A statistical framework for natural feature representation

This paper presents a robust stochastic framework for the incorporation of visual observations into conventional estimation, data fusion, navigation and control algorithms. The representation combines Isomap, a non-linear dimensionality reduction algorithm, with expectation maximization, a statistical learning scheme. The joint probability distribution of this representation is computed offline based on existing training data. The training phase of the algorithm results in a nonlinear and non-Gaussian likelihood model of natural features conditioned on the underlying visual states. This generative model can be used online to instantiate likelihoods corresponding to observed visual features in real-time. The instantiated likelihoods are expressed as a Gaussian mixture model and are conveniently integrated within existing non-linear filtering algorithms. Example applications based on real visual data from heterogenous, unstructured environments demonstrate the versatility of the generative models.

A stochastic model for natural feature representation

This paper presents a robust stochastic model for the incorporation of natural features within data fusion algorithms. The representation combines Isomap, a non-linear manifold learning algorithm, with Expectation Maximization, a statistical learning scheme. The representation is computed offline and results in a non-linear, non-Gaussian likelihood model relating visual observations such as color and texture to the underlying visual states. The likelihood model can be used online to instantiate likelihoods corresponding to observed visual features in real-time. The likelihoods are expressed as a Gaussian Mixture Model so as to permit convenient integration within existing nonlinear filtering algorithms. The resulting compactness of the representation is especially suitable to decentralized sensor networks. Real visual data consisting of natural imagery acquired from an Unmanned Aerial Vehicle is used to demonstrate the versatility of the feature representation.

Improved design method for biosecurity surveillance and early detection of non-indigenous rats

A recent advance in biosecurity surveillance design aims to benefit island conservation through early and improved detection of incursions by non-indigenous species. The novel aspects of the design are that it achieves a specified power of detection in a cost-managed system, while acknowledging heterogeneity of risk in the study area and stratifying the area to target surveillance deployment. The design also utilises a variety of surveillance system components, such as formal scientific surveys, trapping methods, and incidental sightings by non-biologist observers. These advances in design were applied to black rats (Rattus rattus) representing the group of invasive rats including R. norvegicus, and R. exulans, which are potential threats to Barrow Island, Australia, a high value conservation nature reserve where a proposed liquefied natural gas development is a potential source of incursions. Rats are important to consider as they are prevalent invaders worldwide, difficult to detect early when present in low numbers, and able to spread and establish relatively quickly after arrival. The ‘exemplar’ design for the black rat is then applied in a manner that enables the detection of a range of non-indigenous species of rat that could potentially be introduced. Many of the design decisions were based on expert opinion as data gaps exist in empirical data. The surveillance system was able to take into account factors such as collateral effects on native species, the availability of limited resources on an offshore island, financial costs, demands on expertise and other logistical constraints. We demonstrate the flexibility and robustness of the surveillance system and discuss how it could be updated as empirical data are collected to supplement expert opinion and provide a basis for adaptive management. Overall, the surveillance system promotes an efficient use of resources while providing defined power to detect early rat incursions, translating to reduced environmental, resourcing and financial costs.

Natural balancing of flying capacitor voltages in multicell inverter under PD carrier-based PWM

The flying capacitor multicell inverter (FCMI) possesses natural balancing property. With the phase-shifted (PS) carrier-based scheme, natural balancing can be achieved in a straightforward manner. However, to achieve natural balancing with the harmonically optimal phase-disposition (PD) carrierbased scheme, the conventional approaches require (n-1) x (n-1) trapezoidal carrier signals for an n-level inverter, which is (n-1) x (n-2) times more than that in the standard PD scheme. This paper proposes two improved natural balancing strategies for FMI under PD scheme, which use the same (n-1) carrier signals as used in the standard PD scheme. In the first scheme, on-line detection is performed of the band in which the modulation signal is located, corresponding period number of the carrier, and rising or falling half cycle of the carrier waveform to generate the switching signals based on certain rules. In the second strategy, the output voltage level selection is first processed and the switching signals are then generated according to a rule based on preferential cell selection algorithm. These methods are easy to use and can be simply implemented as compared to the other available methods. Simulation and experimental results are presented for a five-level inverter to verify these proposed schemes.

Technical report : acoustic analysis of the natural environment

This technical report is concerned with one aspect of environmental monitoring—the detection and analysis of acoustic events in sound recordings of the environment. Sound recordings offer ecologists the advantage of cheaper and increased sampling but make available so much data that automated analysis becomes essential. The report describes a number of tools for automated analysis of recordings, including noise removal from spectrograms, acoustic event detection, event pattern recognition, spectral peak tracking, syntactic pattern recognition applied to call syllables, and oscillation detection. These algorithms are applied to a number of animal call recognition tasks, chosen because they illustrate quite different modes of analysis: (1) the detection of diffuse events caused by wind and rain, which are frequent contaminants of recordings of the terrestrial environment; (2) the detection of bird and calls; and (3) the preparation of acoustic maps for whole ecosystem analysis. This last task utilises the temporal distribution of events over a daily, monthly or yearly cycle.

