Hierarchical Topology Structure Research Articles

Arrays of Hierarchical Zincophilic Nanorods with Trapping‐and‐Leveling Deposition for Ultrastable Zn Metal Anodes

AbstractRechargeable aqueous zinc‐ion batteries (ZIBs) are highly promising for large‐scale sustainable energy storage applications, but there remain serious problems such as Zn dendrites and side reactions that limit the cycling performance. Herein, arrays of core–shell nanorods on Cu foam are developed to stabilize zinc anodes, which have a hierarchical topological structure consisting of N‐doped carbon layers embedded with a zincophilic component of Cu5Zn8 alloy (Cu5Zn8@NC). It is found that the inner Cu5Zn8 alloys have minimized nucleation barriers and act as preferred nucleation sites, and the hierarchical arrays provide the protective layers to further accommodate the high‐capacity plating Zn, leading to a trapping‐and‐leveling process of Zn deposition. The as‐obtained Zn layers play an important role in homogenizing the interfacial ionic fluxes and reducing the local current densities. As a result, the optimized Cu5Zn8@NC host yields a superb Coulombic efficiency of 99.7% over 5000 plating/stripping cycles, and the corresponding symmetric cell delivers an ultralong dendrite‐free cycle life of 7000 h with a low overpotential of 16.5 mV at 1 mA cm−2 and 1 mAh cm−2. The ZIB assembled with the zinc anode and a V2O5 cathode exhibits long‐term charging/discharging cycles as well, up to 89.2% capacity retention after 10 000 cycles.

Gabor and Non-Gabor Neural Representations Are Shared between Visual Perception and Mental Imagery.

Visual perception and mental imagery have been shown to share a hierarchical topological visual structure of neural representation, despite the existence of dissociation of neural substrate between them in function and structure. However, we have limited knowledge about how the visual hierarchical cortex is involved in visual perception and visual imagery in a unique and shared fashion. In this study, a data set including a visual perception and an imagery experiment with human participants was used to train 2 types of voxel-wise encoding models. These models were based on Gabor features and voxel activity patterns of high-level visual cortex (i.e., fusiform face area, parahippocampal place area, and lateral occipital complex) to predict activity in the early visual cortex (EVC, i.e., V1, V2, V3) during perception, and then tested with respect to the generalization of these models to mental imagery. Our results showed that during perception and imagery, activities in the EVC could be independently predicted by the Gabor features and activity of high-level visual cortex via voxel-wise encoding models, which suggested that perception and imagery might share neural representation in the EVC. We further found Gabor-specific and non-Gabor-specific patterns of neural response to stimuli in the EVC, which were shared by perception and imagery. These findings provide insight into the mechanisms of how visual perception and imagery share representation in the EVC.

Locally informed gravitational search algorithm with hierarchical topological structure

Recently, gravitational search algorithm (GSA) has been successfully applied to solve various optimization problems. However, GSA tends to fall into local optimum because it ignores the environmental heterogeneity of agents. Therefore, locally informed gravitational search algorithm (LIGSA) based on neighborhood structure is proposed to balance exploration and exploitation. However, LIGSA ignores the differences in the evolutionary states between agents in the same neighborhood, and also ignores the differences in the evolutionary states between neighbors, which affects the performance of the algorithm. Therefore, a hierarchical locally informed gravitational search algorithm (HLIGSA) is proposed in this paper. This algorithm designs a hierarchical topology. In the lower layer, there are several non-overlapping neighborhoods, which constitute the whole population. Each agent in the neighborhood adaptively adjusts the gravitational constant according to its evolutionary state, so as to fully search the region of the neighborhood. The upper layer is a population composed of the best agents in each neighborhood, which performs gravity strategy or merging strategy for those neighborhoods that have lost the evolutionary capability, so as to balance the algorithm’s exploration and exploitation. The two layers work together to complete the entire search process. Experimental results show that HLIGSA outperforms many variants of GSA and many current state-of-the-art heuristic algorithms.

