Stability analysis of two-dimensional Markovian jump state-delayed systems in the Roesser model with uncertain transition probabilities

This paper is concerned with the problem of stochastic stability analysis of discrete-time two-dimensional (2-D) Markovian jump systems (MJSs) described by the Roesser model with interval time-varying delays. The transition probabilities of the jumping process/Markov chain are assumed to be uncertain, that is, they are not exactly known but can be estimated. A Lyapunov-like scheme is first extended to 2-D MJSs with delays. Based on some novel 2-D summation inequalities proposed in this paper, delay-dependent stochastic stability conditions are derived in terms of linear matrix inequalities (LMIs) which can be computationally solved by various convex optimization algorithms. Finally, two numerical examples are given to illustrate the effectiveness of the obtained results.

Slope stability analysis using Phase 2

Analysing the rock slope stability is a classical problem for geotechnicalengineers. Recently, Hoek-Brown failure criterion has drawn more and more attentionfor rock slope stability assessments. It would be due to the fact that the nonlinearity ismore pronounced at the low confining stresses that are operational in slope stabilityproblems. However, it is still not popular yet. Therefore, in this study, slope stabilityanalyses will be performed based on the generalised Hoek-Brown failure criterionusing a commercial software, Phase 2. The Hoek-Brown strength parameters will beused as direct inputs in numerical simulations. In addition, two rock slope cases willbe investigated. It is expected that better understandings of rock slope mechanisms canbe obtained.

Adaptive blind channel equalization based on constellation information of MPSK signal

In this paper, we propose a new adaptive algorithm for the blind equalization of an FIR (finite impulse response) channel excited by an M-ary phase shift keying (MPSK) signal. Different from the conventional constant modulus algorithm (CMA), which exploits the amplitude information of the input signal, the proposed algorithm exploits the full constellation information of the input signal. Theoretical analysis shows that the new algorithm has less mean square error (MSE), namely better equalization performance, in steady state than the CMA. Numerical simulations show the effectiveness of the new algorithm

Convergence analysis of multi-stage PIC detectors

This paper analyses the convergence behaviour of the parallel interference cancellation (PIC) detector in code division multiple access (CDMA) systems. Using the results from previous stability analysis of an iterated-map neural network, the paper derives a general condition from which the sufficient condition for convergence of the PIC detector with tentative decision functions that are monotonically increasing at a sublinear rate can be calculated. As examples, the paper derives the sufficient conditions for convergence of the PIC detector with the clip decision and the hyperbolic tangent decision functions. The paper also examines the convergence behaviour of the PIC detector with hyperbolic tangent decision function via computer simulation and compares it with the analytical results.

Observer-based control of multi-agent systems under decentralized information structure

This paper addresses the problem of decentralized implementation of a global state feedback controller for multi-agent systems. The system is assumed to be under the constraint of a complete decentralized information structure. The decentralization of the control task is achieved through the construction of low-order decentralized functional observers with the purpose of generating the required corresponding control signal for each local control station. A design procedure is developed for obtaining an approximate solution to the design of the observers. Stability analysis is provided for the global system using the proposed observer-based approach. A numerical example is given to illustrate the design procedure and cases when the observers' order increases from the lowest value.

A new blind-equalization algorithm for an FIR SIMO system driven by MPSK signal

We consider the problem of blind equalization of a finite impulse response and single-input multiple-output system driven by an M-ary phase-shift-keying signal. The existing single-mode algorithms for this problem include the constant modulus algorithm (CMA) and the multimodulus algorithm (MMA). It has been shown that the MMA outperforms the CMA when the input signal has no more than four constellation points, i.e., Mles4. In this brief, we present a new adaptive equalization algorithm that jointly exploits the amplitude and phase information of the input signal. Theoretical analysis shows that the proposed algorithm has less mean square error, i.e., better equalization performance, at steady state than the CMA regardless of the value of M. The superior performance of our algorithm to the CMA and the MMA is validated by simulation examples

Between context-aware media capture and multimedia content analysis - where do we find the promised land?

Various issues related to the multimedia information retrieval and media access are discussed. The feasible solutions for automatic signal-based analysis of media content are analyzed. The extent of user involvement in the content creation process is emphasized. The applications driving the creation and usage of context and metadata are also elaborated.

Exponential stability for markovian jumping stochastic BAM neural networks with mode-dependent probabilistic time-varying delays and impulse control

Abstract
In this article, an exponential stability analysis of Markovian jumping stochastic bidirectional associative memory (BAM) neural networks with mode-dependent probabilistic time-varying delays and impulsive control is investigated. By establishment of a stochastic variable with Bernoulli distribution, the information of probabilistic time-varying delay is considered and transformed into one with deterministic time-varying delay and stochastic parameters. By fully taking the inherent characteristic of such kind of stochastic BAM neural networks into account, a novel Lyapunov-Krasovskii functional is constructed with as many as possible positive definite matrices which depends on the system mode and a triple-integral term is introduced for deriving the delay-dependent stability conditions. Furthermore, mode-dependent mean square exponential stability criteria are derived by constructing a new Lyapunov-Krasovskii functional with modes in the integral terms and using some stochastic analysis techniques. The criteria are formulated in terms of a set of linear matrix inequalities, which can be checked efficiently by use of some standard numerical packages. Finally, numerical examples and its simulations are given to demonstrate the usefulness and effectiveness of the proposed results.

