Hydraulic instability onset detection in Kaplan turbines by monitoring shaft vibrations

Design of hydraulic turbines has often to deal with hydraulic instability. It is well-known that Francis and Kaplan types present hydraulic instability in their design power range. Even if modern CFD tools may help to define these dangerous operating conditions and optimize runner design, hydraulic instabilities may fortuitously arise during the turbine life and should be timely detected in order to assure a long-lasting operating life. In a previous paper, the authors have considered the phenomenon of helical vortex rope, which happens at low flow rates when a swirling flow, in the draft tube conical inlet, occupies a large portion of the inlet. In this condition, a strong helical vortex rope appears. The vortex rope causes mechanical effects on the runner, on the whole turbine and on the draft tube, which may eventually produce severe damages on the turbine unit and whose most evident symptoms are vibrations. The authors have already shown that vibration analysis is suitable for detecting vortex rope onset, thanks to an experimental test campaign performed during the commissioning of a 23 MW Kaplan hydraulic turbine unit. In this paper, the authors propose a sophisticated data driven approach to detect vortex rope onset at different power load, based on the analysis of the vibration signals in the order domain and introducing the so-called "residual order spectrogram", i.e. an order-rotation representation of the vibration signal. Some experimental test runs are presented and the possibility to detect instability onset, especially in real-time, is discussed.

Systematic evaluation of GoSoapBox in tertiary education : a student response system for improving learning experiences and outcomes

There is currently a wide range of research into the recent introduction of student response systems in higher education and tertiary settings (Banks 2006; Kay and Le Sange, 2009; Beatty and Gerace 2009; Lantz 2010; Sprague and Dahl 2009). However, most of this pedagogical literature has generated ‘how to’ approaches regarding the use of ‘clickers’, keypads, and similar response technologies. There are currently no systematic reviews on the effectiveness of ‘GoSoapBox’ – a more recent, and increasingly popular student response system – for its capacity to enhance critical thinking, and achieve sustained learning outcomes. With rapid developments in teaching and learning technologies across all undergraduate disciplines, there is a need to obtain comprehensive, evidence-based advice on these types of technologies, their uses, and overall efficacy. This paper addresses this current gap in knowledge. Our teaching team, in an undergraduate Sociology and Public Health unit at the Queensland University of Technology (QUT), introduced GoSoapBox as a mechanism for discussing controversial topics, such as sexuality, gender, economics, religion, and politics during lectures, and to take opinion polls on social and cultural issues affecting human health. We also used this new teaching technology to allow students to interact with each other during class – both on both social and academic topics – and to generate discussions and debates during lectures. The paper reports on a data-driven study into how this interactive online tool worked to improve engagement and the quality of academic work produced by students. This paper will firstly, cover the recent literature reviewing student response systems in tertiary settings. Secondly, it will outline the theoretical framework used to generate this pedagogical research. In keeping with the social and collaborative features of Web 2.0 technologies, Bandura’s Social Learning Theory (SLT) will be applied here to investigate the effectiveness of GoSoapBox as an online tool for improving learning experiences and the quality of academic output by students. Bandura has emphasised the Internet as a tool for ‘self-controlled learning’ (Bandura 2001), as it provides the education sector with an opportunity to reconceptualise the relationship between learning and thinking (Glassman & Kang 2011). Thirdly, we describe the methods used to implement the use of GoSoapBox in our lectures and tutorials, and which aspects of the technology we drew on for learning purposes, as well as the methods for obtaining feedback from the students about the effectiveness or otherwise of this tool. Fourthly, we report cover findings from an examination of all student/staff activity on GoSoapBox as well as reports from students about the benefits and limitations of it as a learning aid. We then display a theoretical model that is produced via an iterative analytical process between SLT and our data analysis for use by academics and teachers across the undergraduate curriculum. The model has implications for all teachers considering the use of student response systems to improve the learning experiences of their students. Finally, we consider some of the negative aspects of GoSoapBox as a learning aid.

