为实现网络控制系统(Networked Control Systems,NCS)中重放攻击的检测,在现有研究利用物理水印检测重放攻击的启发下,设计了利用主动丢包对重放攻击进行实时检测的方法 .首先,在理论层面上,利用系统输出的残差构建检测函数,并通过受攻...为实现网络控制系统(Networked Control Systems,NCS)中重放攻击的检测,在现有研究利用物理水印检测重放攻击的启发下,设计了利用主动丢包对重放攻击进行实时检测的方法 .首先,在理论层面上,利用系统输出的残差构建检测函数,并通过受攻击前后检测函数的变化,证明该检测方法的有效性.然后,以一辆四轮汽车为被控对象,比较车辆受攻击前后速度与检测函数的变化.最后,综合考虑车辆对重放攻击的检测结果与速度跟踪结果,确定车辆的最优主动丢包率的范围区间.结果表明:加入主动丢包前,车辆受到重放攻击时,速度会发生剧烈变化而检测函数几乎没有变化;加入主动丢包后,车辆受到重放攻击时,速度剧烈变化的同时检测函数也产生了剧烈的变化;主动丢包率为12%~16%时,系统既能够准确地检测出重放攻击,又能够保证车辆平稳行驶,为后续的重放攻击检测研究提供了参考.展开更多
The resilient modulus(MR)of subgrade soils is usually used to characterize the stiffness of subgrade and is a crucial parameter in pavement design.In order to determine the resilient modulus of compacted subgrade soil...The resilient modulus(MR)of subgrade soils is usually used to characterize the stiffness of subgrade and is a crucial parameter in pavement design.In order to determine the resilient modulus of compacted subgrade soils quickly and accurately,an optimized artificial neural network(ANN)approach based on the multi-population genetic algorithm(MPGA)was proposed in this study.The MPGA overcomes the problems of the traditional ANN such as low efficiency,local optimum and over-fitting.The developed optimized ANN method consists of ten input variables,twenty-one hidden neurons,and one output variable.The physical properties(liquid limit,plastic limit,plasticity index,0.075 mm passing percentage,maximum dry density,optimum moisture content),state variables(degree of compaction,moisture content)and stress variables(confining pressure,deviatoric stress)of subgrade soils were selected as input variables.The MR was directly used as the output variable.Then,adopting a large amount of experimental data from existing literature,the developed optimized ANN method was compared with the existing representative estimation methods.The results show that the developed optimized ANN method has the advantages of fast speed,strong generalization ability and good accuracy in MR estimation.展开更多
文摘为实现网络控制系统(Networked Control Systems,NCS)中重放攻击的检测,在现有研究利用物理水印检测重放攻击的启发下,设计了利用主动丢包对重放攻击进行实时检测的方法 .首先,在理论层面上,利用系统输出的残差构建检测函数,并通过受攻击前后检测函数的变化,证明该检测方法的有效性.然后,以一辆四轮汽车为被控对象,比较车辆受攻击前后速度与检测函数的变化.最后,综合考虑车辆对重放攻击的检测结果与速度跟踪结果,确定车辆的最优主动丢包率的范围区间.结果表明:加入主动丢包前,车辆受到重放攻击时,速度会发生剧烈变化而检测函数几乎没有变化;加入主动丢包后,车辆受到重放攻击时,速度剧烈变化的同时检测函数也产生了剧烈的变化;主动丢包率为12%~16%时,系统既能够准确地检测出重放攻击,又能够保证车辆平稳行驶,为后续的重放攻击检测研究提供了参考.
基金Project(51878078)supported by the National Natural Science Foundation of ChinaProject(2018-025)supported by the Training Program for High-level Technical Personnel in Transportation Industry,ChinaProject(CTKY-PTRC-2018-003)supported by the Design Theory,Method and Demonstration of Durability Asphalt Pavement Based on Heavy-duty Traffic Conditions in Shanghai Area,China。
文摘The resilient modulus(MR)of subgrade soils is usually used to characterize the stiffness of subgrade and is a crucial parameter in pavement design.In order to determine the resilient modulus of compacted subgrade soils quickly and accurately,an optimized artificial neural network(ANN)approach based on the multi-population genetic algorithm(MPGA)was proposed in this study.The MPGA overcomes the problems of the traditional ANN such as low efficiency,local optimum and over-fitting.The developed optimized ANN method consists of ten input variables,twenty-one hidden neurons,and one output variable.The physical properties(liquid limit,plastic limit,plasticity index,0.075 mm passing percentage,maximum dry density,optimum moisture content),state variables(degree of compaction,moisture content)and stress variables(confining pressure,deviatoric stress)of subgrade soils were selected as input variables.The MR was directly used as the output variable.Then,adopting a large amount of experimental data from existing literature,the developed optimized ANN method was compared with the existing representative estimation methods.The results show that the developed optimized ANN method has the advantages of fast speed,strong generalization ability and good accuracy in MR estimation.
