significantly reduces computational complexity of the optimization problem. The sensor deployment optimization problem is then solved by a practical and feasible polynomial algorithm, where its solutions are theoretically proven to be guaranteed. The proposed method is also theoretically and experimentally compared with the existing works. The effectiveness of the proposed algorithm is demonstrated by implementation in a real tested space in a university building, where the obtained results are highly promising.
Ulapane, N & Nguyen, L 2019, 'Review of Pulsed Eddy Current Signal Feature Extraction Methods for Conductive Ferromagnetic Material Thickness Quantification', Electronics (Basel).View/Download from: Publisher's site
Thickness quantification of conductive ferromagnetic materials has become a common necessity in present-day structural health monitoring and infrastructure maintenance. Recent research has found Pulsed Eddy Current (PEC) sensing, especially the detector-coil-based PEC sensor architecture, to effectively serve as a nondestructive sensing technique for this purpose. As a result, several methods of varying complexity have been proposed in recent years to extract PEC signal features, against which conductive ferromagnetic material thickness behaves as a function, in return enabling thickness quantification owing to functional behaviours. It can be seen that almost all features specifically proposed in the literature for the purpose of conductive ferromagnetic material-thickness quantification are in some way related to the diffusion time constant of eddy currents. This paper examines the relevant feature-extraction methods through a controlled experiment in which the methods are applied to a single set of experimentally captured PEC signals, and provides a review by discussing the quality of the extractable features, and their functional behaviours for thickness quantification, along with computational time taken for feature extraction. Along with this paper, the set of PEC signals and some MATLAB codes for feature extraction are provided as supplementary materials for interested readers.
Nguyen, T, Thai, N, Pham, H, Phan, T, Le, H, Nguyen, H & Nguyen, L 2019, 'Adaptive neural network based backstepping sliding mode control approach for dual arm robots', Journal of Control, Automation and Electrical Systems, vol. 30, pp. 512-521.View/Download from: UTS OPUS or Publisher's site
The paper introduces an adaptive strategy to effectively control a nonlinear dual arm robot under external disturbances and uncertainties. By the use of the backstepping sliding mode control (BSSMC) method,
the proposed algorithm rst allows the manipulators to be able to robustly track the desired trajectories. Furthermore, due to the nonlinear, uncertain and unmodelled dynamics of the dual arm robot, it is proposed to employ the radial basis function network (RBFN) to adaptively estimate the robot's dynamic model. Though the estimation of the dynamics is approximate, the adaptation law is derived from the Lyapunov theory, which provides the controller with ability to guarantee stability of the whole system in spite of its nonlinearities, parameter uncertainties and external load variations. The effectiveness of the proposed RBFN-BSSMC approach is demonstrated by implementation in a simulation environment with realistic parameters, where the obtained results 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: UTS OPUS or Publisher's site
In wastewater industry, real-time sensing of surface temperature variations on concrete sewer pipes is paramount in assessing the rate of microbial-induced corrosion. However, the sensing systems are prone to failures due to the aggressively corrosive environmental conditions inside sewer assets. Therefore, reliable sensing in such infrastructures is vital for water utilities to enact efficient wastewater management. In this context, this paper presents a sensor failure detection and faulty data accommodation (SFDFDA) approach that aids to digitally monitor the health conditions of the sewer monitoring sensors. The SFDFDA approach embraces seasonal autoregressive integrated moving average model with a statistical hypothesis testing technique for enabling temporal forecasting of sensor variable. Then, it identifies and isolates anomalies in a continuous stream of sensor data whilst detecting early sensor failure. Finally, the SFDFDA approach provides reliable estimates of sensor data in the event of sensor failure or during the scheduled maintenance period of sewer monitoring systems. The SFDFDA approach was evaluated by using the surface temperature data sourced from the instrumented wastewater infrastructure and the results have demonstrated the effectiveness of the SFDFDA approach and its applicability to surface temperature monitoring sensor suites.
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
This paper addresses the problem of driving robotic sensors for an energy-constrained mobile wireless network in efficiently monitoring and predicting spatial phenomena, under data locational errors. The paper first discusses how errors of mobile sensor locations affect estimating and predicting the spatial physical processes, given that spatial field to be monitored is modeled by a Gaussian process. It then proposes an optimality criterion for designing optimal sampling paths for the mobile robotic sensors given the localization uncertainties. Although the optimization problem is optimally intractable, it can be resolved by a polynomial approximation algorithm, which is proved to be practically feasible in an energy-constrained mobile sensor network. More importantly, near-optimal solutions of this navigation problem are guaranteed by a lower bound within 1-(1/e) of the optimum. The performance of the proposed approach is evaluated on simulated and real-world data sets, where impact of sensor location errors on the results is demonstrated by comparing the results with those obtained by using noise-less data locations.
