Community detection in signed networks based on discrete-time model

Community detection in signed networks has been studied widely in recent years. In this paper, a discrete difference equation is proposed to imitate the consistently changing phases of the nodes. During the interaction, each node will update its phase based on the difference equation. Each node has many different nodes connected with it, and these neighbors have different influences on it. The similarity between two nodes is applied to describe the influences between them. Nodes with high positive similarities will get together and nodes with negative similarities will be far away from each other.Communities are detected ultimately when the phases of the nodes are stable. Experiments on real world and synthetic signed networks show the efficiency of detection performance. Moreover, the presented method gains better detection performance than two existing good algorithms.

Sampled-data Observer Design for a Class of Stochastic Nonlinear Systems Based on the Approximate Discrete-time Models

In this paper,we studied the approximate sampleddata observer design for a class of stochastic nonlinear systems.Euler-Maruyama approximation was investigated in this paper because it is the basis of other higher precision numerical methods,and it preserves important structures of the nonlinear systems.Also,the form of Euler-Maruyama model is simple and easy to be calculated.The results provide a reference for sampled-data observer design method for such stochastic nonlinear systems,and may be useful to many practical control applications,such as tracking control in mechanical systems.And the effectiveness of the approach is demonstrated by a simulation example.

LARGE SIGNAL DISCRETE-TIME MODEL FOR PARALLELED BUCK CONVERTERS

As a number of switch combinations are involved in operation of multi converter system, conventional methods for obtaining discrete time large signal model of these converter systems result in a very complex solution. A simple sampled data technique for modeling distributed dc dc PWM converters system (DCS) was proposed. The resulting model is nonlinear and can be linearized for analysis and design of DCS. These models are also suitable for fast simulation of these networks. As the input and output of dc dc converters are slow varying, suitable model for DCS was obtained in terms of the finite order input/output approximation.

Adaptive Optimal Discrete-Time Output-Feedback Using an Internal Model Principle and Adaptive Dynamic Programming

In order to address the output feedback issue for linear discrete-time systems, this work suggests a brand-new adaptive dynamic programming(ADP) technique based on the internal model principle(IMP). The proposed method, termed as IMP-ADP, does not require complete state feedback-merely the measurement of input and output data. More specifically, based on the IMP, the output control problem can first be converted into a stabilization problem. We then design an observer to reproduce the full state of the system by measuring the inputs and outputs. Moreover, this technique includes both a policy iteration algorithm and a value iteration algorithm to determine the optimal feedback gain without using a dynamic system model. It is important that with this concept one does not need to solve the regulator equation. Finally, this control method was tested on an inverter system of grid-connected LCLs to demonstrate that the proposed method provides the desired performance in terms of both tracking and disturbance rejection.

Finite-Time Stabilization for Constrained Discrete-time Systems by Using Model Predictive Control

In this paper, a model predictive control(MPC)framework is proposed for finite-time stabilization of linear and nonlinear discrete-time systems subject to state and control constraints. The proposed MPC framework guarantees the finite-time convergence property by assigning the control horizon equal to the dimension of the overall system, and only penalizing the terminal cost in the optimization, where the stage costs are not penalized explicitly. A terminal inequality constraint is added to guarantee the feasibility and stability of the closed-loop system.Initial feasibility can be improved via augmentation. The finite-time convergence of the proposed MPC is proved theoretically,and is supported by simulation examples.

Discrete-time movement model of a group of trains on a rail line with stochastic disturbance

This paper presents a discrete-time model to describe the movements of a group of trains, in which some operational strategies, including traction operation, braking operation and impact of stochastic disturbance, are defined. To show the dynamic characteristics of train traffic flow with stochastic disturbance, some numerical experiments on a railway line are simulated. The computational results show that the discrete-time movement model can well describe the movements of trains on a rail line with the moving-block signalling system. Comparing with the results of no disturbance, it finds that the traffic capacity of the rail line will decrease with the influence of stochastic disturbance. Additionally, the delays incurred by stochastic disturbance can be propagated to the subsequent trains, and then prolong their traversing time on the rail line. It can provide auxiliary information for rescheduling trains When the stochastic disturbance occurs on the railway.

