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

ISBN: (数字)9798350372359

ISBN: (纸本)9798350372366

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.

关键词： Proteins Biological system modeling Estimation Electromagnetic fields

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Advancing Medical Innovation through Blockchain-Secured Federated Learning for Smart Health

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IEEE Journal of Biomedical and Health Informatics 2025年 PP卷 PP页

作者： Khan, Salabat Khan, Mansoor Khan, Muhammad Asghar Wang, Lu Wu, Kaishun Qilu Institute of Technology School of Computer and Information Engineering Jinan China Qilu Institute of Technology School of Intelligent Manufacturing and Control Engineering Jinan China Prince Mohammad Bin Fahd University Department of Electrical Engineering P.O. Box 1664 Al-Khobar31952 Saudi Arabia Shenzhen University College of Computer Science and Software Engineering Shenzhen China Thrust Guangzhou China

The rapid digitization of healthcare systems has led to a vast accumulation of electronic medical records (EMRs), offering an invaluable source of patient data that can significantly advance medical research and improve patient care. However, sharing EMRs for research purposes presents challenges, particularly concerning data privacy, security, and the limitations of traditional centralized data-sharing models. This paper introduces a novel approach that leverages blockchain technology to facilitate federated learning with EMRs, thereby addressing these challenges. Federated learning enables multiple institutions to collaboratively train a robust machine learning model without sharing raw data, preserving privacy and security. By integrating blockchain, this framework enhances data integrity, immutability, and trust, all in a decentralized environment. The blockchain serves as a transparent and secure ledger, recording model updates and aggregating them through a consensus-based mechanism. Smart contracts further enforce data usage policies, allowing only authorized access and maintaining control over data ownership and sharing. This approach empowers medical researchers and institutions to collaborate more effectively, accelerating the discovery of treatments, advancements in personalized medicine, and insights into rare diseases. It also enables patients to contribute to medical research while retaining control over their personal data, fostering a patient-centered approach to healthcare innovation. Experimental results confirm the efficacy and efficiency of this blockchain-enabled federated learning framework, highlighting its potential to transform medical research and adhere to stringent privacy and security standards. This study emphasizes the pivotal role of blockchain in enhancing big data analytics within healthcare, paving the way for improved collaboration, innovation, and patient outcomes. © 2025 IEEE.

关键词： Federated learning

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Vertical Take-Off and Landing fixed-wing designed for autonomous missions

Vertical Take-Off and Landing fixed-wing designed for autono...

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IEEE/ASME (AIM) International Conference on Advanced Intelligent Mechatronics

作者： Krzysztof Lewandowski Jakub Tomczak Jakub Zeifert Szymon Nowacki Marcel Król Dawid Rudy Jacek Grzybowski Paweł Piórkowski Roman Czyba Marcin Lemanowicz Piotr Czekalski Department of Automatic Control Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Gliwice Poland Department of Chemical Engineering and Process Design Faculty of Chemistry Silesian University of Technology Gliwice Poland

This paper presents the design and development of a Vertical Take-off and Landing (VTOL) fixed-wing aircraft intended for autonomous missions. It provides an overview of the current state of VTOL technology and its applications. The paper focuses on fixed-wing VTOL aircraft created by Academic Scientific Association High Flyers from the Silesian University of Technology in Poland. The design process and considerations are discussed in detail, including aerodynamics, selection of materials, hardware, control systems and software. Finally, the paper discusses real-world scenarios that the designed UAV could be used to solve real-world problems, such as targeted plant protection or the deployment of oral vaccines for wildlife. The authors successfully tested solutions presented in the paper during competitions and real practical applications. Overall, this paper provides a comprehensive look into the design and development of a VTOL aircraft for autonomous missions and presents its effectiveness and capabilities in solving real-life problems.

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ESFL: Efficient Split Federated Learning over Resource-Constrained Heterogeneous Wireless Devices

arXiv

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arXiv 2024年

作者： Zhu, Guangyu Deng, Yiqin Chen, Xianhao Zhang, Haixia Fang, Yuguang Wong, Tan F. Department of Electrical and Computer Engineering University of Florida GainesvilleFL32611 United States School of Control Science and Engineering Shandong Key Laboratory of Wireless Communication Technologies Shandong University Shandong Jinan250061 China Department of Electrical and Electronic Engineering University of Hong Kong Pok Fu Lam Hong Kong Department of Computer Science City University of Hong Kong Kowloon Hong Kong

Federated learning (FL) allows multiple parties (distributed devices) to train a machine learning model without sharing raw data. How to effectively and efficiently utilize the resources on devices and the central server is a highly interesting yet challenging problem. In this paper, we propose an efficient split federated learning algorithm (ESFL) to take full advantage of the powerful computing capabilities at a central server under a split federated learning framework with heterogeneous end devices (EDs). By splitting the model into different submodels between the server and EDs, our approach jointly optimizes user-side workload and server-side computing resource allocation by considering users’ heterogeneity. We formulate the whole optimization problem as a mixed-integer non-linear program, which is an NP-hard problem, and develop an iterative approach to obtain an approximate solution efficiently. Extensive simulations have been conducted to validate the significantly increased efficiency of our ESFL approach compared with standard federated learning, split learning, and splitfed learning. © 2024, CC BY-NC-SA.

