This paper investigated an electromagnetic torque ripple level of BLDC drives with multiple three-phase (TP) permanent magnet (PM) motors for electric vehicles. For this purpose, mathematical models of PM machines of different armature winding sets-single (STP), dual (DTP), triple (TTP), and quadruple (QTP) ones of asymmetrical configuration and optimal angular displacement between winding sets were developed and corresponding computer models in the Matlab/Simulink environment were created. In conducted simulation, the influence of various factors on the electromagnetic torque ripple of the multiple-TP BLDC drives was investigated—degree of modularity, magnetic coupling between armature winding sets, and drive operation in open and closed-loop control systems. Studies have shown an increase of the electromagnetic torque ripple generated by one module in the multiple TP BLDC drives with magnetically coupled winding sets, due to additional current pulsations caused by magnetic interactions between the machine modules. However, the total electromagnetic torque ripples are much lower than in similar drives with magnetically insulated winding sets. Compared with the STP BLDC drive, the multiple TP BLDC drives with the same output parameters showed a reduction of the electromagnetic torque ripple by 27.6% for the DTP, 32.3% for the TTP, and 34.0% for the QTP BLDC drive.

Pseudorandom sequence generation is used in many industries, including cryptographic information security devices, measurement technology, and communication systems. The purpose of the present work is to research additive Fibonacci generators (AFG) and modified AFG (MAFG) with modules p prime numbers, designed primarily for their hardware implementation. The known AFG and MAFG, as with any cryptographic generators of pseudorandom sequences, are used in arguments with tremendous values. At the same time, there are specific difficulties in defining of their statistical characteristics. In this regard, the following research methodologies were used in work: for each variant of AFG and MAFG, two models were created—abstract, which is not directly related to the circuit solution, and hardware, which corresponds to the proposed structure; for relatively small values of arguments, the identity of models was proved; the research of statistical characteristics, with large values of arguments, was carried out using an abstract model and static tests NIST. Proven identity of hardware and abstract models suggest that the principles laid down in the organization of AFG and MAFG structures with modules of prime numbers ensure their effective hardware implementation in compliance with all requirements for their statistical characteristics and the possibility of application in cryptographic information security devices.

The integration of machine vision systems with programmable logic controllers (PLCs) is increasingly crucial for automated quality assurance in Industry 4.0 environments. This paper presents an applied case study of vision–PLC integration, focusing on real-time synchronization, deterministic communication, and practical industrial deployment. The proposed platform combines a Cognex In-Sight 2802C smart camera (Cognex Corporation, Natick, MA, USA) with an Allen-Bradley Compact GuardLogix PLC through Ethernet/IP implicit cyclic exchange. Three representative case studies were investigated: 3D-printed prototypes with controlled defects, automotive electrical connectors inspected using Cognex ViDi supervised learning tools, and fiber optic tubes evaluated via a custom fixture-based heuristic method. Across all scenarios, detection accuracy exceeded 95%, while PLC-level triple verification reduced false classifications by 28% compared to camera-only operation. The work highlights the benefits of PLC-driven inspection, including robustness, real-time performance, and dynamic tolerance adjustment via HMI interfaces. At the same time, several limitations were identified, including sensitivity to lighting variations, limited dataset size, and challenges in scaling to full production environments. These findings demonstrate a replicable integration framework that supports intelligent manufacturing. Future research will focus on hybrid AI–PLC architectures, extended validation on industrial production lines, and predictive maintenance enabled by edge computing.

The paper proposes a method of automatic detection of parameters of a distribution transformer (model, type, and power) from a distance, based on its low-frequency noise spectra. The spectra are registered by sensors and processed by a method based on evolutionary algorithms and machine learning. The method, as input data, uses the frequency spectra of sound pressure levels generated during operation by transformers in the real environment. The model also uses the background characteristic to take under consideration the changing working conditions of the transformers. The method searches for frequency intervals and its resolution using both a classic genetic algorithm and particle swarm optimization. The interval selection was verified using five state-of-the-art machine learning algorithms. The research was conducted on 16 different distribution transformers. As a result, a method was proposed that allows the detection of a specific transformer model, its type, and its power with an accuracy greater than 84%, 99%, and 87%, respectively. The proposed optimization process using the genetic algorithm increased the accuracy by up to 5%, at the same time reducing the input data set significantly (from 80% up to 98%). The machine learning algorithms were selected, which were proven efficient for this task.

Automated monitoring of cutting tool wear is of paramount importance in the manufacturing industry, as it directly impacts production efficiency and product quality. Traditional manual inspection methods are time-consuming and prone to human error, necessitating the adoption of more advanced techniques. This study explores the application of ViDiDetect, a deep learning-based defect detection solution, in the context of machine vision for assessing cutting tool wear. By capturing high-resolution images of machining tools and analyzing wear patterns, machine vision systems offer a non-contact and non-destructive approach to tool wear assessment, enabling continuous monitoring without disrupting the machining process. In this research, a smart camera and an illuminator were utilized to capture images of a car suspension knuckle’s machined surface, with a focus on detecting burrs, chips, and tool wear. The study also employed a mask to narrow the region of interest and enhance classification accuracy. This investigation demonstrates the potential of machine vision and ViDiDetect in automating cutting tool wear assessment, ultimately enhancing manufacturing processes’ efficiency and product quality. The project is at the implementation stage in one of the automotive production plants located in southern Poland.

Pseudorandom number and bit sequence generators are widely used in cybersecurity, measurement, and other technology fields. A special place among such generators is occupied by additive Fibonacci generators (AFG). By itself, such a generator is not cryptographically strong. Nevertheless, when used as a primary it can be quite resistant to cryptanalysis generators. This paper proposes a modification to AGF, the essence of which is to use prime numbers as modules of recurrent equations describing the operation of generators. This modification made it possible to ensure the constancy of the repetition period of the output pseudorandom pulse sequence in the entire range of possible values of the initial settings–keys (seed) at specific values of the module. In addition, it has proposed a new generator scheme, which consists of two generators: the first of which is based on a modified AFG and the second is based on a linear feedback shift register (LFSR). The output pulses of both generators are combined through a logic element XOR. The results of the experiment show that the specific values of modules provide a constant repetition period of the output pseudorandom pulse sequence in a whole range of possible values of the initial settings–keys (seed) and provide all the requirements of the NIST test to statistical characteristics of the sequence. Modified AFGs are designed primarily for hardware implementation, which allows them to provide high performance.