基于扩展自相似过程的ATM业务模型

在扩展自相似过程的基础上提出了一种新的业务模型。该业务模型可以实现任意的边缘概率分布，并且可以实现众多形状的相关函数，较之现有的ＡＴＭ业务模型具有更广的适用范围。     

基于网络处理器的自相似流量仿真系统

为了解决大部分网络流量的自相似性研究中缺少高速真实的数据流的问题,提出了一个使用网络处理器IXP2400建立的自相似流量仿真系统。该系统利用分形布朗运动(FBM)模型产生自相似序列,以该序列作为数据发送带宽的依据;使用基于微引擎的发送控制算法实现高精度数据发送,以减少输出数据流赫斯特参数的误差,并利用IXP2400的硬件资源缩短随机中点置位(RMD)算法的计算时间。实验结果表明,该系统能够根据输入的赫斯特参数产生真实的自相似TCP数据流,最高数据发送速度接近900Mb/s,赫斯特参数的误差控制在5%以内。     

基于Petri网和语义网络的虚拟维修过程建模与应用

针对当前虚拟维修过程中模型庞大,维修流程不可反复,部件间关系描述混乱等问题,论文提出一种Petri网和语义网络结合的过程建模方法——TJ(Training Join)网。TJ网上层利用语义网络对部件进行层次结构分解和资源规范聚类,实现子部件间逻辑互联;下层运用Petri网的变迁和触发规则提炼出Petri网元素模型,提高模型的通用性和建模效率。同时模型中的状态收集模块可实现部件属性状态间信息共享。最后,以虚拟维修平台中飞机电子设备架的维护为例,验证了TJ网的有效性。     

基于业务建模的软件产品系统分析过程与特点分析

系统分析的方法多种多样,本文介绍了一种产品系统分析过程,这个过程使用了实体与行为分离的业务建模的思想来完成系统的分析,在文章中仔细分析了每个步骤怎么样做,输出入什么样的内容,得到什么样的输出,经过这样的分析过程后,使系统最核心的业务被固化下来,达到平台无关、具备更强的扩展性,每个领域内部关系及外部关系、系统中的每一个元素都得到集中的管理,对系统的理解不易产生歧义。本文所研究的系统分析过程具有很强的操作性,具备较高推广价值。     

基于循环自编码网络的冷轧轧制力建模方法

在钢铁冷连轧生产过程中,轧制力预测结果直接影响带材的轧制精度和产品质量。为进一步提高轧制力预测精度,同时实现模型的在线更新,避免漂移问题,提出了基于循环自编码网络的轧制力模型。首先,使用循环自编码网络对处理好的输入数据进行特征提取,为了加速网络训练,加入了小批量训练方法,然后使用高斯过程回归模型对提取到的特征进行回归拟合。仿真结果表明,该模型预测精度可达3%以内,能够实现轧制力的高精度在线预测。     

基于自回归与长短期记忆网络混合模型的热电偶动态补偿方法研究

热电偶在动态温度测量时由于热惯性存在动态误差。为补偿热电偶的动态误差,提出一种基于自回归与长短期记忆网络混合模型的补偿算法。该算法通过自回归模型对热电偶动态响应进行辨识,再由长短期记忆网络作为非线性补偿器校正动态误差。采用不同强度的高斯白噪声模拟噪声环境,仿真构建热电偶模拟测量数据集。在模拟测量数据集上对算法做验证。计算结果表明,该算法在不同噪声环境下均能有效地减少动态误差。搭建热电偶动态温度测量实验平台,以K型镍铬/镍硅热电偶为实验对象,取得实验测量数据集。实验和计算结果表明,经算法补偿后的热电偶动态响应得到改善,平均动态误差为0.002 8,标准差为0.010 2。     

基于过程神经网络算法的航天器非平稳随机振动时频分析

采用时变参数模型对航天器某时段非平稳随机振动信号(NSRVS)进行建模分析,利用过程神经元网络(PNN)求解模型的时变参数并以此确定信号的时变自功率谱密度.计算结果表明:由PNN估计的NSRVS时变参数与自相关Levinson法估计的该参数基本一致,但前者建模物理意义明确,和传统的方法相比避免了计算信号的自相关矩阵,减少了存储空间,提高了频率分辨率和计算速度.     

自相似业务下光分组交换边缘节点排队方案性能分析

研究了自相似业务下光分组交换网中边缘节点的工作性能,建立了轮询机制下的排队模型,分析了平均组装／发送时延与最大汇聚分组长度等参数的关系。研究表明：平均时延与最大汇聚长度及边缘节点数目成线性关系,与发射速率成反比关系,随业务强度的增加而增大。所得结论具有轮询机制的共性,为进一步研究其他排队方案奠定了基础。     

Simulating Timescale Dynamics of Network Traffic Using Homogeneous Modeling

Simulating and understanding traffic dynamics in large networks are difficult and challenging due to the complexity of such networks and the limitations inherent in simulation modeling. Typically, simulation models used to study traffic dynamics include substantial detail representing protocol mechanisms across several layers of functionality. Such models must be restricted in space and time in order to be computationally tractable. We propose an alternative simulation approach that uses homogeneous modeling with an increased level of abstraction, in order to explore networks at larger space-time scales than otherwise feasible and to develop intuition and insight about the space-time dynamics of large networks. To illustrate the utility of our approach, we examine some current understandings of the timescale dynamics of network traffic, and we discuss some speculative results obtained with homogeneous modeling. Using a wavelet-based technique, we show correlation structures, and changes in correlation structures, of network traffic under variations in traffic sources, transport mechanisms, and network structure. Our simulation results justify further investigation of our approach, which might benefit from cross-verifications against more detailed simulation models.     

