Publications

Sequential and Square-Root Algorithms

No abstract is provided for this article.

PD-controller: a new active queue management scheme

This paper describes a proportional-differential (PD) control algorithm as a new active queue management (AQM) scheme for TCP/IP congestion control. From the viewpoint of the control theory, TCP congestion control system can be regarded as a feedback regulating system. In this paper, a robust AQM called PD-controller is proposed. The design principles of PD-controller are presented in details. Its performance is extensively evaluated by simulations. The results demonstrate that the PD-controller AQM is stable and robust against traffic load fluctuations, UDP and HTTP disturbances. Its superiority over other AQMs is also demonstrated.

A study of design approach of spreading schemes for viral marketing based on human dynamics

Before launching a real viral marketing campaign, it is needed to design a spreading scheme by simulations. Based on a categorization of spreading patterns in real world and models, we point out that the existing research (especially Yang et al. (2010) Ref.  [16]) implicitly assume that if a user decides to post a received message (is activated), he/she will take the reposting action promptly (Prompt Action After Activation, or PAAA). After a careful analysis on a real dataset however, it is found that the observed time differences between action and activation exhibit a heavy-tailed distribution. A simulation model for heavy-tailed pattern is then proposed and performed. Similarities and differences of spreading processes between the heavy-tailed and PAAA patterns are analyzed. Consequently, a more practical design approach of spreading scheme for viral marketing on QQ platform is proposed. The design approach can be extended and applied to the contexts of non-heavy-tailed pattern, and viral marketing on other instant messaging platforms.

State-of-the-Art and Research Opportunities for Next-Generation Consumer Electronics

The tremendous advancement of Internet-of-Things (IoT) has proliferated the interaction between the physical and cyber worlds. Consumer electronics, as the first tier in the physical world, would play a crucial role to enable ubiquitous information exchange. The adoption of IoT technologies will inevitably enhance the maturity of consumer products and services, proliferating the market of consumer electronics. Nonetheless, the ever-growing number of uncoordinated connections and unregulated integrations of technologies would pose various challenges and threats, resulting in vulnerability to consumer electronics. Thus, there is an urgent need for defining the trusted infrastructure and research directions for consumer electronics. In this paper, the next-generation (next-gen) consumer electronics would be defined for the first time. Also, the state-of-the-art architecture of next-gen consumer electronics would be presented, in compliance with the emerging IoT standard IEEE 2668. To address the underlying challenges in next-gen consumer electronics, this paper will classify the essential research topics for consumer electronics and present future research opportunities, such as the exploitation of complex network analysis, cybersecurity measures, etc., to foster the development of consumer electronics in a reliable, efficient, safe, and secure manner.

IEEE Systems, Man, and Cybernetics Society Information

No abstract is provided for this article.

CHAOTIFICATION OF DISCRETE-TIME DYNAMICAL SYSTEMS: AN EXTENSION OF THE CHEN–LAI ALGORITHM

In this article, we propose and study an extension of the Chen–Lai algorithm for chaotification of discrete-time dynamical systems. The proposed method is a simple but mathematically rigorous feedback control design method that can gradually make all the Lyapunov exponents of the controlled system strictly positive for any given n-dimensional dynamical system that has a uniformly bounded Jacobian but otherwise could be originally nonchaotic or even asymptotically stable.

An Overview of Recent Progress in the Study of Distributed Multi-Agent Coordination

This paper reviews some main results and progress in distributed multi-agent coordination, focusing on papers published in major control systems and robotics journals since 2006. Distributed coordination of multiple vehicles, including unmanned aerial vehicles, unmanned ground vehicles, and unmanned underwater vehicles, has been a very active research subject studied extensively by the systems and control community. The recent results in this area are categorized into several directions, such as consensus, formation control, optimization, and estimation. After the review, a short discussion section is included to summarize the existing research and to propose several promising research directions along with some open problems that are deemed important for further investigations.

