Recently published results of field and laboratory experiments on the seismic/acoustic response to injection of direct current （DC） pulses into the Earth crust or stressed rock samples raised a question on a possibi...Recently published results of field and laboratory experiments on the seismic/acoustic response to injection of direct current （DC） pulses into the Earth crust or stressed rock samples raised a question on a possibility of electrical earthquake triggering. A physical mechanism of the considered phenomenon is not clear yet in view of the very low current density （10-7-10-s A/m^2） generated by the pulsed power systems at the epicenter depth （5-10 km） of local earthquakes occurred just after the current injection. The paper describes results of laboratory ＂earthquake＂ triggering by DC pulses under conditions of a spring-block model simulated the seismogenic fault. It is experimentally shown that the electric triggering of the laboratory ＂earthquake＂ （sharp slip of a movable block of the spring-block system） is possible only within a range of subcritical state of the system, when the shear stress between the movable and fixed blocks obtains 0.98-0.99 of its critical value. The threshold of electric triggering action is about 20 A/m^2 that is 7-8 orders of magnitude higher than estimated electric current density for Bishkek test site （Northern Tien Shan, Kirghizia） where the seismic response to the man-made electric action was observed. In this connection, the electric triggering phenomena may be explained by contraction of electric current in the narrow conductive areas of the faults and the corresponding increase in current density or by involving the secondary triggering mechanisms like electromagnetic stimulation of conductive fluid migration into the fault area resulted in decrease in the fault strength properties.展开更多
提出一种具有自适应预测时域的输入重构弹性自触发模型预测控制(self-triggered model predictive control,ST-MPC)算法,平衡机器人系统网络安全和资源受限之间的矛盾.首先,基于自触发非周期采样特征和虚假数据注入(false data injectio...提出一种具有自适应预测时域的输入重构弹性自触发模型预测控制(self-triggered model predictive control,ST-MPC)算法,平衡机器人系统网络安全和资源受限之间的矛盾.首先,基于自触发非周期采样特征和虚假数据注入(false data injection,FDI)攻击模型设计输入重构机制,确保机器人系统可快速重构,能削弱FDI攻击影响的可行控制序列.其次,结合输入重构机制设计关键数据选取条件和预测时域调节机制,从实现最大化触发间隔和降低优化问题复杂度两个方面降低资源消耗.然后,基于输入重构和预测时域调节机制设计弹性ST-MPC镇定控制算法,并推导FDI攻击下算法的可行性和闭环系统稳定性条件.最后,通过仿真实验验证所提出算法能够在抵御FDI攻击前提下保持较好的控制性能及资源利用率.展开更多
An extraordinary earthquake swarm occurred at Rushan on the Jiaodong Peninsula from October 1, 2013, onwards, and more than 12,000 aftershocks had been detected by December 31, 2015. All the activities of the whole sw...An extraordinary earthquake swarm occurred at Rushan on the Jiaodong Peninsula from October 1, 2013, onwards, and more than 12,000 aftershocks had been detected by December 31, 2015. All the activities of the whole swarm were recorded at the nearest station, RSH, which is located about 12 km from the epicenter. We examine the statistical characteristics of the Rushan swarm in this paper using RSH station data to assess the arrival time difference, ts p, of Pg and Sg phases. A temporary network comprising 18 seismometers was set up on May 6, 2014, within the area of the epicenter; based on the data from this network and use of the double difference method, we determine precise hypocenter locations. As the distribution of relocated sources reveals migration of seismic activity, we applied the mean-shift cluster method to perform clustering analysis on relocated catalogs. The results of this study show that there were at least 16 clusters of seismic activities between May 6, 2014, and June 30, 2014, and that each was characterized by a hypocenter spreading process. We estimated the hydraulic diffusivity, D, of each cluster using envelope curve fitting; the results show that D values range between 1.2 and 3.5 m2/d and that approximate values for clusters on the edge of the source area are lower than those within the central area. We utilize an epidemic-type aftershock sequence （ETAS） model to separate external triggered events from self-excited aftershocks within the Rushan swarm. The estimated parameters for this model suggest that α = 1.156, equiva- lent to sequences induced by fluid-injection, and that the forcing rate （μ） implies just 0.15 events per day. These estimates indicate that around 3% of the events within the swarm were externally triggered. The fact that variation in μ is synchronous with swarm activity implies that pulses in fluid pressure likely drove this series of earthquakes.展开更多
Energy consumption is the core issue in wireless sensor networks (WSN). To generate a node energy model that can accurately reveal the energy consumption of sensor nodes is an extremely important part of protocol deve...Energy consumption is the core issue in wireless sensor networks (WSN). To generate a node energy model that can accurately reveal the energy consumption of sensor nodes is an extremely important part of protocol development, system design and performance evaluation in WSNs. In this paper, by studying component energy consumption in different node states and within state transitions, the authors present the energy models of the node core components, including processors, RF modules and sensors. Furthermore, this paper reveals the energy correlations between node components, and then establishes the node energy model based on the event-trigger mechanism. Finally, the authors simulate the energy models of node components and then evaluate the energy consumption of network protocols based on this node energy model. The proposed model can be used to analyze the WSNs energy consumption, to evaluate communication protocols, to deploy nodes and then to construct WSN applications.展开更多