Tuning interface density and electronic structure of NiS/Ni3S4 by Mo, Co co-doping for efficient urea electrooxidation reaction

Mo, Co co-doped NiS/Ni 3 S 4 with high activity, low onset potential, fast kinetics, more active sites, excellent intrinsic catalytic activity and stability demonstrates excellent catalytic activity toward alkaline urea electrolysis. • Mo, Co co-doped NiS/Ni 3 S 4 heterojunction with 3D flower-like shape assembled by 2D ultrathin nanosheets are successfully prepared. • The amount of heterointerfaces and the electronic interaction are simultaneously modified by adjusting experimental parameters. • Mo, Co co-doped NiS/Ni 3 S 4 catalysts exhibit rich exposed active sites and facilitated electron transportation. • The assembled direct urea hydrogen peroxide fuel cell (DUHPFC) employing Mo 0.05 ,Co-NSH has an open-circle voltage (OCV) of 0.86 V and a maximum power density ( P max ) of 5.96 mW cm −2 . Electrocatalysts exhibiting impressive catalytic capability and strong stability for urea oxidation reaction (UOR) are highly desired for direct urea fuel cells (DUFCs). Herein, a series of flower-like Mo, Co co-doped NiS/Ni 3 S 4 heterostructure (Mo, Co-NSH) are controllable synthesized by a one-step hydrothermal method and utilized as catalysts for UOR. Taking advantages of massive heterojunctions, tunable electronic configuration and hierarchical flower-like topological structure together, the optimized component demonstrates advanced UOR performance compared to pure NiS/Ni 3 S 4 and mono Co/Mo element doped NiS/Ni 3 S 4 , it delivers 147.1 mA cm −2 at 1.6 V vs. RHE and works for more than 18 h without decay. The assembly direct urea hydrogen peroxide fuel cell (DUHPFC) with Mo 0.05 , Co-NSH catalyst has an open-circuit voltage of 0.86 V and a maximum power density of 5.96 mW cm −2 at 27 °C. The work provides a promising avenue for exploiting efficient electrocatalysts on both multi-metal doping and heterojunction engineering for electrochemical energy conversion and sewage treatment applications.

Multilayer Batch Learning Growing Neural Gas for Learning Multiscale Topologies

Hierarchical topological structure learning methods are expected to be developed in the field of data mining for extracting multiscale topological structures from an unknown dataset. However, most methods require user-defined parameters, and it is difficult for users to determine these parameters and effectively utilize the method. In this paper, we propose a new parameter-less hierarchical topological structure learning method based on growing neural gas (GNG). First, we propose batch learning GNG (BL-GNG) to improve the learning convergence and reduce the user-designed parameters in GNG. BL-GNG uses an objective function based on fuzzy C-means to improve the learning convergence. Next, we propose multilayer BL-GNG (MBL-GNG), which is a parameter-less unsupervised learning algorithm based on hierarchical topological structure learning. In MBL-GNG, the input data of each layer uses parent nodes to learn more abstract topological structures from the dataset. Furthermore, MBL-GNG can automatically determine the number of nodes and layers according to the data distribution. Finally, we conducted several experiments to evaluate our proposed method by comparing it with other hierarchical approaches and discuss the effectiveness of our proposed method.

Towards a Trust-Enhanced Blockchain P2P Topology for Enabling Fast and Reliable Broadcast

Blockchain technology offers an intelligent amalgamation of distributed ledger, Peer-to-Peer (P2P), cryptography, and smart contracts to enable trustworthy applications without any third parties. Existing blockchain systems have successfully either resolved the scalability issue by advancing the distributed consensus protocols from the control plane, or complemented the security issue by updating the block structure and encryption algorithms from the data plane. Yet, we argue that the underlying P2P network plane remains as an important but unaddressed barrier for accelerating the overall blockchain system performance, which can be discussed from how fast and reliable the network is. In order to improve the blockchain network performance about enabling fast and reliable broadcast, we establish a trust-enhanced blockchain P2P topology which takes transmission rate and transmission reliability into consideration. Transmission rate reflects blockchain network speed to disseminate transactions and blocks, and transmission reliability reveals whether transmission rate changes drastically on unreliable network connection. This paper presents BlockP2P-EP , a novel trust-enhanced blockchain topology to accelerate transmission rate and meanwhile retain transmission reliability. BlockP2P-EP first operates the geographical proximity sensing clustering, which leverages K-Means algorithm for gathering proximity peer nodes into clusters. It follows by the hierarchical topological structure that ensures strong connectivity and small diameter based on node attribute classification. Then we propose establishing trust-enhanced network topology. On top of the trust-enhanced blockchain topology, BlockP2P-EP conducts the parallel spanning tree broadcast algorithm to enable fast data broadcast among nodes both intra- and inter- clusters. Finally, we adopt an effective node inactivation detection method to reduce network load. To verify the validity of BlockP2P-EP protocol, we carefully design and implement a blockchain network simulator. Evaluation results show that BlockP2P-EP can exhibit promising network performance in terms of transmission rate and transmission reliability compared to Bitcoin and Ethereum.