A new epidemic modeling approach:Multi-regions discrete-time model with travel-blocking vicinity optimal control strategy

First,we devise in this paper,a multi-regions discrete-time model which describes the spatial-temporal spread of an epidemic which starts from one region and enters to regions which are connected with their neighbors by any kind of anthropological movement.We suppose homogeneous Susceptible-Infected-Removed(SIR)populations,and we consider in our simulations,a grid of colored cells,which represents the whole domain affected by the epidemic while each cell can represent a sub-domain or region.Second,in order to minimize the number of infected individuals in one region,we propose an optimal control approach based on a travel-blocking vicinity strategy which aims to control only one cell by restricting movements of infected people coming from all neighboring cells.Thus,we show the influence of the optimal control approach on the controlled cell.We should also note that the cellular modeling approach we propose here,can also describes infection dynamics of regions which are not necessarily attached one to an other,even if no empty space can be viewed between cells.The theoretical method we follow for the characterization of the travel-locking optimal controls,is based on a discrete version of Pontryagin's maximum principle while the numerical approach applied to the multi-points boundary value problems we obtain here,is based on discrete progressive-regressive iterative schemes.We illustrate our modeling and control approaches by giving an example of 100 regions.

An Improved Discrete-Time Model for Heterogeneous High-Speed Train Traffic Flow

This paper aims to present a simulation model for heterogeneous high-speed train traffic flow based on an improved discrete-time model（IDTM）.In the proposed simulation model,four train control strategies,including departing strategy,traveling strategy,braking strategy,overtaking strategy,are well defined to optimize train movements.Based on the proposed simulation model,some characteristics of train traffic flow are investigated.Numerical results indicate that the departure time intervals,the station dwell time,the section length,and the ratio of fast trains have different influence on traffic capacity and train average velocity.The results can provide some theoretical support for the strategy making of railway departments.

Scheduling of high-speed rail traffic based on discrete-time movement model

In this paper, a new simulation approach for solving the mixed train scheduling problem on the high-speed double-track rail line is presented. Based on the discrete-time movement model, we propose control strategies for mixed train movement with different speeds on a high-speed double-track rail line, including braking strategy, priority rule, travelling strategy, and departing rule. A new detailed algorithm is also presented based on the proposed control strategies for mixed train movement. Moreover, we analyze the dynamic properties of rail traffic flow on a high-speed rail line. Using our proposed method, we can effectively simulate the mixed train schedule on a rail line. The numerical results demonstrate that an appropriate decrease of the departure interval can enhance the capacity, and a suitable increase of the distance between two adjacent stations can enhance the average speed. Meanwhile, the capacity and the average speed will be increased by appropriately enhancing the ratio of faster train number to slower train number from 1.

Invasion reproductive numbers for discrete-time models

Although invasion reproductive numbers(IRNs)are utilized frequently in continuous-time models with multiple interacting pathogens,they are yet to be explored in discrete-time systems.Here,we extend the concept of IRNs to discrete-time models by showing how to calculate them for a set of two-pathogen SIS models with coinfection.In our exploration,we address how sequencing events impacts the basic reproductive number(BRN)and IRN.As an illustrative example,our models are applied to rhinovirus and respiratory syncytial virus co-circulation.Results show that while the BRN is unaffected by variations in the order of events,the IRN differs.Furthermore,our models predict copersistence of multiple pathogen strains under cross-immunity,which is atypical of analogous continuous-time models.This investigation shows that sequencing events has important consequences for the IRN and can drastically alter competition dynamics.

