Showing papers on "Network topology published in 2023"

Resilient Bipartite Consensus of Second-Order Multiagent Systems With Event-Triggered Communication

Abstract: This article investigates the resilient bipartite consensus problem for continuous-time second-order multiagent systems in the presence of totally bounded malicious nodes under signed digraphs. An event-based resilient impulsive algorithm is employed, which cannot only mitigate the malicious nodes’ influence on the convergence of normal ones but also reduce the communication loads of agents. A necessary and sufficient condition related to the network topology is established for solving resilient bipartite consensus by using system transformation. A numerical simulation illustrates the effectiveness of the result.

Cooperative Target Tracking of Multiple Autonomous Surface Vehicles Under Switching Interaction Topologies

Abstract: This paper is concerned with the cooperative target tracking of multiple autonomous surface vehicles (ASVs) under switching interaction topologies. For the target to be tracked, only its position can be measured/received by some of the ASVs, and its velocity is unavailable to all the ASVs. A distributed extended state observer taking into consideration switching topologies is designed to integrally estimate unknown target dynamics and neighboring ASVs' dynamics. Accordingly, a novel kinematic controller is designed, which takes full advantage of known information and avoids the approximation of some virtual control vectors. Moreover, a disturbance observer is presented to estimate unknown time-varying environmental disturbance. Furthermore, a distributed dynamic controller is designed to regulate the involved ASVs to cooperatively track the target. It enables each ASV to adjust its forces and moments according to the received information from its neighbors. The effectiveness of the derived results is demonstrated through cooperative target tracking performance analysis for a tracking system composed of five interacting ASVs.

Learning Multi-View Interactional Skeleton Graph for Action Recognition

Abstract: Capturing the interactions of human articulations lies in the center of skeleton-based action recognition. Recent graph-based methods are inherently limited in the weak spatial context modeling capability due to fixed interaction pattern and inflexible shared weights of GCN. To address above problems, we propose the multi-view interactional graph network (MV-IGNet) which can construct, learn and infer multi-level spatial skeleton context, including view-level (global), group-level, joint-level (local) context, in a unified way. MV-IGNet leverages different skeleton topologies as multi-views to cooperatively generate complementary action features. For each view, separable parametric graph convolution (SPG-Conv) enables multiple parameterized graphs to enrich local interaction patterns, which provides strong graph-adaption ability to handle irregular skeleton topologies. We also partition the skeleton into several groups and then the higher-level group contexts including inter-group and intra-group, are hierarchically captured by above SPG-Conv layers. A simple yet effective global context adaption (GCA) module facilitates representative feature extraction by learning the input-dependent skeleton topologies. Compared to the mainstream works, MV-IGNet can be readily implemented while with smaller model size and faster inference. Experimental results show the proposed MV-IGNet achieves impressive performance on large-scale benchmarks: NTU-RGB+D and NTU-RGB+D 120.

Observer-Based Fully Distributed Containment Control for MASs Subject to DoS Attacks

Abstract: The problem of the observer-based fully distributed containment control for multiagent systems (MASs) subject to denial-of-service (DoS) attacks is investigated in this article. First, a switched fully distributed control framework is established for a class of DoS attacks constrained by the attack duration. Then, a novel attack-resilient control scheme is developed to accomplish the containment control task. The major advantages of the devised control scheme are that any information of the whole network topology structure is not involved and only the information from neighbor agents is used. What is more, a novel observer-based attack compensator is devised to resist DoS attacks. Finally, a practical example of the mobile robot system is presented to testify the validity of the designed control scheme by a comparison.

Distributed Adaptive Fuzzy Formation Control of Uncertain Multiple Unmanned Aerial Vehicles With Actuator Faults and Switching Topologies

Abstract: This article investigates a distributed fuzzy adaptive formation control for quadrotor multiple unmanned aerial vehicles (UAVs) under unmodeled dynamics and switching topologies. The UAVs dynamics model is described by the Newton–Euler formula, and the actuator faults are considered in the system model in the form of multiplicative factors and additive factors. Due to the underactuated characteristics of the UAVs, two objective attitude commands are generated by designing a virtual control signal, which are transmitted to the attitude subsystem, and then the position controller is solved. By constructing a distributed communication mechanism between UAVs, an adaptive formation control strategy is proposed, which can enable UAVs to update their position and speed online according to their neighbor information, and then achieve the required formation. In addition, a fuzzy adaptive sliding mode controller is designed to ensure that the tracking errors of UAVs converge to the neighborhood of the origin. Finally, the simulation results verify the effectiveness of the proposed control strategy.

