Effects of CCEP and Sc on superplasticity of Al–5.6Mg–0.7Mn alloys

Trace amount (0.3?wt%) of scandium is added to Al–5.6Mg–0.7Mn alloy to form uniformly distributed Al₃Sc precipitates for producing a fine-grained and stable microstructure at high temperature through cross-channel extrusion process. Superplasticity and hot workability of the Sc-containing Al–5.6Mg–0.7Mn alloy, after extrusion, are also examined. The result indicates that Al–5.6Mg–0.7Mn alloys with and without 0.3?wt% Sc after extrusion of six passes at 300°C, fine-grained structures were observed with grain sizes of 1–2?µm and improvement of mechanical properties. Furthermore, Al₃Sc phase can effectively retard recrystallization to increase the thermal stability and remain equiaxed. The elongation of Al–5.6Mg–0.7Mn alloy with Sc addition to failure is extended to 873% maximum at high temperature of 450°C at strain rate of 1?×?10^?1?s^?1after six passes in the CCEP.     

Feature Fusion-Based Deep Learning Network to Recognize Table Tennis Actions

A system for classifying four basic table tennis strokes using wearable devices and deep learning networks is proposed in this study. The wearable device consisted of a six-axis sensor, Raspberry Pi 3, and a power bank. Multiple kernel sizes were used in convolutional neural network (CNN) to evaluate their performance for extracting features. Moreover, a multiscale CNN with two kernel sizes was used to perform feature fusion at different scales in a concatenated manner. The CNN achieved recognition of the four table tennis strokes. Experimental data were obtained from 20 research participants who wore sensors on the back of their hands while performing the four table tennis strokes in a laboratory environment. The data were collected to verify the performance of the proposed models for wearable devices. Finally, the sensor and multi-scale CNN designed in this study achieved accuracy and F1 scores of 99.58% and 99.16%, respectively, for the four strokes. The accuracy for five-fold cross validation was 99.87%. This result also shows that the multi-scale convolutional neural network has better robustness after five-fold cross validation.     

M/M/1 retrial queue with working vacations

In this paper we introduce the new M/M/1 retrial queue with working vacations which is motivated by the performance analysis of a Media Access Control function in wireless systems. We give a condition for the stability of the model, which has an important impact on setting the retrial rate for such systems. We derive the closed form solution in equilibrium for the retrial M/M/1 queue with working vacations, and we also show that the conditional stochastic decomposition holds for this model as well.     

Application of neural processing paradigm in visual landmark recognition and autonomous robot navigation

This article addresses the issue of visual landmark recognition in autonomous robot navigation along known routes, by intuitively exploiting the functions of the human visual system and its navigational ability. A feedforward–feedbackward architecture has been developed for recognising visual landmarks in real time. It integrates the theoretical concepts from the pre-attentive and attentive stages in the human visual system, the selective attention adaptive resonance theory neural network and its derivatives, and computational approaches towards object recognition in computer vision. The architecture mimics the pre-attentive and attentive stages in the context of object recognition, embedding neural network processing paradigm into a computational template-matching approach in computer vision. The real-time landmark recognition capability is achieved by mimicking the pre-attentive stage, where it models a selective attention mechanism for optimal computational resource allocation, focusing only on the regions of interest to address the computational restrictive nature of current computer processing power. Similarly, the recognition of visual landmarks in both clean and cluttered backgrounds is implemented in the attentive stage by developing a memory feedback modulation (MFM) mechanism that enables knowledge from the memory to interact and enhance the efficiency of earlier stages in the architecture. Furthermore, it also incorporates both top-down and bottom-up facilitatory and inhibition pathways between the memory and the earlier stages to enable the architecture to recognise a 2D landmark, which is partially occluded by adjacent features in the surroundings. The results show that the architecture is able to recognise objects in cluttered backgrounds using real-images in both indoor and outdoor scenes. Furthermore, the architecture application in autonomous robot navigation has been demonstrated through a number of real-time trials in both indoor and outdoor environments.