Jiangxi University of Science and Technology

About: Jiangxi University of Science and Technology is a education organization based out in Ganzhou, China. It is known for research contribution in the topics: Microstructure & Alloy. The organization has 6958 authors who have published 5576 publications receiving 50650 citations.

Mercury under Pressure acts as a Transition Metal: Calculated from First Principles

Abstract: The inclusion of Hg among the transition metals is readily debated. Recently, molecular HgF4 was synthesized in a low-temperature noble gas but the potential of Hg to form compounds beyond a +2 oxidation state in a stable solid remains unresolved. We propose high-pressure techniques to prepare unusual oxidation states of Hg-based compounds. Using an advanced structure search algorithm and first-principles electronic structure calculations, we find that under high pressure Hg in Hg-F compounds transfers charge from the d orbitals to the F, thus behaving as a transition metal. Oxidizing Hg to +4 and +3 yielded the thermodynamically stable compounds HgF4 and HgF3. The former consists of HgF4 planar molecules, a typical geometry for d(8) metal centers. HgF3 is metallic and ferromagnetic owing to the d(9) configuration of Hg, with a large gap between its partially occupied and unoccupied bands under high pressure.

Discrimination of mining microseismic events and blasts using convolutional neural networks and original waveform

Abstract: Microseismic monitoring system is one of the effective methods for deep mining geo-stress monitoring. The principle of microseismic monitoring system is to analyze the mechanical parameters contained in microseismic events for providing accurate information of rockmass. The accurate identification of microseismic events and blasts determines the timeliness and accuracy of early warning of microseismic monitoring technology. An image identification model based on Convolutional Neural Network (CNN) is established in this paper for the seismic waveforms of microseismic events and blasts. Firstly, the training set, test set, and validation set are collected, which are composed of 5250, 1500, and 750 seismic waveforms of microseismic events and blasts, respectively. The classified data sets are preprocessed and input into the constructed CNN in CPU mode for training. Results show that the accuracies of microseismic events and blasts are 99.46% and 99.33% in the test set, respectively. The accuracies of microseismic events and blasts are 100% and 98.13% in the validation set, respectively. The proposed method gives superior performance when compared with existed methods. The accuracies of models using logistic regression and artificial neural network (ANN) based on the same data set are 54.43% and 67.9% in the test set, respectively. Then, the ROC curves of the three models are obtained and compared, which show that the CNN gives an absolute advantage in this classification model when the original seismic waveform are used in training the model. It not only decreases the influence of individual differences in experience, but also removes the errors induced by source and waveform parameters. It is proved that the established discriminant method improves the efficiency and accuracy of microseismic data processing for monitoring rock instability and seismicity.