Synthesis of an algorithm for grouping air objects in radar systems

Any radar complex exercising control over the airspace has a limited capacity. When processing information about a large number of air objects, overloads occur in information systems and part of the information is inevitably lost. To solve this problem, you need to group the goals. If you refuse to group, then this can lead to oversaturation of the displayed situation on the monitor and the human operator, according to his physical capabilities, will not be able to cope with its assessment. That is, the more goals, the worse the quality of the tasks being solved. One of the ways to solve this problem is to reduce the number of tracked objects by grouping them. Therefore, grouping is a necessary measure taken to simplify the presentation and assessment of the situation. The currently used algorithms for grouping air objects are based on spatial gating, which have low efficiency in a difficult environment. Therefore, the development of new strobe-free methods of grouping air objects is an urgent task. Objective – to develop and research a new strobe-free algorithm for grouping air objects in radar complexes for detecting and tracking air targets. In work a new algorithm for grouping air objects is synthesized, which is based on one of the methods of cluster analysis and an optimal decision rule according to the Neumann-Pearson criterion about the belonging of the considered set of air objects to one group. Some results of simulation modeling of the synthesized algorithm in a complex air environment in comparison with the algorithm based on production rules are presented. Practical significance – the developed algorithm for grouping air objects is more efficient than both the grouping algorithm in the spatial strobe and the algorithm based on production rules. This is especially noticeable in cases of flight of several groups to air objects on intersecting trajectories. The developed algorithm does not impose great requirements on the computing system of the radar complex and can be implemented in practice without restrictions.

Residual Analysis of Predictive Modelling Data for Automated Fault Detection in Building’s Heating, Ventilation and Air Conditioning Systems

Faults in Heating, Ventilation and Air Conditioning (HVAC) systems affect the energy efficiency of buildings. To date, there rarely exist methods to detect and diagnose faults during the operation of buildings that are both cost-effective and sufficient accurate. This study presents a method that uses artificial intelligence to automate the detection of faults in HVAC systems. The automated fault detection is based on a residual analysis of the predicted total heating power and the actual total heating power using an algorithm that aims to find an optimal decision rule for the determination of faults. The data for this study was provided by a detailed simulation of a residential case study house. A machine learning model and an ARX model predict the building operation. The model for fault detection is trained on a fault-free data set and then tested with a faulty operation. The algorithm for an optimal decision rule uses various statistical tests of residual properties such as the Sign Test, the Turning Point Test, the Box-Pierce Test and the Bartels-Rank Test. The results show that it is possible to predict faults for both known faults and unknown faults. The challenge is to find the optimal algorithm to determine the best decision rules. In the outlook of this study, further methods are presented that aim to solve this challenge.

Cognitive Imprecision and Small-Stakes Risk Aversion

Observed choices between risky lotteries are difficult to reconcile with expected utility maximization, both because subjects appear to be too risk averse with regard to small gambles for this to be explained by diminishing marginal utility of wealth, as stressed by Rabin (2000), and because subjects’ responses involve a random element. We propose a unified explanation for both anomalies, similar to the explanation given for related phenomena in the case of perceptual judgments: they result from judgments based on imprecise (and noisy) mental representations of the decision situation. In this model, risk aversion results from a sort of perceptual bias—but one that represents an optimal decision rule, given the limitations of the mental representation of the situation. We propose a quantitative model of the noisy mental representation of simple lotteries, based on other evidence regarding numerical cognition, and test its ability to explain the choice frequencies that we observe in a laboratory experiment.

An Optimal Decision Rule for a Multiple Selling Problem with a Variable Rate of Offers

An asset selling problem is one of well-known problems in the decision making literature. The problem assumes a stream of bidders who would like to buy one or several identical objects (assets). Offers placed by the bidders once rejected cannot be recalled. The seller is interested in an optimal selling strategy that maximizes the total expected revenue. In this paper, we consider a multi-asset selling problem when the seller wants to sell several identical assets over a finite time horizon with a variable number of offers per time period and no recall of past offers. We consider the problem within the framework of the optimal stopping theory. Using the method of backward induction, we find an optimal sequential procedure which maximizes the total expected revenue in the selling problem with independent observations.

Identification information sensors of robot systems

<span lang="IN">At the present time, the complexity of identification is to find such a description, in which the image (information) of each class would have identified similar properties. The task is to make the transformed description includes the whole set of input images, united by the similarity class by the given ratio.</span><span lang="IN">Using the ordinates of an autocorrelation function is an inseparable shift in the center of gravity of an image, which leads to a change of such description.</span><span lang="IN">Nicest, the concept of an invariant description of information arises, this is an autocorrelation function, which is invariant to the description of any displacements of the image in the vertical and horizontal directions.</span><span lang="IN">The problem of finding an optimal decision rule arises, which, in a number of cases, can be constructed on the basis of a method, based on the definition of the maximum incomplete coefficient of similarity.</span><span lang="IN">Using this method, the solutions, that are almost unintelligible to the errors that arise due to the effects of interference, are found. Therefore, in increments</span><span lang="EN-US"> k</span><span lang="IN">, this rule passes into the Bayes’ rule.</span>

An LP based approximate dynamic programming model to address airline overbooking under cancellation, refund and no-show

In this paper we simultaneously address four constraints relevant to airline revenue management problem: flight cancellation, customer no-shows, overbooking, and refunding. We develop a linear program closely related to the dynamic program formulation of the problem, which we later use to approximate the optimal decision rule for rejecting or accepting customers. First, we give a novel proof that the optimal objective function of this linear program is always an upper bound for the dynamic program. Secondly, we construct a decision rule based on this linear program and prove that it is asymptotically optimal under certain circumstances. Finally, using Monte Carlo simulation, we demonstrate that, numerically, the result of the linear programming policy presented in this paper has a short distance to the upper bound of the optimal answer, which makes it a fairly good approximate answer to the intractable dynamic program.