Coal Seam Thickness Estimation Using GPR and Higher Order Statistics - The Near-Surface Case

A novel pattern recognition-based approach to detect near-surface interfaces using ground penetrating radar (GPR) has been reported in [1]. The approach was used to successfully detect interfaces within 5 cm of the ground surface. This technique has been adapted for the important task of layer thickness estimation in the near-surface range. This is inherently a difficult problem to solve in practice because the radar echo is often dominated by unwanted components such as antenna crosstalk and ring-down, ground reflection effects and clutter. Features derived from the bispectrum and a nearest-neighbour classifier have been utilized for this processing task. It is shown that unlike traditional second order correlation based methods such as matched filtering which can fail in known conditions, layer thickness estimation using this approach can be reliably extended to the near-surface region

Near-Surface Interface Detection for Coal Mining Applications Using Bispectral Features and GPR

The use of ground penetrating radar (GPR) for detecting the presence of near-surface interfaces is a scenario of special interest to the underground coal mining industry. The problem is difficult to solve in practice because the radar echo from the near-surface interface is often dominated by unwanted components such as antenna crosstalk and ringing, ground-bounce effects, clutter, and severe attenuation. These nuisance components are also highly sensitive to subtle variations in ground conditions, rendering the application of standard signal pre-processing techniques such as background subtraction largely ineffective in the unsupervised case. As a solution to this detection problem, we develop a novel pattern recognition-based algorithm which utilizes a neural network to classify features derived from the bispectrum of 1D early time radar data. The binary classifier is used to decide between two key cases, namely whether an interface is within, for example, 5 cm of the surface or not. This go/no-go detection capability is highly valuable for underground coal mining operations, such as longwall mining, where the need to leave a remnant coal section is essential for geological stability. The classifier was trained and tested using real GPR data with ground truth measurements. The real data was acquired from a testbed with coal-clay, coal-shale and shale-clay interfaces, which represents a test mine site. We show that, unlike traditional second order correlation based methods such as matched filtering which can fail even in known conditions, the new method reliably allows the detection of interfaces using GPR to be applied in the near-surface region. In this work, we are not addressing the problem of depth estimation, rather confining ourselves to detecting an interface within a particular depth range

Cost-effectiveness of cognitive behavioural and personalised exercise interventions for reducing fatigue in inflammatory rheumatic diseases

Acknowledgements The authors would like to thank all the participants who supported this trial. We acknowledge the contribution of the Trial Steering Committee and Data Monitoring Committee, and Brian Taylor and Mark Forrest (Centre for Healthcare Randomised Trials [CHaRT], University of Aberdeen, Aberdeen, UK) for their technical assistance. Funding: This work was supported by Versus Arthritis (formerly Arthritis Research UK) grant number 21175.Peer reviewe

New genetic loci link adipose and insulin biology to body fat distribution.

Body fat distribution is a heritable trait and a well-established predictor of adverse metabolic outcomes, independent of overall adiposity. To increase our understanding of the genetic basis of body fat distribution and its molecular links to cardiometabolic traits, here we conduct genome-wide association meta-analyses of traits related to waist and hip circumferences in up to 224,459 individuals. We identify 49 loci (33 new) associated with waist-to-hip ratio adjusted for body mass index (BMI), and an additional 19 loci newly associated with related waist and hip circumference measures (P < 5 × 10(-8)). In total, 20 of the 49 waist-to-hip ratio adjusted for BMI loci show significant sexual dimorphism, 19 of which display a stronger effect in women. The identified loci were enriched for genes expressed in adipose tissue and for putative regulatory elements in adipocytes. Pathway analyses implicated adipogenesis, angiogenesis, transcriptional regulation and insulin resistance as processes affecting fat distribution, providing insight into potential pathophysiological mechanisms

Signal Processing to Improve Target Detection using Ground Penetrating Radar

This paper focuses on some important GPR pre-processing tasks. These tasks are necessary to improve formal target detection and estimation stages. Evaluations performed using a conventional matched filter on real GPR data demonstrate the benefits of pre-processing. This effort is conducted with the ultimate goal of realising a reliable GPR-based coal-thickness sensor

Safe automation: A practical guide to understand and manage sensor RF exposure risk

The increasing interest in remote and automation technology by the underground coal mining industry has resulted in the introduction of sensors and devices that emit electromagnetic radiation. The Australian Radiation Protection and Nuclear Safety Agency (ARPANSA) has defined clear maximum limits for exposure to electromagnetic radiation to ensure health and safety of people and the environment. However, it is often difficult to determine the actual radio frequency (RF) energy level a sensor radiates in a given operation context. Further, the ARPANSA regulation and associated Australian standards for electromagnetic radiation exposure can be complex to practically interpret. Unfortunately, this may leave the mining personnel in the position where they need to either solely rely on vendor device specifications or hope that the sensor is not presenting any radiation exposure risk. This paper provides an overview of the ARPANSA exposure regulations and sets out a practical approach to measure the radiation as per the Australian Standard. By focussing on an RF range from 3 kHz to 300 GHz, a number of commonly used active devices such as radars, wireless communication systems, and related RF imaging devices can be assessed. A simple method to allow basic in-house testing is described to allow end-users to make independent quantitative assessments of sensor radiation levels. The assessment method is demonstrated with a practical example using two ground penetrating radar systems as test cases

Thermal infrared-based seam tracking for intelligent longwall shearer horizon control

Longwall mining remains one of the most efficient methods for underground coal recovery. A key aspect in achieving safe and productive longwall operations relies on maintaining the shearer in an optimal position for extraction within the coal seam. The typical approach to this resource identification issue is labour intensive so is subject to safety and productivity drawbacks. As a solution, this paper describes the use of thermal infrared-based sensing to provide a means to automatically measure the vertical position of the mining machine with respect to the coal seam. This is achieved by identifying and tracking non-optically visible horizontal line-like bands in the main body of coal, which are known as marker bands. These marker bands are strongly linked to the profile of coal seam structure, a geological characteristic often used by operators as an ad hoc datum for maintaining in-seam alignment of the shearer. Details on the theory behind thermal infrared imaging and practical aspects involved in implementation of the method are given. As there are very few real-time solutions available to locate and track coal seam profiles, this approach overcomes a current limitation in implementing intelligent horizon control systems for advanced shearer operation. Measurements from a shearer-based sensing system are given to demonstrate the approach

The Application of wireless LANS in mine automation

The objective of this paper is to provide an overview of mine automation applications, developed at the Queensland Centre for Advanced Technology (QCAT), which make use of IEEE 802.11b wireless local area networks (WLANs). The paper has been prepared for a 2002 conference entitled "Creating the Virtual Enterprise - Leveraging wireless technology within existing business models for corporate advantage". Descriptions of the WLAN components have been omitted here as such details are presented in the accompanying papers. The structure of the paper is as follows. Application overviews are provided in Sections 2 to 7. Some pertinent strengths and weaknesses are summarised in Section 8. Please refer to http://www.mining-automation.com/ or contact the authors for further information