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

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

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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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Semantic Segmentation of Street Scenes Using a Hybridisation between 3D Features and CNNs

Semantic Segmentation of Street Scenes Using a Hybridisation...

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International Conference on Emerging Technologies and Factory Automation (ETFA)

作者： Lavinia Ferariu Teodor-Andrei Sauciuc Department of Automatic Control and Applied Informatics Gheorghe Asachi Technical University of Iasi Iași Romania

ISBN: (数字)9781728189567

ISBN: (纸本)9781728189574

Semantic segmentation (SS) provides pixel-wise label maps explaining the content of the images. To alleviate the accuracy of the labels, this paper proposes SS schemes based on CNNs and 3D features. Mainly, the CNNs receive multidimensional hybrid input arrays of 3D and color features, from which valuable context-aware information could be extracted. The suggested SS method accepts a single CNN working with all the features concomitantly, or an ensemble of CNNs driven by subsets of 3D features. The role of CNNs is discussed both for feature extraction and classification. Thus, the final label map is directly generated by CNNs or is produced by Random Forest (RF) using features provided by the last neural layers. The approach is exemplified for an application assisting persons with visual deficiencies. The experiments show increased performance for the CNNs processing the whole set of 3D features. Also, the CNNs driven by 3D features (in block or subsets) offer refined context-aware features for RF.

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Bio-inspired Coordination and control of Autonomous Vehicles in Future Manufacturing and Goods Transportation

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IFAC-PapersOnLine 2020年第2期53卷 10861-10866页

作者： Constantin F. Caruntu Carlos M. Pascal Anca Maxim Ovidiu Pauca Department of Automatic Control and Applied Informatics Gheorghe Asachi Technical University of Iasi Iasi Romania t) Continental Automotive Romania Iasi Romania

The will to apply bio-inspired techniques to coordinate and control autonomous X vehicles (AXVs) has increased tremendously during the last decade due to their advantages in the face of complexity in today’s demanding applications. Thus, several bio-inspired approaches for multiple-entities optimization have been proposed in the literature for various limited applications, e.g., drone coordination, mobile robot formation maintenance. In all these strategies, the entities must plan their path and control their movements while coordinating their behavior w.r.t. the other members, and they must avoid collisions, so the task could be very difficult in the unstructured environments present in future manufacturing plants and goods transportation. Future applications of these bio-inspired techniques for coordination and control of AXVs include large warehouses, manufacturing, logistics, last-mile delivery, etc. The AXVs could be grouped to carry larger goods or they can act as swarm members when they do not have a common goal, but they must interact while they move to complete the allocated tasks and intersect their paths with the paths of other entities. As such, this paper illustrates the concept of applying such bio-inspired coordination and control techniques for the development of future manufacturing and goods transportation, a discussion being carried out regarding the advantages and disadvantages of several techniques for their use in specific applications.

关键词： Bio-inspired coordination Bio-inspired control Autonomous vehicles Intelligent manufacturing systems Goods transportation Last-mile logistics Formation maintenance

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Pilot Study on Using Hybrid–Cascade Filtering on Brain Signals for the control Purposes

Pilot Study on Using Hybrid–Cascade Filtering on Brain Sign...

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International Conference on Methods and Models in Automation and Robotics (MMAR)

作者： Mariusz Pelc Dariusz Mikołajewski Edward Jacek Gorzelańczyk Anna Wieczorek Henryk Racheniuk Adam Sudoł Kayhan Latifzadeh Luis A. Leiva Aleksandra Kawala-Sterniuk Faculty of Electrical Engineering Automatic Control and Informatics Opole University of Technology Opole Poland Institute of Computer Science Kazimierz Wielki University Bydgoszcz Poland Institute of Philosophy Kazimierz Wielki University Bydgoszcz Poland Faculty of Physical Education and Physiotherapy Opole University of Technology Opole Poland Faculty of Natural and Technical Sciences University of Opole Opole Poland Department of Computer Science University of Luxembourg Luxembourg Luxembourg

We present an initial study conducted on fNIRS signals using Hybrid-Cascade filters for the purpose of their quality improvement. Whilst many studies focus on filtering brain signals, so that their frequency domain properties would allow e.g. widely understood diagnostics, here we focus on the study of time-domain signal characteristics, which is relevant for potential control purposes. Taking into account various kinds of artifacts, we propose a novel cascade 1D Kalman filter to handle fNIRS signals.

