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

Automatic feature detection and interpretation in ground-penetrating radar data

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Mode-converted waves and D-scans for flaw sizing and characterisation in ultrasonic time-of-flight diffraction (TOFD)

Ultrasonic time-of-flight diffraction (TOFD) is known as a reliable non-destructive testing technique for the inspection of weld defects in steel structures. However, the critical stages of data processing and interpretation in TOFD are still performed manually and offline. This is subject to inevitable human errors due to reduced alertness arising from operator fatigue and visual strain when processing large volumes of data. Manual interpretation focuses only on the compression wave portion of the collected TOFD data and overlooks the mode-converted region. Even automatic or semiautomatic TOFD interpretation methods still consider the mode-converted data as irrelevant and redundant. This paper shows some possible employments and utilisations of the data included in the mode-converted returns for accurate sizing and characterisation of weld flaws in D-scans. The results achieved so far have been promising in terms of accuracy, consistency and reliability, and the devised methods are now under thorough investigation to explore their potential further. © (2011) by the British Institute of Non-Destructive Testing. All rights reserved

Deep Learning Models for Automatic Makeup Detection

Makeup can disguise facial features, which results in degradation in the performance of many facial-related analysis systems, including face recognition, facial landmark characterisation, aesthetic quantification and automated age estimation methods. Thus, facial makeup is likely to directly affect several real-life applications such as cosmetology and virtual cosmetics recommendation systems, security and access control, and social interaction. In this work, we conduct a comparative study and design automated facial makeup detection systems leveraging multiple learning schemes from a single unconstrained photograph. We have investigated and studied the efficacy of deep learning models for makeup detection incorporating the use of transfer learning strategy with semi-supervised learning using labelled and unlabelled data. First, during the supervised learning, the VGG16 convolution neural network, pre-trained on a large dataset, is fine-tuned on makeup labelled data. Secondly, two unsupervised learning methods, which are self-learning and convolutional auto-encoder, are trained on unlabelled data and then incorporated with supervised learning during semi-supervised learning. Comprehensive experiments and comparative analysis have been conducted on 2479 labelled images and 446 unlabelled images collected from six challenging makeup datasets. The obtained results reveal that the convolutional auto-encoder merged with supervised learning gives the best makeup detection performance achieving an accuracy of 88.33% and area under ROC curve of 95.15%. The promising results obtained from conducted experiments reveal and reflect the efficiency of combining different learning strategies by harnessing labelled and unlabelled data. It would also be advantageous to the beauty industry to develop such computational intelligence methods.</jats:p

Augmentation of transient stability margin based on rapid assessment of rate of change of kinetic energy

A fast-load injection through a resistive dynamic brake with appropriate power dissipation capacity can absorb the excess transient energy caused by a large and sudden disturbance and thus improve the transient stability margin of a power system. However, fast assessment of the transient stability and the effective insertion/removal instants of the brake are longstanding challenges. This paper proposes a new criterion based on the rate of change of kinetic energy to rapidly evaluate system transient stability and identify conditions of effective insertion/removal instants of a dynamic brake. Unlike reported studies where the superiority of this criterion was only demonstrated through off-line simulation, both the theoretical modeling and practical implementation of this criterion is presented here using the one machine infinite bus system. A microprocessor controller based on a single-variable measurement, i.e. generator deviation speed, is proposed and implemented to control the dynamic brake during the disturbance periods. The observed behavior of the power system under sudden disturbances and the effect of timely insertion/removal of the dynamic brake on the transient stability of the power system under study are presented and evaluated. The proposed method has been successfully validated, demonstrating its suitability for practical and rapid assessment of transient stability