Delay aware routing is now widely used to provide efficient network transmission. However, for newly developing or developed mobile communication networks(MCN), only limited delay data can be obtained. In such a netwo...Delay aware routing is now widely used to provide efficient network transmission. However, for newly developing or developed mobile communication networks(MCN), only limited delay data can be obtained. In such a network, the delay is with epistemic uncertainty, which makes the traditional routing scheme based on deterministic theory or probability theory not applicable. Motivated by this problem, the MCN with epistemic uncertainty is first summarized as a dynamic uncertain network based on uncertainty theory, which is widely applied to model epistemic uncertainties. Then by modeling the uncertain end-toend delay, a new delay bounded routing scheme is proposed to find the path with the maximum belief degree that satisfies the delay threshold for the dynamic uncertain network. Finally, a lowEarth-orbit satellite communication network(LEO-SCN) is used as a case to verify the effectiveness of our routing scheme. It is first modeled as a dynamic uncertain network, and then the delay bounded paths with the maximum belief degree are computed and compared under different delay thresholds.展开更多
Beamspace super-resolution methods for elevation estimation in multipath environment has attracted significant attention, especially the beamspace maximum likelihood(BML)algorithm. However, the difference beam is rare...Beamspace super-resolution methods for elevation estimation in multipath environment has attracted significant attention, especially the beamspace maximum likelihood(BML)algorithm. However, the difference beam is rarely used in superresolution methods, especially in low elevation estimation. The target airspace information in the difference beam is different from the target airspace information in the sum beam. And the use of difference beams does not significantly increase the complexity of the system and algorithms. Thus, this paper applies the difference beam to the beamformer to improve the elevation estimation performance of BML algorithm. And the direction and number of beams can be adjusted according to the actual needs. The theoretical target elevation angle root means square error(RMSE) and the computational complexity of the proposed algorithms are analyzed. Finally, computer simulations and real data processing results demonstrate the effectiveness of the proposed algorithms.展开更多
Using flexible damping technology to improve tunnel lining structure is an emerging method to resist earthquake disasters,and several methods have been explored to predict mechanical response of tunnel lining with dam...Using flexible damping technology to improve tunnel lining structure is an emerging method to resist earthquake disasters,and several methods have been explored to predict mechanical response of tunnel lining with damping layer.However,the traditional numerical methods suffer from the complex modelling and time-consuming problems.Therefore,a prediction model named the random forest regressor(RFR)is proposed based on 240 numerical simulation results of the mechanical response of tunnel lining.In addition,circle mapping(CM)is used to improve Archimedes optimization algorithm(AOA),reptile search algorithm(RSA),and Chernobyl disaster optimizer(CDO)to further improve the predictive performance of the RFR model.The performance evaluation results show that the CMRSA-RFR is the best prediction model.The damping layer thickness is the most important feature for predicting the maximum principal stress of tunnel lining containing damping layer.This study verifies the feasibility of combining numerical simulation with machine learning technology,and provides a new solution for predicting the mechanical response of aseismic tunnel with damping layer.展开更多
