Earticle

Network cloud computing has become a convenient tool for people to continuous learning, it provides conditions to engage in scientific research for our new generation of sports workers future employment and development. In this paper, a cloud computing model was established, and research and development of network course education based on cloud computing is a kind of practice and the accumulation of the technology, which can develop a perfect function, with good interactive performance, can solve various problems of physical training. And the effect of prediction model time is more close to actual time, can efficiently limit the possibility of falling into local convergence, the optimal solution’s time of objective function value is shorten which meet the student’s needs .

Energy harvesting (EH) based on surrounding radio frequency (RF) has lastly become advanced approach to prolong the lifetime of the wireless networks. This paper deals with energy harvesting architecture of the half duplex and the full duplex relaying networks. By applying time switching based relaying (TSR) protocol and Amplify-and-Forward (AF) scheme, we derive the closed-form expression of the instantaneous throughput and then compare instantaneous throughput of two networks. An important result can be seen clearly that the time fraction, the position of relay, noise at relay, energy conversion factor as well as target rate in TSR impact on their throughput. Finally, numerical results show an efficient relaying strategy in half-duplex and full duplex cooperative networks.

Features created in rotating and scanning operations are very complex. Naming and identifying their topological faces is an important problem in CAD fields. In this paper, a new method of coding topological faces in rotating and scanning features is proposed. Firstly, contour segments are numbered. Secondly, an angle between contour segment and rotating axis is computed. Thirdly, all topological faces are named based on contour segments’ numbers, rotating axis and other information. When a face splits and several subfaces merge, a method of processing their codes is given. The proposed method is applied to HUST-CAID feature modeling system. Experimental results show that it can name and identify topological faces effectively in operations.

In view of the problem of data heterogeneity in enterprise application integration, we proposed a solution, which is based on XML, WSDL and Web Service technology to achieve the elimination of data heterogeneity, which can reduce the complexity of the system, and has certain significance for the information communication between enterprises and enterprises.

Cloud computing is a technology in which the Cloud Service Providers (CSP) provide many virtual servers to the users to store their information in the cloud. The faults occurring on the assignment and dismission of the virtual machines, the processing cost in the allocation of resources must also be considered. The parallel processing of the information on the virtual machines must be done effectively and in an efficient manner. A variety of systems were developed to facilitate Many Task Computing (MTC). These systems aim to hide the issues of parallelism and fault tolerant and they are used in many applications. In this paper, we introduced Nephele, a data processing framework to exploit dynamic resource provisioning offered by IaaS clouds. The performance evaluation of the virtual machines has been evaluated and the allocation and de-allocation of job tasks to the specific virtual machines has also been considered. A performance comparison with the well known data processing framework hadoop has been done. Thus this paper tells about the effective and efficient manner of processing the data by parallel processing and allocating the correct resources for the desired task. It also helps to reduce the cost of resource utilization by exploiting the dynamic resource utilization.

As a new protection, wide-area protection enables protective relaying, and automatic control based on electric power system network communications and comprehensive judgment of multi-point information, which plays an increasingly important role in the secure and stable operation of electric power system. Interaction of wide-area information relies on communication network featuring high reliability and low time delay. On the other hand, most service of power transformation station is gradually towards IP and data oriented, along with the development of smart grid. This paper aims to introduce the wide-area protection technology supported by Packet Transport Network (PTN) communication technology, with analyzing the QoS (Quality of Service) network assurance architecture of PTN network, which establishes three planes, including transport plane, management plane, and control plane, based on ASON (Automatically Switched Optical Network) technology. After demonstrating the QoS assurance system of PTN from traffic control and transmission route, this paper introduces PTN networking test. Based on detailed parameters in the test results, the transmission performance of PTN on time delay, protection, and time synchronization of various electric power communication services are analyzed, with showing that PTN can fully meet the requirements of electric power communication.

With the prevalence of Web 2.0, people increasingly prefer to express opinions and exchange information through CGM (consumer-generated media), such as blog, Internet forum and etc. Many studies pay attention to extract and analysis user opinions in consumer reviews. This paper studies how to automatically extract Chinese comparative sentences from consumer reviews. At first, the paper describes a method for solving the class imbalance problem of comparatives and non-comparatives in review data. Then we built a support vector machine learning model to classify comparatives and non-comparatives into different group on a balanced dataset. Experiments were conducted on consumer-generated product reviews, including 9600 sentences, of which 1,624 (16.92% of the total) were comparisons. Experiments show an overall F-score of 87.26%, which presents the effectiveness of the proposed approach.

This paper presents a survey on Radio over fiber for Wireless Broadband Access technology like WiMAX (Fixed and Mobile). Radio over Fiber has achieved an effective delivery of wireless and baseband signal, and has also reduces the power consumption. Whereas recent fast growing broadband access technology through wireless is WiMAX (Worldwide Interoperability for Microwave Access). In this survey, the Radio over fiber implements into wireless broadband access technologies like WiMAX. And also we will be carried out different keying and technical concept from the different methodology.

