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This paper presents machine learning classification on simulated data of permeable conducting spheres in air and seawater irradiated by low frequency electromagnetic pulses. Classification accuracy greater than 90% was achieved. The simulated data were generated using an analytical model of a magnetic dipole in air and seawater placed 1.5–3.5 m above the center of the sphere in 50 cm increments. The spheres had radii of 40 cm and 50 cm and were of permeable materials, such as steel, and non-permeable materials, such as aluminum. A series RL circuit was analytically modeled as the transmitter coil, and an RLC circuit as the receiver coil. Additive white Gaussian noise was added to the simulated data to test the robustness of the machine learning algorithms to noise. Multiple machine learning algorithms were used for classification including a perceptron and multiclass logistic regression, which are linear models, and a neural network, 1D convolutional neural network (CNN), and 2D CNN, which are nonlinear models. Feature maps are plotted for the CNNs and provide explainability of the salient parts of the time signature and spectrogram data used for classification. The pulses investigated, which expand the literature, include a two-sided decaying exponential, Heaviside step-off, triangular, Gaussian, rectangular, modulated Gaussian, raised cosine, and rectangular down-chirp. Propagation effects, including dispersion and frequency dependent attenuation, are encapsulated by the analytical model, which was verified using finite element modeling. The results in this paper show that machine learning methods are a viable alternative to inversion of electromagnetic induction (EMI) data for metallic sphere classification, with the advantage of real-time classification without the use of a physics-based model. The nonlinear machine learning algorithms used in this work were able to accurately classify metallic spheres in seawater even with significant pulse distortion caused by dispersion and frequency dependent attenuation. This paper presents the first effort towards the use of machine learning to classify metallic objects in seawater based on EMI sensing. [ABSTRACT FROM AUTHOR]

This paper presents a metallic object classification method using various electromagnetic pulses. Each electromagnetic pulse irradiated eight metallic objects placed at increasing distances from 10 mm to 40 mm relative to the electromagnetic sensing system. The electromagnetic sensing system consisted of two RL circuits placed in close proximity. Objects were classified using linear (perceptron and multiclass logistic regression) and non-linear (neural network, 1D convolutional neural network (CNN) and 2D CNN) machine learning classifiers. The machine learning classifiers were trained on experimental data collected in an electromagnetically shielded laboratory. A 10-fold cross-validation mean classification accuracy of 99.4 ± 0.3% for the 1D CNN classifier, and 92.9 ± 1.2% for the 2D CNN classifier, was achieved using a rectangular chirp electromagnetic pulse. The rectangular chirp pulse outperformed two-sided decaying exponential, Gaussian, triangular, raised cosine, and rectangular pulses. All pulses had equal energy. While the rectangular chirp performed best overall, other pulses more accurately distinguished between some objects. [ABSTRACT FROM AUTHOR]

Advances in numerical simulation techniques play an important role in helpingmining engineers understand those parts of the rock mass that cannot be readily observed. The Material Point Method (MPM) is an example of such a tool that is gaining popularity for studying geotechnical problems. In recent years, the original formulation of MPM has been extended to not only account for simulating the mechanical behaviour of rock under different loading conditions, but also to describe the coupled interaction of pore water and solid phases in materials. These methods assume that the permeability of mediums is homogeneous, and we show that these MPM techniques fail to accurately capture the correct behaviour of the fluid phase if the permeability of the material is heterogeneous. In this work, we propose a novel implementation of the coupled MPM to address this problem. We employ an approach commonly used in coupled Finite Volume Methods, known as the Two Point Flux Approximation (TPFA). Our new method is benchmarked against two well-known analytical expressions (a one-dimensional geostatic consolidation and the so-called Mandel-Cryer effect). Its performance is compared to existing coupled MPM approaches for homogeneous materials. In order to gauge the possible effectiveness of our technique in the field, we apply ourmethod to a case study relating to a mine known to experience severe problems with pore water. [ABSTRACT FROM AUTHOR]

