Optimization of Air Route Network Nodes to Avoid ″Three Areas″ Based on An Adaptive Ant Colony Algorithm

Air route network(ARN)planning is an efficient way to alleviate civil aviation flight delays caused by increasing development and pressure for safe operation.Here,the ARN shortest path was taken as the objective function,and an air route network node(ARNN)optimization model was developed to circumvent the restrictions imposed by″three areas″,also known as prohibited areas,restricted areas,and dangerous areas(PRDs),by creating agrid environment.And finally the objective function was solved by means of an adaptive ant colony algorithm(AACA).The A593,A470,B221,and G204 air routes in the busy ZSHA flight information region,where the airspace includes areas with different levels of PRDs,were taken as an example.Based on current flight patterns,a layout optimization of the ARNN was computed using this model and algorithm and successfully avoided PRDs.The optimized result reduced the total length of routes by 2.14% and the total cost by 9.875%.

Ranking important nodes in complex networks by simulated annealing

In this paper, based on simulated annealing a new method to rank important nodes in complex networks is presented.First, the concept of an importance sequence（IS） to describe the relative importance of nodes in complex networks is defined. Then, a measure used to evaluate the reasonability of an IS is designed. By comparing an IS and the measure of its reasonability to a state of complex networks and the energy of the state, respectively, the method finds the ground state of complex networks by simulated annealing. In other words, the method can construct a most reasonable IS. The results of experiments on real and artificial networks show that this ranking method not only is effective but also can be applied to different kinds of complex networks.

Evolutionary dynamics analysis of complex network with fusion nodes and overlap edges

Multiple complex networks, each with different properties and mutually fused, have the problems that the evolving process is time varying and non-equilibrium, network structures are layered and interlacing, and evolving characteristics are difficult to be measured. On that account, a dynamic evolving model of complex network with fusion nodes and overlap edges（CNFNOEs） is proposed. Firstly, we define some related concepts of CNFNOEs, and analyze the conversion process of fusion relationship and hierarchy relationship. According to the property difference of various nodes and edges, fusion nodes and overlap edges are subsequently split, and then the CNFNOEs is transformed to interlacing layered complex networks（ILCN）. Secondly,the node degree saturation and attraction factors are defined. On that basis, the evolution algorithm and the local world evolution model for ILCN are put forward. Moreover, four typical situations of nodes evolution are discussed, and the degree distribution law during evolution is analyzed by means of the mean field method.Numerical simulation results show that nodes unreached degree saturation follow the exponential distribution with an error of no more than 6%; nodes reached degree saturation follow the distribution of their connection capacities with an error of no more than 3%; network weaving coefficients have a positive correlation with the highest probability of new node and initial number of connected edges. The results have verified the feasibility and effectiveness of the model, which provides a new idea and method for exploring CNFNOE＇s evolving process and law. Also, the model has good application prospects in structure and dynamics research of transportation network, communication network, social contact network,etc.

Analysis on the Network Node of Konjac Glucomannan Molecular Chain

The dynamic changes of the complex network and the material form and function were actuated by the molecular chains. The interaction behavior between molecular chains was difficult to illuminate because the dynamic changes of macromolecules were observed difficultly by normal spectrum method and the methods to test and evaluate the complex network evolution prediction and intervention are rare. The mathematic model of domino offect of molecular chains was established based on the topological structure of molecular chain aggregation of Konjac glucomannan, and the molecular entanglement mechanism of Konjac glucamannan blends was studied through molecular simulation and knot theory analysis combined with experimental verification. The results suggested that two network models （topological entanglement and solid knot） of Konjac glucomannon blends were formed through hydrogen bond nodes. The topological entanglement was strengthened with the increase of concentration and the form of molecular chains was Gaussian chain which could not allow traverse moving owing to the intermolecular cross and entanglement and the shield of intramolecular interaction. Besides, the structures of Konjac glucomannon blends became more stable due to the solid knot. Both of them were verified by the experimental results. This experimental method simplifies the microscopic description of Konjac glucomannon, and there is important guiding significance of the experimental results for the prediction and control ofpolysaccharides＇ structure and function.

Bidirectional WDM Self-Healing Ring Network for Hub/Remote Nodes

We demonstrate a bidirectional WDM self-healing ring network for hub/remote nodes with one fiber. In this network, self-healing can be achieved within 8 ms. The transmission capacity can be doubled in the operating state.

A QoS-Guanranteed Scheduling Algorithm with High Throughput for Edge Nodes of Optical Burst Switching Networks

A scheduling algorithm for the edge nodes of optical burst switching (OBS) networks is proposed to guarantee the delay requirement of services with different CoS (Class of Service) and provide lower burst loss ratio at the same time. The performance of edge nodes based on the proposed algorithm is presented.

