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13th International Winter Conference on Brain-computer Interface, BCI 2025

作者： Kim, Sejin Kang, Hyunwook Jeong, Ji-Hoon Lee, Minji Dept. Artificial Intelligence The Catholic University of Korea Bucheon Korea Republic of Dept. Electrical and Computer Engineering Inha University Incheon Korea Republic of Dept. Computer Science Chungbuk National University Cheongju Korea Republic of Dept. Biomedical Software Engineering The Catholic University of Korea Bucheon Korea Republic of

ISBN: (纸本)9798331521929

Imagined speech is gaining attention as a next-generation paradigm for brain-computer interfaces in terms of its intuitiveness in communication. Many studies have focused on classifying imagined words as the basis of communication, but some studies on phoneme classification based on imagined speech have begun to be conducted in terms of the fact that combinations of phonemes can further expand sound images. In addition, explainability is a major issue in classification results in machine learning, but there have been few studies that analyzed the explainability of imagined speech classification. In this study, we examined explainability using SHAP for phoneme classification based on imagined speech. FEIS dataset was used, and 16 phonemes were classified using the XGBoost classifier. In 21 subjects, the average accuracy was 40.36%, the average precision was 41.7%, the average recall was 40.34%, and the average F1-score was 40.7%. In addition, when looking at Sub 04, which had the highest performance, the SHAP values were high in AF3 and F7 channels, which correspond to Wernicke’s area related to speech. In this regard, we could observe the feature importance of this area. These results prove that Wernicke’s area actually plays an important role in classifying phoneme units in imagined speech, and can provide important insights to improve the performance of imagined speech for intuitive communication based on brain-computer interface in the future. © 2025 IEEE.

关键词： Adversarial machine learning

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Impact of the DC-DC Stage on Grid-Connection Stability in Solid-State Transformer

Impact of the DC-DC Stage on Grid-Connection Stability in So...

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2023 IEEE Energy Conversion Congress and Exposition, ECCE 2023

作者： Granata, Samuele Tresca, Giulia Benzi, Francesco Zanchetta, Pericle University of Pavia Dept. of Electrical Computer and Biomedical Engineering Pavia Italy University of Nottingham Dept. Electrical and Electronic Engineering Nottingham United Kingdom

ISBN: (纸本)9798350316445

This paper addresses the impact of the dc-dc stage on the grid-connection stability of a three-phase Solid-State Transformer based on the Cascaded H-Bridge and Dual Active Bridge topologies. In particular, the analysis aimed to reveal and discuss the impact of the isolated bidirectional dc-dc stage on the input dq-frame impedance matrix properties in terms of its passivity. For this purpose, the d-axis input impedance has been mainly addressed in this work. Its low-frequency simplified expression has been derived, by means of which its real part and the negative-resistance region upper limit can be analytically deduced, enabling a passivity-oriented design procedure. It is demonstrated that the dc-dc converter lowers that limit thus enhancing the grid-connected Solid-State Transformer passivity. The analysis performed in this work can be applied to a generic ac-dc-dc topology. © 2023 IEEE.

关键词： Cascaded H-Bridge (CHB) Dual Active Bridge (DAB) grid interaction passivity theory Solid-State Transformer (SST) Stability analysis

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Transformer-Based Embeddings for Greek Language Categorization 24

Transformer-Based Embeddings for Greek Language Categorizati...

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24th IEEE/ACIS International Conference on computer and Information Science, ICIS 2024

作者： Liapis, Charalampos M. Kyritsis, Konstantinos Perikos, Isidoros Paraskevas, Michael University of Patras Computer Technology Institute & Press 'Diophantus' Mathematics Dept. Patras26504 Greece University of Peloponnese Electrical and Computer Engineering Dept. Patras Greece University of Patras Computer Engineering and Informatics Department Patras Greece University of Peloponnese Computer Technology Institute and Press 'Diophantus' Electrical and Computer Engineering Dept. Patras Greece

ISBN: (纸本)9798350376647

The Greek School Network (GSN) provides support to students, teachers, and school units in secondary education across Greece. Handling numerous user queries manually can be challenging, necessitating the development of an automated system for accurate categorization of these queries. This paper presents a comparative study of various transformer-based models for multi-class text categorization of Greek language queries submitted to the GSN helpdesk. We introduce a new experimental balanced dataset and extract vector representations from eleven transformer-based models. These representations are evaluated using ten classic machine learning classifiers. Our findings highlight the superior performance of the Multilingual E5 Text Embeddings model, particularly when paired with the extreme gradient-boosting classifier. This combination demonstrates a clear advantage in accurately categorizing user queries, paving the way for more efficient automated helpdesk systems. © 2024 IEEE.

