An output delay approach to fault estimation for sampled-data systems

The problem of fault estimation for a class of non-uniformly sampled-data systems is investigated from the time delay point of view in this paper.Firstly,the output delay approach is employed to model the sampled-data system as a continuous-time one with time-varying delay output.Then,based on the analysis of the inapplicability of the adaptive fault diagnosis observer in such class of time-delay systems,a novel augmented fault estimation observer design method is proposed to guarantee the exponential convergence of the estimation errors.Furthermore,an extension to the case of time varying fault estimation for the noisy sampled-data systems is studied.Finally,simulation results of a flight control system are presented to demonstrate the effectiveness of the proposed method.     

An approach for analyzing the reliability of industrial systems using soft-computing based technique

The purpose of this paper is to present a novel technique for analyzing the behavior of an industrial system by utilizing vague, imprecise, and uncertain data. In this, two important tools namely traditional Lambda–Tau and artificial bee colony algorithm have been used to build a technique named as an artificial bee colony (ABC) algorithm based Lambda–Tau (ABCBLT). In real-life situation, data collected from various resources contains a large amount of uncertainties due to human errors and hence it is not easy to analyze the behavior of such system up to a desired accuracy. If somehow behavior of these systems has been calculated, then they have a high range of uncertainty. For handling this situation, a fuzzy set theory has been used in the analysis and an artificial bee colony has been used for determining their corresponding membership functions. To strengthen the analysis, various reliability parameters, which affects the system performance directly, have been computed in the form of fuzzy membership functions. Sensitivity as well as performance analysis has also been analyzed and their computed results are compared with the existing techniques result. The butter–oil processing plant, a complex repairable industrial system has been taken to demonstrate the approach.     

Total variance approach to software reliability estimation

This paper presents a mathematical model for a new approach to calculating the confidence intervals for software reliability projections. Unlike those calculated by current methods, these confidence intervals account for any uncertainty concerning the operational profile of the system     

An alternative approach to state estimation in linear time-invariant systems

An alternative approach to state estimation problem in linear, time-invariant dynamic systems is presented in this paper. The approach developed first identifies the initial state of the system by using a proportional plus integral parameter identification method. The Lyapunov design technique is used to guarantee the asymptotic convergence of the initial state identifier. A state estimator is then constructed to operate in series with the initial state identifier. The estimator generates an estimate of the unobserved part of the system state. Simulation studies have shown that satisfactory state estimation can be achieved in the presence of measurement or disturbance noise. An example problem is considered to demonstrate the response characteristics of the estimator-identifier combination.     

An uncertainty-based approach to discrete-time fault estimation observer design for nonuniformly sampled systems

This paper considers the fault estimation problem of nonuniformly sampled system in which sensor sampling is performed at aperiodic interval. After being discretized at sampling instant, the nonuniformly sampled system is modeled as an equivalent polytopic system with norm bounded uncertainties. A discrete-time time-varying fault estimation observer with multiple design freedom is then constructed, and a sufficient condition given in linear matrix inequality (LMI) is provided to obtain the constant filter gain and ensure not only the asymptotic stability of fault estimation error but also the robustness of uncertainties. Compared with the existing observer designed based on continuous-time delay approach, the proposed one has a better estimation accuracy and less conservatism and is easy for digital implementation. A numerical simulation and a quadruple-tank benchmark are used to demonstrate the effectiveness and superiority of the proposed method.     

An effective approach for low-complexity maximum likelihood based automatic modulation classification of STBC-MIMO systems

Frontiers of Information Technology & Electronic Engineering - A low-complexity likelihood methodology is proposed for automatic modulation classification of orthogonal space-time block code...     

基于状态验证覆盖的Bayes软件可靠性评估

传统的可靠性评估方法都是基于系统软件运行期间的失效,对于武器系统软件,由于其使用试验耗费巨资且周期很长,不可能对系统进行过多的使用试验,导致难以采集到高质量的失效数据。提出一种基于系统状态验证覆盖的Bayes软件可靠性评估方法,该方法以Bayes可靠性模型为评估准则,通过状态覆盖率来保证充分性,通过状态测试验证来保证可靠性,提倡可信性与可靠性并行增长。     

An enhanced approach to improve the encryption of big data using intelligent classification technique

Multimedia Tools and Applications - Data is undoubtedly one of the most significant assets in the current competitive era and to ensure its value is retained, data safety emerges as a principle...     

An optimal approach for random signals classification

A method is proposed which solves the problem of the Bayes classification of ARMA (autoregressive moving average) signals when the models of classes and samples are not exactly known but only estimated from finite-length data sequences. Justified approximations and the hypothesis lead to decision rules including the variances of the estimations. The results obtained on a large set of simulated data show that this approach is superior to the best classical methods (cepstral distance or Kullback divergence), particularly in the common case where the hypothesis of those methods is not verified (short samples. small training sets. random classes)     

Model-driven estimation approach for system reliability using integrated tasks and resources

The increasing complexity of software systems in embedded systems or industrial business domains has led to the importance of reliability analysis for current systems. Reliability analysis has become a crucial part of the system development life cycle, and a new approach is needed to enable an early analysis for reliability estimation, especially for the system under design. However, the existing approach neglects the correlation between system resource and system task for estimating system reliability. This subsequently restricts accuracy of the estimation as well as causing difficulties in identifying critical resources and tasks during the design phase. This paper proposes a model-driven system reliability estimation using a scenario-based approach to estimate system reliability and identify its critical resources and system tasks during the design phase. This model is based on the PerFAM model, which can specifically view timing failures through a system scenario. The proposed approach is validated by the application of a sensitivity analysis into one case study. The case study demonstrates an essential relationship between system reliability, as well as both resources and tasks, which ultimately becomes the integral part for a system reliability estimation assessment.     

