Applications of sketches in network traffic measurement: A survey

Accurate and timely network traffic measurement is essential for network status monitoring, network fault analysis, network intrusion detection, and network security management. With the rapid development of the network, massive network traffic brings severe challenges to network traffic measurement. However, existing measurement methods suffer from many limitations for effectively recording and accurately analyzing big-volume traffic. Recently, sketches, a family of probabilistic data structures that employ hashing technology for summarizing traffic data, have been widely used to solve these problems. However, current literature still lacks a thorough review on sketch-based traffic measurement methods to offer a comprehensive insight on how to apply sketches for fulfilling various traffic measurement tasks. In this paper, we provide a detailed and comprehensive review on the applications of sketches in network traffic measurement. To this end, we classify the network traffic measurement tasks into four categories based on the target of traffic measurement, namely cardinality estimation, flow size estimation, change anomaly detection, and persistent spreader identification. First, we briefly introduce these four types of traffic measurement tasks and discuss the advantages of applying sketches. Then, we propose a series of requirements with regard to the applications of sketches in network traffic measurement. After that, we perform a fine-grained classification for each sketch-based measurement category according to the technologies applied on sketches. During the review, we evaluate the performance, advantages and disadvantages of current sketch-based traffic measurement methods based on the proposed requirements. Through the thorough review, we gain a number of valuable implications that can guide us to choose and design proper traffic measurement methods based on sketches. We also review a number of general sketches that are highly expected in modern network systems to simultaneously perform multiple traffic measurement tasks and discuss their performance based on the proposed requirements. Finally, through our serious review, we summarize a number of open issues and identify several promising research directions.     

Interval analysis of mode shapes to identify damage in beam structures

Various damage detection methods have been proposed by several researchers in the past few decades. Amongst them, the efficiency of mode shapes in detecting damage has been demonstrated by many researchers when further processed. In most cases, the processing involves expansion or reduction of the mode shape data. However, vital information that are damage-prints are often lost during processing of the mode shape data. In addition, most of these processes involve long and complex computation, thus, leading to inaccurate damage identification. In this study, a simple and fast damage identification technique is proposed to identify damage in beam structures. Interval analysis is applied to the mode shapes of a beam structure in the damaged and undamaged states. The interval situations of each of the beam's segment via mode shape are derived to obtain the upper and lower bounds and the derived bounds are compared. To establish a relationship for identify the damaged point, a possibility of damage existence is defined for each segment of the beam structure. The mode shape increment is defined as the increase in the mode shape value. Furthermore, a damage measure index that provide enhance damage information is obtained as the product of the possibility of damage existence and mode shape increment. A numerical model of a simply supported steel beam is applied to demonstrate this method by imposing damage through thickness reduction of elements in segments. In addition, a parametric analysis is carried out to evaluate noise effect by considering varying damage severities and different noise levels. The results showed that this method is simple and provides considerable accurate results.     

环境检测技术存在的问题及对策分析

环境检测技术对于环境的决策及监管有着很大的影响,但目前在我国的环境检测技术仍然还存在一定的问题,仍然有待完善。因此,本文针对环境检测技术现存问题进行了研究分析,并且提出了一些相应的有效对策。     

Assessment of the prevalence of Streptococcus uberis in dairy cow feces and implications for herd health

Streptococcus uberis is a major causative agent of bovine mastitis worldwide, negatively affecting both milk production and animal welfare. Mammary infections result from environmental reservoirs, with cattle themselves required to propagate the infection cycle. Two longitudinal studies were performed to investigate the prevalence of Streptococcus uberis within feces and to evaluate factors which may affect gastrointestinal carriage. Bacterial detection was confirmed using a PCR-based method directed against sub0888 that detected S. uberis at an analytical sensitivity of 12 cfu/g of bovine feces. The first study sampled an entire herd at 8-wk intervals, over a 10-mo period and identified that maintenance of S. uberis within the dairy cow environment was due to a high proportion of animals shedding S. uberis and not due to a low number of “super-shedding” cows within the herd. Seasonality influenced detection rates, with detection levels significantly higher for housed cattle compared with those at pasture. Multilevel logistic regression was used to identify significant factors that affected S. uberis detection; these included parity, stage of lactation, and body condition score. An additional study involved screening a smaller cohort of cows housed over a 4-wk period and identified an increased probability of detection if cows were housed in loose straw yards, compared those in straw cubicles. This study highlighted several cow and management related factors that affect both detection of S. uberis and future infection risks.     

地表水中石油类监测存在的问题与对策

为使我国地表水中石油类检测过程更具可操作性和推广性,使各实验室石油类检测结果更具可对比性和量值溯源性,本文从地表水中石油类样品采集、样品检测及标准物质选取等三个方面对当前国内外相关标准中规定内容进行了汇总梳理,得出目前采用的样品采集方法均难以保证样品代表性、各种检测方法关注点不同且各有利弊、标准物质的选取对最终测定结果有较大影响的结论。为解决上述难题,建议开展以下3方面工作:(1)研发一种符合地表水中石油类赋存状况的水样采集器;(2)将地表水环境质量标准中"石油类"指标分为"低沸点挥发性石油类"和"高沸点半挥发性石油类"指标分别检测;(3)分流域或河流开展石油类标准物质研制工作。     

Dynamic load-altering attack detection based on adaptive fading Kalman filter in power systems

This paper presents an effective and feasible method for detecting dynamic load-altering attacks (D-LAAs) in a smart grid. First, a smart grid discrete system model is established in view of D-LAAs. Second, an adaptive fading Kalman filter (AFKF) is designed for estimating the state of the smart grid. The AFKF can completely filter out the Gaussian noise of the power system, and obtain a more accurate state change curve (including consideration of the attack). A Euclidean distance ratio detection algorithm based on the AFKF is proposed for detecting D-LAAs. Amplifying imperceptible D-LAAs through the new Euclidean distance ratio improves the D-LAA detection sensitivity, especially for very weak D-LAA attacks. Finally, the feasibility and effectiveness of the Euclidean distance ratio detection algorithm are verified based on simulations.     

A robust locating multi-optima approach for damage identification of plate-like structures

This paper proposes a robust optimization approach for multiple damage identification of plate-like structures. Different from traditional particle swarm optimizations (PSOs), a combined PSO and niche technique (NPSO) is proposed to solve multimodal optimization problems, with the full consideration of subswarm creation, merging and absorbing mechanism. As a hypersensitive parameter to damage, the curvature mode shape is adopted to construct the objective function. Case studies are conducted to investigate the effectiveness and robustness of the algorithm on multi-damage identification. Simulation results show that the proposed algorithm exhibits robust search performance on identifying damage locations accurately with good convergence behavior. It is hoped that this study can provide guidance on robust damage detection, especially when the structure is subject to multiple damages and external disturbances.     

一种基于改进NMS的牛脸检测方法

针对牲畜牛身份认证的多牛脸检测场景，本文给出一种基于改进Faster R-CNN的牛脸检测方法。使用Inception v2替换ZF网络作为Faster R-CNN的基础网络，模型精度得到显著提升；针对多牛检测场景对NMS（Non-Maximum Suppression）进行相应优化，使模型的召回率得到显著提升。通过和其他目标检测模型对比实验，本文的改进模型在精确率和召回率上均优于其他模型。