浅谈初中语文教学网络资源的有效利用

众所周知,语文是学习科目中最基础的一门,它是最重要的交际工具,也是人类文化的重要组成部分。语文是一门基础学科,语文学习决定着其他学科的学习成果,好的语文基础会让学生终身受益,全面提高初中语文教学质量对学生的发展有重要的作用,同时也能促进全民族整体素质的提高。现如今随着互联网的发展,大数据网络时代已经成为主流,充分将网络资源利用到教学中,成为许多学校的研究课题,本篇文章就针对初中语文的网络资源利用进行了研究。

Non-dominated sorting quantum particle swarm optimization and its application in cognitive radio spectrum allocation

In order to solve discrete multi-objective optimization problems, a non-dominated sorting quantum particle swarm optimization (NSQPSO) based on non-dominated sorting and quantum particle swarm optimization is proposed, and the performance of the NSQPSO is evaluated through five classical benchmark functions. The quantum particle swarm optimization (QPSO) applies the quantum computing theory to particle swarm optimization, and thus has the advantages of both quantum computing theory and particle swarm optimization, so it has a faster convergence rate and a more accurate convergence value. Therefore, QPSO is used as the evolutionary method of the proposed NSQPSO. Also NSQPSO is used to solve cognitive radio spectrum allocation problem. The methods to complete spectrum allocation in previous literature only consider one objective, i.e. network utilization or fairness, but the proposed NSQPSO method, can consider both network utilization and fairness simultaneously through obtaining Pareto front solutions. Cognitive radio systems can select one solution from the Pareto front solutions according to the weight of network reward and fairness. If one weight is unit and the other is zero, then it becomes single objective optimization, so the proposed NSQPSO method has a much wider application range. The experimental research results show that the NSQPS can obtain the same non-dominated solutions as exhaustive search but takes much less time in small dimensions; while in large dimensions, where the problem cannot be solved by exhaustive search, the NSQPSO can still solve the problem, which proves the effectiveness of NSQPSO.

The Application of BP Networks to Land Suitability Evaluation

The back propagation (BP) model of artificial neural networks (ANN) has many good qualities comparing with ordinary methods in land suitability evaluation.Through analyzing ordinary methods’ limitations,some sticking points of BP model used in land evaluation,such as network structure,learning algorithm,etc.,are discussed in detail,The land evaluation of Qionghai city is used as a case study.Fuzzy comprehensive assessment method was also employed in this evaluation for validating and comparing.

A Game Theoretic Approach for Energy-Efficient In-Network Caching in Content-Centric Networks

Recently, content-centric networking （CCN） has become a hot research topic for the diffusion of contents over the Internet. Most existing works on CCN focus on the improvement of network resource utilization. Consequently, the energy consumption aspect of CCN is largely ignored. In this paper, we propose a distributed energyefficient in-network caching scheme for CCN, where each content router only needs locally available information to make caching decisions considering both caching energy consumption and transport energy consumption. We formulate the in-network caching problem as a non-cooperative game. Through rigorous mathematical analysis, we prove that pure strategy Nash equilibria exist in the proposed scheme, and it always has a strategy profile that implements the socially optimal configuration, even if the touters are self-interested in nature. Simulation results are presented to show that the distributed solution is competitive to the centralized scheme, and has superior performance compared to other popular caching schemes in CCN. Besides, it exhibits a fast convergence speed when the capacity of content routers varies.

APO-Based Parallel Algorithm of Channel Allocation for Cognitive Networks

This article investigates channel allocation for cognitive networks, which is difficult to obtain the optimal allocation distribution. We first study interferences between nodes in cognitive networks and establish the channel allocation model with interference constraints. Then we focus on the use of evolutionary algorithms to solve the optimal allocation distribution. We further consider that the search time can be reduced by means of parallel computing, and then a parallel algorithm based APO is proposed. In contrast with the existing algorithms, we decompose the allocation vector into a number of sub-vectors and search for optimal allocation distribution of sub-vector in parallel. In order to speed up converged rate and improve converged value, some typical operations of evolutionary algorithms are modified by two novel operators. Finally, simulation results show that the proposed algorithm drastically outperform other optimal solutions in term of the network utilization.

Global Optimization for the Synthesis of Integrated Water Systems with Particle Swarm Optimization Algorithm

The problem of optimal synthesis of an integrated water system is addressed in this study, where water using processes and water treatment operations are combined into a single network such that the total cost of fresh water and wastewater treatment is globally minimized. A superstructure that incorporates all feasible design alterna- tives for wastewater treatment, reuse and recycle, is synthesized with a non-linear programming model. An evolutionary approach--an improved particle swarm optimization is proposed for optimizing such systems. Two simple examples are .Presented.to illustrate the global op.timization of inte.grated water networks using the proposed algorithm.

