In this study, the dataset of as-cast A380 aluminum alloy involving chemical composition, processing parameter and mechanical properties was experimentally established. On this basis, the prediction model of back propagation neural network optimized by particle swarm optimization (PSO-BP) was...

Context: Convolutional neural networks (CNNs) for spectroscopic modelling are usually designed and tuned manually and decisions regarding the CNN components are made heuristically. This can lead to poorly designed CNN models that are not fully optimised, or to models that take a long time to...

The present study is devoted to the design of the main flow parameters of a conveyor control system with a large number of sections. For the design of the control system, a neural network is used. The architecture of the neural network is justified and the rules for the formation of nodes for...

The rapid and accurate identification of different types of faults in the power grid is of great significance to the stable operation of the power grid. An identification model of transient fault recording data for distribution network based on double convolutional neural network is proposed in...

Acoustic source localization in the spherical harmonic domain with reverberation has hitherto not been investigated extensively. When the joint direction-of-arrival (DOA) estimation is treated as a classification task, it needs a lot of different classes and large-scale dataset, which is...

In this paper, a novel conditional focus probability learning model, termed MCNN, is proposed for multi-focus image fusion (MFIF). Given a pair of source images, their conditional focus probabilities can be generated by using our MCNN trained well, which is further converted into the binary...

In this paper we propose a method to perform non-parametric reconstructions using artificial neural networks based solely on cosmological data without any previous theoretical assumptions. Data modelling using neural networks makes it possible to increase the amount of information from small...

MXene materials proved to be an excellent contender for improving thermal systems' high energy and power density. MXene nanoparticles are synthesized and combined with ionic liquids to create an innovative fluid known as "MXene Ionanofluids," which has recently sparked an interest. Significantly...

Prediction of nonlinear dynamic process remains a challenging and active research topic. In this paper, adaptive nonlinear prediction based on a Hammerstein-modeling approach is investigated. Motivated by the desire to represent the best fit to the nonlinear dynamical plant with the Hammerstein...

The task of recovering a low-rank matrix given an incomplete matrix, also termed as matrix completion, arises in various applications. The numerous methods for matrix completion can be classified into linear and nonlinear categories. In this paper, a novel and interpretable neural network is...