Modeling the congestion cost and vehicle emission within multimodal traffic network under the condition of equilibrium

Traditional system optimization models for traffic network focus on the treatment of congestion, which usually have an objective of minimizing the total travel time.However,the negative externality of congestion,such as environment pollution,is neglected in most cases.Such models fall short in taking Greenhouse Gas(GHG) emissions and its impact on climate change into consideration.In this paper,a social-cost based system optimization(SO) model is proposed for the multimodal traffic network considering both traffic congestion and corresponding vehicle emission.Firstly,a variation inequality model is developed to formulate the equilibrium problem for such network based on the analysis of travelers’ combined choices.Secondly,the computational models of traffic congestion and vehicle emission of whole multimodal network are proposed based on the equilibrium link-flows and the corresponding travel times.A bi-level programming model,in which the social-cost based SO model is treated as the upper-level problem and the combined equilibrium model is processed as the lower-level problem,is then presented with its solution algorithm.Finally,the proposed models are illustrated through a simple numerical example.The study results confirm and support the idea of giving the priority to the development of urban public transport,which is an effective way to achieve a sustainable urban transportation.     

The Dynamics of Two-State Public Opinion Propagation On Signed Networks

The formation of public opinion on the network is a hot issue in the field of complex network research, and some classical dynamic models are used to solve this problem. The signed network is a particular form of the complex network, which can adequately describe the amicable and hostile relationships in complex real-world systems. However, the methods for studying the dynamic process of public opinion propagation on signed networks still require to be further discussed. In this paper, the authors pay attention to the influence of negative edges in order to design a two-state public opinion propagation mechanism suitable for signed networks. The authors first set the interaction rules between nodes and the transition rules of node states and then apply the model to synthetic and real-world signed networks. The simulation results show that there is a critical value of the negative edge ratio.When the negative edge ratio exceeds this critical value, the evolutionary result of public opinion will change from a consistent state to a split state. This conclusion is also consistent with the distribution result of opinions within communities in the signed network. Besides, the research on the network structural balance shows that the model makes the network evolve in a more balanced direction.     

Learning and fatigue inspired method for optimized HTN planning

Learning is widely used in intelligent planning to shorten the planning process or improve the plan quality.This paper aims at introducing learning and fatigue into the classical hierarchical task network (HTN) planning process so as to create better highquality plans quickly.The process of HTN planning is mapped during a depth-first search process in a problem-solving agent,and the models of learning in HTN planning is conducted similar to the learning depth-first search (LDFS).Based on the models,a learning method integrating HTN planning and LDFS is presented,and a fatigue mechanism is introduced to balance exploration and exploitation in learning.Finally,experiments in two classical domains are carried out in order to validate the effectiveness of the proposed learning and fatigue inspired method.     

Operational Patterns of Industrial Symbiosis Network in Industrial Cluster District Based on Circular Economy Theory

Industrial symbiosis network （ISN） is an efficient organizational form for improving resource recycling and efficiency in industrial cluster district. Because of the variety of industrial cluster district formation model, the industrial symbiosis network is different with each other. Based on the circular economy theory, combing with international tendency of cluster district, the paper puts forward the relying-on-oriented industrial symbiosis network. Meanwhile, it also analyzes its organizational mechanism, operating pattern and environmental performance. Through the above efforts, we hope it could be helpful for industry cluster district sustainable development in China.     

Adaptive dynamic reconfiguration mechanism of unmanned swarm topology based on an evolutionary game

Autonomous cooperation of unmanned swarms is the research focus on “new combat forces” and “disruptive technologies” in military fields. The mechanism design is the fundamental way to realize autonomous cooperation. Facing the realistic requirements of a swarm network dynamic adjustment under the background of high dynamics and strong confrontation and aiming at the optimization of the coordination level, an adaptive dynamic reconfiguration mechanism of unmanned swarm topology based on an evolut...     

