基于最小集覆盖理论的拥塞链路推理算法,仅对共享瓶颈链路进行推理,当拥塞路径存在多条链路拥塞时,算法的推理性能急剧下降.针对该问题,提出一种基于贝叶斯最大后验(Bayesian maximum a-posterior,简称BMAP)改进的拉格朗日松弛次梯度推...基于最小集覆盖理论的拥塞链路推理算法,仅对共享瓶颈链路进行推理,当拥塞路径存在多条链路拥塞时,算法的推理性能急剧下降.针对该问题,提出一种基于贝叶斯最大后验(Bayesian maximum a-posterior,简称BMAP)改进的拉格朗日松弛次梯度推理算法(Lagrange relaxation sub-gradient algorithm based on BMAP,简称LRSBMAP).针对推理算法中链路覆盖范围对算法推理性能的影响,以及探针部署及额外E2E路径探测发包的开销问题,提出设置度阈值(degree threshold value,简称DTV)参数预选待测IP网络收发包路由器节点,通过引入优选系数?,在保证链路覆盖范围的基础上,兼顾开销问题,确保算法的推理性能.针对大规模IP网络多链路拥塞场景下,链路先验概率求解方程组系数矩阵的稀疏性,提出一种对称逐次超松弛(symmetry successive over-relaxation,简称SSOR)分裂预处理共轭梯度法(preconditioned conjugate gradient method based on SSOR,简称PCG_SSOR)求解链路先验概率近似唯一解的方法,防止算法求解失败.实验验证了所提算法的准确性及鲁棒性.展开更多
Leakage is one of the most important reasons for failure of hydraulic systems.The accurate positioning of leakage is of great significance to ensure the safe and reliable operation of hydraulic systems.For early stage...Leakage is one of the most important reasons for failure of hydraulic systems.The accurate positioning of leakage is of great significance to ensure the safe and reliable operation of hydraulic systems.For early stage of leakage,the pressure of the hydraulic circuit does not change obviously and therefore cannot be monitored by pressure sensors.Meanwhile,the pressure of the hydraulic circuit changes frequently due to the influence of load and state of the switch,which further reduces the accuracy of leakage localization.In the work,a novel Bayesian networks(BNs)-based data-driven early leakage localization approach for multi-valve systems is proposed.Wavelet transform is used for signal noise reduction and BNs-based leak localization model is used to identify the location of leakage.A normalization model is developed to improve the robustness of the leakage localization model.A hydraulic system with eight valves is used to demonstrate the application of the proposed early micro-leakage detection and localization approach.展开更多
文摘基于最小集覆盖理论的拥塞链路推理算法,仅对共享瓶颈链路进行推理,当拥塞路径存在多条链路拥塞时,算法的推理性能急剧下降.针对该问题,提出一种基于贝叶斯最大后验(Bayesian maximum a-posterior,简称BMAP)改进的拉格朗日松弛次梯度推理算法(Lagrange relaxation sub-gradient algorithm based on BMAP,简称LRSBMAP).针对推理算法中链路覆盖范围对算法推理性能的影响,以及探针部署及额外E2E路径探测发包的开销问题,提出设置度阈值(degree threshold value,简称DTV)参数预选待测IP网络收发包路由器节点,通过引入优选系数?,在保证链路覆盖范围的基础上,兼顾开销问题,确保算法的推理性能.针对大规模IP网络多链路拥塞场景下,链路先验概率求解方程组系数矩阵的稀疏性,提出一种对称逐次超松弛(symmetry successive over-relaxation,简称SSOR)分裂预处理共轭梯度法(preconditioned conjugate gradient method based on SSOR,简称PCG_SSOR)求解链路先验概率近似唯一解的方法,防止算法求解失败.实验验证了所提算法的准确性及鲁棒性.
基金Project(51779267)supported by the National Natural Science Foundation of ChinaProject(2019YFE0105100)supported by the National Key Research and Development Program of China+2 种基金Project(tsqn201909063)supported by the Taishan Scholars Project,ChinaProject(20CX02301A)supported by the Fundamental Research Funds for the Central Universities,ChinaProject(2019KJB016)supported by the Science and Technology Support Plan for Youth Innovation of Universities in Shandong Province,China。
文摘Leakage is one of the most important reasons for failure of hydraulic systems.The accurate positioning of leakage is of great significance to ensure the safe and reliable operation of hydraulic systems.For early stage of leakage,the pressure of the hydraulic circuit does not change obviously and therefore cannot be monitored by pressure sensors.Meanwhile,the pressure of the hydraulic circuit changes frequently due to the influence of load and state of the switch,which further reduces the accuracy of leakage localization.In the work,a novel Bayesian networks(BNs)-based data-driven early leakage localization approach for multi-valve systems is proposed.Wavelet transform is used for signal noise reduction and BNs-based leak localization model is used to identify the location of leakage.A normalization model is developed to improve the robustness of the leakage localization model.A hydraulic system with eight valves is used to demonstrate the application of the proposed early micro-leakage detection and localization approach.