Single crystal Raman spectroscopy of natural leiteite ZnAs2O4 and in comparison with the synthesised mineral

The oriented single crystal Raman spectrum of leiteite has been obtained and the spectra related to the structure of the mineral. The intensities of the observed bands vary according to orientation allowing them to be assigned to either Ag or Bg modes. Ag bands are generally the most intense in the CAAC spectrum, followed by ACCA, CBBC, and ABBA whereas Bg bands are generally the most intense in the CBAC followed by ABCA. The CAAC and ACCA spectra are identical, as are those obtained in the CBBC and ABBA orientations. Both cross-polarised spectra are identical. Band assignments were made with respect to bridging and non-bridging As-O bonds.

Towards low emissions in the electricity generation sector : creating a coherent legal model for Australia

Australia’s efforts to transition to a low-emissions economy have stagnated following the successive defeats of the Carbon Pollution Reduction Scheme. This failure should not, however, be regarded as the end of Australia’s efforts to make this transition. In fact, the opportunity now exists for Australia to refine its existing arrangements to enable this transition to occur more effectively. The starting point for this analysis is the legal arrangements applying to the electricity generation sector, which is the largest sectoral emitter of anthropogenic greenhouse gas emissions in Australia. Without an effective strategy to mitigate this sector’s contribution to anthropogenic climate change, it is unlikely that Australia will be able to transition towards a low-emissions economy. It is on this basis that this article assesses the dominant national legal arrangement – the Renewable Energy Target – underpinning the electricity generation sector's efforts to become a low-emissions sector.

Asset life prediction and maintenance decision-making using a non-linear non-Gaussian state space model

Estimating and predicting degradation processes of engineering assets is crucial for reducing the cost and insuring the productivity of enterprises. Assisted by modern condition monitoring (CM) technologies, most asset degradation processes can be revealed by various degradation indicators extracted from CM data. Maintenance strategies developed using these degradation indicators (i.e. condition-based maintenance) are more cost-effective, because unnecessary maintenance activities are avoided when an asset is still in a decent health state. A practical difficulty in condition-based maintenance (CBM) is that degradation indicators extracted from CM data can only partially reveal asset health states in most situations. Underestimating this uncertainty in relationships between degradation indicators and health states can cause excessive false alarms or failures without pre-alarms. The state space model provides an efficient approach to describe a degradation process using these indicators that can only partially reveal health states. However, existing state space models that describe asset degradation processes largely depend on assumptions such as, discrete time, discrete state, linearity, and Gaussianity. The discrete time assumption requires that failures and inspections only happen at fixed intervals. The discrete state assumption entails discretising continuous degradation indicators, which requires expert knowledge and often introduces additional errors. The linear and Gaussian assumptions are not consistent with nonlinear and irreversible degradation processes in most engineering assets. This research proposes a Gamma-based state space model that does not have discrete time, discrete state, linear and Gaussian assumptions to model partially observable degradation processes. Monte Carlo-based algorithms are developed to estimate model parameters and asset remaining useful lives. In addition, this research also develops a continuous state partially observable semi-Markov decision process (POSMDP) to model a degradation process that follows the Gamma-based state space model and is under various maintenance strategies. Optimal maintenance strategies are obtained by solving the POSMDP. Simulation studies through the MATLAB are performed; case studies using the data from an accelerated life test of a gearbox and a liquefied natural gas industry are also conducted. The results show that the proposed Monte Carlo-based EM algorithm can estimate model parameters accurately. The results also show that the proposed Gamma-based state space model have better fitness result than linear and Gaussian state space models when used to process monotonically increasing degradation data in the accelerated life test of a gear box. Furthermore, both simulation studies and case studies show that the prediction algorithm based on the Gamma-based state space model can identify the mean value and confidence interval of asset remaining useful lives accurately. In addition, the simulation study shows that the proposed maintenance strategy optimisation method based on the POSMDP is more flexible than that assumes a predetermined strategy structure and uses the renewal theory. Moreover, the simulation study also shows that the proposed maintenance optimisation method can obtain more cost-effective strategies than a recently published maintenance strategy optimisation method by optimising the next maintenance activity and the waiting time till the next maintenance activity simultaneously.