A Network Traffic Classification Model Based on Metric Learning

Attacks on websites and network servers are among the most critical threats in network security. Network behavior identification is one of the most effective ways to identify malicious network intrusions. Analyzing abnormal network traffic patterns and traffic classification based on labeled network traffic data are among the most effective approaches for network behavior identification. Traditional methods for network traffic classification utilize algorithms such as Naive Bayes, Decision Tree and XGBoost. However, network traffic classification, which is required for network behavior identification, generally suffers from the problem of low accuracy even with the recently proposed deep learning models. To improve network traffic classification accuracy thus improving network intrusion detection rate, this paper proposes a new network traffic classification model, called ArcMargin, which incorporates metric learning into a convolutional neural network (CNN) to make the CNN model more discriminative. ArcMargin maps network traffic samples from the same category more closely while samples from different categories are mapped as far apart as possible. The metric learning regularization feature is called additive angular margin loss, and it is embedded in the object function of traditional CNN models. The proposed ArcMargin model is validated with three datasets and is compared with several other related algorithms. According to a set of classification indicators, the ArcMargin model is proofed to have better performances in both network traffic classification tasks and open-set tasks. Moreover, in open-set tasks, the ArcMargin model can cluster unknown data classes that do not exist in the previous training dataset.     

企业过程建模体系的研究

企业过程建模作为一项支持企业过程集成与优化的共性技术，是对企业系统中与企业过程有关的特性加以抽象表达的工具和方法。首先对企业建模和过程建模进行了探讨，在此基础上，提出了一种企业过程建模体系框架（EPMF），并从四个维对EPMF进行了详细的讨论。最后对EPMF进行了简要的总结。     

公交网络换乘问题的一种实现

公交换乘问题是公共交通信息查询的重要内容。针对公交网络换乘问题构造了公共交通网络模型。基于该模型,提出了基于改进Dijkstra算法的公交网络最短路径问题的求解。将求解最短路径获得的站点作为搜索站点,并将这些站点及经过这些站点的线路构成换乘矩阵,结合换乘次数的要求,给出了换乘的实现算法,确定可行的换乘方案。     

An Object-Oriented Modeling Approach in Materials Processing: Case Study of the Acheson and Carburizing Processes

Numerical results can be conveyed effectively to any one using an object-oriented framework. Therefore, it becomes essential to couple the object-oriented approach with numerical analysis. A simple object-oriented methodology for creating a graphical user interface (GUI) as a framework is presented. This work tries to fill a gap between scientific computer code written in a noninteractive way and interactive programming in materials processing. The GUI interface framework has been created in such a way that at any point of time one need not create a new GUI framework. The user only needs to create the process object and attach it to the framework. Two examples have been presented involving interactive finite volume-based gas carburizing and interactive finite difference-based Acheson process (used for the production of silicon carbide).     

MalDetect: A Structure of Encrypted Malware Traffic Detection

Recently, TLS protocol has been widely used to secure the application data carried in network traffic. It becomes more difficult for attackers to decipher messages through capturing the traffic generated from communications of hosts. On the other hand, malwares adopt TLS protocol when accessing to internet, which makes most malware traffic detection methods, such as DPI (Deep Packet Inspection), ineffective. Some literatures use statistical method with extracting the observable data fields exposed in TLS connections to train machine learning classifiers so as to infer whether a traffic flow is malware or not. However, most of them adopt the features based on the complete flow, such as flow duration, but seldom consider that the detection result should be given out as soon as possible. In this paper, we propose MalDetect, a structure of encrypted malware traffic detection. MalDetect only extracts features from approximately 8 packets (the number varies in different flows) at the beginning of traffic flows, which makes it capable of detecting malware traffic before the malware behaviors take practical impacts. In addition, observing that it is inefficient and time-consuming to re-train the offline classifier when new flow samples arrive, we deploy Online Random Forest in MalDetect. This enables the classifier to update its parameters in online mode and gets rid of the re-training process. MalDetect is coded in C++ language and open in Github. Furthermore, MalDetect is thoroughly evaluated from three aspects: effectiveness, timeliness and performance.     

基于BP神经网络的轿车造型意象设计与量化预测模型建立

目的 构建产品造型意象设计与产品造型量化间的预测模型,将产品的设计元素与消费者的需求和感知相关联。方法 分析小轿车造型与感性意象之间的关系,基于用户对产品外观形态感受进行打分,采用感性工学方法进行量化;用双BP神经网络训练的思路,建立外观设计元素与感性意象间的对应关系,得到小轿车外观方案的感性意象预测模型。结论 获得产品造型设计方案的感性意象预测模型,能高效解决设计师的产品造型设计方案与消费者的目标达成趋同,通过高效的产品外观设计,为未来轿车及机电类产品的造型设计转向以消费者精神需求为导向提供了一种新的设计方法。     

基于灰色模糊数的公交线网优化研究

在考虑信息不完全的情况下,用灰色模糊数对公交线网优化问题进行了研究。在给出了公交线网优化的约束条件与优化目标的基础上,利用灰色模糊数建立了公交线网优化的决策模型。灰色模糊数是用三参数区间参与优化过程,在允许参数在一定范围内变化的情况下,计算后得到的结果是一个区间向量,其中向量值最大的为所求结果,适用于城市公交线网的优化问题。实例应用分析表明,优化后的公交线网效率提高,可达性良好,可满足城市公交可持续发展的要求。     

基于神经网络的复杂系统杂交建模研究

利用先验知识和神经网络黑箱建模相结合,对一类复杂滞后非线性系统进行了杂交建模研究.先验知识建模确定滞后系统模型的形式及主体部分,神经网络估计滞后系统恢复力的未确定部分,所建模型不仅能够进行系统响应预测分析,而且能够获得系统的主体结构特征,对结构设计修改具有指导作用.