Bottleneck Analysis for Data Acquisition in High-Speed Network Traffic Monitoring

The increasing network throughput challenges the current network traffic monitor systems to have compatible high-performance data processing.The design of packet processing systems is guided by the requirements of high packet processing throughput.In this paper,we depict an in-depth research on the related techniques and an implementation of a high-performance data acquisition mechanism.Through the bottleneck analysis with the aid of queuing network model,several performance optimising methods,such as service rate increasing,queue removing and model simplification,are integrated.The experiment results indicate that this approach is capable of reducing the CPU utilization ratio while improving the efficiency of data acquisition in high-speed networks.

A multi-path routing algorithm of LEO satellite networks based on an improved ant colony system

Geography rectangle is used to reduce signaling overhead of the LEO satellite networks.Moreover,a multi-path routing algorithm based on an improved ant colony system(MPRA-AC) is proposed.Matrix indicating the importance of the link between satellites is introduced into MPRA-AC in order to find the optimal path more quickly.Simulation results show that MPRA-AC reduces the number of iterations to achieve a satisfactory solution.At the same time,the packet delivery ratio of LEO satellite networks when running MPRA-AC and DSR-LSN(dynamic source routing algorithm for LEO satellite networks) is compared.The packet delivery ratio is about 7.9%lower when running DSR-LSN.Moreover,because of the mechanism of active load balancing of MPRA-AC,simulation results show that MPRA-AC outperforms DSR-LSN in link utilization when data packets are transmitted in the networks.

Transport Layer Optimization for Cloud Computing Applications via Satellite： TCP Noordwijk＋

Cloud computing can significantly improve efficiency in Internet utilization and data management.Several cloud applications(file sharing,backup,data up/download etc.) imply transfers of large amount of data without real-time requirements.In several use-cases cloud-computing solutions reduce operational costs and guarantee target QoS.These solutions become critical when satellite systems are utilized,since resources are limited,network latency is huge and bandwidth costs are high.Using satellite capacity for cloud-computing bulk traffic,keeping acceptable performance of interactive applications,is very important and can limit the connectivity costs.This goal can be achieved installing in the Set Top Box(STB) a proxy agent,to differentiate traffic and assign bandwidth according to priority,leaving spare capacity to bulk cloud computing traffic.This aim is typically reached using a specific QoS architecture,adding functional blocks at network or lower layers.We propose to manage such a process at transport layer only.The endpoint proxy implements a new transport protocol called TCP Noordwijk+,introducing a flow control differentiation capability.The proxy includes TPCN+ which efficiently transfers low-priority bulk data and handles interactive data,keeping a high degree of friendliness.The outcomes of Ns-2simulations confirm applicability and good performance of the proposed solution.

Distributed Power Control for Energy Conservation in Hybrid Cellular Network with Device-to-Device Communication

Device-to-Device(D2D) communication has been proposed as a promising implementation of green communication to benefit the existed cellular network.In order to limit cross-tier interference while explore the gain of short-range communication,we devise a series of distributed power control(DPC) schemes for energy conservation(EC)and enhancement of radio resource utilization in the hybrid system.Firstly,a constrained opportunistic power control model is built up to take advantage of the interference avoidance methodology in the presence of service requirement and power constraint.Then,biasing scheme and admission control are added to evade ineffective power consumption and maintain the feasibility of the system.Upon feasibility,a non-cooperative game is further formulated to exploit the profit in EC with minor influence on spectral efficiency(SE).The convergence of the DPC schemes is validated and their performance is confirmed via simulation results.

Adaptive Spectr um Decision Method for Heterogeneous Cognitive Radio Networks

To improve spectrum utilization and minimize interference to Primary User (PU), an adaptive spectrum decision method is proposed for Secondary User (SU), while taking traffic load balancing and spectrum heterogeneity into consideration. Long-term statistics and current sensing results are integrated into the proposed decision method of spectrum access. Two decision methods, namely probability based and sensing based, are presented, compared and followed by performance analysis in terms of delay. For probability based spectrum decision, Short-Time-Job-First (STJF) priority queuing discipline is employed to minimize average residual time and theoretical conclusion is derived in a novel way. For sensing based decision we treat the interrupted service of SU as newly incoming and re-decision process is initialized to find available spectrum in a First-Available-First-Access (FAFA) fashion. Effect of sensing error in PHY layer is also analyzed in terms of extended average residual time. Simulation results show that, for relatively low arriving rate of SU traffic, the proposed spectrum decision method yields at least a delay reduction of 39.5% compared with non-adaptive method. The proposed spectrum decision can significantly improve delay performance even facing sensing errors, which cause performance degeneration to both PU and SU.

