In this research we proposed a strategy for location privacy protection which addresses the issues related with existing location privacy protection techniques. Mix-Zones and pseudonyms are considered as the basic bui...In this research we proposed a strategy for location privacy protection which addresses the issues related with existing location privacy protection techniques. Mix-Zones and pseudonyms are considered as the basic building blocks for location privacy; however, continuously changing pseudonyms process at multiple locations can enhance user privacy. It has been revealed that changing pseudonym at improper time and location may threat to user's privacy. Moreover, certain methods related to pseudonym change have been proposed to attain desirable location privacy and most of these solutions are based upon velocity, GPS position and direction of angle. We analyzed existing methods related to location privacy with mix zones, such as RPCLP, EPCS and MODP, where it has been observed that these methods are not adequate to attain desired level of location privacy and suffered from large number of pseudonym changes. By analyzing limitations of existing methods, we proposed Dynamic Pseudonym based multiple mix zone(DPMM) technique, which ensures highest level of accuracy and privacy. We simulate our data by using SUMO application and analysis results has revealed that DPMM outperformed existing pseudonym change techniques and achieved better results in terms of acquiring high privacy with small number of pseudonym change.展开更多
针对经典提前合流和延迟合流对动态流量适应性差,以及上游速度差导致合流车辆“错位”问题,研究了基于深度强化学习方法的作业区智能网联车(connected and autonomous vehicle,CAV)分段控制合流模型。通过依次进行车速引导、间距创建和...针对经典提前合流和延迟合流对动态流量适应性差,以及上游速度差导致合流车辆“错位”问题,研究了基于深度强化学习方法的作业区智能网联车(connected and autonomous vehicle,CAV)分段控制合流模型。通过依次进行车速引导、间距创建和位置对齐,解决换道期多辆封闭车道合流车辆同时申请汇入1个开放车道间距而导致的汇入冲突和效率降低问题。模型将基于柔性演员-评论家算法的纵向轨迹控制与规则的换道决策相结合,共同优化合流轨迹。其中纵向轨迹优化首先选取自车速度与加速度、前车速度与到其距离、相邻车道前后车速度与到其距离、到合流点距离9个特征作为智能体状态,用以刻画自车所处的局部和全局交通状态;其次以降低加速度幅值及其变化率、避免碰撞、创建合流间距、对齐开放车道间距中心、抑制前后车速度差、按推荐速度引导、增加后车让行为目标,分别从舒适、安全、效率角度构建了作业区分段式奖励函数。特别地,基于目标车道后车速度差构建的效率惩罚性函数,解决了混行交通流合流点停车延误多的问题。仿真结果表明:在中、高流量下,与提前合流、延迟合流和新英格兰合流方法相比,本文模型平均车速和最小碰撞时间分别提升了约4.76%和19.71%,进一步加强了作业区行车效率及安全;此外,在含异质人工驾驶车辆的混行交通下,随着CAV市场渗透率的提高,平均车速、最小碰撞时间和合流成功率均呈增大趋势,且均能实现不停车合流。展开更多
开展了Mach数为1.23和1.41的冲击波作用下的Air/SF6斜界面不稳定性激波管实验,并利用王涛等人发展的可压缩多介质粘性流体和湍流大涡模拟程序MVFT(multi-viscous-fluid and turbulence),对该激波管实验进行了数值模拟,二者相比较一致性...开展了Mach数为1.23和1.41的冲击波作用下的Air/SF6斜界面不稳定性激波管实验,并利用王涛等人发展的可压缩多介质粘性流体和湍流大涡模拟程序MVFT(multi-viscous-fluid and turbulence),对该激波管实验进行了数值模拟,二者相比较一致性较好,包括界面图像、湍流混合区TMZ(turbulent mixing zone)宽度、气泡和尖钉位移,确认了该计算代码对界面不稳定性问题模拟的可靠性和有效性.数值模拟再现了冲击波作用下,Air/SF6斜界面的演化过程及流动中复杂波系结构的发展如冲击波的传播、折射和反射.结果还显示冲击波Mach数较大时,冲击波和界面相互作用时混合区获得的能量也较大,扰动界面发展的也更快.展开更多
基金supported by the National Natural Science Foundation of China (Grant No.61401040,Grant No.61372110)
文摘In this research we proposed a strategy for location privacy protection which addresses the issues related with existing location privacy protection techniques. Mix-Zones and pseudonyms are considered as the basic building blocks for location privacy; however, continuously changing pseudonyms process at multiple locations can enhance user privacy. It has been revealed that changing pseudonym at improper time and location may threat to user's privacy. Moreover, certain methods related to pseudonym change have been proposed to attain desirable location privacy and most of these solutions are based upon velocity, GPS position and direction of angle. We analyzed existing methods related to location privacy with mix zones, such as RPCLP, EPCS and MODP, where it has been observed that these methods are not adequate to attain desired level of location privacy and suffered from large number of pseudonym changes. By analyzing limitations of existing methods, we proposed Dynamic Pseudonym based multiple mix zone(DPMM) technique, which ensures highest level of accuracy and privacy. We simulate our data by using SUMO application and analysis results has revealed that DPMM outperformed existing pseudonym change techniques and achieved better results in terms of acquiring high privacy with small number of pseudonym change.
文摘针对经典提前合流和延迟合流对动态流量适应性差,以及上游速度差导致合流车辆“错位”问题,研究了基于深度强化学习方法的作业区智能网联车(connected and autonomous vehicle,CAV)分段控制合流模型。通过依次进行车速引导、间距创建和位置对齐,解决换道期多辆封闭车道合流车辆同时申请汇入1个开放车道间距而导致的汇入冲突和效率降低问题。模型将基于柔性演员-评论家算法的纵向轨迹控制与规则的换道决策相结合,共同优化合流轨迹。其中纵向轨迹优化首先选取自车速度与加速度、前车速度与到其距离、相邻车道前后车速度与到其距离、到合流点距离9个特征作为智能体状态,用以刻画自车所处的局部和全局交通状态;其次以降低加速度幅值及其变化率、避免碰撞、创建合流间距、对齐开放车道间距中心、抑制前后车速度差、按推荐速度引导、增加后车让行为目标,分别从舒适、安全、效率角度构建了作业区分段式奖励函数。特别地,基于目标车道后车速度差构建的效率惩罚性函数,解决了混行交通流合流点停车延误多的问题。仿真结果表明:在中、高流量下,与提前合流、延迟合流和新英格兰合流方法相比,本文模型平均车速和最小碰撞时间分别提升了约4.76%和19.71%,进一步加强了作业区行车效率及安全;此外,在含异质人工驾驶车辆的混行交通下,随着CAV市场渗透率的提高,平均车速、最小碰撞时间和合流成功率均呈增大趋势,且均能实现不停车合流。
文摘开展了Mach数为1.23和1.41的冲击波作用下的Air/SF6斜界面不稳定性激波管实验,并利用王涛等人发展的可压缩多介质粘性流体和湍流大涡模拟程序MVFT(multi-viscous-fluid and turbulence),对该激波管实验进行了数值模拟,二者相比较一致性较好,包括界面图像、湍流混合区TMZ(turbulent mixing zone)宽度、气泡和尖钉位移,确认了该计算代码对界面不稳定性问题模拟的可靠性和有效性.数值模拟再现了冲击波作用下,Air/SF6斜界面的演化过程及流动中复杂波系结构的发展如冲击波的传播、折射和反射.结果还显示冲击波Mach数较大时,冲击波和界面相互作用时混合区获得的能量也较大,扰动界面发展的也更快.
