- Dr David Stirling
School of Electrical, Computer and Telecommunications Engineering
Faculty of Engineering and Information Sciences
University of Wollongong
Wollongong, NSW 2522, AUSTRALIA - +61-242213419
- Medical Bioengineering, Machine Learning, Data Mining, Data Mining and Knowledge Discovery, KDD (Knowledge Discovery Databases), Geoinformatics , Geospatial Modeling , GeoHazards, and 5 moreSpatio Temporal Data Mining (Data Mining), Human Motion Analysis, Time Series Data Mining, Clustering and Classification Methods, and Artificial Intelligenceedit
- My research is essentially interdisciplinary in nature and particularly associated data mining and innovative applica... moreMy research is essentially interdisciplinary in nature and particularly associated data mining and innovative applications of machine learning techniques to challenging, real world problems, i.e. “Big Data” problems. Building on a strong industrial research background, I have over the past 20 years been able to significantly contribute to a number of existing research programs at the University of Wollongong (UoW), as well as facilitating new areas where my expertise has enabled previously unachievable outcomes. I have developed a broad portfolio of research interests here at UoW.
Areas of active Research
• Pattern Analysis and Symbolic Machine Learning
• Data Mining – active research areas:
— Radiomics: tumour characterizations from 4D-CT-images
— Motion analysis: sports injuries & rehabilitation, aged-health; objective metrics for frailty assessment, balance & motion control
— Oncology: objective kinematic fatigue assessment
— Spatial data: geo-hazards, modelling erosion & landslip susceptibilities
— Spatiotemporal data: general manufacturing, industrial processes, iron & steel making
— Time series data: power distribution modelling, reliability & non-stationary power quality
• Pattern-based Knowledge discovery
• Autonomous control, Hybrid Intelligent Systems and Fuzzy Systemsedit
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This paper proposes the use of deep learning classification for acoustic monitoring of an industrial process. Specifically, the application is to process sound recordings to detect when additional air leaks through gaps between grate bars... more
This paper proposes the use of deep learning classification for acoustic monitoring of an industrial process. Specifically, the application is to process sound recordings to detect when additional air leaks through gaps between grate bars lining the bottom of the sinter strand pallets, caused by thermal cycling, aging and deterioration. Detecting holes is not possible visually as the hole is usually small and covered with a granular bed of sinter/blend material. Acoustic signals from normal operation and periods of air leakage are fed into the basic supervised classification methods (SVM and J48) and the deep learning networks, to learn and distinguish the differences. Results suggest that the applied deep learning approach can effectively detect the acoustic emissions from holes time segments with a minimum 79% of accuracy.
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A variety of artificial intelligence induction systems are being success fully used for knowledge acquisition. In some of these systems, knowl edge acquisition is not an automatic procesii hut rather an interactive process between... more
A variety of artificial intelligence induction systems are being success fully used for knowledge acquisition. In some of these systems, knowl edge acquisition is not an automatic procesii hut rather an interactive process between expert/user and system, with induction serving as the focus. We refer to this generic approach as interactive induction. In this paper we examine some case studies, and discuss the approach and software facilities required to support it. Key features for success are the methods and man-machine environment employed to elicit prior, subjective information from an expert (in order to augment the knowl edge implicit in the training data), together with the expert’s acceptance and validation of the knowledge induced.
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A novel algorithm for simultaneous force estimation and friction compensation of constrained motion of robot manipulators is presented. This represents an extension of the improved extended active observer (IEAOB) algorithm reported... more
A novel algorithm for simultaneous force estimation and friction compensation of constrained motion of robot manipulators is presented. This represents an extension of the improved extended active observer (IEAOB) algorithm reported earlier and proposes a higher order IEAOB or N−th order IEAOB (IEAOB −N) for a n−DOF robot manipulator. Central to this observer is the use of extra system states modeled as a Gauss-Markov (GM) formulation to estimate the force and disturbances including robot inertial parameters and friction. The stability of IEAOB −N is verified through stability analysis. The IEAOB-1 is validated by applying it to a Phantom Omni haptic device against a Nicosia observer, disturbance observer (DOB)/reaction torque observer (RTOB), and nonlinear disturbance observer (NDO), respectively. The results show that the proposed IEAOB-1 is superior to the compared observers in terms of force estimation. Then, the performance of the IEAOB − N is experimentally studied and compared to the IEAOB-1. Results demonstrate that the IEAOB − N has an improved capability in tracking nonlinear external forces.
