Search

In practice, many systems exhibit load‐sharing behavior, where the surviving components share the total load imposed on the system. Different from general systems, the components of load‐sharing systems are interdependent in nature, in such a way that when one component fails, the system load has to be shared by the remaining components, which increases the failure rate or degradation rate of the remaining components. Because of the load‐sharing mechanism among components, parameter estimation and reliability assessment are usually complicated for load‐sharing systems. Although load‐sharing systems with components subject to sudden failures have been intensely studied in literatures with detailed estimation and analysis approaches, those with components subject to degradation are rarely investigated. In this paper, we propose the parameter estimation method for load‐sharing systems subject to continuous degradation with a constant load. Likelihood function based on the degradation data of components is established as a first step. The maximum likelihood estimators for unknown parameters are deduced and obtained via expectation‐maximization (EM) algorithm considering the nonclosed form of the likelihood function. Numerical examples are used to illustrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Profile monitoring is a vast area of research underneath the statistical process monitoring (SPM). Several methods for univariate and multivariate process control are found in literature to monitor the profile data, including parametric, nonparametric, and some semiparametric methods. The main idea behind monitoring the linear profiles in mixed effects is to model the possible individual differences between similar set of profiles for future monitoring. In this paper, nonparametric and semiparametric approaches are proposed to model the profile data in a linear mixed effect setting by considering the residuals from a parametric model. A simulation study was carried out to compare the efficiency of the proposed methods. At first step, the residuals from a parametric linear mixed model are obtained. A nonparametric approach (NPR) is then used to model these residuals. Finally, a semiparametric method (MMRRPM) is proposed as a convex combination of the parametric (P) and nonparametric estimations based on the residuals (NPR) to model the profile data in mix effects. Two Hoteling's T2 statistics were computed for each technique based on fitted values and the estimated random effects. The results show that the proposed methods are most effective to monitor the autocorrelated profile data compared with the state‐of‐the‐art. [ABSTRACT FROM AUTHOR]

Summary: In this paper, we study the problem of adaptive trajectory tracking control for a class of nonlinear systems with structured parametric uncertainties. We propose to use an iterative modular approach: we first design a robust nonlinear state feedback that renders the closed‐loop input‐to‐state stable (ISS). Here, the input is considered to be the estimation error of the uncertain parameters, and the state is considered to be the closed‐loop output tracking error. Next, we propose an iterative adaptive algorithm, where we augment this robust ISS controller with an iterative data‐driven learning algorithm to estimate online the parametric uncertainties of the model. We implement this method with two different learning approaches. The first one is a data‐driven multiparametric extremum seeking method, which guarantees local convergence results, and the second is a Bayesian optimization‐based method called Gaussian Process Upper Confidence Bound, which guarantees global results in a compact search set. The combination of the ISS feedback and the data‐driven learning algorithms gives a learning‐based modular indirect adaptive controller. We show the efficiency of this approach on a two‐link robot manipulator numerical example. [ABSTRACT FROM AUTHOR]

Summary: One of the most important steps in designing a model predictive control strategy is selecting appropriate parameters for the relative weights of the objective function. Typically, these are selected through trial and error to meet the desired performance. In this paper, a reinforcement learning technique called learning automata is used to select appropriate parameters for the controller of a differential drive robot through a simulation process. Results of the simulation show that the parameters always converge, although to different values. A controller chosen by the learning process is then ported to a real platform. The selected controller is shown to control the robot better than a standard model predictive control. [ABSTRACT FROM AUTHOR]

For the current generation of cellular communication systems, long-term evolution (LTE) has been the major protocol to support high-speed data transmission. It is critical to allocate downlink spectral resource in LTE, namely, resource blocks (RBs), but the issue is not well addressed in the standard. Therefore, the paper develops an efficient RB allocation algorithm with 4 mechanisms to improve both fairness and throughput in LTE. For fairness concern, our RB allocation algorithm uses a resource-reservation mechanism to prevent cell-edge user equipments from starvation, and a credit-driven mechanism to keep track of the amount of resource given to each user equipment. For throughput concern, it adopts both weight-assignment and RB-matching mechanisms to allocate each RB to a packet according to its flow type and length. Through simulations, we demonstrate that the proposed RB allocation algorithm can significantly increase both throughput and fairness while reducing packet dropping and delays of real-time flows, as compared with previous methods. [ABSTRACT FROM AUTHOR]

