Analytical Model of an Induction Motor Taking into Account the Punching Process Influence on the Material Properties’ Change of Lamination

The technologies of cutting the cores of electric machines change the magnetic properties and the loss of the electrical sheets used, affecting the machine’s parameters, mainly power losses and efficiency. This is particularly important in the case of induction motors, which are a significant consumer of electricity. Therefore, the problem of increasing their efficiency is important from the point of view of environmental impact. The article presents a method of approximating a material’s magnetic properties based on the results of measurements carried out with specimens of various widths. The presented method allows for an approximate representation of the changes in the structure of the material caused by the cutting technology. It is used in the analytical method for calculating motor parameters, and gives results that are in good agreement with the measurement. This method can determine the operating parameters of electrical machines of various sizes and rated powers.

A Deep-Learning Framework for the Detection of Oil Spills from SAR Data

Oil leaks onto water surfaces from big tankers, ships, and pipeline cracks cause considerable damage and harm to the marine environment. Synthetic Aperture Radar (SAR) images provide an approximate representation for target scenes, including sea and land surfaces, ships, oil spills, and look-alikes. Detection and segmentation of oil spills from SAR images are crucial to aid in leak cleanups and protecting the environment. This paper introduces a two-stage deep-learning framework for the identification of oil spill occurrences based on a highly unbalanced dataset. The first stage classifies patches based on the percentage of oil spill pixels using a novel 23-layer Convolutional Neural Network. In contrast, the second stage performs semantic segmentation using a five-stage U-Net structure. The generalized Dice loss is minimized to account for the reduced oil spill representation in the patches. The results of this study are very promising and provide a comparable improved precision and Dice score compared to related work.

Algebraic Approach to Modeling the Management Systems of the Sixth Technological Mode

The aim of this research is to highlight the issues of building the models of control for automotion of the control process and, in particular, by using the artificial intelligence. The urgency of the problem is due to the passage of the economy from the fifth technological mode to the sixth one [1-5]. This passage is accompanied by the use of very complex automatic control systems, which are not limited only by automation of the executed algorithms. Management automation requires formalization of this process, construction and use of various management models. The latter requires the formalization and use of various management models that reflect various aspects of the management system. For simultaneous application of several fundamentally different models, an algebraic approach to modeling is proposed, which consists in distinguishing the following three components: 1) system of the basic models; 2) systems of standard transformations and standard combinations of models; 3) approximation mechanism intended to an approximate representation of the required model as the result of typical transformations and typical combinations of the basic models. The process plan is considered as the main activity model, we consider the strategy as the mechanism for creating the plan. Two ways of describing a strategy are proposed: a hierarchical model and an algebraic representation of a strategy. The study is a theoretical one. The methodological basis is the system analysis (A.A. Bogdanov) and the theory of modeling by Yu.B. Melnikov, in particular, the theory of adequacy, the theory of strategies, etc. Keywords: management model, algebraic approach to modeling, strategy of activity, purpose of activity

A Comparison of Evolutionary and Tree-Based Approaches for Game Feature Validation in Real-Time Strategy Games with a Novel Metric

When it comes to game playing, evolutionary and tree-based approaches are the most popular approximate methods for decision making in the artificial intelligence field of game research. The evolutionary domain therefore draws its inspiration for the design of approximate methods from nature, while the tree-based domain builds an approximate representation of the world in a tree-like structure, and then a search is conducted to find the optimal path inside that tree. In this paper, we propose a novel metric for game feature validation in Real-Time Strategy (RTS) games. Firstly, the identification and grouping of Real-Time Strategy game features is carried out, and, secondly, groups are included into weighted classes with regard to their correlation and importance. A novel metric is based on the groups, weighted classes, and how many times the playtesting agent invalidated the game feature in a given game feature scenario. The metric is used in a series of experiments involving recent state-of-the-art evolutionary and tree-based playtesting agents. The experiments revealed that there was no major difference between evolutionary-based and tree-based playtesting agents.

Adaptive Parameterization for Solving of Thermal/Compositional Nonlinear Flow and Transport With Buoyancy

Summary The nonlinear nature of flow and transport in porous media requires a linearization of the governing numerical-model equations. We propose a new linearization approach and apply it to complex thermal/compositional problems. The key idea of the approach is the transformation of discretized mass- and energy-conservation equations to an operator form with separate space-dependent and state-dependent components. The state-dependent operators are parameterized using a uniformly distributed mesh in parameter space. Multilinear interpolation is used during simulation for a continuous reconstruction of state-dependent operators that are used in the assembly of the Jacobian and residual of the nonlinear problem. This approach approximates exact physics of a simulation problem, which is similar to an approximate representation of space and time discretization performed in conventional simulation. Maintaining control of the error in approximate physics, we perform an adaptive parameterization to improve the performance and flexibility of the method. In addition, we extend the method to compositional problems with buoyancy. We demonstrate the robustness and convergence of the approach using problems of practical interest.