A response surface approach for structural reliability analysis using evidence theory

Evidence theory employs a much more general and flexible framework to quantify the epistemic uncertainty, and thereby it is adopted to conduct reliability analysis for engineering structures recently. However, the large computational cost caused by its discrete property significantly influences the practicability of evidence theory. This paper proposes an efficient response surface (RS) method to evaluate the reliability for structures using evidence theory, and hence improves its applicability in engineering problems. A new design of experiments technique is developed, whose key issue is the search of the important control points. These points are the intersections of the limit-state surface and the uncertainty domain, thus they have a significant contribution to the accuracy of the subsequent established RS. Based on them, a high precise radial basis functions RS to the actual limit-state surface is established. With the RS, the reliability interval can be efficiently computed for the structure. Four numerical examples are investigated to demonstrate the effectiveness of the proposed method.     

A semiparametric density estimation approach to pattern classification

A new multivariate density estimator suitable for pattern classifier design is proposed. The data are first transformed so that the pattern vector components with the most non-Gaussian structure are separated from the Gaussian components. Nonparametric density estimation is then used to capture the non-Gaussian structure of the data while parametric Gaussian conditional density estimation is applied to the rest of the components. Both simulated and real data sets are used to demonstrate the potential usefulness of the proposed approach.     

Surrogate model-based reliability analysis for structural systems with correlated distribution parameters

Structural and Multidisciplinary Optimization - Uncertainties are usually modeled by random variables, and the values of distribution parameters are estimated from the collected samples. In...     

An enhanced client-centric approach for efficient video broadcast

Periodic broadcast is a cost-effective solution for large-scale distribution of popular videos. Regardless of the number of video requests, this strategy guarantees a constant worst service latency to all clients, making it possible to serve a large community with a minimal amount of broadcast bandwidth. Although many efficient periodic broadcast techniques have been proposed, most of them impose rigid requirements on client receiving bandwidth. They either demand clients to have the same bandwidth as the video server, or limit them to receive no more than two video streams at any one time. In our previous work, we addressed this problem with a Client-Centric Approach (CCA). This scheme takes into consideration both server broadcast bandwidth and client receiving bandwidth and allows clients to use all their receiving capability for prefetching broadcast data. As a result, given a fixed broadcast bandwidth, a shorter broadcast period can be achieved with an improved client communication capability. In this paper, we present an enhanced version of CCA to further leverage client bandwidth for more efficient video broadcast. The new scheme reduces the broadcast latency up to 50% as compared to CCA. We prove the correctness of this new technique and provide an analytical evaluation to show its performance advantage as compared with some existing techniques.

A modal approach for estimation in distributed parameter systems

A scheme, based on eigenmode expansion, for the estimation of system responses in distributed parameter systems with non-linear sensors is presented. The associated joint conditional probability distribution is realized by using the partition theorem and Wiener functional expansions. The system state and parameters are subsequently computed by obtaining the innovation processes from the observations.     

An enhanced fuzzy c-means algorithm for audio segmentation and classification

Automated audio segmentation and classification play important roles in multimedia content analysis. In this paper, we propose an enhanced approach, called the correlation intensive fuzzy c-means (CIFCM) algorithm, to audio segmentation and classification that is based on audio content analysis. While conventional methods work by considering the attributes of only the current frame or segment, the proposed CIFCM algorithm efficiently incorporates the influence of neighboring frames or segments in the audio stream. With this method, audio-cuts can be detected efficiently even when the signal contains audio effects such as fade-in, fade-out, and cross-fade. A number of audio features are analyzed in this paper to explore the differences between various types of audio data. The proposed CIFCM algorithm works by detecting the boundaries between different kinds of sounds and classifying them into clusters such as silence, speech, music, speech with music, and speech with noise. Our experimental results indicate that the proposed method outperforms the state-of-the-art FCM approach in terms of audio segmentation and classification.     

A fast approximate method for parameter sensitivity estimation in Monte Carlo structural reliability

In estimating the effect of a change in a random variable parameter on the (time-invariant) probability of structural failure estimated through Monte Carlo methods the usual approach is to carry out a duplicate simulation run for each parameter being varied. The associated computational cost may become prohibitive when many random variables are involved. Herein a procedure is proposed in which the numerical results from a Monte Carlo reliability estimation procedure are converted to a form that will allow the basic ideas of the first order reliability method to be employed. Using these allows sensitivity estimates of low computational cost to be made. Illustrative examples with sensitivities computed both by conventional Monte Carlo and the proposed procedure show good agreement over a range of probability distributions for the input random variables and for various complexities of the limit state function.