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SECURITY systems IN GREEK HEALTH CARE INSTITUTIONS: A SCOPING REVIEW TOWARDS AN EFFECTIVE BENCHMARKING APPROACH 22

SECURITY SYSTEMS IN GREEK HEALTH CARE INSTITUTIONS: A SCOPIN...

22nd International Conference on e-Society 2024, ES 2024 and 20th International Conference on Mobile Learning 2024, ML 2024

作者： Mariettou, Savina Koutsojannis, Constantinos Triantafyllou, Vassilis University of Peloponnese Electrical and Computer Engineering Department Patras Greece Health Physics & Computational Intelligence Laboratory Physiotherapy Department School of Health Rehabilitation Sciences University of Patras Patras Greece Network Technologies and Digital Transformation lab Electrical and Computer Engineering Dpt. University of Peloponnese Patras Greece

Healthcare has undoubtedly brought many advancements through information technology. Specifically, healthcare informatics involves the use of various technologies, data management and communication systems to collect, store, analyze and effectively disseminate information. Therefore, the above serves to improve care as well as patient outcomes, improve efficiency, promote innovation and data analysis, as digitization has become an integral part of our daily lives in today's world. However, with the increased use of interconnected systems and electronic health records, hospitals have become prime targets for cyber-attacks. In this article we will delve into the critical topic of cybersecurity in hospitals as well as nursing facilities. The deepening of this issue was carried out with extensive analysis of the security systems of Greek hospitals. We investigated all their web sites and the security systems already in place on those sites. By checking we found a number vulnerabilities and potential risks. The most important weakness is the lack of legal as well as technical information for all interested citizens and patients. More organizational as well as technical work concerning internal architecture of information systems is needed, towards the improvement of security. In conclusion, we present our proposals on strategies to strengthen healthcare facilities against cyber threats. © 2024 International Conferences e-Society 2024 and Mobile Learning 2024. All rights reserved.

关键词： Cybersecurity Digitization Healthcare Information Patients systems

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The Impact of Cross-Validation Schemes for EEG-Based Auditory Attention Detection with Deep Neural Networks 46

The Impact of Cross-Validation Schemes for EEG-Based Auditor...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Ivucic, Gabriel Pahuja, Saurav Putze, Felix Cai, Siqi Li, Haizhou Schultz, Tanja University of Bremen Cognitive Systems Lab Germany University of Bremen Machine Listening Lab Germany National University of Singapore Department of Electrical and Computer Engineering Singapore

ISBN: (纸本)9798350371499

This study assesses the performance of different cross-validation splits for brain-signal-based Auditory Attention Decoding (AAD) using deep neural networks on three publicly available Electroencephalography datasets. We investigate the effect of trial-specific knowledge during training and assess adaptability to diverse scenarios with a trial-independent split. Introducing a causal time-series split, and simulating online decoding, our results demonstrate a consistent performance increase for auditory attention classification. These positive outcomes provide valuable insights for the development of future brain-signal-based AAD systems, emphasizing the potential for practical, person-dependent AAD applications. The results highlight the importance of diverse evaluation methodologies for enhancing generalizability in developing effective neurofeedback systems and assistive technologies for auditory processing disorders under more real-life conditions. © 2024 IEEE.

关键词： Deep neural networks

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The Impact of the 2024 Total Solar Eclipse on the Power System Frequency of US Interconnections: A Case Study

The Impact of the 2024 Total Solar Eclipse on the Power Syst...

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2025 IEEE SoutheastCon, SoutheastCon 2025

作者： Dulal, Saurav Wen, Yan Liu, Yilu The University of Tennessee Department of Electrical Engineering and Computer Science KnoxvilleTN United States Oak Ridge National Laboratory Oak RidgeTN United States

ISBN: (纸本)9798331504847

With the deployment of wide-area monitoring systems, power systems can be continuously monitored and analyzed, particularly during unusual events. Frequency fluctuations in the United States (US) interconnected power grid are influenced by various factors, including weather conditions and variability in solar power generation. This paper investigates the impact of the solar eclipse on April 8, 2024, on the frequency stability of the US power grid. Phasor measurement data from multiple interconnections are collected and analyzed. Methods of approximate frequency estimation for each interconnection are employed. The study involves comparing the frequency distribution, probability density, and variance on the eclipse day with those of non-eclipse days. The results provide insight into the grid's response to reduced solar generation and underscore the importance of predictive measures to maintain stability during solar eclipses and similar disruptions. The study shows that the Electric Reliability Council of Texas (ERCOT) experienced a dramatic increase in frequency fluctuations - 198% higher compared to periods without eclipse - highlighting the grid's vulnerability to reduced solar irradiance. Conversely, Eastern Interconnection (EI) demonstrated remarkable stability during the eclipse with minimal frequency fluctuations. © 2025 IEEE.

