Harmonic elimination technique for a single-phase multilevel converter with unequal DC link voltage levels

Multilevel converters, because of the benefits they attract in generating high quality output voltage, are used in several applications. Various modulation and control techniques are introduced by several researchers to control the output voltage of the multilevel converters like space vector modulation and harmonic elimination (HE) methods. Multilevel converters may have a DC link with equal or unequal DC voltages. In this study a new HE technique based on the HE method is proposed for multilevel converters with unequal DC link voltage. The DC link voltage levels are considered as additional variables for the HE method and the voltage levels are defined based on the HE results. Increasing the number of voltage levels can reduce lower order harmonic content because of the fact that more variables are created. In comparison to previous methods, this new technique has a positive effect on the output voltage quality by reducing its total harmonic distortion, which must take into consideration for some applications such as uninterruptable power supply, motor drive systems and piezoelectric transducer excitation. In order to verify the proposed modulation technique, MATLAB simulations and experimental tests are carried out for a single-phase four-level diode-clamped converter.

Designing a new robust on-line secondary path modeling technique for feedforward active noise control systems

Several approaches have been introduced in the literature for active noise control (ANC) systems. Since the filtered-x least-mean-square (FxLMS) algorithm appears to be the best choice as a controller filter, researchers tend to improve performance of ANC systems by enhancing and modifying this algorithm. This paper proposes a new version of the FxLMS algorithm, as a first novelty. In many ANC applications, an on-line secondary path modeling method using white noise as a training signal is required to ensure convergence of the system. As a second novelty, this paper proposes a new approach for on-line secondary path modeling on the basis of a new variable-step-size (VSS) LMS algorithm in feed forward ANC systems. The proposed algorithm is designed so that the noise injection is stopped at the optimum point when the modeling accuracy is sufficient. In this approach, a sudden change in the secondary path during operation makes the algorithm reactivate injection of the white noise to re-adjust the secondary path estimate. Comparative simulation results shown in this paper indicate the effectiveness of the proposed approach in reducing both narrow-band and broad-band noise. In addition, the proposed ANC system is robust against sudden changes of the secondary path model.

Preparation of porous nanofibers from electrospun polyacrylonitrile/calcium carbonate composite nanofibers using porogen leaching technique

Production of nanofibrous polyacrylonitrile/calcium carbonate (PAN/CaCO3) nanocomposite web was carried out through solution electrospinning process. Pore generating nanoparticles were leached from the PAN matrices in hydrochloric acid bath with the purpose of producing an ultimate nanoporous structure. The possible interaction between CaCO3 nanoparticles and PAN functional groups was investigated. Atomic absorption method was used to measure the amount of extracted CaCO3 nanoparticles. Morphological observation showed nanofibers of 270–720 nm in diameter containing nanopores of 50–130 nm. Monitoring the governing parameters statistically, it was found that the amount of extraction (ε) of CaCO3was increased when the web surface area (a) was broadened according to a simple scaling law (ε = 3.18 a0.4). The leaching process was maximized in the presence of 5% v/v of acid in the extraction bath and 5 wt % of CaCO3 in the polymer solution. Collateral effects of the extraction time and temperature showed exponential growth within a favorable extremum at 50°C for 72 h. Concentration of dimethylformamide as the solvent had no significant impact on the extraction level.

A variable step-size FxLMS algorithm for feedforward active noise control systems based on a new online secondary path modelling technique

Several approaches have been introduced in literature for active noise control (ANC) systems. Since FxLMS algorithm appears to be the best choice as a controller filter, researchers tend to improve performance of ANC systems by enhancing and modifying this algorithm. This paper proposes a new version of FxLMS algorithm. In many ANC applications an online secondary path modelling method using a white noise as a training signal is required to ensure convergence of the system. This paper also proposes a new approach for online secondary path modelling in feedfoward ANC systems. The proposed algorithm stops injection of the white noise at the optimum point and reactivate the injection during the operation, if needed, to maintain performance of the system. Benefiting new version of FxLMS algorithm and not continually injection of white noise makes the system more desirable and improves the noise attenuation performance. Comparative simulation results indicate effectiveness of the proposed approach.

