This technical note introduces the closed form maximum likelihood estimator for estimating the coefficients of the non-parametric frequency response function from system identification experiments. It is assumed that the experiments consist of repeated pulse excitations and that both the excitation and system response are measured which leads to an error-in-variables setting. Monte Carlo simulations indicate that the estimator achieves efficiency at low signal-to-noise ratios with only few measurements. Comparison with the least-squares estimator shows that better, unbiased results are obtained.

This paper presents modeling of wave propagation in pavements from a system identification point of view. First, a model based on the physical structure is derived. Second, experiment design and evaluation are discussed and maximum-likelihood estimators for estimating the model parameters are introduced, assuming an error-in-variables setting. Finally, the proposed methods are applied to measurement data from two experiments under varying environmental conditions. It is found that the proposed methods can be used to estimate the dispersion curves of the considered waveguide and the results can be used for further analysis

This paper addresses a novel method for vehicle tracking using an extended Kalman filter and measurements of road surface vibrations from a single accelerometer. First, a measurement model for vibrations caused by vehicular road traffic is developed. Then the identifiability of the involved parameters is analyzed. Finally, the measurement model is combined with a constant speed motion model and the Kalman filter is derived. Simulation and measurement results indicate that the approach is feasible and show where further development is needed.

In this paper, modeling of the pavement as a wave propagation medium and estimation of the corresponding model parameters is approached from a system identification perspective. A model based on the physical background is proposed and the corresponding parameters are then estimated from measurement data. In order to achieve the latter, two estimators are proposed, their performance evaluated, and then applied to the measurement data. It is found that the proposed methods are applicable and the results show that different eigenmodes of the structure are excited.

This article proposes a Bayesian procedure to calculate posterior probabilities of active effects for unreplicated two-level factorials. The results from a literature survey are used to specify individual prior probabilities for the activity of effects and the posterior probabilities are then calculated in a three-step procedure where the principles of effects sparsity, hierarchy, and heredity are successively considered. We illustrate our approach by reanalyzing experiments found in the literature.

In traditional methods for reliability analysis, one complex system is often considered as being composed by some subsystems in series. Usually, the failure of any subsystem would be supposed to lead to the failure of the entire system. However, some subsystems' lifetimes are long enough and even never fail during the life cycle of the entire system. Moreover, such subsystems' lifetimes will not be influenced equally under different circumstances. In practice, such interferences will affect the model's accuracy, but it is seldom considered in traditional analysis. To address these shortcomings, this paper presents a new approach to do reliability analysis for complex systems. Here a certain fraction of the subsystems is defined as a "cure fraction" under the consideration that such subsystems' lifetimes are long enough and even never fail during the life cycle of the entire system. By introducing environmental covariates and the joint power prior, the proposed model is developed within the Bayesian survival analysis framework, and thus the problem for censored (or truncated) data in reliability tests can be resolved. In addition, a Markov chain Monte Carlo computational scheme is implemented and a numeric example is discussed to demonstrate the proposed model

This paper addresses multi-input multi-output (MIMO) communications over sparse acoustic channels suffering from frequency modulations. An extension of the recently introduced SLIM algorithm, which stands for sparse learning via iterative minimization, is presented to estimate the sparse and frequency modulated acoustic channels. The extended algorithm is referred to as generalization of SLIM (GoSLIM). The sparseness is exploited through a hierarchical Bayesian model, and because GoSLIM is user parameter free, it is easy to use in practical applications. Moreover this paper considers channel equalization and symbol detection for various MIMO transmission schemes, including both space-time block coding and spatial multiplexing, under the challenging channel conditions. The effectiveness of the proposed approaches is demonstrated using in-water experimental measurements recently acquired during WHOI09 and ACOMM10 experiments.

Inventory management differs from other manufacturing inventory managements, mainly due to its specialists in function with maintenance. So far, enormous attention has been paid by standing on spares’ manufacturingfactories, sales companies, end users’ purchasing departments, or maintenance engineers, separately. However, not only “bullwhip effect” in forecasting spares demands, but also deteriorated relationships among the spares supply chains have shown that, spares optimization strategies with isolated consideration couldonly bring short-term or partial improvements. In this paper, the spares demand system with consideration of integration management is promoted, the new Solid-Net relationships among four main components are elaborated. Then, the root causes of ineffective in spares demand system are analyzed. Also, distinctoptimization policies are illustrated. What’s more, successful stories in practice are cited.

This paper presents a noise-adaptive estimator for the linear model. The strategy is based on a hierarchical approach where in each step, a decreasing number of unbiased estimates for the parameter of interest is produced. In this way, the complexity is greatly reduced compared to standard estimators, like the adaptive maximum likelihood (AML) estimator. Also, since the method combines solutions to sub-problems of smaller dimensionality, the required size of the noise training data set is also reduced. As a result, the derived scheme performs better than AML for small sample support. The results are verified by simulations and show that the derived scheme is a very appropriate choice for a large class of problems with high dimensionality.