Research on the English Teaching Platform Based on the Campus Network Multimedia

The traditional teaching and the multimedia network teaching have their own advantages and limitations. The harmonious integration of these two teaching modes can make up for each other's weaknesses and give full play to the best overall effects o f our teaching. Guided by the constructivism theory, the humanism theory and the harmonious education theory, this paper explores the contradiction between the traditional foreign language teaching and the multimedia network teaching, expounds the characteristics of the two kinds of the teaching harmony, and puts forward solutions to how to achieve the harmony of the foreign language teaching.

Research on the Ideological and Political Education Service Platform Based on the Campus Network Environment

Nowadays, with the increasingly mature construction of the campus network, new changes have taken place in the way students participate in their learning and understanding of the knowledge. For the ideological and political educational activities, we need to build a brand new ideological and political education service platform based on the new changes in the campus network environment. Starting from the analysis of the characteristics, connotation and the teaching impact of the campus network environment, this paper analyses the new changes of the ideological and political education services under the campus network environment, and combines the values and significances o f the ideological and political education services under the campus network environment, so as to analyze the implementation strategy of constructing the ideological and political education service platform based on the campus network environment.

Distributed Fault-Tolerant Containment Control for Nonlinear Multi-Agent Systems Under Directed Network Topology via Hierarchical Approach

This paper investigates the distributed fault-tolerant containment control(FTCC)problem of nonlinear multi-agent systems(MASs)under a directed network topology.The proposed control framework which is independent on the global information about the communication topology consists of two layers.Different from most existing distributed fault-tolerant control(FTC)protocols where the fault in one agent may propagate over network,the developed control method can eliminate the phenomenon of fault propagation.Based on the hierarchical control strategy,the FTCC problem with a directed graph can be simplified to the distributed containment control of the upper layer and the fault-tolerant tracking control of the lower layer.Finally,simulation results are given to demonstrate the effectiveness of the proposed control protocol.

An improved de-interleaving algorithm of radar pulses based on SOFM with self-adaptive network topology

As a core part of the electronic warfare(EW) system,de-interleaving is used to separate interleaved radar signals. As interleaved radar pulses become more complex and denser, intelligent classification of radar signals has become very important. The self-organizing feature map(SOFM) is an excellent artificial neural network, which has huge advantages in intelligent classification of complex data. However, the de-interleaving process based on SOFM is faced with the problems that the initialization of the map size relies on prior information and the network topology cannot be adaptively adjusted. In this paper, an SOFM with self-adaptive network topology(SANT-SOFM) algorithm is proposed to solve the above problems. The SANT-SOFM algorithm first proposes an adaptive proliferation algorithm to adjust the map size, so that the initialization of the map size is no longer dependent on prior information but is gradually adjusted with the input data. Then,structural optimization algorithms are proposed to gradually optimize the topology of the SOFM network in the iterative process,constructing an optimal SANT. Finally, the optimized SOFM network is used for de-interleaving radar signals. Simulation results show that SANT-SOFM could get excellent performance in complex EW environments and the probability of getting the optimal map size is over 95% in the absence of priori information.

Analysis of Campus Network Security

Campus network provides a critical stage to student service and campus administration,which assumes a paramount part in the strategy of‘Rejuvenating the Country through Science and Education’and‘Revitalizing China through Talented Persons’.However,with the rapid development and continuous expansion of campus network,network security needs to be an essential issue that could not be overlooked in campus network construction.In order to ensure the normal operation of various functions of the campus network,the security risk level of the campus network is supposed to be controlled within a reasonable range at any moment.Through literature research,theory analysis and other methods,this paper systematically combs the research on campus network security at home and abroad,analyzing and researching the campus network security issues from a theoretical perspective.A series of efficient solutions accordingly were also put forward.

