In order to solve the problem of property test of large flow-rate safety, the property parameter of safety valve test system was analyzed, and a device for property oflarge flow-rate safety valve test was designed.The...In order to solve the problem of property test of large flow-rate safety, the property parameter of safety valve test system was analyzed, and a device for property oflarge flow-rate safety valve test was designed.The device used accumulators as power source and a united function cylinder, which can realized the large flow-rate output for the test system.Analyzed the test data and made a particular research on the test device by testing different flow-rate safety valves;it verifies that the test device can be used tode-sign larger flow-rate safety valve test system and can make the flow-rate test and analysis and dynamic characteristics for the large-flow safety valve.展开更多
The load-bearing characters of hydraulic-powered roof support with dual telescopic legs were analyzed. With a specific type hydraulic-powered roof support with dual telescopic legs for research object, the inside load...The load-bearing characters of hydraulic-powered roof support with dual telescopic legs were analyzed. With a specific type hydraulic-powered roof support with dual telescopic legs for research object, the inside load test problems in factories was analyzed, and the correct test methods were given, which can enhance the test efficiency and make the factories away from the error design of hydraulic-powered roof supports and legs.展开更多
This paper presented a design of an automatic lifting system. It is used for large load powered support and improves the old method wherein powered support lifting depends on manual control. This system applies a high...This paper presented a design of an automatic lifting system. It is used for large load powered support and improves the old method wherein powered support lifting depends on manual control. This system applies a high accuracy gear shunt motor to match the flow for 4 lifting cylinders, and also allocates bypass throttles to realize automatic lifting. Through the dis- placement sensor feedback the height deviation among 4 lifting cylinders during the whole lifting process, when the deviation is up to the sitting value, the corresponding bypass throttle is operated immediately to reduce the deviation, so that the moving platform of the powered support would not be stuck. Through real application, it is shown that this system can realize automatic lifting of powered support; the lifting speed is controlled between 5 and 10 mm/s, and the final aligning accuracy is up to 1 mm.展开更多
[目的]DDoS攻击作为一种破坏性极强的网络威胁,严重影响电力系统的稳定运行。由于电力监控局域网中的数据流量复杂多变,DDoS攻击流量与正常流量在表现形式上存在较高相似性,导致二者难以有效区分。传统的静态阈值方法虽能在一定程度上...[目的]DDoS攻击作为一种破坏性极强的网络威胁,严重影响电力系统的稳定运行。由于电力监控局域网中的数据流量复杂多变,DDoS攻击流量与正常流量在表现形式上存在较高相似性,导致二者难以有效区分。传统的静态阈值方法虽能在一定程度上实现流量监测,但因无法适应流量的动态变化,常出现误判,从而削弱了对DDoS攻击的检测效果,难以为电力监控局域网提供可靠的安全保障。为此,提出一种基于动态阈值的电力监控局域网DDoS攻击检测方法。[方法]通过网络流量采集设备实时获取电力监控局域网的流量数据,并利用信息熵理论计算流量熵值。信息熵可反映数据的混乱程度:正常流量通常具有一定规律性,熵值相对稳定;而DDoS攻击流量因异常数据包的大量涌入,导致熵值显著波动。基于此特性,本文设定动态阈值,当流量熵值超过阈值时判定为异常流量。随后,提取异常流量的六元组特征集(包括平均流包数、平均字节数、源IP地址增速、流表生存时间变化、端口增速以及对流比),并将其输入预训练的最小二乘支持向量机(least squares support vector machine,LSSVM)分类器中。LSSVM通过对已知样本的学习建立特征与类别的映射关系,从而实现对异常流量的分类与判断,确定其是否为DDoS攻击流量。[结果]实验结果表明,本文方法在ROC曲线和PR曲线上均表现较好,ROC-AUC和PR-AUC值均较传统方法有所提高。这表明该方法在检测DDoS攻击时具备更高的准确率与召回率,能够有效识别隐藏于正常流量中的攻击流量,并显著降低误判率。[结论]基于动态阈值与LSSVM分类器的检测方法能够有效应对电力监控局域网中DDoS攻击与正常流量难以区分的问题,提升检测的准确性与可靠性,为电力监控局域网提供更为有效的DDoS攻击防护手段,有助于增强电力系统的安全性与稳定性,保障电力供应的可靠运行,对电力行业网络安全防护具有重要的实际应用价值。展开更多
基金Supported by China Coal Research Institute Innovation Item(2007CX06)
文摘In order to solve the problem of property test of large flow-rate safety, the property parameter of safety valve test system was analyzed, and a device for property oflarge flow-rate safety valve test was designed.The device used accumulators as power source and a united function cylinder, which can realized the large flow-rate output for the test system.Analyzed the test data and made a particular research on the test device by testing different flow-rate safety valves;it verifies that the test device can be used tode-sign larger flow-rate safety valve test system and can make the flow-rate test and analysis and dynamic characteristics for the large-flow safety valve.
