期刊文献+
共找到2篇文章
< 1 >
每页显示 20 50 100
A Sensorless State Estimation for A Safety-Oriented Cyber-Physical System in Urban Driving:Deep Learning Approach 被引量:3
1
作者 Mohammad Al-Sharman David Murdoch +4 位作者 Dongpu Cao Chen Lv Yahya Zweiri Derek Rayside William Melek 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2021年第1期169-178,共10页
In today's modern electric vehicles,enhancing the safety-critical cyber-physical system(CPS)'s performance is necessary for the safe maneuverability of the vehicle.As a typical CPS,the braking system is crucia... In today's modern electric vehicles,enhancing the safety-critical cyber-physical system(CPS)'s performance is necessary for the safe maneuverability of the vehicle.As a typical CPS,the braking system is crucial for the vehicle design and safe control.However,precise state estimation of the brake pressure is desired to perform safe driving with a high degree of autonomy.In this paper,a sensorless state estimation technique of the vehicle's brake pressure is developed using a deep-learning approach.A deep neural network(DNN)is structured and trained using deep-learning training techniques,such as,dropout and rectified units.These techniques are utilized to obtain more accurate model for brake pressure state estimation applications.The proposed model is trained using real experimental training data which were collected via conducting real vehicle testing.The vehicle was attached to a chassis dynamometer while the brake pressure data were collected under random driving cycles.Based on these experimental data,the DNN is trained and the performance of the proposed state estimation approach is validated accordingly.The results demonstrate high-accuracy brake pressure state estimation with RMSE of 0.048 MPa. 展开更多
关键词 Brake pressure state estimation cyber-physical system(CPS) deep learning dropout regularization approach
下载PDF
Intelligent Recognition Method of Insufficient Fluid Supply of Oil Well Based on Convolutional Neural Network 被引量:1
2
作者 Yanfeng He Zhenlong Wang +2 位作者 Bin Liu Xiang Wang Bingchao Li 《Open Journal of Yangtze Oil and Gas》 2021年第3期116-128,共13页
Traditional methods for judging the degree of insufficient fluid supply in oil wells have low efficiency and limited accuracy. To address this problem, a method for intelligently identifying the degree of insufficient... Traditional methods for judging the degree of insufficient fluid supply in oil wells have low efficiency and limited accuracy. To address this problem, a method for intelligently identifying the degree of insufficient fluid supply in oil wells based on convolutional neural networks is proposed in this paper. Firstly, 5000 indicator diagrams with insufficient liquid supply were collected from the oilfield site, and a sample set was established after preprocessing;then based on the AlexNet model, combined with the characteristics of the indicator diagram, a convolutional neural network model including 4 layers of convolutional layers, 3 layers of down-pooling layers and 2 layers of fully connected layers is established. The backpropagation, ReLu activation function and dropout regularization method are used to complete the training of the convolutional neural network;finally, the performance of the convolutional neural network under different iteration times and network structure is compared, and the super parameter optimization of the model is completed. It has laid a good foundation for realizing the self-adaptive and intelligent matching of oil well production parameters and formation fluid supply conditions. It has certain application prospects. The results show that the accuracy of training and verification of the method exceeds 98%, which can meet the actual application requirements on site. 展开更多
关键词 Degree of Insufficient Fluid Supply in Oil Wells Indicator Diagram Convolutional Neural Network Alexnet Backpropagation Algorithm ReLu Activation Function dropout Regularization
下载PDF
上一页 1 下一页 到第
使用帮助 返回顶部