Hierarchical recognition of intentional human gestures for sports video annotation

We present a novel technique for the recognition of complex human gestures for video annotation using accelerometers and the hidden Markov model. Our extension to the standard hidden Markov model allows us to consider gestures at different levels of abstraction through a hierarchy of hidden states. Accelerometers in the form of wrist bands are attached to humans performing intentional gestures, such as umpires in sports. Video annotation is then performed by populating the video with time stamps indicating significant events, where a particular gesture occurs. The novelty of the technique lies in the development of a probabilistic hierarchical framework for complex gesture recognition and the use of accelerometers to extract gestures and significant events for video annotation.

Mathematical modelling for Singapore primary classrooms : from a teacher’s lens

Limited Singapore research indicated a lack of exposure of modelling tasks at primary levels. Teacher reflection is used as a tool in design research cycles exploring the potentials of modelling tasks in a Singapore primary five classroom. Findings reveal that the teacher identified three potentials of a modelling task on children’s mathematisation process: the task provided a platform for children to (a) identify variables and form relationships between them, (b) relate school-based math learning to real-world experiences, and (c) justify their mathematical models. Implications on the promotion of modelling tasks at primary schools as well as teacher education are drawn.

Building awareness of mathematical modelling at teacher education: a case study in Indonesia

A growing interest to teach mathematics closely connected to its use in daily life has taken place in Indonesia for over a decade (Sembiring, Hadi, and Dolk 2008). This chapter  reports an exploratory case study of  the building of an awareness of mathematical modelling in teacher education in Indonesia. A modelling task, re-designing a parking lot (Ang 2009), was assigned to groups of pre-service secondary mathematics teachers. All groups undertook the stages of collecting data on a parking lot, identifying limitations in the current design of the parking lot, and proposing a new design based on their observations and analyses. The nature of the mathematical models elicited by pre-service teachers during various stages of completing the modelling task will be examined. Implications of this study suggest the need to encourage pre-service teachers to state the assumptions and real-world considerations and link them to the mathematical model in order to validate their models.

Boron carbide reinforced aluminium matrix composite : physical, mechanical characterization and mathematical modelling

This paper investigates the manufacturing of aluminium-boron carbide composites using the stir casting method. Mechanical and physical properties tests to obtain hardness, ultimate tensile strength (UTS) and density are performed after solidification of specimens. The results show that hardness and tensile strength of aluminium based composite are higher than monolithic metal. Increasing the volume fraction of B4C, enhances the tensile strength and hardness of the composite; however over-loading of B4C caused particle agglomeration, rejection from molten metal and migration to slag. This phenomenon decreases the tensile strength and hardness of the aluminium based composite samples cast at 800 °C. For Al-15 vol% B4C samples, the ultimate tensile strength and Vickers hardness of the samples that were cast at 1000 °C, are the highest among all composites. To predict the mechanical properties of aluminium matrix composites, two key prediction modelling methods including Neural Network learned by Levenberg-Marquardt Algorithm (NN-LMA) and Thin Plate Spline (TPS) models are constructed based on experimental data. Although the results revealed that both mathematical models of mechanical properties of Al-B4C are reliable with a high level of accuracy, the TPS models predict the hardness and tensile strength values with less error compared to NN-LMA models.

Modelling of fabric energy storage systems - a review

Fabric energy storage (FES) systems have gained in popularity in the recent years in response to the demand for energy efficient buildings. The dynamic heat transfer mechanisms of an FES require specialised techniques to predict its thermal performance. This requirement has been one of the barriers to the wider use of FES systems. Based on the research literature, this paper presents a critical review of the published mathematical models of FES systems. The paper discusses the usefulness of these models based on the following criteria: the inputs required; the accuracy of predictions; the ability to link with commercially available simulation software: and the degree of difficulty in using the models. The review found that the currently available mathematical models are either not able to predict the thermal behaviour of a building space with an FES system reliably or the models are too complicated and/or require too much specialised knowledge to make them useful.

Bayesian classification of catchments using spatial data: a first step to improved modelling of catchment effects on stream ecological condition

A major challenge facing freshwater ecologists and managers is the development of models that link stream ecological condition to catchment scale effects, such as land use. Previous attempts to make such models have followed two general approaches. The bottom-up approach employs mechanistic models, which can quickly become too complex to be useful. The top-down approach employs empirical models derived from large data sets, and has often suffered from large amounts of unexplained variation in stream condition.

