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.

Network topology optimization of supply chain information systems

Network topology optimization has been widely researched. Since market competition has gradually developed into competition among the supply chain information systems, the network to- pology optimization of supply chain information systems has been in urgent need. However, the net- work topology optimization of supply chain information systems is still in its early stages and still has some challenges. So a description of typical seven network topologies for various supply chain infor- mation systems has been given. The generic characteristics of each network topology can be summa- rized. To analyze the optimization of network topology optimization of supply chain information sys- tems, a numeric model has been established based on these general characteristics. A genetic algo- rithm is applied in the network topology optimization of supply chain information systems model to a- chieve the minimum cost and shortest path. Finally, our experiment results are provided to demon- strate the robustness and effectiveness of the proposed model.

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.

Day-ahead Voltage-stability-constrained Network Topology Optimization with Uncertainties

A day-ahead voltage-stability-constrained network topology optimization(DVNTO)problem is proposed to find the day-ahead topology schemes with the minimum number of operations(including line switching and bus-bar splitting)while ensuring the sufficient hourly voltage stability margin and the engineering operation requirement of power systems.The AC continuation power flow and the uncertainty from both renewable energy sources and loads are incorporated into the formulation.The proposed DVNTO problem is a stochastic,largescale,nonlinear integer programming problem.To solve it tractably,a tailored three-stage solution methodology,including a scenario generation and reduction stage,a dynamic period partition stage,and a topology identification stage,is presented.First,to address the challenges posed by uncertainties,a novel problem-specified scenario reduction process is proposed to obtain the representative scenarios.Then,to obtain the minimum number of necessary operations to alter the network topologies for the next 24-hour horizon,a dynamic period partition strategy is presented to partition the hours into several periods according to the hourly voltage information based on the voltage stability problem.Finally,a topology identification stage is performed to identify the final network topology scheme.The effectiveness and robustness of the proposed three-stage solution methodology under different loading conditions and the effectiveness of the proposed partition strategy are evaluated on the IEEE 118-bus and 3120-bus power systems.

Interaction Between Memory Load and Experimental Design on Brain Connectivity and Network Topology

The conventional approach to investigating functional connectivity in the block-designed study usually concatenates task blocks or employs residuals of task activation.While providing many insights into brain functions,the block design adds more manipulation in functional network analysis that may reduce the purity of the blood oxygenation level-dependent signal.Recent studies utilized one single long run for task trials of the same condition,the so-called continuous design,to investigate functional connectivity based on task functional magnetic resonance imaging.Continuous brain activities associated with the single-task condition can be directly utilized for task-related functional connectivity assessment,which has been examined for working memory,sensory,motor,and semantic task experiments in previous research.But it remains unclear how the block and continuous design influence the assessment of task-related functional connectivity networks.This study aimed to disentangle the separable effects of block/continuous design and working memory load on task-related functional connectivity networks,by using repeated-measures analysis of variance.Across 50 young healthy adults,behavioral results of accuracy and reaction time showed a significant main effect of design as well as interaction between design and load.Imaging results revealed that the cingulo-opercular,fronto-parietal,and default model networks were associated with not only task activation,but significant main effects of design and load as well as their interaction on intra-and inter-network functional connectivity and global network topology.Moreover,a significant behavior-brain association was identified for the continuous design.This work has extended the evidence that continuous design can be used to study task-related functional connectivity and subtle brain-behavioral relationships.

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.

Data-driven Transient Stability Assessment Model Considering Network Topology Changes via Mahalanobis Kernel Regression and Ensemble Learning

Transient stability assessment(TSA)is of great importance in power system operation and control.One of the usual tasks in TSA is to estimate the critical clearing time(CCT)of a given fault under the given network topology and pre-fault power flow.Data-driven methods try to obtain models describing the mapping between these factors and the CCT from a large number of samples.However,the influence of network topology on CCT is hard to be analyzed and is often ignored,which makes the models inaccurate and unpractical.In this paper,a novel data-driven TSA model combining Mahalanobis kernel regression and ensemble learning is proposed to deal with the problem.The model is a weighted sum of several sub-models.Each sub-model only uses the data of one topology to construct a kernel regressor.The weights are determined by both the topological similarity and numerical similarity between the samples.The similarities are decided by the parameters in Mahalanobis distance,and the parameters are to be trained.To reduce the model complexity,sub-models within the same topology category share the same parameters.When estimating CCT,the model uses not only the sub-model which the sample topology belongs to,but also other sub-models.Thus,it avoids the problem that there may be too few data under some topologies.It also efficiently utilizes information of data under all the topologies.Moreover,its decision-making process is clear and understandable,and an effective training algorithm is also designed.Test results on both the IEEE 10-machine 39-bus and a real system verify the effectiveness of the proposed model.

