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A Dynamic Security Assessment Tool for Modern Power Systems with Massive RES Penetration

A Dynamic Security Assessment Tool for Modern Power Systems ...

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International Conference on Energy Transition in the Mediterranean Area (SyNERGY MED)

作者： Phivos Therapontos Charalambos A. Charalambous Petros Aristidou Distribution System Operator Electricity Authority of Cyprus Nicosia Cyprus Department of Electrical and Computer Engineering University of Cyprus Nicosia Cyprus Dept. of Electrical & Computer Engineering & Informatics Cyprus University of Technology Limassol Cyprus

ISBN: (数字)9798350375923

ISBN: (纸本)9798350375930

Modern power systems are facing significant challenges due to the massive penetration of renewable energy sources (RES). Recently, issues related to frequency security, system strength, and excessive fault levels have been experienced in some power systems. Classical dynamic security assessment (DSA) tools have not been developed to identify and analyze these new challenges that are critical in low-inertia grids. In this paper, a DSA tool has been developed that evaluates frequency security, system strength, and maximum fault levels to allow for secure system planning and operation. Furthermore, to increase the accuracy of the DSA tool, inverter-based resources’ (IBRs’) protections and capabilities have been incorporated in the DSA tool. A real-time operation and an operational planning application of the DSA tool are showcased using the islanded Cyprus Power System.

关键词： Renewable energy sources Adaptation models Accuracy Power system dynamics Real-time systems Planning Security Protection Faces

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Increasing the RES hosting capacity of the Cyprus distribution system

Increasing the RES hosting capacity of the Cyprus distributi...

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CIRED 2024 Vienna Workshop

作者： Phivos Therapontos Rafail Konstantinou Charalambos A. Charalambous Petros Aristidou Distribution System Operator Electricity Authority of Cyprus Nicosia Cyprus University of Cyprus Department of Electrical and Computer Engineering Nicosia Cyprus Dept. of Electrical & Computer Engineering & Informatics Cyprus University of Technology Limassol Cyprus

ISBN: (数字)9781837241224

The Renewable Energy Sources (RES) penetration in the power system of Cyprus has dramatically increased over the last years. As a result, the system is already facing significant challenges limiting the RES hosting capacity of the Distribution Network (DN). The major limitation factors are network congestion and voltage security. In this paper, alternative solutions for increasing the RES hosting capacity in DNs are reviewed, and a methodology is introduced to evaluate their effectiveness. More specifically, different inverter settings and an advanced centralised voltage control from power transformers are used to mitigate voltage-related issues, while network reinforcements and upgrading the operating voltage are considered for further increase in hosting capacity. The solutions and evaluation methodology are tested using a real MV network of the Cyprus distribution system and the strategic plan of the Cyprus Distribution System Operator (DSO) for maximizing RES hosting capacity is outlined.

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An Investigation of Running Load Comparisons of ARCore on Native Android and Unity for Outdoor Navigation System Using Smartphone 6

An Investigation of Running Load Comparisons of ARCore on Na...

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6th International Conference on Vocational Education and Electrical engineering, ICVEE 2023

作者： Brata, Komang Candra Funabiki, Nobuo Sukaridhoto, Sritrusta Fajrianti, Evianita Dewi Mentari, Mustika Okayama University Dept. of Information and Communication Systems Okayama Japan Universitas Brawijaya Dept. of Informatics Engineering Malang Indonesia Politeknik Elektronika Negeri Surabaya Dept. of Informatic and Computer Surabaya Indonesia State Polytechnic of Malang Dept. of Information Technology Malang Indonesia

ISBN: (纸本)9798350326642

In recent years, the utilization of location-based augmented reality (LAR) has gained popularity across various domains, including tourism, education, gaming, and advertising. The advancements in AR technology have made it easier to develop immersive LAR applications. ARCore, a widely adopted AR development library, enables the creation of location-based AR apps on Android devices. However, the implementation of ARCore in real-Time scenarios that demand significant computational resources, like outdoor pedestrian navigation, can vary depending on the chosen development environment. This research aims to explore and compare the running load performance of ARCore implementation in location-based AR applications developed with Unity and Android Native, which are two prevalent AR development platforms. The study involves building a prototype LAR application using both Unity and native Android, followed by comprehensive running load performance evaluations. The findings reveal that while both Unity and native Android apps can deliver compelling location-based AR experiences, native Android apps demonstrate superior performance and resource efficiency in outdoor scenarios, whereas Unity offers the advantage of multi-platform support and ease of development for complex 3D assets. These insights provide valuable guidance to developers in selecting the most suitable approach for developing location-based AR applications for navigation, aligning with their specific needs and requirements. © 2023 IEEE.

