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Cybernetics & informatics (K&I)

作者： Ruth Bars Gyula Max J. Anthony Rossiter László Keviczky Department of Automation and Applied Informatics Budapest University of Technology and Economics Budapest Hungary School of Electrical and Electronic Engineering University of Sheffield Sheffield UK Systems and Control Laboratory Institute for Computer Science and Control Budapest Hungary

ISBN: (数字)9798331541811

ISBN: (纸本)9798331541828

The first course of control is under a critical review. Both the teaching material covered and the teaching methods require new considerations. Introducing interactivity in the education process makes the learning more successful and enjoyable. MATLAB provides an effective environment for learning and applying different disciplines. control101 is a new MATLAB toolbox under development which provides tools for interactive learning of control disciplines. This paper presents the framework for teaching discrete control algorithms applied for processes containing large dead times.

关键词： Visualization Systematics PI control Reviews Education Laboratories Process control Prediction algorithms PD control MATLAB

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A Hybrid Adaptive Filter for Head Tracking in Augmented Reality (AR)-based Flight Simulators

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IEEE Transactions on Computers 2025年

作者： Nwobodo, Onyeka J. Kuaban, Godlove Suila Nkemeni, Valery Wereszczynski, Kamil Cyran, Krzysztof A. Silesian University of Technology Department of Computer Graphics Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Gliwice Poland Polish Academy of Sciences Institute of Theoretical and Applied Informatics Poland University of Buea Laboratory of Electrical Engineering and Computing Faculty of Engineering and Technology Buea Cameroon

This paper presents a novel approach for head tracking in augmented reality (AR) flight simulators using an adaptive fusion of Kalman and particle filters. This fusion dynamically balances the strengths of both algorithms, leveraging Kalman filters for computational efficiency and particle filters for handling non-linearities based on real-time factors such as sensor noise and head movement patterns. Our method demonstrates superior tracking precision and reduced latency, making it particularly effective for immersive pilot training in AR-based flight simulations. While focused on flight simulation, our approach holds high potential for broader applications in other AR and virtual reality (VR) environments where precise, real-time head tracking is crucial. These results provide actionable design guidance for developers optimizing tracking systems in environments that require fast response times and high accuracy. Future extensions of this research could explore the generalization of our approach across diverse AR applications, including human-computer interaction (HCI), medical simulations, and gaming. © 1968-2012 IEEE.

关键词： Flight simulators

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Intelligent model for assessing the suitability of water based on its chemical and biological parameters 24

Intelligent model for assessing the suitability of water bas...

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24th International Symposium INFOTEH-JAHORINA, INFOTEH 2025

作者： Tynchenko, Yadviga Tynchenko, Vadim Degtyareva, Ksenia Kuznetsov, Georgy Kukartseva, Svetlana Artificial Intelligence Technology Scientific and Education Center Bauman Moscow State Technical University Moscow Russia Department of Technological Machines and Equipment of Oil and Gas Complex School of Petroleum and Natural Gas Engineering Siberian Federal University Krasnoyarsk Russia Department of Systems Analysis and Operations Research Reshetnev Siberian State University of Science and Technology Krasnoyarsk Russia Department of Applied Informatics Moscow Timiryazev Agricultural Academy Moscow Russia Center for Continuing Education Bauman Moscow State Technical University Moscow Russia Information-Control Systems Department Reshetnev Siberian State University of Science and Technology Krasnoyarsk Russia

ISBN: (纸本)9798331515799

This paper presents a study on the development and testing of a machine learning model for classifying water according to its purpose, based on chemical and microbiological indicators. The aim of the study is to create an accurate and stable model that can effectively determine the categories of water - drinking, industrial, agricultural and technical - based on the analysis of its qualitative characteristics. The gradient boosting method was chosen as the algorithm for classification, which demonstrated high accuracy and reliability when working with multidimensional data. The study included the processing of a synthetic dataset containing information on water parameters such as calcium concentration, total bacterial content, phosphate levels, alkalinity, sulfates and oxidizability. The analysis of the correlation matrix made it possible to identify the relationships between the features, which made it possible to optimize the operation of the model and improve its accuracy. The quality of the model was tested using accuracy, completeness and F1-score metrics, which demonstrated almost perfect results for all categories. In conclusion, it is concluded that the proposed model can be successfully used to automate the analysis of water quality in ecology, industry and agriculture. The use of intelligent technologies opens up new opportunities for monitoring water resources, increasing the efficiency and reliability of quality control. © 2025 IEEE.

