A network application model with operational process feature

A network application serves as the response process to the user request. The network application, taking the traffic as operation carrier, is closely related with process features. The existing investigations mainly focus on the traffic generation by capturing request feature, which is insufficient to characterize the application operation with specific processes. Thus, this paper presents a network application model with operational process feature, where the operation process is introduced in network applications in addition to characterizing traffic element from request feature. And the process feature is represented by the random, customized and routine processes, while request feature is described by the heavy-tailed ON/OFF source. Our analysis and simulation show that the traffic of our model admits the ubiquitous statistical laws: the self-similarity and the mean-variance relationship, which further validate our model. Moreover, compared with the traffic generation model without considering complex process features, where traffic distribution is found being positively correlated with node betweenness centrality (BC), the traffic of our model is both positively related with node BC, and much higher on nodes in the specific processes. The proposed model is thus beneficial for traffic control and network enhancement with complex process features.     

A stochastic process of software fault detection and correction for business operations

Automatic software fault detection and repair is made possible by autonomic software recovery. By incorporating this function, the software will run more efficiently and aggressively while requiring much less time and resources for maintenance. The focus of this article is on a suggested automated approach to Software Fault Detection and Recovery. The Software Fault Detection and Recovery (SFDR) procedure identifies when a software component has been damaged due to a fault and then restores the damaged component so that the software can continue functioning normally. During the design process, the SFDR is examined and created independently from the intended program. The proposed technique was implemented into an application that demonstrates the SFDR's performance and effectiveness to guarantee its practicality in real-world scenarios. The results of this experiment were encouraging. Results from experiments and comparisons to prior works show that the proposed methodology is successful.     

Synchronization of biological neural network systems with stochastic perturbations and time delays

With advances in biochemistry, molecular biology, and neurochemistry there has been impressive progress in the understanding of the molecular properties of anesthetic agents. However, despite these advances, we still do not understand how anesthetic agents affect the properties of neurons that translate into the induction of general anesthesia at the macroscopic level. There is extensive experimental verification that collections of neurons may function as oscillators and the synchronization of oscillators may play a key role in the transmission of information within the central nervous system. This may be particularly relevant to understand the mechanism of action for general anesthesia. In this paper, we develop a stochastic synaptic drive firing rate model for an excitatory and inhibitory cortical neuronal network in the face of system time delays and stochastic input disturbances. In addition, we provide sufficient conditions for global asymptotic and exponential mean-square synchronization for this model.     

Pulse nonstationary processes generated by dynamic systems with random structure

Dynamic system with a random structure described by a set of the first-order stochastic differential equations (SDE) is used as a generating model of nonstationary pulse stochastic processes. Physically the system presents the combination of the so-called partial filters related to the isolated states of the considered process, switched by a Poissonian point process and excited by a vector delta-correlated stream of impulses with the randomly distributed energy. The filters’ outputs are components of the vector Markov continuous-jump process with statistics depending on the partial SDEs operators, intensity of switching process and distributions of the exciting impulses’ energies. The approach proposed was used as a simulation model of the Middleton “Class-A “generally non-Gaussian noise. The results demonstrate that the main features of statistical characteristics of the noise envelope are reproduced rather well with the help of a bistate system with random structure.     

Contraction-based model predictive control for stochastic nonlinear discrete-time systems with time-varying delays via multi-dimensional Taylor network

For stochastic nonlinear systems with time-varying delays, the existing robust control approaches are unnecessarily conservative in most practical scenarios. Within this context, a mathematically rigorous and computationally tractable tube-based model predictive control scheme is proposed in the framework of contraction theory. A contraction metric is systematically constructed via convex optimization by forming a differential LyapunovKrasovskii function on tangent space. It guarantees the perturbed actual solution trajectories to be contained within a robust positive invariant tube centered along the reference trajectories and results in an explicit exponential bound on the deviation. The application scenarios of the control contraction metric controller are extended from constant delay systems into time-varying delay systems thereby. Compared with the existing robust mechanism for time-delay systems based on min-max optimization formulation with a linear feedback controller, the proposed scheme greatly reduces the design conservativeness and yields a larger region of attraction. A sparse multi-dimensional Taylor network (MTN) is designed to parameterize the family of the geodesic. Compared to conventional NNs and MTN surrogates, sparse MTN features a more concise topology that enhances its computational efficiency conspicuously. Results of the numerical simulations verify the effectiveness of the proposed method.     