Which estimating technique?

This paper presents a unified view of the relationship between (1) quantity and (2) price generating mechanisms in estimating individual prime construction costs/prices. A brief review of quantity generating techniques is provided with particular emphasis on experientially based assumptive approaches and this is compared with the level of pricing data available for the quantities generated in terms of reliability of the ensuing prime cost estimates. It is argued that there is a tradeoff between the reliability of quantity items and reliability of rates. Thus it is shown that the level of quantity generation is optimised by maximising the joint reliability function of the quantity items and their associated rates. Some thoughts on how this joint reliability function can be evaluated and quantified follow. The application of these ideas is described within the overall strategy of the estimator's decision - "Which estimating technique shall I use for a given level of contract information? - and a case is made for the computer generation of estimates by several methods, with an indication of the reliability of each estimate, the ultimate choice of estimate being left to the estimator concerned. Finally, the potential for the development of automatic estimating systems within this framework is examined.

Instructional constraints on movement and performance of players in the penalty kick

The influence of different instructional constraints on movement organisation and performance outcomes of the penalty kick (PK) was investigated according to participant age. Sixty penalty takers and twelve goalkeepers from two age groups (under 15 and under 17) performed 300 PKs under five different task conditions, including: no explicit instructional constraints provided (Control); instructional constraints on immobility (IMMOBILE) and mobility (MOBILE) of goalkeepers; and, use of keeper-dependent (DEP) and independent (INDEP) strategies by penalty takers. Every trial was video recorded and digitised using motion analysis techniques. Dependent variables (DVs) were: movement speed of penalty takers and the angles between the goalkeeper's position and the goal line (i.e., diving angle), and between the penalty taker and a line crossing the penalty spot and the centre of the goal (i.e., run up angle). Instructions significantly influenced the way that goalkeepers (higher values in MOBILE relative to Control) and penalty takers (higher values in Control than in DEP) used movement speed during performance, as well as the goalkeepers' movements and diving angle (less pronounced dives in the MOBILE condition compared with INDEP). Results showed how different instructions constrained participant movements during performance, although players' performance efficacy remained constant, reflecting their adaptive variability.

Which procurement system? Towards a universal procurement selection technique

Two approaches are described, which aid the selection of the most appropriate procurement arrangements for a building project. The first is a multi-attribute technique based on the National Economic Development Office procurement path decision chart. A small study is described in which the utility factors involved were weighted by averaging the scores of five 'experts' for three hypothetical building projects. A concordance analysis is used to provide some evidence of any abnormal data sources. When applied to the study data, one of the experts was seen to be atypical. The second approach is by means of discriminant analysis. This was found to provide reasonably consistent predictions through three discriminant functions. The analysis also showed the quality criteria to have no significant impact on the decision process. Both approaches provided identical and intuitively correct answers in the study described. Some concluding remarks are made on the potential of discriminant analysis for future research and development in procurement selection techniques.