关键词： Taxonomies

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Recursive Identification of Fractional Time-Delay systems Using Discrete-Time Model

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IFAC-PapersOnLine 2018年第15期51卷 939-944页

作者： Higo, Toshiaki Kawaguchi, Takahiro Itamoto, Masaki Adachi, Shuichi Department of Applied Physics and Physico-Informatics Keio University Yokohama Japan Department of Systems and Control Engineering Tokyo Institute of Technology Tokyo Japan

In this paper, a recursive identification method for fractional time-delay systems using a discrete-time model is proposed. The fractional time delay is a non-integer multiple of the sampling period for the discrete-time system. We describe the discretization of the fractional time-delay systems and propose a new formulation for system identification. Single-input, single-output systems are transformed to two-input, single-output systems: one transfer function represents system dynamics and the other represents the influence of the time delay. Then, we propose a new recursive identification method for fractional time-delay systems. Finally we verify the effectiveness of the proposed method through numerical examples. © 2018

关键词： Time delay Delay control systems Digital control systems Discrete time control systems Numerical methods Timing circuits Discrete time systems Discrete time model Discretizations Recursive identification Single input , single outputs Single output systems System Dynamics Time delay systems

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Assessment of the mineral potential of rocks using intelligent data analysis technologies 24

Assessment of the mineral potential of rocks using intellige...

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24th International Symposium INFOTEH-JAHORINA, INFOTEH 2025

作者： Tynchenko, Yadviga Tynchenko, Vadim Degtyareva, Ksenia Kalmykova, Ekaterina Kukartseva, Svetlana Bauman Moscow State Technical University Artificial Intelligence Technology Scientific and Education Center Moscow Russia Siberian Federal University School of Petroleum and Natural Gas Engineering Department of Technological Machines and Equipment of Oil and Gas Complex Krasnoyarsk Russia Reshetnev Siberian State University of Science and Technology Information-Control Systems Department Krasnoyarsk Russia Reshetnev Siberian State University of Science and Technology Department of Systems Analysis and Operations Research Krasnoyarsk Russia Bauman Moscow State Technical University Center for Continuing Education Moscow Russia Moscow Timiryazev Agricultural Academy Department of Applied Informatics Moscow Russia

ISBN: (纸本)9798331515799

This paper presents a study aimed at developing and evaluating a classification model for the mineral potential of geological layers using machine learning methods. The main goal was to create an algorithm capable of predicting the categories of mineral potential - low, medium and high - based on key geological characteristics such as ore content, density, depth, permeability and rock type. The results obtained demonstrated the overall accuracy of the model at 98%, with an almost ideal classification for the categories of medium and high mineral potential. The analysis of the significance of the features showed that the ore content and density have the greatest impact on the classification. The work also revealed certain difficulties in classifying low mineral potential, which is due to limited data or the intersection of characteristics between classes. These results highlight the importance of further research in feature selection and improving data balance. The findings demonstrate the effectiveness of using machine learning methods in geological research and their potential for automating mineral resource assessment processes. The developed methodology can be adapted to solve other problems in the field of geology, such as forecasting deposits and optimizing mining. © 2025 IEEE.

关键词： Radioactive prospecting

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Intelligent model for assessing the suitability of water based on its chemical and biological parameters

Intelligent model for assessing the suitability of water bas...