Exploiting Geo-Social Correlations to Improve Pairwise Ranking for Point-of-Interest Recommendation

Recently, as location-based social network(LBSN) rapidly grow, point-of-interest(POI) recommendation has become an important way to help people locate interesting places. Nowadays, there have been deep studies conducted on the geographical and social influence in the point-of-interest recommendation model based on the rating prediction. The fact is, however, relying solely on the rating fails to reflect the user's preferences very accurately, because the users are most concerned with the list of ranked point-of-interests(POIs) on the actual output of recommender systems. In this paper, we propose a co-pairwise ranking model called Geo-Social Bayesian Personalized Ranking model(GSBPR), which is based on the pairwise ranking with the exploiting geo-social correlations by incorporating the method of ranking learning into the process of POI recommendation. In this model, we develop a novel BPR pairwise ranking assumption by injecting users' geo-social preference. Based on this assumption, the POI recommendation model is reformulated by a three-level joint pairwise ranking model. And the experimental results based on real datasets show that the proposed method in this paper enjoys better recommendation performance compared to other state-of-the-art POI recommendation models.

Degree-Based Probabilistic Caching in Content-Centric Networking

Content-Centric Networking is a novel future network architecture that attracts increasing research interests in recent years. In-network caching has been regarded as a prominent feature of Content-Centric Networking since it is able to reduce the network traffic, alleviate the server bottleneck and decrease the user access latency. However, the CCN default caching scheme results in a high caching redundancy, causing an urgent need for an efficient caching scheme. To address this issue, we propose a novel implicit cooperative caching scheme to efficiently reduce the caching redundancy and improve the cache resources utilization. The simulation results show that our design achieves a higher hit ratio and a shorter cache hit distance in comparison with the other typical caching schemes.

Allocation optimization of bicycle-sharing stations at scenic spots

Bicycle-sharing system is considered as a green option to provide a better connection between scenic spots and nearby metro/bus stations. Allocating and optimizing the layout of bicycle-sharing system inside the scenic spot and around its influencing area are focused on. It is found that the terrain, land use, nearby transport network and scenery point distribution have significant impact on the allocation of bicycle-sharing system. While the candidate bicycle-sharing stations installed at the inner scenic points, entrances/exits and metro stations are fixed, the ones installed at bus-stations and other passenger concentration buildings are adjustable. Aiming at minimizing the total cycling distance and overlapping rate, an optimization model is proposed and solved based on the idea of cluster concept and greedy heuristic. A revealed preference/stated preference （RP/SP） combined survey was conducted at Xuanwu Lake in Nanjing, China, to get an insight into the touring trip characteristics and bicycle-sharing tendency. The results reveal that 39.81% visitors accept a cycling distance of 1-3 km and 62.50% respondents think that the bicycle-sharing system should charge an appropriate fee. The sttrvey indicates that there is high possibility to carry out a bicycle-sharing system at Xuanwu Lake. Optimizing the allocation problem cluster by cluster rather than using an exhaustive search method significantly reduces the computing amount from O（2^43） to O（43 2）. The 500 m-radius-coverage rate for the alternative optimized by 500 m-radius-cluster and 800 m-radius-cluster is 89.2% and 68.5%, respectively. The final layout scheme will provide decision makers engineering guidelines and theoretical support.

A stepwise optimal design of water network

In order to take full advantage of regeneration process to reduce fresh water consumption and avoid the accumulation of trace contaminants, regeneration reuse and regeneration recycle should be distinctive. A stepwise optimal design for water network is developed to simplify solution procedures for the formulated MINLP problem. In this paper, a feasible water reuse network framework is generated. Some heuristic rules from water reuse network are used to guide the placement of regeneration process. Then the outlet stream of regeneration process is considered as new water source. Regeneration reuse network structure is obtained through an iterative optimal procedure by taking the insights from reuse water network structure. Furthermore, regeneration recycle is only utilized to eliminate fresh water usage for processes in which regeneration reuse is impossible. Compared with the results obtained by relevant researches for the same example, the present method not only provides an appropriate regeneration reuse water network with minimum fresh water and regenerated water flow rate but also suggests a water network involving regeneration recycle with minimum recycle water flow rate. The design can utilize reuse, regeneration reuse and regeneration recycle step by step with minor water network structure change to achieve better flexibility. It can satisfy different demands for new plants and modernization of existing plants.