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This paper aims to characterise the 2-year survival of non-small cell lung cancer patients. It involves a novel approach that explores the rind around the tumour volume that is delineated by an oncologist as the ground truth. This study... more
This paper aims to characterise the 2-year survival of non-small cell lung cancer patients. It involves a novel approach that explores the rind around the tumour volume that is delineated by an oncologist as the ground truth. This study also compares various machine learning techniques to determine the ideal method for predicting cancer survival. This paper found improved prediction results at 6 pixels outside the tumour volume, a distance of approximately 5mm outside the original GTV, when applying a support vector machine achieving an accuracy of 71.18%. This paper challenges the traditional clinical ideas of radiotherapy where the centre of the tumour is treated with the highest dose, however this research indicates the periphery of the tumour is highly predictive of survival.
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This paper investigates the modelling of dynamic body motion and postures using multiple inertial measurement units. The ultimate goal of this work is to determine a way to model back posture during manual handling activities to prevent... more
This paper investigates the modelling of dynamic body motion and postures using multiple inertial measurement units. The ultimate goal of this work is to determine a way to model back posture during manual handling activities to prevent lower back pain. This is achieved using Gaussian Mixture Modelling to produce a model with twenty clusters. This model is then employed to predict the order in which the clusters occur during the movement. These clusters are then analysed using various methods to determine whether good or bad posture may be associated with these clusters. This is a two-fold problem and involves evaluating clusters which are statistically good or bad on their own or dynamic clusters which become good or bad after a certain sequence of clusters occur. Cluster means which indicate statistically significant postures are also discussed, which is vital for predicting bad posture use before a lift has even occurred. The key outcome of this work is the development of a decision tree which defines the posture observed, based on the order of the static and dynamic clusters associated with good and bad posture. This final decision tree has a precision of 75.3%, which is excellent considering the changes in movement between good and bad posture during a lift are very subtle.
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Frailty is a prevailing phenomena in older people. It is an age related syndrome that can increase the risk of fall in elderly. The people with age above 65 suffers from various functional decline and cognitive impairments. Such... more
Frailty is a prevailing phenomena in older people. It is an age related syndrome that can increase the risk of fall in elderly. The people with age above 65 suffers from various functional decline and cognitive impairments. Such deficiencies are conventionally measured subjectively by geriatrics using questionnaire-based methods and clinical tests. Activities of daily living are also assessed in clinical settings by analysing simple tasks performed by the subject such as sit to stand and walking some distances. The clinical methods used to assess frailty and analyse the activity of daily living are subjective in nature and prone to human error. An objective method is proposed to quantitatively measure frailty using inertial sensor mounted on healthy, frail and nonfrail subjects while performing the sit to stand test (SiSt). An artificial neural networks based algorithm is developed to classify the frailty by extracting a unique set of features from 2D -Centre of Mass (CoM) trajectories derived from SiSt clinical test. The results indicate that the proposed algorithms provides an objective assessment of frailty that can be used by geriatrics in turn to make a more objective judgement of frailty status of older people.
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This study describes an automated medicine dispenser that has enhanced functions provided by with the advent of the Internet of Things (IoT) paradigm. The prototype device outlined here is capable of delivering two different medicines at... more
This study describes an automated medicine dispenser that has enhanced functions provided by with the advent of the Internet of Things (IoT) paradigm. The prototype device outlined here is capable of delivering two different medicines at approximately the same time. This work describes the development of a medicine dispenser that incorporates some of the features of IoT devices for the home. It uses an Archimedes screw to deliver the tablets and incorporates basic levels of visual communication with the client, such as an LCD display and dispensing push buttons. The prototype developed illustrates the possible applications for the home that can be provided by the IoT paradigm.