Short-term Transmission Maintenance Scheduling Considering Network Topology Optimization

With the increasing penetration of renewable energy sources,transmission maintenance scheduling(TMS)will have a larger impact on the accommodation of wind power.Meanwhile,the more flexible transmission network topology owing to the network topology optimization(NTO)technique can ensure the secure and economic operation of power systems.This paper proposes a TMS model considering NTO to decrease the wind curtailment without adding control devices.The problem is formulated as a two-stage stochastic mixed-integer programming model.The first stage arranges the maintenance periods of transmission lines.The second stage optimizes the transmission network topology to minimize the maintenance cost and system operation in different wind speed scenarios.The proposed model cannot be solved efficiently with off-theshelf solvers due to the binary variables in both stages.Therefore,the progressive hedging algorithm is applied.The results on the modified IEEE RTS-79 system show that the proposed method can reduce the negative impact of transmission maintenance on wind accommodation by 65.49%,which proves its effectiveness.

Impact of power system network topology errors on real-time locational marginal price

This paper examines the impact of power transmission network topology change on locational marginal price(LMP) in real-time power markets. We consider the case where the false status of circuit breakers(CBs) that bypass topology error processing can generate an incorrect power system network topology, subsequently distorting the results of the state estimation and economic dispatch.The main goal of this paper is to assess the economic impact of this misconfigured network topology on realtime LMP in an entire power system with network congestion. To this end, we start with our prior result, a simple and analytical congestion price equation, which can be applied to any single line congestion scenario. This equation can be extended to better understand the degree to which the LMP at any bus changes due to any line status error. Furthermore, it enables a rigorous analysis of the relationship between the change in LMP at any bus with respect to any line error and various physical/economical grid conditions such as the bidding prices for marginal generators and the locations of the congested/erroneous lines. Numerical examples on the impact analysis of this topology error are illustrated in IEEE 14-bus and 118-bus systems.

Network dynamics and its relationships to topology and coupling structure in excitable complex networks

All dynamic complex networks have two important aspects, pattern dynamics and network topology. Discovering different types of pattern dynamics and exploring how these dynamics depend or/network topologies are tasks of both great theoretical importance and broad practical significance. In this paper we study the oscillatory behaviors of excitable complex networks （ECNs） and find some interesting dynamic behaviors of ECNs in oscillatory probability, the multiplicity of oscillatory attractors, period distribution, and different types of oscillatory patterns （e.g., periodic, quasiperiodic, and chaotic）. In these aspects, we further explore strikingly sharp differences among network dynamics induced by different topologies （random or scale-free topologies） and different interaction structures （symmetric or asymmetric couplings）. The mechanisms behind these differences are explained physically.

Bitcoin Network Measurement and a New Approach to Infer the Topology

Bitcoin has made an increasing impact on the world's economy and financial order,which attracted extensive attention of researchers and regulators from all over the world.Most previous studies had focused more on the transaction layer,but less on the network layer.In this paper,we developed BNS(Bitcoin Network Sniffer),which could find and connect nodes in the Bitcoin network,and made a measurement in detail.We collected nearly 4.1 million nodes in 1.5 hours and identified 9,515 reachable nodes.We counted the reachable nodes'properties such as:service type,port number,client version and geographic distribution.In addition,we analyzed the stability of the reachable nodes in depth and found nearly 60%kept stable during 15 days.Finally,we proposed a new approach to infer the Bitcoin network topology by analyzing the Neighbor Addresses of Adjacent Nodes and their timestamps,which had an accuracy over 80%.

A Genetic Algorithm and Analysis of Designing Large Enterprise Network Topology

The design of enterprise network topology is in fact a multi-object nonlinear programming problem. In this paper, distance, traffic distribution and transmission delay are chosen as the important factors to be considered in the subnetwork partition of the network topology design. A mathematical model is presented and The Genetic Algorithm is used to solve the optimization object function. The application results demonstrate that the method can well solve the problem of subnetwork partition.

