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21st RoEduNet Conference on Networking in Education and Research, RoEduNet 2022

作者： Contasel, Cristian Tranca, Dumitru-Cristian Palacean, Alexandru-Viorel Rosner, Daniel University Politehnica of Bucharest Faculty of Automatic Control and Computers Department of Computer Engineering Bucharest Romania

ISBN: (数字)9781665455282

ISBN: (纸本)9781665455282

The market of Medical Devices has an annual growth rate perspective of 5.4% in the 2022 - 2028 period. The growth rate is mainly supported by the increased usage of devices for chronic diseases prevention and for remote patient monitoring. The growth rate is also supported by the population's appetite for mobile devices. The continuously growing number of mobile devices has led to an increase in the number of cyber-attacks against them. In this paper we present Personal Medical Hub, an optimized security solution for Bluetooth enabled medical devices, that enhances data privacy and is aimed to offer protection against man-in-the-middle attacks and other security breaches and backdoors. © 2022 IEEE.

关键词： Cybersecurity

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Security Issues in Software-Defined Networking (SDN) Environments 23

Security Issues in Software-Defined Networking (SDN) Environ...

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23rd RoEduNet Conference: Networking in Education and Research, RoEduNet 2024

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

ISBN: (纸本)9798331540388

Software-Defined Networking (SDN) represents a significant shift in network architecture, providing exceptional programmability, flexibility, and simplified management. However, this paradigm shift introduces a unique set of security challenges that must be addressed to fully realize the potential of SDN. This paper examines the security issues in SDN environments, detailing the threats and vulnerabilities at various layers of the SDN architecture, including the control plane, data plane, and application plane. Through an extensive review of current literature, critical security challenges such as controller attacks, data plane breaches, and vulnerabilities in inter-plane communications are identified. Existing security solutions and mitigation strategies, such as authentication and authorization mechanisms, encryption techniques, and intrusion detection systems, are also explored. Furthermore, the paper discusses recent advances and emerging trends in SDN security, offering insights into ongoing research and future directions. The findings underscore the importance of robust security measures in ensuring the reliability and integrity of SDN deployments, providing a foundation for future innovation and development in this dynamic field. © 2024 IEEE.

关键词： Network intrusion

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A Zero-Sequence Current Analysis Approach for Rotating Machinery Fault Diagnosis of Induction Motor Drivetrain Based on Sparse Learning

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IEEE Transactions on Power Electronics 2025年第7期40卷 9800-9810页

作者： Liu, Zhao-Hua Long, Jun-Jie Wei, Hua-Liang Liu, Kan Zhang, Ying-Jie Li, Xiao-Hua Hunan University of Science and Technology School of Information and Electrical Engineering Xiangtan411201 China The University of Sheffield School of Electrical and Electronic Engineering Department of Automatic Control and Systems Engineering SheffieldS1 3JD United Kingdom Hunan University College of Mechanical and Vehicle Engineering Changsha410082 China Hunan University College of Computer Science and Electronic Engineering Changsha410082 China

Fault diagnosis of rotating machinery driven by induction motors has received increasing attention. Current diagnostic methods, which can be performed on existing inverters or current transformers of three-phase induction machines, have become a more economical and reliable alternative to vibration diagnostic methods. Existing references mainly focus on the stator current analysis of single-phase, but most single-phase current analysis methods utilize only a fraction of the total information accessible in the three-phase system. Field experience shows that zero-sequence current (ZSC) has more distinctive fault characteristics compared to single-phase current, and therefore it is better to use in mechanical fault detection and diagnostic tasks. This article proposes a novel sparse learning-based method for ZSC analysis for induction rotating motor drive fault diagnosis. First, it elaborates and compares the effectiveness of ZSC in revealing fault characteristics compared with single-phase current by analyzing the fault diagnosis mechanism of ZSC. In addition, the method proposes a correlation entropy-enhanced sparse learning method for the problem of high-frequency noise and interference in the ZSC signals, so as to enhance the learning of the features of the noise-containing signals. The diagnostic efficacy of the proposed method has been verified through experiments on two real datasets. © 1986-2012 IEEE.