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 cluster‐head 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 well‐known algorithms.
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 & Nguyen, HT, 'Mobility based network lifetime in wireless sensor networks: A review'.
Increasingly emerging technologies in micro-electromechanical systems and
wireless communications allows a mobile wireless sensor networks (MWSN) to be a
more and more powerful mean in many applications such as habitat and
environmental monitoring, traffic observing, battlefield surveillance, smart
homes and smart cities. Nevertheless, due to sensor battery constraints,
energy-efficiently operating a MWSN is paramount importance in those
applications; and a plethora of approaches have been proposed to elongate the
network longevity at most possible. Therefore, this paper provides a
comprehensive review on the developed methods that exploit mobility of sensor
nodes and/or sink(s) to effectively maximize the lifetime of a MWSN. The survey
systematically classifies the algorithms into categories where the MWSN is
equipped with mobile sensor nodes, one mobile sink or multiple mobile sinks.
How to drive the mobile sink(s) for energy efficiency in the network is also
fully reviewed and reported.
Pham, DT, Nguyen, TV, Le, HX, Nguyen, L, Thai, NH, Phan, TA, Pham, HT & Duong, AH, 'Adaptive neural network based dynamic surface control for uncertain dual arm robots'.
The paper discusses an adaptive strategy to effectively control nonlinear
manipulation motions of a dual arm robot (DAR) under system uncertainties
including parameter variations, actuator nonlinearities and external
disturbances. It is proposed that the control scheme is first derived from the
dynamic surface control (DSC) method, which allows the robot's end-effectors to
robustly track the desired trajectories. Moreover, since exactly determining
the DAR system's dynamics is impractical due to the system uncertainties, the
uncertain system parameters are then proposed to be adaptively estimated by the
use of the radial basis function network (RBFN). The adaptation mechanism is
derived from the Lyapunov theory, which theoretically guarantees stability of
the closed-loop control system. The effectiveness of the proposed RBFN-DSC
approach is demonstrated by implementing the algorithm in a synthetic
environment with realistic parameters, where the obtained results are highly
Nguyen, L & Valls Miro, J 2019, 'Acoustic Sensor Networks and Mobile Robotics for Sound Source Localization', IEEE International Conference on Control & Automation, IEEE International Conference on Control & Automation, Edinburgh, UK.View/Download from: UTS OPUS
Localizing a sound source is a fundamental but still challenging issue in many applications, where sound information is gathered by static and local microphone sensors. Therefore, this work proposes a new system by exploiting advances in sensor networks and robotics to more accurately address the problem of sound source localization. By the use of the network infrastructure, acoustic sensors are more efficient to spatially monitor acoustical phenomena. Furthermore, a mobile robot is proposed to carry an extra microphone array in order to collect more acoustic signals when it travels around the environment. Driving the robot is guided by the need to increase the quality of the data gathered by the static acoustic sensors, which leads to better probabilistic fusion of all the information gained, so that an increasingly accurate map of the sound source can be built. The proposed system has been validated in a real-life environment, where the obtained results are highly promising.
Nguyen, L, Miro, JV, Shi, L & Vidal-Calleja, T 2019, 'Gaussian Mixture Marginal Distributions for Modelling Remaining Pipe Wall Thickness of Critical Water Mains in Non-Destructive Evaluation', IEEE International Conference on Cybernetics and Intelligent Systems, and Robotics, Automation and Mechatronics, Bangkok, Thailand.View/Download from: UTS OPUS
Rapidly estimating the remaining wall thickness (RWT) is paramount for the non-destructive condition assessment evaluation of large critical metallic pipelines. A robotic vehicle with embedded magnetism-based sensors has been developed to traverse the inside of a pipeline and conduct inspections at the location of a break. However its sensing speed is constrained by the magnetic principle of operation, thus slowing down the overall operation in seeking dense RWT mapping. To ameliorate this drawback, this work proposes the partial scanning of the pipe and then employing Gaussian Processes (GPs) to infer RWT at the unseen pipe sections. Since GP prediction assumes to have normally distributed input data - which does correspond with real RWT measurements - Gaussian mixture (GM) models are proven in this work as fitting marginal distributions to effectively capture the probability of any RWT value in the inspected data. The effectiveness of the proposed approach is extensively validated from real-world data collected in collaboration with a water utility from a cast iron water main pipeline in Sydney, Australia.
