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21st Polish control Conference, PCC 2023

作者： Placzek, Marek Krauze, Piotr Ryfa, Arkadiusz Jablonka, Tomasz Balys, Karol Helmich, Magdalena Badora, Dawid Florkiewicz, Bartosz Polak, Kamil Slonka, Piotr Trybulowski, Mateusz Faculty of Mechanical Engineering Silesian University of Technology Konarskiego 18A Gliwice44-100 Poland Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 16 Gliwice44-100 Poland Faculty of Energy and Environmental Engineering Silesian University of Technology Konarskiego 18 Gliwice44-100 Poland MOSTOSTAL ZABRZE Biprohut S.A Dubois 16 Gliwice44-100 Poland

ISBN: (纸本)9783031351723

The work is a report on the implementation of interdisciplinary group project classes conducted in accordance with the Project Based Learning approach. The aim of the project was to develop an automated station for measuring the curvature of metallurgical charge and to create its technical documentation. The station is intended to work in cooperation with devices of an automated line for spreading and transporting metallurgical charges, which is a part of the technological line for hot rolling. The task of the station is to identify the curvature value, so that a charge with exceeded parameters does not proceed to the next stage of the process (the goal is significant in terms of eliminating defects). The final outcome of the project was to be a complete technical documentation of the designed station along with the measurement algorithm. Additionally, a laboratory test station and a system design for use in real industrial conditions were developed. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Hot rolling

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FAST FOURIER TRANSFORM ALGORITHM FOR EXTRACTION OF ATTENUATION, PHASE, AND SCATTERING IMAGES IN PHASE-CONTRAST IMAGING

UPB Scientific Bulletin, Series C: Electrical Engineering an...

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UPB Scientific Bulletin, Series C: Electrical Engineering and computer science 2023年第4期85卷 137-150页

作者： Ciobanu, Ionuț-Cristian Safca, Nicoleta Anghel, Elena Rășinar, Cezara Popescu, Dan Faculty of Automatic Control and Computer Science University POLITEHNICA of Bucharest Romania Extreme Light Infrastructure – Nuclear Physics Magurele Romania University POLITEHNICA of Bucharest Bucharest Romania Faculty of Chemical Engineering and Biotechnologies University POLITEHNICA of Bucharest Romania

Phase contrast imaging is based on the refraction of the X-ray beam as it passes through the material and shows excellent results for biological tissues. In this paper, we present a new approach for a Python algorithm that can filtrate phase contrast images (attenuation, phase, and scattering) and improve the identification of the interest object. The images used in this work were obtained with a 5.66 m long Talbot-Lau interferometer with a record angular sensitivity of 0.82 µradians, a phase stepping procedure of 16 steps, and a mean energy of 30 keV. The sample used was 0.89 mm diameter fiber from an accredited mammographic phantom. The proposed algorithm extracts the attenuation, phase, and scattering images from the dataset using Fast Fourier Transform. For filtration, we implemented a non-linear filter (median) to eliminate the noise generated by the acquisition system and the projection processing and to improve the contrast. The results were analyzed by calculation of the contrast-to-noise ratio (CNR). © 2023, Politechnica University of Bucharest. All rights reserved.

关键词： Fast Fourier transforms

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Decentralized Machine Learning for Face Recognition

Decentralized Machine Learning for Face Recognition

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International Symposium on Parallel and Distributed Computing

作者： Ioana Brănescu Radu-Ioan Ciobanu Ciprian Dobre Constandinos Mavromoustakis Faculty of Automatic Control and Computers University Politehnica of Bucharest Romania National Institute for Research and Development in Informatics Bucharest Romania Department of Computer Science University of Nicosia Cyprus

As the fields of machine learning and computer vision are developing, facial recognition systems are becoming increasingly popular and are slowly being widely used in various fields like security, surveillance and medicine. This paper presents the design and development of a facial recognition solution that works in a distributed context, given that the devices used for capturing the images do not have the ability to train models capable of achieving good enough accuracy on large amounts of data. Thus, a method is presented in which the detection of human faces and the characteristics extraction are done locally based on a pre-trained FaceNet model. These characteristics are sent to a strong processing unit where a global model is trained and then transferred back to the clients, where it can be used for recognition. Through experimental evaluation, we show that our solution is efficient and exhibits high accuracy values.

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Machine Learning-Based Cascade Filtering System for fNIRS Data Analysis

Machine Learning-Based Cascade Filtering System for fNIRS Da...

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2023 Progress in Applied Electrical Engineering, PAEE 2023

作者： Pelc, Mariusz Mikolajewski, Dariusz Ruotsalo, Tuukka Leiva, Luis A. Sudol, Adam Gorzelanczyk, Edward Jacek Lysiak, Adam Kawala-Sterniuk, Aleksandra Opole University of Technology Faculty of Electrical Engineering Automatic Control and Informatics Opole Poland Institute of Computer Science Kazimierz Wielki University Bydgoszcz Poland University of Helsinki Finland University of Copenhagen Denmark Faulty of Natural and Technical Sciences University of Opole Opole Poland University of Opole Faculty of Natural and Technical Sciences Opole Poland Institute of Philosophy Kazimierz Wielki University Bydgoszcz Poland

ISBN: (纸本)9798350316254

This paper presents a preliminary study on the use of machine learning-based methods to select the appropriate parameters of cascade filters in the analysis of brain signals recorded using functional infrared spectroscopy (fNIRS), which shows the level of oxygenation in the brain and, unlike EEG signals (showing electrical brain activity), are less prone to potential interference, disturbances or artifacts occurrence. © 2023 IEEE.