We present a method for online detection of land cover change based on remotely sensed time series. Change is detected by monitoring deviations between observations and forecasts made using the time series historical data and similar time series in the geographical region. This method and several others were applied to MODIS 8-day surface reflectance data for problems of detecting settlement expansion in Limpopo Province, South Africa, and detecting deforestation in New South Wales, Australia. The proposed method had significantly shorter median detection delay (DD) for equivalent rates of false alarms compared with the other evaluated methods. We obtained a median DD of seven samples for settlement detection and 14 samples for deforestation detection corresponding to 56 days and 112 days, respectively. This is compared with a median DD of 224 and 544 days for the best other methods evaluated. We suggest that the proposed method is an excellent candidate for land cover change detection where rapid detection is essential. [ABSTRACT FROM AUTHOR]

The paper evaluated the Landsat Automated Land Cover Update Mapping (LALCUM) system designed to rapidly update a land cover map to a desired nominal year using a pre-existing reference land cover map. The system uses the Iteratively Reweighted Multivariate Alteration Detection (IRMAD) to identify areas of change and no change. The system then automatically generates large amounts of training samples (n > 1 million) in the no-change areas as input to an optimized Random Forest classifier. Experiments were conducted in the KwaZulu-Natal Province of South Africa using a reference land cover map from 2008, a change mask between 2008 and 2011 and Landsat ETM+ data for 2011. The entire system took 9.5 h to process. We expected that the use of the change mask would improve classification accuracy by reducing the number of mislabeled training data caused by land cover change between 2008 and 2011. However, this was not the case due to exceptional robustness of Random Forest classifier to mislabeled training samples. The system achieved an overall accuracy of 65%-67% using 22 detailed classes and 72%-74% using 12 aggregated national classes. "Water", "Plantations", "Plantations--clearfelled", "Orchards--trees", "Sugarcane", "Built-up/dense settlement", "Cultivation--Irrigated" and "Forest (indigenous)" had user's accuracies above 70%. Other detailed classes (e.g., "Low density settlements", "Mines and Quarries", and "Cultivation, subsistence, drylands")which are required for operational, provincial-scale land use planning and are usually mapped using manual image interpretation, could not be mapped using Landsat spectral data alone. However, the system was able to map the 12 national classes, at a sufficiently high level of accuracy for national scale land cover monitoring. This update approach and the highly automated, scalable LALCUM system can improve the efficiency and update rate of regional land cover mapping. [ABSTRACT FROM AUTHOR]

Background: Oxygen saturation (SpO2) targeting in the preterm infant may be improved with a better understanding of the SpO2 responses to changes in inspired oxygen (FiO2). Objective: We investigated the first-order FiO2-SpO2 relationship, aiming to quantify the parameters governing that relationship, the influences on these parameters and their variability. Methods: In recordings of FiO2 and SpO2 from preterm infants on continuous positive airway pressure and supplemental oxygen, we identified unique FiO2 adjustments and mapped the subsequent SpO2 responses. For responses identified as first-order, the delay, time constant and gain parameters were determined. Clinical and physiological predictors of these parameters were sought in regression analysis, and intra- and inter-subject variability was evaluated. Results: In 3,788 h of available data from 47 infants at 31 (28-33) post-menstrual weeks [median (interquartile range)], we identified 993 unique FiO2 adjustments followed by a first-order SpO2 response. All response parameters differed between FiO2 increments and decrements, with increments having a shorter delay, longer time constant and higher gain [2.9 (1.7-4.8) vs. 1.3 (0.58-2.6), p < 0.05]. Gain was also higher in less mature infants and in the setting of recent SpO2 instability, and was diminished with increasing severity of lung dysfunction. Intra-subject variability in all parameters was prominent. Conclusions: First-order SpO2 responses show variable gain, influenced by the direction of FiO2 adjustment and the severity of lung disease, as well as substantial intra-subject parameter variability. These findings should be taken into account in adjustment of FiO2 for SpO2 targeting in preterm infants. © 2015 S. Karger AG, Basel [ABSTRACT FROM AUTHOR]