Nguyen, L, Valls Miro, J & Qiu, X 2019, 'Can a Robot Hear the Shape and Dimensions of a Room?', IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2019), IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2019), IEEE, Macau, China.View/Download from: UTS OPUS
Knowing the geometry of a space is desirable for many applications, e.g. sound source localization, sound field reproduction or auralization. In circumstances where only acoustic signals can be obtained, estimating the geometry of a room is a challenging proposition. Existing methods have been proposed to reconstruct a room from the room impulse responses (RIRs). However, the sound source and microphones must be deployed in a feasible region of the room for it to work, which is impractical when the room is unknown. This work propose to employ a robot equipped with a sound source and four acoustic sensors, to follow a proposed path planning strategy to moves around the room to collect first image sources for room geometry estimation. The strategy can effectively drives the robot from a random initial location through the room so that the room geometry is guaranteed to be revealed. Effectiveness of the proposed approach is extensively validated in a synthetic environment, where the results obtained are highly promising.
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
Sensor technologies play a significant role in monitoring the health conditions of urban sewer assets. Currently, the concrete sewer systems are undergoing corrosion due to bacterial activities on the concrete surfaces. Therefore, water utilities use predictive models to estimate the corrosion by using observations such as relative humidity or surface moisture conditions. Surface moisture conditions can be estimated by electrical resistivity based moisture sensing. However, the measurements of such sensors are influenced by the proximal presence of reinforcing bars. To mitigate such effects, the moisture sensor needs to be optimally oriented on the concrete surface. This paper focuses on developing a machine learning model for localizing the reinforcing bars inside the concrete through non-invasive measurements. This work utilizes a resistivity meter that works based on the Wenner technique to obtain electrical measurements on the concrete sample by taking measurements at different angles. Then, the measured data is fed to a Gaussian Markov Random Fields based spatial prediction model. The spatial prediction outcome of the proposed model demonstrated the feasibility of localizing the reinforcing bars with reasonable accuracy for the measurements taken at different angles. This information is vital for decision-making while deploying the moisture sensors in sewer systems.
Ulapane, N, Nguyen, LV, Valls Miro, J & Dissanayake, G 2018, 'A Solution to the Inverse Pulsed Eddy Current Problem Enabling 3D Profiling', 2018 13th IEEE Conference on Industrial Electronics and Applications (ICIEA), IEEE Conference on Industrial Electronics and Applications, IEEE, Wuhan, China, pp. 1267-1272.View/Download from: UTS OPUS or Publisher's site
When a Pulsed Eddy Current (PEC) sensor assesses a metallic surface (i.e., a wall of finite thickness), the inverse problem involves quantification of the geometry and material properties of the wall. Once a PEC sensor is calibrated for a particular material, and the material under test happens to be considerably homogeneous, the inverse problem reduces to quantification of geometry alone. The state-of-the-art in the industry produces a quantification of this geometry only in the form of average wall thickness remaining underneath the sensor footprint, and produces a 2.5D map containing wall thickness information. Therefore, this paper contributes by proposing a solution that can jointly estimate the remaining wall thickness as well as lift-off (i.e., offset from the sensor to the surface of healthy material), in order to advance PEC sensing outputs by enabling estimation of wall condition in 3D. Since PEC maps are used as inputs for stress calculation and remaining life prediction of certain infrastructure like critical pipes, 3D profiles may become a richer form of input for such applications than 2.5D maps. Since PEC sensing is commonly used to assess ferromagnetic materials, this paper focuses on similar materials as well. The solution is demonstrated in simulation alone and future work should focus on experimental implementations.
Nguyen, LV, Ulapane, N & Valls Miro, J 2018, 'Adaptive Sampling for Spatial Prediction in Environmental Monitoring using Wireless Sensor Networks: A Review', 2018 13th IEEE Conference on Industrial Electronics and Applications (ICIEA), IEEE Conference on Industrial Electronics and Applications, IEEE, Wuhan, China, pp. 346-351.View/Download from: UTS OPUS or Publisher's site
The paper presents a review of the spatial prediction problem in the environmental monitoring applications by utilizing stationary and mobile robotic wireless sensor networks. First, the problem of selecting the best subset of stationary wireless sensors monitoring environmental phenomena in terms of sensing quality is surveyed. Then, predictive inference approaches and sampling algorithms for mobile sensing agents to optimally observe spatially physical processes in the existing works are analysed.
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
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.
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, 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.
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, 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.
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.
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.