We believe that the lack of success of both modelling approaches may be at least partly explained by scientists considering too wide a breadth of catchment type. Thus, we believe that by stratifying large sets of catchments into groups of similar types prior to modelling, both types of models may be improved. This paper describes preliminary work using a Bayesian classification software package, ‘Autoclass’ (Cheeseman and Stutz 1996) to create classes of catchments within the Murray Darling Basin based on physiographic data.

Autoclass uses a model-based classification method that employs finite mixture modelling and trades off model fit versus complexity, leading to a parsimonious solution. The software provides information on the posterior probability that the classification is ‘correct’ and also probabilities for alternative classifications. The importance of each attribute in defining the individual classes is calculated and presented, assisting description of the classes. Each case is ‘assigned’ to a class based on membership probability, but the probability of membership of other classes is also provided. This feature deals very well with cases that do not fit neatly into a larger class. Lastly, Autoclass requires the user to specify the measurement error of continuous variables.

Catchments were derived from the Australian digital elevation model. Physiographic data werederived from national spatial data sets. There was very little information on measurement errors for the spatial data, and so a conservative error of 5% of data range was adopted for all continuous attributes. The incorporation of uncertainty into spatial data sets remains a research challenge.

The results of the classification were very encouraging. The software found nine classes of catchments in the Murray Darling Basin. The classes grouped together geographically, and followed altitude and latitude gradients, despite the fact that these variables were not included in the classification. Descriptions of the classes reveal very different physiographic environments, ranging from dry and flat catchments (i.e. lowlands), through to wet and hilly catchments (i.e. mountainous areas). Rainfall and slope were two important discriminators between classes. These two attributes, in particular, will affect the ways in which the stream interacts with the catchment, and can thus be expected to modify the effects of land use change on ecological condition. Thus, realistic models of the effects of land use change on streams would differ between the different types of catchments, and sound management practices will differ.

A small number of catchments were assigned to their primary class with relatively low probability. These catchments lie on the boundaries of groups of catchments, with the second most likely class being an adjacent group. The locations of these ‘uncertain’ catchments show that the Bayesian classification dealt well with cases that do not fit neatly into larger classes.

Although the results are intuitive, we cannot yet assess whether the classifications described in this paper would assist the modelling of catchment scale effects on stream ecological condition. It is most likely that catchment classification and modelling will be an iterative process, where the needs of the model are used to guide classification, and the results of classifications used to suggest further refinements to models.

Design of a distributed power system stabiliser

A new design method for a distributed power system stabiliser for interconnected power systems is introduced in this paper. The stabiliser is of a low order, dynamic and robust. To generate the required local control signals, each local stabiliser requires information about either the rotor speed or the load angle of the other subsystems. A simple MATLAB based design algorithm is given and used on a three-machine unstable power system. The resulting stabiliser is simulated and sample results are presented.

Toward a synergy between simulation and knowledge management for business intelligence

The adoption of simulation as a powerful enabling method for knowledge management is hampered by the relatively high cost of model construction and maintenance. A two-step procedure, based on a divide and conquer strategy, is proposed in this paper. First, a simulation program is partitioned based on a reinterpretation of the model-view-controller architecture. Individual parts are then connected, in terms of abstraction, to guard against possible changes that resulted from shifting user requirements. We explore the applicability of these design principles through a detailed discussion of an industry case study. The knowledge-based perspective guides the design of architecture to accommodate the need of emulation without compromising the integrity of the simulation program. The synergy between simulation and a knowledge management perspective, as shown in the case study, has the potential to achieve the objectives of rapid development of models, with low maintenance cost. This could, in turn, facilitate an extension of the use of simulation in the knowledge management domain.