eHealth-as-a-Service (eHaaS) : towards universal stakeholder engagement

With the introduction of the Personally Controlled Health Record (PCEHR), the Australian public is being asked to accept greater responsibility for their healthcare by taking an active role in the management of personal health information. Although well designed, constructed and intentioned, policy and privacy concerns have resulted in an eHealth model that may impact future health sharing requirements. Hence, as a case study for a consumer eHealth initative in the Australian context, eHealth-as-a-Service (eHaaS) serves as a disruptive step in in the aggregation and transformation of health information for use as real-world knowledge. The strategic value of extending the community Health Record Bank (HRB) model lies in the ability to automatically draw on a multitude of relevant data repositories and sources to create a single source of the truth and to engage market forces to create financial sustainability. The opportunity to transform the beleaguered Australian PCEHR into a realisable and sustainable technology consumption model for patient safety is explored. Moreover, the current clerical focus of healthcare practitioners acting in the role of de facto record keepers is renegotiated to establish a shared knowledge creation landscape of action for safer patient interventions. To achieve this potential however requires a platform that will facilitate efficient and trusted unification of all health information available in real-time across the continuum of care. eHaaS provides a sustainable environment and encouragement to realise this potential.

Computation of ECG signal features using MCMC modelling in software and FPGA reconfigurable hardware

Computational optimisation of clinically important electrocardiogram signal features, within a single heart beat, using a Markov-chain Monte Carlo (MCMC) method is undertaken. A detailed, efficient data-driven software implementation of an MCMC algorithm has been shown. Initially software parallelisation is explored and has been shown that despite the large amount of model parameter inter-dependency that parallelisation is possible. Also, an initial reconfigurable hardware approach is explored for future applicability to real-time computation on a portable ECG device, under continuous extended use.

Computational science for undergraduate biologists via QUT.Bio.Excel

Molecular biology is a scientific discipline which has changed fundamentally in character over the past decade to rely on large scale datasets – public and locally generated - and their computational analysis and annotation. Undergraduate education of biologists must increasingly couple this domain context with a data-driven computational scientific method. Yet modern programming and scripting languages and rich computational environments such as R and MATLAB present significant barriers to those with limited exposure to computer science, and may require substantial tutorial assistance over an extended period if progress is to be made. In this paper we report our experience of undergraduate bioinformatics education using the familiar, ubiquitous spreadsheet environment of Microsoft Excel. We describe a configurable extension called QUT.Bio.Excel, a custom ribbon, supporting a rich set of data sources, external tools and interactive processing within the spreadsheet, and a range of problems to demonstrate its utility and success in addressing the needs of students over their studies.

Degradation modeling and monitoring of machines using operation-specific hidden Markov models

In this paper, a novel data-driven approach to monitoring of systems operating under variable operating conditions is described. The method is based on characterizing the degradation process via a set of operation-specific hidden Markov models (HMMs), whose hidden states represent the unobservable degradation states of the monitored system while its observable symbols represent the sensor readings. Using the HMM framework, modeling, identification and monitoring methods are detailed that allow one to identify a HMM of degradation for each operation from mixed-operation data and perform operation-specific monitoring of the system. Using a large data set provided by a major manufacturer, the new methods are applied to a semiconductor manufacturing process running multiple operations in a production environment.

An MRF based abnormal event detection approach using motion and appearance features

Abnormal event detection has attracted a lot of attention in the computer vision research community during recent years due to the increased focus on automated surveillance systems to improve security in public places. Due to the scarcity of training data and the definition of an abnormality being dependent on context, abnormal event detection is generally formulated as a data-driven approach where activities are modeled in an unsupervised fashion during the training phase. In this work, we use a Gaussian mixture model (GMM) to cluster the activities during the training phase, and propose a Gaussian mixture model based Markov random field (GMM-MRF) to estimate the likelihood scores of new videos in the testing phase. Further-more, we propose two new features: optical acceleration, and the histogram of optical flow gradients; to detect the presence of any abnormal objects and speed violations in the scene. We show that our proposed method outperforms other state of the art abnormal event detection algorithms on publicly available UCSD dataset.

