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This paper aims to present an approach for the probabilistic characterization of the response of linear structural systems subjected to random time-dependent non-Gaussian actions.

Its fundamental property is working directly on the probability density functions of the actions and responses. This avoids passing through the evaluation of the response statistical moments or cumulants, reducing the computational effort in a consistent measure.

It is an efficient method, for both its computational effort and its accuracy, above all when the input and output processes are strongly non-Gaussian.

This approach can be considered as a dynamic generalization of the probability transformation method recently used for static applications.

We propose a generic algorithm for numerically accurate likelihood evaluation of a broad class of spatial models characterized by a high-dimensional latent Gaussian process and non-Gaussian response variables. The class of models under consideration includes specifications for discrete choices, event counts and limited-dependent variables (truncation, censoring, and sample selection) among others. Our algorithm relies upon a novel implementation of efficient importance sampling (EIS) specifically designed to exploit typical sparsity of high-dimensional spatial precision (or covariance) matrices. It is numerically very accurate and computationally feasible even for very high-dimensional latent processes. Thus, maximum likelihood (ML) estimation of high-dimensional non-Gaussian spatial models, hitherto considered to be computationally prohibitive, becomes feasible. We illustrate our approach with ML estimation of a spatial probit for US presidential voting decisions and spatial count data models (Poisson and Negbin) for firm location choices.

In marine structures, the long‐term non‐stationary response of flexible lines, due to random environmental loads, may be regarded as successive short‐term stationary processes in which current, wind and ocean wave conditions remain constant. The power spectrum of each stationary process can be characterized by its linear and non‐linear energy components: the linear energy defines a Gaussian process, and the additional nonlinear energy characterizes a non‐Gaussian process. Within this scope, digital bispectral analysis has enabled one to describe non‐linear stationary response of flexible lines in the frequency domain, so that the complex coefficients of a quadratic model, in the frequency domain, can be estimated. The real and symmetrical matrix constructed from these coefficients has eigenvalues and eigenvectors useful to describe the characteristic function of the response from where the probability density function can be obtained by using a fast Fourier transform algorithm. The bases of the method presented here have already been treated, in a similar but pure algebraic method, to obtain the asymptotic probability function applicable to the response of non‐linear systems in closed form.

The purpose of this paper is to propose a robust correntropy assisted blind channel estimator for multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) for improved channel gains estimation and channel ordering and sign ambiguities resolution in non-Gaussian noise channel.

The correntropy independent component analysis with L₁-norm cost function is used for blind channel estimation. Then a correntropy-based method is formulated to resolve the sign and order ambiguities of the channel estimates.

Simulation study on Gaussian noise scenario shows that the proposed method achieves almost the same performance as the conventional L₂-norm based method. However, in non-Gaussian noise scenarios performance of the proposed method significantly outperforms the conventional and other popular estimators in terms of mean square error (MSE). To solve the ordering and sign ambiguities problems, an auto-correntropy-based method is proposed and compared with the extended cross-correlation-based method. Simulation study shows improved performance of the proposed method in terms of MSE.

This paper presents for the first time, a correntropy-based blind channel estimator for MIMO-OFDM as well as simulated comparison results with traditional correlation-based methods in non-Gaussian noise environment.

This paper aims to develop a method to improve the accuracy of tolerance analysis considering the spatial distribution characteristics of part surface morphology (SDCPSM) and local surface deformations (LSD) of planar mating surfaces during the assembly process.

First, this paper proposes a skin modeling method considering SDCPSM based on Non-Gaussian random field. Second, based on the skin model shapes, an improved boundary element method is adopted to solve LSD of nonideal planar mating surfaces, and the progressive contact method is adopted to obtain relative positioning deviation of mating surfaces. Finally, the case study is given to verify the proposed approach.

Through the case study, the results show that different SDCPSM have different influences on tolerance analysis, and LSD have nonnegligible and different influence on tolerance analysis considering different SDCPSM. In addition, the LSD have a greater influence on translational deviation along the z-axis than rotational deviation around the x- and y-axes.

The surface morphology with different spatial distribution characteristics leads to different contact behavior of planar mating surfaces, especially when considering the LSD of mating surfaces during the assembly process, which will have further influence on tolerance analysis. To address the above problem, this paper proposes a tolerance analysis method with skin modeling considering SDCPSM and LSD of mating surfaces, which can help to improve the accuracy of tolerance analysis.

This review aims to give a critical view of the wheel/rail high frequency vibration-induced vibration fatigue in railway bogie.

Vibration fatigue of railway bogie arising from the wheel/rail high frequency vibration has become the main concern of railway operators. Previous reviews usually focused on the formation mechanism of wheel/rail high frequency vibration. This paper thus gives a critical review of the vibration fatigue of railway bogie owing to the short-pitch irregularities-induced high frequency vibration, including a brief introduction of short-pitch irregularities, associated high frequency vibration in railway bogie, typical vibration fatigue failure cases of railway bogie and methodologies used for the assessment of vibration fatigue and research gaps.