Short-Term Traffic Flow Prediction Based on Road Network Topology

Accurate short-term traffic flow prediction plays a crucial role in intelligent transportation system (ITS), because it can assist both traffic authorities and individual travelers make better decisions. Previous researches mostly focus on shallow traffic prediction models, which performances were unsatisfying since short-term traffic flow exhibits the characteristics of high nonlinearity, complexity and chaos. Taking the spatial and temporal correlations into consideration, a new traffic flow prediction method is proposed with the basis on the road network topology and gated recurrent unit (GRU). This method can help researchers without professional traffic knowledge extracting generic traffic flow features effectively and efficiently. Experiments are conducted by using real traffic flow data collected from the Caltrans Performance Measurement System (PEMS) database in San Diego and Oakland from June 15, 2017 to September 27, 2017. The results demonstrate that our method outperforms other traditional approaches in terms of mean absolute percentage error (MAPE), symmetric mean absolute percentage error (SMAPE) and root mean square error (RMSE).

Clustering Network Topology Control Method Based on Responsibility Transmission

The topology control is an effective approach which can improve the quality of wireless sensor network at all sides. Through studying the mechanism of sensor network data transmission, the nature of data transmission in wireless sensor network is concluded as a kind of responsibility transmission. By redefining the responsibility and availability of nodes, the strategy for cluster head selection is studied, the responsibility and availability is determined by the combination of the residual energy, location and current flow of nodes. Based on the above, new clustering network topology control algorithm based on responsibility transmission CNTCABRT and hierarchical multi-hop CNTCABRT is presented in this paper, whose algorithm structure is along the famous LEACH algorithm. Experimental result demonstrates its promising performance over the famous LEACH algorithm in the cluster head selection, the size of cluster, the deployment of nodes and the lifetime of nodes, and several innovative conclusions are proposed finally.

Designing a practical ATM Campus Network

A typical distributed ATM campus network——BUPT(Beijing University of Posts andTelecommunications)ATM Campus Network is presented in this paper.This network iscomposed of several small ATM switches in a distributed manner and will be connected tothe China Education and Research Network(CERNET).First,an architecture of BUPTATM Campus Network is introduced.Then the interworking and protocol conversion be-tween ATM network and existing 802.X LAN are discussed.Finally,some of the typicalbroadband applications such as multimedia communications,virtual workgroups,remotevideo-teaching and LAN emulation are described.

A Localized Inter-Actuator Network Topology Repair Scheme for Wireless Sensor and Actuator Networks

Node failure is one of the most severe problems that wireless sensor and actuator networks(WSANs) have to deal with. The failure of actuator nodes, in particular, may result in substantial consequences such as network partitioning, incorrect and incomplete decision execution for WSANs. This paper proposes an efficient localized scheme, called LANTR, to repair the damaged topology of inter-actuator network while single actuator node paralyzes. For the failure of an ordinary actuator node, LANTR can rapidly repair the topology through relocating only one-hop neighbors of the failure node, meanwhile, keep the original topology structure as much as possible. Given the magnitude of cut vertex actuators playing on the connectivity, LANTR designs a novel method for each cut vertex to select out a specific guardian node with the minimum degree or minimum cumulative degree from its neighbors, which can reduce the repair influence on the original topology and effectively reduce the coverage loss rate. The performance of the proposed scheme is evaluated and compared with several existing representative topology repair schemes, and the results indicate that LANTR can more effectively and efficiently repair the topology of inter-actuator networks.

Study on Documents ofCampus Network

Campus network establishment belongs to the field of system engineering. It is necessary to carry on cooperation among departments. Standardization is the key to solve the problem, and its core is standardization of documents. Therefore, this paper will be concentrated on the discussion of relevant problems in combination with our campus network practice.

Unicast Network Topology Inference Algorithm Based on Hierarchical Clustering

Network topology inference is one of the important applications of network tomography.Traditional network topology inference may impact network normal operation due to its generation of huge data traffic.A unicast network topology inference is proposed to use time to live(TTL)for layering and classify nodes layer by layer based on the similarity of node pairs.Finally,the method infers logical network topology effectively with self-adaptive combination of previous results.Simulation results show that the proposed method holds a high accuracy of topology inference while decreasing network measuring flow,thus improves measurement efficiency.

