MERGING AND SPLITTING SECOND-ORDER SELF-SIMILAR PROCESSES (TRAFFICS)

Recent traffic measurements in corporate LANs, Variable Bit Rate (VBR) video sources, ISDN control channels, and other communication systems, have indicated traffic behavior of self similar nature, which has implications for design, control and analysis of high speed networks. Merging and splitting are two basic networking operations. This paper gave the necessary and sufficient conditions for that merging of second order self similar traffic streams also results in a second order self similar stream. It shows that splitting traffic streams of the second order self similar stream are still self similar streams by the independent splitting operation.

An Intrusion Alarming System Based on Self-Similarity of Network Traffic

Intrusion detection system ean make effective alarm for illegality of networkusers, which is absolutely necessarily and important to build security environment of communicationbase service According to the principle that the number of network traffic can affect the degree ofself-similar traffic, the paper investigates the variety of self-similarity resulted fromunconventional network traffic. A network traffic model based on normal behaviors of user isproposed and the Hursl parameter of this model can be calculated. By comparing the Hurst parameterof normal traffic and the self-similar parameter, we ean judge whether the network is normal or notand alarm in time.

Providing QoS guarantees for self-similar traffic flows

Provisioning network resource to meet the quality of Service (QoS) demand is a key issue for future network services. Such functions may be realized by an admission control algorithm, which determines whether or not a new traffic flow can be admitted into the network. It is widely accepted that many traffic flows have self-similar character that has detrimental influence on network performance. This characteristic has made old mathematical models invalid, and a new model must work with self-similar fractal instead. This paper applies Fractional Brownian Motion(FBM) model and integrates it into the comprehensive admission control scheme, which takes account of aggregated traffic behavior to get the statistical multiplexing performance gain. Experiment verifies that FBM model can be used to realistically describe packet traffic in modern packet networks and accurately predict their performance.

Comparative Analysis of TCP-Protocol Operation Algorithms in Self-Similar Traffic

This paper presents simulation modelling of network under the conditions of self-similar traffic and bottleneck occurrence. The comparative analysis of different TCPs (NewReno, Reno, Tahoe and etc.) has been conducted along with the testing of various algorithms of these protocols activity. The use of TCP Vegas has been proved to be the most effective.

On use of the alpha stable self-similar stochastic process to model aggregated VBR video traffic

The alpha stable self-similar stochastic process has been proved an effective model for high variable data traffic. A deep insight into some special issues and considerations on use of the process to model aggregated VBR video traffic is made. Different methods to estimate stability parameter a and self-similar parameter H are compared. Processes to generate the linear fractional stable noise （LFSN） and the alpha stable random variables are provided. Model construction and the quantitative comparisons with fractional Brown motion （FBM） and real traffic are also examined. Open problems and future directions are also given with thoughtful discussions.

Identification of Similar Air Traffic Scenes with Active Metric Learning

The rapid growth of air traffic has continuously increased the workload of controllers,which has become an important factor restricting sector capacity.If similar traffic scenes can be identified,the historical decision-making experience may be used to help controllers decide control strategies quickly.Considering that there are many traffic scenes and it is hard to label them all,in this paper,we propose an active SVM metric learning(ASVM2L)algorithm to measure and identify the similar traffic scenes.First of all,we obtain some traffic scene samples correctly labeled by experienced air traffic controllers.We design an active sampling strategy based on voting difference to choose the most valuable unlabeled samples and label them.Then the metric matrix of all the labeled samples is learned and used to complete the classification of traffic scenes.We verify the effectiveness of ASVM2L on standard data sets,and then use it to measure and classify the traffic scenes on the historical air traffic data set of the Central South Sector of China.The experimental results show that,compared with other existing methods,the proposed method can use the information of traffic scene samples more thoroughly and achieve better classification performance under limited labeled samples.