Unsupervised online learning of condition-invariant images for place recognition

This paper presents an online, unsupervised training algorithm enabling vision-based place recognition across a wide range of changing environmental conditions such as those caused by weather, seasons, and day-night cycles. The technique applies principal component analysis to distinguish between aspects of a location’s appearance that are condition-dependent and those that are condition-invariant. Removing the dimensions associated with environmental conditions produces condition-invariant images that can be used by appearance-based place recognition methods. This approach has a unique benefit – it requires training images from only one type of environmental condition, unlike existing data-driven methods that require training images with labelled frame correspondences from two or more environmental conditions. The method is applied to two benchmark variable condition datasets. Performance is equivalent or superior to the current state of the art despite the lesser training requirements, and is demonstrated to generalise to previously unseen locations.

The role of human factors in led outdoor activity accidents: literature review and exploratory analysis

While the exact rate of incidence is unknown (due to the paucity of exposure data), it is acknowledged that safety compromising accidents and incidents occur in the led outdoor activity domain, and that they represent an important issue. Despite this, compared to other safety critical domains, very little is currently known about the key causal factors involved in such accidents and incidents. This report presents the findings derived from a review of the literature, the aim of which was to identify the Human Factors-related issues involved in accidents and incidents occurring in this area. In addition, to demonstrate the utility of systems-based, theoretically underpinned accident analysis methodologies for identifying the systemic and human contribution to accidents and incidents occurring in the led outdoor activity domain, three case-study accidents were analysed using two such approaches. In conclusion, the review identified a range of causal factors cited in the literature; however, it was noted that the majority of the research undertaken to date lacks theoretical underpinning and focuses mainly on instructor or activity leader causal factors, as opposed to the wider system failures involved. The accident analysis presented highlighted the utility of systems-based, theoretically underpinned accident analysis methodologies for analysing and learning from accidents and incidents in the led outdoor activity sector. In closing, the need for further research in the area is articulated, in particular focussing on the development of standardised and universally accepted accident and incident reporting systems and databases, the development of data driven, theoretically underpinned causal factor taxonomies, and the development and application of systems-based accident analysis methodologies.

DS-VBF : dual sink vector-based routing protocol for underwater wireless sensor network

Underwater wireless sensor networks (UWSNs) have become the seat of researchers' attention recently due to their proficiency to explore underwater areas and design different applications for marine discovery and oceanic surveillance. One of the main objectives of each deployed underwater network is discovering the optimized path over sensor nodes to transmit the monitored data to onshore station. The process of transmitting data consumes energy of each node, while energy is limited in UWSNs. So energy efficiency is a challenge in underwater wireless sensor network. Dual sinks vector based forwarding (DS-VBF) takes both residual energy and location information into consideration as priority factors to discover an optimized routing path to save energy in underwater networks. The modified routing protocol employs dual sinks on the water surface which improves network lifetime. According to deployment of dual sinks, packet delivery ratio and the average end to end delay are enhanced. Based on our simulation results in comparison with VBF, average end to end delay reduced more than 80%, remaining energy increased 10%, and the increment of packet reception ratio was about 70%.

Analyzing the impact of learning inputs on near-to-far terrain traversability estimation

With the increasing need to adapt to new environments, data-driven approaches have been developed to estimate terrain traversability by learning the rover’s response on the terrain based on experience. Multiple learning inputs are often used to adequately describe the various aspects of terrain traversability. In a complex learning framework, it can be difficult to identify the relevance of each learning input to the resulting estimate. This paper addresses the suitability of each learning input by systematically analyzing the impact of each input on the estimate. Sensitivity Analysis (SA) methods provide a means to measure the contribution of each learning input to the estimate variability. Using a variance-based SA method, we characterize how the prediction changes as one or more of the input changes, and also quantify the prediction uncertainty as attributed from each of the inputs in the framework of dependent inputs. We propose an approach built on Analysis of Variance (ANOVA) decomposition to examine the prediction made in a near-to-far learning framework based on multi-task GP regression. We demonstrate the approach by analyzing the impact of driving speed and terrain geometry on the prediction of the rover’s attitude and chassis configuration in a Marsanalogue terrain using our prototype rover Mawson.