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Assessing the frailty of older people quantitatively is critical to prevent potential accidents and to ensure their well-being. The older people with high frailty score are at the risk of fall, which increases the rate of hospitalization... more
Assessing the frailty of older people quantitatively is critical to prevent potential accidents and to ensure their well-being. The older people with high frailty score are at the risk of fall, which increases the rate of hospitalization and reducing the number of independent activities carried out. The conventional clinical tools used for frailty assessment are subjective, qualitative and are prone to human error. The balance assessment, activity of daily living (ADL) and gait analysis are practiced as clinical and quantitative tools for risk of fall and frailty assessments. An objective approach to classify the frailty levels using ADL is proposed. The "pick up an object from floor" as an ADL is deployed to differentiate the signal patterns obtained through inertial measurement unit (IMU) for frail and non-frail subjects. The data from single inertial unit mounted on pelvis is analyzed. The experimental work is carried out on three groups of healthy/control, frail and non-frail subjects. The various signal attributes are used to classify the frailty quantitatively using IMU data and machine learning methods. The results demonstrate that frail subjects have clear irregularities in their signal trajectories. Using the proposed algorithm two classes of frailty (non-frail and frail) are identified objectively. The study demonstrates the potential of deploying IMU for advanced classification of frailty levels in older people.
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Quantitative measurement of physical exercises-induced fatigue is a crucial need in sports engineering. Fatigue reduces physical performance of sports players and lack of appropriate measurement causes harmful injuries. This study... more
Quantitative measurement of physical exercises-induced fatigue is a crucial need in sports engineering. Fatigue reduces physical performance of sports players and lack of appropriate measurement causes harmful injuries. This study provides a comprehensive objective evaluation of physical exercises-induced fatigue by using adaptive neuro fuzzy inference system (ANFIS) method. A set of kinematic data of 23 body segments are collected through 17 wireless inertial sensors attached to body segments. The inertial data are subsequently trained through ANFIS to identify the relationship between acceleration data, body postures and the amount of kinetic energies produced by 8 subjects including male and female. The results clearly show the advantages of ANFIS in measuring the fatigue effects on general ambulatory performance.
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This study investigated the application of Symbolic Aggregate approXimation (SAX) to modelling dynamic body motion using a single inertial measurement unit (IMU). In addition this study demonstrates how IMUs located at different positions... more
This study investigated the application of Symbolic Aggregate approXimation (SAX) to modelling dynamic body motion using a single inertial measurement unit (IMU). In addition this study demonstrates how IMUs located at different positions around the body produce comparable results. This study investigates the output of multiple IMU sensors, employed to monitor movement. Next a comparison of the sternum, pelvis, head and lower back sensor locations is conducted by analysing the measured rotation and position IMU data. Additionally, the classifier has been improved by increasing the information in the training data to avoid incorrect classification of similar activities. The results obtained in this study also prove that the sternum and head sensors provided comparable data to the pelvis sensor when using TSBs for classification, especially when used to classify dynamic activities. To pre-process the data, sub-dimensional motif discovery was employed to find features within the data from multiple IMUs. This improves on previous studies which illustrated difficulty classifying fast movements using the sternum IMU. This data was also approximated using SAX and classified by comparing Time Series Bitmaps (TSB's) to find the least Euclidean distance between the reference TSB's and the sliding window TSB's.
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Monitoring and interpreting of the Driver behaviour is a challenging area of research as the driver behaves unpredictably under the influence of factors such as fatigue, drowsiness, inattention, weather, traffic, and roads variations. In... more
Monitoring and interpreting of the Driver behaviour is a challenging area of research as the driver behaves unpredictably under the influence of factors such as fatigue, drowsiness, inattention, weather, traffic, and roads variations. In the studies reported in the literature, the activities of the driver are monitored through longitudinal and lateral behaviours using physiological characteristics and computer vision. However, an effective method to understand and monitor the sudden changes in the postural behaviour of the driver leading to catastrophic conditions is still outstanding. Towards developing such method, the driver head posture under different driving states is monitored in this study using inertial sensors. The data produced by the sensors is modelled using Gaussian Mixture unsupervised clustering approach. The experiments were conducted on a total of 10 young healthy subjects on MATHWORKS driver-in-loop simulator, interfaced with a virtual environment designed in Unreal Engine studio. A criteria of minimum abundance range between 0.5-1% is deployed to identify the most optimum clusters. The information contained in the clusters are analyzed to find the maximum magnitude and standard deviation of each cluster and then organized in descending order for assignment the symbols. Finally, the patterns of each driver state are validated. The results indicate that the proposed approach is effective in identifying the driver state in an unsupervised manner. Moreover, the patterns identified can be deployed in a smart early intervention system to correct the mistakes made during driving. The limitations of the current work and directions for future work are discussed.