关键词： Electric power transmission networks

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Wind Power Prediction Based on Multi-class Autoregressive Moving Average Model with Logistic Function

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Journal of Modern Power systems and Clean Energy 2022年第5期10卷 1184-1193页

作者： Yunxuan Dong Shaodan Ma Hongcai Zhang Guanghua Yang IEEE State Key Laboratory of Internet of Things for Smart City and the Department of Electrical and Computer Engineering University of MacaoMacao S.A.R.999078China Institute of Physical Internet and the School of Intelligent Systems Scienceand Engineering Jinan UniversityZhuhai 519070China

The seasonality and randomness of wind present a significant challenge to the operation of modern power systems with high penetration of wind generation. An effective shortterm wind power prediction model is indispensable to address this challenge. In this paper, we propose a combined model, i.e.,a wind power prediction model based on multi-class autoregressive moving average(ARMA). It has a two-layer structure: the first layer classifies the wind power data into multiple classes with the logistic function based classification method;the second layer trains the prediction algorithm in each class. This two-layer structure helps effectively tackle the seasonality and randomness of wind power while at the same time maintaining high training efficiency with moderate model parameters. We interpret the training of the proposed model as a solvable optimization problem. We then adopt an iterative algorithm with a semi-closed-form solution to efficiently solve it. Data samples from open-source projects demonstrate the effectiveness of the proposed model. Through a series of comparisons with other state-of-the-art models, the experimental results confirm that the proposed model improves not only the prediction accuracy,but also the parameter estimation efficiency.

关键词： Wind power prediction wind generation time series analysis logistic function based classification

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Robust Stochastic Shortest-Path Planning via Risk-Sensitive Incremental Sampling 63

Robust Stochastic Shortest-Path Planning via Risk-Sensitive ...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Enwerem, Clinton Noorani, Erfaun Baras, John S. Sadler, Brian M. Institute for Systems Research University of Maryland Department of Electrical & Computer Engineering College ParkMD United States University of Texas at Austin TX United States

ISBN: (纸本)9798350316339

With the pervasiveness of Stochastic Shortest-Path (SSP) problems in high-risk industries, such as last-mile autonomous delivery and supply chain management, robust planning algorithms are crucial for ensuring successful task completion while mitigating hazardous outcomes. Mainstream chance-constrained incremental sampling techniques for solving SSP problems tend to be overly conservative and typically do not consider the likelihood of undesirable tail events. We propose an alternative risk-aware approach inspired by the asymptoticallyoptimal Rapidy-Exploring Random Trees (RRT*) planning algorithm, which selects nodes along path segments with minimal Conditional Value-at-Risk (CVaR). Our motivation rests on the step-wise coherence of the CVaR risk measure and the optimal substructure of the SSP problem. Thus, optimizing with respect to the CVaR at each sampling iteration necessarily leads to an optimal path in the limit of the sample size. We validate our approach via numerical path planning experiments in a two-dimensional grid world with obstacles and stochastic pathsegment lengths. Our simulation results show that incorporating risk into the tree growth process yields paths with lengths that are significantly less sensitive to variations in the noise parameter, or equivalently, paths that are more robust to environmental uncertainty. Algorithmic analyses reveal similar query time and memory space complexity to the baseline RRT∗ procedure, with only a marginal increase in processing time. This increase is offset by significantly lower noise sensitivity and reduced planner failure rates. © 2024 IEEE.

关键词： Stochastic systems

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Secured Framework for Vehicle Management Using Blockchain

Secured Framework for Vehicle Management Using Blockchain

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2024 International Conference on System, Computation, Automation and Networking, ICSCAN 2024

作者： Poonguzhali, N. Amarabalan, N. School of Computer and Systems Sciences Jawaharlal Nehru University New Delhi India Manakula Vinayagar Institute of Technology Department of Electrical and Electronics Engineering Puducherry India

ISBN: (纸本)9798331510022

For many years, there were many different ways to arrange a vehicle's information using just the license plate. The number on a vehicle's license plate is the primary symbol used to identify it. The industries used to reciprocate the number plates in order to further uphold their basic standards. Anonymization through encryption is the preferred privacy setting for a license plate. We will have access to all the services required for faultless data maintenance through the Vehicle Management system. The entire system depends on the data, which is technologically encrypted and decrypted. However, data stored on a chip will be more secure than image recognition. It reduces the likelihood of mistakes and misunderstandings of the required information. Vehicle-related data is stored in a decentralized system. The suggested system either retrieves data from the smart number plate or uses an image processing technique to determine the license plate number through edge detection. The required data can be seen with this number. In order to ensure security, these data are encrypted using the SHA-256 algorithm and stored in a decentralized system using the Hyperledger Sawtooth framework, which also employs the PoET and BFT algorithms. © 2024 IEEE.

