Luo Zhong

Wuhan University of Technology, Wu-han-shih, Hubei, China

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Publications (56)3.02 Total impact

  • Luo Zhong, Hui Yu, Jingling Yuan, Lin Li
    International Conference on Information Engineering; 03/2014
  • Aiyan Lu, Luo Zhong, Lin Li, Qingbo Wang
    10/2013; 11(10). DOI:10.11591/telkomnika.v11i10.3465
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    Luo Zhong, Bo Zhu, Li Yang, Huazhu Song
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    ABSTRACT: Grid aims at sharing all kinds of geographically distributed and heterogeneous resources. The key to resource organization is how to accomplish the resource discovery efficiently. In this paper, a Layered Resource Discovery Model (LRDM) was proposed firstly according to different resource type in grid. Then the components of LRDM were analyzed and designed in detail with resource discovery module in Globus Tookit 4, mainly including resource description, resource information storage, resource information organization, resource request processing, and resource selection. Finally, the model was implemented and proved to have the characteristics high discovery efficiency and scalability because of combining the advantages of both centralized and distributed mechanisms.
    09/2013; 11:77–84. DOI:10.1016/j.proenv.2011.12.013
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    ABSTRACT: A kind of tunnel environment monitoring system based on SCADA (Supervisory Control and Data Acquisition) and Qt integrated development environment was designed and realized in this paper. The data center module, interface module and data processing module were integrated. The observer pattern was applied to this system including custom widget technology, XML technology, and database technology and so on. A lot of configuration parameters were selected for the extension of this system. This system was proved having a good expansibility, reusability, flexibility and maintainability in the practical engineering.
    Proceedings of the 2012 Second International Conference on Electric Technology and Civil Engineering; 05/2012
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    ABSTRACT: The current city tunnel monitoring system is commonly composed by several independent subsystems, and the monitoring data has feature of multi source and heterogeneous. As a result, it is difficult to analyze this data and realize the linked control between subsystems. A novel linked control scheme based on XML was proposed in this paper. The unified record of each subsystem data, the storage of control strategies and the implement of control commands exist with the form of XML file in this scheme. The realization of linked control scheme has greatly improved the practicability and intelligence of city tunnel monitoring system.
    Proceedings of the 2012 Second International Conference on Electric Technology and Civil Engineering; 05/2012
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    ABSTRACT: Considering the fact that free calcium oxide content is an important parameter to evaluate the quality of cement clinker, it is very significant to predict the change of free calcium oxide content through adjusting the parameters of processing technique. In fact, the making process of cement clinker is very complex. Therefore, it is very difficult to describe this relationship using the conventional mathematical methods. Using several models, i e, linear regression model, nonlinear regression model, Back Propagation neural network model, and Radial Basis Function (RBF) neural network model, we investigated the possibility to predict the free calcium oxide content according to selected parameters of the production process. The results indicate that RBF neural network model can predict the free lime content with the highest precision (1.3%) among all the models.
    Journal of Wuhan University of Technology-Mater Sci Ed 02/2012; 27(1). DOI:10.1007/s11595-012-0433-3 · 0.42 Impact Factor
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    ABSTRACT: More and more attention is paid for energy consumption aware and power control for data center with the emergency of energy crisis. The use of virtualization technology makes it possible for dynamic configuration of data center resources. The N:1 mapping visualization technology is employed to integrate many physical machines into an virtual resource pool to control resources centralized, and then reinforcement learning is applied to resource management and decision making for an uncertain task flow data center. Finally, an automatic resource control algorithm with energy consumption aware is proposed. This algorithm is implemented in the Cloud Sim platform to improve the energy consumption of the data center. The experimental results show that our algorithm can reduce about 40% of the energy consumption of the non-power-aware data center and reduce 1.7% of that of the greedy scheduling algorithm in data center.