极端灾害将导致电力系统与通信系统同时发生大面积瘫痪,基于“保电救灾、通信先行”原则,优先恢复故障通信是支撑保障电力系统安全运行的关键。该文针对灾后电力通信高效恢复问题,提出了一种计及电力侧状态感知需求与运行调控能力保障...极端灾害将导致电力系统与通信系统同时发生大面积瘫痪,基于“保电救灾、通信先行”原则,优先恢复故障通信是支撑保障电力系统安全运行的关键。该文针对灾后电力通信高效恢复问题,提出了一种计及电力侧状态感知需求与运行调控能力保障的增广路径最大流电力通信网络恢复算法。首先考虑到电力通信系统与电力物理系统的紧耦合特性,设计了计及电力侧影响的状态感知与运行调控能力量化指标,辨识关键信息节点。然后,通过图论最大流理论,搜寻关键信息节点的增广路径集。在此基础上,引入恢复贡献度,从增广路径集中选择具有大容量、低延时以及少故障链路的通信路径进行优先重建,为灾后电力系统快速恢复过程提供可达、可靠的通信支撑。最后,以IEEE39标准系统作为仿真算例,验证了所提恢复策略下的通信系统具有更高的通信服务质量(quality of service,QoS),避免了在恢复过程中由于带宽容量不足而发生业务频繁掉线风险。展开更多
光伏阵列P-U特性曲线在局部遮阴状态下呈现多峰状态,传统的最大功率追踪算法容易陷入局部最优状态。针对此问题,提出了一种基于改进麻雀搜索算法的最大功率点跟踪(maximum power point tracking,MPPT)方法。在麻雀搜索算法中引入遗传算...光伏阵列P-U特性曲线在局部遮阴状态下呈现多峰状态,传统的最大功率追踪算法容易陷入局部最优状态。针对此问题,提出了一种基于改进麻雀搜索算法的最大功率点跟踪(maximum power point tracking,MPPT)方法。在麻雀搜索算法中引入遗传算法和Lévy飞行策略,使算法的全局搜索能力得以增强,并且可以跳出局部最优解。在MATLAB/Simulink中建立仿真模型,并与粒子群优化算法和原始麻雀搜索算法进行比较。仿真结果表明,基于改进麻雀搜索算法的MPPT方法在不同光照条件下均显示出更高的效率和稳定性。展开更多
针对最大功率点跟踪(Maximum power point tracking, MPPT)算法中传统滑模控制存在收敛速度慢、抖振显著等不足,提出一种基于RBF神经网络的光伏系统非线性反步积分滑模(Nonlinear backstepping integral sliding mode control, NBISMC)...针对最大功率点跟踪(Maximum power point tracking, MPPT)算法中传统滑模控制存在收敛速度慢、抖振显著等不足,提出一种基于RBF神经网络的光伏系统非线性反步积分滑模(Nonlinear backstepping integral sliding mode control, NBISMC)最大功率点跟踪策略。首先,采用RBF神经网络对各种气象条件下的光伏电池输出电压进行预测;其次,设计非线性积分滑模面以改善传统滑模控制存在稳态误差及超调量大的问题;最后,设计新型指数趋近律,在加快收敛速度的同时有效削弱了系统高频抖振;通过Lyapunov函数分析非线性反步积分滑模控制的可达性与稳定性,并在静态、动态和遮光条件下进行仿真试验。仿真试验结果表明,在温度和光照强度发生变化的工况下,相比于传统滑模控制,基于RBF神经网络的非线性反步积分滑模控制能在各种气象条件下快速、准确地跟踪光伏系统最大功率点,具有较强的鲁棒性。展开更多
基金National Natural Science Foundation of China (61773044,62073009)National key Laboratory of Science and Technology on Reliability and Environmental Engineering(WDZC2019601A301)。
文摘Delay aware routing is now widely used to provide efficient network transmission. However, for newly developing or developed mobile communication networks(MCN), only limited delay data can be obtained. In such a network, the delay is with epistemic uncertainty, which makes the traditional routing scheme based on deterministic theory or probability theory not applicable. Motivated by this problem, the MCN with epistemic uncertainty is first summarized as a dynamic uncertain network based on uncertainty theory, which is widely applied to model epistemic uncertainties. Then by modeling the uncertain end-toend delay, a new delay bounded routing scheme is proposed to find the path with the maximum belief degree that satisfies the delay threshold for the dynamic uncertain network. Finally, a lowEarth-orbit satellite communication network(LEO-SCN) is used as a case to verify the effectiveness of our routing scheme. It is first modeled as a dynamic uncertain network, and then the delay bounded paths with the maximum belief degree are computed and compared under different delay thresholds.
基金supported by the Fund for Foreign Scholars in University Research and Teaching Programs (B18039)。
文摘Beamspace super-resolution methods for elevation estimation in multipath environment has attracted significant attention, especially the beamspace maximum likelihood(BML)algorithm. However, the difference beam is rarely used in superresolution methods, especially in low elevation estimation. The target airspace information in the difference beam is different from the target airspace information in the sum beam. And the use of difference beams does not significantly increase the complexity of the system and algorithms. Thus, this paper applies the difference beam to the beamformer to improve the elevation estimation performance of BML algorithm. And the direction and number of beams can be adjusted according to the actual needs. The theoretical target elevation angle root means square error(RMSE) and the computational complexity of the proposed algorithms are analyzed. Finally, computer simulations and real data processing results demonstrate the effectiveness of the proposed algorithms.