关键词： Contrastive Learning

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Detection and Classification of Skin Cancer Using YOLOv8n 11

Detection and Classification of Skin Cancer Using YOLOv8n

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11th International Conference on electrical engineering, computer Science and informatics, EECSI 2024

作者： Riyadi, Munawar A Ayuningtias, Adela Isnanto, R Rizal Universitas Diponegoro Dept. of Electrical Engineering Semarang Indonesia Universitas Diponegoro Dept. of Computer Engineering Semarang Indonesia

ISBN: (纸本)9798350355314

Skin cancer is a disease caused by the growth of abnormal cells in skin tissues. The World Health Organization (WHO) has recorded an 88% increase in deaths due to skin cancer caused by exposure to ultraviolet rays. Currently, in the medical field, the diagnosis of skin cancer involves a biopsy process, which requires considerable time and cost. Therefore, this study aims to develop a system for detecting and classifying skin cancer based on the shape of skin lesions using the You Only Look Once version 8 nano (YOLOv8n), which can detect lesions rapidly. The dataset used is ISIC 2019, comprising of 4289 images of cancerous skin lesions divided into 9 classes: Basal Cell Carcinoma, Squamous Cell Carcinoma, Melanoma, Actinic Keratosis, Dermatofibroma, Nevus, Seborrheic Keratosis, Pigmented Benign Keratosis, and Vascular Lesion. Experimental results show that the designed system performs well in detecting and classifying the lesions, achieving an overall accuracy of 93.5%, with a Precision of 93.5%, Recall of 93.7%, and an F1-Score of 93.5%. © 2024 IEEE.

关键词： Lung cancer

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Influence of parallel kinematic machine dimensional tolerances on the estimation of payload inertia

Influence of parallel kinematic machine dimensional toleranc...

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2023 International Conference on electrical, computer, Communications and Mechatronics engineering, ICECCME 2023

作者： Farías, José Alfonso Sergenti, Carol Carnevale, Marco Giberti, Hermes Università di Pavia Dept. Electrical Computer and Biomedical Engineering Pavia Italy

ISBN: (纸本)9798350322972

Estimating the inertial parameters of a rigid body linked to the end effector of a parallel kinematic machine may be useful in many applications (e.g., set-up of control parameters, orientation estimation, inertial compensation). The quality of the estimation depends on different aspects such as the implemented method, the excitation movement trajectories and the disturbances of the input signals. The model inputs are generally the forces and torques at the interface between the end effector and the workpiece under estimation, commonly measured through a 6-axis load cell, and the related motion. However, when evaluating the imposed motion based on the motion of the actuators, rather than the most common adoption of an Inertial Measurement Unit for direct measurement, geometrical tolerances of the machine links are likely to affect the identification results, since they introduce uncertainty in the terms of the Jacobian *** paper studies the influence of the variability of the robot link lengths on the accuracy in the estimation of the inertial properties of the payload, when the end effector motion is evaluated based on the actuator motion and forward kinematics. It is found that, under a certain limit of geometrical tolerances, the error in the estimation of the center of gravity and inertia tensor can be contained to acceptable values, comparable to those achievable through direct measurement of the end effector *** a given geometrical error in the link lengths, the influence of the excitation trajectory (i.e. matrix conditioning number) on the identification error is also addressed. Finally, the analysis points out how much the accelerations used as input can differ from the real ones, which is useful information to size the sensitivity of the IMU sensor to be used when direct measurement is possible or required. © 2023 IEEE.

关键词： End effectors

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A novel charging approach to Temperature and State of Charge management in BEV

A novel charging approach to Temperature and State of Charge...

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2023 IEEE Energy Conversion Congress and Exposition, ECCE 2023

作者： Gemma, Filippo Tresca, Giulia Di Salvo, Salvatore Riccardo Formentini, Andrea Zanchetta, Pericle University of Genova Electrical Electronic Telecommunications Engineering and Naval Architecture Dept. Italy University of Pavia Electrical Computer and Biomedical Engineering Dept. Italy University of Nottingham Electrical and Electronic Engineering Dept. United Kingdom

ISBN: (纸本)9798350316445

This paper proposes a novel approach to simultaneously manage the State of Charge and Temperature balancing during the charging process of the battery cells installed within a Reconfigurable Cascaded Multilevel Converter, used in the powertrain of Battery Electric Vehicles. Depending on the charging process scenario, the algorithm prioritizes temperature or SOC balancing, respectively, still ensuring low charging times and adherence to safety temperature limits. The method is here validated through simulations, showing a final balanced dynamic for both temperature and SOC without compromising the overall efficiency of the charging process. © 2023 IEEE.