关键词： Learning algorithms

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Condition Monitoring & Fault Detection in Photovoltaic Modules Using Machine Learning 1

Condition Monitoring & Fault Detection in Photovoltaic Modul...

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1st International Conference on Sustainable Technology for Power and Energy Systems, STPES 2022

作者： Tyagi, Shivam Dhingra, Bhavya Tomar, Anuradha Netaji Subhas University of Technology Department of Computer Science Delhi78 India Netaji Subhas University of Technology Department of Electrical Engineering Delhi78 India Netaji Subhas University of Technology Department of Inst. and Control Engg. Delhi78 India

ISBN: (纸本)9781665459150

There has been a significant increase in the use of renewable energy solutions such as Photovoltaic (PV) power plant projects to decrease the dependency on fossil fuels while still meeting the global energy demands. However, these technologies come with their own set of problems. Condition monitoring and maintenance of PV modules are one of the challenges that need to be tackled to make these new energy solutions even more efficient. The cleaning of PV modules and the maintenance of hardware components used in a power plant are significantly important to the total energy yield and efficiency of the project. This paper provides a detailed look into the application of machine learning algorithms for condition monitoring and fault detection in photovoltaic modules. This paper employs various regression models to predict the power output of the PV modules accurately. The model takes inputs from the inverter connected to an array mesh of PV modules and applies a trained prediction model that can work in real-time. The system is based on comparing linear, Bayesian and non-linear regression models on their ability to explain the variance of the yield by PV modules. © 2022 IEEE.

关键词： Fault detection

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Hierarchical Continual Learning for Domain-Knowledge Retention in Healthcare Federated Learning

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IEEE Transactions on Consumer Electronics 2025年

作者： Iqbal, Saeed Zhong, Xiaopin Khan, Muhammad Attique Wu, Zongze AlHammadi, Dina Abdulaziz Liu, Weixiang Choudhry, Imran Arshad Shenzhen University College of Mechatronics and Control Engineering Shenzhen518060 China Prince Mohammad Bin Fahd University Department of AI Alkhobar Saudi Arabia Princess Nourah bint Abdulrahman University College of Computer and Information Sciences Department of Information Systems Riyadh11671 Saudi Arabia University of Central Punjab Faculty of Information Technology and Computer Science Department of Software Engineering Lahore Pakistan

Internet of Medical Things (IoMT) applications encounter issues with data protection, continual adaptation, and domain-specific knowledge retention, especially in consumer-centric IoMT scenarios. We overcome these obstacles and facilitate effective knowledge retention and task adaptation in IoMT applications. This study attempts to create a unique privacy-preserving federated learning framework that combines a hierarchical learning structure with Continual Learning (CL). Despite the advancements in Federated Learning (FL), current models have trouble integrating changing datasets in real-time while protecting privacy, as well as catastrophic forgetting, which occurs when previously learned knowledge is lost when adjusting to new tasks. We present a hierarchical learning framework that makes use of three levels of models -Junior Model (JM), Consultant Model (CM), and Senior Consultant Model (SCM) -to overcome these drawbacks. Each level of the model aids in archived retention and domain-knowledge adaptation. To guarantee that the model maintains valuable information over time and adapts to new tasks with ease, our method blends domain adaptation strategies with ongoing learning approaches like knowledge distillation and elastic weight consolidation (EWC). We compare the suggested methodology with current state-of-the-art (SOTA) models on healthcare datasets for tasks like illness diagnosis and medical image categorization. According to our findings, the hierarchical continual learning model performs better than SOTA techniques in terms of accuracy, task adaptability, and privacy protection. In the healthcare industry, our study sets a new standard for privacy-preserving, continuously adaptable federated learning systems, allowing for real-time, scalable IoMT applications that can adapt dynamically to a variety of changing datasets. © 1975-2011 IEEE.

关键词： Patient treatment

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A Comparative Study of Machine Learning Algorithms for Photovoltaic Degradation Rate Prediction 3

A Comparative Study of Machine Learning Algorithms for Photo...

