The role of HPC facilies in development of a novel road safety barrier

Water-filled portable road safety barriers are a common fixture in road works, however their use of water can be problematic, both in terms of the quantity of water used and the transportation of the water to the installation site. This project aims to develop a new design of portable road safety barrier, which will make novel use of composite and foam materials in order to reduce the barrier’s reliance on water in order to control errant vehicles. The project makes use of finite element (FE) techniques in order to simulate and evaluate design concepts. FE methods and models that have previously been tested and validated will be used in combination in order to provide the most accurate numerical simulations available to drive the project forward. LS-DYNA code is as highly dynamic, non-linear numerical solver which is commonly used in the automotive and road safety industries. Several complex materials and physical interactions are to be simulated throughout the course of the project including aluminium foams, composite laminates and water within the barrier during standardised impact tests. Techniques to be used include FE, smoothed particle hydrodynamics (SPH) and weighted multi-parameter optimisation techniques. A detailed optimisation of several design parameters with specific design goals will be performed with LS-DYNA and LS-OPT, which will require a large number of high accuracy simulations and advanced visualisation techniques. Supercomputing will play a central role in the project, enabling the numerous medium element count simulations necessary in order to determine the optimal design parameters of the barrier to be performed. Supercomputing will also allow the development of useful methods of visualisation results and the production of highly detailed simulations for end-product validation purposes. Efforts thus far have been towards integrating various numerical methods (including FEM, SPH and advanced materials models) together in an efficient and accurate manner. Various designs of joining mechanisms have been developed and are currently being developed into FE models and simulations.

Constraints on the complete optimization of human motion

In sport and exercise biomechanics, forward dynamics analyses or simulations have frequently been used in attempts to establish optimal techniques for performance of a wide range of motor activities. However, the accuracy and validity of these simulations is largely dependent on the complexity of the mathematical model used to represent the neuromusculoskeletal system. It could be argued that complex mathematical models are superior to simple mathematical models as they enable basic mechanical insights to be made and individual-specific optimal movement solutions to be identified. Contrary to some claims in the literature, however, we suggest that it is currently not possible to identify the complete optimal solution for a given motor activity. For a complete optimization of human motion, dynamical systems theory implies that mathematical models must incorporate a much wider range of organismic, environmental and task constraints. These ideas encapsulate why sports medicine specialists need to adopt more individualized clinical assessment procedures in interpreting why performers' movement patterns may differ.

QoS-aware reference station placement for regional network RTK

Network RTK (Real-Time Kinematic) is a technology that is based on GPS (Global Positioning System) or more generally on GNSS (Global Navigation Satellite System) measurements to achieve centimeter-level accuracy positioning in real-time. Reference station placement is an important problem in the design and deployment of network RTK systems as it directly affects the quality of the positioning service and the cost of the network RTK systems. This paper identifies a new reference station placement for network RTK, namely QoS-aware regional network RTK reference station placement problem, and proposes an algorithm for the new reference station placement problem. The algorithm can always produce a reference station placement solution that completely covers the region of network RTK.

Gateways placement in backbone wireless mesh networks

This paper presents a novel algorithm for the gateway placement problem in Backbone Wireless Mesh Networks (BWMNs). Different from existing algorithms, the new algorithm incrementally identifies gateways and assigns mesh routers to identified gateways. The new algorithm can guarantee to find a feasible gateway placement satisfying Quality-of-Service (QoS) constraints, including delay constraint, relay load constraint and gateway capacity constraint. Experimental results show that its performance is as good as that of the best of existing algorithms for the gateway placement problem. But, the new algorithm can be used for BWMNs that do not form one connected component, and it is easy to implement and use.

The strategic and operational dimensions of staff training and professional development for information professionals : what neXus2 has revealed

The paper seeks to continue the debate about the need for professionals in the library and information services (LIS) sector to continually engage in career-long learning to sustain and develop their knowledge and skills in a dynamic industry. Aims: The neXus2 workforce study has been funded by the ALIA and the consortium of National and State Libraries Australasia (NSLA). It builds on earlier research work (the neXus census) that looked at the demographic, educational and career perspectives of individual library and information professions, to critically examine institutional policies and practices associated with the LIS workforce. The research aims to develop a clearer understanding of the issues impacting on workforce sustainability, workforce capability and workforce optimisation. Methods: The research methodology involved an extensive online survey conducted in March 2008 which collected data on organisational and general staffing; recruitment and retention; staff development and continuing professional education; and succession planning. Encouragement to participate was provided by key industry groups, including academic, public, health, law and government library and information agencies, with the result that around 150 institutions completed the questionnaire. Results: The paper will specifically discuss the research findings relating to training and professional development, to measure the scope and distribution of training activities across the workforce, to consider the interrelationship between the strategic and operational dimensions of staff development in individual institutions and to analyse the common and distinctive factors evident in the different sectors of the profession. Conclusion: The neXus2 project has successfully engaged LIS institutions in the collection of complex industry data that is relevant to the future education and workforce strategies for all areas of the profession. Cross-sector forums such as Information Online 2009 offer the opportunity for stimulating professional dialogue on the key issues.