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Universities around the world require project-based subjects and effective team allocation. Team allocation is a very time-consuming task in complex project-based coursework subjects. This gives rise to the need of automated team... more
Universities around the world require project-based subjects and effective team allocation. Team allocation is a very time-consuming task in complex project-based coursework subjects. This gives rise to the need of automated team allocation software, which can help the subject coordinator quickly allocate students into optimal or sub-optimal teams based on a set of predetermined criteria. This paper details our team allocation software, which was developed in Java in 2012 for the project-based engineering projects and was actually implemented in two annual project-based subjects in the University of Wollongong in the years 2013 through 2017. The actual use of our developed team allocation software shows that this software is able to find, in a very short time, the solutions highly compliant to the team allocation criteria selected using a simple algorithm. Our developed software reduces significantly the time required to form student teams, compared to manual allocations.
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Virtual reality (VR) has been shown to have significant impacts on the efficacy of rehabilitation, improving a patient\u27s motivation and participation, as well as improving scores in functional assessments when used to enhance... more
Virtual reality (VR) has been shown to have significant impacts on the efficacy of rehabilitation, improving a patient\u27s motivation and participation, as well as improving scores in functional assessments when used to enhance traditional therapy. However, movements in VR have been demonstrated to have significant differences in movement profiles whilst performing simple reaching tasks compared to their real counterparts. The lack of tactile perception in VR systems is often attributed to be one of the causes of these differences. Therefore, to investigate the degree to which the lack of haptic feedback impacts movement profiles in VR, we have reintroduced the sense of touch through vibration motors on the fingertips. Participants were required to reach to virtual targets, both with and without haptic feedback. Their movements were quantified using motion capture, and the virtual targets were rendered using the Oculus Rift. The motions to both targets were compared using a number of measures to characterize the velocity profiles. Preliminary results suggest that the reintroduction of haptic feedback improves performance based indicators in virtual reaching tasks, such as the time to complete a reach, and the stability of the reaching hand whilst touching the virtual target
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The status of functional performance (FP) is a key component in making a decision about termination or continuation of adjuvant chemotherapy. A questionnaire-based method known as eastern cooperative oncology group(ECOG) is often utilized... more
The status of functional performance (FP) is a key component in making a decision about termination or continuation of adjuvant chemotherapy. A questionnaire-based method known as eastern cooperative oncology group(ECOG) is often utilized to assess the FP of cancer patients during adjuvant chemotherapy. The ECOG is primarily a subjective method and consequently prone to error and inaccuracy. This paper proposed an objective FP assessment method using an array of 23 body-mounted inertial sensors collecting kinematic motion data during walking test (WT). A Gaussian mixture model (GMM) was subsequently applied to data to identify postural states representing the adjuvant chemotherapy effects. The method applied to a prototypical model simulating pre- and post-chemotherapy FP was developed in consultation with an oncologist. The method was subsequently validated on a breast cancer patient. The results were promising as they can clearly and quantitatively identify the effects of adjuvant...
