To ensure the control of the precision of air-fuel ratio(AFR)of port fuel injection(PFI)spark ignition(SI)engines,a chaos radial basis function(RBF)neural network is used to predict the air intake flow of the engine.T...To ensure the control of the precision of air-fuel ratio(AFR)of port fuel injection(PFI)spark ignition(SI)engines,a chaos radial basis function(RBF)neural network is used to predict the air intake flow of the engine.The data of air intake flow is proved to be multidimensionally nonlinear and chaotic.The RBF neural network is used to train the reconstructed phase space of the data.The chaos algorithm is employed to optimize the weights of output layer connection and the radial basis center of Gaussian function in hidden layer.The simulation results obtained from Matlab/Simulink illustrate that the model has higher accuracy compared to the conventional RBF model.The mean absolute error and the mean relative error of the chaos RBF model can reach 0.0017 and 0.48,respectively.展开更多
Resource allocation is an important problem in ubiquitous network. Most of the existing resource allocation methods considering only wireless networks are not suitable for the ubiquitous network environment, and they ...Resource allocation is an important problem in ubiquitous network. Most of the existing resource allocation methods considering only wireless networks are not suitable for the ubiquitous network environment, and they will harm the interest of individual users with instable resource requirements. This paper considers the multi-point video surveillance scenarios in a complex network environment with both wired and wireless networks. We introduce the utility estimated by the total costs of an individual network user. The problem is studied through mathematical modeling and we propose an improved problem-specific branch-and-cut algorithm to solve it. The algorithm follows the divide-and-conquer principle and fully considers the duality feature of network selection. The experiment is conducted by simulation through C and Lingo. And it shows that compared with a centralized random allocation scheme and a cost greed allocation scheme, the proposed scheme has better per- formance of reducing the total costs by 13.0% and 30.6% respectively for the user.展开更多
An ultra-dense network scenario is a scene where a large number of people assemble in a limited area to generate centralized broadband data traffic requirements.Because ultra-dense networks generate enormous traffic p...An ultra-dense network scenario is a scene where a large number of people assemble in a limited area to generate centralized broadband data traffic requirements.Because ultra-dense networks generate enormous traffic pressure,traditional network capabilities are not enough to accommodate the user s needs.Based on the description of ultra-dense network architecture,we analyze millimeter wave radio spectrum,high gain beam forming,physical layer frame structure,resource concentration and edge computing technology.In addition,the cooperative technology required by overlay and interference symbiosis in the dense network architecture as well as the access control technology of centralized access is analyzed and discussed comprehensively.展开更多
To increase the limit of ethernet over coax （EoC） technology in the bidirectional reform of the hybrid fiber coaxial （HFC） network, an ethernet passive electronic network （EPEN） system based on Coax is proposed ...To increase the limit of ethernet over coax （EoC） technology in the bidirectional reform of the hybrid fiber coaxial （HFC） network, an ethernet passive electronic network （EPEN） system based on Coax is proposed and experimentally demonstrated in this paper. The proposed EPEN exploits the existing capabilities of the MAC layer in the ethernet passive optical network （EPON） for reduction of cost of the HFC network. As the MPCP （multi-point control protocol） is introduced in the EPEN, bandwidth control and higher efficiency can be achieved. The experimental results exhibit the throughput of system up to 100Mbps, which meet the requirements of HFC network. To improve the performance of EPEN, frequency division multiplexing （FDM） can be used for further increase of the throughput and more hardware processing modules in the future. can be implemented to enhance the capacity展开更多