In Situ Variation of Interpenetrating Polymer Network Topology using a Photolabile Connector

A photo-controlled approach is developed to regulate the interpenetrating polymer network(IPN)topology by varying the connecting structure between the first and second networks.The approach is based on multifunctional inimer(Vinyl-o NB-Br)possessing three moieties,i.e.,an acrylate-based double bond for incorporation within a polymer network,a Br group for grafting polymerization to get connectIPN(c-IPN),and an o-nitrobenzyl spacer for photocleaving to convert the c-IPN to disconnected-IPN(d-IPN)with UV light irradiation.Such design allows for finely controlling the connection degree between two networks.A systematic study on the mechanical property of a series of samples with different connection degrees thus can be conducted.The results reveal that decreasing the connecting degree between two networks of IPN made a negligible contribution to materials'mechanical properties.

Network-topology-adaptive quantum conference protocols

As an important application of the quantum network communication, quantum multiparty conference has made multiparty secret communication possible. Previous quantum multiparty conference schemes based on quantum data encryption are insensitive to network topology. However, the topology of the quantum network significantly affects the communication efficiency, e.g., parallel transmission in a channel with limited bandwidth. We have proposed two distinctive protocols, which work in two basic network topologies with efficiency higher than the existing ones. We first present a protocol which works in the reticulate network using Greeberger-Horne-Zeilinger states and entanglement swapping. Another protocol, based on quantum multicasting with quantum data compression, which can improve the efficiency of the network, works in the star-like network. The security of our protocols is guaranteed by quantum key distribution and one-time-pa^t eucryption. In general, the two protocols can be applied to any quantum network where the topology can be equivalently transformed to one of the two structures we propose in our protocols.

Performance Evaluation of Topologies for Multi-Domain Software-Defined Networking

Software-defined networking(SDN)is widely used in multiple types of data center networks,and these distributed data center networks can be integrated into a multi-domain SDN by utilizing multiple controllers.However,the network topology of each control domain of SDN will affect the performance of the multidomain network,so performance evaluation is required before the deployment of the multi-domain SDN.Besides,there is a high cost to build real multi-domain SDN networks with different topologies,so it is necessary to use simulation testing methods to evaluate the topological performance of the multi-domain SDN network.As there is a lack of existing methods to construct a multi-domain SDN simulation network for the tool to evaluate the topological performance automatically,this paper proposes an automated multi-domain SDN topology performance evaluation framework,which supports multiple types of SDN network topologies in cooperating to construct a multi-domain SDN network.The framework integrates existing single-domain SDN simulation tools with network performance testing tools to realize automated performance evaluation of multidomain SDN network topologies.We designed and implemented a Mininet-based simulation tool that can connect multiple controllers and run user-specified topologies in multiple SDN control domains to build and test multi-domain SDN networks faster.Then,we used the tool to perform performance tests on various data center network topologies in single-domain and multi-domain SDN simulation environments.Test results show that Space Shuffle has the most stable performance in a single-domain environment,and Fat-tree has the best performance in a multi-domain environment.Also,this tool has the characteristics of simplicity and stability,which can meet the needs of multi-domain SDN topology performance evaluation.

Multi-constraint quality of service routing algorithm for dynamic topology networks

An adaptive multi-QoS routing algorithm called AMQRA is proposed for dynamic topology networks, such as satellite networks and Ad-hoc networks. The AMQRA is a distributed and mobile-agents-based routing algorithm, which combines ant quantity system （AQS） with ant colony optimization （ACO） that is used in AntNet routing algorithm. In dynamic topology networks, the AMQRA achieves timely optimization for concave metric QoS constraint and fast convergence. The proposed routing algorithm is simulated in Iridium satellite constellation on OPNET. The results show that AMQRA not only outperforms the AntNet in convergence rate in dynamic topology networks but also can optimize concave metric QoS constraint and reasonably allot bandwidth to the load to avoid networks congestion.

Design, Implementation and Verification of Topology Network Architecture of Smart Home Tree

Smart home technology provides consumers with network connectivity,automation or enhanced services for home devices.With the Internet of Things era,a vast data flow makes business platforms have to own the same computing power to match their business services.It achieves computing power through implementing big data algorithms deployed in the cloud data center.However,because of the far long geographical distance between the client and the data center or the massive data capacity gap,potentially high latency and high packet loss will reduce the usability of smart home systems if service providers deploy all services in the cloud data center.Edge computing and fog computing can significantly improve the utilization of network resources and reconstruct the network architecture for the user’s home.This article enables a fog resource-based resource allocation management technology.It provides a method that can more reasonably allocate network resources through a virtualized middle-tier method to ensure low response time and configure Quality of Service to ensure the use of delay-sensitive critical applications to improve the reliability of smart home communication system.Besides,the proposed method has is tested and verified by adjusting the variables of the network environment.We realize the optimization of resource allocation of client network without changing the hardware of client.