Fast reconstruction of aerodynamic shapes using evolutionary algorithms and virtual Nash strategies in a CFD design environment

This paper compares the performances of two different optimisation techniques for solving inverse problems; the first one deals with the Hierarchical Asynchronous Parallel Evolutionary Algorithms software (HAPEA) and the second is implemented with a game strategy named Nash-EA. The HAPEA software is based on a hierarchical topology and asynchronous parallel computation. The Nash-EA methodology is introduced as a distributed virtual game and consists of splitting the wing design variables - aerofoil sections - supervised by players optimising their own strategy. The HAPEA and Nash-EA software methodologies are applied to a single objective aerodynamic ONERA M6 wing reconstruction. Numerical results from the two approaches are compared in terms of the quality of model and computational expense and demonstrate the superiority of the distributed Nash-EA methodology in a parallel environment for a similar design quality.

Scatter difference NAP for SVM speaker recognition

This paper presents Scatter Difference Nuisance Attribute Projection (SD-NAP) as an enhancement to NAP for SVM-based speaker verification. While standard NAP may inadvertently remove desirable speaker variability, SD-NAP explicitly de-emphasises this variability by incorporating a weighted version of the between-class scatter into the NAP optimisation criterion. Experimental evaluation of SD-NAP with a variety of SVM systems on the 2006 and 2008 NIST SRE corpora demonstrate that SD-NAP provides improved verification performance over standard NAP in most cases, particularly at the EER operating point.

Numerical investigations of linear least squares methods for derivative estimation

The results of a numerical investigation into the errors for least squares estimates of function gradients are presented. The underlying algorithm is obtained by constructing a least squares problem using a truncated Taylor expansion. An error bound associated with this method contains in its numerator terms related to the Taylor series remainder, while its denominator contains the smallest singular value of the least squares matrix. Perhaps for this reason the error bounds are often found to be pessimistic by several orders of magnitude. The circumstance under which these poor estimates arise is elucidated and an empirical correction of the theoretical error bounds is conjectured and investigated numerically. This is followed by an indication of how the conjecture is supported by a rigorous argument.

Patterns of engagement in workplace learning amongst employees from social sectors

Continuous learning and development has become increasingly important in the information age. However, employees with limited formal education in lower status occupations may be disadvantaged in their opportunities for development, as their jobs tend to require more limited knowledge and skills. In mature age, such workers may be subject to cumulative disadvantage with respect to work related learning and development, as well as negative stereotyping. This thesis concerns work related learning and development from a lifespan development psychology perspective. Development across the lifespan is grounded in biocultural co-constructivism. That is, the reciprocal influences of the individual and environment produce change in the individual. Existing theories and models of adaptive development attempt to explain how developmental resources are allocated across the lifespan. These included the Meta- theory of Selective Optimisation with Compensation, Dual Process Model of Self Regulation, and Developmental Regulation via Optimisation and Primary and Secondary Control. These models were integrated to create the Model of Adaptive Development for Work Related Learning. The Learning and Development Survey (LDS) was constructed to measure the hypothesised processes of adaptive development for work related learning, which were individual goal selection, individual goal engagement, individual goal disengagement, organisational opportunities (selection and engagement), and organisational constraints. Data collection was undertaken in two phases: the pilot study and the main study. The objective of the pilot study was to test the LDS on a target population of 112 employees from a local government organisation. Exploratory factor analysis reduced the pilot version of the survey to 38 items encompassing eight constructs which covered the processes of the model of adaptive development for work related learning. In the main study, the Revised Learning and Development Survey (R-LDS) was administered to another group of 137 employees from the local government organisation, as well as 110 employees from a private healthcare organisation. The purpose of the main study was to validate the R-LDS on two different groups to provide evidence of stability, and compare survey scores according to age and occupational status to determine construct validity. Findings from the main study indicated that only four constructs of the R-LDS were stable, which were organisational opportunities – selection, individual goal engagement, organisational constraints – disengagement and organisational opportunities – engagement. In addition, MANOVA studies revealed that the demographic variables affected organisational opportunities and constraints in the workplace, although individual goal engagement was not influenced by age. The findings from the pilot and main study partially supported the model of adaptive development for work related learning. Given that only four factors displayed adequate reliability in terms of internal consistency and stability, the findings suggest that individual goal selection and individual goal disengagement are less relevant to work related learning and development. Some recent research which emerged during the course of the current study has suggested that individual goal selection and individual goal disengagement are more relevant when goal achievement is impeded by biological constraints such as ageing. However, correlations between the retained factors support the model of adaptive development for work related learning, and represent the role of biocultural co-constructivism in development. Individual goal engagement was positively correlated with both opportunity factors (selection and engagement), while organisational constraints – disengagement was negatively correlated with organisational opportunities – selection. Demographic findings indicated that higher occupational status was associated with more opportunities for development. Age was associated with fewer opportunities or greater constraints for development, especially for lower status workers.