The detection of ships at sea is a difficult task made more so by uncooperative ships, especially when using transponder-based ship detection systems. Synthetic aperture radar (SAR) imagery provides a means of observation independent of the ships cooperation, and over the years, a vast amount of research has gone into the detection of ships using this imagery. One of the most common methods used for ship detection in SAR imagery is the cell-averaging constant false alarm rate (CA-CFAR) prescreening method. It uses a scalar threshold value to determine how bright a pixel needs to be in order to be classified as a ship, and thus inversely how many false alarms are permitted. This paper presents by a method of converting the scalar threshold into a threshold manifold. The manifold is adjusted using a simulated annealing (SA) algorithm to optimally fit to information provided by the ship distribution map, which is generated from transponder data. By carefully selecting the input solution and threshold boundaries, much of the computational inefficiencies usually associated with SA can be avoided. The proposed method was tested on six ASAR images against five other methods and had a reported detection accuracy (DA) of 85.2% with a corresponding FAR of \mathbf1.01 \times \mathbf10^\mathbf-7. [ABSTRACT FROM PUBLISHER]

In this paper, the internal operations of an Extended Kalman Filter is investigated to observe if information can be derived to detect land cover change in a MODerate-resolution Imaging Spectroradiometer (MODIS) time series. The concept is based on the internal covariance matrix used by the Extended Kalman Filter, which adjusts the internal state of the filter for any changes occurring in the time series. The Extended Kalman Filter expands the internal covariance matrix if a significant change in reflectance value is observed, followed by adapting the state parameters to compensate for this change. The analysis shows that a change detection accuracy above 90% can be attained when evaluating the elements within the internal covariance matrix to detect new human settlements, with a corresponding false alarm rate below 6%. [ABSTRACT FROM PUBLISHER]

An automated land cover change detection method is proposed that uses coarse spatial resolution hyper-temporal earth observation satellite time series data. The study compared three different unsupervised clustering approaches that operate on short term Fourier transform coefficients computed over subsequences of 8-day composite MODerate-resolution Imaging Spectroradiometer (MODIS) surface reflectance data that were extracted with a temporal sliding window. The method uses a feature extraction process that creates meaningful sequential time series that can be analyzed and processed for change detection. The method was evaluated on real and simulated land cover change examples and obtained a change detection accuracy exceeding 76% on real land cover conversion and more than 70% on simulated land cover conversion. [ABSTRACT FROM PUBLISHER]

The potential bias in time estimation known as the planning fallacy was examined for 32 undergraduate students in relation to exam preparation. Students provided estimates of their study plans for the 8 days prior to two midterm exams. These estimates were compared to their study logs for the same period using Ordinal Pattern Analysis. The results indicate that overall students did not demonstrate optimistic biases as predicted by the planning fallacy. Moreover, a median split of the sample on measures of procrastination indicated that the students scoring high on procrastination were just as accurate in their study time predictions as low-scoring participants. Students scoring high on procrastination did commence studying later and studied significantly less than students in the low-procrastination group. No significant difference between these groups was found on exam performance. These results are discussed in relation to the nature of academic tasks, the significance of externally imposed deadlines and the effects of procrastination on task performance. [ABSTRACT FROM AUTHOR]

Apnoea, a pause in respiration, is almost ubiquitous in preterm infants born before completing 30 weeks gestation. Apnoea often begets hypoxemia and/or bradycardia, and has the potential to result in adverse neurodevelopmental consequences. Our current inability to predict apnoeic events in preterm infants requires apnoea to first be detected by monitoring device/s in order to trigger an intervention by bedside (medical or nursing) staff. Such a reactive management approach is laborious, and makes the consequences of apnoeic events inevitable. Recent technological advances and improved signal processing have allowed the possibility of developing prediction models for apnoeic events in preterm infants. However, the development of such models has numerous challenges and is only starting to show potential. This paper identifies requisite components and current gaps in developing prediction models for apnoeic events, and reviews previous studies on predicting apnoeic events in preterm infants., (Copyright © 2020 Lim, Jiang, Marshall, Salmon, Gale and Dargaville.)