IUKF neural network modeling for FOG temperature drift

A novel neural network based on iterated unscented Kalman filter （IUKF） algorithm is established to model and com- pensate for the fiber optic gyro （FOG） bias drift caused by temperature. In the network, FOG temperature and its gradient are set as input and the FOG bias drift is set as the expected output. A 2-5-1 network trained with IUKF algorithm is established. The IUKF algorithm is developed on the basis of the unscented Kalman filter （UKF）. The weight and bias vectors of the hidden layer are set as the state of the UKF and its process and measurement equations are deduced according to the network architecture. To solve the unavoidable estimation deviation of the mean and covariance of the states in the UKF algorithm, iterative computation is introduced into the UKF after the measurement update. While the measure- ment noise R is extended into the state vectors before iteration in order to meet the statistic orthogonality of estimate and mea- surement noise. The IUKF algorithm can provide the optimized estimation for the neural network because of its state expansion and iteration. Temperature rise （-20-20℃） and drop （70-20℃） tests for FOG are carried out in an attemperator. The temperature drift model is built with neural network, and it is trained respectively with BP, UKF and IUKF algorithms. The results prove that the proposed model has higher precision compared with the back- propagation （BP） and UKF network models.     

Stability analysis of extended discrete-time BAMneural networks based on LMI approach

1 .INTRODUCTIONBidirectional associative memory model is a kind ofneural network models in common use with the abili-ty of information memory and association. Since thedistributed memory of the information,the networkcan associate a completed and clear mode stored in itfroman uncompleted and fuzzy mode . Bidirectionalassociative memory (BAM) proposed by B. KoskoinRef .[1] is a generalization of Cohen-Grossberg’smodel from single layer to two layers . Since then,there have beenlots of …     

A GENERAL APPROACH BASED ON AUTOCORRELATION TO DETERMINE INPUT VARIABLES OF NEURAL NETWORKS FOR TIME SERIES FORECASTING

Input selection is probably one of the most critical decision issues in neural network designing, because it has a great impact on forecasting performance. Among the many applications of artificial neural networks to finance, time series forecasting is perhaps one of the most challenging issues. Considering the features of neural networks, we propose a general approach called Autocorrelation Criterion (AC) to determine the inputs variables for a neural network. The purpose is to seek optimal lag periods, which are more predictive and less correlated. AC is a data-driven approach in that there is no prior assumption about the models for time series under study. So it has extensive applications and avoids a lengthy experimentation and tinkering in input selection. We apply the approach to the determination of input variables for foreign exchange rate forecasting and conduct comparisons between AC and information-based in-sample model selection criterion. The experiment results show that AC outperforms inf     

Density-based rough set model for hesitant node clustering in overlapping community detection

Overlapping community detection in a network is a challenging issue which attracts lots of attention in recent years.A notion of hesitant node（HN） is proposed. An HN contacts with multiple communities while the communications are not strong or even accidental, thus the HN holds an implicit community structure.However, HNs are not rare in the real world network. It is important to identify them because they can be efficient hubs which form the overlapping portions of communities or simple attached nodes to some communities. Current approaches have difficulties in identifying and clustering HNs. A density-based rough set model（DBRSM） is proposed by combining the virtue of densitybased algorithms and rough set models. It incorporates the macro perspective of the community structure of the whole network and the micro perspective of the local information held by HNs, which would facilitate the further ＂growth＂ of HNs in community. We offer a theoretical support for this model from the point of strength of the trust path. The experiments on the real-world and synthetic datasets show the practical significance of analyzing and clustering the HNs based on DBRSM. Besides, the clustering based on DBRSM promotes the modularity optimization.     

Analysis of effect factors-based stochastic network planning model

Looking at all the indeterminate factors as a whole and regarding activity durations as independent random variables, the traditional stochastic network planning models ignore the inevitable relationship and dependence among activity durations when more than one activity is possibly affected by the same indeterminate factors. On this basis of analysis of indeterminate effect factors of durations, the effect factors-based stochastic network planning （EFBSNP） model is proposed, which emphasizes on the effects of not only logistic and organizational relationships, but also the dependent relationships, due to indeterminate factors among activity durations on the project period. By virtue of indeterminate factor analysis the model extracts and describes the quantitatively indeterminate effect factors, and then takes into account the indeterminate factors effect schedule by using the Monte Carlo simulation technique. The method is flexible enough to deal with effect factors and is coincident with practice. A software has been developed to simplify the model-based calculation, in VisualStudio.NET language. Finally, a case study is included to demonstrate the applicability of the proposed model and comparison is made with some advantages over the existing models.     

