Publications

ANALYSIS OF STOCHASTIC CYCLES IN THE CHEN SYSTEM

We study the stochastically forced Chen system in its parameter zone under the transition to chaos via period-doubling bifurcations. We suggest a stochastic sensitivity function technique for the analysis of stochastic cycles. We show that this approach allows to construct the dispersion ellipses of random trajectories for any Poincaré sections, and these ellipses reflect the essential features of a spatial arrangement of random trajectories near deterministic cycles. For the Chen system, we demonstrate a growth of stochastic sensitivity of the forced cycles under transition to chaos.

Synchronization Phenomena on Networks

No abstract is provided for this article.

Computation of focus values with applications

No abstract is provided for this article.

Diffeomorphisms with infinitely many Smale horseshoes

A class of planar diffeomorphims is formulated, with infinitely many coexisting Smale horseshoes, where the Lebesgue measure of the parameters with such strange dynamics is infinite. On each horseshoe, there exists a uniformly hyperbolic invariant set, on which the map is topologically conjugate to the two-sided full-shift on two symbols. Moreover, the topological entropy is infinite in certain parameter regions.

Robust fuzzy-model-based controller for uncertain systems

This paper addresses the analysis and design of fuzzy control systems for a class of complex uncertain single input single output nonlinear systems. In the design procedure, we represent the nonlinear system using a Takagi-Sugeno fuzzy model and construct a global fuzzy logic controller by blending all local state feedback controllers with a sliding mode controller. Unlike the commonly used parallel distributed compensation technique, we can design a global stable fuzzy controller without finding a common Lyapunov function for all local control systems, and can obtain a good tracking performance. Furthermore, a stability analysis is carried out not for the fuzzy model but for a real nonlinear system with uncertainties. One simulation example is given to show the effectiveness and feasibility of the proposed fuzzy controller design method.

Chaotic Jerk Systems with Hidden Attractors

No abstract is provided for this article.

Distributed Containment Control of Linear Multi-agent Systems with Multiple Higher-dimensional Leaders

This paper addresses the distributed containment problem for a class of continuous-time multi-agent systems with multiple higher-dimensional leaders. In the present model, the graph describing the communication topology among the followers is assumed to be fixed and directed, but does not need to meet any connectivity condition. Furthermore, it is assumed that output information of the leader is only available to a small fraction of the followers. To achieve containment, a kind of distributed controllers based only on the local state information is exploited. Under the assumptions that the dynamics of the leaders are detectable and that each follower can directly or indirectly receive information from at least one leader, some sufficient conditions for achieving containment are provided and analyzed.

Cryptanalysis of a Chaotic Neural Network Based Multimedia Encryption Scheme

No abstract is provided for this article.

Generalized Synchronization and Generalized Consensus of System Arrays

No abstract is provided for this article.

Design and Implementation of Grid Multiwing Butterfly Chaotic Attractors From a Piecewise Lorenz System

The intrinsic dynamics of the Lorenz system are confined in the positive half-space with respect to the vertical axis due to a limiting threshold effect. To break such a threshold effect, a novel piecewise Lorenz system is introduced, equipped with a staircase function and an even symmetric piecewise-linear function. The new system is autonomous, and yet, it can generate various grid multiwing butterfly chaotic attractors without requiring any external forcing. A module-based circuit is designed for implementation, with experiments reported for verification and demonstration.

The role of tumor microenvironment in lenvatinib resistance of hepatocellular carcinoma

Cryptanalysis of a New Signal Security System for Multimedia Data Transmission

Recently, a new signal security system called TDCEA (two-dimensional circulation encryption algorithm) was proposed for real-time multimedia data transmission. This paper gives a comprehensive analysis on the security of TDCEA. The following security problems are found: (1) there exist some essential security defects in TDCEA; (2) two known-plaintext attacks can break TDCEA; (3) the chosen-plaintext and chosen-ciphertext versions of the aforementioned two known-plaintext attacks can break TDCEA even with a smaller complexity and a better performance. Some experiments are given to show the security defects of TDCEA and the feasibility of the proposed known-plaintext attacks. As a conclusion, TDCEA is not suitable for applications that require a high level of security.

Intelligent control of a planning system for astronaut training

This work intends to design, analyze and solve, from the systems control perspective, a complex, dynamic, and multiconstrained planning system for generating training plans for crew members of the NASA-led International Space Station. Various intelligent planning systems have been developed within the framework of artificial intelligence. These planning systems generally lack a rigorous mathematical formalism to allow a reliable and flexible methodology for their design, modeling, and performance analysis in a dynamical, time-critical, and multiconstrained environment. Formulating the planning problem in the domain of discrete-event systems under a unified framework such that it can be modeled, designed, and analyzed as a control system will provide a self-contained theory for such planning systems. This will also provide a means to certify various planning systems for operations in the dynamical and complex environments in space. The work presented here completes the design, development, and analysis of an intricate, large-scale, and representative mathematical formulation for intelligent control of a real planning system for Space Station crew training. This planning system has been tested and used at NASA-Johnson Space Center.