基金Project(2023YFB2390400)supported by the National Key R&D Programs for Young Scientists,ChinaProjects(U21A20159,52079133,52379112,52309123,41902288)supported by the National Natural Science Foundation of China+5 种基金Project(2024AFB041)supported by the Hubei Provincial Natural Science Foundation,ChinaProject(QTKS0034W23291)supported by the Key Laboratory of Water Grid Project and Regulation of Ministry of Water Resources,ChinaProject(2023SGG07)supported by the Visiting Researcher Fund Program of State Key Laboratory of Water Resources Engineering and Management,ChinaProject(2022KY56(ZDZX)-02)supported by the Key Research Program of FSDI,ChinaProject(SKS-2022103)supported by the Key Research Program of the Ministry of Water Resources,ChinaProject(202102AF080001)supported by the Yunnan Major Science and Technology Special Program,China。
文摘Using flexible damping technology to improve tunnel lining structure is an emerging method to resist earthquake disasters,and several methods have been explored to predict mechanical response of tunnel lining with damping layer.However,the traditional numerical methods suffer from the complex modelling and time-consuming problems.Therefore,a prediction model named the random forest regressor(RFR)is proposed based on 240 numerical simulation results of the mechanical response of tunnel lining.In addition,circle mapping(CM)is used to improve Archimedes optimization algorithm(AOA),reptile search algorithm(RSA),and Chernobyl disaster optimizer(CDO)to further improve the predictive performance of the RFR model.The performance evaluation results show that the CMRSA-RFR is the best prediction model.The damping layer thickness is the most important feature for predicting the maximum principal stress of tunnel lining containing damping layer.This study verifies the feasibility of combining numerical simulation with machine learning technology,and provides a new solution for predicting the mechanical response of aseismic tunnel with damping layer.
文摘极端灾害将导致电力系统与通信系统同时发生大面积瘫痪,基于“保电救灾、通信先行”原则,优先恢复故障通信是支撑保障电力系统安全运行的关键。该文针对灾后电力通信高效恢复问题,提出了一种计及电力侧状态感知需求与运行调控能力保障的增广路径最大流电力通信网络恢复算法。首先考虑到电力通信系统与电力物理系统的紧耦合特性,设计了计及电力侧影响的状态感知与运行调控能力量化指标,辨识关键信息节点。然后,通过图论最大流理论,搜寻关键信息节点的增广路径集。在此基础上,引入恢复贡献度,从增广路径集中选择具有大容量、低延时以及少故障链路的通信路径进行优先重建,为灾后电力系统快速恢复过程提供可达、可靠的通信支撑。最后,以IEEE39标准系统作为仿真算例,验证了所提恢复策略下的通信系统具有更高的通信服务质量(quality of service,QoS),避免了在恢复过程中由于带宽容量不足而发生业务频繁掉线风险。
文摘光伏阵列P-U特性曲线在局部遮阴状态下呈现多峰状态,传统的最大功率追踪算法容易陷入局部最优状态。针对此问题,提出了一种基于改进麻雀搜索算法的最大功率点跟踪(maximum power point tracking,MPPT)方法。在麻雀搜索算法中引入遗传算法和Lévy飞行策略,使算法的全局搜索能力得以增强,并且可以跳出局部最优解。在MATLAB/Simulink中建立仿真模型,并与粒子群优化算法和原始麻雀搜索算法进行比较。仿真结果表明,基于改进麻雀搜索算法的MPPT方法在不同光照条件下均显示出更高的效率和稳定性。
文摘针对最大功率点跟踪(Maximum power point tracking, MPPT)算法中传统滑模控制存在收敛速度慢、抖振显著等不足,提出一种基于RBF神经网络的光伏系统非线性反步积分滑模(Nonlinear backstepping integral sliding mode control, NBISMC)最大功率点跟踪策略。首先,采用RBF神经网络对各种气象条件下的光伏电池输出电压进行预测;其次,设计非线性积分滑模面以改善传统滑模控制存在稳态误差及超调量大的问题;最后,设计新型指数趋近律,在加快收敛速度的同时有效削弱了系统高频抖振;通过Lyapunov函数分析非线性反步积分滑模控制的可达性与稳定性,并在静态、动态和遮光条件下进行仿真试验。仿真试验结果表明,在温度和光照强度发生变化的工况下,相比于传统滑模控制,基于RBF神经网络的非线性反步积分滑模控制能在各种气象条件下快速、准确地跟踪光伏系统最大功率点,具有较强的鲁棒性。
