城市公交路网性能评估的网络图方法

通过建立数学模型 ,研究城市公交路网的拓扑结构 ,计算反映其综合性能的指标——平均换乘次数 ,对该城市的公交路网的性能做出评价 ,据此评价给出该公交路网的发展战略。

A unified graphical method for integration of hydrogen networks with purification reuse

Introducing purifiers into hydrogen network can enhance the recovery and reuse of hydrogen in refineries, further reducing the consumption of fresh hydrogen. Based on previous graphical methods, this work proposes a simple and unified graphical method for integration of hydrogen networks with purification processes. Scenarios with different hydrogen concentrations of purified product can be analyzed by the unified procedure. As a result, the maximum hydrogen saved by purification reuse can he identified and the corresponding purification process can be optimized, The proposed method is easy and non-iterative, and it is valid to purification processes with any feed concentration. A conventional hydrogen network is analyzed to test the effectiveness of the proposed method.

Research on High Resolution Satellite Image Classification Algorithm based on Convolution Neural Network

Artifi cial neural network is a kind of artificial intelligence method to simulate the function of human brain, and deep learning technology can establish a depth network model with hierarchical structure on the basis of artificial neural network. Deep learning brings new development direction to artificial neural network. Convolution neural network is a new artificial neural network method, which combines artificial neural network and deep learning technology, and this new neural network is widely used in many fields of computer vision. Modern image recognition algorithm requires classifi cation system to adapt to different types of tasks, and deep network and convolution neural network is a hot research topic in neural networks. According to the characteristics of satellite digital image, we use the convolution neural network to classify the image, which combines texture features with spectral features. The experimental results show that the convolution neural network algorithm can effectively classify the image.

Graph-Theoretic Approach to Network Analysis

Networks are a class of general systems represented by becomes a weighted graph visualizing the constraints imposed their UC-structure. Suppressing the nature of elements the network by interconnections rather than the elements themselves. These constraints follow generalized Kirchhoff＇s laws derived from physical constraints. Once we have a graph; then the working environment becomes the graph-theory. An algorithm derived from graph theory is developed within the paper in order to analyze general networks. The algorithm is based on computing all the spanning trees in the graph G with an associated weight. This weight is the product ofadmittance＇s of the edges forming the spanning tree. In the first phase this algorithm computes a depth first spanning tree together with its cotree. Both are used as parents for controlled generation of off-springs. The control is represented in selecting the off-springs that were not generated previously. While the generation of off-springs, is based on replacement of one or more tree edges by cycle edges corresponding to cotree edges. The algorithm can generate a frequency domain analysis of the network.

INTERFERENCE COORDINATION METHOD BASED ON GRAPH THEORY IN TWO-TIER CELLULAR NETWORKS

This paper studies an interference coordination method by means of spectrum allocation in Long-Term Evolution (LTE) multi-cell scenario that comprises of macrocells and femtocells. The purpose is to maximize the total throughput of femtocells while ensuring the Signal-to-Interference plus Noise Ratio (SINR) of the edge macro mobile stations (mMSs) and the edge femtocell Mobile Stations (fMSs). A new spectrum allocation algorithm based on graph theory is proposed to reduce the interference. Firstly, the ratio of Resource Blocks (RBs) that mMSs occupy is obtained by genetic algorithm. Then, after considering the impact of the macro Base Stations (mBSs) and small scale fading to the fMS on different RBs, multi-interference graphs are established and the spectrum is allocated dynamically. The simulation results show that the proposed algorithm can meet the Quality of Service (QoS) requirements of the mMSs. It can strike a balance between the edge fMSs' throughput and the whole fMSs' throughput.

Research on Novel Natural Image Reconstruction and Representation Algorithm based on Clustering and Modified Neural Network

In this paper, we conduct research on the novel natural image reconstruction and representation algorithm based on clustenng and modified neural network. Image resolution enhancement is one of the earliest researches of single image interpolation. Although the traditional interpolation and method for single image amplification is effect, but did not provide more useful information. Our method combines the neural network and the clustering approach. The experiment shows that our method performs well and satisfactory.

Independent Source Separation of Multichannel Electroencephalogram Based on Neural Network

A neural network method for independent source separation (ISS) of multichannel electroencephalogram (EEG) is proposed in this paper.Using the denoising function of wavelet multiscale decomposition,the high-frequency noises are removed from the original (raw) EEGs.Then the multichannel EEGs are treated as the weighted mixtures and the expression of weight vector is obtained by seeking the local extrema of the fourth-order cumulants (i.e.kurtosis coefficients) of the mixtures.After these process steps,the weighted mixtures are used as the input of neural network,so the independent source of EEGs can be separated one by one.The experimental results show that our method is effective for ISS of multichannel EEGs.

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.

Global Systemic Financial Risk Spillovers and Their External Shocks

The shock of the global financial crisis sparked widespread concern across the world about systemic financial risk and led to the reexamination of regulatory mechanisms.The traditional principle of“too big to fail”underwent a transformation into the new idea of“too interconnected to fail.”We used Directed Acyclic Graph(DAG)technology and network topology analysis to examine the dynamic evolution of global systemic financial risk and the risk trends in global financial markets from the perspective of network connectivity.Our findings show that financial markets in the Chinese Mainland are net receivers of risk spillovers and that systemic financial risk has a clear cross-market contagion effect due to a global volatility spillover scale of 64 percent.To maintain the stability and security of China’s financial markets,consideration should be given to the regulatory precept of“too interconnected to fail”in establishing macro-prudential risk prevention mechanisms.

Graph Theory Methods for Decomposition w.r.t. Outputs of Boolean Control Networks

This paper focuses graph theory method for the problem of decomposition w.r.t. outputs for Boolean control networks(BCNs). First, by resorting to the semi-tensor product of matrices and the matrix expression of BCNs, the definition of decomposition w.r.t. outputs is introduced. Second, by referring to the graphical structure of BCNs, a necessary and sufficient condition for the decomposition w.r.t. outputs is obtained based on graph theory method. Third, an effective algorithm to realize the maximum decomposition w.r.t. outputs is proposed. Finally, some examples are addressed to validate the theoretical results.

METHOD FOR QUICKLY INFERRING THE MECHANISMS OF LARGE-SCALE COMPLEX NETWORKS BASED ON THE CENSUS OF SUBGRAPH CONCENTRATIONS

A Mechanism-Inferring method of networks exploited from machine learning theory caneffectively evaluate the predicting performance of a network model.The existing method for inferringnetwork mechanisms based on a census of subgraph numbers has some drawbacks,especially the needfor a runtime increasing strongly with network size and network density.In this paper,an improvedmethod has been proposed by introducing a census algorithm of subgraph concentrations.Networkmechanism can be quickly inferred by the new method even though the network has large scale andhigh density.Therefore,the application perspective of mechanism-inferring method has been extendedinto the wider fields of large-scale complex networks.By applying the new method to a case of proteininteraction network,the authors obtain the same inferring result as the existing method,which approvesthe effectiveness of the method.

OBSERVABILITY OF EXTENDED TIMED EVENT GRAPH

In this paper, we study some results of extended timed event graph (ETEG)by using graph theory's methods in the dioid framework. A necessary and sufficient con-dition for the observability of ETEG is obtained and ETEG's standard structure is alsoestablished.