Precise measurement of rail vehicle velocities is an essential prerequisite for the implementation of modern train control systems and the improvement of transportation capacity and logistics. Novel eddy current sensor systems make it possible to estimate velocity by using cross-correlation techniques, which show a decline in precision in areas of high accelerations. This is due to signal distortions within the correlation interval. We propose to overcome these problems by employing algorithms from the field of dynamic programming. In this paper we evaluate the application of correlation optimized warping, an enhanced version of dynamic time warping algorithms, and compare it with the classical algorithm for estimating rail vehicle velocities in areas of high accelerations and decelerations.

The work presented in the paper concerns a very important problem of searching for string alignments. The authors show that the problem of a genome pattern alignment could be interpreted and defined as a measuring task, where the distance between two (or more) patterns is investigated. The problem originates from modern computation biology. Hardware-based implementations have been driving out software solutions in the field recently. The complex programmable devices have become very commonly applied. The paper introduces a new, optimized approach based on the Smith-Waterman dynamic programming algorithm. The original algorithm is modified in order to simplify data-path processing and take advantage of the properties offered by FPGA devices. The results obtained with the proposed methodology allow to reduce the size of the functional block and radically speed up the processing time. This approach is very competitive compared with other related works.

In this work, a novel approach to designing an on-line tracking controller for a nonholonomic wheeled mobile robot (WMR) is presented. The controller consists of nonlinear neural feedback compensator, PD control law and supervisory element, which assure stability of the system. Neural network for feedback compensation is learned through approximate dynamic programming (ADP). To obtain stability in the learning phase and robustness in face of disturbances, an additional control signal derived from Lyapunov stability theorem based on the variable structure systems theory is provided. Verification of the proposed control algorithm was realized on a wheeled mobile robot Pioneer–2DX, and confirmed the assumed behavior of the control system.

The problem of optimally controlling a Wiener process until it leaves an interval (a; b) for the first time is considered in the case when the infinitesimal parameters of the process are random. When a = ��1, the exact optimal control is derived by solving the appropriate system of differential equations, whereas a very precise approximate solution in the form of a polynomial is obtained in the two-barrier case.

A boundary value problem for a non-linear difference equation of order three is considered. We show that this equation can be interpreted as the equation satisfied by the value function in a stochastic optimal control problem. We thus obtain an expression for the solution of the non-linear difference equation that can be used to find an explicit solution to this equation. An example is presented.

We build a mathematical game model of pandemic transmission, including vaccinations of population and budget costs of different acting to eliminate pandemic. We assume the interactions among different groups: vaccinated, susceptible, exposed, infectious, super-spreaders, hospitalized and fatality, defining a system of ordinary differential equations, which describes compartment model of disease and costs of the treatment. The goal of the game is to describe the development disease under different types of treatment, but including costs of them and social restrictions, during the shortest time period. To this effect we construct a dual dynamic programming method to describe open-loop Nash equilibrium for treatment, a group of people having antibodies and budget costs. Next, we calculate numerically an approximate open-loop Nash equilibrium.