An approach for assembly process case discovery using multimedia information source

Assembly process design (APD) is a time-consuming and error-prone activity of complex product manufacturing. Assembly process reuse can efficiently improve the process design quality and shorten design cycle. With the rapid development of digital technologies, the information source, which stores reusable information of an existing APD result, becomes multiple and heterogeneous multimedia, e.g. text, 3D model, even images and videos. Currently, most researches focused on using single and structured information source, there has been very little research on how to use multimedia information source. For reusing assembly process, one appropriate way is to capture assembly process case (APC) from existing APD result, retrieve an APC similar to the new assembly and modify the process of retrieved APC to generate assembly process of new assembly. In this paper, a novel approach is proposed to discover assembly process case for assembly process reuse using multimedia information source. First, the integrated representation of APC is constructed, which consists of hierarchical topological structure and sematic assembly process concept. Then, by multimedia dismantling and single-medium parsing, the assembly process information is extracted uniformly from multimedia information source. Moreover, the process similarity between new assembly and APC is calculated to retrieve the reusable APC. The process similarity calculation is comprehensively accomplished from two aspects: applying graph algorithm to calculate the topological structure similarity and applying weighted bipartite graph to calculate the assembly process concepts similarity. Finally, an illustrative case study is performed to analyze the effectiveness of the proposed approach. The result showed that the proposed approach can build an APC library from multimedia information source and help assembly process designers to discover APC for assembly process reuse.

Energy Efficient Hierarchical Multi-Path Routing Protocol to Alleviate Congestion in WSN

Congestion easily occurs in wireless sensor networks (WSN) due to it's centralized traffic pattern. In order to achieve high energy efficiency, network longevity, better fairness and quality of service, it is important to detect congestion in WSN in a timely manner. In this paper, an energy efficient hierarchical multi-path routing protocol to alleviate congestion and to balance energy is proposed. The algorithm is designed by partitioning the network into equal sized zones to achieve complete network connectivity and to reduce packet transmissions. The zone leaders selected are shifted on different nodes based on network dynamic conditions to avoid hotspots and to provide energy balancing. For an efficient data transmission, quicker and optimal multiple paths are established using merged zone and hierarchical network topology structure. The proposed algorithm detects the congestion by monitoring the path quality. The detected congestion is a result of overloaded links or nodes on the path. In addition, the algorithm proactively controls the congestion by dynamically shifting the transmission paths on their quality and alleviate it reactively using traffic splitting approach. The goal of this approach is to control resources instead of controlling the network load. The simulation results demonstrate that the proposed algorithm performs better as compared to other congestion control algorithms in terms of throughput, energy consumption and packet delivery ratio in a resource constraint wireless sensor network.

Containment Control of Multi-Agent Systems With General Noise Based on Hierarchical Topology Reconfiguration

In this paper, the containment control problem of the second-order multi-agent systems (MASs) with general noise over directed topology is investigated. General noise, which may be the mixture of colored and white noise, is introduced to model the perturbation of MASs. Since constructing an anti-perturbed distributed controller for concerned MASs is difficult, we attend to use the perturbation tolerant character of the convex hull spanned by multiple leaders in the containment control problems of MASs. To this end, the hierarchical topology structure of MASs is used, which can largely reduce the communication loads and computational complexity. It is shown that the MASs are noise-to-state stable and the state of the MASs has an asymptotic gain in the mean square based on random differential equations (RDEs) theorem framework. Moreover, the relative hierarchical topology reconfiguration algorithm is given based on which the system can withstand stronger noise and all followers gradually converge to the convex hull formed by the leaders of the respective level. Finally, the numerical simulation is carried out to substantiate the feasibility of the proposed control theory.

HpGAT: High-Order Proximity Informed Graph Attention Network

Graph neural networks (GNNs) have recently made remarkable breakthroughs in the paradigm of learning with graph-structured data. However, most existing GNNs limit the receptive field of the node on each layer to its connected (one-hop) neighbors, which disregards the fact that large receptive field has been proven to be a critical factor in state-of-the-art neural networks. In this paper, we propose a novel approach to appropriately define a variable receptive field for GNNs by incorporating high-order proximity information extracted from the hierarchical topological structure of the input graph. Specifically, multiscale groups obtained from trainable hierarchical semi-nonnegative matrix factorization are used for adjusting the weights when aggregating one-hop neighbors. Integrated with the graph attention mechanism on attributes of neighboring nodes, the learnable parameters within the process of aggregation are optimized in an end-to-end manner. Extensive experiments show that the proposed method (hpGAT) outperforms state-of-the-art methods and demonstrate the importance of exploiting high-order proximity in handling noisy information of local neighborhood.