Role of street patterns in zone-based traffic safety analysis

Although extensive analyses of road segments and intersections located in urban road networks have examined the role of many factors that contribute to the frequency and severity of crashes, the explicit relationship between street pattern characteristics and traffic safety remains underexplored. Based on a zone-based Hong Kong database, the Space Syntax was used to quantify the topological characteristics of street patterns and investigate the role of street patterns and zone-related factors in zone-based traffic safety analysis. A joint probability model was adopted to analyze crash frequency and severity in an integrated modeling framework and the maximum likelihood estimation method was used to estimate the parameters. In addition to the characteristics of street patterns, speed, road geometry, land-use patterns, and temporal factors were considered. The vehicle hours was also included as an exposure proxy in the model to make crash frequency predictions. The results indicate that the joint probability model can reveal the relationship between zone-based traffic safety and various other factors, and that street pattern characteristics play an important role in crash frequency prediction.

Structure of Chinese City Network as Driven by Technological Knowledge Flows

Based on patent cooperation data,this study used a range of city network analysis approaches in order to explore the structure of the Chinese city network which is driven by technological knowledge flows.The results revealed the spatial structure,composition structure,hierarchical structure,group structure,and control structure of Chinese city network,as well as its dynamic factors.The major findings are:1) the spatial pattern presents a diamond structure,in which Wuhan is the central city;2) although the invention patent knowledge network is the main part of the broader inter-city innovative cooperation network,it is weaker than the utility model patent;3) as the senior level cities,Beijing,Shanghai and the cities in the Zhujiang(Pearl) River Delta Region show a strong capability of both spreading and controlling technological knowledge;4) whilst a national technology alliance has preliminarily formed,regional alliances have not been adequately established;5) even though the cooperation level amongst weak connection cities is not high,such cities still play an important role in the network as a result of their location within ′structural holes′ in the network;and 6) the major driving forces facilitating inter-city technological cooperation are geographical proximity,hierarchical proximity and technological proximity.

A Survey of Security Issues in Cognitive Radio Networks

In the last decade,cognitive radio(CR) has emerged as a major next generation wireless networking technology,which is the most promising candidate solution to solve the spectrum scarcity and improve the spectrum utilization.However,there exist enormous challenges for the open and random access environment of CRNs,where the unlicensed secondary users(SUs) can use the channels that are not currently used by the licensed primary users(PUs) via spectrum-sensing technology.Because of this access method,some malicious users may access the cognitive network arbitrarily and launch some special attacks,such as primary user emulation attack,falsifying data or denial of service attack,which will cause serious damage to the cognitive radio network.In addition to the specifi c security threats of cognitive network,CRNs also face up to the conventional security threats,such as eavesdropping,tampering,imitation,forgery,and noncooperation etc..Hence,Cognitive radio networks have much more risks than traditional wireless networks with its special network model.In this paper,we considered the security threats from passive and active attacks.Firstly,the PHY layer security is presented in the view of passive attacks,and it is a compelling idea of using the physical properties of the radio channel to help provide secure wireless communications.Moreover,malicious user detection is introduced in the view of active attacks by means of the signal detection techniques to decrease the interference and the probabilities of false alarm and missed detection.Finally,we discuss the general countermeasures of security threats in three phases.In particular,we discuss the far reaching effect of defensive strategy against attacks in CRNs.

Dynamic Resource Allocation in TDD-Based Heterogeneous Cloud Radio Access Networks

To fulfill the explosive growth of network capacity, fifth generation(5G) standard has captured the attention and imagination of researchers and engineers around the world. In particular, heterogeneous cloud radio access network(H-CRAN), as a promising network paradigm in 5G system, is a hot research topic in recent years. However, the densely deployment of RRHs in H-CRAN leads to downlink/uplink traffic asymmetry and severe inter-cell interference which could seriously impair the network throughput and resource utilization. To simultaneously solve these two problems, we proposed a dynamic resource allocation(DRA) scheme for H-CRAN in TDD mode. Firstly, we design a clustering algorithm to group the RRHs into different sets. Secondly, we adopt coordinated multipoint technology to eliminate the interference in each set. Finally, we formulate the joint frame structure, power and subcarrier selection problem as a mixed strategy noncooperative game. The simulation results are presented to validate the effectiveness of our proposed algorithm by compared with the existing work.

Energy-Performance Tradeoffs in laaS Cloud with Virtual Machine Scheduling

In the cloud data centers,how to map virtual machines(VMs) on physical machines(PMs) to reduce the energy consumption is becoming one of the major issues,and the existing VM scheduling schemes are mostly to reduce energy consumption by optimizing the utilization of physical servers or network elements.However,the aggressive consolidation of these resources may lead to network performance degradation.In view of this,this paper proposes a two-stage VM scheduling scheme:(1) We propose a static VM placement scheme to minimize the number of activating PMs and network elements to reduce the energy consumption;(2) In the premise of minimizing the migration costs,we propose a dynamic VM migration scheme to minimize the maximum link utilization to improve the network performance.This scheme makes a tradeoff between energy efficiency and network performance.We design a new twostage heuristic algorithm for a solution,and the simulations show that our solution achieves good results.