Automation and Remote Control最新文献

One of the most important problems of game theory—the game of firms in an oligopoly market—is considered. The survey covers classical and modern formulations for the game-theoretic problem of choosing optimal player’s strategies and the recent methodological achievements in oligopoly games with applications, including publications over the past five years.

The paper is devoted to the problem of deriving synchronizing and homing experiments for nondeterministic Input/Output automata; corresponding input sequences are widely used in testing (non-initialized) discrete event systems. In active testing, there is an opportunity to set a system under test into a known initial state; in passive testing, a known current state allows to reduce the number of properties to be checked. In the paper, we note that such experiments for Input/Output automata are different from so-called “gedanken” experiments with classical Finite State Machines; the existence check conditions of such experiments are established for a predefined discipline of applying inputs and a method for its derivation is proposed when such an experiment exists. The obtained results allow to reduce the problem of deriving synchronizing and homing experiments for Input/Output automata to the well developed problem of deriving such experiments for appropriate classes of Finite State Machines.

Three-dimensional (3D) sensors usually require a calibration procedure. In some cases, scale factor errors depend on the signs of the projections of the vector input signal onto the sensitivity axes of the sensor. To eliminate the ambiguity of scale factor errors, the angular positions of the sensor can be restricted so that the corresponding projections have a definite sign. This paper presents an analytical solution of the optimal calibration problem for a 3D sensor under a constraint on its angular positions.

We present a model of a stochastic observation system that allows for time delays between the received observation and the actual state of the observed object that formed these observations. Such delays can occur when observing the movement of an object in a water medium using acoustic sonars and have a significant impact on the accuracy of position tracking. We present equations to solve the optimal mean square filtering problem. Since the practical use of the optimal solution is barely feasible due to its computational complexity, we pay the main attention to an alternative, suboptimal but computationally efficient approach. Specifically, we adapted a conditional minimax nonlinear filter (CMNF) to the proposed model and formulated sufficient existence conditions for its estimate. We conducted a computational experiment on a model that is close to practical needs. The results of the experiment show the effectiveness of CMNF in the model considered. However, they also show a significant decrease in the quality of estimation compared to the model without random observation delays, which can be considered as a motivation for further research into the model and related problems.

The article proposes a method for creating soft sensors using identification models obtained by associative search algorithm. The method consists in constructing an approximating hypersurface of the space of input vectors and their corresponding one-dimensional outputs at each time instant. Case studies are presented and the advantages of the author’s method over traditional approaches are evaluated are revealed.

The problem of optimal flow production planning at the operational scheduling stage is being studied, using the example of the out-of-furnace department of a converter-based steel-making production in the iron metallurgy industry. To solve this problem, a linear integer programming model is proposed, which fully describes the specifics of the investigated technological processes. A major advantage of this approach is its scalability for solving related optimization problems in the industry of plant logistics, as well as flexibility in adapting to changes and fine-tuning the system of constraints and objective function. The software implementation of the developed model forms the basis of the operational scheduling module of the optimal flow production planning system, which is used for a large-scale computational experiment on real-world data.

For a dynamic system given by first-order ordinary differential equations, the problem of identification of periodic regimes is investigated. This problem is the establishment the periodicity of an arbitrary solution via the periodicity of the observed value of solution. The conditions under which the problem of identification of periodic regimes is solvable are found. Formulated and proven theorems supplement the well-known results on the observability of dynamic systems.

This paper considers an optimal movement routing problem with constraints. One such constraint is due to decomposing the original problem into a preliminary subproblem and a final subproblem; the tasks related to the preliminary problem must be executed before the tasks of the final subproblem begin. In particular, this condition may arise in the tool control problem for thermal cutting machines with computer numerical control (CNC): if there are long parts among workpieces, the cutting process near a narrow material boundary should start with these workpieces since such parts are subject to thermal deformations, which may potentially cause rejects. The problem statement under consideration involves two zones for part processing. The aggregate routing process in the original problem includes a starting point, a route (a permutation of indices), and a particular track consistent with the route and the starting point. Each of the subproblems has specific precedence conditions, and the travel cost functions forming the additive criterion may depend on the list of pending tasks. A special two-stage procedure is introduced to apply dynamic programming as a solution method. The structure of the optimal solution is established and an algorithm based on this structure is developed. The algorithm is implemented on a personal computer and a computational experiment is carried out.

This paper is devoted to a multifaceted analysis of person re-identification (ReID) in video surveillance systems and modern solution methods using deep learning. The general principles and application of convolutional neural networks for this problem are considered. A classification of person ReID systems is proposed. The existing datasets for training deep neural architectures are studied and approaches to increasing the number of images in databases are described. Approaches to forming human image features are considered. The backbone models of convolutional neural network architectures used for person ReID are analyzed and their modifications as well as training methods are presented. The effectiveness of person ReID is examined on different datasets. Finally, the effectiveness of the existing approaches is estimated in different metrics and the corresponding results are given.