Monitoring energy performance of air conditioners in classrooms

Air quality and temperatures in classrooms are important factors influencing the student learning process. To improve the thermal comfort of classrooms for Queensland State Schools, Queensland Government initiated the "Cooler Schools Program". One of the key objectives under this program was to develop low energy cooling systems as an alternative to high energy demand conventioanl split system of air conditioning (AC) systems. In order to compare and evaluate the energy performance of different types of air conditioners installed in classrooms, monitoring systems were installed in a state primary school located in the greater outer urban area of Brisbane, Australia. It was found that the installation of monitoring systems could have a significant impact on the accuracy of the data being collected. By comparing the estimated energy efficiency ratio (EER)for four qualified air conditioners included in this study, it was also found that AC6, a hybrid air conditioner newly developed by the Queensland Department of Public Works (DPW), had the best energy performance, although the current data were not able to show the full advantages of the system.

Aerial image analysis using spiking neural networks with application to power line corridor monitoring

Trees, shrubs and other vegetation are of continued importance to the environment and our daily life. They provide shade around our roads and houses, offer a habitat for birds and wildlife, and absorb air pollutants. However, vegetation touching power lines is a risk to public safety and the environment, and one of the main causes of power supply problems. Vegetation management, which includes tree trimming and vegetation control, is a significant cost component of the maintenance of electrical infrastructure. For example, Ergon Energy, the Australia’s largest geographic footprint energy distributor, currently spends over $80 million a year inspecting and managing vegetation that encroach on power line assets. Currently, most vegetation management programs for distribution systems are calendar-based ground patrol. However, calendar-based inspection by linesman is labour-intensive, time consuming and expensive. It also results in some zones being trimmed more frequently than needed and others not cut often enough. Moreover, it’s seldom practicable to measure all the plants around power line corridors by field methods. Remote sensing data captured from airborne sensors has great potential in assisting vegetation management in power line corridors. This thesis presented a comprehensive study on using spiking neural networks in a specific image analysis application: power line corridor monitoring. Theoretically, the thesis focuses on a biologically inspired spiking cortical model: pulse coupled neural network (PCNN). The original PCNN model was simplified in order to better analyze the pulse dynamics and control the performance. Some new and effective algorithms were developed based on the proposed spiking cortical model for object detection, image segmentation and invariant feature extraction. The developed algorithms were evaluated in a number of experiments using real image data collected from our flight trails. The experimental results demonstrated the effectiveness and advantages of spiking neural networks in image processing tasks. Operationally, the knowledge gained from this research project offers a good reference to our industry partner (i.e. Ergon Energy) and other energy utilities who wants to improve their vegetation management activities. The novel approaches described in this thesis showed the potential of using the cutting edge sensor technologies and intelligent computing techniques in improve power line corridor monitoring. The lessons learnt from this project are also expected to increase the confidence of energy companies to move from traditional vegetation management strategy to a more automated, accurate and cost-effective solution using aerial remote sensing techniques.

Monitoring identity of past, current and future : a performance management system perspective

This paper seeks to explore how organisations can effectively use performance management systems (PMS) to monitor collective identities. The monitoring of relationships between identity and an influential PMS—the balanced scorecard (BSC)—are explored. Drawing from identity and management accounting literature, this paper argues that identity products, patternings and processes are commonly positioned, monitored and interpreted through the multiple perspectives and levels of the BSC. Specifically, human, technical and organisational capital under the Learning and Growth perspective of the BSC can incorporate various identity measures that sustain the relative, distinctive and fluid nature of identities. The value of this research is to strengthen the theoretical grounds which position identity as an important dimension of organisational capital in PMS.

Development of a good practice guide to safeguard student learning engagement

This paper reports on the development of a good practice guide that will offer the higher education sector a framework for safeguarding student learning engagement. The good practice guide and framework are underpinned by a set of principles initially identified as themes in the social justice literature which were refined following the consolidation of data collected from eight selected “good practice” Australasian universities and feedback gathered at various forums and presentations. The good practice guide will provide the sector with examples of institutional wide efforts which respond to national priorities for student retention and will also provide exemplars of institutional practices for each principle to facilitate the uptake of sector-wide good practice. Participants will be provided with the opportunity to discuss the social justice principles, the draft good practice guide and identify the practical applications of the guide within individual institutions.

