Fault monitoring and diagnostics are important to ensure reliability of electric motors. Efficient algorithms for fault detection improve reliability, yet development of cost-effective and reliable classifiers for diagnostics of equipment is challenging, in particular due to unavailability of well-balanced datasets, with signals from properly functioning equipment and those from faulty equipment. Thus, we propose to use a Bayesian neural network to detect and classify faults in electric motors, given its efficacy with imbalanced training data. The performance of the proposed network is demonstrated on real life signals, and a robustness analysis of the proposed solution is provided.

This paper explores the use of Gaussian Processes (GPs) and Gaussian Mixture Models (GMMs) for diagnosing induction motors. GPs provide flexible, non-linear models that handle noisy data, while GMMs offer robust probabilistic classification by modeling data as mixtures of Gaussian distributions. By integrating Bayesian inference with GMMs and utilizing Stan for complex model management, the study enhances classification accuracy. Experimental data from induction motors under various conditions were analyzed, identifying patterns indicative of motor health. The approach, leveraging synthetic data generation, demonstrates effectiveness in proactive maintenance and fault detection, reducing downtime and costs.

This study introduces a stochastic model designed to evaluate the long-term degradation of Electronic Security Systems (ESS) as critical components of Physical Protection Systems (PPS). Recognizing the significant financial investments in security systems and the evolving nature of threats, our model aims to provide a more dynamic assessment of PPS effectiveness over time. Traditional methodologies, such as the Estimated Adversary Sequence Interruption (EASI), often overlook the degradation factor, potentially leading to mismanagement and increased vulnerability. Our model incorporates various stochastic parameters, including aging, adversary knowledge growth, and technological improvements, to simulate the decline in system effectiveness. We present a comprehensive computational framework using Python, demonstrating the model’s application through a case study involving different ESS components with varied ages. The results highlight the necessity for proactive maintenance strategies and dynamic policy-making to sustain PPS effectiveness. This paper underscores the importance of integrating degradation factors into PPS evaluation to enhance security infrastructure management against evolving threats.

In this article, we explore the domain of 3D object detection, in particular its usage in autonomous driving systems. To replace expensive LiDAR-based perception systems, the monocular camera object detection methods have been successfully adapted to the 3D detection problem. On the other hand, they tend to be very complex and require lots of resources. Our novel Cross-Domain Spatial Matching (CDSM) method poses a simple yet effective alternative to achieve the same goal. We present the idea of reusing 2D object detection network structure and applying our feature domain adaptation layer that transforms learned representation from 2D image space to 3D. We show how we trained and tested it on popular open automotive datasets and present a comparison of obtained results with respect to current state-of-the-art solutions.

Teaching in pandemic was a challenge for everyone. Keeping students interested in lectures was hard. Motivating them to laboratory work even more so. In this paper we present the case study of our successful course of numerical methods. We describe tools that we have used, its structure, and show survey results suggesting that we are doing something right. Objectively, we can see that challenges of remote learning give an opportunity to reach for tools that are available and generate new qualities in teaching.

A new methodology is presented to assure numerically reliable integration of the magnetization slope in the Jiles-Atherton model of ferromagnetic core hysteresis. Two hardware description language (HDL) implementations of the technique are presented: one in SystemC and the other in very-high-speed integrated circuit (VHSIC) HDL (VHDL) analog and mixed signal (AMS). The new model uses timeless discretization of the magnetization slope equation and provides superior accuracy and numerical stability especially at the discontinuity points that occur in hysteresis. Numerical integration of the magnetization slope is carried out by the model itself rather than by the underlying analog solver. The robustness of the model is demonstrated by practical simulations of examples involving both major and minor hysteresis loops

In this paper, a modified model of ferromagnetic hysteresis suitable for mixed-signal simulations in VHDL-AMS is presented. The aim of this paper is to demonstrate how a numerically stable and accurate implementation of the Jiles-Atherton model can be achieved using a 4th order Runga-Kutta integration of the derivative of magnetization with respect to the field strength (H). While most SPICE-like implementations require inconvenient integration in time to obtain the magnetization derivative, our approach is more general as it does not rely on the underlying differential equation solver for this purpose. The model addresses the non-physical situation of negative BH slopes and proposes an alternative implementation of the anhysteretic function using a polynomial approximation of the Langevin function for low signal levels and a new function with no discontinuities. Model efficiency is improved by monitoring the change in H and only activating the integration function when H changes by a specified amount.