SADI: A Novel Model to Study the Propagation of Social Worms in Hierarchical Networks

As more and more people rely on social networks for business and life, social worms constitute one of the major security threats to our society. Modern social worms exhibit two new features, message notification and the temporal characteristic of human mobility . Message notification indicates a user will get a reminder once a new message comes to a social account. The temporal characteristic of human mobility indicates a user can operate corresponding computer in different locations with different resting time. Previous scholars have proposed some analytical models for the propagation dynamics of social worms. However, they did not consider the above two features and there is one critical problem unrealized, which is structural imperfection of network topology . Previous models have not taken into account the hierarchical topology structure, which results from a many-to-many relationship between users and hosts. To address these problems, we model propagation dynamics of social worms oriented hierarchical networks in this paper, and the proposed model accurately describes the propagation behavior of social worms. We conduct both a theoretical analyses and extensive simulations to show our model can overcome inaccuracy in the number of infected nodes and provide a stronger approximation for the worm propagation. The results show that our model presented in this paper achieves a greater accuracy in characterizing the propagation of modern social worms.

HRTT: A Hierarchical Roof Topology Structure for Robust Building Roof Reconstruction from Point Clouds

The identification and representation of building roof topology are basic, but important, issues for 3D building model reconstruction from point clouds. Always, the roof topology is expressed by the roof topology graph (RTG), which stores the plane–plane adjacencies as graph edges. As the decision of the graph edges is often based on local statistics between adjacent planes, topology errors can be easily produced because of noise, lack of data, and resulting errors in pre-processing steps. In this work, the hierarchical roof topology tree (HRTT) is proposed, instead of traditional RTG, to represent the topology relationships among different roof elements. Building primitives or child structures are taken as inside tree nodes; thus, the plane–model and model–model relations can be well described and further exploited. Integral constraints and extra verifying procedures can also be easily introduced to improve the topology quality. As for the basic plane-to-plane adjacencies, we no longer decide all connections at the same time, but rather we decide the robust ones preferentially. Those robust connections will separate the whole model into simpler components step-by-step and produce the basic semantic information for the identification of ambiguous ones. In this way, the effects from structures of minor importance or spurious ridges can be limited to the building locale, while the common features can be detected integrally. Experiments on various data show that the proposed method can obviously improve the topology quality and produce more precise results. Compared with the RTG-based method, two topology quality indices increase from 80.9% and 79.8% to 89.4% and 88.2% in the test area. The integral model quality indices at the pixel level and the plane level also achieve the precision of 90.3% and 84.7%, accordingly.

A Cross-Layer Wireless Sensor Network Energy-Efficient Communication Protocol for Real-Time Monitoring of the Long-Distance Electric Transmission Lines

Optimization of energy consumption in Wireless Sensor Network (WSN) nodes has become a critical link that constrains the engineering application of the smart grid due to the fact that the smart grid is characterized by long-distance transmission in a special environment. The paper proposes a linear hierarchical network topological structure specific to WSN energy conservation in environmental monitoring of the long-distance electric transmission lines in the smart grid. Based on the topological structural characteristics and optimization of network layers, the paper also proposes a Topological Structure be Layered Configurations (TSLC) routing algorithm to improve the quality of WSN data transmission performance. Coprocessing of the network layer and the media access control (MAC) layer is achieved by using the cross-layer design method, accessing the status for the nodes in the network layer and obtaining the status of the network nodes of the MAC layer. It efficiently saves the energy of the whole network, improves the quality of the network service performance, and prolongs the life cycle of the network.

Comparative Performance Analysis of Flat and Hierarchical Routing in Wireless Sensor Network

Wireless sensor network is composed of a set of sensors in a wireless network of self-organized. Research on topological structure of wireless sensor network is focused in two directions, namely plane topology structure and hierarchical topology structure. This paper gives a detailed analysis of wireless sensor networks in the flat routing protocols and hierarchical routing protocols, and compares the advantages and disadvantages of the performance of two kinds of protocol by simulation experiment.