Meta-evaluation to improve learning, evaluation capacity development and sustainability : Findings from a participatory evaluation project in Nepal

Recent studies suggest that meta-evaluation can be valuable in developing new approaches to evaluation, building evaluation capacities, and enhancing organizational learning. These new extensions of the concept of meta-evaluation are significant, given the growing emphasis on improving the quality and effectiveness of evaluation practices in the South Asian region. Following a review of the literature, this paper presents a case study of the use of concurrent meta-evaluation in the four-year project Assessing Communication for Social Change which developed and trialled a participatory impact assessment methodology in collaboration with a development communication Non-government organization (NGO) in Nepal. Key objectives of the meta-evaluation included to: continuously develop, adapt and improve the impact assessment methodology, Monitoring and Evaluation (M&E) systems and process and other project activities; identify impacts of the project; and build capacities in critical reflection and review. Our analysis indicates that this meta-evaluation was essential to understanding various constraints related to the organizational context that affected the success of the project and the development of improved M&E systems and capacities within the NGO. We identified several limitations of our meta-evaluation methods, which were balanced by the strengths of other methods. Our case study suggests that as well as assessing the quality, credibility and value of evaluation practices, meta-evaluations need to focus on important contextual issues that can have significant impacts on the outcomes of participatory evaluation projects. They include hierarchical organizational cultures, communication barriers, power/knowledge relations, and the time and resources available. Meta-evaluations also need to consider wider issues such as the sustainability of evaluation systems and approaches.

Assessing the representativeness of a groundwater quality monitoring network

The monitoring sites comprising a state of the environment (SOE) network must be carefully selected to ensure that they will be representative of the broader resource. Hierarchical cluster analysis (HCA) is a data-driven technique that can potentially be employed to assess the representativeness of a SOE monitoring network. The objective of this paper is to explore the use of HCA as an approach for assessing the representativeness of the New Zealand National Groundwater Monitoring Programme (NGMP), which is comprised of 110 monitoring sites across the country.

Good practice for safeguarding student learning engagement in higher education institutions : Final Report 2012

The project 'Good practice for safeguarding student learning engagement in higher education institutions' commenced in late 2010 as a Competitive Grant with funding provided by the Australian Learning and Teaching Council. The project is now overseen by the Office for Learning and Teaching within the Australian Department of Industry, Innovation, Science, Research and Tertiary Education. The project was completed in December 2012. The project was lead by QUT and comprised of the project team: Professor Karen Nelson, (project leader), Ms Tracy Creagh, (project manager) and Adjunct Professor John Clarke. Commencing in late 2010 the project invited a total of eight institutions across Australia and New Zealand (including QUT) who had either: existing programs and activities that monitored student learning engagement (MSLE); were in the early stages of implementing MSLE programs, or; who were piloting MSLE activities. As well, the project involved an advisory group and project evaluator comprising of academic and professional staff across two additional universities.

The application of a set of principles to safeguard student learning engagement

Student engagement is a key contributor to student achievement and retention. Increasingly, international and Australasian universities are introducing a range of specific initiatives aimed at monitoring and intervening with students who are at risk of disengaging, particularly in their first year of study. A multi-site case study formed the focus of a national learning and teaching project to develop a suite of resources to guide good practice for safeguarding student learning engagement that were consistent with the notions of equity and social justice. Pivotal to the suite of resources is the Social Justice Framework and a set of social justice principles that emerged through a synthesis of existing literature and were further refined through the examination of qualitative data collected across the participating institutions. These social justice principles reflect general notions of equity and social justice, embrace the philosophical position of recognitive social justice, and are presented in an interconnected and co-dependent way within the framework. Participants will be provided with the opportunity to identify and discuss the practical applications of the principles to student engagement activities in their own institutions.

