Applying ISOMAP to the learning of hyperspectral image

In this paper, we present the application of a non-linear dimensionality reduction technique for the learning and probabilistic classification of hyperspectral image. Hyperspectral image spectroscopy is an emerging technique for geological investigations from airborne or orbital sensors. It gives much greater information content per pixel on the image than a normal colour image. This should greatly help with the autonomous identification of natural and manmade objects in unfamiliar terrains for robotic vehicles. However, the large information content of such data makes interpretation of hyperspectral images time-consuming and userintensive. We propose the use of Isomap, a non-linear manifold learning technique combined with Expectation Maximisation in graphical probabilistic models for learning and classification. Isomap is used to find the underlying manifold of the training data. This low dimensional representation of the hyperspectral data facilitates the learning of a Gaussian Mixture Model representation, whose joint probability distributions can be calculated offline. The learnt model is then applied to the hyperspectral image at runtime and data classification can be performed.

The impact of changes to the graduated driver licensing program in Queensland, Australia on the experiences of learner drivers

Purpose: Graduated driver licensing (GDL) has been introduced in numerous jurisdictions in Australia and internationally in an attempt to ameliorate the significantly greater risk of death and injury for young novice drivers arising from road crashes. The GDL program in Queensland, Australia, was extensively modified in July 2007. This paper reports the driving and licensing experiences of Learner drivers progressing through the current-GDL program, and compares them to the experiences of Learners who progressed through the former-GDL program. ----- ----- Method: Young drivers (n = 1032, 609 females, 423 males) aged 17 to 19 years (M = 17.43, SD = 0.67) were recruited as they progressed from a Learner to a Provisional driver’s licence. They completed a survey exploring their sociodemographic characteristics, driving and licensing experiences as a Learner. Key measures for a subsample (n = 183) of the current-GDL drivers were compared with the former-GDL drivers (n = 149) via t-tests and chi-square analyses. ----- ----- Results: As expected, Learner drivers progressing through the current-GDL program gained significantly more driving practice than those in the former program, which was more likely to be provided by mothers than in the past. Female learners in the current-GDL program reported less difficulty obtaining supervision than those in the former program. The number of attempts needed to pass the practical driving assessment did not change, nor did the amount of professional supervision. The current-GDL Learners held their licence for a significantly longer duration than those in the former program, with the majority reporting that their Logbook entries were accurate on the whole. Compared to those in the former program, a significantly smaller proportion of male current-GDL Learners reported being detected for a driving offence while the females reported significantly lower crash involvement. Most current-GDL drivers reported undertaking their supervised practice at the end of the Learner period. ----- ----- Conclusions: The enhancements to the GDL program in Queensland appear to have achieved many of their intended results. The current-GDL learners participating in the study reported obtaining a significantly greater amount of supervised driving experience compared to former-GDL learners. Encouragingly, the current-GDL Learners did not report any greater difficulty in obtaining supervised driving practice, and there was a decline in the proportion of current-GDL Learners engaging in unsupervised driving. In addition, the majority of Learners do not appear to be attempting to subvert logbook recording requirements, as evidenced by high rates of self-reported logbook accuracy. The results have implications for the development and the evaluation of GDL programs in Australia and around the world.

Asset renewal decision modelling with application to the water utility industry

Reliable infrastructure assets impact significantly on quality of life and provide a stable foundation for economic growth and competitiveness. Decisions about the way assets are managed are of utmost importance in achieving this. Timely renewal of infrastructure assets supports reliability and maximum utilisation of infrastructure and enables business and community to grow and prosper. This research initially examined a framework for asset management decisions and then focused on asset renewal optimisation and renewal engineering optimisation in depth. This study had four primary objectives. The first was to develop a new Asset Management Decision Framework (AMDF) for identifying and classifying asset management decisions. The AMDF was developed by applying multi-criteria decision theory, classical management theory and life cycle management. The AMDF is an original and innovative contribution to asset management in that: · it is the first framework to provide guidance for developing asset management decision criteria based on fundamental business objectives; · it is the first framework to provide a decision context identification and analysis process for asset management decisions; and · it is the only comprehensive listing of asset management decision types developed from first principles. The second objective of this research was to develop a novel multi-attribute Asset Renewal Decision Model (ARDM) that takes account of financial, customer service, health and safety, environmental and socio-economic objectives. The unique feature of this ARDM is that it is the only model to optimise timing of asset renewal with respect to fundamental business objectives. The third objective of this research was to develop a novel Renewal Engineering Decision Model (REDM) that uses multiple criteria to determine the optimal timing for renewal engineering. The unique features of this model are that: · it is a novel extension to existing real options valuation models in that it uses overall utility rather than present value of cash flows to model engineering value; and · it is the only REDM that optimises timing of renewal engineering with respect to fundamental business objectives; The final objective was to develop and validate an Asset Renewal Engineering Philosophy (AREP) consisting of three principles of asset renewal engineering. The principles were validated using a novel application of real options theory. The AREP is the only renewal engineering philosophy in existence. The original contributions of this research are expected to enrich the body of knowledge in asset management through effectively addressing the need for an asset management decision framework, asset renewal and renewal engineering optimisation based on fundamental business objectives and a novel renewal engineering philosophy.

An introduction to the constraints-led approach to learning in outdoor education

Participation in outdoor education is underpinned by a learner's ability to acquire skills in activities such as canoeing, bushwalking and skiing and consequently the outdoor leader is often required to facilitate skill acquisition and motor learning. As such, outdoor leaders might benefit from an appropriate and tested model on how the learner acquires skills in order to design appropriate learning contexts. This paper introduces an approach to skill acquisition based on ecological psychology and dynamical systems theory called the constraints-led approach to skills acquisition. We propose that this student-centred approach is an ideal perspective for the outdoor leader to design effective learning settings. Furthermore, this open style of facilitation is also congruent with learning models that focus on other concepts such as teamwork and leadership.

Assigning metabolic equivalent (MET) values to the 2002 Census Occupational Classification System

Background: The Current Population Survey (CPS) and the American Time Use Survey (ATUS) use the 2002 census occupation system to classify workers into 509 separate occupations arranged into 22 major occupational categories. Methods: We describe the methods and rationale for assigning detailed MET estimates to occupations and present population estimates (comparing outputs generated by analysis of previously published summary MET estimates to the detailed MET estimates) of intensities of occupational activity using the 2003 ATUS data comprised of 20,720 respondents, 5,323 (2,917 males and 2,406 females) of whom reported working 6+ hours at their primary occupation on their assigned reporting day. Results: Analysis using the summary MET estimates resulted in 4% more workers in sedentary occupations, 6% more in light, 7% less in moderate, and 3% less in vigorous compared to using the detailed MET estimates. The detailed estimates are more sensitive to identifying individuals who do any occupational activity that is moderate or vigorous in intensity resulting in fewer workers in sedentary and light intensity occupations. Conclusions: Since CPS/ATUS regularly captures occupation data it will be possible to track prevalence of the different intensity levels of occupations. Updates will be required with inevitable adjustments to future occupational classification systems.