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14th Mediterranean Conference on Power Generation Transmission, Distribution and Energy Conversion, MEDPOWER 2024

作者： Tillyros, Kypros Komodromos, Michael Lestas, Marios Christofides, Nicholas Department of Electrical Engineering Computer Engineering and Informatics Frederick University Nicosia Cyprus Frederick Research Centre Nicosia Cyprus

ISBN: (纸本)9781837242689

This paper presents a simulation study on integrating grid-connected photovoltaic (PV) systems with a potential to implement battery storage in residential settings. It analyses the performance and feasibility of such systems in enhancing self-consumption and energy export. The simulation model considers factors like household energy demand profiles, and PV output profiles. Through this analysis, it is anticipated to offer insights for homeowners, policymakers, and industry stakeholders on achieving greater energy independence and resilience. Finally, findings show that the attempt to effectively utilise battery storage in a residence while aiming for the most economically feasible system heavily depends on the load demand. It was observed that no matter the profile of the building or the energy utilisation, there is no significant change in energy costs. However, the results manifested an upcoming trend for utilising energy that will prove beneficial for costumers. © The Institution of engineering & Technology 2024.

关键词： Battery storage

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Building blocks for complex tasks: Robust generative event extraction for radiology reports under domain shifts 5

Building blocks for complex tasks: Robust generative event e...

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5th Workshop on Clinical Natural Language Processing, ClinicalNLP 2023. held at ACL 2023

作者： Zhou, Sitong Yetisgen, Meliha Ostendorf, Mari University of Washington Electrical & Computer Engineering United States University of Washington Biomedical and Health Informatics United States

ISBN: (纸本)9781959429883

This paper explores methods for extracting information from radiology reports that generalize across exam modalities to reduce requirements for annotated data. We demonstrate that multi-pass T5-based text-to-text generative models exhibit better generalization across exam modalities compared to approaches that employ BERT-based task-specific classification layers. We then develop methods that reduce the inference cost of the model, making largescale corpus processing more feasible for clinical applications. Specifically, we introduce a generative technique that decomposes complex tasks into smaller subtask blocks, which improves a single-pass model when combined with multitask training. In addition, we leverage target-domain contexts during inference to enhance domain adaptation, enabling use of smaller models. Analyses offer insights into the benefits of different cost reduction strategies. © 2023 Association for Computational Linguistics.

关键词： Cost reduction

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Developing an Anti -Phishing Large Language Model: A Focus Group Study on Human, Technological, and Legal Factors 15

Developing an Anti -Phishing Large Language Model: A Focus G...

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15th International Conference on Information, Intelligence, Systems and Applications, IISA 2024

作者： Thomopoulos, George A. Kiortsi, Panagiota Sigalas, Damianos Dumi Kosmopoulos, Dimitris Igglezakis, Ioannis Fidas, Christos University of Patras Department of Electrical and Computer Engineering Greece University of Thessaloniki Faculty of Law Aristotle Greece University of Patras Department of Computer Engineering and Informatics Greece

ISBN: (纸本)9798350368833

The phishing problem poses a significant threat in modern information systems, putting both individuals and businesses at risk of financial and professional harm. Owing to social media's rapid development and widespread appeal, deception of this sort is hurting millions of people and growing more dangerous. The current work, as part of the AILA (Artificial Intelligence-driven Framework and Legal Advice Tools for Phishing Prevention and Mitigation in Information Systems) project, aims to specify and validate an AI-driven multifactor (human, technology and legal) anti-phishing data model, with the implementation of focus group studies. The findings assist to provide human, technology, and legislative user model endpoints that will be identified and discussed for explicit and implicit user modeling, which will guide the development of the corresponding AI-driven user modeling and profiling mechanisms. To this end a Large Language System is planned to be employed. © 2024 IEEE.

关键词： Phishing

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Introduction to Heterogeneous Robotic Systems in 5G and Beyond Networks

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IEEE WIRELESS COMMUNICATIONS 2023年第2期30卷 64-65页

作者： Lu, Huimin Ho, Pin-Han Khalfi, Bassem Verikoukis, Christos Kyushu Institute of Technology Japan Department of Electrical and Computer Engineering The University of Waterloo Canada Oregon State University CorvallisOR United States University of Patras Department of Computer Engineering & Informatics Greece

Remote intelligence in the application of robotics and the autonomous system relies heavily on seamless wireless connections. The 5G mobile network technology meets traditional manufacturing enterprises' application requirements for wireless networks based on robot transformation and upgrading, robot interconnection, and remote interactive applications in production. However, there exists many challenging 5G communication issues, such as different communication protocols in the system varies with different robots and no dis-ruptive changes to the physical layer.