关键词： Blockchain

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Decentralized Droop-based Finite-Control-Set Model Predictive Control of Inverter-based Resources in Islanded AC Microgrid

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IFAC-PapersOnLine 2024年第28期58卷 384-389页

作者： Ayobami Olajube Koto Omiloli Satish Vedula Olugbenga M. Anubi Department of Electrical and Computer Engineering the Center for Advanced Power Systems Florida State University

This paper presents an improved droop control method to ensure effective power sharing, voltage regulation, and frequency stabilization of inverter-based resources (IBRs) connected in parallel in an islanded AC microgrid. In the contemporary droop control algorithm, the distance between connected inverters affects the effectiveness of the active power-frequency and the reactive power-voltage droop characteristics which results in poor power sharing at the primary level of the microgrid. That is, high impedance emanating from long transmission lines results in instability, poor voltage tracking, and ineffective frequency regulation. Hence, in this work, we use a finite-control-set model predictive controller (FCS-MPC) in the inner loop, which gives efficient voltage tracking, good frequency regulation, and faster performance response. FCS-MPC is easy to implement in fast switching converters and does not suffer from computational burden unlike the continuous-set MPC and is also devoid of issues of multiple-loop, parameter variation, and slow response associated with conventional droop control methods. We derived the condition for bounded stability for the FCS-MPC and the proposed method is tested via a numerical simulation on three IBRs. The results show effective power sharing, capacitor voltage tracking, and efficient frequency regulation with reduced oscillations to changes in load.

关键词： Droop control finite-control-set model predictive control active power sharing reactive power sharing voltage regulation inverter-based resources islanded microgrid

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Comprehensive Design, Modeling and Analysis of Grid-Forming Type IV Wind Turbine Generators Using State-Space Methods

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IEEE Access 2025年 13卷 91089-91112页

作者： Harith E. Udawatte Mohammad H. Ravanji Behrooz Bahrani Department of Electrical and Computer Systems Engineering Monash University Clayton VIC Australia Department of Electrical Engineering Sharif University of Technology Tehran Iran

Grid-forming (GFM) control has emerged as a promising solution to the challenges posed by the increasing reliance on inverter-based resources (IBRs). However, unlike in a battery-based IBR, the implementation of GFM in wind turbine generators (WTGs) introduces challenges due to multiple machine-side converter (MSC) and grid-side converter (GSC) interactions. In this work, a GFM-WTG control structure is adopted in which the MSC primarily regulates the DC-link voltage, while the GSC emulates grid-forming behavior using virtual synchronous generator principles. Accordingly, this paper presents a practical control implementation scheme and a systematic small-signal modeling framework for GFM WTGs using the component connection method, enabling a unified state-space representation that captures key electromechanical, aerodynamic and control interactions inside the GFM-WTG system. The proposed model is validated through electromagnetic transient simulations, and eigenvalue and participation factor analyses reveal strong MSC-GSC inter-dependencies. Sensitivity analysis further confirms model accuracy across varying operating conditions. Additionally, a reduced-order model is derived to balance computational efficiency with dynamic fidelity. The findings provide a robust foundation for stability analysis and control tuning of GFM WTGs, supporting their reliable integration into future power grids.

关键词： Aerodynamics Wind turbines Grid forming Power system stability Computational modeling Analytical models Wind energy Voltage control Generators Eigenvalues and eigenfunctions

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On the Importance of Trust in Next-Generation Networked Cyber-Physical systems

On the Importance of Trust in Next-Generation Networked Cybe...

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International Conference on Computing, Networking and Communications (ICNC)

作者： Nariman Torkzaban Anousheh Gholami John S. Baras Department of Electrical & Computer Engineering Institute for Systems Research

ISBN: (数字)9798350370997

ISBN: (纸本)9798350371000

With 5G and beyond promises to realize massive machine-type communications, a wide range of applications have driven interest in complex heterogeneous networked systems, including multi-agent optimization, large-scale distributed learning, 5G service provisioning, etc. This trend highlights the essence of seamless control, management, and security mechanisms to be in place for the next-generation networked cyber-physical systems (CPS). In this paper, we interpret trust as a relation among networked collaborating entities that can set forth a measure for evaluating the status of network components and secure the execution of the collaborative protocol. In this paper, we will first elaborate on the importance of trust as a metric and then present a mathematical framework for trust computation and aggregation within a network. We consider two use-case examples where trust can be incorporated into the next-generation networked CPS and improve the security of decision-making, i.e. i) federated learning (FL), and ii) network resource provisioning. Finally, we explain the challenges associated with aggregating the trust evidence and briefly explain our ideas to tackle them.

关键词： Measurement Protocols 5G mobile communication Federated learning Distance learning Cyber-physical systems Market research

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An Integrated AI-Based Information System for Energy Forecasting 14

An Integrated AI-Based Information System for Energy Forecas...

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14th International Conference on Information, Intelligence, systems and Applications, IISA 2023

作者： Skaloumpakas, Panagiotis Mylona, Zoi Strompolas, Sofoklis Sarmas, Elissaios Doukas, Haris Holistic Ike Athens Greece School of Electrical and Computer Engineering National Technical University of Athens Decision Support Systems Laboratory Athens Greece

ISBN: (纸本)9798350318067

The aim of reducing carbon emissions through the digitization of the energy sector involves several challenges. One potential solution is the integration of renewable energy sources;however, the volatile and intermittent nature of this type of energy requires precise forecasting. This paper presents a novel, intelligent information system that covers the entire data lifecycle in the energy sector, including data acquisition, processing and storage, model development and training, and a user-friendly web application. The system was tested in three real-life use cases and demonstrated effectiveness and versatility in addressing the intricate data challenges faced by the energy industry. The paper concludes with suggestions for further research in this area. © 2023 IEEE.

关键词： Forecasting

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