Multi-objective mission flight planning in civil unmanned aerial systems

Unmanned Aerial Vehicles (UAVs) are emerging as an ideal platform for a wide range of civil applications such as disaster monitoring, atmospheric observation and outback delivery. However, the operation of UAVs is currently restricted to specially segregated regions of airspace outside of the National Airspace System (NAS). Mission Flight Planning (MFP) is an integral part of UAV operation that addresses some of the requirements (such as safety and the rules of the air) of integrating UAVs in the NAS. Automated MFP is a key enabler for a number of UAV operating scenarios as it aids in increasing the level of onboard autonomy. For example, onboard MFP is required to ensure continued conformance with the NAS integration requirements when there is an outage in the communications link. MFP is a motion planning task concerned with finding a path between a designated start waypoint and goal waypoint. This path is described with a sequence of 4 Dimensional (4D) waypoints (three spatial and one time dimension) or equivalently with a sequence of trajectory segments (or tracks). It is necessary to consider the time dimension as the UAV operates in a dynamic environment. Existing methods for generic motion planning, UAV motion planning and general vehicle motion planning cannot adequately address the requirements of MFP. The flight plan needs to optimise for multiple decision objectives including mission safety objectives, the rules of the air and mission efficiency objectives. Online (in-flight) replanning capability is needed as the UAV operates in a large, dynamic and uncertain outdoor environment. This thesis derives a multi-objective 4D search algorithm entitled Multi- Step A* (MSA*) based on the seminal A* search algorithm. MSA* is proven to find the optimal (least cost) path given a variable successor operator (which enables arbitrary track angle and track velocity resolution). Furthermore, it is shown to be of comparable complexity to multi-objective, vector neighbourhood based A* (Vector A*, an extension of A*). A variable successor operator enables the imposition of a multi-resolution lattice structure on the search space (which results in fewer search nodes). Unlike cell decomposition based methods, soundness is guaranteed with multi-resolution MSA*. MSA* is demonstrated through Monte Carlo simulations to be computationally efficient. It is shown that multi-resolution, lattice based MSA* finds paths of equivalent cost (less than 0.5% difference) to Vector A* (the benchmark) in a third of the computation time (on average). This is the first contribution of the research. The second contribution is the discovery of the additive consistency property for planning with multiple decision objectives. Additive consistency ensures that the planner is not biased (which results in a suboptimal path) by ensuring that the cost of traversing a track using one step equals that of traversing the same track using multiple steps. MSA* mitigates uncertainty through online replanning, Multi-Criteria Decision Making (MCDM) and tolerance. Each trajectory segment is modeled with a cell sequence that completely encloses the trajectory segment. The tolerance, measured as the minimum distance between the track and cell boundaries, is the third major contribution. Even though MSA* is demonstrated for UAV MFP, it is extensible to other 4D vehicle motion planning applications. Finally, the research proposes a self-scheduling replanning architecture for MFP. This architecture replicates the decision strategies of human experts to meet the time constraints of online replanning. Based on a feedback loop, the proposed architecture switches between fast, near-optimal planning and optimal planning to minimise the need for hold manoeuvres. The derived MFP framework is original and shown, through extensive verification and validation, to satisfy the requirements of UAV MFP. As MFP is an enabling factor for operation of UAVs in the NAS, the presented work is both original and significant.

