The radio surface brightness-to-diameter (Σ − D) relation for supernova remnants (SNRs) in the starburst galaxy M82 is analyzed in a statistically more robust manner than in the previous studies that mainly discussed sample quality and related selection effects. The statistics of data fits in log Σ−log D plane are analyzed by using vertical (standard) and orthogonal regressions. As the parameter values of D − Σ and Σ − D fits are invariant within the estimated uncertainties for orthogonal regressions, slopes of the empirical Σ − D relations should be determined by using the orthogonal regression fitting procedure. Thus obtained Σ − D relations for samples which are not under severe influence of the selection effects could be used for estimating SNR distances. Using the orthogonal regression fitting procedure Σ − D slope β ≈ 3.9 is obtained for the sample of 31 SNRs in M82. The results of implemented Monte Carlo simulations show that the sensitivity selection effect does not significantly influence the slope of M82 relation. This relation could be used for estimation of distances to SNRs that evolve in denser interstellar environment, with number denisty up to 1000 particles per cm 3 .
Neural networks (NNs) have proven to be a very powerful tool both for one-dimensional (1D) and two-dimensional (2D) direction of arrival (DOA) estimation. By avoiding complex and time-consuming mathematical calculations, NNs estimate DOAs almost instantaneously. This feature makes them very convenient for real-time applications. Further, unlike the well known MUSIC algorithm, neural network-based models provide accurate directions without additional calibration procedure of antenna array and a priori knowledge of the number of sources. In this review paper, the results achieved by the research group at the Faculty of Electronic Engineering in Nis are presented. The problem of DOA estimation of narrowband signals impinging upon different configurations of antenna arrays is addressed. Both Multi-Layer Perceptron (MLP) and Radial Basis Function (RBF) neural networks are considered, and their advantages and disadvantages are discussed. To improve the resolution of DOA estimates, sectorization model is introduced. As shown in this work, neural network-based models demonstrate high-resolution localization capabilities and much better efficiency than the MUSIC. Index Terms-Neural networks, SDMA, DOA, spatial signal processing, MUSIC.
Predicting the mechanical behavior of metallic materials, such as stress-strain curves, is important for studying the plastic behavior of materials. This paper intends to investigate machine learning methods’ capacity to predict the aluminum alloy’s stress-strain curves under different temperature levels. Specifically, three machine learning methods (Gaussian process regression (GPR), neural network (NN), and boosted trees (BT) were employed to predict the stress-strain response of Al6061- T6 at different temperatures, including 25°C, 100°C, 200°C, and 300°C. The performance of the studied machine learning methods has been verified using actual strain-stress measurements collected using uniaxial tensile testing on Al6061-T6. Four statistical scores have been adopted to evaluate the prediction accuracy. Results revealed the potential of machine learning methods in predicting strain-stress measurements. Furthermore, results showed that the NN model dominates the other models by providing a prediction with an averaged mean absolute error percentage of 0.213.
Abstract:In this paper a two-layer feedforward network is studied, a network that stores an association between two sequences in the two layers. Our work shows that neuronal sequences in one area can robustly trigger sequences in the second area if the association between the sequences is stored in the network. A more detailed incorporation into the biological aspects of neural network in the network dynamics may help to improve neutral networks in engineering applications.
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