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International Symposium on INFOTEH-JAHORINA (INFOTEH)

作者： Yadviga Tynchenko Vadim Tynchenko Ksenia Degtyareva Georgy Kuznetsov Svetlana Kukartseva Artificial Intelligence Technology Scientific and Education Center Bauman Moscow State Technical University Moscow Russia Department of Technological Machines and Equipment of Oil and Gas Complex School of Petroleum and Natural Gas Engineering Siberian Federal University Krasnoyarsk Russia Information-Control Systems Department Reshetnev Siberian State University of Science and Technology Krasnoyarsk Russia Department of Applied Informatics Moscow Timiryazev Agricultural Academy Moscow Russia Department of Systems Analysis and Operations Research Reshetnev Siberian State University of Science and Technology Krasnoyarsk Russia Center for Continuing Education Bauman Moscow State Technical University Moscow Russia

ISBN: (数字)9798331515799

ISBN: (纸本)9798331515805

This paper presents a study on the development and testing of a machine learning model for classifying water according to its purpose, based on chemical and microbiological indicators. The aim of the study is to create an accurate and stable model that can effectively determine the categories of water — drinking, industrial, agricultural and technical — based on the analysis of its qualitative characteristics. The gradient boosting method was chosen as the algorithm for classification, which demonstrated high accuracy and reliability when working with multidimensional data. The study included the processing of a synthetic dataset containing information on water parameters such as calcium concentration, total bacterial content, phosphate levels, alkalinity, sulfates and oxidizability. The analysis of the correlation matrix made it possible to identify the relationships between the features, which made it possible to optimize the operation of the model and improve its accuracy. The quality of the model was tested using accuracy, completeness and F1-score metrics, which demonstrated almost perfect results for all categories. In conclusion, it is concluded that the proposed model can be successfully used to automate the analysis of water quality in ecology, industry and agriculture. The use of intelligent technologies opens up new opportunities for monitoring water resources, increasing the efficiency and reliability of quality control.

关键词： Measurement Analytical models Accuracy Correlation Biological system modeling Water quality Boosting Reliability Water resources Chemicals

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Assessment of the mineral potential of rocks using intelligent data analysis technologies

Assessment of the mineral potential of rocks using intellige...

引用

International Symposium on INFOTEH-JAHORINA (INFOTEH)

作者： Yadviga Tynchenko Vadim Tynchenko Ksenia Degtyareva Ekaterina Kalmykova Svetlana Kukartseva Artificial Intelligence Technology Scientific and Education Center Bauman Moscow State Technical University Moscow Russia Department of Technological Machines and Equipment of Oil and Gas Complex School of Petroleum and Natural Gas Engineering Siberian Federal University Krasnoyarsk Russia Information-Control Systems Department Reshetnev Siberian State University of Science and Technology Krasnoyarsk Russia Department of Systems Analysis and Operations Research Reshetnev Siberian State University of Science and Technology Krasnoyarsk Russia Center for Continuing Education Bauman Moscow State Technical University Moscow Russia Department of Applied Informatics Moscow Timiryazev Agricultural Academy Moscow Russia

ISBN: (数字)9798331515799

ISBN: (纸本)9798331515805

This paper presents a study aimed at developing and evaluating a classification model for the mineral potential of geological layers using machine learning methods. The main goal was to create an algorithm capable of predicting the categories of mineral potential — low, medium and high — based on key geological characteristics such as ore content, density, depth, permeability and rock type. The results obtained demonstrated the overall accuracy of the model at 98%, with an almost ideal classification for the categories of medium and high mineral potential. The analysis of the significance of the features showed that the ore content and density have the greatest impact on the classification. The work also revealed certain difficulties in classifying low mineral potential, which is due to limited data or the intersection of characteristics between classes. These results highlight the importance of further research in feature selection and improving data balance. The findings demonstrate the effectiveness of using machine learning methods in geological research and their potential for automating mineral resource assessment processes. The developed methodology can be adapted to solve other problems in the field of geology, such as forecasting deposits and optimizing mining.