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Convolutional neural network-based fast seizure detection from video electroencephalograms

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Biomedical Signal Processing and control 2023年 80卷

作者： Chou, Chi-Hsiang Shen, Tsu-Wang Tung, Hsin Hsieh, Peiyuan F. Kuo, Chih-En Chen, Ting-Mao Yang, Chao-Wei Department of Post-Baccalaureate Medicine College of Medicine National Chung Hsing University Taichung Taiwan The Department of Neurological Institute Taichung Veterans General Hospital Taichung Taiwan Department of Neurology National Yang Ming Chiao Tung University School of Medicine Taipei Taiwan Department of Automatic Control Engineering Feng Chia University Taichung Taiwan Master's Program in Biomedical Informatics and Biomedical Engineering Feng Chia University Taichung Taiwan Department of Applied Mathematics National Chung Hsing University Taichung Taiwan

Objective: Ictal stage detection in electroencephalography (EEG) is important for epilepsy diagnosis. However, it's a laborious and time-consuming task for neurologists, especially with lengthy EEG recordings (such as video-EEG). Here we applied the current deep learning techniques to improve ictal stage detection efficiency. Methods: We enrolled epileptic patients at video-EEG examination beds of our neurology department between Jan 2016 and Apr 2020. Four stages of EEG (interictal, preictal, ictal and postictal) were labeled and confirmed by neurologists. Labeled EEG signals were transformed into second-order Poincaré difference plots. The whole dataset was divided into ‘Training’, ‘Validation’ and ‘Testing’ datasets with the proportions of 45%, 5% and 50% respectively. We applied 4 Convolutional Neural Networks (CNNs), namely, AlexNet, GoogLeNet, ResNet50 and DenseNet201, to learn classifying. The top three CNNs were put into an ensemble model for datasets classification. Results: 30 epileptic patients were included: Male/Female 16/14, mean age 42.8 ± 15.0 (range 22 ∼ 81) years old. Total recorded ictal duration was 47,444 s. Overall accuracies for each of the 4 CNN models (AlexNet, GoogLeNet, ResNet50 and DenseNet201) were 78.0%, 78.3%, 81.7% and 80.8%. We omitted the AlexNet, the other 3 CNNs were put into the ensemble model. The best performance was under test with 1% of the Testing dataset yielded the overall accuracies of 85.9%. The ictal stage accuracy was 97.7%. Conclusion: Our method is a promising computer-aid deep learning approach to detect seizures on video EEG. Significance: For testing phase, selected proportion of testing dataset may influence the performances of deep learning models. © 2022 The Author(s)

关键词： Electroencephalography

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Creating an Application in MATLAB to Visualize Changes in Statistical Parameters 25

Creating an Application in MATLAB to Visualize Changes in St...

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25th IEEE International Conference on Problems of Automated Electric Drive. Theory and Practice, PAEP 2020

作者： Guzan, Milan Feher, Adam Vince, Tibor Beres, Matej Kalinov, Andrii Technical University of Košice Faculty of Electrical Engineering and Informatics Department of Theoretical and Industrial Electrical Engineering Košice Slovakia Mykhailo Ostrohradskyi National University Systems of Automatic Control and Electric Drive Department Kremenchuk Ukraine

ISBN: (纸本)9781728199351

This paper points out the possibilities of using MATLAB in processing a large amount of measured data. An application created calculates and then graphically displays changes in individual statistical parameters of the measured mains voltage. Although the application works with voltage values, it can also be used for similar evaluation of changes in statistical parameters of other measured technical quantities, where the measured data are collected, which will be subsequently evaluated. © 2020 IEEE.

