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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]

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]