关键词： Special issues and sections Robots 5G mobile communication Physical layer Autonomous systems Heterogeneous networks

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Insights on diagnostic signals in single-phase and three-phase induction motors in single-phasing fault 14

Insights on diagnostic signals in single-phase and three-pha...

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14th IEEE International Symposium on Diagnostics for electrical Machines, Power Electronics and Drives, SDEMPED 2023

作者： Minervini, Marcello Frosini, Lucia Meloni, Alberto De Tullio, Riccardo Mantione, Lorenzo University of Pavia Department of Electrical Computer and Biomedical Engineering Pavia Italy

ISBN: (纸本)9798350320770

The aim of this paper is twofold. On the one hand, the applicability of the well-known diagnostic techniques, based on the motor current and stray flux signature analysis, has been experimentally tested both on small single-phase induction motors and on large on-field three-phase induction motors. On the other hand, the single phasing fault in three-phase induction motors, which has rarely been treated in the literature, has been experimentally analyzed and compared to the behavior of the single-phase motor, in order to find characteristic signatures of this fault. Time and frequency domain analyses were performed on current, axial and radial stray flux, and torque signals. The experimental study highlighted the following insights: i) classical motor current signature analysis cannot be easily applied to permanent-capacitor single-phase machines;ii) stray flux analysis reveals the presence of additional harmonic sidebands, even in healthy three-phase induction motors;iii) a rough analysis of the third harmonic in the current spectrum may be sufficient to diagnose the single phasing fault. © 2023 IEEE.

关键词： Induction motors

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Deep Semantic Segmentation of Echocardiographic Images using Vision Transformers

Deep Semantic Segmentation of Echocardiographic Images using...

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2023 IEEE International Ultrasonics Symposium, IUS 2023

作者： Bosco, Edoardo Casula, Filippo Cotogni, Marco Cusano, Claudio Matrone, Giulia University of Pavia Department of Electrical Computer and Biomedical Engineering Pavia Italy

ISBN: (纸本)9798350346459

Deep learning (DL) methods have revolutionized image segmentation by providing tools to automatically identify structures within images, with high levels of accuracy. In particular, Convolutional Neural Networks (CNN), such as the U-Net and its variants, have achieved remarkable results in many segmentation tasks. Only recently, Vision Transformer (ViT)-based models have emerged and in some cases demonstrated to outperform CNNs in semantic segmentation tasks. However, transformers typically require larger amounts of data for training as compared to CNNs. This can result in a significant drawback given the time-consuming nature of collecting and annotating data, especially in a clinical setting. In particular, only a few studies involved the application of ViT networks for ultrasound image *** this study, we propose one of the earliest applications of ViT-based architectures for segmenting the left heart chambers in 2D echocardiographic images. Indeed, the identification of cardiac structures, like e.g. heart chambers, can be used to derive relevant quantitative parameters, such as atrial and ventricular volumes, the ejection fraction, *** trained and tested several ViT models using the publicly available CAMUS dataset, composed by cardiac image sequences from 500 patients, with the corresponding mask labels of the left ventricle and left atrium. ViT networks performances were then compared to an implementation of the U-Net. We demonstrate how, on this type of data, recent ViT variants can reach and even outperform CNNs, despite the limited data availability. © 2023 IEEE.

关键词： Echocardiography

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A Case Study of Rice Paddy Field Detection Using Sentinel-1 Time Series in Northern Italy 7th

A Case Study of Rice Paddy Field Detection Using Sentinel-1 ...

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7th International Conference on computer Vision and Image Processing, CVIP 2022

作者： Dell’Acqua, Fabio Marzi, David Department of Electrical Computer Biomedical Engineering University of Pavia Pavia Italy

ISBN: (纸本)9783031314063

Whereas a vast literature exists reporting on mapping of rice paddy fields in Asia based on spaceborne data, especially from radar sen¬sors, comparatively little has been done so far on the European context, where production is much smaller in absolute terms. From a scientific standpoint, it would be interesting to characterize rice paddy fields in terms of typical annual trend of radar response in a context where seasons follow different patterns with respect to the Asian one. In this manuscript we report a case study on a designated set of rice paddy fields in north-ern Italy, where the largest fraction of European rice paddy fields are located. Building on previous work, more in-depth analysis of the time trends of radar response is carried out, and some preliminary conclusions on features usable in mapping are presented. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Mapping

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Using a 3D-Printed Waveguide Filter with Ridge Resonators as a Dielectric Permittivity Sensor

Using a 3D-Printed Waveguide Filter with Ridge Resonators as...