关键词： Data visualization

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Middle East and North African Health informatics Association (MENAHIA): Technological initiatives for 'One Health'

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Yearbook of Medical informatics 2023年第1期32卷 320-329页

作者： Abd-Alrazaq, Alaa Al-Shorbaji, Najeeb Lakhdari, Kheira Elbokl, Ahmed Farouk, Hani Wahba, Hoda Elgasser, Naema Mohell, Rania Emara, Tamer Ayatollahi, Haleh Kalhori, Sharareh R. Niakan Rabiei, Reza Tara, Mahmood Akhu-Zaheya, Laila Al-Qutob, Raeda Alrazak, Sadam Alabed Al-Jafar, Eiman Alhuwail, Dari Ghazal, Hassan Dehbi, Zineb El Otmani Al Idrissi, Najib Fihri, Ouafaa Fassi Belyamani, Lahcen Shahzad, Tariq Ahmed, Zakiuddin Ahmad, Arfan Shah, Zubair Hamra, Eman Abu Taweel, Adel Nashwan, Abdulqadir J. Househ, Mowafa Hamdi, Mounir Al-Thani, Dena Alam, Tanvir Alshammari, Abdulwahhab Alnafrani, Sana Alali, Haitham AI Center for Precision Health Weill Cornell Medicine-Qatarc Doha Qatar EHealth Development Association Amman Jordan Abou Bekr Belkaid University of Tlemcen Chetouane Algeria Ain Shams University Virtual Hospital Ain Shams University Cairo Egypt Ain Shams University Cairo Egypt World Health Organization Cairo Egypt Alexandria Regional Blood Transfusion Center Alexandria University Alexandria Egypt Health Management and Economics Research Center Health Management Research Institute Iran University of Medical Sciences Tehran Iran Department of Health Information Management School of Health Management and Information Sciences Iran University of Medical Sciences Tehran Iran Department of Health Information Management School of Allied Medical Sciences Tehran University of Medical Sciences Tehran Iran Peter L. Reichertz Institute for Medical Informatics (PLRI) Technical University of Braunschweig Hannover Medical School Braunschweig Germany Department of Health Information Technology and Management School of Allied Medical Sciences Shahid Beheshti University of Medical Sciencesc Tehran Iran Department of Medical Informatics Faculty of Medicine Mashhad University of Medical Sciencesc Mashhad Iran School of Nursing Jordan University of Science and Technology Jordan Jordan Centre for Disease Control and Prevention Amman Jordan Faculty of Applied Science and Engineering University of Toronto Toronto Canada Kuwait Health Informatics Association Kuwait Information Science Department College of Life Sciences Kuwait University Kuwait Health Informatics Unit Dasman Diabetes Institute Kuwait Mohammed VI University of Health Sciences Casablanca Morocco National Center for Scientific and Technical Research (CNRST) Rabat Morocco Hassan II Agronomic and Veterinary Institute Rabat Morocco COMSATS University Islamabad Sahiwal Campus Islamabad Pakistan Digital Care Karachi Pakistan College of Science and Engineering Hamad Bin Khalifa University Doha Qatar Al Quds Unive

MENAHIA (Middle East and North African Health informatics Association) is the International Medical informatics Association chapter dedicated to the Middle East and North Africa region. This region is rapidly growing in terms of the use of health informatics or what has been recently coined digital health. Human health is highly affected by the health of the environment, animal health, food, nutrition, climate change, and many other factors that are beyond the biological or genetic structure of human beings. The impact of animal health and the health of the environment on people's health is an old phenomenon but recent reemerging and appearance of diseases have clearly demonstrated the link between these. The Novel Coronavirus disease (COVID-19) that almost all of us have been suffering from is an example of this. A number of countries in the region have already shown the depth and the work that they do to integrate the concept of 'One Health' in the public health surveillance system as they have described the work that has been done to capture data from databases other than those dealing with human beings. The examples that were provided to monitor the health of animals, agriculture, environmental health, climate change, and man-made and natural disasters are just examples of what countries have been registering in their databases and informing the health authorities of these changes and emerging trends. © The Author(s) 2023.