Due to the problem of low imaging accuracy and slow imaging speed when applying SVM image reconstruction algorithm in ECT system to dealing with a large amount of sample data set, the method of combining feature dimension reduction with SVM algorithm is proposed. This method classifies the sample data by using the way of clustering and extracts the feature parameter, finds out the connection between each sample and the feature, and deals the sample data with dimension reduction, thus finally getting the high-quality training sample. Then it trains the simplified sample data by applying SVM algorithm and obtains decision function, then the decision function is used to predict and image. The experimental results of image reconstruction show that this method greatly reduces the running time and improves the accuracy of imaging compared to using the SVM algorithm alone.

Workflow model based-on homogeneous activity needs to be transformed into basic workflow model in order to run in distributed environment. A workflow model mapping algorithm oriented homogeneous activity is proposed which can generate independent basic workflow fragments for homogeneous activities of different configuration modes using different processing mechanisms, and then form the basic workflow model based on the assembly of these workflow fragments. The algorithm provides a complete set of virtual mapping mechanism to solve it effectively focusing on the problem of the topological relationship of redundant activities difficult to describe in the dynamic selection model of homogeneous activity.

In the field of service computing, reputation of a Web service is usually calculated using feedback ratings provided by service users. However, the existing malicious ratings and different preferences of different service users often lead to a bias towards positive or negative ratings. In this paper, we propose a novel reputation computation approach for accurately measuring the reputation of Web services. The proposed approach uses three phases to measure and compute service reputation such as, feedback data collection, malicious rating detection and reputation computation for improving the reputation computation accuracy. A framework is also proposed to detect the malicious feedback rating and determine the accurate service reputation for reliable service selection.

In this paper, the nonlinear systems with time delay network control is discussed transmission delays and packet dropouts for networked control system with random time delay are analyzed. In this paper, online delay estimation method to obtain the delay value, the delay value is an input parameter of fuzzy adaptive PID controller, fuzzy PID temporal adjustment based on genetic algorithm and Particle swarm optimization algorithm to optimization objective function, three parameters of PID is adjusted Online in order to improve the system stability, through Matlab co-simulation tool True Time, the shows the fuzzy logic control system base on PSO algorithm promote reduce transmission delays and packet dropouts. The paper also shows the superiority of Fuzzy PID controllers over their traditional PID for NCS applications.

Grid use huge number of dynamic, distributed & heterogeneous resources under different organizations domain to executing user applications. Working with these resources, applications face problems due to occurrence of faults. There are number of different faults that occur due to many problems. Therefore grid computing needs an efficient fault tolerance mechanism. This fault tolerance makes the grid system capable to work correctly even in the presence of faults. In this paper we have study various kinds of faults and reasons for occurrence. With this, also study various kinds of existing fault tolerance techniques. We devise a strategy, based on proactive fault tolerance and take appropriate step, if any possibility for fault occurrence. Scheduling of task on available resources, which is provided by GIS consider previous history of these resources. This will decrease the probability of faults, execution time, and increase the execution rate. Proposed strategy also uses technique of check pointing to reduce execution cost. To implement proposed strategy GridSim toolkit is used for simulation

The application of complex network theory can clearly reveal the coopetition activities between inter-members in software industrial virtual cluster. The research result shows that neither simplex cooperation nor competition，but it is a kind of dynamic equilibrium of coopetition. The empirical is analyzed by using the example of cloud computing. In the process of coopetition evolution, the distribution functions of node strength are from random to power law and eventually to random.

Cloud computing is designed as computing as a utility. Customers rent computing resources in the cloud to complete their work. To ensure the quality of service (QoS) requirements defined by customers and guarantee the resource utilization in cloud datacenters, effective resource management systems should be considered. However, with more and more individuals and enterprises migrating their work into the cloud, workloads in the cloud become more and more heterogeneous. Meanwhile, resources are much more heterogeneous as cloud providers constantly scale or update the clusters with new generations of machines. Withal, workloads are dynamic with different resource demands during their execution. Besides, machines are re-engaged frequently after removal due to various reasons such as crushing and updating. The heterogeneity and dynamicity in both workloads and resources are huge barriers to using classic resource management systems. This paper will first introduce the status quo of cloud computing environment, and then give an overview of resource demand prediction and allocation policies. Finally, challenges are proposed to help build adaptive resource management systems. Keywords

Cloud computing as a new and modern technology has become a focus of attention and a platform for future services. Cloud computing services have been categorized into three main groups of services, which are infrastructure, platform, and software. Each service has its own unique characteristic while all services have also some common characteristics and challenges. Such characteristics create a complicated combination of features to serve selection of cloud solutions. This paper presents a new feature based taxonomy which facilitates the process of cloud software product selection. More precisely, Hierarchical Cloud Taxonomy Engine is the proposed method to help developers and consumers choose their appropriate product according to their needs and based on real capabilities of different cloud computing products. Moreover, this paper proposes a new evaluation mechanism which sorts the cloud products according to the customer’s needs and requirements. Besides, this paper presents a classification model for cloud features and subsequently presents a comparison between some business and research cloud products as a case study. Results of comparisons show that our proposed techniques facilitate the process of cloud product selection.

