MPEG VBR video traffic modeling and classification using fuzzytechnique

We present an approach for MPEG variable bit rate (VBR) video modeling and classification using fuzzy techniques. We demonstrate that a type-2 fuzzy membership function, i.e., a Gaussian MF with uncertain variance, is most appropriate to model the log-value of I/P/B frame sizes in MPEG VBR video. The fuzzy c-means (FCM) method is used to obtain the mean and standard deviation (std) of T/P/B frame sizes when the frame category is unknown. We propose to use type-2 fuzzy logic classifiers (FLCs) to classify video traffic using compressed data. Five fuzzy classifiers and a Bayesian classifier are designed for video traffic classification, and the fuzzy classifiers are compared against the Bayesian classifier. Simulation results show that a type-2 fuzzy classifier in which the input is modeled as a type-2 fuzzy set and antecedent membership functions are modeled as type-2 fuzzy sets performs the best of the five classifiers when the testing video product is not included in the training products and a steepest descent algorithm is used to tune its parameters     

An adaptable time-delay neural-network algorithm for image sequence analysis.

We present an algorithm based on a time-delay neural network with spatio-temporal receptive fields and adaptable time delays for image sequence analysis. Our main result is that tedious manual adaptation of the temporal size of the receptive fields can be avoided by employing a method to adapt the corresponding time delay and related network structure parameters during the training process.     

在非线性建模中神经网络模型的构建与选择

主要研究了如何将平时单独使用的数学方法和统计学方法根据它们各自的优点综合运用,以提高非线性建模过程中神经网络模型构建和选择的效率。所使用的统计学工具包括矩阵的条件数,假设检验,交叉验证。文中对每个方法进行综合分析,进而判断它们分别应用在神经网络模型构建与选择过程的哪个阶段是最有效的。在此基础上,提出了一个系统的神经网络模型的构建与选择程序,并最终通过仿真试验来说明这个程序的有效性。     

基于小波变换的MPEG视频流预测模型

提出了一种基于小波变换的MPEG视频流的预测模型,该模型对原始数据做Haar小波变换以后,充分考虑尺度系数和乘子的不同统计特性,对尺度系数和乘子采用不同的预测模型分别预测,最终通过多重分形小波模型建模过程合成MPEG视频流序列。利用仿真和数学分析的方法对预测模型在刻画MPEG视频流的主要统计特性方面进行性能分析,从误差、数据的概率分布特性以及自相似特性等方面,证实了该预测模型的优越性能。     

Simple recursive algorithm for linear-in-theparameters nonlinear model identification

This paper introduces a simple recursive algorithm for nonlinear dynamic system identification using linear-in-the-parameters models for NARX or RBF network where both the structure and parameters can be obtained simultaneously and recursively. The main objective is to improve the numerical stability when the model terms are highly correlated. This is based on the "innovation" idea and net contribution criteria. Using the recursive formulae for the computation of the Moore-Penrose inverse of matrices and th...     

A hybrid neural-network/mathematical prediction model for tandem cold mill

In tandem cold mill, a strip is flattened by stands of rolls to a desired thickness. At Pohang Iron and Steel Company (POSCO) in Pohang, Korea, precalculation determines the mill settings before a strip actually enters the mill and is done by an outdated mathematical model. A corrective neural network model is proposed to impoove the accuracy of the roll force prediction. The network is fed not only the usual mathematical model's input but also a set of additional inputs such as the chemical composition of the coil, its coiling temperature and the aggregated amount of processed strips of each roll. The network was trained using a standard backpropagation with 4,944 process data collected at POSCO from March 1995 through December 1995, then was tested on the unseen 1,586 data from February 1996 through April 1996. The combined model reduced the prediction error by 33.88% on average.     

