Abstract
The paper presents a modification of the Magnitude Optimum Multiple Integration (MOMI) method process non-parametric data in the frequency domain instead of the time domain The required frequency data are obtained directly from the filtered amplitude -shifted process step response and have been shown to be relatively insensitive to normally distributed process noise. All calculations, including the calculation of the PID controller parameters, are performed analytically. The closed loop responses to tested processes with added normally distributed noise were relatively fast with small or no overshoot, all according to the Magnitude Optimum (MO) method. The proposed method is not limited to open loop step responses or to the PID controller structure.

Abstract
Integrating processes can be found in various industries. The main characteristic of such processes is that a limited process input can cause an unlimited process output. In general, they are more difficult to control compared to stable processes. The recently developed Magnitude optimum multiple integration tuning method for integrating processes provides very good closed -loop responses. However, it uses a reference -weighting 2-DOF PI(D) controller structure where the weighting parameters for the P and D term of the controller are equal (therefore the user can only change one parameter). Another drawback of the existing method is that it needs to find the roots of the fourth -order algebraic equation. The method proposed here does not require finding these roots and provides better tracking compared to the original method while maintaining optimal disturbance rejection for different integrating process models.

Abstract
Recently introduced Generalized Pre-trained Transformers (GPT) and conversional chatbots such as ChatGPT are causing deep society transformations. The incorporation of these Artificial Intelligence technologies can be beneficial in multiple science and development areas including Control Engineering. The evaluation of GPTs within Control Engineering Education and PID control is addressed in this work. Different types of interactions with GPTs are evaluated and the use of a personalized GPT for PID tuning explored. Copyright (C) 2024 The Authors. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract
This paper aims to provide an overview of the impact of the COVID-19 pandemic on control engineering education worldwide. The authors, who are educators in the control education field from various countries across all continents, first summarize their experiences to present a global perspective on the different solutions adopted in control education during the pandemic. Afterwards, collected information from the international community through a questionnaire enabled insightful comparisons between pre-pandemic and during-pandemic educational resources and methods, which are shared in this paper. The feedback from the authors’ experiences, along with the questionnaire responses, serves as a valuable resource for learning and improving teaching activities. The questionnaire was distributed among the international control engineering community in collaboration with the International Federation of Automatic Control (IFAC) to explore the diverse alternatives employed globally for conducting online educational activities during the pandemic. These activities include methodologies, tools, theoretical exercises, laboratory experiments, exam types, simulators, and software for online lecturing.

Abstract
Encouraging pre-university students to pursue engineering courses at the university level is essential to meet the industry’s escalating demand for engineers. Each year, universities host hundreds of secondary students who tour their facilities to get a feel for the academic environment. This paper discusses an educational experiment designed as part of a semester-long undergraduate project in Informatics Engineering. The project involves tailoring a Dobot Magician robot, equipped with a standard webcam, to engage in a game of tic-tac-toe against a human user. The camera stream is continuously processed by a computer vision algorithm to detect the pieces placement in the game board. The paper outlines the project development stages, the elements involved, and presents preliminary test results. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

Abstract
The manual monitoring of refilling stations in industrial environments can lead to inefficiencies and errors, which can impact the overall performance of the production line. In this paper, we present an unsupervised detection pipeline for identifying refilling stations in industrial environments. The proposed pipeline uses a combination of image processing, pattern recognition, and deep learning techniques to detect refilling stations in visual data. We evaluate our method on a set of industrial images, and the findings demonstrate that the pipeline is reliable at detecting refilling stations. Furthermore, the proposed pipeline can automate the monitoring of refilling stations, eliminating the need for manual monitoring and thus improving industrial operations’ efficiency and responsiveness. This method is a versatile solution that can be applied to different industrial contexts without the need for labeled data or prior knowledge about the location of refilling stations.