The dynamic wireless communication network is a complex network that needs to consider various influence factors including communication devices,radio propagation,network topology,and dynamic behaviors.Existing works ...The dynamic wireless communication network is a complex network that needs to consider various influence factors including communication devices,radio propagation,network topology,and dynamic behaviors.Existing works focus on suggesting simplified reliability analysis methods for these dynamic networks.As one of the most popular modeling methodologies,the dynamic Bayesian network(DBN)is proposed.However,it is insufficient for the wireless communication network which contains temporal and non-temporal events.To this end,we present a modeling methodology for a generalized continuous time Bayesian network(CTBN)with a 2-state conditional probability table(CPT).Moreover,a comprehensive reliability analysis method for communication devices and radio propagation is suggested.The proposed methodology is verified by a reliability analysis of a real wireless communication network.展开更多
The absence of control over carriers transport during electrochemical cycling,accompanied by the deterioration of the solid electrolyte interphase(SEI)and the growth of lithium dendrites,has hindered the development o...The absence of control over carriers transport during electrochemical cycling,accompanied by the deterioration of the solid electrolyte interphase(SEI)and the growth of lithium dendrites,has hindered the development of lithium metal batteries.Herein,a separator complexion consisting of polyacrylonitrile(PAN)nanofiber and MIL-101(Cr)particles prepared by electrospinning is proposed to bind the anions from the electrolyte utilizing abundant effective open metal sites in the MIL-101(Cr)particles to modulate the transport of non-effective carriers.The binding effect of the PANM separator promotes uniform lithium metal deposition and enhances the stability of the SEI layer and long cycling stability of ultra-high nickel layered oxide cathodes.Taking PANM as the Li||NCM96 separator enables high-voltage cycling stability,maintaining 72%capacity retention after 800 cycles at a charging and discharging rate of 0.2 C at a cut-off voltage of 4.5 V and 0°C.Meanwhile,the excellent high-rate performance delivers a specific capacity of 156.3 mA h g^(-1) at 10 C.In addition,outstanding cycling performance is realized from−20 to 60°C.The separator engineering facilitates the electrochemical performance of lithium metal batteries and enlightens a facile and promising strategy to develop fast charge/discharge over a wide range of temperatures.展开更多
Emerging technologies of wireless and mobile communication enable people to accumulate a large volume of time-stamped locations,which appear in the form of a continuous moving object trajectory.How to accurately predi...Emerging technologies of wireless and mobile communication enable people to accumulate a large volume of time-stamped locations,which appear in the form of a continuous moving object trajectory.How to accurately predict the uncertain mobility of objects becomes an important and challenging problem.Existing algorithms for trajectory prediction in moving objects databases mainly focus on identifying frequent trajectory patterns,and do not take account of the effect of essential dynamic environmental factors.In this study,a general schema for predicting uncertain trajectories of moving objects with dynamic environment awareness is presented,and the key techniques in trajectory prediction arc addressed in detail.In order to accurately predict the trajectories,a trajectory prediction algorithm based on continuous time Bayesian networks(CTBNs) is improved and applied,which takes dynamic environmental factors into full consideration.Experiments conducted on synthetic trajectory data verify the effectiveness of the improved algorithm,which also guarantees the time performance as well.展开更多
The structural model of sodium silicate glass plays a crucial role in understanding the properties and the nature of binary glass and other more complicated silicate glasses.This work proposes a structural model for s...The structural model of sodium silicate glass plays a crucial role in understanding the properties and the nature of binary glass and other more complicated silicate glasses.This work proposes a structural model for sodium silicate glass based on the medium-range ordering structure of silica glass and the information found from the Na_(2)O-SiO_(2) phase diagram.This new model is different from previous ones.First,the sodium silica glass is both structurally and chemically heterogeneous on the nanometer scale.Secondly,the sodium cation distribution is Na_(2)O concentration-dependent.In order to reflect the structural change with Na_(2)O concentration,it requires two different schematic graphs to present the glass structure.The model can be extended to other binary and multiple component silicate glasses and can be experimentally verified.展开更多
False data injection attacks(FDIAs)can manipulate measurement data from Supervisory Control and Data Acquisition(SCADA)system and threat state estimation in smart grids.Blind FDIAs(BFDIAs)enhance traditional FDIAs,whi...False data injection attacks(FDIAs)can manipulate measurement data from Supervisory Control and Data Acquisition(SCADA)system and threat state estimation in smart grids.Blind FDIAs(BFDIAs)enhance traditional FDIAs,which eliminate the limitation of grasping measurement Jacobian matrix H in advance,but when there are outliers in measurement data,attack performance is degraded.In this paper,improved BFDIAs are proposed.In off-line phase,lowdimensional measurement matrix without outliers calculated by Linear Local Tangent Space Alignment algorithm(LLTSA)is sent into Continuous Deep Belief Network(CDBN)as training data to learn their probability distribution.In on-line phase,real-time low-dimensional measurement matrix with outliers are sent into the trained model as inputs,and outputs are reconstructed by the probability distribution in off-line phase,which eliminates the influence of outliers indirectly.Simulations are implemented on PJM 5-bus and IEEE 14-bus systems to verify the performance of proposed strategy compared with PCA-based BFDIAs.展开更多
基金supported by the Chinese Universities Scientific Fund(ZYGX2020ZB022)the National Natural Science Foundation of China(51775090).