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    ABSTRACT: The city tunnel monitoring platform was studied based on the city tunnel monitoring system in order to share information and resources in this paper. The characteristics of the data which are collected from different city tunnel monitoring systems were analyzed. The XML(eXtensive Makeup Language) based multisource description method was proposed to uniformly express various data in the monitoring platform. Then, the common architecture of city tunnel monitoring platform was discussed. The city tunnel monitoring platform aimed at solving the problems that the monitoring data of the city tunnel is isolated and hardly to share, enhancing the monitoring level of city tunnel.
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    ABSTRACT: Using object mathematical model of traditional control theory can not solve the forecasting problem of the chemical components of sintered ore. In order to control complicated chemical components in the manufacturing process of sintered ore, some key techniques for intelligent forecasting of the chemical components of sintered ore are studied in this paper. A new intelligent forecasting system based on SVM is proposed and realized. The results show that the accuracy of predictive value of every component is more than 90%. The application of our system in related companies is for more than one year and has shown satisfactory results. Key wordssintered ore–support vector machine–intelligent forecasting–nonlinear regression–optimized control
    Journal of Wuhan University of Technology-Mater Sci Ed 06/2011; 26(3):583-587. DOI:10.1007/s11595-011-0272-7 · 0.42 Impact Factor
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    ABSTRACT: This paper mainly discusses the simulation aspect of online payment in e-commerce teaching by means of the cloud and virtualization technologies. The various transaction entities in online payment are simulated so that students and teachers access the simulation programs for virtual trading entity through the online or wireless means under cloud architecture. Students are able to fully experience the actual functionality of the entities and roles. The open-source cloud platform Eucalyptus is employed to configure and manage the Virtual Machines(VMs) for various trading entities to achieve persistence mechanisms of trading entities and Citrix ICA/Microsoft RDP/VNC protocol are also used to remotely access and control the VMs of various trading entities.
  • Luo Zhong, Li Yang, Bo Zhu, Huazhu Song
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    ABSTRACT: In the grid environment, resources are distributed, heterogeneous, dynamical and autonomous. Resource discovery takes a part of a bridge between resource requester and resource provider. Its primary function is to devise effective location strategy and find the optimal resource combination to meet the demands of users. In this paper, on the premise of the Layered Resource Discovery Model (LRDM), a layered resource location strategy was proposed, mainly including two parts: local location and global location. At last, the location strategy was simulated by using OPNET in order to demonstrate the feasibility and effectiveness of the proposed strategy.
    Advances in Computer Science, Environment, Ecoinformatics, and Education - International Conference, CSEE 2011, Wuhan, China, August 21-22, 2011. Proceedings, Part V; 01/2011
  • Luo Zhong, Li Yang, Bo Zhu, Huazhu Song
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    ABSTRACT: Combined the technology of service-oriented with learning resource grid, this paper proposed its solution of demonstration system. Firstly, the architecture of learning demonstration system was given, and the services in the system were discussed. Then the learning resource grid demonstration system was designed from the data supporting layer and visualization layer in detail. Finally, the implemented system not only showed the service invocation process visually, but also let learners or users get a deeper understanding about grid.
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    ABSTRACT: With the development of 3G mobile technology, the mobile learning (M-Learning) platform research becomes the demand of the Times. Based on cloud service for convenient hardware resource allocation and ontology for good individual learning, the system architecture of mobile learning platform is proposed and some key techniques are studied in this paper. Finally, a kind of M-learning platform framework is designed and realized.
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    ABSTRACT: This paper discusses the characteristics and key techniques of 3G mobile learning based on cloud services. Our research mainly focuses on mobile learning mode including active mode, passive mode and hybrid mode. And personalized learning method and resource integration approach are applied and analyzed. At last, we propose to employ cloud computing to mobile learning and build basic framework and simulation application for 3G mobile learning based on cloud services.
