The Immense Impact of Reverse Edges on Large Hierarchical Networks

Hierarchical networks are frequently encountered in animal groups,gene networks,and artificial engineering systems such as multiple robots,unmanned vehicle systems,smart grids,wind farm networks,and so forth.The structure of a large directed hierarchical network is often strongly influenced by reverse edges from lower-to higher-level nodes,such as lagging birds’howl in a flock or the opinions of lowerlevel individuals feeding back to higher-level ones in a social group.This study reveals that,for most large-scale real hierarchical networks,the majority of the reverse edges do not affect the synchronization process of the entire network;the synchronization process is influenced only by a small part of these reverse edges along specific paths.More surprisingly,a single effective reverse edge can slow down the synchronization of a huge hierarchical network by over 60%.The effect of such edges depends not on the network size but only on the average in-degree of the involved subnetwork.The overwhelming majority of active reverse edges turn out to have some kind of“bunching”effect on the information flows of hierarchical networks,which slows down synchronization processes.This finding refines the current understanding of the role of reverse edges in many natural,social,and engineering hierarchical networks,which might be beneficial for precisely tuning the synchronization rhythms of these networks.Our study also proposes an effective way to attack a hierarchical network by adding a malicious reverse edge to it and provides some guidance for protecting a network by screening out the specific small proportion of vulnerable nodes.

Distributed Optimization Algorithm for Multi-Robot Formation with Virtual Reference Center

Dear editor,In this letter,we use a distributed optimization approach to solve a class of multi-robot formation problem with virtual reference center.We investigate the design and analysis of the constrained consensus algorithm to solve the optimization problem with a sum of objective functions with some local constraints.In the multi-robot system with virtual reference center,each robot has messages on its own constraints and objective function,as well as the message about the formation that interacts with the virtual reference center.At the same time,all the robots collaborate to find the minimum value of the function defined by the formation.To find the optimal formation,we propose an algorithm with fixed step size with better performance.In addition,we use a combination of the Hungarian assignment algorithm and the proposed formation algorithm to get the optimal matching formation of the multi-robot system.

Selecting pinning nodes to control complex networked systems

One of the fundamental problems in pinning control of complex networks is selecting appropriate pinning nodes, such that the whole system is controlled. This is particularly useful for complex networks with huge numbers of nodes. Recent research has yielded several pinning node selection strategies, which may be efficient. However, selecting a set of pinning nodes and identifying the nodes that should be selected first remain challenging problems. In this paper, we present a network control strategy based on left Perron vector. For directed networks where nodes have the same in-and out-degrees, there has so far been no effective pinning node selection strategy, but our method can find suitable nodes. Likewise, our method also performs well for undirected networks where the nodes have the same degree. In addition, we can derive the minimum set of pinning nodes and the order in which they should be selected for given coupling strengths. Our proofs of these results depend on the properties of non-negative matrices and M-matrices. Several examples show that this strategy can effectively select appropriate pinning nodes, and that it can achieve better results for both directed and undirected networks.

Research on the basic theoretical issues of smart metro construction

The basic conception and theoretical system of smart metro are not clear and there is not a unified understanding at the academic level.Based on this fact,there is no guidance of the smart metro construction.Therefore,this paper conducts a complete and systemic research on several basic theoretical issues of smart metro,such as the development history,concept,connotation,characteristics,evolution stages,system architecture and main construction content.Firstly,the internal and external development demands are analyzed in this paper.In addition,the subject-cycle-function(SCF)model of smart metro concept is obtained.Moreover,the essential connotation,three typical characteristics and the evolution process of smart metro are introduced.Furthermore,the evolution relationships of main function,achievement goal,instrument subject,decision subject,control subject and factor relationship for every evolution stage are clarified.Finally,according to the theory of system engineering,the three-hierarchy system architecture and five main construction contents are proposed.The both can be used to construct the technical system of smart metro at the academic level,and provide theoretical support and technical guidance for promoting the development of smart metro construction in China.

Distributed online bandit tracking for Nash equilibrium under partial-decision information setting

This paper is concerned with a Nash equilibrium(NE)tracking issue in online games with bandit feedback,where cost functions vary with time and agents only have access to the values of these functions at two points during each round.A partial-decision information setting is considered,in which agents have only access to the decisions of their neighbors.The primary objective of this paper is to develop a distributed online NE tracking algorithm that ensures sublinear growth of regret with respect to the total round T,under both the bandit feedback and partial-decision information setting.By utilizing a two-point estimator together with the leader-following consensus method,a new distributed online NE tracking algorithm is established with the estimated gradient and local estimated decisions based on the projection gradient-descent method.Moreover,sufficient conditions are derived to guarantee an improved upper bound of dynamic regret compared to existing bandit algorithms.Finally,a simulation example is presented to demonstrate the effectiveness of the proposed algorithm.

Short-term power load forecasting using integrated methods based on long short-term memory

The development of power system informatization,the massive access of distributed power supply and electric vehicles have increased the complexity of power consumption in the distribution network,which puts forward higher requirements for the accuracy and stability of load forecasting.In this paper,an integrated network architecture which consists of the self-organized mapping,chaotic time series,intelligent optimization algorithm and long short-term memory(LSTM)is proposed to extend the load forecasting length,decrease artificial debugging,and improve the prediction precision for the short-term power load forecasting.Compared with LSTM prediction,the algorithm in this paper improves the prediction accuracy by 61.87%in terms of root mean square error(RMSE),and reduces the prediction error by 50%in the 40-fold forecast window under some circumstances.

DeepTSP:Deep traffic state prediction model based on large-scale empirical data

Real-time traffic state(e.g.,speed)prediction is an essential component for traffic control and management in an urban road network.How to build an effective large-scale traffic state prediction system is a challenging but highly valuable problem.This study focuses on the construction of an effective solution designed for spatiotemporal data to predict the traffic state of large-scale traffic systems.In this study,we first summarize the three challenges faced by large-scale traffic state prediction,i.e.,scale,granularity,and sparsity.Based on the domain knowledge of traffic engineering,the propagation of traffic states along the road network is theoretically analyzed,which are elaborated in aspects of the temporal and spatial propagation of traffic state,traffic state experience replay,and multi-source data fusion.A deep learning architecture,termed as Deep Traffic State Prediction(DeepTSP),is therefore proposed to address the current challenges in traffic state prediction.Experiments demonstrate that the proposed DeepTSP model can effectively predict large-scale traffic states.

Distributed consensus-based algorithm for social welfare in smart grid with transmission losses

In this paper, we present a distributed consensus-based algorithm to solve the social welfare maximization problem. This is one of typical problems of distributed energy management in smart grid. In this problem, we consider not only the generator and demand, but also the transmission losses which make the feasibility set of the formulated problem a non-convex set. In solving this issue, we find a noticeable result that the primal problem has the same solution with a new convex optimization problem by getting the utmost out of the implied term in practice. Considering the general communication topology among generators and demands, we first design a finite step algorithm to make each generator and demand know the information of parameters of others.Then, we design a distributed algorithm and also prove the optimality and convergence of the proposed algorithm. Finally, the convergence and optimality are examined through extensive simulations.