38454938
A Generalized LMI-Based Approach to the Global Exponential Stability of Recurrent Neural Networks with Delay
A new theoretical result on the global exponential stability of recurrent neural networks with delay is presented. It should be noted that the activation functions of recurrent neural network do not require to be bounded. The presented criterion, which has the attractive feature of possessing the structure of linear matrix inequality (LMI), is a generalization and improvement over some previous criteria. A example is given to illustrate our results.
27820093
A Modified Projection Neural Network for Linear Variational Inequalities and Quadratic Optimization Problems
Variational inequalities provide us with a tool to study a wide class of optimization arising in pure and applied sciences. In the paper,we present a neural network for solving linear variational inequalities and quadratic optimization by using a projection techniques. We also consider the global uniqueness of the solution of the neural network as well as the convergence of the modified projection neural network. Our results present a significant improvement of previously known projection methods for solving variational inequalities and related optimization problems. Two simulation examples are provided to show the effectiveness of the approach and applicability of the proposed criteria.
27173246
Stochastic Lotka-Volterra Competitive Systems with Variable Delay
In this paper we reveal that the environmental noise will not only suppress a potential population explosion in the stochastic Lotka-Volterra competitive systems with variable delay, but also make the solutions to be stochastically ultimately bounded. To reveal these interesting facts, we stochastically perturb the Lotka-Volterra competitive systems with variable delay ẋ(t)=diag(x1(t),...,xn(t))[b+Ax(t−δ(t))] into the Ito form dx(t)=diag(x1(t),...,xn(t))[b+Ax(t−δ(t))]dt+σx(t)dw(t) and show that although the solution to the original delay systems may explode to infinity in a finite time, with probability one that of the associated stochastic delay systems do not. We also show that the stochastic systems will be stochastically ultimately bounded without any additional conditions on the matrix A.
2869717
Global Exponential Stability of Cohen-Grossberg Neural Networks with Time-Varying Delays and Continuously Distributed Delays
In this paper, we investigate the global exponential stability for a class of Cohen-Grossberg neural networks (CGNN) with time-varying delays and continuously distributed delays. CGNN herein is a general neural networks model which includes some well-known neural networks as its special cases. Firstly, applying the homeomorphism theory, we establish the new sufficient condition of existence and uniqueness of the equilibrium point to CGNN. Then, the sufficient criteria of global exponential stability of CGNN, which are easy to verify, are given by M-matrix. Our results imply and generalize some existed ones in previous literature. Furthermore, it is convenient to estimate the exponential convergence rates of the neural networks by using the criteria. Compared with the previous methods, our method does not resort to any Lyapunov functions or functionals. Finally, a example is given to illustrate the effective of our results.
3242735
Feature Mining Paradigms for Scientific Data
Numerical simulation is replacing experimentation as a means to gain insight into complex physical phenomena. Analyzing the data produced by such simulations is extremely challenging, given the enormous sizes of the datasets involved. In order to make efficient progress, analyzing such data must advance from current techniques that only visualize static images of the data, to novel techniques that can mine, track, and visualize the important features in the data. In this paper, we present our research on a unified framework that addresses this critical challenge in two science domains: computational fluid dynamics and molecular dynamics. We offer a systematic approach to detect the significant features in both domains, characterize and track them, and formulate hypotheses with regard to their complex evolution. Our framework includes two paradigms for feature mining, and the choice of one over the other, for a given application, can be determined based on local or global influence of relevant features in the data.
11028073
The Hamiltonicity of Directed sigma - tao Cayley Graphs (or: A Tale of Backtracking)
Abstract Let τ be the 2-cycle (1 2) and σ the n-cycle (12 … n). These two cycles generate the symmetric group Sn. Let Gn denote the directed Cayley graph Cay(τ, σ: Sn). Based on erroneous computer calculations, Nijenhuis and Wilf (1975, p. 238; 1978, p. 288) give as an exercise to show that G5 does not have a Hamiltonian path. To the contrary, we show that G5 is Hamiltonian. Furthermore, we show that G6 has a Hamilton path. Our results illustrate how a little theory and some good luck can save a lot of time in backtracking searches.
15586921
Vortex Visualization for Practical Engineering Applications
In order to understand complex vortical flows in large data sets, we must be able to detect and visualize vortices in an automated fashion. In this paper, we present a feature-based vortex detection and visualization technique that is appropriate for large computational fluid dynamics data sets computed on unstructured meshes. In particular, we focus on the application of this technique to visualization of the flow over a serrated wing and the flow field around a spinning missile with dithering canards. We have developed a core line extraction technique based on the observation that vortex cores coincide with local extrema in certain scalar fields. We also have developed a novel technique to handle complex vortex topology that is based on k-means clustering. These techniques facilitate visualization of vortices in simulation data that may not be optimally resolved or sampled. Results are included that highlight the strengths and weaknesses of our approach. We conclude by describing how our approach can be improved to enhance robustness and expand its range of applicability
32545163
Access Protocol with Efficient Slot Reuse in WDM Metro Ring
This letter provides a scalable slotted ring network architecture with nodes using one fixed transmitter, one tunable transmitter and multiple fixed receivers. Furthermore, the novel access protocol with efficient slot reuse is proposed. Theoretical analysis and simulation results show that protocol can achieve high node throughput and low queuing delay.
11507469
Automated extraction of fine features of kinetochore microtubules and plus-ends from electron tomography volume
Kinetochore microtubules (KMTs) and the associated plus-ends have been areas of intense investigation in both cell biology and molecular medicine. Though electron tomography opens up new possibilities in understanding their function by imaging their high-resolution structures, the interpretation of the acquired data remains an obstacle because of the complex and cluttered cellular environment. As a result, practical segmentation of the electron tomography data has been dominated by manual operation, which is time consuming and subjective. In this paper, we propose a model-based automated approach to extracting KMTs and the associated plus-ends with a coarse-to-fine scale scheme consisting of volume preprocessing, microtubule segmentation and plus-end tracing. In volume preprocessing, we first apply an anisotropic invariant wavelet transform and a tube-enhancing filter to enhance the microtubules at coarse level for localization. This is followed with a surface-enhancing filter to accentuate the fine microtubule boundary features. The microtubule body is then segmented using a modified active shape model method. Starting from the segmented microtubule body, the plus-ends are extracted with a probabilistic tracing method improved with rectangular window based feature detection and the integration of multiple cues. Experimental results demonstrate that our automated method produces results comparable to manual segmentation but using only a fraction of the manual segmentation time.