债务清偿的网络流方法

本文提出了一种债务网络回路存在性的判别方法 ,并给出了债务清偿的网络流方法 。

双向编组站列车调度调整的优化模型及算法

研究双向编组站调度优化问题,以解决到达列车接入系统和出发列车编组系统的实时调度调整。在分析双向编组站作业机理和规律的基础上,以列车的编成辆数、编组内容、接续时间、集结地点和作业能力为约束条件,以列车的走行距离、所产生的交换车数为综合优化目标,构造双向编组站列车调度调整的非线性优化模型。根据模型NP-Hard性和变量高度相关性的特点,建立基于网络流技术的遗传算法求解理论。算法的主要思想是在假定0-1变量已经确定的条件下,将整数变量的确定归结为求解网络最小费用流问题。以郑州北编组站为背景,给出算法的实际求解过程。求解算例表明,提出的方法能够有效解决到达列车和出发列车作业地点的实时选择问题。

Application of the Network Flow Method for Eigenvalue Control in Power System Dispatch

A kind of dispatch method for power system eigenvalue control is proposed-in this paper. With the help of this method, not only the low-frequency oscillation of a power system can be prevented and controlled, but also the probabilistic power oscillatoin on the interconnection lines of an interconnected power system can be reduced. The proposed method has the advantages of high calculation speed and good convergency. Therefore, the method has much prospect of on-line application.

Detecting Encrypted Botnet Traffic Using Spatial-Temporal Correlation

In this paper, we to detect encrypted botnet propose a novel method traffic. During the traffic preprocessing stage, the proposed payload extraction method can identify a large amount of encrypted applications traffic. It can filter out a large amount of non-malicious traffic, greatly in, roving the detection efficiency. A Sequential Probability Ratio Test （SPRT）-based method can find spatialtemporal correlations in suspicious botnet traffic and make an accurate judgment. Experimental resuks show that the false positive and false nega- tive rates can be controlled within a certain range.

A novel internet traffic identification approach using wavelet packet decomposition and neural network

Internet traffic classification plays an important role in network management, and many approaches have been proposed to classify different kinds of internet traffics. A novel approach was proposed to classify network applications by optimized back-propagation (BP) neural network. Particle swarm optimization (PSO) algorithm was used to optimize the BP neural network. And in order to increase the identification performance, wavelet packet decomposition (WPD) was used to extract several hidden features from the time-frequency information of network traffic. The experimental results show that the average classification accuracy of various network applications can reach 97%. Moreover, this approach optimized by BP neural network takes 50% of the training time compared with the traditional neural network.

PPP： Towards Parallel Protocol Parsing

Network traffic classification plays an important role and benefits many practical network issues,such as Next-Generation Firewalls(NGFW),Quality of Service(QoS),etc.To face the challenges brought by modern high speed networks,many inspiring solutions have been proposed to enhance traffic classification.However,taking many factual network conditions into consideration,e.g.,diversity of network environment,traffic classification methods based on Deep Inspection(DI) technique still occupy the top spot in actual usage.In this paper,we propose a novel classification system employing Deep Inspection technique,aiming to achieve Parallel Protocol Parsing(PPP).We start with an analytical study of the existing popular DI methods,namely,regular expression based methods and protocol parsing based methods.Motivated by their relative merits,we extend traditional protocol parsers to achieve parallel matching,which is the representative merit of regular expression.We build a prototype system,and evaluation results show that significant improvement has been made comparing to existing open-source solutions in terms of both memory usage and throughput.

Artificial neural network approach for rheological characteristics of coal-water slurry using microwave pre-treatment

Detailed experimental investigations were carried out for microwave pre-treatment of high ash Indian coal at high power level(900 W) in microwave oven. The microwave exposure times were fixed at60 s and 120 s. A rheology characteristic for microwave pre-treatment of coal-water slurry(CWS) was performed in an online Bohlin viscometer. The non-Newtonian character of the slurry follows the rheological model of Ostwald de Waele. The values of n and k vary from 0.31 to 0.64 and 0.19 to 0.81 Pa·sn,respectively. This paper presents an artificial neural network(ANN) model to predict the effects of operational parameters on apparent viscosity of CWS. A 4-2-1 topology with Levenberg-Marquardt training algorithm(trainlm) was selected as the controlled ANN. Mean squared error(MSE) of 0.002 and coefficient of multiple determinations(R^2) of 0.99 were obtained for the outperforming model. The promising values of correlation coefficient further confirm the robustness and satisfactory performance of the proposed ANN model.

