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SSRN 2024年

作者： Tsekouras, George J. Lamprinidi, Eleni P. Orfanos-Pepainas, Stamatios Kanellos, Fotios D. Prousalidis, John M. Vlachos, Andreas Tsirekis, Constantinos D. School of Electrical & Computer Engineering National Technical University of Athens Greece Department of Electrical and Electronics Engineering Faculty of Engineering University of West Attica Greece School of Electrical & Computer Engineering Technical University of Crete Greece School of Naval Architecture & Marine Engineering National Technical University of Athens Greece Department of Long Term Energy Planning Renewable Energy Sources & Environmental Policy Regulatory Authority of Energy Greece Strategy & System Planning Department Hellenic Independent Power Transmission Operator Greece

According to European regulation 2023/1804/EU, all member states shall ensure the availability of cold ironing (CI) in large ports for at least 90% of the total number of port calls of seagoing ships above 5,000 gross tonnes, by the end of 2029. This is a measure expected to improve air quality, especially in the vicinity of the port’s urban environment. Preliminary studies show that there are financial viability issues related to CI installations, under the current electricity pricing policies and the high investment cost for CI infrastructure. In this paper, the financial viability of the penetration of photovoltaic (PV) units optimized from spatial and power produced point of view is investigated. The proposed method is applied to various ship types, indicatively for the Thessaloniki Port, taking into consideration the connection with the Medium Voltage (MV) electricity distribution grid, as well as the construction, operation, and maintenance costs, under modified economic criterion of benefit cost ratio (BCR). © 2024, The Authors. All rights reserved.

关键词： Air quality

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Dynamic Reward Adjustment in Multi-Reward Reinforcement Learning for Counselor Reflection Generation

arXiv

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arXiv 2024年

作者： Min, Do June Pérez-Rosas, Verónica Resnicow, Kenneth Mihalcea, Rada Department of Electrical Engineering and Computer Science Ann ArborMI United States School of Public Health University of Michigan Ann ArborMI United States

In this paper, we study the problem of multi-reward reinforcement learning to jointly optimize for multiple text qualities for natural language generation. We focus on the task of counselor reflection generation, where we optimize the generators to simultaneously improve the fluency, coherence, and reflection quality of generated counselor responses. We introduce two novel bandit methods, DynaOpt and C-DynaOpt, which rely on the broad strategy of combining rewards into a single value and optimizing them simultaneously. Specifically, we employ non-contextual and contextual multi-arm bandits to dynamically adjust multiple reward weights during training. Through automatic and manual evaluations, we show that our proposed techniques, DynaOpt and C-DynaOpt, outperform existing naive and bandit baselines, demonstrating their potential for enhancing language models. © 2024, CC BY.

关键词： Reinforcement learning

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Design and Implementation of a Smart Human Posture Monitoring Device Using a Wearable Vest 7

Design and Implementation of a Smart Human Posture Monitorin...

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7th International Conference on Advances in Biomedical engineering, ICABME 2023

作者： Daou, Roy Abi Zeid Geha, Elio Aoun, Youssef Hayek, Ali Boercsoek, Josef Olmedo, Jose Javier Serrano Université la Sagesse Biomedical Engineering Department Furn El Chebbak Beirut Lebanon Mart Learning Education and Research Center Chananir Lebanon Lebanese German University Faculty of Public Health Biomedical Technologies Department Jounieh Lebanon University of Applied Science Hamm-Lippstadt Department Lippstadt 2 Hardware Engineering Marker Allee 76-78 Hamm59063 Germany University of Kassel Faculty of Electrical Engineering /Computer Science Department of Computer Architecture and System Programming Germany Universidad Politecnica de Madrid Lab of Bioinstrumentation and Nanomedicine Centro de Tecnologia Biomedica Spain

ISBN: (纸本)9798350325850

Chronic back pain can present a serious health concern, with symptoms that can significantly affect an individual's well-being, mobility, and overall quality of life over an extended period. While chronic back pain may manifest suddenly in some cases, it often develops gradually and persists for weeks, and in untreated cases, it can linger for years. Hence, the utilization of assistive devices such as wearable posture-monitoring vests can offer valuable assistance and guidance to users. This research paper is dedicated to the development of a system for detecting, diagnosing, and correcting poor posture, specifically leaning posture. The vest is designed to provide users with visual, auditory, and tactile cues to help them address this issue, thereby reducing the risk associated with leaning. Additionally, an integrated electrical box has been designed to consolidate all components directly onto the main board in a secure enclosure. This box also displays the daily count of instances where the user has leaned. This system is characterized by its electrical safety, portability, compactness, comfort, and affordability. A comprehensive analysis of the system's performance has been conducted with a meticulous evaluation of accuracy. Each component of the system has undergone successful testing, and the system as a whole is currently in the testing phase. The results of these tests have indicated a lack of faulty errors and have demonstrated outstanding accuracy and detection rates. Over 100 individuals of varying ages, genders, and BMI categories were involved in testing, with each person wearing the device for an average of six hours. The accuracy rate achieved was 98.85%, with an average of 54.35 instances of poor posture detected per participant. © 2023 IEEE.