Thrust estimation is a significant part of aeroengine thrust control systems.The traditional estimation methods are either low in accuracy or large in computation.To further improve the estimation effect,a thrust esti...Thrust estimation is a significant part of aeroengine thrust control systems.The traditional estimation methods are either low in accuracy or large in computation.To further improve the estimation effect,a thrust estimator based on Multi-layer Residual Temporal Convolutional Network(M-RTCN)is proposed.To solve the problem of dead Rectified Linear Unit(ReLU),the proposed method uses the Gaussian Error Linear Unit(GELU)activation function instead of ReLU in residual block.Then the overall architecture of the multi-layer convolutional network is adjusted by using residual connections,so that the network thrust estimation effect and memory consumption are further improved.Moreover,the comparison with seven other methods shows that the proposed method has the advantages of higher estimation accuracy and faster convergence speed.Furthermore,six neural network models are deployed in the embedded controller of the micro-turbojet engine.The Hardware-in-the-Loop(HIL)testing results demonstrate the superiority of M-RTCN in terms of estimation accuracy,memory occupation and running time.Finally,an ignition verification is conducted to confirm the expected thrust estimation and real-time performance.展开更多
Based on the experimental ignition delay results of n-butane/hydrogen mixtures in a rapid compression machine,a Genetic Algorithm(GA)optimized Back Propagation(BP)neural network model is originally developed for ignit...Based on the experimental ignition delay results of n-butane/hydrogen mixtures in a rapid compression machine,a Genetic Algorithm(GA)optimized Back Propagation(BP)neural network model is originally developed for ignition delay prediction.In the BP model,the activation function,learning rate and the neurons number in the hidden layer are optimized,respectively.The prediction ability of the BP model is validated in wide operating ranges,i.e.,compression pressures from 20 to 25 bar,compression temperatures from 722 to 987 K,equivalence ratios from 0.5 to 1.5 and molar ratios of hydrogen(X_(H2))from 0 to 75%.Compared with the BP model,the GA optimized BP model could increase the average correlation coefficient from 0.9745 to 0.9890,in the opposite,the average Mean Square Error(MSE)decreased from 2.21 to 1.06.On the other hand,to assess the BP-GA model prediction ability in the never-seen-before cases,a limited BP-GA model is fostered in the𝑋X_(H2) range from 0 to 50%to predict the ignition delays at the cases of𝑋X_(H2)=75%.It is found that the predicted ignition delays are underestimated due to the training dataset lacking of“acceleration feature”that happened at𝑋X_(H2)=75%.However,three possible options are reported to improve the prediction accuracy in such never-seen-before cases.展开更多
基金Project(51176014)supported by the National Natural Science Foundation of ChinaProject(2016JJ2003)supported by Natural Scienceof Hunan Province,ChinaProject(KF1605)supported by Key Laboratory of Safety Design and Reliability Technology of Engineering Vehicle in Hunan Province,China。
文摘To ensure the control of the precision of air-fuel ratio(AFR)of port fuel injection(PFI)spark ignition(SI)engines,a chaos radial basis function(RBF)neural network is used to predict the air intake flow of the engine.The data of air intake flow is proved to be multidimensionally nonlinear and chaotic.The RBF neural network is used to train the reconstructed phase space of the data.The chaos algorithm is employed to optimize the weights of output layer connection and the radial basis center of Gaussian function in hidden layer.The simulation results obtained from Matlab/Simulink illustrate that the model has higher accuracy compared to the conventional RBF model.The mean absolute error and the mean relative error of the chaos RBF model can reach 0.0017 and 0.48,respectively.
基金Supported by the National Science and Technology Major Project (No.2011ZX03005-004-04)the National Grand Fundamental Research 973 Program of China (No.2011CB302-905)+2 种基金the National Natural Science Foundation of China (No.61170058,61272133,and 51274202)the Research Fund for the Doctoral Program of Higher Education of China (No.20103402110041)the Suzhou Fundamental Research Project (No.SYG201143)
文摘Resource allocation is an important problem in ubiquitous network. Most of the existing resource allocation methods considering only wireless networks are not suitable for the ubiquitous network environment, and they will harm the interest of individual users with instable resource requirements. This paper considers the multi-point video surveillance scenarios in a complex network environment with both wired and wireless networks. We introduce the utility estimated by the total costs of an individual network user. The problem is studied through mathematical modeling and we propose an improved problem-specific branch-and-cut algorithm to solve it. The algorithm follows the divide-and-conquer principle and fully considers the duality feature of network selection. The experiment is conducted by simulation through C and Lingo. And it shows that compared with a centralized random allocation scheme and a cost greed allocation scheme, the proposed scheme has better per- formance of reducing the total costs by 13.0% and 30.6% respectively for the user.
