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

Rapid Humanitarian Assessments and Rationality: A Value of Information Study from Iraq, 2003-2004

This is an extended case study of a rapid humanitarian assessment and the factors that shaped a long series of decisions during its design and execution. The subject matter-contamination with landmine and Unexploded Ordonance (UXO)- and the settings- Iraq in 2003 and 2004- are almost incidental. The focus is on the rationality of decision-making during the assessment, meaning in this case, the value of the information produced versus the effort needed to produce it

Study of the Effects of Aging on Landmines

Most of the mines that currently threaten populations were manufactured more than 50 years ago and many have been in the ground for 30 years or more. Despite the inevitable and obvious deterioration, there has been very little research into the effects of aging on landmines. In 2008, James Madison University (JMU), the Center for International Stabilization and Recovery (CISR), and C King Associates Ltd (CKA) began a study designed to understand the aging process and the range of implications for the various components of mine action. The two-and-a-half year study was funded by grants from the US Department of State, Bureau of Political-Military Affairs/Office of Weapons Removal and Abatement

Scoping Study of the Effects of Aging on Landmines phase 2

Most of the mines that currently threaten populations were manufactured more than 50 years ago and many have been in the ground for 30 years or more. Despite the inevitable and obvious deterioration, there has been very little research into the effects of aging on landmines. In 2008, James Madison University (JMU), the Center for International Stabilization and Recovery (CISR), and C King Associates Ltd (CKA) began a study designed to understand the aging process and the range of implications for the various components of mine action. The two-and-a-half year study was funded by grants from the US Department of State, Bureau of Political-Military Affairs/Office of Weapons Removal and Abatement

UAV for Landmine Detection Using SDR-Based GPR Technology

This chapter presents an approach for explosive-landmine detection on-board an autonomous aerial drone. The chapter describes the design, implementation and integration of a ground penetrating radar (GPR) using a software defined radio (SDR) platform into the aerial drone. The chapter?s goal is first to tackle in detail the development of a custom-designed lightweight GPR by approaching interplay between hardware and software radio on an SDR platform. The SDR-based GPR system results on a much lighter sensing device compared against the conventional GPR systems found in the literature and with the capability of re-configuration in real-time for different landmines and terrains, with the capability of detecting landmines under terrains with different dielectric characteristics. Secondly, the chapter introduce the integration of the SDR-based GPR into an autonomous drone by describing the mechanical integration, communication system, the graphical user interface (GUI) together with the landmine detection and geo-mapping. This chapter approach completely the hardware and software implementation topics of the on-board GPR system given first a comprehensive background of the software-defined radar technology and second presenting the main features of the Tx and Rx modules. Additional details are presented related with the mechanical and functional integration of the GPR into the UAV system

Conic Multi-Task Classification

Traditionally, Multi-task Learning (MTL) models optimize the average of task-related objective functions, which is an intuitive approach and which we will be referring to as Average MTL. However, a more general framework, referred to as Conic MTL, can be formulated by considering conic combinations of the objective functions instead; in this framework, Average MTL arises as a special case, when all combination coefficients equal 1. Although the advantage of Conic MTL over Average MTL has been shown experimentally in previous works, no theoretical justification has been provided to date. In this paper, we derive a generalization bound for the Conic MTL method, and demonstrate that the tightest bound is not necessarily achieved, when all combination coefficients equal 1; hence, Average MTL may not always be the optimal choice, and it is important to consider Conic MTL. As a byproduct of the generalization bound, it also theoretically explains the good experimental results of previous relevant works. Finally, we propose a new Conic MTL model, whose conic combination coefficients minimize the generalization bound, instead of choosing them heuristically as has been done in previous methods. The rationale and advantage of our model is demonstrated and verified via a series of experiments by comparing with several other methods.Comment: Accepted by European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD)-201

The Journal of Conventional Weapons Destruction, Issue 24.1 (2020)

Mine Action on the Korean Peninsula Raising the Profile of Mine Action A New Approach to IMAS Compliance Disposal of EO and Environmental Risk Mitigation Explosive Ordnance Risk Education - Measuring Behavior Chang