Energy is the primary constraint on designing wireless sensor networks (WSNs) practically, leading to limited network lifetime of WSN. Different communication protocols and algorithms are investigated to find ways to reduce power consumption. Cluster formation and clusterhead selection are critical issues in WSN, which can prolong the lifetime and also improve the network’s performance. In order to maintain the stability of clusters, energy of wireless sensor nodes and connectivity are taken as the basis of clusterhead election. We consider the problem of appropriate clusterhead selection in WSNs. This paper proposes a Novel Energy Entropy Based on Clusterhead Selection Algorithm for WSNs (EE-CSAW). The protocol constructs a new metric of node stability and selects a stable clusterhead with the help of entropy metric to reduce the number of clusterhead reconstruction. It selects the nodes which have the most weight and stability to be the clusterheaders. Simulations demonstrate the performance benefits of our proposal EE-CSAW over SLEACH and Thein’s proposed algorithm in WSNs.

Cloud computing is a kind of formula mode that is based on internet, and in this way, the sharing software and hardware resource and information can be provided for the computer and the other terminal devices on demand. However, due to technical differences among main service vendors, there is a compatibility issue on appropriate format that is based on the virtual machine manager of different cloud platforms. Therefore, it brings some hinder to flexible deployment of cloud workloads to some extent. While with the great development of open source standers, such issues can be solved in another train of thought. In this paper, it proposes a constructive solution to deploy cloud workloads based on open source standers. In the whole structure, the key point is that a cloud middleware is introduced and the application of MapReduce algorithm. The cloud middleware contains four kinds of modules, namely, OVF converter, classifier, interface matched and security check. And the improved MapReduce algorithm is used to the classification phase of the cloud workloads. In the program, cloud workloads are sent to the cloud middleware, and in the process of "classified → packaged →matching interface → security check → deployment", which can make cloud workloads deployed easily and flexibly. And this paper will elaborate the progress of each section through a theory case. The biggest construction of this program is manifested in four aspects that are function modular, operation independence, security and scalability. This program must be further improvement, based on the addition of functional roles and the development of open source standards, and has a strong theoretical and practical significance.

Scheduling jobs on computational grids is identified as NP-hard problem due to the heterogeneity of resources; the resources belong to different administrative domains and apply different management policies. Genetic algorithm which is a metaheuristic search on the basis of the idea of the natural evolution of living organisms generate solutions in order to reach the best solution, using techniques inspired by nature, such as the selection, crossover and mutation. One of the most important processes in the genetic algorithm is the crossover process that combines two chromosomes (parents) to produce a new chromosome (offspring). The parents with the highest fitness functions are selected to participate in the process. The idea behind crossover is that the new chromosome will be better than both parents because it takes the best qualities of both of them. This paper proposed a new job scheduling mechanism based on increasing the crossover rate in genetic algorithm in order to reach the best solution faster to improve the functionality of the genetic algorithm. To evaluate the proposed mechanism this study conducted a simulation using GridSim simulator and different workloads. The results of the simulation process revealed that the increase in the exploitation process decrease the finish time.

In this paper, a robust kalman filter is designed for the uncertainty time-varying discrete systems with state delay in process and output matrices combined with the possibility of missing measurements. The uncertainties are expected in the process, output and white noise covariance matrices. A formula for a candidate upper bound on the actual state estimation error variances for all admissible parameter uncertainties and possible missing measurements is obtained. The filter parameters are optimized to give a minimal upper bound on the state estimation error covariance for all admissible uncertainties and missing measurements.

This paper studies ε-greedy algorithm and softmax algorithm in obstacle avoidance and balance study. In the experiment, Sarsa algorithm and Q-Learning algorithm were used to appropriately simplify and build the model of obstacle avoidance; softmax algorithm was used to address how to balance exploration and utilisation; and two classical algorithms of reinforcement learning were adopted to deal with obstacle avoidance. The results generated by simulation prove that Sarsa algorithm and Q-Learning algorithm can handle obstacle avoidance and balance study in limited time step, which makes the intelligent agent improve the non-maximum estimated value of the value function of the state so as to choose the best action that has been carried out. In addition, Sarsa algorithm and Q-Learning algorithm can also enable the intelligent agent to try new actions and find out the optimal one.