Human gut Bifidobacterium longum subsp. suillum is enriched in vitro by a pectic polysaccharide isolated from the flowers of Lilium lancifolium

Undetermination of the relation between network synchronizability and betweenness centrality

Betweenness centrality is taken as a sensible indicator of the synchronizability of complex networks. To test whether betweenness centrality is a proper measure of the synchronizability in specific realizations of random networks, this paper adds edges to the networks and then evaluates the changes of betweenness centrality and network synchronizability. It finds that the two quantities vary independently.

A comprehensive multi-local-world model for complex networks

The nodes in a community within a network are much more connected to each other than to the others outside the community in the same network. This phenomenon has been commonly observed from many real-world networks, ranging from social to biological even to technical networks. Meanwhile, the number of communities in some real-world networks, such as the Internet and most social networks, are evolving with time. To model this kind of networks, the present Letter proposes a multi-local-world (MLW) model to capture and describe their essential topological properties. Based on the mean-field theory, the degree distribution of this model is obtained analytically, showing that the generated network has a novel topological feature as being not completely random nor completely scale-free but behaving somewhere between them. As a typical application, the MLW model is applied to characterize the Internet against some other models such as the BA, GBA, Fitness and HOT models, demonstrating the superiority of the new model.

Coevolution of strategy-selection time scale and cooperation in spatial prisoner's dilemma game

In this paper, we investigate a networked prisoner's dilemma game where individuals' strategy-selection time scale evolves based on their historical learning information. We show that the more times the current strategy of an individual is learnt by his neighbors, the longer time he will stick on the successful behavior by adaptively adjusting the lifetime of the adopted strategy. Through characterizing the extent of success of the individuals with normalized payoffs, we show that properly using the learned information can form a positive feedback mechanism between cooperative behavior and its lifetime, which can boost cooperation on square lattices and scale-free networks.

Specified-Time Affine Formation Maneuver Control of Multiagent Systems Over Directed Networks

In this article, the specified-time affine formation maneuver control (AFMC) problem is studied with a leader-follower structure over a directed communication network. Firstly, by means of Pontraygin's maximum principle and motion-planning techniques, a specified-time AFMC algorithm is designed for the second-integrator systems with dynamic leaders which can lead the followers to achieve the desired affine formation at a specified settling time according to mission requirements and environmental constraints. Furthermore, the specified-time AFMC problem without velocity measurement is studied. The global convergence and stability of the closed-loop system with proposed control algorithms are analyzed. Compared with the existing finite-time AFMC algorithms, a prominent advantage of the proposed specified-time algorithms is that the convergence moment of AFMC can be accurately scheduled and appointed in advance. Finally, an example is simulated to verify the effectiveness of the proposed algorithms.

Stochastic sensitivity synthesis in nonlinear systems with incomplete information

A problem of stabilizing stochastically forced equilibria in nonlinear dynamic systems with incomplete information is studied. A new control approach based on the idea of synthesizing a desired stochastic sensitivity for an equilibrium is developed. The focus is on the case when the system states are observed only partially, with noisy observations. For the task of control, a dynamic regulator composed by feedback and filter is used. The problem of synthesizing the assigned stochastic sensitivity by this dynamic regulator is then considered. For a general nonlinear system, an algebraic equation connecting the stochastic sensitivity matrix with parameters of the regulator is derived. For the important case of two-dimensional stochastic nonlinear oscillators, explicit formulas of the dependence of the stochastic sensitivity on the regulator parameters are obtained. These theoretical results are constructively applied to the stabilization of the randomly forced equilibrium of the van der Pol oscillator.

Chaotifying a stable map via smooth small-amplitude high-frequency feedback control

In this paper, the problem of making a stable map chaotic by using smooth small-amplitude high-frequency feedback control is studied. The controlled map is mathematically proven to be chaotic in the sense of Li and Yorke. To demonstrate the practical usefulness of the proposed method, it is applied to a feedback boost switching regulator model operating in discontinuous mode. Copyright © 2000 John Wiley & Sons, Ltd.