Anticontrol of chaos for discrete-time fuzzy hyperbolic model with uncertain parameters

This paper proposes a new method to chaotify the discrete-time fuzzy hyperbolic model （DFHM） with uncertain parameters. A simple nonlinear state feedback controller is designed for this purpose. By revised Marotto theorem, it is proven that the chaos generated by this controller satisfies the Li-Yorke definition. An example is presented to demonstrate the effectiveness of the approach.

Robustness Analysis of Discrete-time Indirect Model Reference Adaptive Control with Normalized Adaptive Laws

For a class of discrete-time systems with unmodeled dynamics and bounded disturbance, the design and analysis of robust indirect model reference adaptive control （MRAC） with normalized adaptive law are investigated. The main work includes three parts. Firstly, it is shown that the constructed parameter estimation algorithm not only possesses the same properties as those of traditional estimation algorithms, but also avoids the possibility of division by zero. Secondly, by establishing a relationship between the plant parameter estimate and the controller parameter estimate, some similar properties of the latter are also established. Thirdly, by using the relationship between the normalizing signal and all the signals of the closed-loop system, and some important mathematical tools on discrete-time systems, as in the continuous-time case, a systematic stability and robustness analysis approach to the discrete indirect robust MRAC scheme is developed rigorously.

The Dynamic Properties of a Deterministic SIR Epidemic Model in Discrete-Time

In this paper, a class of discrete deterministic SIR epidemic model with vertical and horizontal transmission is studied. Based on the population assumed to be a constant size, we transform the discrete SIR epidemic model into a planar map. Then we find out its equilibrium points and eigenvalues. From discussing the influence of the coefficient parameters effected on the eigenvalues, we give the hyperbolicity of equilibrium points and determine which point is saddle, node or focus as well as their stability. Further, by deriving equations describing flows on the center manifolds, we discuss the transcritical bifurcation at the non-hyperbolic equilibrium point. Finally, we give some numerical simulation examples for illustrating the theoretical analysis and the biological explanation of our theorem.

H_∞ Control of Looper-Tension Control Systems Based on a Discrete-Time Model

High control performances cannot be obtained by most of the existing looper-tension control approaches through only considering controller designs based on continuous time models, which cannot meet the requirements of modern computer control systems with high control accuracy. In order to solve the above problems, a state feedback H∞ control method based on a discrete-time model for looper-tension control systems in hot rolling mills is presen- ted. The considered system is approximated by a discrete-time loope∞tension control system model. Based on a Lya- punov functional method, a state feedback H∞ control law is developed which makes the closed-loop system asymp totically stable with guaranteed H∞ performance. The controller gains are obtained by solving a set of linear matrix inequalities （LMIs）. In contrast to the existing results, the proposed approach can obtain good H∞ performance and effectively reduce external disturbances. The strip tension is also less affected by the change of looper angel, so good control performances can be obtained. Moreover, this control scheme is easy to implement, and can be applied to other linear systems. A simulation example with practical parameters is provided to illustrate the effectiveness of the developed method.

Exponential stability for networked control systems based on the model of nonlinear discrete-time system with time-varying delay

An uncertain nonlinear discrete-time system model with time-varying input delays for networked control systems (NCSs) is presented. The problem of exponential stability for the system is considered and some new criteria of exponential stability are obtained based on norm inequality methods. A numerical example is given todemonstrate that those criteria are useful to analyzing the stability of nonlinear NCSs.

Design of Discrete-time Repetitive Control System Based on Two-dimensional Model

This paper presents a novel design method for discrete-time repetitive control systems （RCS） based on two-dimensional （2D） discrete-time model. Firstly, the 2D model of an RCS is established by considering both the control action and the learning action in RCS. Then, through constructing a 2D state feedback controller, the design problem of the RCS is converted to the design problem of a 2D system. Then, using 2D system theory and linear matrix inequality （LMI） method, stability criterion is derived for the system without and with uncertainties, respectively. Parameters of the system can be determined by solving the LMI of the stability criterion. Finally, numerical simulations validate the effectiveness of the proposed method.

