Electrical and Electronic Engineering - Research Publications
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ItemImplementation of marker training exercises to improve marking reliability and consistency(Australasian Association for Engineering Education, 2018)CONTEXT: One of the challenges present in teaching a large engineering subject is that of achieving marking consistency of assessments across multiple markers. Several measures of standardising markers exist, such as calibrated review, and are commonly used in the humanities, particularly for assessments that could be prone to a wide variation in marks such as essays. The application of such methods, in an engineering context, is somewhat less documented but of particular importance in the case of reflective writing. This study contrasts the implementation of several different methods of using marker training exercises prior to the actual assessment marking and provides an analysis of the results in order to minimise the effect of multiple marker irregularities and to provide effective high-quality formative feedback on a piece of reflective writing. PURPOSE: This paper presents several different methods of marker training exercises, run prior to the actual assessment marking, and provides analyses to determine the effect of each in terms of minimising marking inconsistency among multiple markers on a piece of reflective writing. APPROACH: In all three marker training exercises, markers are given samples of a piece of reflective writing, of differing quality, along with a rubric outlining the marking criteria for the piece of writing and exemplars for indicative marking standards. Each of the methods employed differ in how the reference standard was set and how feedback was delivered to the markers. Statistics comparing the marking results across markers from before the introduction of the training exercises and between each of the three training methods were analysed to investigate marking reliability and consistency. RESULTS: A significant reduction in the spread of the marker means has been achieved through the introduction of the marker training, indicating an improvement in consistency. Some differences in results between the alternative methods employed has also been observed. CONCLUSIONS: Marking consistency can be improved with the introduction of a marker training exercise prior to the actual assessment marking. Different methods of implementing the marking training exercise, and how the feedback is provided, can have an effect of the amount of improvement in terms of consistency and reliability.
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ItemMoving Horizon Estimation for Linear Cascade Systems(IEEE, 2018-01-01)A structured approach to the problem of state estimation for cascaded linear sub-systems is studied in terms of minimizing a measure of the error relative to a model over a moving horizon of past system input and output observations. A quadratic programming formulation of this optimization problem is considered and two approaches are explored. One approach involves solving the Karush-Kuhn-Tucker conditions directly, and the other is based on the alternating direction method of multipliers. In both cases, the problem structure can be exploited to yield distributed computations in the following sense: Construction of the estimate for each sub-system component of the state involves information pertaining to the two immediate neighbours only. Numerical simulations based on model data from an automated irrigation channel are used to investigate and compare the computational burden of the two approaches.
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ItemStructured moving horizon estimation for linear system chains(IEEE, 2019-06-01)Computational aspects of moving horizon state estimation are studied for a class of chain networks with bidirectional coupling in the linear state dynamics, and measured outputs. Moving horizon estimation involves solving a quadratic program to minimize the estimation error relative to a model over a fixed window of past input-output observations. By exploiting the spatial structure of a chain, two algorithms for solving this quadratic program are considered. Both algorithms can be distributed in the sense that the computations associated with each sub-system component of the state depend only on information associated with the immediate neighbours. The algorithms differ in the way that the linear Karush-Kuhn-Tucker conditions for optimality are solved. Computational and information dependency overheads are analyzed. Numerical results are presented for a 1-D mass-spring-damper chain.
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ItemCluster-based Crowd Movement Behavior Detection(IEEE, 2019-01-01)Crowd behaviour monitoring and prediction is an important research topic in video surveillance that has gained increasing attention. In this paper, we propose a novel architecture for crowd event detection, which comprises methods for object detection, clustering of various groups of objects, characterizing the movement patterns of the various groups of objects, detecting group events, and finding the change point of group events. In our proposed framework, we use clusters to represent the groups of objects/people present in the scene. We then extract the movement patterns of the various groups of objects over the video sequence to detect movement patterns. We define several crowd events and propose a methodology to detect the change point of the group events over time. We evaluated our scheme using six video sequences from benchmark datasets, which include events such as walking, running, global merging, local merging, global splitting and local splitting. We compared our scheme with state of the art methods and showed the superiority of our method in accurately detecting the crowd behavioral changes.
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ItemArytenoid Cartilage Feature Point Detection Using Laryngeal 3D CT Images in Parkinson's Disease(IEEE, 2017-01-01)Parkinson's disease is a neurodegenerative disorder that results in progressive degeneration of nerve cells. It is generally associated with the deficiency of dopamine, a neurotransmitter involved in motor control of humans and thus affects the motor system. This results in abnormal vocal fold movements in majority of the Parkinson's patients. Analysis of vocal fold abnormalities may provide useful information to assess the progress of Parkinson's disease. This is accomplished by measuring the distance between the arytenoid cartilages during phonation. In order to automate this process of identifying arytenoid cartilages from CT images, in this work, a rule-based approach is proposed to detect the arytenoid cartilage feature points on either side of the airway. The proposed technique detects feature points by localizing the anterior commissure and analyzing airway boundary pixels to select the optimal feature point based on detected pixels. The proposed approach achieved 83.33% accuracy in estimating clinically-relevant feature points, making the approach suitable for automated feature point detection. To the best of our knowledge, this is the first such approach to detect arytenoid cartilage feature points using laryngeal 3D CT images.