文摘The dynamic wireless communication network is a complex network that needs to consider various influence factors including communication devices,radio propagation,network topology,and dynamic behaviors.Existing works focus on suggesting simplified reliability analysis methods for these dynamic networks.As one of the most popular modeling methodologies,the dynamic Bayesian network(DBN)is proposed.However,it is insufficient for the wireless communication network which contains temporal and non-temporal events.To this end,we present a modeling methodology for a generalized continuous time Bayesian network(CTBN)with a 2-state conditional probability table(CPT).Moreover,a comprehensive reliability analysis method for communication devices and radio propagation is suggested.The proposed methodology is verified by a reliability analysis of a real wireless communication network.
基金financially supported by the National Key Research and Development Program of China(No.2021YFB2400300)the IPE Talent Start-up Program of Institute of Process Engineering of Chinese Academy of Sciences(Grant No.E0293507)。
文摘The absence of control over carriers transport during electrochemical cycling,accompanied by the deterioration of the solid electrolyte interphase(SEI)and the growth of lithium dendrites,has hindered the development of lithium metal batteries.Herein,a separator complexion consisting of polyacrylonitrile(PAN)nanofiber and MIL-101(Cr)particles prepared by electrospinning is proposed to bind the anions from the electrolyte utilizing abundant effective open metal sites in the MIL-101(Cr)particles to modulate the transport of non-effective carriers.The binding effect of the PANM separator promotes uniform lithium metal deposition and enhances the stability of the SEI layer and long cycling stability of ultra-high nickel layered oxide cathodes.Taking PANM as the Li||NCM96 separator enables high-voltage cycling stability,maintaining 72%capacity retention after 800 cycles at a charging and discharging rate of 0.2 C at a cut-off voltage of 4.5 V and 0°C.Meanwhile,the excellent high-rate performance delivers a specific capacity of 156.3 mA h g^(-1) at 10 C.In addition,outstanding cycling performance is realized from−20 to 60°C.The separator engineering facilitates the electrochemical performance of lithium metal batteries and enlightens a facile and promising strategy to develop fast charge/discharge over a wide range of temperatures.
基金supported by the National Natural Science Foundation of China (Nos.61100045,61165013,61003142,60902023,and 61171096)the China Postdoctoral Science Foundation (Nos.20090461346,201104697)+3 种基金the Youth Foundation for Humanities and Social Sciences of Ministry of Education of China (No.10YJCZH117)the Fundamental Research Funds for the Central Universities (Nos.SWJTU09CX035,SWJTU11ZT08)Zhejiang Provincial Natural Science Foundation of China (Nos.Y1100589,Y1080123)the Natural Science Foundation of Ningbo,China (No.2011A610175)
文摘Emerging technologies of wireless and mobile communication enable people to accumulate a large volume of time-stamped locations,which appear in the form of a continuous moving object trajectory.How to accurately predict the uncertain mobility of objects becomes an important and challenging problem.Existing algorithms for trajectory prediction in moving objects databases mainly focus on identifying frequent trajectory patterns,and do not take account of the effect of essential dynamic environmental factors.In this study,a general schema for predicting uncertain trajectories of moving objects with dynamic environment awareness is presented,and the key techniques in trajectory prediction arc addressed in detail.In order to accurately predict the trajectories,a trajectory prediction algorithm based on continuous time Bayesian networks(CTBNs) is improved and applied,which takes dynamic environmental factors into full consideration.Experiments conducted on synthetic trajectory data verify the effectiveness of the improved algorithm,which also guarantees the time performance as well.
基金the Office of Science,Office of Basic Energy Sciences,of the U.S.Department of Energy under Contract No.DE-AC02-05CH11231.
文摘The structural model of sodium silicate glass plays a crucial role in understanding the properties and the nature of binary glass and other more complicated silicate glasses.This work proposes a structural model for sodium silicate glass based on the medium-range ordering structure of silica glass and the information found from the Na_(2)O-SiO_(2) phase diagram.This new model is different from previous ones.First,the sodium silica glass is both structurally and chemically heterogeneous on the nanometer scale.Secondly,the sodium cation distribution is Na_(2)O concentration-dependent.In order to reflect the structural change with Na_(2)O concentration,it requires two different schematic graphs to present the glass structure.The model can be extended to other binary and multiple component silicate glasses and can be experimentally verified.
基金supported by the Funds of the National Key Research and Development Program of China(Grant No.2020YFE0201100)the Funds of National Science of China(Grant nos.61973062,61973068)the Fundamental Research Funds for the Central Universities(Grant nos.N2004010,N2104021,N182008004).
文摘False data injection attacks(FDIAs)can manipulate measurement data from Supervisory Control and Data Acquisition(SCADA)system and threat state estimation in smart grids.Blind FDIAs(BFDIAs)enhance traditional FDIAs,which eliminate the limitation of grasping measurement Jacobian matrix H in advance,but when there are outliers in measurement data,attack performance is degraded.In this paper,improved BFDIAs are proposed.In off-line phase,lowdimensional measurement matrix without outliers calculated by Linear Local Tangent Space Alignment algorithm(LLTSA)is sent into Continuous Deep Belief Network(CDBN)as training data to learn their probability distribution.In on-line phase,real-time low-dimensional measurement matrix with outliers are sent into the trained model as inputs,and outputs are reconstructed by the probability distribution in off-line phase,which eliminates the influence of outliers indirectly.Simulations are implemented on PJM 5-bus and IEEE 14-bus systems to verify the performance of proposed strategy compared with PCA-based BFDIAs.