DESIGN METHODOLOGY OF NETWORKED SOFTWARE EVOLUTION GROWTH BASED ON SOFTWARE PATTERNS

Recently,some new characteristics of complex networks attract the attentions of scientistsin different fields,and lead to many kinds of emerging research directions.So far,most of the researchwork has been limited in discovery of complex network characteristics by structure analysis in large-scalesoftware systems.This paper presents the theoretical basis,design method,algorithms and experiment results ofthe research.It firstly emphasizes the significance of design method of evolution growth for networktopology of Object Oriented(OO)software systems,and argues that.the selection and modulationof network models with various topology characteristics will bring un-ignorable effect on the processof design and implementation of OO software systems.Then we analyze the similar discipline of“negation of negation and compromise”between the evolution of network models with different topologycharacteristics and the development of software modelling methods.According to the analysis of thegrowth features of software patterns,we propose an object-oriented software network evolution growthmethod and its algorithms in succession.In addition,we also propose the parameter systems for OOsoftware system metrics based on complex network theory.Based on these parameter systems,it cananalyze the features of various nodes,links and local-world,modulate the network topology and guidethe software metrics.All these can be helpful to the detailed design,implementation and performanceanalysis.Finally.we focus on the application of the evolution algorithms and demonstrate it by a casestudy.Comparing the results from our early experiments with methodologies in empirical software engi-neering,we believe that the proposed software engineering design method is a computational softwareengineering approach based on complex network theory.We argue that this method should be greatlybeneficial for the design,implementation,modulation and metrics of functionality,structure and per-formance in large-scale OO software complex system.     

Multiple vehicle signals separation based on particle filtering in wireless sensor network

A novel statistical method based on particle filtering is presented for multiple vehicle acoustic signals separation problem in wireless sensor network. The particle filtering method is able to deal with non-Gaussian and nonlinear models and non-stationary sources. Using some instantaneously mixed observations of several real-world vehicle acoustic signals, the proposed statistical method is compared with a conventional non-stationary Blind Source Separation algorithm and attractive simulation results are achieved. Moreover, considering the natural convenience to transmit particles between sensor nodes, the algorithm based on particle filtering is believed to have potential to enable the task of multiple vehicles recognition collaboratively performed by sensor nodes in distributed wireless sensor network.     

Accurate energy model for WSN node and its optimal design

With the development of CMOS and MEMS technologies, the implementation of a large number of wireless distributed micro-sensors that can be easily and rapidly deployed to form highly redundant, self-configuring, and ad hoc sensor networks. To facilitate ease of deployment, these sensors operate on battery for extended periods of time. A particular challenge in maintaining extended battery lifetime lies in achieving communications with low power. For better understanding of the design tradeoffs of wireless sensor network （WSN）, a more accurate energy model for wireless sensor node is proposed, and an optimal design method of energy efficient wireless sensor node is described as well. Different from power models ever shown which assume the power cost of each component in WSN node is constant, the new one takes into account the energy dissipation of circuits in practical physical layer. It shows that there are some parameters, such as data rate, carrier frequency, bandwidth, Tsw, etc, which have a significant effect on the WSN node energy consumption per useful bit （EPUB）. For a given quality specification, how energy consumption can be reduced by adjusting one or more of these parameters is shown.     

Universality of Competitive Networks in Complex Networks

The paper proposes a model which helps to investigate the competitive aspect of real networks in quantitative terms. Through theoretical analysis and numerical simulations, it shows that the competitive model has the universality for a weighted network. The relation between parameters in the weighted network and the competitiveness in the competitive network is obtained by theoretical analysis. Based on the expression of the degree distribution of the competitive network, the strength and degree distributions of the weighted network can be calculated. The analytical solution reveals that the degree distribution of the weighted network is correlated with the increment and initial value of edge weights, which is verified by numerical simulations. Moreover, the evolving pattern of a clustering coefficient along with network parameters such as the size of a network, an updating coefficient, an initial weight and the competitiveness are obtained by further simulations.     