关键词： Augmented reality

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Context-aware Container Orchestration in Serverless Edge Computing

Context-aware Container Orchestration in Serverless Edge Com...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Peiyuan Guan Chen Chen Ziru Chen Lin X. Cai Xing Hao Amir Taherkordi Dept. of Informatics University of Oslo Oslo Norway Dept. of Computer Science and Technology University of Cambridge Cambridge UK Dept. of Electrical and Computer Engineering Illinois Institute of Technology Chicago USA School of Information Science and Technology Northwest Univesity Xi’an China

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

Adopting serverless computing to edge networks benefits end-users from the pay-as-you-use billing model and flexible scaling of applications. This paradigm extends the boundaries of edge computing and remarkably improves the quality of services. However, due to the heterogeneous nature of computing and bandwidth resources in edge networks, it is challenging to dynamically allocate different resources while adapting to the burstiness and high concurrency in serverless workloads. This article focuses on serverless function provisioning in edge networks to optimize end-to-end latency, where the challenge lies in jointly allocating wireless bandwidth and computing resources among heterogeneous computing nodes. To address this challenge, We devised a context-aware learning framework that adaptively orchestrates a wide spectrum of resources and jointly considers them to avoid resource fragmentation. Extensive simulation results justified that the proposed algorithm reduces over 95% of converge time while the end-to-end delay is comparable to the state of the art.

关键词： Wireless communication Simulation Serverless computing Bandwidth Quality of service Containers Heterogeneous networks Delays Global communication Edge computing

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Enhanced Outsourced and Secure Inference for Tall Sparse Decision Trees

Enhanced Outsourced and Secure Inference for Tall Sparse Dec...

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2024 IEEE International Performance, Computing, and Communications Conference, IPCCC 2024

作者： Quijano, Andrew Halkidis, Spyros T. Gallagher, Kevin Akkaya, Kemal Samaras, Nikolaos New York University Dept. of Computer Science and Engineering New YorkNY United States University of Macedonia Dept. of Applied Informatics Thessaloniki Greece Nova Lincs Nova School of Science and Technology Lisbon Portugal Florida International University Advanced Wireless and Security Lab MiamiFL United States

ISBN: (纸本)9798350367942

A decision tree is an easy-to-understand tool that has been widely used for classification tasks. On the one hand, due to privacy concerns, there has been an urgent need to create privacy-preserving classifiers that conceal the user's input from the classifier. On the other hand, with the rise of cloud computing, data owners are keen to reduce risk by outsourcing their model, but want security guarantees that third parties cannot steal their decision tree model. To address these issues, Joye and Salehi introduced a theoretical protocol that efficiently evaluates decision trees while maintaining privacy by leveraging their comparison protocol that is resistant to timing attacks. However, their approach was not only inefficient but also prone to side-channel attacks. Therefore, in this paper, we propose a new decision tree inference protocol in which the model is shared and evaluated among multiple entities. We partition our decision tree model by each level to be stored in a new entity we refer to as a "level-site."Utilizing this approach, we were able to gain improved average run time for classifier evaluation for a non-complete tree, while also having strong mitigations against side-channel attacks. © 2024 IEEE.

关键词： Differential privacy

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Emotion Recognition from Video Frame Sequence using Face Mesh and Pre-Trained Models of Convolutional Neural Network

Emotion Recognition from Video Frame Sequence using Face Mes...

