• Structural Engineering and Mechanics
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• v.38 no.3
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• pp.283-300
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• 2011

A new conception of fundamental tasks in dynamics of the bridge-track-train systems (BTT), with the aim to evaluate moving load's models adequacy, has been developed. The 2D physical models of BTT systems, corresponding to the fundamental tasks, have been worked out taking into account one-way constraints between the moving unsprung masses and the track. A method for deriving the implicit equations of motion, governing vibrations of BTT systems' models, as well as algorithms for numerical integration of these equations, leading to the solutions of high accuracy and relatively short times of simulations, have been also developed. The derived equations and formulated algorithms constitute the basis for numerical simulation of vibrations of the considered systems.

• Structural Engineering and Mechanics
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• v.38 no.3
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• pp.301-314
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• 2011

The paper constitutes the second part of the author's study. The first part (Podworna 2010) formulates four fundamental tasks in dynamics of the bridge-track-train systems. The following cyclic moving loads are considered: a concentrated forces stream (model P), an unsprung masses stream (model M), a single-mass viscoelastic oscillators stream (model $M_o$) and a double-mass viscoelastic oscillators stream (model $MM_o$). Three problems precluding to the numerical simulations have been developed, i.e., prediction of the forced resonances, the parameters of integration of equations of motion, the output results. A computer programme was written in Pascal and numerical research in the scope of the fundamental tasks was worked out. The investigations were focused on adequacy evaluation of the moving load models, P, M, $M_o$, $MM_o$, in predicting dynamic processes in railway bridges.

• The KIPS Transactions:PartD
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• v.13D no.3 s.106
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• pp.347-356
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• 2006

A video stream can be represented by a sequence of data points in a multidimensional space. In this paper, we introduce a trend vector that approximates values of data points in a sequence and represents the moving trend of points in the sequence, and present a pattern similarity matching method for data sequences using the trend vector. A sequence is partitioned into multiple segments, each of which is represented by a trend vector. The query processing is based on the comparison of these vectors instead of scanning data elements of entire sequences. Using the trend vector, our method is designed to filter out irrelevant sequences from a database and to find similar sequences with respect to a query. We have performed an extensive experiment on synthetic sequences as well as video streams. Experimental results show that the precision of our method is up to 2.1 times higher and the processing time is up to 45% reduced, compared with an existing method.