Dynamical Behaviors of a Large Class of General Delayed Neural Networks

Research of delayed neural networks with varying self-inhibitions, interconnection weights, and inputs is an important issue. In the real world, self-inhibitions, interconnection weights, and inputs should vary as time varies. In this letter, we discuss a large class of delayed neural networks with periodic inhibitions, interconnection weights, and inputs. We prove that if the activation functions are of Lipschitz type and some set of inequalities, for example, the set of inequalities 3.1 in theorem 1, is satisfied, the delayed system has a unique periodic solution, and any solution will converge to this periodic solution. We also prove that if either set of inequalities 3.20 in theorem 2 or 3.23 in theorem 3 is satisfied, then the system is exponentially stable globally. This class of delayed dynamical systems provides a general framework for many delayed dynamical systems. As special cases, it includes delayed Hopfield neural networks and cellular neural networks as well as distributed delayed neural networks with periodic self-inhibitions, interconnection weights, and inputs. Moreover, the entire discussion applies to delayed systems with constant self-inhibitions, interconnection weights, and inputs.

Realization of Boolean Functions and Gene Bank of Cellular Neural Networks

A paradigm for nonlinear spatial-temporal processing, cellular neural networks (CNN), was created by inspiration from the cellular automata and neural networks. This article is an exploration of the important aspect of realizing Boolean functions by using standard CNN. A neat CNN truth table of n binary variables and an essential formula of an uncoupled CNN are discovered, and an effective method of realizing all linearly separable Boolean functions (LSBF) via CNN is proposed. Borrowed from biological concepts and terms, the parameter group in a CNN is a metaphor for gene which completely determines the dynamical properties of the CNN. The CNN gene bank, which consists of the family of all linearly separable Boolean genes (LSBG) that are associated with all the LSBF, can be easily determined and progressively established. An interesting phenomenon is that the number of LSBG with the von Neumann neighborhood is 94572, which is close to the number of genes existing in the human genome.

Synchronization in general complex dynamical networks with coupling delays

Complex networks have attracted increasing attention from various fields of science and engineering today. Due to the finite speeds of transmission and spreading as well as traffic congestions, a signal or influence travelling through a complex network often is associated with time delays, and this is very common in biological and physical networks. In this paper, we introduce complex dynamical network models with coupling delays for both continuous- and discrete-time cases and then investigate their synchronization phenomena and criteria. Based on these new complex network models, we derive synchronization conditions for both delay-independent and delay-dependent asymptotical stabilities in terms of linear matrix inequalities (LMI). We finally use a network with a fixed delay and a specific coupling scheme as an example to illustrate the theoretical results.

Boosting Adversarial Transferability via Ensemble Non-Attention

Ensemble attacks integrate the outputs of surrogate models with diverse architectures, which can be combined with various gradient-based attacks to improve adversarial transferability. However, previous work shows unsatisfactory attack performance when transferring across heterogeneous model architectures. The main reason is that the gradient update directions of heterogeneous surrogate models differ widely, making it hard to reduce the gradient variance of ensemble models while making the best of individual model. To tackle this challenge, we design a novel ensemble attack, NAMEA, which for the first time integrates the gradients from the non-attention areas of ensemble models into the iterative gradient optimization process. Our design is inspired by the observation that the attention areas of heterogeneous models vary sharply, thus the non-attention areas of ViTs are likely to be the focus of CNNs and vice versa. Therefore, we merge the gradients respectively from the attention and non-attention areas of ensemble models so as to fuse the transfer information of CNNs and ViTs. Specifically, we pioneer a new way of decoupling the gradients of non-attention areas from those of attention areas, while merging gradients by meta-learning. Empirical evaluations on ImageNet dataset indicate that NAMEA outperforms AdaEA and SMER, the state-of-the-art ensemble attacks by an average of 15.0% and 9.6%, respectively. This work is the first attempt to explore the power of ensemble non-attention in boosting cross-architecture transferability, providing new insights into launching ensemble attacks.

USING CHAOS TO IMPROVE GENERALIZATION IN SMART NN CONTROL DESIGN

In this paper, a smart NN control scheme is proposed. This scheme is designed such that the current control action can utilize the knowledge that the NN learned from the past control process. A chaotic signal is employed as the reference signal to improve the generalization ability of the NN in the training phase of the scheme, where the complex chaotic signal offers much more information for NN learning thereby significantly improving the efficiency of the NN generalization. Compared with most of the adaptive neural controllers, the smart neural controller (in the operational phase) is a static and low-order controller, and thus needs much less computational resources, and is more feasible in practical implementation. Simulation studies are included to demonstrate the effectiveness of the new control scheme.