基金funded by Russian Foundation for Basic Research according to research project No.15-55-53104National Natural Science Foundation of China according to International cooperation project No.41511130032
文摘Recently published results of field and laboratory experiments on the seismic/acoustic response to injection of direct current （DC） pulses into the Earth crust or stressed rock samples raised a question on a possibility of electrical earthquake triggering. A physical mechanism of the considered phenomenon is not clear yet in view of the very low current density （10-7-10-s A/m^2） generated by the pulsed power systems at the epicenter depth （5-10 km） of local earthquakes occurred just after the current injection. The paper describes results of laboratory ＂earthquake＂ triggering by DC pulses under conditions of a spring-block model simulated the seismogenic fault. It is experimentally shown that the electric triggering of the laboratory ＂earthquake＂ （sharp slip of a movable block of the spring-block system） is possible only within a range of subcritical state of the system, when the shear stress between the movable and fixed blocks obtains 0.98-0.99 of its critical value. The threshold of electric triggering action is about 20 A/m^2 that is 7-8 orders of magnitude higher than estimated electric current density for Bishkek test site （Northern Tien Shan, Kirghizia） where the seismic response to the man-made electric action was observed. In this connection, the electric triggering phenomena may be explained by contraction of electric current in the narrow conductive areas of the faults and the corresponding increase in current density or by involving the secondary triggering mechanisms like electromagnetic stimulation of conductive fluid migration into the fault area resulted in decrease in the fault strength properties.
文摘提出一种具有自适应预测时域的输入重构弹性自触发模型预测控制(self-triggered model predictive control,ST-MPC)算法,平衡机器人系统网络安全和资源受限之间的矛盾.首先,基于自触发非周期采样特征和虚假数据注入(false data injection,FDI)攻击模型设计输入重构机制,确保机器人系统可快速重构,能削弱FDI攻击影响的可行控制序列.其次,结合输入重构机制设计关键数据选取条件和预测时域调节机制,从实现最大化触发间隔和降低优化问题复杂度两个方面降低资源消耗.然后,基于输入重构和预测时域调节机制设计弹性ST-MPC镇定控制算法,并推导FDI攻击下算法的可行性和闭环系统稳定性条件.最后,通过仿真实验验证所提出算法能够在抵御FDI攻击前提下保持较好的控制性能及资源利用率.
基金supported financially by the Science and Technology Development Plan Project of Shandong Province(2014GSF120007)Shandong Earthquake Agency,China Earthquake Administration(SD1250501)
文摘An extraordinary earthquake swarm occurred at Rushan on the Jiaodong Peninsula from October 1, 2013, onwards, and more than 12,000 aftershocks had been detected by December 31, 2015. All the activities of the whole swarm were recorded at the nearest station, RSH, which is located about 12 km from the epicenter. We examine the statistical characteristics of the Rushan swarm in this paper using RSH station data to assess the arrival time difference, ts p, of Pg and Sg phases. A temporary network comprising 18 seismometers was set up on May 6, 2014, within the area of the epicenter; based on the data from this network and use of the double difference method, we determine precise hypocenter locations. As the distribution of relocated sources reveals migration of seismic activity, we applied the mean-shift cluster method to perform clustering analysis on relocated catalogs. The results of this study show that there were at least 16 clusters of seismic activities between May 6, 2014, and June 30, 2014, and that each was characterized by a hypocenter spreading process. We estimated the hydraulic diffusivity, D, of each cluster using envelope curve fitting; the results show that D values range between 1.2 and 3.5 m2/d and that approximate values for clusters on the edge of the source area are lower than those within the central area. We utilize an epidemic-type aftershock sequence （ETAS） model to separate external triggered events from self-excited aftershocks within the Rushan swarm. The estimated parameters for this model suggest that α = 1.156, equiva- lent to sequences induced by fluid-injection, and that the forcing rate （μ） implies just 0.15 events per day. These estimates indicate that around 3% of the events within the swarm were externally triggered. The fact that variation in μ is synchronous with swarm activity implies that pulses in fluid pressure likely drove this series of earthquakes.
文摘Energy consumption is the core issue in wireless sensor networks (WSN). To generate a node energy model that can accurately reveal the energy consumption of sensor nodes is an extremely important part of protocol development, system design and performance evaluation in WSNs. In this paper, by studying component energy consumption in different node states and within state transitions, the authors present the energy models of the node core components, including processors, RF modules and sensors. Furthermore, this paper reveals the energy correlations between node components, and then establishes the node energy model based on the event-trigger mechanism. Finally, the authors simulate the energy models of node components and then evaluate the energy consumption of network protocols based on this node energy model. The proposed model can be used to analyze the WSNs energy consumption, to evaluate communication protocols, to deploy nodes and then to construct WSN applications.
