Hysteresis modulation of multilevel inverters

The hysteresis modulation for power electronic converters is attractive in many different applications because of its unmatched dynamic response and wide command-tracking bandwidth. Its application and beneftis for two-level converters are well understood, but the extension of this strategy to multilevel converters is still under development. This paper summarizes and reviews the various hysteresis modulation approaches available in the literature for multilevel converters. The pros and cons of various techniques are described and compared for tracking the reference signal in order to attain an adequate switching optimization, excellent dynamic responses and high accuracy in steady-state operation. By using the recently developed multilevel hysteresis modulation approaches the advantages of using several accessible dc potentials in a multilevel inverter has been fully exploited. All of these hysteresis modulation approaches are testing for tracking a current reference when applied to a fivelevel inveter. The relevant simulation and experimental result are also presented. This study will provide a useful framweork and point of reference for the future development of hysteresis modulation for multilevel converters.

Addressing sustainable living using a collaborative approach and multi-disciplinary techniques

In order to achieve meaningful reductions in individual ecological footprints, individuals must dramatically alter their day to day behaviours. Effective interventions will need to be evidence based and there is a necessity for the rapid transfer or communication of information from the point of research, into policy and practice. A number of health disciplines, including psychology and public health, share a common mission to promote health and well-being and it is becoming clear that the most practical pathway to achieving this mission is through interdisciplinary collaboration. This paper argues that an interdisciplinary collaborative approach will facilitate research that results in the rapid transfer of findings into policy and practice. The application of this approach is described in relation to the Green Living project which explored the psycho-social predictors of environmentally friendly behaviour. Following a qualitative pilot study, and in consultation with an expert panel comprising academics, industry professionals and government representatives, a self-administered mail survey was distributed to a random sample of 3000 residents of Brisbane and Moreton Bay (Queensland, Australia). The Green Living survey explored specific beliefs which included attitudes, norms, perceived control, intention and behaviour, as well as a number of other constructs such as environmental concern and altruism. This research has two beneficial outcomes. First, it will inform a practical model for predicting sustainable living behaviours and a number of local councils have already expressed an interest in making use of the results as part of their ongoing community engagement programs. Second, it provides an example of how a collaborative interdisciplinary project can provide a more comprehensive approach to research than can be accomplished by a single disciplinary project.

A methodology for object identification and tracking in construction based on spatial modeling and image matching techniques

Understanding the motion characteristics of on-site objects is desirable for the analysis of construction work zones, especially in problems related to safety and productivity studies. This article presents a methodology for rapid object identification and tracking. The proposed methodology contains algorithms for spatial modeling and image matching. A high-frame-rate range sensor was utilized for spatial data acquisition. The experimental results indicated that an occupancy grid spatial modeling algorithm could quickly build a suitable work zone model from the acquired data. The results also showed that an image matching algorithm is able to find the most similar object from a model database and from spatial models obtained from previous scans. It is then possible to use the matched information to successfully identify and track objects.

Commissioning of a Geant4 based treatment plan simulation tool : linac model and dicom-rt interface

A Geant4 based simulation tool has been developed to perform Monte Carlo modelling of a 6 MV VarianTM iX clinac. The computer aided design interface of Geant4 was used to accurately model the LINAC components, including the Millenium multi-leaf collimators (MLCs). The simulation tool was verified via simulation of standard commissioning dosimetry data acquired with an ionisation chamber in a water phantom. Verification of the MLC model was achieved by simulation of leaf leakage measurements performed using GafchromicTM film in a solid water phantom. An absolute dose calibration capability was added by including a virtual monitor chamber into the simulation. Furthermore, a DICOM-RT interface was integrated with the application to allow the simulation of treatment plans in radiotherapy. The ability of the simulation tool to accurately model leaf movements and doses at each control point was verified by simulation of a widely used intensity-modulated radiation therapy (IMRT) quality assurance (QA) technique, the chair test.

Machine learning techniques to improve software quality

A significant proportion of the cost of software development is due to software testing and maintenance. This is in part the result of the inevitable imperfections due to human error, lack of quality during the design and coding of software, and the increasing need to reduce faults to improve customer satisfaction in a competitive marketplace. Given the cost and importance of removing errors improvements in fault detection and removal can be of significant benefit. The earlier in the development process faults can be found, the less it costs to correct them and the less likely other faults are to develop. This research aims to make the testing process more efficient and effective by identifying those software modules most likely to contain faults, allowing testing efforts to be carefully targeted. This is done with the use of machine learning algorithms which use examples of fault prone and not fault prone modules to develop predictive models of quality. In order to learn the numerical mapping between module and classification, a module is represented in terms of software metrics. A difficulty in this sort of problem is sourcing software engineering data of adequate quality. In this work, data is obtained from two sources, the NASA Metrics Data Program, and the open source Eclipse project. Feature selection before learning is applied, and in this area a number of different feature selection methods are applied to find which work best. Two machine learning algorithms are applied to the data - Naive Bayes and the Support Vector Machine - and predictive results are compared to those of previous efforts and found to be superior on selected data sets and comparable on others. In addition, a new classification method is proposed, Rank Sum, in which a ranking abstraction is laid over bin densities for each class, and a classification is determined based on the sum of ranks over features. A novel extension of this method is also described based on an observed polarising of points by class when rank sum is applied to training data to convert it into 2D rank sum space. SVM is applied to this transformed data to produce models the parameters of which can be set according to trade-off curves to obtain a particular performance trade-off.