关键词： Machine learning

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Methods and tools for automatic or semi-automatic recognition of selected emotions using machine learning algorithms

Methods and tools for automatic or semi-automatic recognitio...

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Progress in Applied Electrical Engineering (PAEE)

作者： Piotr Schneider Dariusz Mikołajewski Anna Bryniarska Magdalena Igras-Cybulska Artur Cybulski Walery Marcinowicz Maciej Janiszewski Aleksandra Kawala-Sterniuk Department of Neuroinformatics and Biomedical Engineering Institute of Computer Science Maria Curie-Sklodowska University Lublin Poland Hishoo Sp. z o.o Gdynia Poland Faculty of Computer Science Kazimierz Wielki University Bydgoszcz Poland Faculty of Electrical Engineering Automatic Control and Informatics Opole University of Technology Opole Poland

ISBN: (数字)9798331527563

ISBN: (纸本)9798331527570

automatic or semi-automatic recognition of selected emotions using Machine Learning (ML) algorithms requires practical, fast and accurate implementation of the steps involved in acquiring the actual signals (e.g. speech, facial expression, posture and body movements), recording them in the form of an optimal feature vector/matrix, correctly interpreting the above data and reproducing and generalising this process within the ML system. Depending on the accuracy and speed required and the degree of its ‘invasiveness’ (e.g. by attaching sensors to the user’s body), single or multi-modal interpretations of biometric signals associated with different groups of emotions are possible. This group of studies, despite their relative non-invasiveness, requires the caution prescribed by the Artificial Intelligence (AI) Act and RODO Act and the possibility of being replaced (if necessary) by alternatives. This article aims to analyse the opportunities and risks associated with this group of technologies, with a particular focus on directions for further development.

关键词： Emotion recognition Machine learning algorithms Accuracy Video on demand Speech recognition Machine learning Feature extraction Recording Convolutional neural networks Web sites

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Machine Learning Detects Anomalies in OPS-SAT Telemetry 23rd

Machine Learning Detects Anomalies in OPS-SAT Telemetry

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23rd International Conference on Computational science, ICCS 2023

作者： Ruszczak, Bogdan Kotowski, Krzysztof Andrzejewski, Jacek Musial, Alicja Evans, David Zelenevskiy, Vladimir Bammens, Sam Laurinovics, Rodrigo Nalepa, Jakub Faculty of Electrical Engineering Automatic Control and Informatics Department of Informatics Opole University of Technology Prószkowska 76 Opole45-758 Poland Faculty of Automatic Control Electronics and Computer Science Department of Algorithmics and Software Silesian University of Technology Akademicka 16 Gliwice44-100 Poland KP Labs Konarskiego 18C Gliwice44-100 Poland European Space Agency/ESOC Robert-Bosch-Str. 5 Darmstadt64293 Germany Telespazio Germany GmbH Europaplatz 5 Darmstadt64293 Germany IrbGS ltd "Viraki" Ance Parish IrbeneLV-3601 Latvia

ISBN: (纸本)9783031359941

Detecting anomalies in satellite telemetry data is pivotal in ensuring its safe operations. Although there exist various data-driven techniques for the task of determining abnormal parts of the signal, they are virtually never validated over real telemetries. Analyzing such data is challenging due to its intrinsic characteristics, as telemetry may be noisy and affected by incorrect acquisition, resulting in missing parts of the signal. In this paper, we tackle this issue and propose a machine learning approach for detecting anomalies in single-channel satellite telemetry. To validate its capabilities in a practical scenario, we build a dataset capturing the nominal and anomalous telemetry data captured on board OPS-SAT—a nanosatellite launched and operated by the European Space Agency. Our extensive experimental study showed that the proposed algorithm offers high-quality anomaly detection in real-life satellite telemetry, reaching 98.4% accuracy over the unseen test set. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Machine learning

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Solutions for Digital Interaction of a Resilient Energy Community in a Service-oriented Framework 12

Solutions for Digital Interaction of a Resilient Energy Comm...