Topology-aware Virtual Network Embedding Using Multiple Characteristics

Network virtualization provides a promising tool to allow multiple heterogeneous virtual networks to run on a shared substrate network simultaneously. A long-standing challenge in network virtualization is the Virtual Network Embedding (VNE) problem: how to embed virtual networks onto specific physical nodes and links in the substrate network effectively. Recent research presents several heuristic algorithms that only consider single topological attribute of networks, which may lead to decreased utilization of resources. In this paper, we introduce six complementary characteristics that reflect different topological attributes, and propose three topology-aware VNE algorithms by leveraging the respective advantages of different characteristics. In addition, a new KS-core decomposition algorithm based on two characteristics is devised to better disentangle the hierarchical topological structure of virtual networks. Due to the overall consideration of topological attributes of substrate and virtual networks by using multiple characteristics, our study better coordinates node and link embedding. Extensive simulations demonstrate that our proposed algorithms improve the long-term average revenue, acceptance ratio, and revenue/cost ratio compared to previous algorithms.

Access Authentication Scheme Based on Authorized Certificate in Wireless Mesh Networks

The main benefits of the access authentication in IEEE 802.11s are both easy administration of the subscribers and compatibility with IEEE 802.11i. However, there still exist some shortcomings, such as worse authentication delay, suffering from intermediate attack, lower expansibility and the inequality among users. In order to overcome these shortcomings, a zone-based hierarchical topology structure, virtual certification authority (CA), off-line CA, authorized certificates and authorized keys are used in the access authentication scheme proposed in this paper. Qualitative analysis and simulation show that the access authentication scheme would improve authentication of the 802.11s in authentication latency, security, expansibility and the inequality in users.

Access Authentication Scheme Based on Authorized Certificate in Wireless Mesh Networks

The main benefits of the access authentication in IEEE 802.11s are both easy administration of the subscribers and compatibility with IEEE 802.11i. However, there still exist some shortcomings, such as worse authentication delay, suffering from intermediate attack, lower expansibility and the inequality among users. In order to overcome these shortcomings, a zone-based hierarchical topology structure, virtual certification authority (CA), off-line CA, authorized certificates and authorized keys are used in the access authentication scheme proposed in this paper. Qualitative analysis and simulation show that the access authentication scheme would improve authentication of the 802.11s in authentication latency, security, expansibility and the inequality in users.

Map Merging for Multi-robot Systems Based on Hierarchical Topology Structure under RTM Framework

For large-scale unknown environments,a method of topological node matching based on visual feature is presented,and a local scan matching strategy is integrated to realize map merging for multi-robot system under RTM(robot technology middleware) framework.A main-auxiliary structure model of multiple robots is developed,and an improved SP~2ATM algorithm is adopted to incrementally constructing topological map in unknown environments.Based on this,the hierarchical topology structure including SIFT(scale-invariant feature transform) feature information is presented,which is combined with ICP(iterative closest point) algorithm to realize map merging of multi-robot systems.RTM is taken as communication platform to improve the realtime performance,flexibility and robustness of the system.Simulation on USARSim and experimental results in actual environments verify the effectiveness of the proposed method.

Adaptive neighbor-based topology control protocol for wireless multi-hop networks

Topology control protocols have been proposed to construct efficient network topologies with several design goals, e.g., network-wide connectivity, minimal energy cost, symmetry, lower nodal degree, and therefore higher spatial reuse or lower interferences. Neighbor-based topology control protocols are simple and assume that each node in the network is connected to its k least-distant neighbors. There have been several empirical and theoretical research efforts that recommend a network-wide optimal value of the local parameter k. However, since most of the design goals often run against each other the suggested lower and upper bounds on the values of k are not sufficient to provide a controllable trade-off among various design goals. In this article, an adaptive neighbor-based topology control protocol is presented where the neighboring nodes collaborate and provide feedback on the network connectivity to decide on their respective transmission ranges. Since every node adaptively adjusts its number of neighbors, the parameter k acts as a performance knob to choose a set of backbone nodes and to form a hierarchical topology structure consisting of symmetric links. Through extensive simulation-based study, it is shown that the value of k can be tuned to generate fully connected network topologies while offering an efficient trade-off among various design goals.

An effective Denial of Service Attack Detection Method in Wireless Mesh Networks

Abstract In order to detect the DoS attack (Denial-of-Service attack) when wireless mesh networks adopt AODV routing protocol of Ad Hoc networks. Such technologies as an end-to-end authentication, utilization rate of cache memory, two pre-assumed threshold value and distributed voting are used in this paper to detect DoS attacker, which is on the basic of hierarchical topology structure in wireless mesh networks. Through performance analysis in theory and simulations experiment, the scheme would improve the flexibility and accuracy of DoS attack detection, and would obviously improve its security in wireless mesh networks.