Switched Capacitor Network Analysis by Means of TCM

The totally coded method (TCM) reveals the same objective law, which governs the gain calculating for signal flow graph as Mason formula does. This algorithm is carried out merely in the domain of code operation. Based on pure code algorithm, it is more efficient because figure searching is no longer necessary. The code-series ( CS ), which are organized from node association table, have the holoinformation nature, so that both the content and the sign of each gain-term can be determined via the coded method.The principle of this method is obvious and it is suited for computer programming. The capability of the computeraided analysis for Switched Capacitor (SCN) can be enhanced.

A Stream Pattern Matching Method for Traffic Analysis

In order to identify any traces of suspicious activities for the networks security, Network Traffic Analysis has been the basis of network security and network management. With the continued emergence of new applications and encrypted traffic, the currently available approaches can not perform well for all kinds of network data. In this paper, we propose a novel stream pattern matching technique which is not only easily deployed but also includes the advantages of different methods. The main idea is： first, defining a formal description specification, by which any series of data stream can be unambiguously descrbed by a special stream pattern; then a tree representation is constructed by parsing the stream pattern; at last, a stream pattern engine is constructed with the Non-t-mite automata （S-CG-NFA） and Bit-parallel searching algorithms. Our stream pattern analysis system has been fully prototyped on C programming language and Xilinx Vn-tex2 FPGA. The experimental results show the method could provides a high level of recognition efficiency and accuracy.

Internet-Oriented Optimization Schemes for Joint Compression and Encryption

Compression and encryption are widely used in network traffic in order to improve efficiency and security of some systems.We propose a scheme to concatenate both functions and run them in a paralle pipelined fashion,demonstrating both a hardware and a software implementation.With minor modifications to the hardware accelerators,latency can be reduced to half.Furthermore,we also propose a seminal and more efficient scheme,where we integrate the technology of encryption into the compression algorithm.Our new integrated optimization scheme reaches an increase of 1.6X by using parallel software scheme However,the security level of our new scheme is not desirable compare with previous ones.Fortunately,we prove that this does not affect the application of our schemes.

Compressor Map Prediction by Neural Networks

This paper presents a study where artificial neural networks are used as a curve fitting method applying measured data from an axial compressor test rig to predict the compressor map. Emphasis is on models for prediction of pressure ratio, compressor mass flow and mechanical efficiency. Except for evaluation of interpolation and extrapolation capabilities, this study also investigates the effect of the design parameters such as number of neurons and size of training data. To reduce the effect of noise, the auto associative neural network has been applied for noise filtering of the data from the parameters used to calculate the efficiency. In summary, the results show that artificial neural network can be used for compressor map prediction, but it should be emphasized that the selection of data normalisation scale is crucial for the model where compressor mass flow is predicted. Furthermore, it is shown that the AANN （auto associative neural network） can be used to the reduce noise in measured data and thereby enhance the quality of the data.

MRT-LBM-based numerical simulation of seepage flow through fractal fracture networks

The study on seepage flow passing through single fractures is essential and critical for understanding of the law of seepage flow passing through fracture networks and the coupling mechanisms of seepage field and stress field in rock masses.By using the fractal interpolation to reconstruct a natural coarse fracture,as well as taking into account the microstructure of the fracture,the numerical simulation of seepage flow passing through the coarse fractures with two distinct vertical scaling factors is conducted based on the MRT-LBM model of the lattice Boltzmann method.Then,after obtaining the length of the preferential flow pathway,the permeability of the two kinds of fractures is estimated respectively.In view of difficulties in locating the preferential flow pathway of natural fracture networks,by numerical tests a transect permeability weighted algorithm for estimating the fracture network permeability is proposed.The algorithm is not specific to one or more particular preferential flow pathways,but considers the contribution of each section to hinder the fluid passing through the medium.In order to apply the new algorithm,by capturing the structure of fracture networks based on the image-processing technique,the numerical simulations of seepage flow passing through two groups of natural fracture networks is carried out,the permeability is forecasted and the partial flows are reproduced for both cases.It is found that the preferential flow pathway emerges at the beginning of evolution,then is strengthened subsequently,and finally reaches a steady status.Furthermore,by using the proposed method some details on local flow can be clearly observed such as backflows and vortices at local branches can exist simultaneously and so forth,suggesting the validness of the proposed method for multiscale simulations of seepage flow.