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International Seminar on Intelligent technology and Its Applications (ISITIA)

作者： Derry Pramono Adi Eko Mulyanto Yuniarno Diah Puspito Wulandari Dept. of Electrical Engineering Faculty of Intelligent Electrical and Informatics Technology Institut Teknologi Sepuluh Nopember Surabaya Indonesia Dept. of Electrical Engineering Dept. of Computer Engineering Faculty of Intelligent Electrical and Informatics Technology Institut Teknologi Sepuluh Nopember Surabaya Indonesia Dept. of Computer Engineering Faculty of Intelligent Electrical and Informatics Technology Institut Teknologi Sepuluh Nopember Surabaya Indonesia

Emotions are a collection of subjective cognitive experiences and psychological and physiological characteristics that express a wide range of feelings, thoughts, and behaviors in human interaction. Emotions can be represented through several means, such as facial expressions, tone of voice, and behavior. Deep Learning (DL) research has focused on incorporating facial expressions. Images with facial expressions are commonly used as data input for the DL model. Unfortunately, most DL models in Facial Emotion Recognition (FER) use static images. This method does not take into consideration all conceivable facial expressions. The static image of facial expressions is insufficient for recognizing emotions, but a sequential image from a video is required. In this study, we extract MediaPipe’s face mesh feature, the state-of-the-art multidimensional expression key points embedded in the video image sequence. Furthermore, we feed sequence image data into the pre-trained Convolutional Neural Network (CNN) model. The data we used is from The Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) with the emotion classes of “Anger,” “Fearful,” “Happy,” and “Sad.” For this specific FER task, we found that the best pre-trained CNN model achieved 92.8% accuracy (using the VGG-19 model), with the fastest runtime of $\sim2.3$ seconds (achieved using the SqueezeNet model).

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Heart Failure: Machine Learning Prediction Within a 5-Year Framework 24

Heart Failure: Machine Learning Prediction Within a 5-Year F...

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24th IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2024

作者： Tsarapatsani, Konstantina-Helen Tsakanikas, Vasilios D. Sakellarios, Antonis Trampisch, Hans J. Karapintzou, Efterpi Rudolf, Henrik Matsopoulos, George K. Fotiadis, Dimitrios I. Biomedical Research Institute Foundation for Research and Technology-Hellas Ioannina Greece Ruhr University Bochum Department of Medical Informatics Biometry and Epidemiology Bochum Germany University of Ioannina Unit of Medical Technology and Intelligent Information Systems Dept. of Materials Science and Engineering Greece Institute for Biostatistics and Informatics in Medicine and Ageing Research University Medical Center Rostock Germany School of Electrical and Computer Engineering National Technical University of Athens Athens Greece University of Ioannina and Biomedical Research Institute-FORTH Unit of Medical Technology and Intelligent Information Systems Dept. of Materials Science and Engineering Ioannina Greece

ISBN: (纸本)9798331518622

Heart failure (HF) is a complex syndrome that is affected by many factors and causes. It is crucial to early recognize the disease subtypes and the unidentified clinical pathways that give rise to it. Machine learning (ML) is the tool that assist to deal with these challenges and improve the prediction of HF. In this work, the HF risk prediction was implemented by employed ML classifiers, such as Random Forest (RF), Extreme Grading Boosting (XGBoost) and Light Gradient-Boosting Machine (LGBM). We utilized the data from the German epidemiological trial on ankle brachial index - getABI cohort, which includes 6,454 patients. The performance of classifiers was estimated by Accuracy (ACC), Sensitivity, Specificity and the area under the receiver operating characteristic curve (AUC) in mean values for each ML classifier. The results were also interpreted using the Explainable artificial intelligence (XAI) approach, the Shapley Additive exPlanations (SHAP) values. Our work reveals that LGBM classifier predict the HF risk within 5 years follow-up in general population with 68 % accuracy. Moreover, the N-terminal pro-B-type natriuretic peptide (NT-proBNP) was identified as the most important feature for HF risk prediction. © 2024 IEEE.

关键词： Risk perception

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DeepFuzzLoc Positioning: A Unified Fusion of Fuzzy Clustering and Deep Learning for Scalable Indoor Localization Using Wi-Fi RSSI

DeepFuzzLoc Positioning: A Unified Fusion of Fuzzy Clusterin...