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12th International Conference and Exposition on Electrical and Power Engineering, EPE 2022

作者： Sanduleac, Mihai Ionescu, Constantin Mandis, Alexandru Gropa, Victor Efremov, Cristina Sanduleac, Vlad Department of Power Systems Faculty of Energy Engineering University Politehnica of Bucharest Bucharest Romania Dep. of Energy Generation and Use Faculty of Energy Engineering University Politehnica of Bucharest Bucharest Romania Faculty of Energetics and Electrical Engineering Technical University of Moldova Chisinau Moldova Department of Computer Science and Engineering Faculty of Automatic Control and Computers University Politehnica of Bucharest Romania

ISBN: (数字)9781665489942

ISBN: (纸本)9781665489942

Energy communities are emerging entities which need their own Information and Communication System. Resilience is a key metric of such communities, and it has to be implemented for both energy supply versus public network outages and for its information system versus cyber-attacks, acting as cyber-citadels which have to resist external malicious attacks while having also digital interactions with external entities. The paper presents solutions for a resilient energy community with appropriate implementations of digital interaction for data exchange, in order to acquire external energy service for specialized companies and to deliver also information and communication-related services for external users. Principles of a Contractual Data Protection Regulation are presented, as an adaptation of data protection tailored for automated energy services and based on secure data exchange. The principles are verified in a demonstration project which has also external entities with digital interaction. © 20XX IEEE.

关键词： Cybersecurity

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Evaluation of Neurological Disorders in Isokinetic Dynamometry and Surface Electromyography Activity of Biceps and Triceps Muscles 23rd

Evaluation of Neurological Disorders in Isokinetic Dynamom...

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Proceedings of the 23rd Polish Conference on Biocybernetics and Biomedical Engineering, PCBEE 2023

作者： Roksela, Anna Poświata, Anna Śmieja, Jaroslaw Kozak, Dominika Bienias, Katarzyna Ślaga, Jakub Mikulski, Michal Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Gliwice44-100 Poland EGZOTech Sp. z o.o. Gliwice44-100 Poland Department of International Learning Centre Wellness Progressive Group Ltd. 63/66 Hatton Garden Fifth Floor Suite 23 LondonEC1N 8L United Kingdom

ISBN: (纸本)9783031384295

Diagnosing neurological disorders and tracking rehabilitation progress requires objective, quantitative measures. Isokinetic exercises are commonly employed to evaluate muscle strength and activity. However, repetitive, quantitative assessment of the tests performed during these exercises can be difficult, especially for patients with neurological disorders. Usually, either dynamometric or sEMG measurements are used. This study is concerned with the evaluation of the applicability and reliability of these two measurement techniques, based on analysis of elbow flexion and extension supported by the Luna EMG rehabilitation robot, aimed at the evaluation of the activity of the biceps and triceps muscles. Dynamometric and surface electromyography measurements are compared with respect to their repeatability between sessions and days, and their ability to differentiate impaired limbs from healthy ones. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Muscle

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Network Management devices in an SDN environment

Network Management devices in an SDN environment

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International Conference on control Systems and computer science (CSCS)

作者： Diyar Jamal Hamad Khirota Gorgees Yalda Nicolae Tapus Ibrahim Taner Okumus Faculty of Automatic Control and Computer Science Politehnica University of Bucharest Bucharest Romania Soran Technical college Erbil Polytechnic University Iraq Erbil Computer Engineering Kahramanmaras Sutcu Imam University Kahramanmaras Turkey

Network optimization and continued availability depend on a number of capabilities that are part of network management. The Maintenance, operating and also offering a safeguarded interaction network is very complicated. It calls for the network operators to grapple with low-level vendor particular arrangements to execute the high degree network policies which are complicated. A method for monitoring networks with OpenFlow controller is presented in this paper in two separate functions for the same networks. Bandwidth utilization, Meter values, charts and statistics are provided by the method to extend controller monitoring capabilities. The method architecture and implementation will be introduced in order to present the feature set. Additionally, softswitches are used as a switch and Mininet to evaluate a virtualized network. This analysis shows whether Meters or ports value is better for network management.

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Continual Learning of a Time Series Model Using a Mixture of HMMs with Application to the IoT Fuel Sensor Verification 18

Continual Learning of a Time Series Model Using a Mixture of...

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18th Conference on computer science and Intelligence Systems, FedCSIS 2023

作者： Glomb, Przemyslaw Cholewa, Michal Foszner, Pawel Bularz, Jakub Institute of Theoretical and Applied Informatics Polish Academy of Sciences Batycka 5 Gliwice44-100 Poland Department of Computer Graphics Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 2A Gliwice44-100 Poland Aiut Sp. Z O.o. ul. Wyczókowskiego 113 Gliwice44-109 Poland

ISBN: (纸本)9788396744784

This paper presents an application of a mixture of Hidden Markov Models (HMMs) as a tool for verification of IoT fuel sensors. The IoT fuel sensors report the level of fuel in tanks of a petrol station, and are a key component for monitoring system reliability (billing), safety (fuel/oil leak detection) and security (theft prevention). We propose an algorithm for learning a mixture of HMMs based on a continual learning principle, i.e. it adapts the model while monitoring a sensor over time, signalling unexpected or anomalous sensor reports. We have tested the proposed approach on a real-life data of 15 fuel tanks being monitored with the FuelPrime system, where it has shown a very good performance (average area under ROC curve of 0.94) of detecting anomalies in the sensor data. Additionally we show that the proposed method can be used for trend monitoring and present qualitative analysis of the short and long term learning performance. The proposed method has promising performance score, the resulting model has a high degree of explainability, limited memory and computation requirements and can be easily generalized to other domains of sensor verification. © 2023 Polish Information Processing Society.

关键词： Hidden Markov models

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