Machine prognostics based on health state probability estimation

The ability to accurately predict the remaining useful life of machine components is critical for machine continuous operation and can also improve productivity and enhance system’s safety. In condition-based maintenance (CBM), maintenance is performed based on information collected through condition monitoring and assessment of the machine health. Effective diagnostics and prognostics are important aspects of CBM for maintenance engineers to schedule a repair and to acquire replacement components before the components actually fail. Although a variety of prognostic methodologies have been reported recently, their application in industry is still relatively new and mostly focused on the prediction of specific component degradations. Furthermore, they required significant and sufficient number of fault indicators to accurately prognose the component faults. Hence, sufficient usage of health indicators in prognostics for the effective interpretation of machine degradation process is still required. Major challenges for accurate longterm prediction of remaining useful life (RUL) still remain to be addressed. Therefore, continuous development and improvement of a machine health management system and accurate long-term prediction of machine remnant life is required in real industry application. This thesis presents an integrated diagnostics and prognostics framework based on health state probability estimation for accurate and long-term prediction of machine remnant life. In the proposed model, prior empirical (historical) knowledge is embedded in the integrated diagnostics and prognostics system for classification of impending faults in machine system and accurate probability estimation of discrete degradation stages (health states). The methodology assumes that machine degradation consists of a series of degraded states (health states) which effectively represent the dynamic and stochastic process of machine failure. The estimation of discrete health state probability for the prediction of machine remnant life is performed using the ability of classification algorithms. To employ the appropriate classifier for health state probability estimation in the proposed model, comparative intelligent diagnostic tests were conducted using five different classifiers applied to the progressive fault data of three different faults in a high pressure liquefied natural gas (HP-LNG) pump. As a result of this comparison study, SVMs were employed in heath state probability estimation for the prediction of machine failure in this research. The proposed prognostic methodology has been successfully tested and validated using a number of case studies from simulation tests to real industry applications. The results from two actual failure case studies using simulations and experiments indicate that accurate estimation of health states is achievable and the proposed method provides accurate long-term prediction of machine remnant life. In addition, the results of experimental tests show that the proposed model has the capability of providing early warning of abnormal machine operating conditions by identifying the transitional states of machine fault conditions. Finally, the proposed prognostic model is validated through two industrial case studies. The optimal number of health states which can minimise the model training error without significant decrease of prediction accuracy was also examined through several health states of bearing failure. The results were very encouraging and show that the proposed prognostic model based on health state probability estimation has the potential to be used as a generic and scalable asset health estimation tool in industrial machinery.

Single-crystal Raman spectroscopy of natural schafarzikite FeSb2O4 from Pernek, Slovak Republic

The single crystal Raman spectra of natural mineral schafarzikite FeSb2O4 from the Pernek locality of the Slovak Republic are presented for the first time. Raman spectra of natural mineral apuanite Fe2+Fe43+Sb4O12S, originating from the Apuan Alps in Italy, as well as spectra of synthetic ZnSb2O4 and arsenite mineral trippkeite CuAs2O4 are also presented for the first time. The spectra of the antimonite minerals are characterized by a strong band in the region 660 – 680 cm-1 with shoulders on either side, and a band of medium intensity near 300 cm-1. The spectrum of the arsenite mineral is characterized by a medium band near 780 cm-1 with a shoulder on the high wavenumber side and a strong band at 370 cm-1. Assignments are proposed based on the spectral comparison between the compounds, symmetry modes of the bands and prior literature. The single crystal spectra of schafarzikite showed good mode separation, allowing bands to be assigned a symmetry species of A1g, B1g, B2g or Eg.

From the bottom-up : redesigning the international legal response to anthropogenic climate change

The design of the Kyoto Protocol renders it incapable of effectively responding to the problem of anthropogenic climate change. Therefore, this article explores the opportunity to construct a new, principled legal approach to respond to climate change that is premised on nationally derived legal responses. To do so, this article considers the theoretical foundation of the international legal response to climate change – Hardin's "The Tragedy of the Commons‟ – and the systemic design faults of the Kyoto Protocol. This article also suggests four principles – a judicious mix of legal instruments, flexibility, intrinsic legal coherence, and quantifiable and achievable targets for the reduction of greenhouse gas intensity – that are necessary to guide the creation of a nationally derived legal response to climate change. This approach is intended to provide the catalyst for new bilateral and multilateral arrangements that can, with the passing of time, generate sufficient momentum to drive the creation of a new and effective cooperative international legal framework to mitigate anthropogenic climate change.

Nursing and competencies - a natural fit : the politics of skill/competency formation in nursing

The last two decades have seen a significant restructuring of work across Australia and other industrialised economies, a critical part of which has been the appearance of competency based education and assessment. The competency movement is about creating a more flexible and mobile labour force to increase productivity and it does so by redefining work as a set of transferable or ‘soft’ generic skills that are transportable and are the possession of the individual. This article sought to develop an analysis of competency based clinical assessment of nursing students across a bachelor of nursing degree course. This involved an examination of a total of 406 clinical assessment tools that covered the years 1992-2009 and the three years of a bachelor degree. Data analysis generated three analytical findings: the existence of a hierarchy of competencies that prioritises soft skills over intellectual and technical skills; the appearance of skills as personal qualities or individual attributes; and the absence of context in assessment. The article argues that the convergence in nursing of soft skills and the professionalisation project reform has seen the former give legitimacy to the enduring invisibility and devaluation of nursing work.

Natural and engineered kallikrein inhibitors: an emerging pharmacopoeia

The kallikreins and kallikrein-related peptidases are serine proteases that control a plethora of developmental and homeostatic phenomena, ranging from semen liquefaction to skin desquamation and blood pressure. The diversity of roles played by kallikreins has stimulated considerable interest in these enzymes from the perspective of diagnostics and drug design. Kallikreins already have well-established credentials as targets for therapeutic intervention and there is increasing appreciation of their potential both as biomarkers and as targets for inhibitor design. Here, we explore the current status of naturally occurring kallikrein protease-inhibitor complexes and illustrate how this knowledge can interface with strategies for rational re-engineering of bioscaffolds and design of small-molecule inhibitors.