关键词： Induction motors

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Visual Tracking Nonlinear Model Predictive control Method for Autonomous Wind Turbine Inspection 21

Visual Tracking Nonlinear Model Predictive Control Method fo...

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21st International Conference on Advanced Robotics, ICAR 2023

作者： Amer, Abdelhakim Mehndiratta, Mohit Le Fevre Sejersen, Jonas Pham, Huy Xuan Kayacan, Erdal Department of Electrical and Computer Engineering Aarhus C8000 Denmark Gim Robotics Espoo Finland Automatic Control Group Paderborn University Department of Electrical Engineering and Information Technology Paderborn Germany

ISBN: (纸本)9798350342291

Automated visual inspection of on- and off-shorewind turbines using aerial robots provides several benefits, namely, a safe working environment by circumventing the need for workers to be suspended high above the ground, reduced inspection time, preventive maintenance, and access to hard-to-reach areas. A novel nonlinear model predictive control (NMPC) framework alongside a global wind turbine path planner is proposed to achieve distance-optimal coverage for wind turbine inspection. Unlike traditional MPC formulations, visual tracking NMPC (VT-NMPC) is designed to track an inspection surface, instead of a position and heading trajectory, thereby circumventing the need to provide an accurate predefined trajectory for the drone. An additional capability of the proposed VT-NMPC method is that by incorporating inspection requirements as visual tracking costs to minimize, it naturally achieves the inspection task successfully while respecting the physical constraints of the drone. Multiple simulation runs and real-world tests demonstrate the efficiency and efficacy of the proposed automated inspection framework, which outperforms the traditional MPC designs, by providing full coverage of the target wind turbine blades as well as its robustness to changing wind conditions. The implementation codes11https://***/open-airlab/VTNMPC-Autonomous-Wind-Turbine-Inspection are open-sourced. © 2023 IEEE.

关键词： Inspection

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Biological Influence of Low-Frequency Electromagnetic Field Exposition Based on Selected Cell Death Markers 15

Biological Influence of Low-Frequency Electromagnetic Field ...

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15th International Conference Elektro, ELEKTRO 2024

作者： Cichorowski, Robert Kocikowska, Olga Bajtos, Marek Radil, Roman Janousek, Ladislav De Carrillo, Daria Gendosz Student, Sebastian Silesian University of Technology Faculty of Automatic Control Electronics and Computer Science Gliwice Poland Faculty of Automatic Control Electronics and Computer Science Department of Engineering and Systems Biology Gliwice Poland Silesian University of Medicine Faculty of Medical Sciences Department of Physiology Katowice Poland Faculty of Electrical Engineering and Informational Technology Department of Electromagnetic and Biomedical Engineering Žilina Slovakia Silesian University of Medicine Faculty of Medical Sciences in Zabrze Department of Histology Zabrze Poland Silesian University of Technology Biotechnology Center Gliwice Poland

ISBN: (纸本)9798350372359

This work presents the results of the examination of the HeLa cell line exposure on the ELF-EMF (extremely low-frequency electromagnetic field). In particular, the relationship between ELF-EMF exposition time and cell death. The examination of cell death hallmarks were examined by estimation levels of selected proteins - FACL4 (a protein that is a part of the ferroptosis pathway) and CK18 (Cytokeratin related to necrosis and apoptosis pathways) in the proposed model of workers exposed to ELF-EMF week. © 2024 IEEE.