The periodic Markov modulated batch Bernoulli process and its application to MPEG video traffic

The Markov modulated batch Bernoulli process (MMBBP) is widely used for modeling a superposition of multiple voices and data at multiplexers in asynchronous transfer mode (ATM) networks, because the MMBBP has the time correlation property and its autocorrelation function is exponentially decaying. However, experimental studies on the MPEG (Motion Pictures Expert Group) video traffic show that its autocorrelation function is periodic as well as exponentially decaying. So, the MMBBP is not adequate to describe the MPEG video traffic.

In this paper, we propose a new stochastic process which we call the periodic Markov modulated batch Bernoulli process (P-MMBBP) to describe the MPEG video traffic adequately. We derive the autocorrelation function of the P-MMBBP and show that the autocorrelation functions of the P-MMBBP and the MPEG video traffic are of the same pattern. We also propose a simple way to match parameters of the P-MMBBP from real MPEG video traffic. We consider the P-MMBBP/D/1 queue and derive the queue length distribution analytically. From numerical results, we show that periodicity gives a significant effect on the tail behavior of the queue length distribution. We also compare tail probabilities obtained by using the P-MMBBP with those obtained by using the 2-state MMBBP. Our comparison shows that the use of the 2-state MMBBP underestimates tail probabilities.     

QoE prediction model for mobile video telephony

Interactive online video applications, such as video telephony, are known for their vulnerability to network condition. With the increasing usage of hand-held wireless mobile devices, which are capable of capturing and processing good quality videos, combined with the flexibility in an end-user movements have added new challenging factors for application providers and network operators. These factors affect the perceived video quality of mobile video telephony applications, unlike conventional video telephony over desktop computers. We investigate this impact on video quality of mobile video telephony in varying network conditions and end-users movement scenarios. Based on 312 live traces, we quantitatively derive the correlation between the perceived video quality and the network Quality of Service (QoS) and user mobility. With the results, we develop a Quality of Experience (QoE) prediction model for mobile video telephony using Support Vector Regression techniques. The prediction models display ≈ 0.8 pearson correlation with experimental data. Our methodology and findings can be used to guide the video telephony application providers and network operators to work towards satisfying end-user experience.     

Analytical model for MPEG video frame loss rates and playback interruptions on packet networks

This paper presents and studies objective video quality evaluation techniques for a network where frame losses can be considered independent, for example a best effort not heavy loaded packet switching network. The total or partial loss of a frame’s information affects the quality of video playback, as the frame cannot be decoded and other frames that depend on it cannot be correctly decoded too. Therefore, during some time the video playback has errors in the image and the user will perceive them as interruptions. In this paper, the total number of decoded frames and the video playback interruptions duration will be considered important parameters to quantify the video quality. The analytical formulation for them will be presented and the importance of considering them together will be highlighted.     

Feature modeling for configurable and adaptable modular buildings

Current design approaches for new buildings do not sufficiently plan for or adapt to changing conditions that could be used to extend the useful life of buildings, as part of a circular economy. While notable advances have emerged for using BIM-based configurators to improve building design and project execution, there is a need expand such configurators to look at how buildings can be adapted and re-configured across their lifecycle. This paper develops and demonstrates an innovative feature modeling approach for configuring and adapting modular buildings. This study uses BIM for structuring intricate feature relationships of specific design aspects in terms of product circularity. The design aspects considered are structural design, dimensional variation control, and disassembly planning design. Feature parameter maps, which are a general constraint relation representation, are implemented to describe the data models since they are an efficient way to visualize feature elements and interdependencies, to avoid the creation of redundant information, and to improve data structure consistency. The application of the proposed methodology is validated with a functional demonstration of a conceptual design and optimization for a single module that is meant to be part of a modular construction project. The product model was synthesized in a parametric BIM environment for iterative configuration, analysis of results, and final optimization of the single module assembly. The demonstration case study shows that BIM can be adapted to assist on modeling specific design aspects for modular buildings and to create design alternatives. Also, the method shows a considerable benefit that the designer can produce diverse accurate design alternatives within a reduced amount of time.     

