Applications of real-time machine vision to the control of mining equipment

The mining industry presents us with a number of ideal applications for sensor based machine control because of the unstructured environment that exists within each mine. The aim of the research presented here is to increase the productivity of existing large compliant mining machines by retrofitting with enhanced sensing and control technology. The current research focusses on the automatic control of the swing motion cycle of a dragline and an automated roof bolting system. We have achieved: * closed-loop swing control of an one-tenth scale model dragline; * single degree of freedom closed-loop visual control of an electro-hydraulic manipulator in the lab developed from standard components.

Aspect-based opinion mining from product reviews using conditional random fields

Product reviews are the foremost source of information for customers and manufacturers to help them make appropriate purchasing and production decisions. Natural language data is typically very sparse; the most common words are those that do not carry a lot of semantic content, and occurrences of any particular content-bearing word are rare, while co-occurrences of these words are rarer. Mining product aspects, along with corresponding opinions, is essential for Aspect-Based Opinion Mining (ABOM) as a result of the e-commerce revolution. Therefore, the need for automatic mining of reviews has reached a peak. In this work, we deal with ABOM as sequence labelling problem and propose a supervised extraction method to identify product aspects and corresponding opinions. We use Conditional Random Fields (CRFs) to solve the extraction problem and propose a feature function to enhance accuracy. The proposed method is evaluated using two different datasets. We also evaluate the effectiveness of feature function and the optimisation through multiple experiments.

Psychological distress and pain reporting in Australian coal miners

BACKGROUND: Coal mining is of significant economic importance to the Australian economy. Despite this fact, the related workforce is subjected to a number of psychosocial risks and musculoskeletal injury, and various psychological disorders are common among this population group. Because only limited research has been conducted in this population group, we sought to examine the relationship between physical (pain) and psychological (distress) factors, as well as the effects of various demographic, lifestyle, and fatigue indicators on this relationship. METHODS: Coal miners (N = 231) participated in a survey of musculoskeletal pain and distress on-site during their work shifts. Participants also provided demographic information (job type, age, experience in the industry, and body mass index) and responded to questions about exercise and sleep quality (on- and off-shift) as well as physical and mental tiredness after work. RESULTS: A total of 177 workers (80.5%) reported experiencing pain in at least one region of their body. The majority of the sample population (61.9%) was classified as having low-level distress, 28.4% had scores indicating mild to moderate distress, and 9.6% had scores indicating high levels of distress. Both number of pain regions and job type (being an operator) significantly predicted distress. Higher distress score was also associated with greater absenteeism in workers who reported lower back pain. In addition, perceived sleep quality during work periods partially mediated the relationship between pain and distress. CONCLUSION: The study findings support the existence of widespread musculoskeletal pain among the coal-mining workforce, and this pain is associated with increased psychological distress. Operators (truck drivers) and workers reporting poor sleep quality during work periods are most likely to report increased distress, which highlights the importance of supporting the mining workforce for sustained productivity.

Acoustic classification of Australian anurans using syllable features

Acoustic classification of anurans (frogs) has received increasing attention for its promising application in biological and environment studies. In this study, a novel feature extraction method for frog call classification is presented based on the analysis of spectrograms. The frog calls are first automatically segmented into syllables. Then, spectral peak tracks are extracted to separate desired signal (frog calls) from background noise. The spectral peak tracks are used to extract various syllable features, including: syllable duration, dominant frequency, oscillation rate, frequency modulation, and energy modulation. Finally, a k-nearest neighbor classifier is used for classifying frog calls based on the results of principal component analysis. The experiment results show that syllable features can achieve an average classification accuracy of 90.5% which outperforms Mel-frequency cepstral coefficients features (79.0%).

Acoustic features for multi-level classification of Australian frogs

Over past few decades, frog species have been experiencing dramatic decline around the world. The reason for this decline includes habitat loss, invasive species, climate change and so on. To better know the status of frog species, classifying frogs has become increasingly important. In this study, acoustic features are investigated for multi-level classification of Australian frogs: family, genus and species, including three families, eleven genera and eighty five species which are collected from Queensland, Australia. For each frog species, six instances are selected from which ten acoustic features are calculated. Then, the multicollinearity between ten features are studied for selecting non-correlated features for subsequent analysis. A decision tree (DT) classifier is used to visually and explicitly determine which acoustic features are relatively important for classifying family, which for genus, and which for species. Finally, a weighted support vector machines (SVMs) classifier is used for the multi- level classification with three most important acoustic features respectively. Our experiment results indicate that using different acoustic feature sets can successfully classify frogs at different levels and the average classification accuracy can be up to 85.6%, 86.1% and 56.2% for family, genus and species respectively.