A social justice framework for safeguarding student learning engagement

The set of social justice principles and the Social Justice Framework (SJF), developed as resources for the sector as part of an Australian Government Office for Learning and Teaching project, adopt a recognitive approach to social justice and emphasise full participation and contribution within democratic society (Gale, 2000; Gale & Densmore, 2000). The SJF is contained within the major deliverable of the project, which is A Good Practice Guide for Safeguarding Student Learning Engagement (Nelson & Creagh, 2013) and is focused on good practice for activities that monitor student learning engagement and identify students at risk of disengaging in their first year. Examination of the social justice literature and its application to the higher education sector produced a set of five principles: Self-determination, Rights, Access, Equity and Participation. Each principle was defined and elucidated by a rationale and implications for practice, thus completing the SJF. The framework: reflects the notions of equity and social justice; provides a strategic approach for safeguarding engagement activities; and is supported by a suite of resources for practice and practitioners. The aim of this poster session is to engage in conversations about the SJF and how it might be applied to other types of student engagement activities critical to the first year of university life, such as orientation and transition programs, teamwork activities, peer programs and other academic support initiatives.

Learning in Helicoverpa armigera (Lepidoptera: Noctuidae): a new look at the behaviour and control of a polyphagous pest

Recent experimental evidence has shown that learning occurs in the host selection behaviour of Helicoverpa armigera (Hübner), one of the world‘s most important agricultural pests. This paper discusses how the occurrence of learning changes our understanding of the host selection behaviour of this polyphagous moth. Host preferences determined from previous laboratory studies may be vastly different from preferences exhibited by moths in the field, where the abundance of particular hosts may be more likely to determine host preference. In support of this prediction, a number of field studies have shown that the ‘attractiveness’ of different hosts for H. armigera oviposition may depend on the relative abundance of these host species. Insect learning may play a fundamental role in the design and application of present and future integrated pest management strategies such as the use of host volatiles, trap crops and resistant crop varieties for monitoring and controlling this important pest species

Collaborative learning and critical thinking in technology-enhanced environments : an instructional design framework

The nature and characteristics of how learners learn today are changing. As technology use in learning and teaching continues to grow, its integration to facilitate deep learning and critical thinking becomes a primary consideration. The implications for learner use, implementation strategies, design of integration frameworks and evaluation of their effectiveness in learning environments cannot be overlooked. This study specifically looked at the impact that technology-enhanced learning environments have on different learners’ critical thinking in relation to eductive ability, technological self-efficacy, and approaches to learning and motivation in collaborative groups. These were explored within an instructional design framework called CoLeCTTE (collaborative learning and critical thinking in technology-enhanced environments) which was proposed, revised and used across three cases. The field of investigation was restricted to three key questions: 1) Do learner skill bases (learning approach and eductive ability) influence critical thinking within the proposed CoLeCTTE framework? If so, how?; 2) Do learning technologies influence the facilitation of deep learning and critical thinking within the proposed CoLeCTTE framework? If so, how?; and 3) How might learning be designed to facilitate the acquisition of deep learning and critical thinking within a technology-enabled collaborative environment? The rationale, assumptions and method of research for using a mixed method and naturalistic case study approach are discussed; and three cases are explored and analysed. The study was conducted at the tertiary level (undergraduate and postgraduate) where participants were engaged in critical technical discourse within their own disciplines. Group behaviour was observed and coded, attributes or skill bases were measured, and participants interviewed to acquire deeper insights into their experiences. A progressive case study approach was used, allowing case investigation to be implemented in a "ladder-like" manner. Cases 1 and 2 used the proposed CoLeCTTE framework with more in-depth analysis conducted for Case 2 resulting in a revision of the CoLeCTTE framework. Case 3 used the revised CoLeCTTE framework and in-depth analysis was conducted. The findings led to the final version of the framework. In Cases 1, 2 and 3, content analysis of group work was conducted to determine critical thinking performance. Thus, the researcher used three small groups where learner skill bases of eductive ability, technological self-efficacy, and approaches to learning and motivation were measured. Cases 2 and 3 participants were interviewed and observations provided more in-depth analysis. The main outcome of this study is analysis of the nature of critical thinking within collaborative groups and technology-enhanced environments positioned in a theoretical instructional design framework called CoLeCTTE. The findings of the study revealed the importance of the Achieving Motive dimension of a student’s learning approach and how direct intervention and strategies can positively influence critical thinking performance. The findings also identified factors that can adversely affect critical thinking performance and include poor learning skills, frustration, stress and poor self-confidence, prioritisations over learning; and inadequate appropriation of group role and tasks. These findings are set out as instructional design guidelines for the judicious integration of learning technologies into learning and teaching practice for higher education that will support deep learning and critical thinking in collaborative groups. These guidelines are presented in two key areas: technology and tools; and activity design, monitoring, control and feedback.