关键词： Electromagnetic fields

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TCIP: Transformed Colour Intensity Profiles analysis for fault detection in power line insulators

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Multimedia Tools and Applications 2025年第8期84卷 4503-4534页

作者： Tomaszewski, Michal Gasz, Rafal Kasana, Singara Singh Osuchowski, Jakub Singh, Simranjit Zator, Slawomir Department of Computer Science Faculty of Electrical Engineering Automatic Control and Informatics Opole University of Technology Prószkowska 76 Opole45-758 Poland Department of Computer Science and Information Technology Central University of Haryana Haryana Mahendergarh123031 India Department of Information Technology Dr B. R. Ambedkar National Institute of Technology Punjab Jalandhar India Department of Safety Engineering and Technical Systems Faculty of Production Engineering and Logistics Opole University of Technology Prószkowska 76 Opole45-758 Poland

In response to the escalating demand for electricity, the aging process and inherent failures in power lines have become unavoidable challenges in their operational integrity. This research addresses the imperative need for a robust method to detect damage in power insulator disc images, introducing a novel feature extraction technique grounded in color intensity profile transformation and ensemble learning. For the purposes of the research, a specific dataset was developed based on publicly available images of power insulators. The collection consists of 9232 images of insulator discs which included 2184 images of damaged discs. The discernible damages include breakages and burnouts. Two distinct analysis variants were employed. In the first variant (V1), two classification groups were selected: disc with damages and disc without damages. In the second variant (V2), three classification groups were selected: discs without damage, broken discs, and burnout discs. The described research was divided into two successive stages. In the first of them (S1), the transformed color intensity profiles for the prepared data set were built and various machine learning models for their classification were trained and evaluated. In the next stage (S2), previously developed, highest efficient machine learning models, were used to make the detection of various damaged types of overhead line insulator discs. For the first stage of research high-performance results and a high value of the F1 score parameters were obtained (F1 score = 0.999 for the first variant (S1V1) and F1 score = 0.992 for the second variant (S1V2). In the second stage of research and the first variant (S2V1), the highest result of the F1 score was 0.932 and for the second variant (S2V2) F1 score was 0.930. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Feature extraction

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Small Signal Stability-Guaranteed Moving Target Defense Against Load Redistribution Attack On IoT-based Smart Grid

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IEEE Internet of Things Journal 2025年第11期12卷 17413-17429页

作者： Wang, Bingdong Zhang, Zhenyong Wang, Mufeng Liu, Mengxiang Ye, Rui Deng, Ruilong Zhang, Xin Guizhou University State Key Laboratory of Public Big Data College of Computer Science and Technology Guiyang550025 China China Industrial Control Systems Cyber Emergency Response Team Beijing100040 China University of Sheffield Department of Automatic Control and System Engineering Sheffield United Kingdom Guizhou Power Grid Co. Ltd. Intelligent Operation Center China Zhejiang University State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Hangzhou310027 China Huzhou Institute of Industrial Control Technology Huzhou313000 China

Moving target defense (MTD) is a promising approach to defend against load redistribution attacks on the internet-of-things (IoT)-based smart grid networks by probing the distorted state estimates with the distributed flexible AC transmission system. However, existing studies mainly focus on optimizing the performance of MTD and ignore the safety effect of it on the system’s operation. In this paper, we fill this gap by deeply analyzing the effect of MTD on the small signal stability and aim to alleviate the negative impact and guarantee its defending performance simultaneously. Firstly, the stability is formally described using the eigenvalue sensitivity. The relationship between the MTD-induced perturbation (MTDper) and the stability criteria is derived. Secondly, a new indicator is proposed to measure the effectiveness of MTDper. Thirdly, a constrained optimization problem is formulated to compute the bound of MTDper for guaranteeing the small signal stability. In addition, a surprising finding is that the stability margin can be improved and enhanced by optimizing the value of MTDper without losing the MTD’s effectiveness. Finally, we evaluate the performance of MTDper and its impact on the small signal stability with extensive simulations on the IEEE 30-bus, 39-bus, and 68-bus test power systems. © 2014 IEEE.

关键词： Constrained optimization

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ORB-Net: End-to-end Planning Using Feature-based Imitation Learning for Autonomous Drone Racing 56

ORB-Net: End-to-end Planning Using Feature-based Imitation L...