基于频繁序列挖掘的出租车轨迹特性分析

为进一步厘清不同出租车路径选择行为的差异性,采用频繁序列挖掘方法提取了同一个OD对间的频繁路径,构建路径选择集,分别从静态和动态两个角度分析路径集的相似特性。以西安市出租车的轨迹数据为研究对象,通过栅格划分与路网匹配,获得了不同OD对之间的路径集合。重新定义了频繁路径,采用PrefixSpan演变算法,在得到频繁子序列的基础上引入动态阈值和频繁度指标挖掘频繁路径,提取了最短路径和其他路径,完成了3类有效路径集的构建,并分析了路径集的一般属性。其后,将路径上二维时间序列(轨迹)间的相似度表示为动态相似度,将一维有向序列(路段)间的相似度表示为静态相似度,基于改进的最长公共子序列和动态时间规整算法对3类路径进行了相似性分析。结果表明:频繁路径与最短路径的相似度较高,意味着大多数出租车仍然选择具有最低出行时间的路段,但不一定会选择最短路径;时间和距离仍是出行者选择路径时主要考虑的因素,但出行者并不完全追求时间最短或距离最短;试验得到的动态相似度计算结果显著高于静态相似度计算结果,说明路径上的二维时序相似度高于一维形状相似度;两种方法下频繁路径和最短路径的相似度均最高,最短路径和其他路径的相似度均最低,比较结果的一致性说明可以用动态轨迹的相似度来大致度量静态路径的相似度。文中的频繁路径挖掘算法具有一定的可靠性,可为城市交通管理者进行路径推荐、道路规划等提供支持。

基于流量自相似性的网络队列管理算法

网络流量的自相似性会导致数据突发状态持续,传统队列管理算法无法对网络流量突发状态进行预测,从而影响网络端到端时延、丢包率和吞吐性能。针对该问题,提出一种基于网络流量预测的主动队列管理算法P-ARED。基于网络流量的均值和方差给出网络流量等级的概念,讨论网络流量等级转移概率与Hurst参数之间的关系,提出基于贝叶斯估计思想的网络流量等级预测方法。在此基础上,在对自相似网络流量环境下的平均队列长度、缓存队列长度最小阈值等参数优化设置的基础上,基于Hurst参数和自相似流量等级预测结果,重新设计ARED算法中分组丢弃概率的计算方法,以提高缓存队列长度的稳定性。仿真结果表明,P-ARED算法与对比的主动队列管理算法相比,降低了网络端到端时延和丢包率,提高了端到端吞吐性能,其中平均吞吐量最高提升7.63%,平均时延最多降低17.52%。

基于CBR视图相似度与HFLTS方法的车货匹配研究

为提高整车运输装载率及货主满意度,基于案例推理(CBR)技术中的案例检索思维和犹豫模糊综合术语集(HFLTS),提出了一种基于视图相似度并考虑货主评价的车货匹配方法。依据案例检索思维,将货物条件属性集视为待解问题(准案例),将车辆条件属性集视为既有案例条件属性集,保留车主的信赖属性指标;基于自适应K均值法确定最近聚类,只保留最近聚类内堆积在聚类中心附近的车辆数据,双重压缩匹配空间;通过熵权法确定条件属性权重;基于欧式距离计算待匹配货物数据集与匹配空间中的车辆数据集之间的视图相似度,降序排列计算结果并保留TOP 3车辆数据集所对应的车主信息;基于HFLTS方法将货主对车主犹豫模糊的评价信息通过得分函数转化为具体的数值,并综合考虑视图相似度及评价结果为货主推荐最优匹配车主。通过某案例分析,验证了该方法的匹配效率比常规方法提升了88.79%;待匹配货物与推荐车辆条件属性比值最大,能有效提升车辆装载率。

公路突发事件应急演练动态脚本生成方法

为了实现应急预案与应急演练脚本双向应用,形成反馈机制,提出一种基于预案解析的公路突发事件应急演练动态脚本生成方法。以应急演练脚本为研究对象,首先,通过应急预案数字化解析,提取并确定应急演练脚本关键要素;其次,构建静态应急演练脚本集,并进行结构化处理,形成应急演练数据与演练逻辑库;然后,构建相似度匹配方法,根据拟演练突发事件关键信息指标与属性值,匹配应急演练程序、具体处置方案、风险防范点;最后,关联演练突发事件状态,有序匹配演练脚本集,形成公路突发事件应急演练动态脚本生成方法,包含事件类型与级别、应急响应程序、现场处置方案、应急处置风险防范点四部分内容。以某一交通事故为例,使用该方法进行应急演练脚本动态生成的验证,结果表明该方法具备合理可行性。

