Ironmaking process(IP)is indispensable to modern iron and steel industry,where real-time monitoring is crucial for achieving high molten iron quality(MIQ)with low energy consumption.While neural network-based models s...Ironmaking process(IP)is indispensable to modern iron and steel industry,where real-time monitoring is crucial for achieving high molten iron quality(MIQ)with low energy consumption.While neural network-based models show some promising results,they are generally limited by non-negligible drawbacks such as interpretability issues of feature learning.To address these issues,we propose a novel concept based on the shallow-to-deep correlation network representation regression(Sh-to-De CNRR).Our approach,shallow correlation network representation regression(ShCNRR),combines neural network and canonical correlation analysis thoughts to generate explainable features via shallow correlation network representation(CNR).A twin inverse network is then derived to obtain the explicit model output,leveraging the shallow CNR.To capture deeper nonlinear information,we extend ShCNRR into a hierarchical deep correlation network representation regression(DeCNRR)model that features stacked neural networks,enabling us to learn deeper CNR from process data.The feasibility and advantages of our proposals are validated by theoretical derivations and practical IP cases,which contain one MIQ regression and three MIQ-related fault detection tasks.The results reveal that highly fused statistical and neural network models yield superior monitoring performance compared to current state-of-the-art models,while statistical tests verify the convincing feature mining.展开更多
针对传统的IP欺骗攻击缓解方法存在运算开销大、缺乏灵活性等问题,提出了一种基于动态限制策略的软件定义网络(software defined network,SDN)中IP欺骗攻击缓解方法。首先,利用Packet-In消息中三元组信息回溯攻击路径,定位IP欺骗攻击源...针对传统的IP欺骗攻击缓解方法存在运算开销大、缺乏灵活性等问题,提出了一种基于动态限制策略的软件定义网络(software defined network,SDN)中IP欺骗攻击缓解方法。首先,利用Packet-In消息中三元组信息回溯攻击路径,定位IP欺骗攻击源头主机;然后,由控制器制定动态限制策略对连接攻击源头主机的交换机端口的新流转发功能进行限制,待限制期满再恢复其转发新流的功能,限制期的大小随着被检测为攻击源的次数而增长。研究结果表明:这种动态的限制策略可阻隔攻击流进入SDN网络,从而有效避免SDN交换机、控制器以及链路过载;由于在限制期间无需再对这些限制的交换机端口进行实时监测,该方法在应对长时攻击时较传统方法具有更高的缓解效率和更少的资源消耗。展开更多
基金supported in part by the Pioneer Research and Development Program of Zhejiang(2025C01021)Zhejiang Province Postdoctoral Research Project Selection Fund(ZJ2025061)+3 种基金the National Science and Technology Major Project-Intelligent Manufacturing Systems and Robotics of China(2025ZD1602000,2025ZD1601800)the National Natural Science Foundation of China(61933015,62273030,62573387)the Natural Science Foundation of Zhejiang province,China(LY24F030004)the Fundamental Research Funds of Zhejiang Sci-Tech University(25222139-Y)。
文摘Ironmaking process(IP)is indispensable to modern iron and steel industry,where real-time monitoring is crucial for achieving high molten iron quality(MIQ)with low energy consumption.While neural network-based models show some promising results,they are generally limited by non-negligible drawbacks such as interpretability issues of feature learning.To address these issues,we propose a novel concept based on the shallow-to-deep correlation network representation regression(Sh-to-De CNRR).Our approach,shallow correlation network representation regression(ShCNRR),combines neural network and canonical correlation analysis thoughts to generate explainable features via shallow correlation network representation(CNR).A twin inverse network is then derived to obtain the explicit model output,leveraging the shallow CNR.To capture deeper nonlinear information,we extend ShCNRR into a hierarchical deep correlation network representation regression(DeCNRR)model that features stacked neural networks,enabling us to learn deeper CNR from process data.The feasibility and advantages of our proposals are validated by theoretical derivations and practical IP cases,which contain one MIQ regression and three MIQ-related fault detection tasks.The results reveal that highly fused statistical and neural network models yield superior monitoring performance compared to current state-of-the-art models,while statistical tests verify the convincing feature mining.
文摘针对传统的IP欺骗攻击缓解方法存在运算开销大、缺乏灵活性等问题,提出了一种基于动态限制策略的软件定义网络(software defined network,SDN)中IP欺骗攻击缓解方法。首先,利用Packet-In消息中三元组信息回溯攻击路径,定位IP欺骗攻击源头主机;然后,由控制器制定动态限制策略对连接攻击源头主机的交换机端口的新流转发功能进行限制,待限制期满再恢复其转发新流的功能,限制期的大小随着被检测为攻击源的次数而增长。研究结果表明:这种动态的限制策略可阻隔攻击流进入SDN网络,从而有效避免SDN交换机、控制器以及链路过载;由于在限制期间无需再对这些限制的交换机端口进行实时监测,该方法在应对长时攻击时较传统方法具有更高的缓解效率和更少的资源消耗。