关键词： 'One Health' 'One Health' informatics Algeria Artificial Intelligence Climate Change Digital Health Egypt Health informatics Health Information Technologies International Medical informatics Association (IMIA) Iran Jordan Kuwait MENAHIA Middle East and North Africa Morocco Pakistan Palestine Qatar Saudi Arabia United Arab Emirates Zoonitic Diseases

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Distributed Optimal Power Flow for VSC-MTDC Meshed AC/DC Grids Using ALADIN

Distributed Optimal Power Flow for VSC-MTDC Meshed AC/DC Gri...

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IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT Europe)

作者： Junyi Zhai Xinliang Dai Yuning Jiang Ying Xue Veit Hagenmeyer Colin N. Jones Xiao-Ping Zhang College of New Energy China University of Petroleum (UPC) China Karlsruhe Institute of Technology (KIT) Institute for Automation and Applied Informatics Germany Automatic Control Laboratory École Polytechnique Fédérale de Lausanne (EPFL) Switzerland School of Electric Power Engineering South China University of Technology (SCUT) China Department of Electronic Electrical and Systems Engineering University of Birmingham (UBir) Birmingham United Kingdom

The increasing application of voltage source converter (VSC) based high voltage direct current (VSC-HVDC) technology in power grids has raised the importance of incorporating DC grids and converters into the existing transmission network. This poses significant challenges in dealing with the resulting optimal power flow (OPF) problem. In this paper, a recently proposed nonconvex distributed optimization algorithm — Augmented Lagrangian based Alternating Direction Inexact Newton method (ALADIN), is tailored to solve the nonconvex AC/DC OPF problem for emerging voltage source converter (VSC) based multiterminal high voltage direct current (VSC-MTDC) meshed AC/DC hybrid systems. The proposed scheme decomposes this AC/DC hybrid OPF problem and handles it in a fully distributed way. Compared to the existing state-of-art Alternating Direction Method of Multipliers (ADMM), which is in general, not applicable for nonconvex problems, ALADIN has a theoretical convergence guarantee. Applying these two approaches to VSC-MTDC coupled with an IEEE benchmark AC power system illustrates that the tailored ALADIN outperforms ADMM in convergence speed and numerical robustness.

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Closed-loop control of anesthesia and hemodynamic system: a Model Predictive control approach

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IFAC-PapersOnLine 2021年第15期54卷 37-42页

作者： Anca Maxim Dana Copot “Gheorghe Asachi” Technical University of Iasi Department of Automatic Control and Applied Informatics Blvd. D. Mangeron 27 700050 Iasi Romania Research group of Dynamical Systems and Control Ghent University Tech Lane Science Park 125 9052 Ghent Belgium EEDT—Core Lab on Decision and Control Flanders Make Consortium Tech Lane Science Park 131 9052 Ghent Belgium

This paper proposes a Model Predictive control (MPC) approach of both anesthesia and hemodynamic systems. The designed control strategy has been validated on a novel and unique patient simulator. The aim of this paper is to evaluate the feasibility MIMO closed-loop control of anesthesia and hemodynamic variables taking into account the interaction (synergic and antagonistic) between subsystems. The proposed methodology takes into account patient variability, is robust to subsystems interaction and meets the clinical objectives. The algorithm is tested in simulation on a hypnosis-hemodynamic combined model for use during general anesthesia. The preliminary results are promising and show the effectiveness of the control procedure.

关键词： anesthesia hemodynamic regulation closed-loop control of anesthesia Model Predictive control optimal drug dosing

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