Converting a General 2D Quadratic Autonomous System to a 2D Lorenz-Type System

Under three necessary conditions for preserving the essential qualitative properties of the 3D Lorenz system, a general 2D quadratic autonomous system is converted to a 2D Lorenz-type system (2DLTS). A canonical form of the 2DLTS is derived with aid of a normalization technique. It is found that the 2DLTS can be converted to the 2D Duffing oscillator model under certain conditions. Furthermore, it is shown that the 2DLTS undergoes pitchfork bifurcation and Hopf bifurcation. Finally, approximate periodic solutions of both the 2DLTS near the Hopf bifurcation point and a time-periodically forced system are obtained.

A novel stream encryption scheme with avalanche effect

This paper proposes a novel stream encryption scheme with avalanche effect (SESAE). Using this scheme and an ideal pseudorandom number generator (PRNG) to generate d-bit segment binary key streams, one can encrypt a plaintext such that by using any key stream generated from a different seed to decrypt the ciphertext, the decrypted plaintext will become an avalanche-like text which has 2 d − 1 consecutive one’s with a high probability. As a cost, the required bits of the ciphertext are d times those of the plaintext. A corresponding avalanche-type encryption theorem is established. Two chaotic 12-bit segment PRNGs are designed. A generalized FIPS140 test and SESAE test for the two chaotic PRNGs, RC4 12-bit segment PRNG and 12-bit segment Matlab PRNG are implemented. The SESAE tests for 16-bit segment PRNGs are also compared. The results suggest that those PRNGs are able to generate the SESAEs which are similar to those generated via ideal PRNGs.

Naming Game on Networks: Let Everyone be Both Speaker and Hearer

To investigate how consensus is reached on a large self-organized peer-to-peer network, we extended the naming game model commonly used in language and communication to Naming Game in Groups (NGG). Differing from other existing naming game models, in NGG everyone in the population (network) can be both speaker and hearer simultaneously, which resembles in a closer manner to real-life scenarios. Moreover, NGG allows the transmission (communication) of multiple words (opinions) for multiple intra-group consensuses. The communications among indirectly-connected nodes are also enabled in NGG. We simulated and analyzed the consensus process in some typical network topologies, including random-graph networks, small-world networks and scale-free networks, to better understand how global convergence (consensus) could be reached on one common word. The results are interpreted on group negotiation of a peer-to-peer network, which shows that global consensus in the population can be reached more rapidly when more opinions are permitted within each group or when the negotiating groups in the population are larger in size. The novel features and properties introduced by our model have demonstrated its applicability in better investigating general consensus problems on peer-to-peer networks.

Deterministic learning of nonlinear dynamical systems

In this paper, we present an approach for neural networks (NN) based identification of unknown nonlinear dynamical systems undergoing periodic or periodic-like (recurrent) motions. Among various types of NN architectures, we use a dynamical version of the localized RBF neural network, which is shown to be particularly suitable for identification in a dynamical framework. With the associated properties of localized RBF networks, especially the one concerning the persistent excitation (PE) condition for periodic trajectories, the proposed approach achieves sufficiently accurate identification of system dynamics in a local region along the experienced system trajectory. In particular, for neurons whose centers are close to the trajectories, the neural weights converge to a small neighborhood of a set of optimal values; while for other neurons with centers far away from the trajectories, the neural weights are not updated and are almost unchanged. The proposed approach implements a sort of "deterministic learning" in the sense that deterministic features of nonlinear dynamical systems are learned not by algorithms from statistical principles, but in a dynamical, deterministic manner, utilizing results from adaptive systems theory. The nature of this deterministic learning is closely related to the exponentially stability of a class of nonlinear adaptive systems. Simulation studies are included to demonstrate the effectiveness of the proposed approach.