Sharif Digital Repository

In this paper, a new approach to control continuous time chaotic systems with an unknown governing equation and limitation on the measurement of states, has been investigated. In many chaotic systems, disability to measure all of the states is a usual limitation, like in some economical, biological and many other engineering systems. Takens showed that a chaotic attractor has an astonishing feature in which it can embed to a mathematically similar attractor by using time series of one of the states. The new embedded attractor saves much information from the original attractor. This phenomenon has been deployed to present a new way to control continuous time chaotic systems, when only one of... 

Background: The abundance of gene expression microarray data has led to the development of machine learning algorithms applicable for tackling disease diagnosis, disease prognosis, and treatment selection problems. However, these algorithms often produce classifiers with weaknesses in terms of accuracy, robustness, and interpretability. This paper introduces fuzzy support vector machine which is a learning algorithm based on combination of fuzzy classifiers and kernel machines for microarray classification.Results: Experimental results on public leukemia, prostate, and colon cancer datasets show that fuzzy support vector machine applied in combination with filter or wrapper feature selection... 

In this paper, the problem of chaos control in discrete-time chaotic systems with unknown governing equations and limited measurable states is investigated. Using the time-series of only one measurable state, an algorithm is proposed to stabilize unstable fixed points. The approach consists of three steps: first, using Takens embedding theory, a delayed phase space preserving the topological characteristics of the unknown system is reconstructed. Second, a dynamic model is identified by recursive least squares method to estimate the time-series data in the delayed phase space. Finally, based on the reconstructed model, an appropriate linear delayed feedback controller is obtained for... 

In this paper, an adaptive nonlinear delayed feedback control is proposed for stabilizing an unstable periodic orbit (UPO) of a class of uncertain chaotic systems using the sliding mode approach. The chaotic system is subjected to external disturbances with unknown bounds. Using the Lyapunov stability theorem and the concept of sliding mode control, an adaptive delayed feedback controller has been designed. The controller and adaptation law guarantee the asymptotic stability of the closed loop system on a periodic trajectory which can be sufficiently close to the UPO of the chaotic system. The capability of the proposed control method in stabilizing the UPO of chaotic systems is established... 

In this paper, the problem of chaos control in a chaotic Henon-like system without using the governing equations of the system is investigated. It is also assumed that the system has only one measurable state. The time-series of the measurable state is used to stabilize chaos by a three-step method. First, using Takens embedding theory, a delayed phase space is reconstructed preserving the topological characteristics of the system. Then, an appropriate dynamic model is identified to estimate the time-series data in the reconstructed phase space. Finally, the unstable fixed point of the system is stabilized using an appropriate linear delayed feedback controller with controller gains... 

Modeling and analysis of information system vulnerabilities helps us to predict possible attacks to networks using the network configuration and vulnerabilities information. As a fact, exploiting most of vulnerabilities result in access rights alteration. In this paper, we propose a new vulnerability analysis method based on the Take-Grant protection model. We extend the initial Take-Grant model to address the notion of vulnerabilities and introduce the vulnerabilities rewriting rules to specify how the protection state of the system can be changed by exploiting vulnerabilities. Our analysis is based on a bounded polynomial algorithm, which generates the closure of the Take-Grant graph... 

We study the translocation of stiff polymers through a nanopore, driven by the chemical-potential gradient exerted by binding proteins (chaperones) on the trans side of the pore. Bound chaperones prevent backsliding through the pore and, therefore, partially rectify the polymer passage. We show that the sequence of chain monomers with different binding affinity for the chaperones significantly affects the translocation dynamics. In particular, we investigate the effect of the nearest-neighbor adjacency probability of the two monomer types. Depending on the magnitude of the involved binding energies, the translocation speed may either increase or decrease with the adjacency probability. We... 

Combining the advection-diffusion equation approach with Monte Carlo simulations we study chaperone driven polymer translocation of a stiff polymer through a nanopore. We demonstrate that the probability density function of first passage times across the pore depends solely on the Péclet number, a dimensionless parameter comparing drift strength and diffusivity. Moreover it is shown that the characteristic exponent in the power-law dependence of the translocation time on the chain length, a function of the chaperone-polymer binding energy, the chaperone concentration, and the chain length, is also effectively determined by the Péclet number. We investigate the effect of the chaperone size on... 

The amount of rippling in graphene sheets is related to the interactions with the substrate or with the suspending structure. Here, we report on an irreversibility in the response to forces that act on suspended graphene sheets. This may explain why one always observes a ripple structure on suspended graphene. We show that a compression-relaxation mechanism produces static ripples on graphene sheets and determine a peculiar temperature Tc, such that for T