Application reliability for communication networks and its analysis method

The network reliability is difficult to be evaluated because of the complex relationship among the network components.It can be quite different for different users running different applications on the same network.This paper proposes a new concept and a model of application reliability.Different from the existing models that ignores the effects of applications,the proposed application reliability model considers the effects of different applications on the network performance and different types of network faults and makes the analysis of network components relationship possible.This paper also provides a method to evaluate the application reliability when the data flow satisfies Markov properties.Finally,a case study is presented to illustrate the proposed network reliability model and the analysis method.     

Relationships of the Monetary Policy, Stock Market and Real Investment in China Based on Markov-Switching Model

This paper studies the relationships of the monetary policy, stock market and real investment in China based on Markov-Switching-Vector Error Correction Model. It shows that there is a cointegration relationship among the three ones. We disclose the riddle that the stock market is in recession, but the growth rate of economy is very high in recent years. We also find that Chinese economy operated stably most of the time during the past 8 years. But if the economy is difficult to continue its high growth, it is more likely to appear ＂hard landing＂ than ＂soft landing＂. The impulse response analysis indicates the asymmetry between the ＂too cold＂ economy regime and the ＂too hot＂ regime. And the economy will oscillate during the subsequent time when it is shocked under the ＂too hot＂ regime.     

Designing cost-effective network-on-chip by dual-channel access mechanism

A dual-channel access mechanism to overcome the drawback of traditional single-channel access mechanism for network-on-chip (NoC) is proposed.In traditional single-channel access mechanism,every Internet protocol (IP) has only one channel to access the on-chip network.When the network is relatively idle,the injection rate is too small to make good use of the network resource.When the network is relatively busy,the ejection rate is so small that the packets in the network cannot leave immediately,and thus the probability of congestion is increased.In the dual-channel access mechanism,the injection rate of IP and the ejection rate of the network are increased by using two optional channels in network interface (NI) and local port of routers.Therefore,the communication performance is improved.Experimental results show that compared with traditional single-channel access mechanism,the proposed scheme greatly increases the throughput and cuts down the average latency with reasonable area increase.     

Using Multi-input-layer Wavelet Neural Network to Model Product Quality of Continuous Casting Furnace and Hot Rolling Mill

A new architecture of wavelet neural network with multi-input-layer is proposed and implemented for modeling a class of large-scale industrial processes. Because the processes are very complicated and the number of technological parameters, which determine the final product quality, is quite large, and these parameters do not make actions at the same time but work in different procedures, the conventional feed-forward neural networks cannot model this set of problems efficiently. The network presented in this paper has several input-layers according to the sequence of work procedure in large-scale industrial production processes. The performance of such networks is analyzed and the network is applied to model the steel plate quality of continuous casting furnace and hot rolling mill. Simulation results indicate that the developed methodology is competent and has well prospects to this set of problems.     

A Study on System Availability Vs System Administration Efforts with Mathematical Models

Abstract: Two mathematical models are developed in this paper to study the effectiveness of system administration effortson the improvement of system availability, based on the assumption that there exists a transitional state for a computer sys-tem in operation before it is brought down by some hardware or software problems and with intensified system administra-tion efforts, it is possible to discover and fix the problems in time to bring the system back to normal state before it isdown. Markov chain is used to simulate the transition of system states. A conclusion is made that increasing system admin-istration efforts may be a cost-effective way to meet the requirements for moderate improvement on system availability, buthigher demand on this aspect still has to be met by advanced technologies.     

Application of quantum neural networks in localization of acoustic emission

Due to defects of time-difference of arrival localization,which influences by speed differences of various model waveforms and waveform distortion in transmitting process,a neural network technique is introduced to calculate localization of the acoustic emission source.However,in back propagation(BP) neural network,the BP algorithm is a stochastic gradient algorithm virtually,the network may get into local minimum and the result of network training is dissatisfactory.It is a kind of genetic algorithms with the form of quantum chromosomes,the random observation which simulates the quantum collapse can bring diverse individuals,and the evolutionary operators characterized by a quantum mechanism are introduced to speed up convergence and avoid prematurity.Simulation results show that the modeling of neural network based on quantum genetic algorithm has fast convergent and higher localization accuracy,so it has a good application prospect and is worth researching further more.