INTS-MFS:A novel method to predict microRNA-disease associations by integrating network topology similarity and microRNA function similarity

Identifying associations between microRNAs(miRNAs)and diseases is very important to understand the occurrence and development of human diseases.However,these existing methods suffer from the following limitation:first,some disease-related miRNAs are obtained from the miRNA functional similarity networks consisting of heterogeneous data sources,i.e.,disease similarity,protein interaction network,gene expression.Second,little approaches infer disease-related miRNAs depending on the network topological features without the functional similarity of miRNAs.In this paper,we develop a novel model of Integrating Network Topology Similarity and MicroRNA Function Similarity(INTS-MFS).The integrated miRNA similarities are calculated based on miRNA functional similarity and network topological characteristics.INTS-MFS obtained AUC of 0.872 based on five-fold cross-validation and was applied to three common human diseases in case studies.As a results,30 out of top 30 predicted Prostatic Neoplasm-related miRNAs were included in the two databases of dbDEMC and PhenomiR2.0.29 out of top 30 predicted Lung Neoplasm-related miRNAs and Breast Neoplasm-related miRNAs were included in dbDEMC,PhenomiR2.0 and experimental reports.Moreover,INTS-MFS found unknown association with hsa-mir-371a in breast cancer and lung cancer,which have not been reported.It provides biologists new clues for diagnosing breast and lung cancer.

An Optimization Algorithm of Network Topology Discovery Based on SNMP Protocol

Through the analysis of network topology discovery algorithm used ICMP protocol and FDB address, a novel layer topology discovery and link layer topology discovery algorithm which is suitable for campus network environment is proposed based on SNMP protocol. This algorithm can rapidly and accurately calculate the second and third floors topology of the whole pipe network.

Model of Computer Network Topology Optimization Based on Pattern Recognition Technology

The topological structure of the computer network has an important influence on the efficiency of the whole network system,the exertion of the technical performance, the reliability and the cost. The concept of computer network topology classification, introduces thecharacteristics, based on the analysis of the complex network structure, explore the effective construction of computer network topology model,for its practical application in the design of redundancy is studied, to improve the network system reliability and system safety design，

Multi-Material Topology Optimization of 2D Structures Using Convolutional Neural Networks

In recent years,there has been significant research on the application of deep learning(DL)in topology optimization(TO)to accelerate structural design.However,these methods have primarily focused on solving binary TO problems,and effective solutions for multi-material topology optimization(MMTO)which requires a lot of computing resources are still lacking.Therefore,this paper proposes the framework of multiphase topology optimization using deep learning to accelerate MMTO design.The framework employs convolutional neural network(CNN)to construct a surrogate model for solving MMTO,and the obtained surrogate model can rapidly generate multi-material structure topologies in negligible time without any iterations.The performance evaluation results show that the proposed method not only outputs multi-material topologies with clear material boundary but also reduces the calculation cost with high prediction accuracy.Additionally,in order to find a more reasonable modeling method for MMTO,this paper studies the characteristics of surrogate modeling as regression task and classification task.Through the training of 297 models,our findings show that the regression task yields slightly better results than the classification task in most cases.Furthermore,The results indicate that the prediction accuracy is primarily influenced by factors such as the TO problem,material category,and data scale.Conversely,factors such as the domain size and the material property have minimal impact on the accuracy.

Optimization of a Network Topology Generation Algorithm Based on Spatial Information Network

Spatial information network(SIN)is a network with high speed and periodicity of node operation.In recent days,China will build a complete asteroid monitoring and warning system and a near-Earth asteroid defense system.This requires launching more low-Earth orbit satellites.In order to adapt to the increase in the number of near-Earth satellites,the dynamic optimization of space informa-tion network topology between satellites will have research significance.Consid-ering the visibility of satellite networking,the connectivity of satellite nodes,and the number of links connected to the whole network,with the goal of minimizing the end-to-end delay between satellite nodes in the network as the optimization goal,a network topology optimization model that meets multiple constraints is constructed,and the model is solved using greedy algorithm and simulated anneal-ing algorithm.In the process of simulated annealing,the networkflow algorithm is innovatively proposed for neighborhood solution.Experiments show that the simulated annealing hybrid neighborhood algorithm is significantly better than the simulated annealing random neighborhood algorithm.