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This paper outlines the status of the landslide susceptibility modelling of the Sydney Basin region within the state of NSW, Australia. This area extends from Newcastle in the north to Batemans Bay in the south and west to include the... more
This paper outlines the status of the landslide susceptibility modelling of the Sydney Basin region within the state of NSW, Australia. This area extends from Newcastle in the north to Batemans Bay in the south and west to include the Blue Mountains, an area of approximately 31,000 km2. The University of Wollongong NSW Landslide Inventory includes 1863 landslides (134 falls, 278 flows and 1451 slides) to date. The region supports approximately one quarter of the population of Australia. Individual susceptibility models for both slide category and flow category landslides have been developed for the entire Sydney Basin region. Rockfall Susceptibility has also been developed for portions of the Wollongong Local Government Area. The susceptibility models are suitable for use at local scale, Advisory level Local Government Area Development Control Plans. As the models cover the three dominant landslide types identified within the inventory, a trial Total Landslide Susceptibility model has now been developed. As each landslide susceptibility model is a 10 m pixel resolution numerical grid, with values ranging from 0 to 1, the total susceptibility model has been developed, quite simply, by summing the three individual models producing a Total Susceptibility numerical grid. However, classifying this grid into zones is not simple. Furthermore, the Australian regulatory requirements are varied and the outcomes often complex involving a spatial query of the total susceptibility grid and its three contributing landslide susceptibility grids.
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Prolonged exercise-induced muscular fatigue adversely affects physical performance. The fatigue increases the risk of sport injuries, whereas early fatigue detection and assessment can prevent injuries. An assessment of the most crucial... more
Prolonged exercise-induced muscular fatigue adversely affects physical performance. The fatigue increases the risk of sport injuries, whereas early fatigue detection and assessment can prevent injuries. An assessment of the most crucial impacts of fatigue on physical performance leads to the development of an accurate, non-invasive and objective muscular fatigue measurement method. The most common manifestation of exercise-induced fatigue is a significant decline in force produced by the muscles that in turn affects motion characteristics. This directly alters body postural behaviour and decreases the amount of kinetic energy produced by the subject. The current non-invasive and objective methods to measure fatigue and analyse motion characteristics cannot provide a comprehensive information about muscular fatigue because of their limited ability to record different aspects of motion. An objective and non-invasive assessment method of exercise-induced fatigue, in which the variation...
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Exercise-induced fatigue evolves from the initiation of physical work. Nonetheless, the development of an objective method for detecting fatigue based on variation in ambulatory motion parameters measured during exercise is yet to be... more
Exercise-induced fatigue evolves from the initiation of physical work. Nonetheless, the development of an objective method for detecting fatigue based on variation in ambulatory motion parameters measured during exercise is yet to be explored. In this study, the ambulatory motion parameters consisting of kinematic parameters of 23 body segments in addition to muscle tissue oxygen saturation (SmO2), heart rate, and vertical work of eight healthy male subjects during stair climbing tests (SCT) were measured before and after a fatigue protocol utilizing Wingate cycling test. The impacts of fatigue on ambulatory motion and postural behaviors were analyzed using an unsupervised machine learning method classifying angular joint motions. The average of total distance traveled by subjects and the overall body postural behavior showed about 25% decline and 90% variation after fatigue protocol, respectively. Also, higher relative desaturation in SCT1 −64.0 (1.1) compared SCT2 −54.8 (1.1) was ...
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Abstract The most common side-effect of chemotherapy is fatigue. Because of its impact on Physical Performance Status (PPS), the degree of fatigue is a factor considered in chemotherapy administration. Conventionally, a... more
Abstract The most common side-effect of chemotherapy is fatigue. Because of its impact on Physical Performance Status (PPS), the degree of fatigue is a factor considered in chemotherapy administration. Conventionally, a questionnaire-based method known as the ECOG table, devised by the Eastern Cooperation Oncology Group (ECOG), is employed to assess the chemotherapy-induced fatigue. The approach is qualitative, subjective, inaccurate and prone to error. To achieve a more reliable method, an objective, quantitative and precise method is proposed to assess the PPS of different groups of cancer patients. The approach was developed based on a six-minute walk test (6MWT) during which the kinematic data of 23 body segments were measured using body-mounted inertial sensors. The data streams were subsequently segmented by a clustering algorithm known as ‘minimum-message-length-encoding’ (MML) producing a Gaussian mixture model (GMM). Several postural states were captured from the model to derive a holistic index representing the PPS of a patient undergoing chemotherapy. The proposed method was validated by applying it to simulated and real data. For the simulation study, a typical gait behaviour simulating post-chemotherapy conditions was devised in consultation with an oncologist while the real data comprised the gait information obtained from 4 cancer patients. The results indicate that the proposed algorithm clearly identifies the characteristics of ambulatory motion affected by chemotherapy and provides a more accurate measure of fatigue that can assist oncologists to make a more objective decision regarding continuation or termination of treatment.