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3rd International Conference on Intelligent Computing, Instrumentation and control Technologies, ICICICT 2022

作者： Dhingra, Bhavya Tyagi, Shivam Tomar, Anuradha Netaji Subhas University of Technology Department of Electrical Engineering Delhi78 India Netaji Subhas University of Technology Department of Computer Science Delhi78 India Netaji Subhas University of Technology Department of Inst. and Control Engg. Delhi78 India

ISBN: (数字)9781665410052

ISBN: (纸本)9781665410052

Solar energy is the most versatile, harmless, and non-exhaustive energy present in nature because of this, the number of photovoltaic modules that have been integrated into the electrical grid is increasing every day. As a result, reliable forecasting of falling power output over the period of time is required for an acceptable return on investment made for these interactions, to estimate the power delivered to the power system by these photovoltaic modules, photovoltaic degradation rates must be known. In this study degradation rates of photovoltaic modules are estimated using the application of nine machine learning models and the effectiveness of these models is compared in order to determine which model is most efficient. All the models are tested on various evaluation metrics like mean absolute error, root mean squared error, and mean percentage error for an unbiased evaluation, and the run time of these models is also calculated and compared to determine the overall efficiency of the models. © 2022 IEEE.

关键词： Machine learning

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An adaptive observer-based SOC estimation method for lithium-ion batteries against sensor measurement uncertainties

An adaptive observer-based SOC estimation method for lithium...

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Youth Academic Annual Conference of Chinese Association of Automation (YAC)

作者： Xiaoyi Xu Hao Luo Mo-Yuen Chow Cong-Sheng Huang Yuchen Jiang Department of Control Science and Engineering Harbin Institute of Technology Harbin P. R. China Department of Electrical and Computer Engineering North Carolina State University Raleigh NC USA

Accurate estimation of battery state of charge (SOC) is critical for efficient and safe battery applications. The measurement uncertainties of sensors, including measurement noises and sensor bias will affect the estimation accuracy inevitably. Therefore, quantifying the relationship between the sensor measurement uncertainties and the SOC estimation errors and seeking better SOC estimation methods has been a hot topic of research. In this paper, model errors and SOC estimation errors under measurement noise and sensor bias are derived. The resulting analyses can be used to assess the robustness of the SOC estimates. In addition, an adaptive observer-based SOC estimation method for lithium-ion batteries is proposed to cope with the measurement uncertainty. Simulation experiments demonstrate the effectiveness of the proposed method.

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Navigation in Time-Varying Densities: An Operator Theoretic Approach

Navigation in Time-Varying Densities: An Operator Theoretic ...

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European control Conference (ECC)

作者： Shankar A. Deka Umesh Vaidya Dimos V. Dimarogonas Department of Electrical Engineering Computer Science Decision and Control Systems division KTH Royal Institute of Technology Stockholm Sweden Department of Mechanical Engineering Clemson University Clemson South Carolina USA

This paper considers the problem of optimizing robot navigation with respect to a time-varying objective encoded into a navigation density function. We are interested in designing state feedback control laws that lead to an almost everywhere stabilization of the closed-loop system to an equilibrium point while navigating a region optimally and safely (that is, the transient leading to the final equilibrium point is optimal and satisfies safety constraints). Though this problem has been studied in literature within many different communities, it still remains a challenging non-convex control problem. In our approach, under certain assumptions on the time-varying navigation density, we use Koopman and Perron-Frobenius Operator theoretic tools to transform the problem into a convex one in infinite dimensional decision variables. In particular, the cost function and the safety constraints in the transformed formulation become linear in these functional variables. Finally, we present some numerical examples to illustrate our approach, as well as discuss the current limitations and future extensions of our framework to accommodate a wider range of robotics applications.

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Performing Effective Generative Learning from a Single Image Only

Performing Effective Generative Learning from a Single Image...

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International Conference on Wireless and Optical Communications ( WOCC)

作者： Qihui Xu Jinshu Chen Jiacheng Tang Qi Kang MengChu Zhou Department of Control Science and Engineering Tongji University Shanghai China The Helen and John C. Hartmann Department of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ USA

Generative adversarial networks (GANs) can be well used for image generation. Yet their training typically requires large amounts of data, which may not be available. This paper proposes a new algorithm for effective generative learning given a single image only. The proposed method involves building GAN models with a hierarchical pyramid structure and a parallel-branch design that enables independent learning of the foreground and background areas. This work conducts a set of well-designed experiments. The results well demonstrate that the proposed method produces the images of higher quality and better diversity than existing methods do. Thus, this work advances the field of generative learning for image generation.

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