The method of discretization signals to minimize the fallibility of information recovery

The paper proposes a fundamentally new approach to the formulation of the problem of optimizing the discretization interval (frequency). The well-known traditional methods of restoring an analog signal from its discrete implementations consist of sequentially solving two problems: restoring the output signal from a discrete signal at the output of a digital block and restoring the input signal of an analog block from its output signal. However, this approach leads to methodical fallibility caused by interpolation when solving the first problem and by regularizing the equation when solving the second problem. The aim of the work is to develop a method for the signal discretization to minimize the fallibility of information recovery to determine the optimal discretization frequency.The proposed method for determining the optimal discretization rate makes it possible to exclude both components of the methodological fallibility in recovering information about the input signal. This was achieved due to the fact that to solve the reconstruction problem, instead of the known equation, a relation is used that connects the input signal of the analog block with the output discrete signal of the digital block.The proposed relation is devoid of instabilities inherent in the well-known equation. Therefore, when solving it, neither interpolation nor regularization is required, which means that there are no components of the methodological fallibility caused by the indicated operations. In addition, the proposed ratio provides a joint consideration of the properties of the interference in the output signal of the digital block and the frequency properties of the transforming operator, which allows minimizing the fallibility in restoring the input signal of the analog block and determining the optimal discretization frequency.A widespread contradiction in the field of signal information recovery from its discrete values has been investigated. A decrease in the discretization frequency below the optimal one leads to an increase in the approximation fallibility and the loss of some information about the input signal of the analog-to-digital signal processing device. At the same time, unjustified overestimation of the discretization rate, complicating the technical implementation of the device, is not useful, since not only does it not increase the information about the input signal, but, if necessary, its restoration leads to its decrease due to the increase in the effect of noise in the output signal on the recovery accuracy. input signal. The proposed method for signal discretization based on the minimum information recovery fallibility to determine the optimal discretization rate allows us to solve this contradiction.

Temporal Variability of River Inflow to the Lake Baikal and Reservoirs of the Angara Cascade of HPS

Main properties of long-term and seasonal fluctuations of runoff are studied based on the prolonged observation of the inflow to reservoirs of the Angarsk cascade of HPS. Statistical characteristics (parameters of distributions) of seasonal and annual inflow are estimated. The analysis of sequence of annual inflow to the Lake Baikal led to the conclusion about heterogeneity of the long-term fluctuations. It is shown that spatial variability of runoff value in the studied region, which is high, is defined by differences in conditions of the hydrological regime formation. This variability is expressed as the matrix of pair correlations characterized by small values. With the inflow to the Lake Baikal as an example, the parameters of a seasonal inflow for two uniform periods providing a possibility of stochastic modeling of the inflow with a discretization interval one month are estimated. For the months with negative values of an inflow, the application of the IV-type distribution of Pearson is recommended. The results received in the work are intended for the solution of the optimum control of the Angarsk reservoir cascade problems.

The impact of time discretization on solvency measurement

Purpose The purpose of this paper is to study how the discretization interval affects the solvency measurement of a property-liability insurance company. Design/methodology/approach Starting with a basic solvency model, the authors study the impact of the discretization interval on risk measures. The analysis considers the sensitivity of the discrepancy between the risk measures in continuous and discrete time to various parameters, such as the asset-to-liability ratio, the characteristics of the asset and liability processes, as well as the correlation between assets and liabilities. Capital requirements for the one-year planning horizon in continuous vs discrete time are reported as well. The purpose is to report the degree to which the deviations in risk measures, due to the different discretization intervals, can be reduced by means of increasing the frequency with which the risk measures are assessed. Findings The simulation results suggest that the risk measures of an insurance company are consistently underestimated when assessed on an annual basis (as it is currently done under insurance regulation such as Solvency II). The authors complement the analysis with the capital requirements of an insurance company and conclude that more frequent discretization translates into higher capital requirements for the insurance company. Both the probability of ruin and the expected policyholder deficit (EPD) can be reduced through intermediate financial reports. Originality/value The results from our simulation analysis suggest that that the choice of discretization interval has an impact on the risk assessment of an insurance company which uses the probability of ruin and the EPD as risk measures. By assessing the risk measures once a year, both risk measures and the capital requirements are consistently underestimated. Therefore, the recommendation for risk managers is to complement the capital requirements in solvency regulation with sensitivity analyses of the risk measures presented with respect to time discretization. On the one hand, it seems to us that there is value in knowing about the substantial discrepancy between the focused time discrete ruin probability and EPD compared to the continuous version. On the other hand, and if there are no substantial transaction costs associated with more frequent monitoring of solvency figures, a frequent update would be helpful to increase the accuracy of the calculations and reduce the EPD.

Path integral of a relativistic spinning particle in (1+1) dimension with vector and scalar linear potentials in the presence of a minimal length

Using the path integral formalism in (1+1) dimension of the energy–momentum space representation, we calculate the Green function of relativistic spinning particle subjected to the action of combined vector and scalar linear potentials in the framework of deformed Heisenberg algebra which distinguish to the appearance of nonzero minimum position uncertainty given by [Formula: see text] where β is the deformation parameter of the modified Heisenberg uncertainty relation [Formula: see text]. We adapt the space–time transformation method to evaluate quantum corrections and this latter are dependent on the α-point discretization interval. The exact bound states spectrum and the corresponding momentum space wave function are obtained.

Milling Process Simulation Based on Quadtree-Array Representation

Model representation of workpiece in machining process simulation does directly influence on simulation accuracy and efficiency. The algorithm to acquire cutting width and cutting depth based on quadtree-array representation is discussed in this paper. In milling process simulation, workpiece is represented with quadtree-array and the cutting width and cutting depth are predicted real-time. The results of controlled simulation shows that the predicted cutting depth is accordance with that of the designed and the predicted cutting width is close to that of the designed within accuracy of half a discretization interval.