文摘The load-bearing characters of hydraulic-powered roof support with dual telescopic legs were analyzed. With a specific type hydraulic-powered roof support with dual telescopic legs for research object, the inside load test problems in factories was analyzed, and the correct test methods were given, which can enhance the test efficiency and make the factories away from the error design of hydraulic-powered roof supports and legs.
文摘This paper presented a design of an automatic lifting system. It is used for large load powered support and improves the old method wherein powered support lifting depends on manual control. This system applies a high accuracy gear shunt motor to match the flow for 4 lifting cylinders, and also allocates bypass throttles to realize automatic lifting. Through the dis- placement sensor feedback the height deviation among 4 lifting cylinders during the whole lifting process, when the deviation is up to the sitting value, the corresponding bypass throttle is operated immediately to reduce the deviation, so that the moving platform of the powered support would not be stuck. Through real application, it is shown that this system can realize automatic lifting of powered support; the lifting speed is controlled between 5 and 10 mm/s, and the final aligning accuracy is up to 1 mm.
文摘[目的]DDoS攻击作为一种破坏性极强的网络威胁,严重影响电力系统的稳定运行。由于电力监控局域网中的数据流量复杂多变,DDoS攻击流量与正常流量在表现形式上存在较高相似性,导致二者难以有效区分。传统的静态阈值方法虽能在一定程度上实现流量监测,但因无法适应流量的动态变化,常出现误判,从而削弱了对DDoS攻击的检测效果,难以为电力监控局域网提供可靠的安全保障。为此,提出一种基于动态阈值的电力监控局域网DDoS攻击检测方法。[方法]通过网络流量采集设备实时获取电力监控局域网的流量数据,并利用信息熵理论计算流量熵值。信息熵可反映数据的混乱程度:正常流量通常具有一定规律性,熵值相对稳定;而DDoS攻击流量因异常数据包的大量涌入,导致熵值显著波动。基于此特性,本文设定动态阈值,当流量熵值超过阈值时判定为异常流量。随后,提取异常流量的六元组特征集(包括平均流包数、平均字节数、源IP地址增速、流表生存时间变化、端口增速以及对流比),并将其输入预训练的最小二乘支持向量机(least squares support vector machine,LSSVM)分类器中。LSSVM通过对已知样本的学习建立特征与类别的映射关系,从而实现对异常流量的分类与判断,确定其是否为DDoS攻击流量。[结果]实验结果表明,本文方法在ROC曲线和PR曲线上均表现较好,ROC-AUC和PR-AUC值均较传统方法有所提高。这表明该方法在检测DDoS攻击时具备更高的准确率与召回率,能够有效识别隐藏于正常流量中的攻击流量,并显著降低误判率。[结论]基于动态阈值与LSSVM分类器的检测方法能够有效应对电力监控局域网中DDoS攻击与正常流量难以区分的问题,提升检测的准确性与可靠性,为电力监控局域网提供更为有效的DDoS攻击防护手段,有助于增强电力系统的安全性与稳定性,保障电力供应的可靠运行,对电力行业网络安全防护具有重要的实际应用价值。