Load–frequency control : a GA-based multi-agent reinforcement learning

The load–frequency control (LFC) problem has been one of the major subjects in a power system. In practice, LFC systems use proportional–integral (PI) controllers. However since these controllers are designed using a linear model, the non-linearities of the system are not accounted for and they are incapable of gaining good dynamical performance for a wide range of operating conditions in a multi-area power system. A strategy for solving this problem because of the distributed nature of a multi-area power system is presented by using a multi-agent reinforcement learning (MARL) approach. It consists of two agents in each power area; the estimator agent provides the area control error (ACE) signal based on the frequency bias estimation and the controller agent uses reinforcement learning to control the power system in which genetic algorithm optimisation is used to tune its parameters. This method does not depend on any knowledge of the system and it admits considerable flexibility in defining the control objective. Also, by finding the ACE signal based on the frequency bias estimation the LFC performance is improved and by using the MARL parallel, computation is realised, leading to a high degree of scalability. Here, to illustrate the accuracy of the proposed approach, a three-area power system example is given with two scenarios.

Robust speech recognition using speech enhancement

Automatic Speech Recognition (ASR) has matured into a technology which is becoming more common in our everyday lives, and is emerging as a necessity to minimise driver distraction when operating in-car systems such as navigation and infotainment. In “noise-free” environments, word recognition performance of these systems has been shown to approach 100%, however this performance degrades rapidly as the level of background noise is increased. Speech enhancement is a popular method for making ASR systems more ro- bust. Single-channel spectral subtraction was originally designed to improve hu- man speech intelligibility and many attempts have been made to optimise this algorithm in terms of signal-based metrics such as maximised Signal-to-Noise Ratio (SNR) or minimised speech distortion. Such metrics are used to assess en- hancement performance for intelligibility not speech recognition, therefore mak- ing them sub-optimal ASR applications. This research investigates two methods for closely coupling subtractive-type enhancement algorithms with ASR: (a) a computationally-efficient Mel-filterbank noise subtraction technique based on likelihood-maximisation (LIMA), and (b) in- troducing phase spectrum information to enable spectral subtraction in the com- plex frequency domain. Likelihood-maximisation uses gradient-descent to optimise parameters of the enhancement algorithm to best fit the acoustic speech model given a word se- quence known a priori. Whilst this technique is shown to improve the ASR word accuracy performance, it is also identified to be particularly sensitive to non-noise mismatches between the training and testing data. Phase information has long been ignored in spectral subtraction as it is deemed to have little effect on human intelligibility. In this work it is shown that phase information is important in obtaining highly accurate estimates of clean speech magnitudes which are typically used in ASR feature extraction. Phase Estimation via Delay Projection is proposed based on the stationarity of sinusoidal signals, and demonstrates the potential to produce improvements in ASR word accuracy in a wide range of SNR. Throughout the dissertation, consideration is given to practical implemen- tation in vehicular environments which resulted in two novel contributions – a LIMA framework which takes advantage of the grounding procedure common to speech dialogue systems, and a resource-saving formulation of frequency-domain spectral subtraction for realisation in field-programmable gate array hardware. The techniques proposed in this dissertation were evaluated using the Aus- tralian English In-Car Speech Corpus which was collected as part of this work. This database is the first of its kind within Australia and captures real in-car speech of 50 native Australian speakers in seven driving conditions common to Australian environments.

Evolutionary algorithms for sustainable building design

This approach to sustainable design explores the possibility of creating an architectural design process which can iteratively produce optimised and sustainable design solutions. Driven by an evolution process based on genetic algorithms, the system allows the designer to “design the building design generator” rather than to “designs the building”. The design concept is abstracted into a digital design schema, which allows transfer of the human creative vision into the rational language of a computer. The schema is then elaborated into the use of genetic algorithms to evolve innovative, performative and sustainable design solutions. The prioritisation of the project’s constraints and the subsequent design solutions synthesised during design generation are expected to resolve most of the major conflicts in the evaluation and optimisation phases. Mosques are used as the example building typology to ground the research activity. The spatial organisations of various mosque typologies are graphically represented by adjacency constraints between spaces. Each configuration is represented by a planar graph which is then translated into a non-orthogonal dual graph and fed into the genetic algorithm system with fixed constraints and expected performance criteria set to govern evolution. The resultant Hierarchical Evolutionary Algorithmic Design System is developed by linking the evaluation process with environmental assessment tools to rank the candidate designs. The proposed system generates the concept, the seed, and the schema, and has environmental performance as one of the main criteria in driving optimisation.