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Physical exercise-induced fatigue causes a significant decline in functional performance and subsequently increases the risk of sport injuries. The fatigability tolerance is generally increased by, improving muscular strength and... more
Physical exercise-induced fatigue causes a significant decline in functional performance and subsequently increases the risk of sport injuries. The fatigability tolerance is generally increased by, improving muscular strength and endurance using physical exercise training programs. However, a simple quantitative assessment of fatigue remains as an important step in such programs. The nature of fatigue and its impact on gait and posture is studied using inertial sensors. In this approach, the flexion/extension of knees of a subject during stair climbing test (SCTs) before and after performing a specific set of fatigue-inducing exercises is measured. The knee motion data obtained during SCT before after the exercise are compared using dynamic time warping (DTW). The amount of difference is an indication of the degree of fatigue. The method is applied to twenty subjects including both male and female. The result is encouraging and confirms the feasibility and validity of the approach.
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This paper describes a novel online biodynamic model parameter estimation algorithm applied to data acquired from a small Inertial Measurement Unit network. The predicted biodynamic model represents the response motion of a human seated... more
This paper describes a novel online biodynamic model parameter estimation algorithm applied to data acquired from a small Inertial Measurement Unit network. The predicted biodynamic model represents the response motion of a human seated in a vehicle subjected to mechanical vibrations. The motivations for identifying the body model parameters are, to aid vibration isolation technologies and to continuously monitor the transmission of vibrations through the seated occupant. The algorithm consists of a bank of parallel Extended Kalman Filters for estimating each biodynamic parameter. During the posterior state prediction update step of the Extended Kalman Filter, an Adaptive Sliding Window concept drift detection algorithm maintains a variable window of past priori estimation errors. The model process error statistics are then updated based upon the statistics of the priori state errors in the window. The algorithm updates the estimator parameters incrementally and is suitable for streaming data. The estimator is applicable for cases where the distribution of the biodynamic model process noise is unknown or is abruptly varying, and when the model parameters are unknown. This estimator was evaluated experimentally with data sourced from a network of wearable wireless Inertial Measurement Units affixed to the test subject. The test subjects were then exposed to an external vibrating excitation source. The results validate that this algorithm provides accurate online estimates of the human biodynamic model parameters for a second order transmissibility model. The algorithm presented mitigates problems associated with the estimation of biodynamic parameters such as biomechanical nonlinearities, process noise drift and divergence.
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Purpose: To investigate the segmentation approaches for bone metastases in differentiating benign from malignant bone lesions and characterizing malignant bone lesions. Method: The literature search was conducted in Scopus, PubMed, IEEE... more
Purpose: To investigate the segmentation approaches for bone metastases in differentiating benign from malignant bone lesions and characterizing malignant bone lesions. Method: The literature search was conducted in Scopus, PubMed, IEEE and MedLine, and Web of Science electronic databases following the guidelines of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). A total of 77 original articles, 24 review articles, and 1 comparison paper published between January 2010 and March 2022 were included in the review. Results: The results showed that most studies used neural network-based approaches (58.44%) and CT-based imaging (50.65%) out of 77 original articles. However, the review highlights the lack of a gold standard for tumor boundaries and the need for manual correction of the segmentation output, which largely explains the absence of clinical translation studies. Moreover, only 19 studies (24.67%) specifically mentioned the feasibility of their propos...
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This paper proposes the application of an improved extended active observer (IEAOB) based teleoperation controller for both master and slave manipulators. The proposed approach successfully deals w...
