A Permutation-Equivariant Neural Network Architecture For Auction Design

Designing an incentive compatible auction that maximizes expected revenue is a central problem in Auction Design. Theoretical approaches to the problem have hit some limits in the past decades and analytical solutions are known for only a few simple settings. Computational approaches to the problem through the use of LPs have their own set of limitations. Building on the success of deep learning, a new approach was recently proposed by Duetting et al. (2019) in which the auction is modeled by a feed-forward neural network and the design problem is framed as a learning problem. The neural architectures used in that work are general purpose and do not take advantage of any of the symmetries the problem could present, such as permutation equivariance. In this work, we consider auction design problems that have permutation-equivariant symmetry and construct a neural architecture that is capable of perfectly recovering the permutation-equivariant optimal mechanism, which we show is not possible with the previous architecture. We demonstrate that permutation-equivariant architectures are not only capable of recovering previous results, they also have better generalization properties

Computational Shape Derivatives in Heat Conduction: An Optimization Approach for Enhanced Thermal Performance

We analyze an optimization problem of the conductivity in a composite material arising in a heat conduction energy storage problem. The model is described by the heat equation that specifies the heat exchange between two types of materials with different conductive properties with Dirichlet-Neumann boundary conditions on the external part of the domain, and on the interface characterized by the resisting coefficient between the highly conductive material and the less conductive material. The main purpose of the paper is to compute a shape gradient of an optimization functional in order to accurately determine the optimal location of the conductive material using a classical shape optimization strategy. We also present some numerical experiments to illustrate the efficiency of the proposed method

A Multiple-Sensor Fault-Tolerant Control of a Single-Phase Pulse-Width Modulated Rectifier Based on MRAS and GPI Observers

Due to their advantages in ensuring low harmonic distortion and high power factors, single-phase Pulse-Width Modulated (PWM) rectifiers are widely employed in several industrial applications. Generally, the conventional control loop of a single-phase PWM rectifier uses both voltage and current sensors. Hence, in case of sensor fault, the performance and the availability of the converter can be seriously compromised. Therefore, diagnosis approaches and fault-tolerant control (FTC) strategies are mandatory to monitor these systems. Accordingly, this paper introduces a novel multiple-sensor FTC scheme for a single-phase PWM rectifier. The proposed fault diagnosis approach relies on joining several Generalized Proportional Integral (GPI) and Model Reference Adaptive System (MRAS) observers with a residual generation technique to detect and isolate sensor faults in a simple and reliable manner. While conventional sensor FTC methods dedicated to PWM rectifiers can only deal with single faults, the suggested approach guarantees a very good effectiveness level of sensor fault detection, isolation (FDI) and FTC of multiple-sensor fault occurrence scenarios. Consequently, the single-phase PWM rectifier can work with only the survivable single sensor with the guarantee of very good performance as in healthy operation mode. The effectiveness of the proposed sensor FDI approach and its control reconfiguration performance are demonstrated through both extensive simulation and experimental results

Educational robotics as an Innovative teaching practice using technology: minimization of risks

This research is focused on studying educational robotics, specifically robots which provide functions of educational activity. We have considered the questions of intelligent agents' behavior and have studied their educational opportunities. Educational robotics is a powerful tool of developing person's skills and abilities in various fields of technical creativity and professional activity. The evolutionary development of robotics is connected with development of artificial intelligence, where emotions play a great role in operations. Nowadays the main thing is to form the ability and skills of optimum interaction with social environment when a person, based on gained knowledge, is capable to put goals of the activity in strict accordance with laws and society conditions and using current technology