关键词： Accuracy Machine learning algorithms Ores Machine learning Predictive models Rocks Minerals Permeability Decision trees Forecasting

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Distributed AC Optimal Power Flow: A Scalable Solution for Large-Scale Problems

arXiv

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arXiv 2025年

作者： Dai, Xinliang Jiang, Yuning Guo, Yi Jones, Colin N. Diehl, Moritz Hagenmeyer, Veit Institute for Automation and Applied Informatics The Karlsruhe Institute of Technology Germany Automatic Control Laboratory EPFL Switzerland Urban Energy Systems Laboratory Swiss Federal Laboratory for Materials Science and Technology Switzerland Department of Mathematics University of Freiburg Germany

This paper introduces a novel distributed optimization framework for large-scale AC Optimal Power Flow (OPF) problems, offering both theoretical convergence guarantees and rapid convergence in practice. By integrating smoothing techniques and the Schur complement, the proposed approach addresses the scalability challenges and reduces communication overhead in distributed AC OPF. Additionally, optimal network decomposition enables efficient parallel processing under the single program multiple data (SPMD) paradigm. Extensive simulations on large-scale benchmarks across various operating scenarios indicate that the proposed framework is 2 to 5 times faster than the state-of-the-art centralized solver IPOPT on modest hardware. This paves the way for more scalable and efficient distributed optimization in future power system applications. Copyright © 2025, The Authors. All rights reserved.

关键词： Parallel processing systems

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Complex system dedicated to monitoring and control of hydroponic greenhouse environment 17

Complex system dedicated to monitoring and control of hydrop...

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17th International Multidisciplinary Scientific Geoconference, SGEM 2017

作者： Ştefan, Mocanu Alexandru, Dumitraşcu Cosmin, Popa Department of Automatic Control and Applied Informatics Bucharest Romania Department of Automatic Control and Systems Engineering Faculty of Automatic Control and Computers UPB Bucharest Romania

ISBN: (纸本)9786197408263

Fast increase of Earth population during the last decades raised a serious problem when it came to providing fresh and good quality food (mainly vegetables) in big quantity over a short period of time. Due to relatively long lifecycles of plants when growing in gardens or fields, the best solution consisted in using greenhouses. Greenhouses proved to be a very important solution since they are capable of operating regardless of local environments if minimum requirements (heating and water) are provided. By offering optimum growing conditions for the plants, the greenhouse environments not only stimulate a shorter lifecycle but also offer almost perfect protection against pests or plants diseases. An even better solution is represented by hydroponic greenhouses which exhibit several advantages over traditional greenhouses: they do not need earth for growing plants, the size and location are less restrictive, the plant cultures can be overlapped and the nutrients and other parameters can be controlled and personalized more efficiently. However, this comes with a higher cost for implementing and using automatic control architecture. In addition to specialized sensors, efficient solutions for data transmission, control and command must be used. In this paper a hydroponic system is presented. It aims to control not only a greenhouse but a set of greenhouses by aggregating them into a complex grid alike mechanism of monitoring and control. Intermediate results prove the solution is functional but the final architecture is not yet finalized. © SGEM2017.

关键词： Embedded systems

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User-Friendly Tools for New Digital Education in Computer Sience 22

User-Friendly Tools for New Digital Education in Computer Si...

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22nd International Conference on Emerging eLearning Technologies and Applications, ICETA 2024

作者： Kanalikova, Alžbeta Rakovská, Eva University of Žilina Department of Control and Information Systems Žilina Slovakia University of Economics in Bratislava Department of Applied Informatics Bratislava Slovakia

ISBN: (纸本)9798331527716

In today's world of pandemic era, it is important to develop new ways of e-learning. E-learning is also associated with finding creative innovation as a part of digital learning, which includes a variety of IT tools as multimedia and smart tools. The article is focused on preparing and implementing innovative tools in computer science teaching. Computer science teaching is traditionally based on explicit exact knowledge. It leads to memorizing the steps or searching the ready solutions on websites from the students' side. In the article, we analyse the problems of computer science teaching online and the experience of using a few innovative elements in e-learning courses last year. Based on gained experience we choose appropriate tools for collaboration, interactivity, tools that record the sequence of steps, and tools for creating scenarios, focusing on gamification in teaching. The aim of the article comes from such experience and shows examples of how to implement multimedia and tools in a creative way for algorithmization and programming. We also try to use the interaction with the chatbot, which responds directly to the students' suggestions and helps them with basic terms. The specific results of our analyses and practical use are discussed and supported with figures. © 2024 IEEE.

关键词： Algorithmization Chatbot Computer science teaching Digital learning Programming

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