The results showed that the resulting excitation frequencies of short-pitch irregularity vary substantially due to different track types and formation mechanisms. The axle box-mounted components are much more vulnerable to vibration fatigue compared with other components. The wheel polygonal wear and rail corrugation-induced high frequency vibration is the main driving force of fatigue failure, and the fatigue crack usually initiates from the defect of the weld seam. Vibration spectrum for attachments of railway bogie defined in the standard underestimates the vibration level arising from the short-pitch irregularities. The current investigations on vibration fatigue mainly focus on the methods to improve the accuracy of fatigue damage assessment, and a systematical design method for vibration fatigue remains a huge gap to improve the survival probability when the rail vehicle is subjected to vibration fatigue.

The research can facilitate the development of a new methodology to improve the fatigue life of railway vehicles when subjected to wheel/rail high frequency vibration.

The purpose of this study is to research the influence of wire’s surface topography on interwire contact performance of simple spiral strand.

The mechanical model of the simple spiral strand imposed by a tensile load is first established, into which the surface topography, Poisson’s ratio effect and radial deformation are incorporated simultaneously. Meanwhile, the Gaussian and non-Gaussian rough surfaces of the steel wires are obtained with the fast Fourier transform (FFT) and digital filter technology. Then, the rough interwire contact performance of the simple spiral strand is calculated by using conjugate gradient method and FFT.

As compared with smooth wire surface, both the longitudinal orientation for the Gaussian wire surface and large kurtosis or small skewness for the non-Gaussian surface yield a small contact pressure and stress.

This study conducts detailed discussion of the influence of wire’s surface topography on the interwire contact performance for the simple spiral strand and gives a beneficial reference for the design and application of a wire rope.

Self-localization of an underwater robot using global positioning sensor and other radio positioning systems is not possible, as an alternative onboard sensor-based self-location estimation provides another possible solution. However, the dynamic and unstructured nature of the sea environment and highly noise effected sensory information makes the underwater robot self-localization a challenging research topic. The state-of-art multi-sensor fusion algorithms are deficient in dealing of multi-sensor data, e.g. Kalman filter cannot deal with non-Gaussian noise, while parametric filter such as Monte Carlo localization has high computational cost. An optimal fusion policy with low computational cost is an important research question for underwater robot localization.

In this paper, the authors proposed a novel predictive coding-biased competition/divisive input modulation (PC/BC-DIM) neural network-based multi-sensor fusion approach, which has the capability to fuse and approximate noisy sensory information in an optimal way.

Results of low mean localization error (i.e. 1.2704 m) and computation cost (i.e. 2.2 ms) show that the proposed method performs better than existing previous techniques in such dynamic and unstructured environments.

To the best of the authors’ knowledge, this work provides a novel multisensory fusion approach to overcome the existing problems of non-Gaussian noise removal, higher self-localization estimation accuracy and reduced computational cost.

This paper analyzes the potential presence of time-varying asymmetries in the preference parameters of the Central Bank of Brazil during the inflation targeting regime.

Given the econometric issues inherent to classical time-varying parameter (TVP) regressions, a Bayesian estimation procedure is implemented in order to provide more robust parameter estimates. A stochastic volatility specification is also included to take into account the potential presence of conditional heteroskedasticity.

The obtained results show that the reduced form and structural parameters were not constant during the period considered. Moreover, the subsequent analysis of the preference parameters provided evidences of short periods in which asymmetry was an important feature to the conduction of monetary policy in Brazil. Yet, during most of the sample period, the loss function was considered to be symmetrical.

This paper aims to contribute to the rather scarce monetary debate on time-varying central bank preferences. The study of Lopes and Aragón (2014) is, to the best of the authors’ knowledge, the only study for Brazil considering specifically TVPs. The authors applied Kalman filter estimation to data from 2000:M1 to 2011:M12. Despite the similar structure of TVPs, the present paper extends the latter study by controlling for stochastic volatility. Ignoring conditional heteroskedasticity might lead to spurious movements in time-varying variables and inaccurate inference (Hamilton, 2010). Thus, the stochastic volatility specification is included to take this issue into account. The authors follow the theoretical scheme put forward by Surico (2007) and Aragón and Portugal (2010), in which the economy is modeled from a New Keynesian perspective and the central bank loss function is assumed to be asymmetric regarding the responses to inflation and output deviations from their targets. On the empirical side, the authors propose a TVP univariate regression with stochastic volatility for the Brazilian reduced-form reaction function, following closely the Bayesian econometric procedure developed by Nakajima (2011). Given the nonlinear non-Gaussian nature of the TVP regression with stochastic volatility, the choice of a nonlinear Bayesian approach using the Markov chain Monte Carlo (MCMC) method is justified due to the intractability of the associated likelihood function (Primiceri, 2005). Finally, based on the theoretical model specification, the authors intend to recover the central bank preference parameters as to further evaluate the degree of asymmetry and its potential time-variation under the inflation targeting regime.

This study aims to investigate dynamic relationships among residential housing price indices of ten major Chinese cities for the years 2005–2021.

Using monthly data, this study uses vector error correction modeling and the directed acyclic graph for characterization of contemporaneous causality among the ten indices.

The PC algorithm identifies the causal pattern and the Linear Non-Gaussian Acyclic Model algorithm further determines the causal path, from which this study conducts innovation accounting analysis. Sophisticated price dynamics are found in price adjustment processes following price shocks, which are generally dominated by the top tiers of cities.

This study suggests that policies on residential housing prices in the long run might need to be planned with particular attention paid to these top tiers of cities.