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2023 IEEE/MTT-S International Microwave Symposium, IMS 2023

作者： Romano, Francesco Delmonte, Nicolo Perregrini, Luca Bozzi, Maurizio University of Pavia Computer and Biomedical Engineering Department of Electrical Pavia Italy

ISBN: (纸本)9798350347647

This paper presents the use of a waveguide filter with ridge resonators that exhibits transmission zeros to implement a dielectric permittivity sensor. Thanks to its geometry, the electric field is intense under the ridge resonators, where the dielectric slab to be characterized is located. The operation principle is based on the evaluation of the frequency shift of transmission zeros, and exhibits high sensitivity and tolerance to vertical positioning errors. The designed sensor operates around the frequency of 10 GHz. The manufacturing process is based on the 3D printing with plastic material and the subsequent metallization. A set of commercial dielectric laminates have been adopted to validate the performance of the sensor. © 2023 IEEE.

关键词： 3D printing

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Adaptive 3D descattering with a dynamic synthesis network

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Light(Science & Applications) 2022年第5期11卷 766-786页

作者： Waleed Tahir Hao Wang Lei Tian Department of Electrical and Computer Engineering Boston UniversityBostonMA 02215USA Department of Biomedical Engineering Boston UniversityBostonMA 02215USA

Deep learning has been broadly applied to imaging in scattering applications.A common framework is to train a descattering network for image recovery by removing scattering *** achieve the best results on a broad spectrum of scattering conditions,individual“expert”networks need to be trained for each ***,the expert’s performance sharply degrades when the testing condition differs from the *** alternative brute-force approach is to train a“generalist”network using data from diverse scattering *** generally requires a larger network to encapsulate the diversity in the data and a sufficiently large training set to avoid ***,we propose an adaptive learning framework,termed dynamic synthesis network(DSN),which dynamically adjusts the model weights and adapts to different scattering *** adaptability is achieved by a novel“mixture of experts”architecture that enables dynamically synthesizing a network by blending multiple experts using a gating *** demonstrate the DSN in holographic 3D particle imaging for a variety of scattering *** show in simulation that our DSN provides generalization across a continuum of scattering *** addition,we show that by training the DSN entirely on simulated data,the network can generalize to experiments and achieve robust 3D *** expect the same concept can find many other applications,such as denoising and imaging in scattering ***,our dynamic synthesis framework opens up a new paradigm for designing highly adaptive deep learning and computational imaging techniques.

关键词： network scattering synthesis

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Classification of Arrhythmias 12-Lead ECG Signals Based on 1 Dimensional Convolutional Neural Networks

Classification of Arrhythmias 12-Lead ECG Signals Based on 1...

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2024 International Conference on Smart Computing, IoT and Machine Learning, SIML 2024

作者： Kurniawan, Arief Triwibowo, Bayu Aditya Fandiantoro, Dion Hayu Institut Teknologi Sepuluh Nopember Faculty of Intelligent Electrical and Informatics Technology Department of Computer Engineering Indonesia

ISBN: (纸本)9798350364101

Heart disease is the highest cause of death in the world. Arrhythmia is an abnormality in the rhythm of the heartbeat. The heart beats too fast, too slow, or irregularly. Arrhythmias are not always dangerous, e.g., someone who does excessive activity has a faster heart rate. Then, a diagnosis is needed to classify arrhythmias. One method used is ECG (Electrocardiogram) signal analysis. The ECG signal consists of P, QRS Complex, and T waves. The morphology of the QRS is used for arrhythmia classification. Currently, cardiologists analyze ECG signals by observing directly. This method is depending on the level of expertise of the cardiologist. Previous research classified arrhythmias based on the QRS morphology from a single ECG lead. As 12-lead ECG devices have now become standard in ECG examinations because abnormalities can be observed from multiple angles. This study proposes the classification of arrhythmias in 12-lead ECG signals based on the morphology of QRS complex waves using a deep learning 1-dimensional Convolutional Neural Network. The output of deep learning is the classification of arrhythmias into four classes, namely: Normal, Right Bundle Branch Block, Premature Ventricular Contraction, and Atrial Premature Beat. The outcome of the proposed system is that each QRS segment is used as input for deep learning, which can improve classification performance compared to the classification carried out by each lead. The experimental results show the method can be done well, with an average Accuracy, Precision, Sensitivity, and F1-Score were 98.8%, 99.2%, 99.2%, and 99.2%, respectively. © 2024 IEEE.

关键词： Convolutional neural networks

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