Wireless Chargers for Electric Vehicle: A Systematic Review on Converter Topologies, Environmental Assessment, and Review Policy

Abstract: The delivery of electricity employing an electromagnetic field that extends across an intervening region is called a wireless power transfer (WPT). This approach paves the way for electric vehicles (EVs) to use newly available options to reduce their environmental impact. This article is a review that examines the WPT technology for use in electric vehicle applications from both the technical aspect and the environmental impact. This review will attempt to accomplish the following objectives: (1) describe the present state of the technology behind the development and application of a WPT across the transportation industry; (2) substantiate the actual implementation of WPT EV systems; and (3) estimate the functioning of the autonomous system, as well as detect the potential stumbling blocks and openings for enhancement. The most recent advancements and implementation in compensating topologies, power electronics converters, and control techniques are dissected and debated scientifically to improve the system’s performance. To evaluate the performance from a sustainable perspective, energy, environmental, and economic factors are utilized, and at the same time, policy drivers and health and safety problems are researched.

Gridtopo-GAN for Distribution System Topology Identification

Abstract: Due to the limited presence of monitoring and measurement devices, timely identification of distribution grid topology has been challenging. Therefore, this article proposes a power grid topological generative adversarial network (Gridtopo-GAN) model to identify the distribution grid topology of either meshed or radial structure with limited measurements. By leveraging the topology preserved node embedding architecture, this model can efficiently handle large-scale systems with different topological configurations. Because of the generative capability of GAN, the model is robust enough when fed with bad measurement data, including missing data, commonly encountered in practical applications. Numerical simulations are carried out on the IEEE 33-node system, 118-node, 415-node, and real 76-node distribution systems to demonstrate the effectiveness and efficiency of the proposed topology identification model.

A Review of Single-Stage Multiport Inverters for Multisource Applications

Abstract: Conventional power electronic interfaces for multisource applications always need multiple conversion stages as intermediate dc–dc power conversion stages are inevitably installed for matching the different dc voltage levels and regulating the power distribution among dc sources. Dually, single-stage multiport inverter for multisource applications enables direct connection from the dc source to ac side without an extra conversion stage and has witnessed an upward trend in recent years. The salient advantages of single-stage multiport inverters comprise fewer power losses, fewer passive filters, and higher system efficiency. To capture the advancement in the field, the article reviews various single-stage multiport inverters for multisource applications, offers a comprehensive understanding of the current development of this configuration, and provides an insightful forward look at potential research issues of single-stage multiport inverters. This article presents various state-of-the-art single-stage multiport topologies as well as their applications and comparisons. In addition, systemic topology derivation methods of single-stage multiport inverters are discussed to provide inspiration for exploring new topologies. Moreover, the main challenging issues of this topology, such as modulation, control, and energy management strategies are analyzed and summarized. Finally, some open questions and future trends of single-stage multiport inverters are also discussed to motivate future research and explore new alternatives.

Reversible switching between two common protein folds in a designed system using only temperature

Abstract: Significance Metamorphic proteins play an important role in both native biological processes and disease states through their shape-shifting properties, which allow them to expand their functional and dysfunctional capacity. However, the design of monomeric proteins that can shift between differently folded states remains a challenge. This paper describes a designed system that can switch reversibly between two of the most common protein topologies, 3α and α/β-plait, over a relatively narrow temperature range that is relevant to biology. The results provide mechanistic insights into how proteins can become shape-shifters, as well as adding to the protein design toolkit. The reversible 3α/αβ switch may also serve as a model system for further understanding the fundamentals of protein folding and fold switching.

Learning Emergent Random Access Protocol for LEO Satellite Networks

Abstract: A mega-constellation of low-altitude earth orbit (LEO) satellites (SATs) are envisaged to provide a global coverage SAT network in beyond fifth-generation (5G) cellular systems. LEO SAT networks exhibit extremely long link distances of many users under time-varying SAT network topology. This makes existing multiple access protocols, such as random access channel (RACH) based cellular protocol designed for fixed terrestrial network topology, ill-suited. To overcome this issue, in this paper, we propose a novel contention-based random access solution for LEO SAT networks, dubbed emergent random access channel protocol (eRACH). In stark contrast to existing model-based and standardized protocols, eRACH is a model-free approach that emerges through interaction with the non-stationary network environment, using multi-agent deep reinforcement learning (MADRL). Furthermore, by exploiting known SAT orbiting patterns, eRACH does not require central coordination or additional communication across users, while training convergence is stabilized through the regular orbiting patterns. Compared to RACH, we show from various simulations that our proposed eRACH yields 54.6% higher average network throughput with around two times lower average access delay while achieving 0.989 Jain’s fairness index.

