Dynamics of Plate Equations with Memory Driven by Multiplicative Noise on Bounded Domains

This article examines the dynamics for stochastic plate equations with linear memory in the case of bounded domain. We investigate the existence of solutions and bounded absorbing set by using the uniform pullback attractors on the tails estimates, and the asymptotic compactness of the random dynamical system is proved by decomposition method, and then we obtain the existence of a random attractor.

Foundations and Applications of Information Systems Dynamics

Although numerous advances have been made in information technology in the past decades,there is still a lack of progress in information systems dynamics(ISD),owing to the lack of a mathematical foundation needed to describe information and the lack of an analytical framework to evaluate information systems.The value of ISD lies in its ability to guide the design,development,application,and evaluation of largescale information system-of-systems(So Ss),just as mechanical dynamics theories guide mechanical systems engineering.This paper reports on a breakthrough in these fundamental challenges by proposing a framework for information space,improving a mathematical theory for information measurement,and proposing a dynamic configuration model for information systems.In this way,it establishes a basic theoretical framework for ISD.The proposed theoretical methodologies have been successfully applied and verified in the Smart Court So Ss Engineering Project of China and have achieved significant improvements in the quality and efficiency of Chinese court informatization.The proposed ISD provides an innovative paradigm for the analysis,design,development,and evaluation of large-scale complex information systems,such as electronic government and smart cities.

A solution method for decomposing vector fields in Hamilton energy

Hamilton energy,which reflects the energy variation of systems,is one of the crucial instruments used to analyze the characteristics of dynamical systems.Here we propose a method to deduce Hamilton energy based on the existing systems.This derivation process consists of three steps:step 1,decomposing the vector field;step 2,solving the Hamilton energy function;and step 3,verifying uniqueness.In order to easily choose an appropriate decomposition method,we propose a classification criterion based on the form of system state variables,i.e.,type-I vector fields that can be directly decomposed and type-II vector fields decomposed via exterior differentiation.Moreover,exterior differentiation is used to represent the curl of low-high dimension vector fields in the process of decomposition.Finally,we exemplify the Hamilton energy function of six classical systems and analyze the relationship between Hamilton energy and dynamic behavior.This solution provides a new approach for deducing the Hamilton energy function,especially in high-dimensional systems.

Computing large deviation prefactors of stochastic dynamical systems based on machine learning

We present a large deviation theory that characterizes the exponential estimate for rare events in stochastic dynamical systems in the limit of weak noise.We aim to consider a next-to-leading-order approximation for more accurate calculation of the mean exit time by computing large deviation prefactors with the aid of machine learning.More specifically,we design a neural network framework to compute quasipotential,most probable paths and prefactors based on the orthogonal decomposition of a vector field.We corroborate the higher effectiveness and accuracy of our algorithm with two toy models.Numerical experiments demonstrate its powerful functionality in exploring the internal mechanism of rare events triggered by weak random fluctuations.

A Dynamical System-Based Framework for Dimension Reduction

We propose a novel framework for learning a low-dimensional representation of data based on nonlinear dynamical systems,which we call the dynamical dimension reduction(DDR).In the DDR model,each point is evolved via a nonlinear flow towards a lower-dimensional subspace;the projection onto the subspace gives the low-dimensional embedding.Training the model involves identifying the nonlinear flow and the subspace.Following the equation discovery method,we represent the vector field that defines the flow using a linear combination of dictionary elements,where each element is a pre-specified linear/nonlinear candidate function.A regularization term for the average total kinetic energy is also introduced and motivated by the optimal transport theory.We prove that the resulting optimization problem is well-posed and establish several properties of the DDR method.We also show how the DDR method can be trained using a gradient-based optimization method,where the gradients are computed using the adjoint method from the optimal control theory.The DDR method is implemented and compared on synthetic and example data sets to other dimension reduction methods,including the PCA,t-SNE,and Umap.

AN AUTOMATIC CONSTRAINT VIOLATION STABI- LIZATION METHOD FOR DIFFERENTIAL/ ALGEBRAIC EQUATIONS OF MOTION IN MULTIBODY SYSTEM DYNAMICS

A new automatic constraint violation stabilization method for numerical integration of Euler_Lagrange equations of motion in dynamics of multibody systems is presented. The parameters α,β used in the traditional constraint violation stabilization method are determined according to the integration time step size and Taylor expansion method automatically. The direct integration method, the traditional constraint violation stabilization method and the new method presented in this paper are compared finally.

Editorial: Dynamics on delayed systems

In the representation of natural and social phenom- ena, there exist not only ordinary or partial differential equations which are independent of the past states and determined solely by the present states~ but also time delay differential equations which are related to some of the past states. Time delay is ubiquitous in mechanical, physical, ecological, physiological, biological, economic, electronic, and chemical systems due to finite propaga- tion speeds of signals, finite reaction times, and finite processing times. These realistic backgrounds drive var- ious investigations to consider effects of time delays on realistic phenomena which are modeled by delay differ- ential equations. It is impossible to make achievements only by extending theory of ordinary or partial differen- tial equations since time delay is of uncertainty in time scale. Therefore, the study will be very difficult for a problem in the infinite-dimensional space.