Comparison of scoring systems for invasive pests using ROC analysis and Monte Carlo simulation

Different international plant protection organisations advocate different schemes for conducting pest risk assessments. Most of these schemes use structured questionnaire in which experts are asked to score several items using an ordinal scale. The scores are then combined using a range of procedures, such as simple arithmetic mean, weighted averages, multiplication of scores, and cumulative sums. The most useful schemes will correctly identify harmful pests and identify ones that are not. As the quality of a pest risk assessment can depend on the characteristics of the scoring system used by the risk assessors (i.e., on the number of points of the scale and on the method used for combining the component scores), it is important to assess and compare the performance of different scoring systems. In this article, we proposed a new method for assessing scoring systems. Its principle is to simulate virtual data using a stochastic model and, then, to estimate sensitivity and specificity values from these data for different scoring systems. The interest of our approach was illustrated in a case study where several scoring systems were compared. Data for this analysis were generated using a probabilistic model describing the pest introduction process. The generated data were then used to simulate the outcome of scoring systems and to assess the accuracy of the decisions about positive and negative introduction. The results showed that ordinal scales with at most 5 or 6 points were sufficient and that the multiplication-based scoring systems performed better than their sum-based counterparts. The proposed method could be used in the future to assess a great diversity of scoring systems.

Business process simulation in virtual worlds

This is an invited presentation made as a short preview of the virtual environment research work being undertaken at QUT in the Business Process Management (BPM) research group, known as BPMVE. Three projects are covered, spatial process visualisation, with applications to airport check-in processes, collaborative process modelling using a virtual world BPMN editing tool and business process simulation in virtual worlds using Open Simulator and the YAWL workflow system. In addition, the relationship of this work to Organisational Psychology is briefly explored. Full Video/Audio is available at: http://www.youtube.com/user/BPMVE#p/u/1/rp506c3pPms

HIFiRE re-entry observation using an image-based visual servoing system

This paper presents an image based visual servoing system that is intended to be used for tracking and obtaining scientific observations of the HIFiRE vehicles. The primary aim of this tracking platform is to acquire and track the thermal signature emitted from the surface of the vehicle during the re-entry phase of the mission using an infra-red camera. The implemented visual servoing scheme uses a classical image based approach to identify and track the target using visual kinematic control. The paper utilizes simulation and experimental results to show the tracking performance of the system using visual feedback. Discussions on current implementation and control techniques to further improve the performance of the system are also explored.

Human resource behaviour simulation in business processes

The structure and dynamics of a modern business environment are very hard to model using traditional methods. Such complexity raises challenges to effective business analysis and improvement. The importance of applying business process simulation to analyze and improve business activities has been widely recognized. However, one remaining challenge is the development of approaches to human resource behavior simulation. To address this problem, we describe a novel simulation approach where intelligent agents are used to simulate human resources by performing allocated work from a workflow management system. The behavior of the intelligent agents is driven a by state transition mechanism called a Hierarchical Task Network (HTN). We demonstrate and validate our simulator via a medical treatment process case study. Analysis of the simulation results shows that the behavior driven by the HTN is consistent with design of the workflow model. We believe these preliminary results support the development of more sophisticated agent-based human resource simulation systems.

Numerical simulation of flow in a photocatalytic reactor containing baffles

In this study a new immobilized flat plate photocatalytic reactor for wastewater treatment has been investigated using computational fluid dynamics (CFD). The reactor consists of a reactor inlet, a reactive section where the catalyst is coated, and outlet parts. For simulation, the reactive section of the reactor was modelled with an array of baffles. In order to optimize the fluid mixing and reactor design, this study attempts to investigate the influence of baffles with differing heights on the flow field of the flat plate reactor. The results obtained from the simulation of a baffled flat plate reactor hydrodynamics for differing baffle heights for certain positions are presented. Under the conditions simulated, the qualitative flow features, such as the distribution of local stream lines, velocity contours, and high shear region, boundary layers separation, vortex formation, and the underlying mechanism are examined. At low and high Re numbers, the influence of baffle heights on the distribution of species mass fraction of a model pollutant are also highlighted. The simulation of qualitative and quantitative properties of fluid dynamics in a baffled reactor provides valuable insight to fully understand the effect of baffles and their role on the flow pattern, behaviour, and features of wastewater treatment using a photocatalytic reactor.