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Application of fuzzy NARX to human gait modelling and identification
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Research Interests: Image Processing, Intelligent Control, Image Analysis, Image segmentation, Control Systems, and 15 moreDigital Image Processing, Unmanned Aerial Vehicles, Navigation, Search and Rescue, Visual Servoing, Robustness, Spectrum, Autonomic System, Autonomous Vehicle, Hough Transform, Object Detection, Indoor Environment, Unmanned Aerial Vehicle, Image processing techniques, and Navigational tracking
University of Wollongong Landslide Research Team has completed a GIS-based Landslide Susceptibility model for the entire Sydney Basin region. According to the Australian Bureau of Statistics and the 2011 Census data, the population within... more
University of Wollongong Landslide Research Team has completed a GIS-based Landslide Susceptibility model for the entire Sydney Basin region. According to the Australian Bureau of Statistics and the 2011 Census data, the population within the Sydney Basin Study area is approximately one quarter of the population of Australia. This model has been developed with the aid of a large scale Landslide Inventory for NSW, which contains 1823 landslides to date. A composite geology dataset has also been developed using commercially available geology datasets including those from NSW Department of Primary Industries and elsewhere. The model employs a 10m pixel Digital Elevation Model (DEM) across the entire study area derived from either Local Government sourced Airborne Laser Scan data and where absent the 30m pixel year 2000 Shuttle Radar Topography Mission (SRTM) data. Using techniques developed over the last decade and refined ArcGIS tools developed over the last three years, Data Mining m...
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Batch annealing of tightly coiled steel strip involves a heating and cooling cycle in a protective atmosphere. The last region of the coiled material to reach the required annealing temperature conditions, the cold spot, is unobservable.... more
Batch annealing of tightly coiled steel strip involves a heating and cooling cycle in a protective atmosphere. The last region of the coiled material to reach the required annealing temperature conditions, the cold spot, is unobservable. This paper describes an on-line prediction of this cold spot thermal history that may be used in the control of the batch annealing process. This affords, amongst other benefits, energy savings and more consistent and improved product. The distributed control system developed implements coil interior temperature prediction, as well as providing enhanced automatic batch process management and monitoring.
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Research Interests: Engineering, Mathematics, Computer Science, MIMO, Sts, and 9 moreSystems, Effects, Promising, Sequences, Set, MAI, Science and Technology Studies, mitigate, and Spreading
The modeling and measurement of the biodynamic response of the seated human body has recently been an active research topic, with major applications to ergonomics and automotive suspension control system technologies. This paper presents... more
The modeling and measurement of the biodynamic response of the seated human body has recently been an active research topic, with major applications to ergonomics and automotive suspension control system technologies. This paper presents a holistic literature survey of topics including the latest research in the area of vibration signal processing and modeling of the biodynamic response of the seated human to vibrations. This paper reviews recent sensing systems that are reported to measure the motion of the seated body. The data processing techniques that are currently accepted are surveyed and these include impedance, transmissibility measures, frequency response function estimation, and model development. A review of applications of biodynamic response analysis and modeling to seating vibration isolation technologies and vibration monitoring systems is presented within this paper. This survey paper provides a discussion on the direction that the future research in this field will aim toward based on the trends in the recent research and the introduction and application of new technologies.
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This paper examines a new technique for uncovering and synthesising control skills evolved by human agents involved in controlling complex machines or devices. The repetitive application of such skills often renders them as automatic,... more
This paper examines a new technique for uncovering and synthesising control skills evolved by human agents involved in controlling complex machines or devices. The repetitive application of such skills often renders them as automatic, sub-cognitive responses such as driving (controlling), a car or riding a bicycle. Uncovering and imitating such sub-symbolic abilities has received much attention in the field of machine learning of late, particularly through the technique of behavioural cloning. However, to date, the advantages afforded by this technique, typically, transforming such implicit skills to symbolic forms are often eclipsed by a lack of robustness, particularly in domains involving dynamic control skills. By employing Compressed Heuristic Universal Reaction Planners, or CHURPs, which offers a different perspective on control skills, all of these disadvantages can be overcome. CHURPs also provide a human-like synthetic form of knowledge, substantially better control performance and offer a range of additional benefits, such as surrogate control and goal sharing behaviours. In this paper the structure of CHURPs is explained and comparative performance results on Sammut's flight domain are given.
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On the effects of spreading sequences over MIMO systems