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56th International Symposium on Robotics, ISR Europe 2023

作者： Pham, Huy Xuan Heiß, Micha Tran, Dung Nguyen, Minh Anh Nguyen, Anh Quang Kayacan, Erdal Department of Electrical and Computer Engineering Aarhus University Aarhus C8000 Denmark Hanoi Viet Nam Automatic Control Group Department of Electrical Engineering and Information Technology Paderborn University Paderborn Germany

ISBN: (纸本)9783800761418

Deep learning-based methods have enhanced the performance of many robot applications thanks to their superior ability to robustly extract rich high-dimensional features. However, it comes with a high computational cost that often increases the latency of the overall system. On the other hand, traditional feature extraction methods, such as oriented FAST and rotated BRIEF (ORB), can be computed efficiently and remains the backbone of some critical robot algorithms, e.g. ORB-SLAM for robot state estimation. In this work, the usefulness of the aforementioned features for a robot navigation task is investigated. The features are experimentally incorporated with a deep-learning method, called ORB-Net1, which allows an agile aerial robot to learn a motion policy to complete a racing track in an autonomous drone racing context. The experimental studies demonstrate that it can be beneficial to reuse these computed features for end-to-end motion planning for the agile quadrotor, as the proposed method using combined input of ORB feature position and RGB images outperforms the baseline methods which use only RGB images. © VDE VERLAG GMBH Berlin Offenbach.

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Making children read more and better by integrating natural language processing and gamification techniques into mobile applications 24

Making children read more and better by integrating natural ...

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24th International Conference on control Systems and computer Science, CSCS 2023

作者： Voicu, George Catalin Ciolofan, Sorin N. Dragoicea, Monica Manager Emag Bucharest Romania Politehnica University of Bucharest Department of Computer Science Bucharest Romania Politehnica University of Bucharest Department of Automatic Control and Systems Engineering Bucharest Romania

ISBN: (纸本)9798350313390

It is well known that nowadays children tend to spend lot of time playing games on mobile devices at the expense of reading books. The current research aims to explore ways to design and develop a mobile application, attractive for children, that uses voice recognition technology and gamification techniques to help children read more and better and to track the progress of reading. The application is implemented with Flutter, Dart and Firebase and is addressed to children, the books being made available in encyclopedia format. By integrating technologies such as NLP and gamification the application aims to improve reading fluency among children, but also to give them the opportunity to accumulate new and exciting information in the same time. © 2023 IEEE.

关键词： Human computer interaction

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Estimating Chlorophyll Content from Hyperspectral Data Using Gradient Features 23rd

Estimating Chlorophyll Content from Hyperspectral Data Usin...

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23rd International Conference on Computational Science, ICCS 2023

作者： Ruszczak, Bogdan Wijata, Agata M. Nalepa, Jakub Faculty of Electrical Engineering Automatic Control and Informatics Department of Informatics Opole University of Technology Opole Poland Faculty of Biomedical Engineering Silesian University of Technology Zabrze Poland Faculty of Automatic Control Electronics and Computer Science Department of Algorithmics and Software Silesian University of Technology Gliwice Poland KP Labs Gliwice Poland

ISBN: (纸本)9783031360206

Non-invasive estimation of chlorophyll content in plants plays an important role in precision agriculture. This task may be tackled using hyperspectral imaging that acquires numerous narrow bands of the electromagnetic spectrum, which may reflect subtle features of the plant, and inherently offers spatial scalability. Such imagery is, however, high-dimensional, therefore it is challenging to transfer from the imaging device, store and investigate. We propose a machine learning pipeline for estimating chlorophyll content from hyperspectral data. It benefits from the Savitzky-Golay filtering to smooth the (potentially noisy) spectral curves, and from gradient-based features extracted from such a smoothed signal. The experiments revealed that our approach significantly outperforms the state of the art according to the widely-established estimation quality metrics obtained for four chlorophyll-related parameters. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Machine learning

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