Modeling and simulation for large scale production systems

Computer modeling and simulation provide a foundation upon which industrial processes and systems can be transformed and innovation dramatically accelerated. Computer modeling and simulation is also an indispensable tool of the information age, used extensively in design, analysis, operations, decision-making, optimization, and education and training. Manufacturing, production and design relies upon simulation to develop efficient production systems and factories that produce quality products. Simulation in industry has yet to meet its full potential. The development of models is very time consuming, particularly for geometries of complex engineering systems such as manufacturing plants, automobiles, aircraft and ships. Computer simulation allows scientists and engineers to understand and predict three-dimensional and time-dependent phenomena in science and engineering discipline. This talk will focus on challenges associated with modeling and simulation in the manufacturing sector and through a number of case studies highlight the benefits gained through the use of such technologies.

Skill shortages in health : innovative solutions using vocational education and training

Introduction: This article reports findings of a project funded by the Australian National Council for Vocational Education Research. The project explores solutions to current and projected skills shortages within the health and community services sector, from a vocational education and training perspective. Its purpose is to locate, analyse and disseminate information about innovative models of health training and service delivery that have been developed in response to skill shortages.

Methods: The article begins with a brief overview of Australian statistics and literature on the structure of the national health workforce and perceived skill shortages. The impact of location (state and rurality), demographics of the workforce, and other relevant factors, on health skill shortages is examined. Drawing on a synthesis of the Australian and international literature on innovative and effective models for addressing health skill shortages and nominations by key stakeholders within the health sector, over 70 models were identified. The models represent a mixture of innovative service delivery models and training solutions from Australia, as well as international examples that could be transposed to the Australian context. They include the skill ecosystem approach facilitated by the Australian National Training Authority Skill Ecosystem Project. Models were selected to represent diversity in terms of the nature of skill shortage addressed, barriers overcome in development of the model, healthcare specialisations, and different customer groups.

Results: Key barriers to the development of innovative solutions to skills shortages identified were: policy that is not sufficiently flexible to accommodate changing workplace needs; unwillingness to risk take in order to develop new models; delays in gaining endorsement/accreditation; current vocational education and training (VET) monitoring and reporting systems; issues related to working in partnership, including different cultures, ways of operating, priorities and timelines; workplace culture that is resistant to change; and organisational boundaries. For training-only models, additional barriers were: technology; low educational levels of trainees; lack of health professionals to provide training and/or supervision; and cost of training. Key enhancers for the development of models were identified as: commitment by all partners and co-location of partners; or effective communication channels. Key enhancers for model effectiveness were: first considering work tasks, competencies and job (re)design; high profile of the model within the community; community-based models; cultural fit; and evidence of direct link between skills development and employment, for example VET trained aged care workers upskilling for other health jobs. For training only models, additional enhancers were flexibility of partners in accommodating needs of trainees; low training costs; experienced clinical supervisors; and the provision of professional development to trainers.

Conclusions: There needs to be a balance between short-term solutions to current skill shortages (training only), and medium to longer term solutions (job redesign, holistic approaches) that also address projected skills shortages. Models that focus on addressing skills shortages in aged care can provide a broad pathway to careers in health. Characteristics of models likely to be effective in addressing skill shortages are: responsibility for addressing skills shortage is shared between the health sector, education and training organisations and government, with employers taking a proactive role; the training component is complemented by a focus on retention of workers; models are either targeted at existing employees or identify a target group(s) who may not otherwise have considered a career in health.

Physical theory of organisation and consequent neural model of spatio-temporal pattern acquisition

A neurone model (the FORMON) is proposed which provides a mathematical explanation for a range of psychological phenomena and has potential in Artificial Intelligence applications. A general definition of organisation in terms of entropy and information is formulated. The concept of microcodes is introduced to describe the physical nature of organisation. Spatio-temporal pattern acquisition and processing functions attributable to individual neurones are reviewed. The criterion for self-organisation in a neurone is determined as the maximisation of mutual organisation. A feedback control system is proposed to satisfy this criterion and provide an integrated long-term memory of spatio-temporal pattern. This pattern acquisition system is shown to be applicable to dendritic pattern recognition and axonal pattern generation. Provision is also made for adaptation, short-term memory and operant learning. An electro-chemical model of transmission and processing of neural signals is outlined to provide the pattern acquisition functions of the Formon model. A transverse magnetic mode of electrotonic propagation is postulated in addition to the transverse electromagnetic mode. Configurations of the Formon are categorised in terms of possible pattern processing functions. Connective architectures are proposed as self-organising models of acquisitive semantic and syntactic networks.