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ItemCrowd Activity Change Point Detection in Videos via Graph Stream Mining(IEEE, 2018)In recent years, there has been a growing interest in detecting anomalous behavioral patterns in video. In this work, we address this task by proposing a novel activity change point detection method to identify crowd movement anomalies for video surveillance. In our proposed novel framework, a hyperspherical clustering algorithm is utilized for the automatic identification of interesting regions, then the density of pedestrian flows between every pair of interesting regions over consecutive time intervals is monitored and represented as a sequence of adjacency matrices where the direction and density of flows are captured through a directed graph. Finally, we use graph edit distance as well as a cumulative sum test to detect change points in the graph sequence. We conduct experiments on four real-world video datasets: Dublin, New Orleans, Abbey Road and MCG Datasets. We observe that our proposed approach achieves a high F-measure, i.e., in the range [0.7, 1], for these datasets. The evaluation reveals that our proposed method can successfully detect the change points in all datasets at both global and local levels. Our results also demonstrate the efficiency and effectiveness of our proposed algorithm for change point detection and segmentation tasks.
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ItemStability analysis of discrete-time finite-horizon discounted optimal control(IEEE, 2018)Discounted costs are considered in many fields, like reinforcement learning, for which various algorithms can be used to obtain optimal inputs for finite horizons. The related literature mostly concentrates on optimality and largely ignores stability. In this context, we study stability of general nonlinear discrete- time systems controlled by an optimal sequence of inputs that minimizes a finite-horizon discounted cost computed in a receding horizon fashion. Assumptions are made related to the stabilizability of the system and its detectability with respect to the stage cost. Then, a Lyapunov function for the closed-loop system with the receding horizon controller is constructed and a uniform semiglobal stability property is ensured, where the adjustable parameters are both the discount factor and the horizon length. Uniform global exponential stability is guaranteed by strengthening the initial assumptions, in which case explicit bounds on the discount factor and the horizon length are provided. We compare the obtained bounds in the particular cases where there is no discount or the horizon is infinite, respectively, with related results in the literature and we show our bounds improve existing ones on the examples considered.
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ItemGaussian Processes with Monotonicity Constraints for Preference Learning from Pairwise Comparisons(IEEE, 2018)In preference learning, it is beneficial to incorporate monotonicity constraints for learning utility functions when there is prior knowledge of monotonicity. We present a novel method for learning utility functions with monotonicity constraints using Gaussian process regression. Data is provided in the form of pairwise comparisons between items. Using conditions on monotonicity for the predictive function, an algorithm is proposed which uses the weighted average between prior linear and maximum a posteriori (MAP) utility estimates. This algorithm is formally shown to guarantee monotonicity of the learned utility function in the dimensions desired. The algorithm is tested in a Monte Carlo simulation case study, in which the results suggest that the learned utility by the proposed algorithm performs better in prediction than the standalone linear estimate, and enforces monotonicity unlike the MAP estimate.
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ItemA machine learning approach for tuning model predictive controllers(IEEE, 2018-01-01)Many industrial domains are characterized by Multiple-Input-Multiple-Output (MIMO) systems for which an explicit relationship capturing the nontrivial trade-off between the competing objectives is not available. Human experts have the ability to implicitly learn such a relationship, which in turn enables them to tune the corresponding controller to achieve the desirable closed-loop performance. However, as the complexity of the MIMO system and/or the controller increase, so does the tuning time and the associated tuning cost. To reduce the tuning cost, a framework is proposed in which a machine learning method for approximating the human-learned cost function along with an optimization algorithm for optimizing it, and consequently tuning the controller, are employed. In this work the focus is on the tuning of Model Predictive Controllers (MPCs), given both the interest in their implementations across many industrial domains and the associated high degrees of freedom present in the corresponding tuning process. To demonstrate the proposed approach, simulation results for the tuning of an air path MPC controller in a diesel engine are presented.
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ItemExtremum-Seeking-Based Adaptive Scan for Atomic Force Microscopy(IEEE, 2017)Improving the imaging speed in Atomic Force Microscopy (AFM) is of high interest due to its typically prolonged imaging duration. Conventionally, the line rate of the scan is fixed at a conservative value in order to ensure a safe tip-sample contact force even in the worst case of sample aspect ratio and linear scan speed. In this paper, an adaptive scan method is proposed to adapt the scan line rate based on the extremum-seeking control framework. A performance metric balancing both imaging speed and accuracy is proposed, and an extremum-seeking approach is designed to optimise the metric based on error feedback. Semi-global practical asymptotic stability (SGPAS) result is shown, and the proposed method is demonstrated via simulation.