CFD Simulation of turbulence promoters in a water treatment reactor

A new immobilized flat plate photocatalytic reactor for wastewater treatment has been proposed in this study to avoid subsequent catalyst removal from the treated water. The reactor consists of an inlet, reactive section where catalyst is coated and an outlet parts. In order to optimize the fluid mixing and reactor design, this study aims to investigate the influence of baffles and its arrangement on the flat plate reactor hydrodynamics using computational fluid dynamics (CFD) simulation. For simulation, an array of baffles acting as turbulence promoters is inserted in the reactive zone of the reactor. In this regard, results obtained from the simulation of a baffled- flat plate photoreactor hydrodynamics for different baffle positions, heights and intervals are presented utilizing RNG k-ε turbulence model. Under the conditions simulated, the qualitative flow features, such as the development and separation of boundary layers, vortex formation, the presence of high shear regions and recirculation zones, and the underlying mechanism are examined. The influence of various baffle sizes on the distribution of pollutant concentration is also highlighted. The results presented here indicate that the spanning of recirculation increases the degree of interfacial distortion with a larger interfacial area between fluids which results in substantial enhancement in fluid mixing. The simulation results suggest that the qualitative and quantitative properties of fluid dynamics in a baffled reactor can be obtained which provides valuable insight to fully understand the effect of baffles and its arrangements on the flow pattern, behaviour, and feature.

Check-in processing : simulation of passengers with advanced traits

In order to tackle the growth of air travelers in airports worldwide, it is important to simulate and understand passenger flows to predict future capacity constraints and levels of service. We discuss the ability of agent-based models to understand complicated pedestrian movement in built environments. In this paper we propose advanced passenger traits to enable more detailed modelling of behaviors in terminal buildings, particularly in the departure hall around the check-in facilities. To demonstrate the concepts, we perform a series of passenger agent simulations in a virtual airport terminal. In doing so, we generate a spatial distribution of passengers within the departure hall to ancillary facilities such as cafes, information kiosks and phone booths as well as common check-in facilities, and observe the effects this has on passenger check-in and departure hall dwell times, and facility utilization.

Adaptive Bayesian compound designs for dose finding studies

We consider the problem of how to efficiently and safely design dose finding studies. Both current and novel utility functions are explored using Bayesian adaptive design methodology for the estimation of a maximum tolerated dose (MTD). In particular, we explore widely adopted approaches such as the continual reassessment method and minimizing the variance of the estimate of an MTD. New utility functions are constructed in the Bayesian framework and are evaluated against current approaches. To reduce computing time, importance sampling is implemented to re-weight posterior samples thus avoiding the need to draw samples using Markov chain Monte Carlo techniques. Further, as such studies are generally first-in-man, the safety of patients is paramount. We therefore explore methods for the incorporation of safety considerations into utility functions to ensure that only safe and well-predicted doses are administered. The amalgamation of Bayesian methodology, adaptive design and compound utility functions is termed adaptive Bayesian compound design (ABCD). The performance of this amalgamation of methodology is investigated via the simulation of dose finding studies. The paper concludes with a discussion of results and extensions that could be included into our approach.

Simulation of inplane and out of plane AE source in thin plate

In most materials, short stress waves are generated during the process of plastic deformation, phase transformation, crack formation and crack growth. These phenomena are applied in acoustic emission (AE) for the detection of material defects in a wide spectrum of areas, ranging from nondestructive testing for the detection of materials defects to monitoring of microseismical activity. AE technique is also used for defect source identification and for failure detection. AE waves consist of P waves (primary longitudinal waves), S waves (shear/transverse waves) and Rayleigh (surface) waves as well as reflected and diffracted waves. The propagation of AE waves in various modes has made the determination of source location difficult. In order to use acoustic emission technique for accurate identification of source, an understanding of wave propagation of the AE signals at various locations in a plate structure is essential. Furthermore, an understanding of wave propagation can also assist in sensor location for optimum detection of AE signals along with the characteristics of the source. In real life, as the AE signals radiate from the source it will result in stress waves. Unless the type of stress wave is known, it is very difficult to locate the source when using the classical propagation velocity equations. This paper describes the simulation of AE waves to identify the source location and its characteristics in steel plate as well as the wave modes. The finite element analysis (FEA) is used for the numerical simulation of wave propagation in thin plate. By knowing the type of wave generated, it is possible to apply the appropriate wave equations to determine the location of the source. For a single plate structure, the results show that the simulation algorithm is effective to simulate different stress waves.

Testing and simulation of extruded polystyrene foam at low to moderate strain rates

This paper presents a study into the behaviour of extruded polystyrene foam at low strain rates. The foam is being studied in order assess its potential for use as part of a new innovative design of portable road safety barrier the aim to consume less water and reduce rates of serious injury. The foam was tested at a range of low strain rates, with the stress and strain behaviour of the foam specimens being recorded. The energy absorption capabilities of the foam were assessed as well as the response of the foam to multiple loadings. The experimental data was then used to create a material model of the foam for use in the explicit finite element solver LS-DYNA. Simulations were carried out using the material model which showed excellent correlation between the numerical material model and the experimental data.