Classification of cancer-related death certificates using machine learning

Background Cancer monitoring and prevention relies on the critical aspect of timely notification of cancer cases. However, the abstraction and classification of cancer from the free-text of pathology reports and other relevant documents, such as death certificates, exist as complex and time-consuming activities. Aims In this paper, approaches for the automatic detection of notifiable cancer cases as the cause of death from free-text death certificates supplied to Cancer Registries are investigated. Method A number of machine learning classifiers were studied. Features were extracted using natural language techniques and the Medtex toolkit. The numerous features encompassed stemmed words, bi-grams, and concepts from the SNOMED CT medical terminology. The baseline consisted of a keyword spotter using keywords extracted from the long description of ICD-10 cancer related codes. Results Death certificates with notifiable cancer listed as the cause of death can be effectively identified with the methods studied in this paper. A Support Vector Machine (SVM) classifier achieved best performance with an overall F-measure of 0.9866 when evaluated on a set of 5,000 free-text death certificates using the token stem feature set. The SNOMED CT concept plus token stem feature set reached the lowest variance (0.0032) and false negative rate (0.0297) while achieving an F-measure of 0.9864. The SVM classifier accounts for the first 18 of the top 40 evaluated runs, and entails the most robust classifier with a variance of 0.001141, half the variance of the other classifiers. Conclusion The selection of features significantly produced the most influences on the performance of the classifiers, although the type of classifier employed also affects performance. In contrast, the feature weighting schema created a negligible effect on performance. Specifically, it is found that stemmed tokens with or without SNOMED CT concepts create the most effective feature when combined with an SVM classifier.

Application of machine learning technique in wind turbine fault diagnosis

In this study, a machine learning technique called anomaly detection is employed for wind turbine bearing fault detection. Basically, the anomaly detection algorithm is used to recognize the presence of unusual and potentially faulty data in a dataset, which contains two phases: a training phase and a testing phase. Two bearing datasets were used to validate the proposed technique, fault-seeded bearing from a test rig located at Case Western Reserve University to validate the accuracy of the anomaly detection method, and a test to failure data of bearings from the NSF I/UCR Center for Intelligent Maintenance Systems (IMS). The latter data set was used to compare anomaly detection with SVM, a previously well-known applied method, in rapidly finding the incipient faults.

The Catlin Seaview Survey – kilometre-scale seascape assessment, and monitoring of coral reef ecosystems

1.Marine ecosystems provide critically important goods and services to society, and hence their accelerated degradation underpins an urgent need to take rapid, ambitious and informed decisions regarding their conservation and management. 2.The capacity, however, to generate the detailed field data required to inform conservation planning at appropriate scales is limited by time and resource consuming methods for collecting and analysing field data at the large scales required. 3.The ‘Catlin Seaview Survey’, described here, introduces a novel framework for large-scale monitoring of coral reefs using high-definition underwater imagery collected using customized underwater vehicles in combination with computer vision and machine learning. This enables quantitative and geo-referenced outputs of coral reef features such as habitat types, benthic composition, and structural complexity (rugosity) to be generated across multiple kilometre-scale transects with a spatial resolution ranging from 2 to 6 m2. 4.The novel application of technology described here has enormous potential to contribute to our understanding of coral reefs and associated impacts by underpinning management decisions with kilometre-scale measurements of reef health. 5.Imagery datasets from an initial survey of 500 km of seascape are freely available through an online tool called the Catlin Global Reef Record. Outputs from the image analysis using the technologies described here will be updated on the online repository as work progresses on each dataset. 6.Case studies illustrate the utility of outputs as well as their potential to link to information from remote sensing. The potential implications of the innovative technologies on marine resource management and conservation are also discussed, along with the accuracy and efficiency of the methodologies deployed.