滚-跳多运动模式球形机器人的动力学仿真研究

复杂的探索环境对探测机器人的通行能力提出了更高要求,具有多种运动模式的球形机器人是探测机器人领域的研究热点之一。通过对球形机器人内部构件质量分布的调节,设计了一种基于单摆驱动式的滚-跳多运动模式球形机器人,其具有滚动、转向、爬坡与跳跃4种基本运动能力;随后,又根据球形机器人的设计方案建立了球形机器人4种基本运动动力学方程。利用Automatic Dynamics Analysis of Mechanical Systems(ADAMS)软件构建该球形机器人虚拟样机并对其进行了滚动、转向、爬坡与跳跃的动力学仿真,最后以此为依据计算滚动、转向、爬坡与跳跃运动性能的仿真值,并制作实验室样机验证了动力学仿真的正确性。

Dynamics of Open Systems with Affine Maps

Many quantum systems of interest are initially correlated with their environments and the reduced dynamics of open systems are an interesting while challenging topic. Affine maps, as an extension of completely positive maps, are a useful tool to describe the reduced dynamics of open systems with initial correlations. However, it is unciear what kind of initial state shares an affine map. In this study, we give a sumcient condition of initial states, in which the reduced dynamics can always be described by an affihe map. Our result shows that if the initial states of the combined system constitute a convex set, and if the correspondence between the initial states of the open system and those of the combined system, defined by taking the partial trace, is a bijection, then the reduced dynamics of the open system can be described by an affine map.

The Aysmptotic Behavior of Solution to a Class of Inhomogeneons Systems of Gas Dynamics

Consider an initial-boundary problem vt - ux=0,u, + （）x + f（u） = （）x,θt+ux=（）ux=（）x+ （E） v（x,0） = v0（x）,u（x,0） = u0（x）,θ（0,x） = θ0（x）, （I） u（t,0） = u（t,1） = θx（t,0） = θx（t,1） （J） Sufficient and necessary conditions for （E）, （I） and （J） to have asymptotic stability of the gobal smooth solution are given by means of the elemental L2 energy method.

THE VARIATIONAL PRINCIPLE FOR THE PACKING ENTROPY OF NONAUTONOMOUS DYNAMICAL SYSTEMS

Let(X,φ) be a nonautonomous dynamical system.In this paper,we introduce the notions of packing topological entropy and measure-theoretical upper entropy for nonautonomous dynamical systems.Moreover,we establish the variational principle between the packing topological entropy and the measure-theoretical upper entropy.

ARCHITECTURE OF DYNAMIC DATA DRIVEN SIMULATION FOR WILDFIRE AND ITS REALIZATION

Dynamic data driven simulation （DDDS） is proposed to improve the model by incorporaing real data from the practical systems into the model. Instead of giving a static input, multiple possible sets of inputs are fed into the model. And the computational errors are corrected using statistical approaches. It involves a variety of aspects, including the uncertainty modeling, the measurement evaluation, the system model and the measurement model coupling ,the computation complexity, and the performance issue. Authors intend to set up the architecture of DDDS for wildfire spread model, DEVS-FIRE, based on the discrete event speeification （DEVS） formalism. The experimental results show that the framework can track the dynamically changing fire front based on fire sen- sor data, thus, it provides more aecurate predictions.

Research on robust fault-tolerant control for networked control system with packet dropout

A kind of networked control system with network-induced delay and packet dropout, modeled on asynchronous dynamical systems was tested, and the integrity design of the networked control system with sensors failures and actuators failures was analyzed using hybrid systems technique based on the robust fault-tolerant control theory. The parametric expression of controller is given based on the feasible solution of linear matrix inequality. The simulation results are provided on the basis of detailed theoretical analysis, which further demonstrate the validity of the proposed schema.

Eigenstructure assignment in a class of second-order dynamic systems

Eigenstructure assignment using the proportional-plus-derivative feedback controller in a class of secondorder dynamic system is investigated. Simple, general, complete parametric expressions for both the closed-loop eigenvector matrix and the feedback gains are established based on two simple Smith form reductions. The approach utilizes directly the original system data and involves manipulations only on n-dimensional matrices. Furthermore, it reveals all the degrees of freedom which can be further utilized to achieve additional system specifications. An example shows the effect of the proposed approach.

