Recurrent Neural Networks(RNNs)have been widely applied to deal with temporal problems,such as flood forecasting and financial data processing.On the one hand,traditional RNNs models amplify the gradient issue due to ...Recurrent Neural Networks(RNNs)have been widely applied to deal with temporal problems,such as flood forecasting and financial data processing.On the one hand,traditional RNNs models amplify the gradient issue due to the strict time serial dependency,making it difficult to realize a long-term memory function.On the other hand,RNNs cells are highly complex,which will signifi-cantly increase computational complexity and cause waste of computational resources during model training.In this paper,an improved Time Feedforward Connections Recurrent Neural Networks(TFC-RNNs)model was first proposed to address the gradient issue.A parallel branch was introduced for the hidden state at time t−2 to be directly transferred to time t without the nonlinear transforma-tion at time t−1.This is effective in improving the long-term dependence of RNNs.Then,a novel cell structure named Single Gate Recurrent Unit(SGRU)was presented.This cell structure can reduce the number of parameters for RNNs cell,consequently reducing the computational complexity.Next,applying SGRU to TFC-RNNs as a new TFC-SGRU model solves the above two difficulties.Finally,the performance of our proposed TFC-SGRU was verified through sev-eral experiments in terms of long-term memory and anti-interference capabilities.Experimental results demonstrated that our proposed TFC-SGRU model can cap-ture helpful information with time step 1500 and effectively filter out the noise.The TFC-SGRU model accuracy is better than the LSTM and GRU models regarding language processing ability.展开更多
The fractured-vuggy carbonate oil resources in the western basin of China are extremely rich.The connectivity of carbonate reservoirs is complex,and there is still a lack of clear understanding of the development and ...The fractured-vuggy carbonate oil resources in the western basin of China are extremely rich.The connectivity of carbonate reservoirs is complex,and there is still a lack of clear understanding of the development and topological structure of the pore space in fractured-vuggy reservoirs.Thus,effective prediction of fractured-vuggy reservoirs is difficult.In view of this,this work employs adaptive point cloud technology to reproduce the shape and capture the characteristics of a fractured-vuggy reservoir.To identify the complex connectivity among pores,fractures,and vugs,a simplified one-dimensional connectivity model is established by using the meshless connection element method(CEM).Considering that different types of connection units have different flow characteristics,a sequential coupling calculation method that can efficiently calculate reservoir pressure and saturation is developed.By automatic history matching,the dynamic production data is fitted in real-time,and the characteristic parameters of the connection unit are inverted.Simulation results show that the three-dimensional connectivity model of the fractured-vuggy reservoir built in this work is as close as 90%of the fine grid model,while the dynamic simulation efficiency is much higher with good accuracy.展开更多
It is expected that for a long time the future road trafc will be composed of both regular vehicles(RVs)and connected autonomous vehicles(CAVs).As a vehicle-to-infrastructure technology dedicated to facilitating CAV u...It is expected that for a long time the future road trafc will be composed of both regular vehicles(RVs)and connected autonomous vehicles(CAVs).As a vehicle-to-infrastructure technology dedicated to facilitating CAV under the mixed trafc fow,roadside units(RSUs)can also improve the quality of information received by CAVs,thereby infuencing the routing behavior of CAV users.This paper explores the possibility of leveraging the RSU deployment to afect the route choices of both CAVs and RVs and the adoption rate of CAVs so as to reduce the network congestion and emissions.To this end,we frst establish a logit-based stochastic user equilibrium model to capture drivers’route choice and vehicle type choice behaviors provided the RSU deployment plan is given.Particularly,CAV users’perception error can be reduced by higher CAV penetration and denser RSUs deployed on the road due to the improved information quality.With the established equilibrium model,the RSU deployment problem is then formulated as a mathematical program with equilibrium constraints.An active-set algorithm is presented to solve the deployment problem efciently.Numerical results suggest that an optimal RSU deployment plan can efectively drive the system towards one with lower network delay and emissions.展开更多
