Wyniki wyszukiwania - BazTech

1

Numerical simulation of random fields using correlated random vector and the Karhunen-Loève expansion

Poński M., Pokorska I.

Computer Assisted Methods in Engineering and Science

|

2018

|

Vol. 25, no. 1

47--58

EN

The paper presents an approach of one- and two-dimensional random fields simulation methods using correlated random vector and Karhunen-Loève expansion. Comparison of the authors’ analytical solution of the Fredholm integral equation of the second kind with the numerical solution using finite element method and inverse vector iteration technique is presented. Numerical approach and sample realizations of one- and two-dimensional random fields are presented using described techniques as well as generated probability distribution functions for chosen point of analyzed domain.

2

A dynamic bi-orthogonal field equation approach to efficient Bayesian inversion

Tagade P. M., Choi H. L.

International Journal of Applied Mathematics and Computer Science

|

2017

|

Vol. 27, no. 2

229--243

EN

This paper proposes a novel computationally efficient stochastic spectral projection based approach to Bayesian inversion of a computer simulator with high dimensional parametric and model structure uncertainty. The proposed method is based on the decomposition of the solution into its mean and a random field using a generic Karhunen–Loève expansion. The random field is represented as a convolution of separable Hilbert spaces in stochastic and spatial dimensions that are spectrally represented using respective orthogonal bases. In particular, the present paper investigates generalized polynomial chaos bases for the stochastic dimension and eigenfunction bases for the spatial dimension. Dynamic orthogonality is used to derive closed-form equations for the time evolution of mean, spatial and the stochastic fields. The resultant system of equations consists of a partial differential equation (PDE) that defines the dynamic evolution of the mean, a set of PDEs to define the time evolution of eigenfunction bases, while a set of ordinary differential equations (ODEs) define dynamics of the stochastic field. This system of dynamic evolution equations efficiently propagates the prior parametric uncertainty to the system response. The resulting bi-orthogonal expansion of the system response is used to reformulate the Bayesian inference for efficient exploration of the posterior distribution. The efficacy of the proposed method is investigated for calibration of a 2D transient diffusion simulator with an uncertain source location and diffusivity. The computational efficiency of the method is demonstrated against a Monte Carlo method and a generalized polynomial chaos approach.

3

Specyficzna identyfikacja emiterów radarowych bazująca na analizie składowych głównych

Kawalec A., Owczarek R., Rapacki T., Wnuczek S.

Biuletyn Wojskowej Akademii Technicznej

|

2006

|

Vol. 55, nr 1

41-54

PL

W artykule została przedstawiona problematyka związana z identyfikacją emiterów radarowych należących do tego samego typu i klasy. Jest to specyficzny rodzaj identyfikacji (SEI, ang. Specific Emitter Identification), polegający na rozróżnianiu poszczególnych egzemplarzy tego samego typu radaru. Klasyczna identyfikacja sygnałów bazująca na analizie statystycznej podstawowych parametrów mierzalnych sygnału nie spełnia wymagań stawianych przed SEI. Przedstawiona w artykule metoda identyfikacji opiera się na przekształceniu Karhunena-Loeve'a (KL), która należy do metod analizy składowych głównych (PCA, ang. Principal Component Analysis).

EN

One of the most difficult tasks in the radar signal processing is optimal features extraction and classification. The multifunction radar systems cannot be classified and precisely recognized by most of new and modern Electronic Support Measure and Electronic Intelligence Devices in the real time. In most cases, the modern ESM/ELINT systems cannot recognize the different devices of the same type or class. New method of the radar identification with a high quality of recognizing is the Specific Emitter Identification (SEI). The main task is to find non-intentional modulations in the receiving signals. This paper provides an overview of the new methods of measurement emitter signal features parameters and their transformation. This paper presents some aspects of radar signal features processing using Karhunen-Loeve's expansion as a feature selection and classification transform.