Pore pressure effect on slope stability assessment

Slope stability assessment has been an integral problem for geotechnical engineering all these years. While stability of slopes is affected by various factors, pore pressure is one of the common naturalelements that influence slope stability analysis. This paper studies the effect of pore pressure on slope stability assessment by using Limit Equilibrium Method (LEM). The results will be compared to the solutions of Hoek and Bray charts. In this study, slopes with different levels of water table corresponding to those of Hoek and Bray charts are investigated. It’s interesting to observe that the results obtained from the Hoek and Bray charts yielded different factor of safety compare to those in the study here-in. In fact, the different between the factors of safety could be up to 30%. Hence this issue should be taken into consideration during slope design.

Small-variance asymptotics for bayesian nonparametric models with constraints

The users often have additional knowledge when Bayesian nonparametric models (BNP) are employed, e.g. for clustering there may be prior knowledge that some of the data instances should be in the same cluster (must-link constraint) or in different clusters (cannot-link constraint), and similarly for topic modeling some words should be grouped together or separately because of an underlying semantic. This can be achieved by imposing appropriate sampling probabilities based on such constraints. However, the traditional inference technique of BNP models via Gibbs sampling is time consuming and is not scalable for large data. Variational approximations are faster but many times they do not offer good solutions. Addressing this we present a small-variance asymptotic analysis of the MAP estimates of BNP models with constraints. We derive the objective function for Dirichlet process mixture model with constraints and devise a simple and efficient K-means type algorithm. We further extend the small-variance analysis to hierarchical BNP models with constraints and devise a similar simple objective function. Experiments on synthetic and real data sets demonstrate the efficiency and effectiveness of our algorithms.

Dynamical analysis of neural networks with time-varying delays using the LMI approach

This study is concerned with the delay-range-dependent stability analysis for neural networks with time-varying delay and Markovian jumping parameters. The time-varying delay is assumed to lie in an interval of lower and upper bounds. The Markovian jumping parameters are introduced in delayed neural networks, which are modeled in a continuous-time along with finite-state Markov chain. Moreover, the sufficient condition is derived in terms of linear matrix inequalities based on appropriate Lyapunov-Krasovskii functionals and stochastic stability theory, which guarantees the globally asymptotic stable condition in the mean square. Finally, a numerical example is provided to validate the effectiveness of the proposed conditions.

Investigation of seismic effects on disturbed rock slope stability assessments

For general stability analysis of rock slopes, rock mass strength and rock mass disturbance are definitely should be considered. In addition, the impact of earthquakes must be taken into account. In fact, the rock mass strength is very difficult to be assessed which causes the difficulty of analysing rock slope stability. Therefore, an empirical failure criterion, the Hoek-Brown failure criterion, has been proposed. It is one of the most widely accepted approaches to estimate rock mass strength. The rock mass disturbance is important and was found having significant influence on evaluating rock slope stability, especially for rock slope with poor quality rock mass. In the Hoek-Brown failure criterion, the disturbance factor can represent the level of the rock mass disturbance which would provide a reasonable basis for estimating rock mass strength. This research will not only discuss the slope factor of safety, but also consider the influence of the seismic force on rock slope stability assessment using pseudo-static method. In practice, only horizontal seismic coefficient is used. Various magnitudes of the disturbance factor and recommended blasting damage zone thickness are also taken into account. The blasting damage zone thickness considered ranges from 0.5 to 2.5 times of slope height. The research results have potential to be extended and then sets of comprehensive stability charts can be provided for the rock slope stability evaluations. They will be convenient tools for practising engineers. In this study, finite element upper bound and lower bound limit analysis methods are employed. Their applicability has been investigated in some previous studies. The differences between upper bound and lower bound solutions are less than ±10% which would provide reasonable and acceptable range for rock slope stability safety factor estimation.

Analysis of texture and R value variations in asymmetric rolling of if steel

Asymmetric rolling (ASR) is a potential process to reach better grain refinement than in conventional rolling, thus, can lead to better mechanical properties. It is not known, however, how the introduction of a shear component will change the ideal orientations of the textures, and consequently, the evolution of plastic anisotropy. To understand the effect of the added shear on texture evolution in ASR, a stability analysis is carried out in orientation space and the variations in the position and strength of the ideal orientations are analysed as a function of the shear component. Then, modelling of R values is presented for various cases. On that basis, it is shown that there is an upper limit for the shear component in asymmetric rolling that still retains the 〈1 1 1〉 ND fibre (ND: direction normal to the sheet) which is good for formability. It is also found that better persistence of the ND fibre can be obtained by cyclically alternating the shear component. The theoretical results are well supported by comparison to experimental evidences. © 2011 Elsevier B.V. All rights reserved.

Exponential stability of time-delay systems via new weighted integral inequalities

In this paper, new weighted integral inequalities (WIIs) are first derived based on Jensen's integral inequalities in single and double forms. It is theoretically shown that the newly derived inequalities in this paper encompass both the Jensen inequality and its most recent improvement based on Wirtinger's integral inequality. The potential capability of WIIs is demonstrated through applications to exponential stability analysis of some classes of time-delay systems in the framework of linear matrix inequalities (LMIs). The effectiveness and least conservativeness of the derived stability conditions using WIIs are shown by various numerical examples.