虚拟网络型产业技术学习理论框架初探

技术学习理论是解释发展中国家经济赶超的有力工具。传统的技术学习理论分析框架忽略了技术标准与知识产权因素,难以解释虚拟网络型产业的技术学习过程。本文分析了由技术标准和知识产权决定的虚拟网络型产业的三个特征:网络外部性、技术标准专利化和基于标准的竞争,提出了一个虚拟网络型产业技术学习的三维理论分析框架:国际贸易规则、全球技术演化和企业层面的技术学习模型,分别描述了国际贸易规则的变化,全球技术演化轨迹和企业的技术学习过程。     

Statistical information based two-layer model predictive control with dynamic economy and control performance for non-Gaussian stochastic process

In this paper, a two-layer model predictive control (MPC) hierarchical architecture of dynamic economic optimization (DEO) and reference tracking (RT) is proposed for non-Gaussian stochastic process in the framework of statistical information. In the upper layer, with state feedback and dynamic economic information, the economically optimal trajectories are estimated by entropy and mean based dynamic economic MPC, which uses the nonlinear dynamic model instead of the steady-state model. These estimated optimal trajectories from the upper layer are then employed as the reference trajectories of the lower layer control system. A survival information potential based MPC algorithm is used to maintain the controlled variables at their reference trajectories in the nonlinear system with non-Gaussian disturbances. The stability condition of closed-loop system dynamics is proved using the statistical linearization method. Finally, a numerical example and a continuous stirred-tank reactor are used to illustrate the merits of the proposed economic optimization and control method.     

A multi-agent knowledge integration process for enterprise management innovation from the perspective of neural network

Because the innovation level of enterprise clusters in various regions of China is generally low, this research is focused on the process of knowledge integration of regional innovation subjects. We research, explore and analyze the different connection states between nodes and their impact on the knowledge symbiosis or knowledge spillover ability of the entire innovation network, learn from the characteristics of neurons in the neural network and the information transmission model to investigate the connection between various nodes in innovation network. We then determine the knowledge association mechanism and transmission relationship, and then analyze the trigger conditions of fusion and the transformation model of innovative knowledge flow under this condition, laying the foundation for further theoretical or practical research. Second, we built a model of the knowledge transfer connection that will be used in the innovation process. and select a path of knowledge transfer. Based on the mechanism of multi-agent innovation, we analyzed the incentive relationship of knowledge transfer in the innovation process, constructed the principles of knowledge transfer, analyzed the mutual transfer relationship between different innovation nodes, and analyze the simulation innovation network through certain examples. The knowledge fusion process in China lays the foundation for the improvement of the overall collaborative innovation level of the regional multi-agent innovation network. From the overall structure of the article, this research analyzes the regional innovation network from a new perspective on the basis of domestic and foreign research in knowledge flow management and control technology, knowledge exchange under social networks, and neural network optimization algorithms. The process of knowledge symbiosis or knowledge spillover in the process of innovation, exploring the incentive relationship of knowledge transfer throughout the primary parts innovation process, optimizing the degree of connection or relationship between different main agents, optimizing the knowledge exchange relationship from one node in the innovation network to another and improving the collaborative innovation of the regional mesh lay a theoretical foundation for the level.     

Stability of switching linear systems with switching signals driven by stochastic processes

This paper presents conditions to assure the exponential stability in probability for autonomous switching linear systems. The switching signal acting on the autonomous system produces intervals that follow independent, identically distributed stochastic processes—the stability then follows by verifying simple-to-check linear matrix inequalities.     

Fault detection for discrete-time systems with randomly occurring nonlinearity and data missing: A quadrotor vehicle example

This paper concerns the fault detection (FD) problem for a class of discrete-time systems subject to data missing and randomly occurring nonlinearity modeled by two independent Bernoulli distributed random variables. We propose to design a set of fault detection filters, or residual generation systems, corresponding to each of the fault components, to guarantee that each subsystem is mean square stable and satisfies a prescribed disturbance attenuation level. Sufficient conditions are established in the form of linear matrix inequalities (LMIs). System faults can be effectively detected by generating the residues and comparing them with the dynamic fault thresholds. A quadrotor vehicle example with faults on angles and angular rates illustrates and verifies the effectiveness of the proposed algorithm.     

A fault diagnosis in a nonlinear location network

In location problems, the out-transmission and in-transmission numbers are important indices to evaluate a directed network in which each edge is associated with a positive real number called the length of the edge and each vertex is associated with a positive real number call the weight of the vertex. The generalized indices of the out-transmission and in-transmission numbers are used in this paper. The indices are nonlinear functions of the distances between the vertices which represent the total cost or loss of service among the vertices. By a fault, we mean a change in the length of an edge. A fault diagnosis problem is discussed in a directed network in which the above indices are measurable at all times.     