Faster Monte Carlo simulation of radiotherapy geometries

Using Monte Carlo simulation for radiotherapy dose calculation can provide more accurate results when compared to the analytical methods usually found in modern treatment planning systems, especially in regions with a high degree of inhomogeneity. These more accurate results acquired using Monte Carlo simulation however, often require orders of magnitude more calculation time so as to attain high precision, thereby reducing its utility within the clinical environment. This work aims to improve the utility of Monte Carlo simulation within the clinical environment by developing techniques which enable faster Monte Carlo simulation of radiotherapy geometries. This is achieved principally through the use new high performance computing environments and simpler alternative, yet equivalent representations of complex geometries. Firstly the use of cloud computing technology and it application to radiotherapy dose calculation is demonstrated. As with other super-computer like environments, the time to complete a simulation decreases as 1=n with increasing n cloud based computers performing the calculation in parallel. Unlike traditional super computer infrastructure however, there is no initial outlay of cost, only modest ongoing usage fees; the simulations described in the following are performed using this cloud computing technology. The definition of geometry within the chosen Monte Carlo simulation environment - Geometry & Tracking 4 (GEANT4) in this case - is also addressed in this work. At the simulation implementation level, a new computer aided design interface is presented for use with GEANT4 enabling direct coupling between manufactured parts and their equivalent in the simulation environment, which is of particular importance when defining linear accelerator treatment head geometry. Further, a new technique for navigating tessellated or meshed geometries is described, allowing for up to 3 orders of magnitude performance improvement with the use of tetrahedral meshes in place of complex triangular surface meshes. The technique has application in the definition of both mechanical parts in a geometry as well as patient geometry. Static patient CT datasets like those found in typical radiotherapy treatment plans are often very large and present a significant performance penalty on a Monte Carlo simulation. By extracting the regions of interest in a radiotherapy treatment plan, and representing them in a mesh based form similar to those used in computer aided design, the above mentioned optimisation techniques can be used so as to reduce the time required to navigation the patient geometry in the simulation environment. Results presented in this work show that these equivalent yet much simplified patient geometry representations enable significant performance improvements over simulations that consider raw CT datasets alone. Furthermore, this mesh based representation allows for direct manipulation of the geometry enabling motion augmentation for time dependant dose calculation for example. Finally, an experimental dosimetry technique is described which allows the validation of time dependant Monte Carlo simulation, like the ones made possible by the afore mentioned patient geometry definition. A bespoke organic plastic scintillator dose rate meter is embedded in a gel dosimeter thereby enabling simultaneous 3D dose distribution and dose rate measurement. This work demonstrates the effectiveness of applying alternative and equivalent geometry definitions to complex geometries for the purposes of Monte Carlo simulation performance improvement. Additionally, these alternative geometry definitions allow for manipulations to be performed on otherwise static and rigid geometry.

Pleading - self-incrimination and penalty privileges

In Anderson v Australian Securities and Investments Commission [2012] QCA 301 the Queensland Court of Appeal allowed an appeal from the decision of the primary judge (ASIC v Managed Investments Ltd No 3 [2012] QSC 74. The Court of Appeal was satisfied that the defendants’ non-compliance with the pleading rules in the Uniform Civil Procedure Rules 1999 (Qld) was justified by the claims to privilege against self-incrimination or exposure to a penalty.

The use of electronic portal imaging to verify patient position during intensity-modulated radiotherapy delivered by the dynamic MLC technique

Purpose: The precise shape of the three-dimensional dose distributions created by intensity-modulated radiotherapy means that the verification of patient position and setup is crucial to the outcome of the treatment. In this paper, we investigate and compare the use of two different image calibration procedures that allow extraction of patient anatomy from measured electronic portal images of intensity-modulated treatment beams. Methods and Materials: Electronic portal images of the intensity-modulated treatment beam delivered using the dynamic multileaf collimator technique were acquired. The images were formed by measuring a series of frames or segments throughout the delivery of the beams. The frames were then summed to produce an integrated portal image of the delivered beam. Two different methods for calibrating the integrated image were investigated with the aim of removing the intensity modulations of the beam. The first involved a simple point-by-point division of the integrated image by a single calibration image of the intensity-modulated beam delivered to a homogeneous polymethyl methacrylate (PMMA) phantom. The second calibration method is known as the quadratic calibration method and required a series of calibration images of the intensity-modulated beam delivered to different thicknesses of homogeneous PMMA blocks. Measurements were made using two different detector systems: a Varian amorphous silicon flat-panel imager and a Theraview camera-based system. The methods were tested first using a contrast phantom before images were acquired of intensity-modulated radiotherapy treatment delivered to the prostate and pelvic nodes of cancer patients at the Royal Marsden Hospital. Results: The results indicate that the calibration methods can be used to remove the intensity modulations of the beam, making it possible to see the outlines of bony anatomy that could be used for patient position verification. This was shown for both posterior and lateral delivered fields. Conclusions: Very little difference between the two calibration methods was observed, so the simpler division method, requiring only the single extra calibration measurement and much simpler computation, was the favored method. This new method could provide a complementary tool to existing position verification methods, and it has the advantage that it is completely passive, requiring no further dose to the patient and using only the treatment fields.

