Study on video intelligent early warning and tracking system based on ARM

The protrusion of the planning of numerical intelligent early-warning and tracking system in this study which can ease triggerman's work strength,lay the next generation intelligence supervision system foundation and expand effectively the video resources use etc. In the numerical intelligent early-warning matrix sub-system,the authors have designed a kind dual-core system which includes both ARM and DSP,and designed detailedly traffic dynamics affairs early-warning arithmetic which bases on that system. And then,this system will carry quickly on fixing the right position of license plate,correcting the inclination degree of license plate,and thinning it to get the number of this license and severity grade. Secondly,in the rotated dome camera sub-system,the authors have designed three-dimensional trajectory mathematical model which makes use of a fuzzy PID controller to achieve the high- speed track. At last,Simulation shows that the proposed control method has high profile tracking precision,accuracy and robustness of the disturbance.

Microseismic source location using deep learning:A coal mine case study in China

Microseismic source location is crucial for the early warning of rockburst risks.However,the conventional methods face challenges in terms of the microseismic wave velocity and arrival time accuracy.Intelligent techniques,such as the full convolutional neural network(FCNN),can capture spatial information but struggle with complex microseismic sequence.Combining the FCNN with the long shortterm memory(LSTM)network enables better time-series signal classification by integrating multiscale information and is therefore suitable for waveform location.The LSTM-FCNN model does not require extensive data preprocessing and it simplifies the microseismic source location through feature extraction.In this study,we utilized the LSTM-FCNN as a regression learning model to locate the seismic focus.Initially,the method of short-time-average/long-time-average(STA/LTA)arrival time picking was employed to augment spatiotemporal information.Subsequently,oversampling the on-site data was performed to address the issue of data imbalance,and finally,the performance of LSTM-FCNN was tested.Meanwhile,we compared the LSTM-FCNN model with previous deep-learning models.Our results demonstrated remarkable location capabilities with a mean absolute error(MAE)of only 7.16 m.The model can realize swift training and high accuracy,thereby significantly improving risk warning of rockbursts.