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International Symposium on Soft Computing and Intelligent Systems (SCIS)

作者： Muhammad Izzuddin Mahali Jenq-Shiou Leu Nur Achmad Sulistyo Putro Setya Widyawan Prakosa Cries Avian Dept. of Electronic and Computer Engineering National Taiwan University of Science and Technology Taipei Taiwan Dept. of Electronics and Informatics Engineering Education Universitas Negeri Yogyakarta Yogyakarta Indonesia Dept. of Computer Science and Electronics Universitas Gadjah Mada Yogyakarta Indonesia

ISBN: (数字)9798350373332

ISBN: (纸本)9798350373349

This research introduces a novel method for indoor positioning by integrating Fuzzy C-Means clustering with deep learning autoencoders, leveraging Wi-Fi RSSI fingerprinting for precise location prediction and building and floor level classification within a unified framework. The approach combines the comprehensibility of Fuzzy C-Means (FCM) with the robust feature extraction capabilities of deep autoencoders through a dual-phase strategy: extensive offline training utilizing RSSI fingerprint data followed by real-time online position prediction. The model's performance was rigorously tested using 5-Fold cross-validation, examining its effectiveness both with and without the integration of FCM on various cluster numbers. Evaluation metrics, including Euclidean distance error for regression and accuracy, F1-score, precision, and specificity for classification effectiveness, were used for comparative analysis. The results were visualized through regression plots, confusion matrices, and learning curves, supplemented by t-SNE visualizations that provide profound insights into the feature space. These detailed analyses highlight the considerable promise of this integrated approach in enhancing the accuracy and reliability of indoor positioning systems.

关键词： Location awareness Deep learning Training Measurement Accuracy Clustering algorithms Fingerprint recognition Real-time systems Reliability Wireless fidelity

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Clustering Based User Allocation in 5G Networks∗

Clustering Based User Allocation in 5G Networks∗

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2021 International Symposium on Networks, computers and Communications, ISNCC 2021

作者： Bouras, Christos Kalogeropoulos, Rafail Sina, Ioannis Computer Technology Institute Press 'Diophantus201D Univ. Of Patras Greece Patras Greece Computer Engineering And Informatics Dept.

ISBN: (纸本)9780738113166

Machine learning is an extremely efficient technique for solving complex problems without the use of traditional programming but rather enabling machines to learn from an input of data and train them to cope with various problems. The rapid growth in the number of active mobile devices, mobile applications and services dictates an efficient utilization of mobile and wireless networking infrastructure. Communication networks need to evolve and valorize machine learning methods in order to process large volumes of data without introducing excessive time delay in these computations. Upcoming 5G systems are expected to be the first network infrastructure to support exploding mobile traffic volumes and machine learning techniques can be used in order to help manage the rise in data volumes. We present a mechanism for resource allocation in mobile and wireless networks, that effectively utilizes machine learning techniques. © 2021 IEEE.

关键词： Wireless networks

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Comparison of Fast Frequency Response Methods in the Low-Inertia Grid of Cyprus

Comparison of Fast Frequency Response Methods in the Low-Ine...

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International Conference on Energy Transition in the Mediterranean Area (SyNERGY MED)

作者： Andreas Argyrou Savvas Panagi Petros Aristidou Dept. of Electrical & Computer Engineering & Informatics Cyprus University of Technology Limassol Cyprus Distribution System Operator Electricity Authority of Cyprus Nicosia Cyprus

ISBN: (数字)9798350375923

ISBN: (纸本)9798350375930

Modern electric power systems with high levels of penetration of renewable energy sources (RES) often present frequency security problems. The lack of inertia due to the reduced number of synchronous generators in these systems, in combination with the usual inability of RES to provide frequency support, leads to system operators having to curtail RES or rely heavily on Under Frequency Load Shedding schemes to ensure frequency security. Fast Frequency Reserves (FFR) have been proposed as a solution to strengthen the frequency support of the system and alleviate security problems. FFR allows mitigating Rate of Change of Frequency, Nadir, and post-fault frequency steady-state problems after an event. In this paper, we present, analyze, implement and compare five FFR controllers to alleviate frequency security problems in low-inertia grids. The low-inertia, islanded, Cyprus dynamic model is used to quantify the results and exhibit the impact on a real system.

关键词： Renewable energy sources Power system dynamics Load shedding Power system stability Synchronous generators Stability analysis Steady-state Security Frequency synchronization Frequency control

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