文摘An ultra-dense network scenario is a scene where a large number of people assemble in a limited area to generate centralized broadband data traffic requirements.Because ultra-dense networks generate enormous traffic pressure,traditional network capabilities are not enough to accommodate the user s needs.Based on the description of ultra-dense network architecture,we analyze millimeter wave radio spectrum,high gain beam forming,physical layer frame structure,resource concentration and edge computing technology.In addition,the cooperative technology required by overlay and interference symbiosis in the dense network architecture as well as the access control technology of centralized access is analyzed and discussed comprehensively.
基金Supported by the National High Technology Research and Development Program of China(No.2007AA01Z229)
文摘To increase the limit of ethernet over coax （EoC） technology in the bidirectional reform of the hybrid fiber coaxial （HFC） network, an ethernet passive electronic network （EPEN） system based on Coax is proposed and experimentally demonstrated in this paper. The proposed EPEN exploits the existing capabilities of the MAC layer in the ethernet passive optical network （EPON） for reduction of cost of the HFC network. As the MPCP （multi-point control protocol） is introduced in the EPEN, bandwidth control and higher efficiency can be achieved. The experimental results exhibit the throughput of system up to 100Mbps, which meet the requirements of HFC network. To improve the performance of EPEN, frequency division multiplexing （FDM） can be used for further increase of the throughput and more hardware processing modules in the future. can be implemented to enhance the capacity
基金co-supported by the National Natural Science Foundation of China(Nos.61890920,61890921)。
文摘Thrust estimation is a significant part of aeroengine thrust control systems.The traditional estimation methods are either low in accuracy or large in computation.To further improve the estimation effect,a thrust estimator based on Multi-layer Residual Temporal Convolutional Network(M-RTCN)is proposed.To solve the problem of dead Rectified Linear Unit(ReLU),the proposed method uses the Gaussian Error Linear Unit(GELU)activation function instead of ReLU in residual block.Then the overall architecture of the multi-layer convolutional network is adjusted by using residual connections,so that the network thrust estimation effect and memory consumption are further improved.Moreover,the comparison with seven other methods shows that the proposed method has the advantages of higher estimation accuracy and faster convergence speed.Furthermore,six neural network models are deployed in the embedded controller of the micro-turbojet engine.The Hardware-in-the-Loop(HIL)testing results demonstrate the superiority of M-RTCN in terms of estimation accuracy,memory occupation and running time.Finally,an ignition verification is conducted to confirm the expected thrust estimation and real-time performance.
基金The authors would like to acknowledge the financial support to the research provided by the National Natural Science Foundation of China through the Project of 51922076 and 51706140.
文摘Based on the experimental ignition delay results of n-butane/hydrogen mixtures in a rapid compression machine,a Genetic Algorithm(GA)optimized Back Propagation(BP)neural network model is originally developed for ignition delay prediction.In the BP model,the activation function,learning rate and the neurons number in the hidden layer are optimized,respectively.The prediction ability of the BP model is validated in wide operating ranges,i.e.,compression pressures from 20 to 25 bar,compression temperatures from 722 to 987 K,equivalence ratios from 0.5 to 1.5 and molar ratios of hydrogen(X_(H2))from 0 to 75%.Compared with the BP model,the GA optimized BP model could increase the average correlation coefficient from 0.9745 to 0.9890,in the opposite,the average Mean Square Error(MSE)decreased from 2.21 to 1.06.On the other hand,to assess the BP-GA model prediction ability in the never-seen-before cases,a limited BP-GA model is fostered in the𝑋X_(H2) range from 0 to 50%to predict the ignition delays at the cases of𝑋X_(H2)=75%.It is found that the predicted ignition delays are underestimated due to the training dataset lacking of“acceleration feature”that happened at𝑋X_(H2)=75%.However,three possible options are reported to improve the prediction accuracy in such never-seen-before cases.