Investigation of the chemical and physical basis of oxidative stress generated by particulate matter using the profluorescent probe technique

Following the growing need for adoption of alternative fuels, this project aimed at getting more information on the oxidative potential of biodiesel particulate matter. Within this scope, the physical and chemical characteristics of biodiesel PM were analysed which lead to identification of reactive organic fractions. An in-house developed proflurescent nitroxide probe was used. This project further developed in-depth understanding of the chemical mechanisms following the detection of the oxidative potential of PM. This knowledge made a significant contribution to our understanding of processes behind negative health effects of pollution and enabled us to further develop new techniques to monitor it.

Developing an adaptive radiotherapy technique for virally mediated head and neck cancer

Purpose Virally mediated head and neck cancers (VMHNC) often present with nodal involvement and are highly radioresponsive, meaning that treatment plan adaptation during radiotherapy (RT) in a subset of patients is required. We sought to determine potential risk profiles and a corresponding adaptive treatment strategy for these patients. Methodology 121 patients with virally mediated, node positive nasopharyngeal (Epstein Barr Virus positive) or oropharyngeal (Human Papillomavirus positive) cancers, receiving curative intent RT were reviewed. The type, frequency and timing of adaptive interventions, including source-to-skin distance (SSD) corrections, re-scanning and re-planning, were evaluated. Patients were reviewed based on the maximum size of the dominant node to assess the need for plan adaptation. Results Forty-six patients (38%) required plan adaptation during treatment. The median fraction at which the adaptive intervention occurred was 26 for SSD corrections and 22 for re-planning CTs. A trend toward 3 risk profile groupings was discovered: 1) Low risk with minimal need (< 10%) for adaptive intervention (dominant pre-treatment nodal size of ≤ 35 mm), 2) Intermediate risk with possible need (< 20%) for adaptive intervention (dominant pre-treatment nodal size of 36 mm – 45 mm) and 3) High-risk with increased likelihood (> 50%) for adaptive intervention (dominant pre-treatment nodal size of ≥ 46 mm). Conclusion In this study, patients with VMHNC and a maximum dominant nodal size of > 46 mm were identified at a higher risk of requiring re-planning during a course of definitive RT. Findings will be tested in a future prospective adaptive RT study.

Composite SaaS resource management in cloud computing using evolutionary computation