From 0D to 3D:Hierarchical structured high-performance free-standing silicon anodes based on binder-induced topological network architecture

Free-standing silicon anodes with high proportion of active materials have aroused great attention;however,the mechanical stability and electrochemical performance are severely suppressed.Herein,to resolve the appeal issues,a free-standing anode with a"corrugated paper"shape on micro-scale and a topological crosslinking network on the submicron and nano-scale is designed.Essentially,an integrated three-dimensional electrode structure is constructed based on robust carbon nanotubes network with firmly anchored SiNPs via forming interlocking junctions.In which,the hierarchical interlocking structure is achieved by directional induction of the binder,which ensures well integration during cycling so that significantly enhances mechanical stability as well as electronic and ionic conductivity of electrodes.Benefiting from it,this anode exhibits outsta nding performance under harsh service conditions including high Si loading,ultrahigh areal capacity(33.2 mA h cm^(-2)),and high/low temperatures(-15-60℃),which significantly extends its practical prospect.Furthermore,the optimization mechanism of this electrode is explored to verify the crack-healing and structure-integration maintaining along cycling via a unique self-stabilization process.Thus,from both the fundamental and engineering views,this strategy offers a promising path to produce high-performance free-standing electrodes for flexible device applications especially facing volume effect challenges.

A Privacy Preservation Method for Attributed Social Network Based on Negative Representation of Information

Due to the presence of a large amount of personal sensitive information in social networks,privacy preservation issues in social networks have attracted the attention of many scholars.Inspired by the self-nonself discrimination paradigmin the biological immune system,the negative representation of information indicates features such as simplicity and efficiency,which is very suitable for preserving social network privacy.Therefore,we suggest a method to preserve the topology privacy and node attribute privacy of attribute social networks,called AttNetNRI.Specifically,a negative survey-based method is developed to disturb the relationship between nodes in the social network so that the topology structure can be kept private.Moreover,a negative database-based method is proposed to hide node attributes,so that the privacy of node attributes can be preserved while supporting the similarity estimation between different node attributes,which is crucial to the analysis of social networks.To evaluate the performance of the AttNetNRI,empirical studies have been conducted on various attribute social networks and compared with several state-of-the-art methods tailored to preserve the privacy of social networks.The experimental results show the superiority of the developed method in preserving the privacy of attribute social networks and demonstrate the effectiveness of the topology disturbing and attribute hiding parts.The experimental results show the superiority of the developed methods in preserving the privacy of attribute social networks and demonstrate the effectiveness of the topological interference and attribute-hiding components.

Defense Against Software-Defined Network Topology Poisoning Attacks

Software-Defined Network(SDN)represents a new network paradigm.Unlike conventional networks,SDNs separate control planes and data planes.The function of a data plane is enabled using switches,whereas that of a control plane is facilitated by a controller.The controller learns network topologies and makes traffic forwarding decisions.However,some serious vulnerabilities are gradually exposed in the topology management services of current SDN controller designs.These vulnerabilities mainly exist in host tracking and link discovery services.Attackers can exploit these weak points to poison the network topology information in SDN controllers.In this study,a novel solution is proposed to defend against topology poisoning attacks.By analyzing the existing topology attack principles and threat models,this work constructs legal conditions for host migration to detect host hijacking attacks.The checking of the Link Layer Discovery Protocol(LLDP)source and integrity is designed to defend against link fabrication attacks.A relay-type link fabrication attack detection method based on entropy is also designed.Results show that the proposed solution can effectively detect existing topological attacks and provide complete and comprehensive topological security protection.