空中交通管制中相似航班号解决策略探究

在繁忙时段或繁忙扇区,同一管制员可能会连续指挥多架具有相似航班号的航空器,受通信波道质量不佳或语言发音等影响,极易造成陆空通话双方误听、误复诵、误操作,增加了双方的工作负荷,对飞行安全产生较大威胁。本文将结合相似航班号的概念和分类,通过相关案例,分析相似航班号对飞行安全的影响,提出解决相似航班号问题的建议,以便降低运行风险,更好地保障飞行安全。

基于深度学习的城市主干道路交通拥堵水平预测

为了解决城市主干道路交通拥堵问题,提出一种基于深度学习的城市主干道路交通拥堵水平预测方法。建立城市主干道路交通网络的Katz相似度矩阵,保存路网的结构特征,获得城市主干道路交通流数据。采用局部敏感判别分析模型,将交通流数据映射到低维流形,获得最优投影矩阵,提取城市主干道路交通特征。结合循环神经网络模型(RNN)和长短时记忆网络模型(LSTM),设计长短时记忆循环网络(RNN-LSTM)模型,解决梯度消失问题,输入城市主干道路交通特征,经过训练输出城市主干道路交通拥堵水平预测结果。实验结果表明,所提方法预测准确度在0.8~0.98,预测所需时间平均为24.74 ms,具有一定的应用价值。

Identifying Similar Operation Scenes for Busy Area Sector Dynamic Management

Air traffic controllers face challenging initiatives due to uncertainty in air traffic.One way to support their initiatives is to identify similar operation scenes.Based on the operation characteristics of typical busy area control airspace,an complexity measurement indicator system is established.We find that operation in area sector is characterized by aggregation and continuity,and that dimensionality and information redundancy reduction are feasible for dynamic operation data base on principle components.Using principle components,discrete features and time series features are constructed.Based on Gaussian kernel function,Euclidean distance and dynamic time warping(DTW)are used to measure the similarity of the features.Then the matrices of similarity are input in Spectral Clustering.The clustering results show that similar scenes of trend are not ideal and similar scenes of modes are good base on the indicator system.Finally,actual vertical operation decisions for area sector and results of identification are compared,which are visualized by metric multidimensional scaling(MDS)plots.We find that identification results can well reflect the operation at peak hours,but controllers make different decisions under the similar conditions before dawn.The compliance rate of busy operation mode and division decisions at peak hours is 96.7%.The results also show subjectivity of actual operation and objectivity of identification.In most scenes,we observe that similar air traffic activities provide regularity for initiatives,validating the potential of this approach for initiatives and other artificial intelligence support.

Recognition of Similar Weather Scenarios in Terminal Area Based on Contrastive Learning

In order to improve the recognition accuracy of similar weather scenarios(SWSs)in terminal area,a recognition model for SWS based on contrastive learning(SWS-CL)is proposed.Firstly,a data augmentation method is designed to improve the number and quality of weather scenarios samples according to the characteristics of convective weather images.Secondly,in the pre-trained recognition model of SWS-CL,a loss function is formulated to minimize the distance between the anchor and positive samples,and maximize the distance between the anchor and the negative samples in the latent space.Finally,the pre-trained SWS-CL model is fine-tuned with labeled samples to improve the recognition accuracy of SWS.The comparative experiments on the weather images of Guangzhou terminal area show that the proposed data augmentation method can effectively improve the quality of weather image dataset,and the proposed SWS-CL model can achieve satisfactory recognition accuracy.It is also verified that the fine-tuned SWS-CL model has obvious advantages in datasets with sparse labels.

STRUCTURAL MODELING OF WAN TRAFFIC

High resolution traffic measurements from modern communications networks provide unique opportunities for developing and validating mathematical models for aggregate traffic, or WAN (wide area network) traffic. To exploit these opportunities, this paper emphasized the need for structural model that takes into account specific physical feature of the underlying communication network structure. This approach is in sharp contrast to the traditional black box modeling methodology from this time series analysis that ignores, in general, specific physical structure. The paper demonstrated, in particular, how the proposed structural modeling approach provides a direct link between the observed self similarity characteristic of measured aggregate network traffic and the strong empirical evidence in favor of heavy tailed, infinite variance phenomena at the level of individual network connections.