Cloud computing is an emerging computing paradigm in which IT resources are provided over the Internet as a service to users. One such service offered through the Cloud is Software as a Service or SaaS. SaaS can be delivered in a composite form, consisting of a set of application and data components that work together to deliver higher-level functional software. SaaS is receiving substantial attention today from both software providers and users. It is also predicted to has positive future markets by analyst firms. This raises new challenges for SaaS providers managing SaaS, especially in large-scale data centres like Cloud. One of the challenges is providing management of Cloud resources for SaaS which guarantees maintaining SaaS performance while optimising resources use. Extensive research on the resource optimisation of Cloud service has not yet addressed the challenges of managing resources for composite SaaS. This research addresses this gap by focusing on three new problems of composite SaaS: placement, clustering and scalability. The overall aim is to develop efficient and scalable mechanisms that facilitate the delivery of high performance composite SaaS for users while optimising the resources used. All three problems are characterised as highly constrained, large-scaled and complex combinatorial optimisation problems. Therefore, evolutionary algorithms are adopted as the main technique in solving these problems. The first research problem refers to how a composite SaaS is placed onto Cloud servers to optimise its performance while satisfying the SaaS resource and response time constraints. Existing research on this problem often ignores the dependencies between components and considers placement of a homogenous type of component only. A precise problem formulation of composite SaaS placement problem is presented. A classical genetic algorithm and two versions of cooperative co-evolutionary algorithms are designed to now manage the placement of heterogeneous types of SaaS components together with their dependencies, requirements and constraints. Experimental results demonstrate the efficiency and scalability of these new algorithms. In the second problem, SaaS components are assumed to be already running on Cloud virtual machines (VMs). However, due to the environment of a Cloud, the current placement may need to be modified. Existing techniques focused mostly at the infrastructure level instead of the application level. This research addressed the problem at the application level by clustering suitable components to VMs to optimise the resource used and to maintain the SaaS performance. Two versions of grouping genetic algorithms (GGAs) are designed to cater for the structural group of a composite SaaS. The first GGA used a repair-based method while the second used a penalty-based method to handle the problem constraints. The experimental results confirmed that the GGAs always produced a better reconfiguration placement plan compared with a common heuristic for clustering problems. The third research problem deals with the replication or deletion of SaaS instances in coping with the SaaS workload. To determine a scaling plan that can minimise the resource used and maintain the SaaS performance is a critical task. Additionally, the problem consists of constraints and interdependency between components, making solutions even more difficult to find. A hybrid genetic algorithm (HGA) was developed to solve this problem by exploring the problem search space through its genetic operators and fitness function to determine the SaaS scaling plan. The HGA also uses the problem's domain knowledge to ensure that the solutions meet the problem's constraints and achieve its objectives. The experimental results demonstrated that the HGA constantly outperform a heuristic algorithm by achieving a low-cost scaling and placement plan. This research has identified three significant new problems for composite SaaS in Cloud. Various types of evolutionary algorithms have also been developed in addressing the problems where these contribute to the evolutionary computation field. The algorithms provide solutions for efficient resource management of composite SaaS in Cloud that resulted to a low total cost of ownership for users while guaranteeing the SaaS performance.

A parametric analysis technique for design of fuel cell and hybrid-electric vehicles

This paper presents a new simplified parametric analysis technique for the design of fuel cell and hybrid-electric vehicles. The technique utilizes a comprehensive set of ∼30 parameters to fully characterize the vehicle platform, powertrain components, vehicle performance requirements and driving conditions. It is best applied to the sizing of powertrain components and prediction of energy consumption in a vehicle. This new parametric technique makes a good complement to existing vehicle simulation software packages and therefore represents a potentially valuable tool for the hybrid vehicle designer.

Automatic segmentation of choroidal thickness in optical coherence tomography

The assessment of choroidal thickness from optical coherence tomography (OCT) images of the human choroid is an important clinical and research task, since it provides valuable information regarding the eye’s normal anatomy and physiology, and changes associated with various eye diseases and the development of refractive error. Due to the time consuming and subjective nature of manual image analysis, there is a need for the development of reliable objective automated methods of image segmentation to derive choroidal thickness measures. However, the detection of the two boundaries which delineate the choroid is a complicated and challenging task, in particular the detection of the outer choroidal boundary, due to a number of issues including: (i) the vascular ocular tissue is non-uniform and rich in non-homogeneous features, and (ii) the boundary can have a low contrast. In this paper, an automatic segmentation technique based on graph-search theory is presented to segment the inner choroidal boundary (ICB) and the outer choroidal boundary (OCB) to obtain the choroid thickness profile from OCT images. Before the segmentation, the B-scan is pre-processed to enhance the two boundaries of interest and to minimize the artifacts produced by surrounding features. The algorithm to detect the ICB is based on a simple edge filter and a directional weighted map penalty, while the algorithm to detect the OCB is based on OCT image enhancement and a dual brightness probability gradient. The method was tested on a large data set of images from a pediatric (1083 B-scans) and an adult (90 B-scans) population, which were previously manually segmented by an experienced observer. The results demonstrate the proposed method provides robust detection of the boundaries of interest and is a useful tool to extract clinical data.