Impact of Dopant-induced Ensemble Structure of Hetero Double Atom Catalysts in Electrochemical NH3 Production

Using the spin-polarized density functional theory (DFT) calculations, we examined the electrochemical N2 reduction (N2RR) toward NH3 production on the hetero RuM (M = 3d transition metals) double atom catalysts...

Recognition of Eye-Written Characters Using Deep Neural Network

Eye writing is a human–computer interaction tool that translates eye movements into characters using automatic recognition by computers. Eye-written characters are similar in form to handwritten ones, but their shapes are often distorted because of the biosignal’s instability or user mistakes. Various conventional methods have been used to overcome these limitations and recognize eye-written characters accurately, but difficulties have been reported as regards decreasing the error rates. This paper proposes a method using a deep neural network with inception modules and an ensemble structure. Preprocessing procedures, which are often used in conventional methods, were minimized using the proposed method. The proposed method was validated in a writer-independent manner using an open dataset of characters eye-written by 18 writers. The method achieved a 97.78% accuracy, and the error rates were reduced by almost a half compared to those of conventional methods, which indicates that the proposed model successfully learned eye-written characters. Remarkably, the accuracy was achieved in a writer-independent manner, which suggests that a deep neural network model trained using the proposed method is would be stable even for new writers.

Design of hybrid neural networks of the ensemble structure

This paper considers the structural-parametric synthesis (SPS) of neural networks (NNs) of deep learning, in particular convolutional neural networks (CNNs), which are used in image processing. It has been shown that modern neural networks may possess a variety of topologies. That is ensured by using unique blocks that determine their essential features, namely, the compression and excitation unit, the attention module convolution unit, the channel attention module, the spatial attention module, the residual unit, the ResNeXt block. This, first of all, is due to the need to increase their efficiency in the processing of images. Due to the large architectural space of parameters, including the type of unique block, the location in the structure of the convolutional neural network, its connections with other blocks, layers, computing costs grow nonlinearly. To minimize computational costs while maintaining the specified accuracy this work set tasks of both the generation of possible topology and structural-parametric synthesis of convolutional neural networks. To resolve them, the use of a genetic algorithm (GA) has been proposed. Parameter configuration was implemented using a genetic algorithm and modern gradient methods (GM). For example, stochastic gradient descent with momentum, accelerated Nesterov gradient, adaptive gradient algorithm, distribution of the root of the mean square of the gradient, assessment of adaptive momentum, adaptive Nesterov momentum. It is assumed to use such networks in the intelligent medical diagnostic system (IMDS), for determining the activity of tuberculosis. To improve the accuracy of solving the classification problem in the processing of images, the ensemble structure of hybrid convolutional neural networks (HCNNs) has been proposed in the current work. The parallel structure of the ensemble with the merged layer was used. Algorithms of optimal choice and integration of features in the construction of the ensemble have been developed