High dynamic adaptive mobility network model and performance analysis

Since mobile networks are not currently deployed on a large scale, research in this area is mostly by simulation. Among other simulation parameters, the mobility model plays a very important role in determining the protocol performance in MANET. Based on random direction mobility model, a high dynamic adaptive mobility network model is proposed in the paper. The algorithms and modeling are mainly studied and explained in detail. The technique keystone is that normal distribution is combined with uniform distribution and inertial feedback control is combined with kinematics, through the adaptive control on nodes speed and prediction tracking on nodes routes, an adaptive model is designed, which can be used in simulations to produce realistic and dynamic network scenarios. It is the adaptability that nodes mobile parameters can be adjusted randomly in threedimensional space. As a whole, colony mobility can show some rules. Such random movement processes as varied speed and dwells are simulated realistically. Such problems as sharp turns and urgent stops are smoothed well. The model can be adapted to not only common dynamic scenarios, but also high dynamic scenarios. Finally, the mobility model performance is analyzed and validated based on random dynamic scenarios simulations.

Air route network optimization in fragmented airspace based on cellular automata

Air route network optimization,one of the essential parts of the airspace planning,is an effective way to optimize airspace resources,increase airspace capacity,and alleviate air traffic congestion.However,little has been done on the optimization of air route network in the fragmented airspace caused by prohibited,restricted,and dangerous areas（PRDs）.In this paper,an air route network optimization model is developed with the total operational cost as the objective function while airspace restriction,air route network capacity,and non-straight-line factors（NSLF） are taken as major constraints.A square grid cellular space,Moore neighbors,a fixed boundary,together with a set of rules for solving the route network optimization model are designed based on cellular automata.The empirical traffic of airports with the largest traffic volume in each of the 9 flight information regions in China's Mainland is collected as the origin-destination（OD） airport pair demands.Based on traffic patterns,the model generates 35 air routes which successfully avoids 144 PRDs.Compared with the current air route network structure,the number of nodes decreases by 41.67%,while the total length of flight segments and air routes drop by 32.03% and 5.82% respectively.The NSLF decreases by 5.82% with changes in the total length of the air route network.More importantly,the total operational cost of the whole network decreases by 6.22%.The computational results show the potential benefits of the model and the advantage of the algorithm.Optimization of air route network can significantly reduce operational cost while ensuring operation safety.

Pareto-based multi-objective node placement of industrial wireless sensor networks using binary differential evolution harmony search

The reliability and real time of industrial wireless sensor networks （IWSNs） are the absolute requirements for industrial systems, which are two fore- most obstacles for the large-scale applications of IWSNs. This paper studies the multi-objective node placement problem to guarantee the reliability and real time of IWSNs from the perspective of systems. A novel multi-objective node deployment model is proposed in which the reliabil- ity, real time, costs and scalability of IWSNs are addressed. Considering that the optimal node placement is an NP-hard problem, a new multi-objective binary differential evolu- tion harmony search （MOBDEHS） is developed to tackle it, which is inspired by the mechanism of harmony search and differential evolution. Three large-scale node deploy- ment problems are generated as the benCHmarks to verify the proposed model and algorithm. The experimental results demonstrate that the developed model is valid and can be used to design large-scale IWSNs with guaranteed reliability and real-time performance efficiently. Moreover, the comparison results indicate that the proposed MOB- DEHS is an effective tool for multi-objective node place- ment problems and superior to Pareto-based binary differential evolution algorithms, nondominated sorting genetic algorithm II （NSGA-II） and modified NSGA-II.

Dynamic temperature prediction of electronic equipment under high altitude long endurance conditions

Unmanned Aerial Vehicle（UAV） is developing towards the direction of High Altitude Long Endurance（HALE）. This will have an important influence on the stability of its airborne electronic equipment using passive thermal management. In this paper, a multi-node transient thermal model for airborne electronic equipment is set up based on the thermal network method to predict their dynamic temperature responses under high altitude and long flight time conditions. Some relevant factors are considered into this temperature prediction model including flight environment,radiation, convection, heat conduction, etc. An experimental chamber simulating a high altitude flight environment was set up to survey the dynamic thermal responses of airborne electronic equipment in a UAV. According to the experimental measurement results, the multi-node transient thermal model is verified without consideration of the effects of flight speed. Then, a modified way about outside flight speed is added into the model to improve the temperature prediction performance. Finally, the corresponding simulation code is developed based on the proposed model. It can realize the dynamic temperature prediction of airborne electronic equipment under HALE conditions.

Latent Co-interests' Relationship Prediction

With the development of the social media and Internet, discovering latent information from massive information is becoming particularly relevant to improving user experience. Research efforts based on preferences and relationships between users have attracted more and more attention. Predictive problems, such as inferring friend relationship and co-author relationship between users have been explored. However, many such methods are based on analyzing either node features or the network structures separately, few have tried to tackle both of them at the same time. In this paper, in order to discover latent co-interests＇ relationship, we not only consider users＇ attributes but network information as well. In addition, we propose an Interest-based Factor Graph Model （I-FGM） to incorporate these factors. Experiments on two data sets （bookmarking and music network） demonstrate that this predictive method can achieve better results than the other three methods （ANN, NB, and SVM）.