Cat's claw creeper leaf-mining jewel beetle Hylaeogena jureceki Obenberger (Coleoptera: Buprestidae), a host-specific biological control agent for Dolichandra unguis-cati (Bignoniaceae) in Australia

Cat's claw creeper, Dolichandra unguis-cati (L.) L.G. Lohman (syn: Macfadyena unguis-cati (L.) A.H. Gentry) (Bignoniaceae), a major environmental weed in Queensland and New South Wales, is a Weed of National Significance and an approved target for biological control. A leaf-mining jewel beetle, Hylaeogena jureceki Obenberger (Coleoptera: Buprestidae), first collected in 2002 from D. unguis-cati in Brazil and Argentina, was imported from South Africa into a quarantine facility in Brisbane in 2009 for host-specificity testing. H. jureceki adults chew holes in leaves and lay eggs on leaf margins and the emerging larvae mine within the leaves of D. unguis-cati. The generation time (egg to adult) of H. jureceki under quarantine conditions was 55.4 ± 0.2 days. Host-specificity trials conducted in Australia on 38 plant species from 11 families supplement and support South African studies which indicated that H. jureceki is highly host-specific and does not pose a risk to any non-target plant species in Australia. In no-choice tests, adults survived significantly longer (>32 weeks) on D. unguis-cati than on non-target test plant species (<3 weeks). Oviposition occurred on D. unguis-cati and one non-target test plant species, Citharexylum spinosum (Verbenaceae), but no larval development occurred on the latter species. In choice tests involving D. unguis-cati, C. spinosum and Avicennia marina (Avicenniaceae), feeding and oviposition were evident only on D. unguis-cati. The insect was approved for field release in Australia in May 2012.

A crop productivity research protocol for long wall mining

Springsure Creek Coal (SCC) intends to develop a coal mine using the long wall mining process under grain farming land near Emerald in Central Queensland (CQ). While this technology will result in some subsidence of the land surface, SCC wishes to maintain productivity of the grain cropping land in the precinct after coal mining. However, the impact of the surface subsidence resulting from that mining process on productivity of cropping land in any Australian landscape is currently unclear. A research protocol to investigate the impacts of subsidence on grain productivity for when the SCC project becomes operational is proposed. The protocol has wider application for other similar mining projects throughout the country. A copy of the full report is accessible on www.aginstitute.com.au.

Australian fly-in, fly-out operations: Impacts on communities, safety workers and their families

Background Australia’s mineral, resource and infrastructure sectors continues to expand as operations in rural and remote locations increasingly rely on fly-in, fly-out or drive-in, drive-out workforces in order to become economically competitive. The issues in employing these workforces are becoming more apparent and include a range of physical, mental, psychosocial, safety and community challenges. Objectives This review aims to consolidate a range of research conducted to communicate potential challenges for industry in relation to a wide variety of issues when engaging and using FIFO/DIDO workforces which includes roster design, working hours, fatigue, safety performance, employee wellbeing, turnover, psychosocial relationships and community concerns. Methods A wide literature review was performed using EBSCOhost and Google Scholar, with a focus on FIFO or DIDO workforces engaged within the resources sector. Results A number of existing gaps in the management of FIFO workforces and potential for future research were identified. This included the identification of various roster designs and hours worked across the resources industry and how to best understand the influences of roster swings, and work hours on fatigue, safety, psychological wellbeing and job satisfaction. Fatigue management, particularly in relation to travelling after extended work shifts can increase the risk for road safety and influence safety performance while at work due to a culmination of long hours, roster cycle and accumulated sleep debt. Further challenges associated with the engagement of this workforce include feelings of isolation, physiological and general health and lifestyle concerns. Conclusions FIFO workforces appear to be at an increased risk physically and mentally due to a wide range of influences of this unique lifestyle, particularly in relation to rosters, length of shift and feelings of community disengagement. Research and data collected has been limited in understanding the influences on employee engagement, satisfaction, retention and safety. Ensuring the challenges associated with FIFO employment are understood, addressed and communicated to workers and their families may assist.

Venomics of the Australian eastern brown snake (Pseudonaja textilis): detection of new venom proteins and splicing variants

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)