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
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
Aiming at the problems of the complex process of the Turbo decoding algorithm and the large delay in processing the received signal,an adaptive Turbo decoding method based on an improved convolutional neural network( ...Aiming at the problems of the complex process of the Turbo decoding algorithm and the large delay in processing the received signal,an adaptive Turbo decoding method based on an improved convolutional neural network( ATDIC) was proposed in this paper. Firstly,an adaptive unreliable bit and adaptive iteration number method is proposed to simplify the traditional Turbo algorithm. While reducing computational complexity,these refinements still demand considerable computational resources. Leveraging networks exceptional generalization capabilities,strong adaptability,and high parallel process ability,a convolutional neural network( CNN) optimized by Adam's algorithm was employed to predict the Turbo decoding results in this paper,and at the same time,based on the traditional CNN,batch normalization and dropout regularization were performed to accelerate computational speed and mitigate overfitting tendencies inherent in prior approaches. Simulation results show that ATDIC enhances the decoding performance while increasing the decoding rate and reducing resource consumption.展开更多
文摘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.
文摘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.
基金supported by the Science and Technology Innovation 2030-Major Project,China (2022ZD0119001)。
文摘Aiming at the problems of the complex process of the Turbo decoding algorithm and the large delay in processing the received signal,an adaptive Turbo decoding method based on an improved convolutional neural network( ATDIC) was proposed in this paper. Firstly,an adaptive unreliable bit and adaptive iteration number method is proposed to simplify the traditional Turbo algorithm. While reducing computational complexity,these refinements still demand considerable computational resources. Leveraging networks exceptional generalization capabilities,strong adaptability,and high parallel process ability,a convolutional neural network( CNN) optimized by Adam's algorithm was employed to predict the Turbo decoding results in this paper,and at the same time,based on the traditional CNN,batch normalization and dropout regularization were performed to accelerate computational speed and mitigate overfitting tendencies inherent in prior approaches. Simulation results show that ATDIC enhances the decoding performance while increasing the decoding rate and reducing resource consumption.
