Automation and Remote Control最新文献

This paper considers the use of machine learning for diagnosis of diseases that is based on the analysis of a complete gene expression profile. This distinguishes our study from other approaches that require a preliminary step of finding a limited number of relevant genes (tens or hundreds of genes). We conducted experiments with complete genetic expression profiles (20 531 genes) that we obtained after processing transcriptomes of 801 patients with known oncologic diagnoses (oncology of the lung, kidneys, breast, prostate, and colon). Using the indextron (instant learning index system) for a new purpose, i.e., for complete expression profile processing, provided diagnostic accuracy that is 99.75% in agreement with the results of histological verification.

In this paper, the problem of suboptimal stabilization of an object with discrete time, output and control uncertainties, and bounded external perturbation is considered. The autoregressive nominal model coefficients, uncertainty amplification coefficients, norm and external disturbance offset are assumed to be unknown. The quality indicator is the worst-case asymptotic upper bound of the output modulus of the object. The solution of the problem in conditions of non-identifiability of all unknown parameters is based on the method of recurrent target inequalities and optimal online estimation, in which the quality index of the control problem serves as an identification criterion. A non-linear replacement of the unknown parameter perturbations that reduces the optimal online estimation problem to a fractional linear programming problem is proposed. The performance of adaptive suboptimal control is illustrated by numerical simulation results.

In this paper, a heating process control law with steam supply is designed for a fluid in a heat exchanger. The process is described by a linear hyperbolic equation of the first order with a nonlocal boundary condition with a time-delayed argument. The temperature of the supplied steam is found as a linear dependence on fluid temperature values at measurement points in the heat exchanger. Explicit formulas are obtained for the gradient of the objective functional of the control problem in the space of the feedback coefficients (parameters) of this dependence. A numerical scheme is developed for determining the feedback parameters based on these formulas. Finally, an algorithm is proposed for determining the rational (optimal) number of measurement points.

We consider the intractable problem of finding several edge-disjoint spanning trees of the minimum total weight with a given diameter in complete undirected graph in current paper. The weights of edges of a graph are random variables from several continuous distributions: uniform, biased truncated exponential, biased truncated normal. The approximation algorithm with time complexity (mathcal{O})(n²), where n is number of vertices in graph, is proposed for solving this problem. The asymptotic optimality conditions for constructed algorithm is presented for each considered probabilistic distribution.

Continuous random processes with fuzzy states are studied. The properties of their numerical characteristics (expectations and correlation functions) corresponding to those of numerical random processes are established. The results obtained are based on the properties of fuzzy random variables. Applications to the problem of transforming a random signal with fuzzy states by a linear dynamic system are considered.

This paper considers a single-server queueing system with an incoming Markovian Arrival Process (MAP) request flow with two states. Explicit expressions are derived for the stationary probability distribution of the states and several numerical characteristics of the system (the probability of idle time of the server, the expected number of requests in the system, and the mean queue length). The resulting numerical characteristics are presented in tables and plotted in graphical form as well. The recurrent MAP flow with two states as a special case of correlated MAP request flows is studied.

Three variants of the statistical complexity function, which is used as a criterion in the problem of detection of a useful signal in the signal-noise mixture, are considered. The probability distributions maximizing the considered variants of statistical complexity are obtained analytically and conclusions about the efficiency of using one or another variant for detection problem are made. The comparison of considered information characteristics is shown and analytical results are illustrated on an example of synthesized signals. A method is proposed for selecting the threshold of the information criterion, which can be used in decision rule for useful signal detection in the signal-noise mixture. The choice of the threshold depends a priori on the analytically obtained maximum values. As a result, the complexity based on the total variation demonstrates the best ability of useful signal detection.

This paper is devoted to the guaranteeing estimation method with application to the calibration problem of a gyro unit. Mathematical models are constructed to describe the kinematics of the gyro unit on a test bench. The applicability limits and errors of the models are investigated. A numerical solution procedure is developed for guaranteeing estimation problems based on their reduction to l₁-approximation problems.