Iterative identification of discrete-time output-error model with time delay

The problem of discrete-time model identification of industrial processes with time delay was investigated.An iterative and separable method is proposed to solve this problem,that is,the rational transfer function model parameters and time delay are alternately fixed to estimate each other.The instrumental variable technique is applied to guarantee consistent estimation against measurement noise.A noteworthy merit of the proposed method is that it can handle fractional time delay estimation,compared to existing methods commonly assuming that the time delay is an integer multiple of the sampling interval.The identifiability analysis for time delay is addressed and correspondingly,some guidelines are provided for practical implementation of the proposed method.Numerical and experimental examples are presented to illustrate the effectiveness of the proposed method.

Direct model reference adaptive control using K_p = LDU factorization for multivariable discrete-time plants

For a large class of discrete-time multivariable plants with arbitrary relative degrees, the design and analysis of the direct model reference adaptive control scheme are investigated under less restrictive assumptions. The algorithm is based on a new parametrization derived from the high frequency gain matrix factorization Kp=LDU under the condition that the signs of the leading principal minors of/fp are known. By reproving the discrete-time Lp and L2σ norm relationship between inputs and outputs, establishing the properties of discrete-time adaptive law, defining the normalizing signal, and relating the signal with all signals in the closed-loop system, the stability and convergence of the discrete-time multivariable model reference adaptive control scheme are analyzed rigorously in a systematic fashion as in the continuous-time case.

Comparative analysis of empirical and deep learning models for ionospheric sporadic E layer prediction

Sporadic E(Es)layers in the ionosphere are characterized by intense plasma irregularities in the E region at altitudes of 90-130 km.Because they can significantly influence radio communications and navigation systems,accurate forecasting of Es layers is crucial for ensuring the precision and dependability of navigation satellite systems.In this study,we present Es predictions made by an empirical model and by a deep learning model,and analyze their differences comprehensively by comparing the model predictions to satellite RO measurements and ground-based ionosonde observations.The deep learning model exhibited significantly better performance,as indicated by its high coefficient of correlation(r=0.87)with RO observations and predictions,than did the empirical model(r=0.53).This study highlights the importance of integrating artificial intelligence technology into ionosphere modelling generally,and into predicting Es layer occurrences and characteristics,in particular.

Aquaporin-4-IgG-seropositive neuromyelitis optica spectrum disorders:progress of experimental models based on disease pathogenesis

Neuromyelitis optica spectrum disorders are neuroinflammatory demyelinating disorders that lead to permanent visual loss and motor dysfunction.To date,no effective treatment exists as the exact causative mechanism remains unknown.Therefore,experimental models of neuromyelitis optica spectrum disorders are essential for exploring its pathogenesis and in screening for therapeutic targets.Since most patients with neuromyelitis optica spectrum disorders are seropositive for IgG autoantibodies against aquaporin-4,which is highly expressed on the membrane of astrocyte endfeet,most current experimental models are based on aquaporin-4-IgG that initially targets astrocytes.These experimental models have successfully simulated many pathological features of neuromyelitis optica spectrum disorders,such as aquaporin-4 loss,astrocytopathy,granulocyte and macrophage infiltration,complement activation,demyelination,and neuronal loss;however,they do not fully capture the pathological process of human neuromyelitis optica spectrum disorders.In this review,we summarize the currently known pathogenic mechanisms and the development of associated experimental models in vitro,ex vivo,and in vivo for neuromyelitis optica spectrum disorders,suggest potential pathogenic mechanisms for further investigation,and provide guidance on experimental model choices.In addition,this review summarizes the latest information on pathologies and therapies for neuromyelitis optica spectrum disorders based on experimental models of aquaporin-4-IgG-seropositive neuromyelitis optica spectrum disorders,offering further therapeutic targets and a theoretical basis for clinical trials.