基金This work was funded by the National Science Foundation of Hunan Province(2020JJ2029)。
文摘Recurrent Neural Networks(RNNs)have been widely applied to deal with temporal problems,such as flood forecasting and financial data processing.On the one hand,traditional RNNs models amplify the gradient issue due to the strict time serial dependency,making it difficult to realize a long-term memory function.On the other hand,RNNs cells are highly complex,which will signifi-cantly increase computational complexity and cause waste of computational resources during model training.In this paper,an improved Time Feedforward Connections Recurrent Neural Networks(TFC-RNNs)model was first proposed to address the gradient issue.A parallel branch was introduced for the hidden state at time t−2 to be directly transferred to time t without the nonlinear transforma-tion at time t−1.This is effective in improving the long-term dependence of RNNs.Then,a novel cell structure named Single Gate Recurrent Unit(SGRU)was presented.This cell structure can reduce the number of parameters for RNNs cell,consequently reducing the computational complexity.Next,applying SGRU to TFC-RNNs as a new TFC-SGRU model solves the above two difficulties.Finally,the performance of our proposed TFC-SGRU was verified through sev-eral experiments in terms of long-term memory and anti-interference capabilities.Experimental results demonstrated that our proposed TFC-SGRU model can cap-ture helpful information with time step 1500 and effectively filter out the noise.The TFC-SGRU model accuracy is better than the LSTM and GRU models regarding language processing ability.
基金funded by the Natural Science Foundation of Xinjiang Uygur Autonomous Region (No.2022D01A330)the CNPC (China National Petroleum Corporation)Scientific Research and Technology Development Project (Grant No.2021DJ1501)+1 种基金National Natural Science Foundation Project (No.52274030)“Tianchi Talent”Introduction Plan of Xinjiang Uygur Autonomous Region (2022).
文摘The fractured-vuggy carbonate oil resources in the western basin of China are extremely rich.The connectivity of carbonate reservoirs is complex,and there is still a lack of clear understanding of the development and topological structure of the pore space in fractured-vuggy reservoirs.Thus,effective prediction of fractured-vuggy reservoirs is difficult.In view of this,this work employs adaptive point cloud technology to reproduce the shape and capture the characteristics of a fractured-vuggy reservoir.To identify the complex connectivity among pores,fractures,and vugs,a simplified one-dimensional connectivity model is established by using the meshless connection element method(CEM).Considering that different types of connection units have different flow characteristics,a sequential coupling calculation method that can efficiently calculate reservoir pressure and saturation is developed.By automatic history matching,the dynamic production data is fitted in real-time,and the characteristic parameters of the connection unit are inverted.Simulation results show that the three-dimensional connectivity model of the fractured-vuggy reservoir built in this work is as close as 90%of the fine grid model,while the dynamic simulation efficiency is much higher with good accuracy.
基金the National Natural Science Foundation of China(72101153,72061127003)Shanghai Pujiang Program(2020PJC086)+2 种基金Shanghai Chenguang Program(21CGA72)the Joint Laboratory for Internet of Vehicles,Ministry of Education-China Mobile Communications Corporation(2020109)the NYU Shanghai Boost Fund.
文摘It is expected that for a long time the future road trafc will be composed of both regular vehicles(RVs)and connected autonomous vehicles(CAVs).As a vehicle-to-infrastructure technology dedicated to facilitating CAV under the mixed trafc fow,roadside units(RSUs)can also improve the quality of information received by CAVs,thereby infuencing the routing behavior of CAV users.This paper explores the possibility of leveraging the RSU deployment to afect the route choices of both CAVs and RVs and the adoption rate of CAVs so as to reduce the network congestion and emissions.To this end,we frst establish a logit-based stochastic user equilibrium model to capture drivers’route choice and vehicle type choice behaviors provided the RSU deployment plan is given.Particularly,CAV users’perception error can be reduced by higher CAV penetration and denser RSUs deployed on the road due to the improved information quality.With the established equilibrium model,the RSU deployment problem is then formulated as a mathematical program with equilibrium constraints.An active-set algorithm is presented to solve the deployment problem efciently.Numerical results suggest that an optimal RSU deployment plan can efectively drive the system towards one with lower network delay and emissions.