关键词： Battery Electric Vehicle Battery Model Reconfigurable Cascaded Multilevel Converter SOC balancing Temperature Control

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A Comparative Analysis of Transformer Models in Zero-Shot Text Classification 15

A Comparative Analysis of Transformer Models in Zero-Shot Te...

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

作者： Kyritsis, Konstantinos Liapis, Charalampos M. Spatiotis, Nikolaos Perikos, Isidoros Paraskevas, Michael University of Peloponnese Electrical & Computer Engineering Dept. Patras26334 Greece University of Patras Mathematics Dept. Patras26504 Greece Computer Technology Institute & Press 'Diophantus' Patras26504 Greece University of Patras Computer Engineering & Informatics Dept. Patras26504 Greece

ISBN: (纸本)9798350368833

Ahstract-Zero-shot text classification leverages pre-trained transformer models to categorize texts without the need for task-specific training. This paper analyzes a variety of transformer-based pre-trained methods on the task of zero-shot text classi-fication. Specifically, we present a deep comparative analysis of various transformer models, such as BART, DeBerta, DistiIBART, RoBERTa, and their variants, and evaluate their performance in various zero-shot text classification schemes. Furthermore, we examine the models' generalization capabilities. The findings highlight key strengths and weaknesses of each model, providing insights into their suitability for different text categorization tasks. Our research contributes to the broader understanding of transformer-based approaches and offers guidance for selecting models for zero-shot text classification. © 2024 IEEE.

关键词： Zero-shot learning

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Visual Saliency Approaches for Defect Detection in Industrial Processes: A Short Review 29

Visual Saliency Approaches for Defect Detection in Industria...

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29th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2024

作者： Porta, Marco University of Pavia Dept. of Electrical Computer and Biomedical Engineering Via A. Ferrata 5 Pavia27100 Italy

ISBN: (纸本)9798350361230

Finding defects during production is crucial in industrial manufacturing. While various detection methods exist, this paper proposes a short review on techniques based on visual saliency. Saliency approaches try to mimic how humans see and are characterized by advantages such as accuracy, speed, and ability to handle complex situations. With solutions based on deep learning becoming increasingly dominant, this review aims to provide a brief analysis of the current state of research in this field, possibly stimulating continued exploration and development of new techniques. © 2024 IEEE.

关键词： computer vision defect detection industrial production visual saliency

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A Study of Resonance Modes for Microwave Imaging in biomedical Applications 25

A Study of Resonance Modes for Microwave Imaging in Biomedic...

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25th International Conference on Electromagnetics in Advanced Applications, ICEAA 2024

作者： Attar, Amir LoVetri, Joe Dept. of Electrical and Computer Engineering University of Manitoba Winnipeg Canada

ISBN: (纸本)9798350360974

The present numerical study explores the resonant modes generated within a microwave imaging chamber having perfectly conducting walls and loaded by a breast phantom. The objective of this preliminary work is to investigate whether such a resonant set-up, and an understanding of the modal field distribution, can be used to improve microwave imaging. As such the perturbation of the resonant frequencies and associated modes are identified utilizing the eigenmode projection technique. A simplified 2D model is utilized to assess the sensitivity of the resonant modes and frequencies to various tissues, such as different sizes, shapes, and locations of tumour and fibroglandular tissue regions, located within the breast model. It is found that there is a considerable variation in the sensitivity of the resulting modes to tissue anomalies located within the breast. © 2024 IEEE.

关键词： Natural frequencies

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Deep Learning Approaches for Stress Detection: A Survey

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IEEE Transactions on Affective Computing 2024年第2期16卷 499-517页

作者： Kyrou, Maria Kompatsiaris, IoannisSenior Petrantonakis, Panagiotis C. Aristotle University of Thessaloniki Artificial Intelligence and Information Analysis Laboratory Dept. of Informatics GR 54124 Greece Information Technologies Institute Centre for Research and Technology Hellas GR 57001 Greece Aristotle University of Thessaloniki Dept. of Electrical and Computer Engineering GR 54124 Greece

Stress has a severe impact on individuals irrespective of age, sex, work, or background. The reliable development of stress detection techniques enhances the social, educational, physical, economic, and professional quality of life, preventing chronic stress and proposing alleviation strategies. Research studies examine psychological, cognitive, behavioral, and physiological reactions to identify stress adequately. Deep Learning (DL) has received significant attention in recent years as it deals with high-dimensional, heterogeneous data and automatically learns representative features. This paper presents a survey on stress detection with recent DL approaches, leveraging data from all possible sources (physiological, speech, facial expressions, gestures, and social media content). The methodological outlines, the best results, and the main contributions of each study are discussed. We also describe publicly available datasets used by several of the presented works. Finally, we emphasize various open issues within the field of research and highlight key directions for future work © 2024 IEEE.

关键词： Social psychology

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