The primary focus of this paper is to design a progressive restoration plan for an enterprise data center environment following a partial or full disruption. Repairing and restoring disrupted components in an enterpri...The primary focus of this paper is to design a progressive restoration plan for an enterprise data center environment following a partial or full disruption. Repairing and restoring disrupted components in an enterprise data center requires a significant amount of time and human effort. Following a major disruption, the recovery process involves multiple stages, and during each stage, the partially recovered infrastructures can provide limited services to users at some degraded service level. However, how fast and efficiently an enterprise infrastructure can be recovered de- pends on how the recovery mechanism restores the disrupted components, considering the inter-dependencies between services, along with the limitations of expert human operators. The entire problem turns out to be NP- hard and rather complex, and we devise an efficient meta-heuristic to solve the problem. By considering some real-world examples, we show that the proposed meta-heuristic provides very accurate results, and still runs 600-2800 times faster than the optimal solution obtained from a general purpose mathematical solver [1].展开更多
考察了采油螺杆泵(PCP)定子橡胶需要具备的力学性能,并在此基础上,采用遗传神经网络方法,在Matlab软件中编程实现基于遗传算法优化的误差反向传播神经网络算法,建立了PCP定子橡胶配方组分与力学性能之间的优化模型。采用遗传神经网络模...考察了采油螺杆泵(PCP)定子橡胶需要具备的力学性能,并在此基础上,采用遗传神经网络方法,在Matlab软件中编程实现基于遗传算法优化的误差反向传播神经网络算法,建立了PCP定子橡胶配方组分与力学性能之间的优化模型。采用遗传神经网络模型对PCP定子橡胶的力学性能进行预测,并与实测值进行对比。结果表明,PCP定子橡胶配方组分与力学性能之间的优化模型的参数设置为:权值初始化范围为[-1,1],种群大小为50,最大进化代数为100,选择率为0.09,交叉率为0.6,变异率为0.05。当NBR用量为197份、硫黄用量为4份、促进剂CZ用量为1.5份、炭黑用量为20份时,PCP定子橡胶的拉伸强度可达27.5 MPa,扯断伸长率为710%,撕裂强度为158 k N/m。应用遗传神经网络对PCP定子橡胶配方的优化设计取得了较好的效果。PCP定子橡胶的力学性能的实测值与遗传神经网络模型的预测值相对误差控制在±5%以内。展开更多
文摘The primary focus of this paper is to design a progressive restoration plan for an enterprise data center environment following a partial or full disruption. Repairing and restoring disrupted components in an enterprise data center requires a significant amount of time and human effort. Following a major disruption, the recovery process involves multiple stages, and during each stage, the partially recovered infrastructures can provide limited services to users at some degraded service level. However, how fast and efficiently an enterprise infrastructure can be recovered de- pends on how the recovery mechanism restores the disrupted components, considering the inter-dependencies between services, along with the limitations of expert human operators. The entire problem turns out to be NP- hard and rather complex, and we devise an efficient meta-heuristic to solve the problem. By considering some real-world examples, we show that the proposed meta-heuristic provides very accurate results, and still runs 600-2800 times faster than the optimal solution obtained from a general purpose mathematical solver [1].
文摘考察了采油螺杆泵(PCP)定子橡胶需要具备的力学性能,并在此基础上,采用遗传神经网络方法,在Matlab软件中编程实现基于遗传算法优化的误差反向传播神经网络算法,建立了PCP定子橡胶配方组分与力学性能之间的优化模型。采用遗传神经网络模型对PCP定子橡胶的力学性能进行预测,并与实测值进行对比。结果表明,PCP定子橡胶配方组分与力学性能之间的优化模型的参数设置为:权值初始化范围为[-1,1],种群大小为50,最大进化代数为100,选择率为0.09,交叉率为0.6,变异率为0.05。当NBR用量为197份、硫黄用量为4份、促进剂CZ用量为1.5份、炭黑用量为20份时,PCP定子橡胶的拉伸强度可达27.5 MPa,扯断伸长率为710%,撕裂强度为158 k N/m。应用遗传神经网络对PCP定子橡胶配方的优化设计取得了较好的效果。PCP定子橡胶的力学性能的实测值与遗传神经网络模型的预测值相对误差控制在±5%以内。