Secure Time-Sensitive Software-Defined Networking in Vehicles

Abstract: Current designs of future In-Vehicle Networks (IVN) prepare for switched Ethernet backbones, which can host advanced LAN technologies such as IEEE Time-Sensitive Networking (TSN) and Software-Defined Networking (SDN). In this article, we present an integrated Time-Sensitive Software-Defined Networking (TSSDN) architecture that simultaneously enables control of synchronous and asynchronous real-time and best-effort communication for all IVN traffic classes. Despite the central SDN controller, we can validate that control can operate without a delay penalty for TSN traffic, provided protocols are properly mapped. We demonstrate how TSSDN adaptably and reliably enhances network security for in-vehicle communication. A systematic investigation of the possible control flow integrations with switched Ether-networks reveals that these strategies allow for shaping the attack surface of a software-defined IVN. We discuss embeddings of control flow identifiers on different layers, covering the range from a fully exposed mapping to deep encapsulation. We experimentally evaluate these strategies in a production vehicle, which we map to a modern Ethernet topology. Our findings indicate that visibility of automotive control flows on lower network layers enables isolation and access control throughout the network infrastructure. Such a TSSDN backbone can establish and survey trust zones within the IVN and reduce the attack surface of connected cars in various attack scenarios.

A Deep Multimodal Adversarial Cycle-Consistent Network for Smart Enterprise System

Abstract: Nowadays, much research leverages the clustering to mine commercial patterns from data in enterprise systems. However, previous methods cannot fully consider local structures and global topology of data, which may cause the degradation of clustering performance. To address the challenges, a deep multimodal adversarial cycle-consistent network (DMACCN) is proposed to mine intrinsic patterns of data, which can capture the local structures from instance reconstructions and the global topology from adversarial games. Specifically, DMACCN is designed as an adversarial encoding-decoding architecture composed of the modality specific-encoder, the modality-common fusion network, the cycle-consistent modality-specific generator, and the modality-fusion discriminator, which can fully fuse complementary information of data. Then, an adversarial cycle-consistent loss is devised to guide the clustering pattern mining from complementary information of data, which can align semantics between modalities and capture clustering structures of instances. The two components collaborate in a seamless manner to capture accurate commercial patterns. Finally, extensive experimental results on four datasets show DMACCN greatly outperforms the comparison methods.

NSRGRN: a network structure refinement method for gene regulatory network inference

Abstract: The elucidation of gene regulatory networks (GRNs) is one of the central challenges of systems biology, which is crucial for understanding pathogenesis and curing diseases. Various computational methods have been developed for GRN inference, but identifying redundant regulation remains a fundamental problem. Although considering topological properties and edge importance measures simultaneously can identify and reduce redundant regulations, how to address their respective weaknesses whilst leveraging their strengths is a critical problem faced by researchers. Here, we propose a network structure refinement method for GRN (NSRGRN) that effectively combines the topological properties and edge importance measures during GRN inference. NSRGRN has two major parts. The first part constructs a preliminary ranking list of gene regulations to avoid starting the GRN inference from a directed complete graph. The second part develops a novel network structure refinement (NSR) algorithm to refine the network structure from local and global topology perspectives. Specifically, the Conditional Mutual Information with Directionality and network motifs are applied to optimise the local topology, and the lower and upper networks are used to balance the bilateral relationship between the local topology's optimisation and the global topology's maintenance. NSRGRN is compared with six state-of-the-art methods on three datasets (26 networks in total), and it shows the best all-round performance. Furthermore, when acting as a post-processing step, the NSR algorithm can improve the results of other methods in most datasets.

DC–DC Converters for Bipolar Microgrid Voltage Balancing: A Comprehensive Review of Architectures and Topologies

Abstract: DC microgrids initiated the change of a paradigm regarding the concept about electrical distribution networks, especially in the context of the distributed generation associated with renewable energies. However, this new reality opens a new area of research, in which several aspects must be carefully studied. Indeed, the bipolar design is one of the principal dc microgrid configurations considering its characteristic wiring. Although holding many promising advantages, the bipolar dc microgrid has a tendency toward voltage and current imbalances due to the unequal distribution of the loads and generators between the two poles. Thus, specific power electronic-based solutions are required to ensure the balance of these dc microgrids. Within this frame, this article gives a comprehensive review of the multiple architectures and power electronic topologies proposed to mitigate/eliminate this undesired condition. The following provides an insightful classification and discussion with the pros and cons of these solutions. This work can serve as a timely review for researcher/engineers who want to enter the voltage balancing field in the bipolar dc grids and promote the innovation of their power electronics-enabled solutions.