呼叫中心分块回归话务量预测

为获得前瞻性话务量数据,解决呼叫中心坐席安排的问题,实现人力资源合理配置,分析历史话务量特性,提出了基于支持向量机和[K]近邻算法的分块回归（SKBR）话务量预测模型。将话务量按日期类型分为工作日话务量、周末话务量以及节假日话务量,采用不同的模型预测相应的话务量。以某省电力呼叫中心话务量为例,在Matlab平台上进行实验。结果证明,相比SVM模型和改进寻参方法的SVM模型,SKBR模型在预测准确性上有所提升。     

An efficient neural-network model for real-time fault detection in industrial machine

Neural Computing and Applications - Induction machines have extensive demand in industries as they are used for large-scale production and, therefore, vulnerable to both electrical and mechanical...     

网络流量预测模型研究进展

网络流量预测的关键是建立相应的数学模型.介绍了网络流量预测模型的基本概念和分类,指出了单一预测模型存在的问题.从模型构建原理出发,重点对组合预测模型进行了系统的分类,将其分为线性组合模型、优化组合模型、分解重构组合模型,介绍了各组合模型的相关研究进展,并分析了它们的特点和适用范围,指出组合预测模型能更精确、全面地预测流量.针对流量预测目前存在的问题,对未来研究趋势提出了几点思考.     

基于RBF神经网络的网络流量建模及预测

随着计算机网络的迅速发展,目前的网络规模极为庞大和复杂,网络流量预测对于网络管理具有至关重要的意义。根据实际网络中测量的大量网络流量数据,建立了一个基于RBF神经网络的流量模型,给出了RBF神经网络的结构设计及基于正交最小二乘的学习算法,并基于该流量模型对网络流量进行预测。仿真结果表明,该模型具有较高的预测效果,相对于传统线性模型及BP神经网络模型具有更高的预测精度和良好的自适应性。     

一种新的网络流量组合预测模型

针对传统网络流量预测方法精度低的问题,提出一种新的网络流量预测模型。该模型首先采用小波变换对网络流量进行分解,基于支持向量机预测各分量,并将预测结果进行重构。仿真实验结果表明该模型性能较好,其预测精度为6.347。     

An architecture modeling framework for probabilistic prediction

In the design phase of business and IT system development, it is desirable to predict the properties of the system-to-be. A number of formalisms to assess qualities such as performance, reliability and security have therefore previously been proposed. However, existing prediction systems do not allow the modeler to express uncertainty with respect to the design of the considered system. Yet, in contemporary business, the high rate of change in the environment leads to uncertainties about present and future characteristics of the system, so significant that ignoring them becomes problematic. In this paper, we propose a formalism, the Predictive, Probabilistic Architecture Modeling Framework (P²AMF), capable of advanced and probabilistically sound reasoning about business and IT architecture models, given in the form of Unified Modeling Language class and object diagrams. The proposed formalism is based on the Object Constraint Language (OCL). To OCL, P²AMF adds a probabilistic inference mechanism. The paper introduces P²AMF, describes its use for system property prediction and assessment and proposes an algorithm for probabilistic inference.     

An artificial neural-network model for impact properties in X70 pipeline steels

An artificial neural-network (ANN) model has been developed for the analysis and simulation of the correlation between the mechanical properties and composition and thermomechanical treatment parameters of high strength, low alloy steels. The input parameters of the model consist of alloy compositions (C, Si, Mn, P, S, Cu, Ni, Cr, Mo, Ti, V, Nb, Ca, Al, B) and tensile test results (yield strength, ultimate tensile strength, percentage elongation). The outputs of the ANN model include impact energy (?10 °C). The model can be used to calculate the properties of low alloy steels as a function of alloy composition and thermomechanical treatment variables. The current study achieved a good performance of the ANN model, and the results are in agreement with experimental knowledge.