Atomic Simulation of Fatigue Crack Growth Mechanism of Single Crystal γ-TiAl Alloy

In order to study the relationship of fatigue property、crack growth and organization form of TiAl alloy, the micro crack growth and micro deformation mechanism of single crystal γ-TiAl alloy with an embedded boundary crack under cyclic loading were studied by means of molecular dynamics and velocity loading. Results show that the crack growth process and micro deformation mechanism of single crystal γ-TiAl alloy under cyclic loading were divided into three stages. The mechanical properties are affected by the defects of crack tip lattice distortion,prismatic dislocation slip, Lomer-cottrell dislocation group formation,stacking fault start, deformation twin, etc. and their interaction results in the loading process. The mechanism of crack growth and the mechanism of plastic deformation at different stages were quite different. The research results provide a strong theoretical guidance for improving the performance of γ-TiAlalloys under complex external loading conditions

A Novel Hybrid Approach: Instance Weighted Hidden Naive Bayes

Naive Bayes (NB) is easy to construct but surprisingly effective, and it is one of the top ten classification algorithms in data mining. The conditional independence assumption of NB ignores the dependency between attributes, so its probability estimates are often suboptimal. Hidden naive Bayes (HNB) adds a hidden parent to each attribute, which can reflect dependencies from all the other attributes. Compared with other Bayesian network algorithms, it offers significant improvements in classification performance and avoids structure learning. However, the assumption that HNB regards each instance equivalent in terms of probability estimation is not always true in real-world applications. In order to reflect different influences of different instances in HNB, the HNB model is modified into the improved HNB model. The novel hybrid approach called instance weighted hidden naive Bayes (IWHNB) is proposed in this paper. IWHNB combines instance weighting with the improved HNB model into one uniform framework. Instance weights are incorporated into the improved HNB model to calculate probability estimates in IWHNB. Extensive experimental results show that IWHNB obtains significant improvements in classification performance compared with NB, HNB and other state-of-the-art competitors. Meanwhile, IWHNB maintains the low time complexity that characterizes HNB

Fault identification for power transformer based on dissolved gas in oil data using sparse convolutional neural networks

Abstract This paper addressed the challenges associated with the complexity, numerous parameters, computational resource demands, and slow processing speed of transformer fault identification models based on deep learning technologies. Sparse convolutional neural network (CNN) approach is proposed for identifying faults related to dissolved gases in oil. Leveraging an improved Gramian angular field, one‐dimensional fault samples are converted into two‐dimensional feature images and data augmentation is implemented to meet the input requirements of deep learning models. Building upon visual geometry group (VGG)19 and residual networks (ResNet)50 networks for fault diagnosis, sparsity techniques are introduced through pruning, the fusion of convolution layers and batch normalization layers, and parameter quantization. Numerical experiments and performance evaluations on dissolved gas in transformer oil fault data demonstrate that the proposed method effectively reduced model complexity, minimized parameter count, conserved computational resources, and improved processing speed while maintaining a considerable level of fault identification accuracy. This made it applicable to edge computing platforms characterized by small form factors and low power consumption in the power industry

DoA Prediction Based Beamforming with Low Training Overhead for Highly-Mobile UAV Communication with Cellular Networks

In supporting communications with unmanned aerial vehicles (UAVs) as aerial user equipments (aUEs) in cellular systems, the current beamforming schemes based on channel state estimation are facing severe challenges from the pilot contamination effect, especially in 5G and future networks where the cell size becomes small and the user density is high. Beamforming schemes based on signal direction of arrival (DoA) are regarded as a highly promising alternative to solve this problem. However, to achieve optimal performance for DoA-based beamforming, the error to DoA estimation during pilot signal intervals, caused by the high mobility of UAVs, must be addressed. In the meantime, the training overheads of traditional DoA estimation algorithms must be reduced to save the bandwidth for data communication. This paper investigates uplink beamforming performance enhancement based on signal DoA estimation to support UAV-cellular network communication. We propose a novel DoA estimation algorithm to predict angle variations during the intervals, which achieves high precision even when UAVs are at high mobility. The prediction process requires no pilot signals and enables timely adjustment of the steering vector when calculating the beamforming weight vector. The proposed algorithm contributes to the realisation of a beamforming scheme with real-time steering vector updates, which simultaneously maintains high beamforming gains and low training overheads. Simulation results show that, compared with the conventional DoA-based beamforming scheme, the proposed method yields more accurate DoA estimation output and higher gains. Furthermore, simulation experiments also suggests that applying the proposed scheme can reduce up to 100 pilot signal transmissions per second