Distributed Event-Triggered Bipartite Consensus for Multiagent Systems Against Injection Attacks

Abstract: This article studies fully distributed data-driven problems for nonlinear discrete-time multiagent systems (MASs) with fixed and switching topologies preventing injection attacks. We first develop an enhanced compact form dynamic linearization model by applying the designed distributed bipartite combined measurement error function of the MASs. Then, a fully distributed event-triggered bipartite consensus (DETBC) framework is designed, where the dynamics information of MASs is no longer needed. Meanwhile, the restriction of the topology of the proposed DETBC method is further relieved. To prevent the MASs from injection attacks, neural network based detection and compensation schemes are developed. Rigorous convergence proof that the bipartite consensus error is ultimately bounded is presented. Finally, the effectiveness of the designed method is verified through simulations and experiments.

DC-based microgrid: Topologies, control schemes, and implementations

Abstract: This article presents a state-of-the-art review of the status, development, and prospects of DC-based microgrids. In recent years, researchers’ focus has shifted to DC-based microgrids as a better and more feasible solution for meeting local loads at the consumer level while complementing a given power system's reliability, stability, and controllability. DC microgrid has an advantage in terms of compatibility with renewable energy systems (RESs), energy storage, modern electrical appliances, high efficiency, and reliability. However, the integration of different distributed generations has complicated the control of bus voltage and current. Therefore, several efforts have been made in the research community to further explore efficient control techniques for a reliable and stable DC microgrid. In spite of the numerous review papers published on DC microgrid control, so far, not any has given sufficient emphasis on the power flow analysis methods used in various DC microgrid topologies and the hybrid control topologies discussed in this review. In general, this paper presents a meticulous explanation of DC microgrid architecture; power flow analysis; control strategies with comparative analysis; challenges with recommendations; as well as classical and intelligent-based energy management strategy (EMS). Finally, suggestion for further research is presented. This review paper will go a long way in helping readers to understand the present state of development on DC microgrid control.

Research on MMC Improved Sub-Module Topology With DC Fault Ride-Through and Negative Level Output Capability

Abstract: Modular multilevel converter (MMC) is a modern and preferred topology among the voltage source converters (VSCs) in high voltage direct current (HVdc) transmission. However, the traditional half-bridge sub-module (SM) cannot block the dc fault current. Hence, the novel SM topology with dc fault ride-through capability is introduced. This article proposes an improved diode-clamp SM (IDCSM) structure. Based on the DCSM topology, the two capacitors’ connection in the IDCSM is modified to operate independently and generate output voltage levels of $- u_{c}$ , 0, $u_{c}$ , and $2u_{c}$ based on the additional switching devices. The IDCSM with dc fault ride-through and negative level output capability can increase the number of output levels and significantly reduce hardware cost. In addition, this article adopts an equivalent half-bridge modulation (EHBM) strategy that can modulate all the MMC SM topologies with the comparison algorithm, simplifying the complicated logic calculation process. Then the IDCSM power loss, cost, and dc fault ride-through characteristics are illustrated in detail. Finally, the simulation and experimental results verify the feasibility of the proposed IDCSM topology and EHBM.

Power Flow Balancing With Decentralized Graph Neural Networks

Abstract: We propose an end-to-end framework based on a Graph Neural Network (GNN) to balance the power flows in energy grids. The balancing is framed as a supervised vertex regression task, where the GNN is trained to predict the current and power injections at each grid branch that yield a power flow balance. By representing the power grid as a line graph with branches as vertices, we can train a GNN that is accurate and robust to changes in topology. In addition, by using specialized GNN layers, we are able to build a very deep architecture that accounts for large neighborhoods on the graph, while implementing only localized operations. We perform three different experiments to evaluate: i) the benefits of using localized rather than global operations and the tendency of deep GNN models to oversmooth the quantities on the nodes; ii) the resilience to perturbations in the graph topology; and iii) the capability to train the model simultaneously on multiple grid topologies and the consequential improvement in generalization to new, unseen grids. The proposed framework is efficient and, compared to other solvers based on deep learning, is robust to perturbations not only to the physical quantities on the grid components, but also to the topology.

Event-Triggered Cooperative Output Regulation of Heterogeneous Multiagent Systems Under Switching Directed Topologies

Abstract: In this article, the cooperative output regulation problem of heterogeneous linear multiagent systems under jointly connected digraphs is addressed. The event-triggered control protocols based on state feedback and output feedback are proposed, respectively. It is shown that the output tracking errors of the resulting closed-loop control systems converge to 0 exponentially via the proposed protocols. One of the key advantages of the proposed event-triggering mechanism is that the information transmissions induced by event triggerings and topology switchings are independent, and data transmissions among agents are thus reduced. Furthermore, an explicit minimum interevent time is provided for all the agents so that the Zeno-behavior is excluded strictly. Finally, three numerical examples are provided to verify the effectiveness of the proposed protocols.