Generalised energy conservation law for chaotic phenomena

Chaotic phenomena are increasingly being observed in all fields of nature,where investigations reveal that a natural phe nomenon exhibits nonlinearities and attempts to reveal their deep underlying mechanisms.Chaos is normally understood as“a state of disorder”,for which there is as yet no universally accepted mathematical definition.A commonly used concept states that,for a dynamical system to be classified as chaotic,it must have the following properties:be sensitive to initial conditions,show topological transitivity,have densely periodical orbits etc.Revealing the rules that govern chaotic motion is thus an important unsolved task for exploring nature.W e present herein a generalised energy conservation law governing chaotic phenomena.Based on two scalar variables,viz.generalised potential and kinetic energies defined in the phase space describing nonlinear dynamical systems,we find that chaotic motion is periodic motion with infinite time period whose time-averaged generalised potential and kinetic energies are conserved over its time period.This implies that,as the averaging time is increased,the time-averaged generalised potential and kinetic energies tend to constants while the time-averaged energy flows,i.e.,their rates of change with time,tend to zero.Numerical simulations on reported chaotic motions,such as the forced van der Pol system,forced Duffing system,forced smooth and discontinuous oscillator,Lorenz’s system,and Rossler's system,show the above conclusions to be correct according to the results presented herein.This discovery may indicate that chaotic phenomena in nature could be controlled because,even though their instantaneous states are disordered,their long-time averages can be predicted.

Study on a new chaotic bitwise dynamical system and its FPGA implementation

In this paper, the structure of a new chaotic bitwise dynamical system （CBDS） is described. Compared to our previous research work, it uses various random bitwise operations instead of only one. The chaotic behavior of CBDS is mathemat- ically proven according to the Devaney＇s definition, and its statistical properties are verified both for uniformity and by a comprehensive, reputed and stringent battery of tests called TestU01. Furthermore, a systematic methodology developing the parallel computations is proposed for FPGA platform-based realization of this CBDS. Experiments finally validate the proposed systematic methodology.

Identification Schemes for Unmanned Excavator Arm Parameters

Parameter identification is a key requirement in the field of automated control of unmanned excavators （UEs）. Furthermore, the UE operates in unstructured, often hazardous environments, and requires a robust parameter identification scheme for field applications. This paper presents the results of a research study on parameter identification for UE. Three identification methods, the Newton-Raphson method, the generalized Newton method, and the least squares method are used and compared for prediction accuracy, robustness to noise and computational speed. The techniques are used to identify the link parameters （mass, inertia, and length） and friction coefficients of the full-scale UE. Using experimental data from a full-scale field UE, the values of link parameters and the friction coefficient are identified. Some of the identified parameters are compared with measured physical values. Furthermore, the joint torques and positions computed by the proposed model using the identified parameters are validated against measured data. The comparison shows that both the Newton-Raphson method and the generalized Newton method are better in terms of prediction accuracy. The Newton-Raphson method is computationally efficient and has potential for real time application, but the generalized Newton method is slightly more robust to measurement noise. The experimental data were obtained in collaboration with QinetiQ Ltd.

Hybrid control of bifurcation and chaos in stroboscopic model of Internet congestion control system

Interaction between transmission control protocol （TCP） and random early detection （RED） gateway in the Internet congestion control system has been modelled as a discrete-time dynamic system which exhibits complex bifurcating and chaotic behaviours. In this paper, a hybrid control strategy using both state feedback and parameter perturbation is employed to control the bifurcation and stabilize the chaotic orbits embedded in this discrete-time dynamic system of TCP/RED. Theoretical analysis and numerical simulations show that the bifurcation is delayed and the chaotic orbits are stabilized to a fixed point, which reliably achieves a stable average queue size in an extended range of parameters and even completely eliminates the chaotic behaviour in a particular range of parameters. Therefore it is possible to decrease the sensitivity of RED to parameters. By using the hybrid strategy, we may improve the stability and performance of TCP/RED congestion control system significantly.

Dynamic soft sensor development based on Gaussian mixture regression for fermentation processes

The dynamic soft sensor based on a single Gaussian process regression(GPR) model has been developed in fermentation processes.However,limitations of single regression models,for multiphase/multimode fermentation processes,may result in large prediction errors and complexity of the soft sensor.Therefore,a dynamic soft sensor based on Gaussian mixture regression(GMR) was proposed to overcome the problems.Two structure parameters,the number of Gaussian components and the order of the model,are crucial to the soft sensor model.To achieve a simple and effective soft sensor,an iterative strategy was proposed to optimize the two structure parameters synchronously.For the aim of comparisons,the proposed dynamic GMR soft sensor and the existing dynamic GPR soft sensor were both investigated to estimate biomass concentration in a Penicillin simulation process and an industrial Erythromycin fermentation process.Results show that the proposed dynamic GMR soft sensor has higher prediction accuracy and is more suitable for dynamic multiphase/multimode fermentation processes.

CONCEPTUAL ANALYSIS AND RANDOM ATTRACTOR FOR DISSIPATIVE RANDOM DYNAMICAL SYSTEMS

The aim of this work is to understand better the long time behaviour of asymptotically compact random dynamical systems （RDS）, which can be generated by solutions of some stochastic partial differential equations on unbounded domains. The conceptual analysis for the long time behavior of RDS will be done through some examples. An application of those analysis will be demonstrated through the proof of the existence of random attractors for asymptotically compact dissipative RDS.