This paper has proposed a new pre-processing stage algorithm to enhance the overall performance of current simplification algorithms in analog circuits. This pre-process stage takes place after the calculation of symbolic transfer function and before the main core of the simplification algorithm. The detailed stages of this pre-process are completely independent of the main simplification and symbolic transfer function algorithm. As such, the pre-process algorithm can be easily added to any simplification algorithm to improve the total simplification performance. The use of this command, which is based on the stack decision method, helps the pre-process algorithm to obtain a high-process speed. Based on the CPU time simulation results for a transfer function with 139 terms, it is expected to have a total CPU time in the simplification of a 9820 term of about 28 to 30 seconds pulse and 1.2 to 4.9 seconds for the pre-process algorithm. Finally, a total method, which involves the combination of this pre-process algorithm with the main simplification algorithm is proposed in, and a CPU time of about 30 to 35 seconds is needed for simplifying a huge transfer function with 9820 terms. The addition of extra pre-process algorithm to the main simplification algorithm will reduce the total CPU time of simulation from 820 to 35 seconds, indicating a reduction of 95% in the total needed simulation time. [ABSTRACT FROM AUTHOR]

SUMMARY This paper proposes an adaptive notch filter that automatically adjusts the notch frequency and notch gain, where they are the null frequency and the depth of the null, respectively. The proposed notch filter consists of an inverse notch filter and a linear prediction filter, and gives an appropriate filter gain which can eliminate a sinusoidal noise and extract only a wide-band signal even if they share the same frequency. We compare the capability of the proposed notch filter with traditional methods. Simulation results show that the proposed system improves the SNR in comparison to the conventional methods. [ABSTRACT FROM AUTHOR]

This paper introduces a highly detailed radar altimeter simulation model, considering geometric and signal processing aspects. The simulation model is deployed in a simulation environment for a better understanding of the different error sources of radar altimeters. To characterize the strengths and weaknesses of the altimeter simulation model, the simulation results are compared to recorded altimeter flight test data. A slant range compensation algorithm is introduced applying the altimeter simulation model inflight. The performance of the slant range compensation algorithm is shown on recorded flight test data of an octocopter trajectory as well as on a fixed-wing aircraft trajectory. Copyright © 2016 Institute of Navigation [ABSTRACT FROM AUTHOR]

Penalized regression methods have for quite some time been a popular choice for addressing challenges in high dimensional data analysis. Despite their popularity, their application to time series data has been limited. This paper concerns bridge penalized methods in a linear regression time series model. We first prove consistency, sparsity and asymptotic normality of bridge estimators under a general mixing model. Next, as a special case of mixing errors, we consider bridge regression with autoregressive and moving average (ARMA) error models and develop a computational algorithm that can simultaneously select important predictors and the orders of ARMA models. Simulated and real data examples demonstrate the effective performance of the proposed algorithm and the improvement over ordinary bridge regression. [ABSTRACT FROM AUTHOR]

ABSTRACT Time-delay systems (TDSs) frequently arise in circuit simulation especially in high-frequency applications. Model order reduction (MOR) techniques can be used to facilitate the simulation of TDSs. On the other hand, many kinds of variations, such as temperature and geometric uncertainties, can have significant impact on the transient responses of TDSs. Therefore, it is important to preserve parametric dependence during the MOR procedure. This paper presents a new parameterized MOR scheme for TDSs with parameter variations. We derive parameterized reduced-order models (ROMs) for TDSs using balanced truncation by approximating the Gramians in the multi-dimensional space of parameters. The resulting ROMs can preserve the parametric dependence, making it efficient for repeated simulations under different parameter settings. Numerical examples are presented to verify the accuracy and efficiency of our proposed algorithm. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Co-simulation is a simulation technique for time dependent coupled problems in engineering that restricts the data exchange between subsystems to discrete communication points in time. In the present paper we follow the block-oriented framework in the recently established industrial interface standard FMI for Model Exchange and Co-Simulation v2.0 and study local and global error of co-simulation algorithms for systems with force-displacement coupling. A rather general convergence result for the co-simulation of coupled systems without algebraic loops shows zero-stability of co-simulation algorithms with force-displacement coupling and proves that order reduction of local errors does not affect the order of global errors. The theoretical investigations are illustrated by numerical tests in the novel FMI-compatible co-simulation environment SNiMoWrapper. (© 2014 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim) [ABSTRACT FROM AUTHOR]

In this paper an inverse and iterative method for the identification of material behavior is presented, based on the Finite Element Model Updating (FEMU) strategy. The FE simulations are performed with a commercial FE software code, using a self-implemented elastic material model at finite strain. The iterative identification procedure is based on an experimental test (numerical) whose measured kinematic values are compared to the corresponding simulated ones. Through an optimization algorithm the material parameters are varied in a way that the least-squares sum of the kinematic values is minimized and the optimal material parameters yielding the material behavior are identified. (© 2014 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim) [ABSTRACT FROM AUTHOR]