    E-Product E-Service and E-Entertainment (ICEEE), 2010 International Conference on; 12/2010
  • Jingling Yuan, Hongfu Du, Luo Zhong
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    ABSTRACT: In order to solve complex knowledge reduction, the relative conditional partition granularity and new knowledge significance, quantitative representations for the relative classification ability of decision attributes are defined in this paper. And new knowledge partition granularity and new relative conditional partition granularity are constructed to transform inconsistent decision tables into "consistent" decision table. On this basis, common knowledge reduction algorithm is proposed for both consistent and inconsistent decision tables. The algorithm can effectively obtain the optimal or a sub-optimal relative reduction of decision table and its time complexity is relatively low as O(|U|2|U|) through theoretical analysis. Finally, we show that this algorithm is effective through an example.
    2010 IEEE International Conference on Granular Computing, GrC 2010, San Jose, California, USA, 14-16 August 2010; 01/2010
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    ABSTRACT: Granular Computing (GrC) is a new concept and novel approach to solve the complex problems and provide a method for massive data-mining in the field of artificial intelligence. This paper applied granular space theory to hierarchical clustering method to generate a more efficient two-stage hierarchical clustering algorithm based on granular theory. This Method can reduce the time and memory complexities significantly and make validation very efficient and accurate. so it is very meaningful to the Hierarchical clustering algorithm.
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    ABSTRACT: Grid job scheduling is an NP complete problem, concerning the large-scale resource and job scheduling, and the adoptive and efficient job scheduling algorithm is required. Genetic algorithms show good capability to solve the problem of the small-scale, but with the increase in the number of jobs and resources, genetic algorithm is hard to convergence or slow convergence. This paper proposed a Memetic Algorithm which designed crossover operators and mutation operator with hill-climbing algorithm and Tabu search algorithm for processing grid job scheduling. Hill Climbing scheduling usually can enhance processor utilization, and Tabu search algorithm have shorter completion times for job scheduling in computing grid. And then the algorithms’ search ability and convergence speed were compared. The simulation results shown that the proposed algorithm can effectively solve the grid job scheduling problem.
    Information and Automation - International Symposium, ISIA 2010, Guangzhou, China, November 10-11, 2010. Revised Selected Papers; 01/2010
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    ABSTRACT: A new prediction model that combining the merits of support vector machine (SVM) and gray RBF neutral network is proposed in this paper. First apply structural risk minimization principle to optimize the modeling method of RBF neutral network, so that the radial basis centers and network weights could be acquired directly. Then use error compensator of RBF neutral network based on structural risk to compensate the predicting results of GM (1,1) model. The comparative experimental results show that this model is capable of improving the data predicting accuracy, as well as the generalization ability of neutral network.
    Computer Science and Software Engineering, 2008 International Conference on; 01/2009
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    ABSTRACT: An improved Greedy based global optimized Placement algorithm was proposed in this paper. Probability controlling was applied to avoid local minimization of traditional Greedy algorithm. Some new technique such as Marker bit method, dynamical selection and weight matrix were introduced to improve efficiency of this algorithm. From the experiments and comparison with Simulated Annealing algorithm, this novel algorithm shows better stability and accuracy.
    The Sixth International Symposium on Neural Networks, ISNN 2009, Wuhan, China, May 26-29, 2009, Proceedings, Part IV; 01/2009
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    ABSTRACT: Finding the minimum MPR set is a NP-complete problem in OLSR protocol, and intelligent computing methods can be used to solve it. Based on analyzing the defects of the strategy of the greedy heuristic algorithm, ant colony algorithm is imported to solve the minimum set of MPR problem. Firstly, defining the out-degree and the in-degree of a node, and in accordance with the out-degree and in-degree constraints, ant colony algorithm based on the graphic is given to find the minimum sets of MPR in this paper. Then, three kinds of ant colony algorithm model such as Ant-Cycle, Ant-Quantity and Ant-Density are improved, and the analysis of the convergence curves about the three kinds of model is described by Matlab. As the result shows that Ant-Cycle model has a faster rate of convergence, the ant colony algorithm of solving a minimum MPR sets based Ant-Cycle Model is determined. After using the OPNET to simulate the algorithm, the statistics show that the connectivity and data consistency of nodes, which also prove the rationality of the algorithm.