Exponential synchronization of stochastic complex networks with multi-weights: A graph-theoretic approach

This paper concerns the exponential synchronization problem of stochastic complex networks with multiple weights (SCNMW). By the method of network split, SCNMW can be modelled as stochastic coupled systems driven by Brownian motion. By combining graph theory, Lyapunov stability theory and state feedback control technique, drive-response synchronization criteria of SCNMW have been obtained. Two kinds of exponential synchronization criteria are obtained, one is given with Lyapunov functions of vertex systems, and the other is shown with the coefficients of SCNMW. The obtained synchronization principles are closely related to the coupling strength of multiple sub-networks and the intensity of noise perturbation. Finally, a numerical example with some simulations is presented to illustrate the theoretical results.     

基于BP神经网络模型的新疆建设用地分析

鉴于BP神经网络在非线性领域预测中的应用,以新疆建设用地为研究对象,构建BP神经网络预测模型,选取1996~2006年总人口、城市化水平、GDP等10个因子,反映新疆人口状况、经济发展水平、产业结构及投资水平作为网络的仿真输入,对2007年新疆建设用地进行模拟预测,预测结果与实际面积的相对误差仅为0.06%.最后针对新疆建设用地中存在的问题,提出了保障经济与社会协调可持续发展的土地利用策略.     

Model free adaptive iterative learning control for a class of nonlinear systems with randomly varying iteration lengths

This paper proposes a novel model free adaptive iterative learning control scheme for a class of unknown nonlinear systems with randomly varying iteration lengths. By applying the dynamic linearization technique along the iteration axis, such systems can be transformed into iteration-depended time varying linear systems. Then, an improved model free adaptive iterative learning control scheme can be constructed only using input and output data of the system. From the rigorous theoretical analysis, it is shown that the mathematical expectation of tracking errors converge to zero as iteration increases. This design does not require any dynamic information of the ILC systems and prior information of randomly varying iteration lengths. An illustrative example verifies the effectiveness of the proposed design.     

Computational investigations of the mixing performance inside liquid slugs generated by a microfluidic T-junction

Droplet-based microfluidics has gained extensive research interest as it overcomes several challenges confronted by conventional single-phase microfluidics. The mixing performance inside droplets/slugs is critical in many applications such as advanced material syntheses and in situ kinetic measurements. In order to understand the effects of operating conditions on the mixing performance inside liquid slugs generated by a microfluidic T-junction, we have adopted the volume of fluid method coupled with the species transport model to study and quantify the mixing efficiencies inside slugs. Our simulation results demonstrate that an efficient mixing process is achieved by the intimate collaboration of the twirling effect and the recirculating flow. Only if the reagents are distributed transversely by the twirling effect, the recirculating flow can bring in convection mechanism thus facilitating mixing. By comparing the mixing performance inside slugs at various operating conditions, we find that slug size plays the key role in influencing the mixing performance as it determines the amount of fluid to be distributed by the twirling effect. For the cases where short slugs are generated, the mixing process is governed by the fast convection mechanism because the twirling effect can distribute the fluid to the flow path of the recirculating flow effectively. For cases with long slugs, the mixing process is dominated by the slow diffusion mechanism since the twirling effect is insufficient to distribute the large amount of fluid. In addition, our results show that increasing the operating velocity has limited effects on improving the mixing performance. This study provides the insight of the mixing process and may benefit the design and operations of droplet-based microfluidics.     

Stability and dissipative analysis for a class of stochastic system with time-delay

This paper deals with the stability and dissipative problem of a class of stochastic hybrid system. The system under study involves Markovian jump, impulsive effects and time delay, which are often encountered in practice and are the sources of instability. Our attention is focused on analysis of whether the stochastic hybrid system with time-delay is stochastically asymptotically stable and strictly (Q, S, R) dissipative. By introducing an extra artificial time instance, the equivalent system is obtained and the sufficient conditions are derived by using linear matrix inequality (LMI) techniques. The main results of this paper unify the existing results on H_∞ control.     

Dynamics of a stochastic multigroup SIQR epidemic model with standard incidence rates

In this paper, we consider a stochastic multigroup SIQR epidemic model with standard incidence rates. By using the stochastic Lyapunov function method, we establish sufficient conditions for the existence of a stationary distribution of the positive solutions to the model. Then we establish sufficient conditions for extinction of the diseases. A stationary distribution means that all the individuals can be coexistent and persistent in the long term. Finally, some examples and numerical simulations are introduced to illustrate our theoretical results.