Implementation of Efficient Burst Assembly Algorithm with Traffic Prediction

This paper reports on the implementation of efficient burst assembly algorithms and traffic prediction. The ultimate goal is to propose a new burst assembly algorithm which is based on time-burst length （hybrid） threshold with traffic prediction to reduce burst assembly delay in OBS （Optical Burst Switching） networks. Research has shown that traffic always change from time to time, hence, any measure that is put in place should be able to adapt to such changes. With our implemented burst assembly algorithm, the traffic rate is predicted and the predicted rate is used to dynamically adjust the burst assembly length. This work further investigates the impact of the proposed algorithm on traffic self similarity.

基于深度学习的城市区域短时交通拥堵预测算法

城市的交通拥堵已经成为各个城市中的普遍现象,严重影响了城市日常交通以及人们的出行。针对城市区域交通流的研究与分析,为了准确地对城市交通状态进行预测,通过网格划分的方法,把城市区域划分为多个区域,根据城市交通数据流的时空特征,提出了一种基于深度学习的城市交通拥堵预测模型(CS-Transformer)。该模型通过使用卷积神经网络(convolutional neural networks,CNN)提取基于网格划分的城市区域交通数据的空间特征,然后采用全连接神经网络增强模型的表达能力,再通过相似性位置编码机制(similarity location encoding mechanism,SPEM)把位置信息加入交通数据中,最后运用Transformer网络捕捉交通数据的时间依赖特征。用成都市出租车全球定位系统(global positioning system,GPS)数据对模型进行验证,结果表明该模型预测结果优于CNN、Transformer和CNN-Transformer等模型,以均方误差(mean square error,MSE)为评价指标,将测试集中交通路网的平均预测精度分别提高了19.6%、26.3%和10%。

自走式三七联合收获机底盘通过性能分析与模型试验

针对自走式三七联合收获机在田间作业安全性不足与行驶稳定性较弱等问题,开展了黏重土壤条件下底盘的行驶通过性能研究。首先,对联合收获机的直行转向、纵横向爬坡和越障等行驶工况进行理论分析,获得了影响行驶通过性能的各因素参数,其次,通过ADAMS ATV开展多体动力学仿真分析,绘制了行驶工况下欧拉角与对应的角速度曲线,最后,运用相似理论的量纲分析法设计了1∶4的联合收获机底盘微缩平台模型,对纵向爬坡、跨越壕沟和翻越田埂3种典型行驶工况开展行驶通过性能模型试验。仿真结果表明,联合收获机底盘具有平稳的直行与转向性能,能够顺利通过30°纵坡、20°横坡、600 mm壕沟及300 mm田埂;模型试验结果表明,模型能够顺利通过30°纵坡、150 mm壕沟和75 mm田埂,以上3种工况下的俯仰角与对应的角速度曲线趋势与仿真一致,且二者所得俯仰角曲线的幅值变化相同,试验误差主要受实际地形平整度与土壤均匀度的影响,因此表明模型试验能够验证仿真分析结果的正确性,通过模型对原型的预测及仿真结果,满足了联合收获机行驶通过性能的设计要求,该研究可为丘陵山区根茎类联合收获机底盘的设计提供理论基础与参考。

改进TOPSIS法的武汉城市轨道交通节点重要度评估

随着城市轨道交通规模的不断发展,如何快速有效地挖掘重要站点对轨道交通的安全运营具有重要意义。以2021年底武汉城市轨道交通为研究对象,运用Space-L方法构建武汉地铁网络拓扑结构;考虑节点所处的局部环境和节点在网络中的全局属性,引入进行网页排名的PageRank算法,分析了客流量对地铁站点的影响;最后通过基于余弦相似性TOPSIS法代替传统TOPSIS法来解决节点重要度排序的问题。研究结果表明:武汉城市轨道交通网络为无标度网络,整个线路呈现出小环线、长放射线的模式,放射线过长会造成长距离通勤放射线过分依赖市中心的换乘,易造成客流的不均衡;重要度排名靠前的车站在轨道交通网络中拥有着较多的邻居节点、位置相对中心化、客流量较大且具有较高的局部重要性和全局重要性;与单一指标评估结果相比,笔者方法更加全面客观并赋予了不同属性指标权重,使得评价更加全面,且能在一定程度上改善因欧式距离失效而产生的逆序现象。