Influencers on quality of life as reported by people living with dementia in long-term care: A descriptive exploratory approach

Background Over half of the residents in long-term care have a diagnosis of dementia. Maintaining quality of life is important, as there is no cure for dementia. Quality of life may be used as a benchmark for caregiving, and can help to enhance respect for the person with dementia and to improve care provision. The purpose of this study was to describe quality of life as reported by people living with dementia in long-term care in terms of the influencers of, as well as the strategies needed, to improve quality of life. Methods A descriptive exploratory approach. A subsample of twelve residents across two Australian states from a national quantitative study on quality of life was interviewed. Data were analysed thematically from a realist perspective. The approach to the thematic analysis was inductive and data-driven. Results Three themes emerged in relation to influencers and strategies related to quality of life: (a) maintaining independence; (b) having something to do, and; (c) the importance of social interaction. Conclusions The findings highlight the importance of understanding individual resident needs and consideration of the complexity of living in large group living situations, in particular in regard to resident decision-making.

Regulating ride-sharing in the peer economy

The rise of the peer economy poses complex new regulatory challenges for policy-makers. The peer economy, typified by services like Uber and AirBnB, promises substantial productivity gains through the more efficient use of existing resources and a marked reduction in regulatory overheads. These services are rapidly disrupting existing established markets, but the regulatory trade-offs they present are difficult to evaluate. In this paper, we examine the peer economy through the context of ride-sharing and the ongoing struggle over regulatory legitimacy between the taxi industry and new entrants Uber and Lyft. We first sketch the outlines of ride-sharing as a complex regulatory problem, showing how questions of efficiency are necessarily bound up in questions about levels of service, controls over pricing, and different approaches to setting, upholding, and enforcing standards. We outline the need for data-driven policy to understand the way that algorithmic systems work and what effects these might have in the medium to long term on measures of service quality, safety, labour relations, and equality. Finally, we discuss how the competition for legitimacy is not primarily being fought on utilitarian grounds, but is instead carried out within the context of a heated ideological battle between different conceptions of the role of the state and private firms as regulators. We ultimately argue that the key to understanding these regulatory challenges is to develop better conceptual models of the governance of complex systems by private actors and the available methods the state has of influencing their actions. These struggles are not, as is often thought, struggles between regulated and unregulated systems. The key to understanding these regulatory challenges is to better understand the important regulatory work carried out by powerful, centralised private firms – both the incumbents of existing markets and the disruptive network operators in the peer-economy.

Regulating ride-sharing in the peer economy

The rise of the peer economy poses complex new regulatory challenges for policy-makers. The peer economy, typified by services like Uber and AirBnB, promises substantial productivity gains through the more efficient use of existing resources and a marked reduction in regulatory overheads. These services are rapidly disrupting existing established markets, but the regulatory trade-offs they present are difficult to evaluate. In this paper, we examine the peer economy through the context of ride-sharing and the ongoing struggle over regulatory legitimacy between the taxi industry and new entrants Uber and Lyft. We first sketch the outlines of ride-sharing as a complex regulatory problem, showing how questions of efficiency are necessarily bound up in questions about levels of service, controls over pricing, and different approaches to setting, upholding, and enforcing standards. We outline the need for data-driven policy to understand the way that algorithmic systems work and what effects these might have in the medium to long term on measures of service quality, safety, labour relations, and equality. Finally, we discuss how the competition for legitimacy is not primarily being fought on utilitarian grounds, but is instead carried out within the context of a heated ideological battle between different conceptions of the role of the state and private firms as regulators. We ultimately argue that the key to understanding these regulatory challenges is to develop better conceptual models of the governance of complex systems by private actors and the available methods the state has of influencing their actions. These struggles are not, as is often thought, struggles between regulated and unregulated systems. The key to understanding these regulatory challenges is to better understand the important regulatory work carried out by powerful, centralised private firms – both the incumbents of existing markets and the disruptive network operators in the peer-economy.

Ethical leadership in a time of increasing accountability

Australia, like many other countries, has embraced national testing as part of wider reforms and increased accountability in schooling. Results for standardised testing programs, such as NAPLAN, are widely published yet form only one part of accountability for educators. We argue that accountability also has moral, ethical and professional dimensions. In this paper we offer a discussion of background to our study of ethical leadership in a time of data driven or contractual accountability. Based on Starratt’s (1996) model, we define ethical leadership as a social, relational practice concerned with the moral purpose of education (Angus, 2006). Our central thesis is that given increasing accountabilities, school leaders need to consider approaches to ethical leadership to improve quality and equity in education and achieve equitable outcomes for all students. The paper concludes with key implications for school leaders.