A Novel Three-Phase Switched-Capacitor Five-Level Multilevel Inverter with Reduced Components and Self-Balancing Ability

Abstract: This paper proposes a step-up 3-Ф switched-capacitor multilevel inverter topology with minimal switch count and voltage stresses. The proposed topology is designed to provide five distinct output voltage levels from a single isolated dc source, making it suitable for medium and low-voltage applications. Each leg of the proposed topology contains four switches, one power diode, and a capacitor. The switching signals are also generated using a staircase universal modulation method. As a result, the proposed topology will operate at both low and high switching frequencies. To highlight the proposed topology’s advantages, a comparison of three-phase topologies wasperformed in terms of the switching components, voltage stress, component count per level factor, and cost function withthe recent literature. The topology achieved an efficiency of about 96.7% with dynamic loading, and 75% of the switches experienced half of the peak output voltage (VDC), whereas the remaining switches experienced peak output voltage (2VDC) as voltage stress. The MATLAB/Simulink environment was used to simulate the proposed topology, and a laboratory prototype was also built to verify the inverter’s theoretical justifications and real-time performance.

Cascading Failure Analysis of Cyber–Physical Power Systems Considering Routing Strategy

Abstract: In this brief, we develop a novel model to study the cascading failure in cyber-physical power systems. We use a discrete packet traffic model that follows a specific routing strategy to describe the dynamic data transmission in the information network. Moreover, the dynamic load flow model that takes into account the power-frequency characteristics of loads and generators is applied to describe the dynamic flow process of the power network. Our proposed model allows to consider the impacts of data packet transmission failures and voltage-related failures in the cascading process. Furthermore, we analyze the effects of routing strategy and information network topology on the severity of cascading failure. Simulation results verify the applicability of the proposed model and reveal the way in which routing strategy affects the cascading failure of cyber-coupled systems. In addition, we show that when the main hub in the information network is used as a dispatching center, a spreadout degree distribution of the information network reduces the severity of cascading failure in the cyber-coupled system.

Model Predictive Control for Networked Multiple Linear Motors System Under DoS Attack and Time Delay

Abstract: The complex cyber layer challenges the accuracy of cooperative tracking motion for the networked multiple linear motors system. This article focuses on model predictive control (MPC) for networked control system with the denial-of-service (DoS) attack and network-induced time delay. In order to improve tracking accuracy and control performance of the system, several topologies for linear motion models are studied. The DoS attack and time delay are modeled as constraint models in the time domain. Combining dynamic prediction horizon with the buffer compensation in the controller to actuator (C-A) channel, the proposed networked control strategy based on MPC effectively solves the impact of DoS attacks and time delays on the tracking performance of multiple linear motors. In order to ensure the recursive feasibility of the MPC optimization problem and the uniform global asymptotic stability of system, the output terminal constraint and dynamic prediction horizon constraint are considered by combining four DoS attack cases of the networked control strategy, and a Lyapunov function is constructed. Finally, three linear motors with different topologies are built, and the simulation results verify the control effectiveness of the proposed control strategy on the motor tracking performance under cyber constraints.

Monotone decreasing LKF method for secure consensus of second-order mass with delay and switching topology

Abstract: This letter investigates secure global asymptotic consensus (SGAC) with non-weighted L2 gain of second-order multi-agent systems (MASs) under deception attacks by designing time-delayed state feedback control with switching topology. Compared with the existing methods in which the Lyapunov–Krasovskii functional (LKF) has to jump high at switching instants, the most important breakthrough is that a discretized LKF is designed such that it is monotone decreasing at switching instants, which not only can obtain low conservative results but also is convenient to study consensus with non-weighted L2 gain without any additional inequality transformation. The Results are universal since they can be applied with no difficulty to any other switched time-delay systems. A numerical simulation illustrates the less conservative SGAC.

Graded multiscale topology optimization using neural networks

Abstract: In this paper, we propose a novel graded multiscale topology optimization framework by exploiting the unique classification capacity of neural networks. The salient features of this framework include: (1) the number of design variables is only weakly dependent on the number of pre-selected microstructures, (2) it guarantees partition of unity while discouraging microstructure mixing, (3) it supports automatic differentiation, thereby eliminating manual sensitivity analysis, and (4) it supports high-resolution re-sampling, leading to smoother variation of microstructure topologies. The proposed framework is illustrated through several examples.