Flexible Deterministic Packet Marking: An IP Traceback System to Find the Real Source of Attacks

IP traceback is the enabling technology to control Internet crime. In this paper we present a novel and practical IP traceback system called Flexible Deterministic Packet Marking (FDPM) which provides a defense system with the ability to find out the real sources of attacking packets that traverse through the network. While a number of other traceback schemes exist, FDPM provides innovative features to trace the source of IP packets and can obtain better tracing capability than others. In particular, FDPM adopts a flexible mark length strategy to make it compatible to different network environments; it also adaptively changes its marking rate according to the load of the participating router by a flexible flow-based marking scheme. Evaluations on both simulation and real system implementation demonstrate that FDPM requires a moderately small number of packets to complete the traceback process; add little additional load to routers and can trace a large number of sources in one traceback process with low false positive rates. The built-in overload prevention mechanism makes this system capable of achieving a satisfactory traceback result even when the router is heavily loaded. It has been used to not only trace DDoS attacking packets but also enhance filtering attacking traffic.     

老化SBS改性沥青二次改性再生工艺及机理研究

为研究老化SBS改性沥青的二次改性再生效果,比较不同工艺对再生效果的影响,首先通过常规试验、SHRP试验选择恰当的SBS改性沥青老化模拟方式,然后对添加SBS改性剂、新沥青进行再生的3种工艺展开研究,并借助红外光谱试验分析不同掺加顺序对老化SBS改性沥青再生效果的影响,确定了改性剂及新、旧沥青的最佳比例和最佳工艺。结果表明:采用RTFOT试验进行老化模拟更加方便、科学,RTFOT试验5h获得的改性沥青指标与服务年龄为7年的SBS改性沥青一致;SBS改性剂、新沥青添加顺序与老化沥青再生效果关系密切,先将SBS改性剂和新沥青混合制备改性沥青,再将改性沥青与老化沥青混合的工艺可使再生沥青性能最佳,其中SBS最佳掺量为4%,新、旧沥青质量比为2。     

Li+增强Ho3+/Yb3+:Y2O3纳米晶的上转化发光研究

采用凝胶溶胶法制备了不同浓度的Y2O3:Ho3+/Yb3+/Li+纳米晶,并且系统研究了不同掺杂浓度对上转化荧光现象的影响。首先,通过XRD图形判断了晶体结构的生成,再通过980 nm的激光器和荧光光谱仪测得的光谱图,发现了位于520~579 nm的绿光、635~674 nm的红光等两条很强的可见光,还有一条较弱的位于743~775 nm的近红外发光。最后,通过与能级图比较和分析可以得出:它们分别是5F4/5S2→5I8,5F5→5I8和5F4/5S2→5I7荧光跃迁。可以看出:在掺杂入Li+之后,上转化荧光得到了极大增强。     

Visual privacy attacks and defenses in deep learning: a survey

The concerns on visual privacy have been increasingly raised along with the dramatic growth in image and video capture and sharing. Meanwhile, with the recent breakthrough in deep learning technologies, visual data can now be easily gathered and processed to infer sensitive information. Therefore, visual privacy in the context of deep learning is now an important and challenging topic. However, there has been no systematic study on this topic to date. In this survey, we discuss algorithms of visual privacy attacks and the corresponding defense mechanisms in deep learning. We analyze the privacy issues in both visual data and visual deep learning systems. We show that deep learning can be used as a powerful privacy attack tool as well as preservation techniques with great potential. We also point out the possible direction and suggestions for future work. By thoroughly investigating the relationship of visual privacy and deep learning, this article sheds insights on incorporating privacy requirements in the deep learning era.

     

新旧动能转换背景下综合能源服务模式

为发挥综合能源服务对新旧动能转换的关键支撑作用,从政策和产业演进两个方面出发,梳理新旧动能转换的基本框架以及产业演进方向,研究适应新旧动能转换的能源供需新格局,得到综合能源服务的基本架构、运营模式以及关键支撑技术,为大范围开展综合能源服务、支撑新旧动能转换提供思路和模式。     

山东电网气温敏感负荷研究

随着社会经济的发展以及居民生活水平的提高,气温敏感负荷对电网生产运行的影响越来越大,气温敏感负荷分析愈发重要。收集山东省2011—2014年用电负荷和相关温度数据,测算空调降温负荷及单位温升负荷,并在此基础上,定量分析降温负荷对日负荷特性的影响,为电网规划和生产调度提供参考。     

An Iteration-Based Differentially Private Social Network Data Release

Online social networks provide an unprecedented opportunity for researchers to analysis various social phenomena. These network data is normally represented as graphs, which contain many sensitive individual information. Publish these graph data will violate users’ privacy. Differential privacy is one of the most influential privacy models that provides a rigorous privacy guarantee for data release. However, existing works on graph data publishing cannot provide accurate results when releasing a large number of queries. In this paper, we propose a graph update method transferring the query release problem to an iteration process, in which a large set of queries are used as update criteria. Compared with existing works, the proposed method enhances the accuracy of query results. The extensive experiment proves that the proposed solution outperforms two state-of-the-art methods, the Laplace method and the correlated method, in terms of Mean Absolute Value. It means our methods can retain more utility of the queries while preserving the privacy.     

K means算法在电力企业同业对标中的应用

鉴于K means聚类算法在电力企业同业对标中的应用,以供电质量为指标对各省电力公司进行企业分类,确定标杆企业,并准确寻求与行业标杆企业之间的差距,进而不断提升自身的管理水平和经营效益,力争实现“一强三优”的发展目标。结果表明,该算法行之有效。     

系统动力学模型在电网公司经营决策支持中的应用

对电网公司关键经营统计指标进行预测及敏感性分析,先利用加权移动平均、多元线性回归、灰色模型预测售电量指标得到三种预测结果,将GDP、人口作为神经网络的输入条件,借助Matlab软件,基于RBF神经网络的综合预测模型将三种预测结果拟合后预测出核心指标售电量;再借助STELLA软件用图形表示因素之间的相互关系和影响,进而构建系统动力学模型,定量分析了各指标间的关系,获得了所有关键指标的预测值,并选取售电量和售电均价做敏感性分析,分析售电量和售电均价变化对电网投资能力的影响程度。     

考虑环境外部性和风光出力不确定性的虚拟电厂运行优化

虚拟电厂在建设运行中产生的环境外部性影响其运行的经济效益与环境效益.首先,单独核算煤电、风电和光伏发电的单位环境外部性成本,并将其计入虚拟电厂的单位发电量成本.在此基础上,考虑风光出力的不确定性,构建基于Wasserstein距离的风光出力预测误差模糊集的两阶段分布鲁棒优化模型,使虚拟电厂在风光出力预测误差满足模糊集内...