关键词： Wearable technology

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UAV weaknesses against deauthentication based hijacking attacks

UAV weaknesses against deauthentication based hijacking atta...

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International Symposium on Applied Machine Intelligence and Informatics (SAMI)

作者： Brúnó Krasnyánszki Sándor Tihamér Brassai András Németh Under Graduate Student in Computer Science Engineering Óbuda University Budapest Hungary Department of Electrical Engineering Sapientia Hungarian University of Transylvania Corunca Romania Department of Electronic Warfare National University of Public Service Budapest Hungary

Unmanned Aerial Vehicles (UAVs) and Unmanned Aerial Systems (UAS) are more important than ever before and we’re using it daily for agriculture, photo making, film industry, Search & Rescue, long range cable quality control, Geo Informatics, cartography, and state use (including border protection, disaster recovery and the military). Most of the UAV/UAS devices are using digital communication protocols nowadays, especially the IEEE 802.11 Wireless Local Area of Network for the cheaper and middle price UAV/UAS. Unfortunately, the protocols specified by the previously mentioned IEEE standard are vulnerable against some attacks. This research paper proposes a method that can interrupt the normal operation of the UAV/UAS that are using the IEEE 802.11 protocol and hijack the interrupted UAV/UAS permanently. For this method, deauthentication attack has been performed against the UAV/UASs. The attack vector is capable of denying the communication between the UAV and the control station. With sufficient prior knowledge it is possible to hijack the UAV that communicates on a 802.11 based protocol. Deauthentication based attacks are much more sophisticated than for example traditional electronic jamming, because the target can be precisely chosen and the attack will not interrupt other devices using the same communication method. The research has unfortunately some limitations in equipment, measurements conducted only in three different types of UAVs from three price categories, but in the future the authors would like to extend the range of UAVs. From this three UAVs two are hijackable by this method and the third should also be hijackable with some modifications. This research could be a base later for UAV/UAS counter measure systems for the safety of all of us.

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Implementation of Robots in Autism Spectrum Disorder Research: Diagnosis and Emotion Recognition and Expression

Implementation of Robots in Autism Spectrum Disorder Researc...

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International Conference on Modern Circuits and Systems Technologies (MOCAST)

作者： Konstantinos-Filippos Kollias Luis Miguel Maia Marques Torres E Silva Panagiotis Sarigiannidis Christine K. Syriopoulou-Delli George F. Fragulis Department of Electrical and Computer Engineering University of Western Macedonia Kozani Greece Faculty of Engineering University of Porto Porto Portugal Department of Educational and Social Policy University of Macedonia Thessaloniki Greece

Early Autism Spectrum Disorder (ASD) diagnosis and intervention with state-of-the-art instruments, such as Artificial Intelligence and Robots is a rather challenging and promising research area which can optimise outcomes regarding the quality of life of ASD individuals and their families. Autistic individuals tend to show, among others, deficits in emotion recognition and expression. The present review study, aiming to review the literature concerning the contribution of Robotics to ASD research, found 27 review studies conducted from 2016 to February 2023 on PubMed and Web of Science databases in which robots were either employed in ASD diagnosis, or emotion recognition and expression. Results pointed out beneficial effects of robots’ employment as far as ASD diagnosis and emotion recognition and expression are concerned. Limitations and recommendations for future research are also involved, i.e., studies combining the Internet of Things (IoT) and Robotics with an aim to early ASD diagnosis, assessment, intervention, and inclusion.

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Effect of Epsilon-Near-Zero Modes on the Casimir Interaction between Ultrathin Films

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Physical Review Letters 2023年第19期130卷 196901-196901页

作者： Tao Gong Iñigo Liberal Benjamin Spreng Miguel Camacho Nader Engheta Jeremy N. Munday Department of Electrical and Computer Engineering University of California Davis California 95616 USA Department of Materials Science and Engineering University of California Davis California 95616 USA Institute of Smart Cities Public University of Navarre Pamplona Navarre 31006 Spain Department of Electronics and Electromagnetism University of Seville Sevilla Sevilla 41012 Spain Department of Electrical and Systems Engineering University of Pennsylvania Philadelphia Pennsylvania 19104 USA

Vacuum fluctuation-induced interactions between macroscopic metallic objects result in an attractive force between them, a phenomenon known as the Casimir effect. This force is the result of both plasmonic and photonic modes. For very thin films, field penetration through the films will modify the allowed modes. Here, we theoretically investigate the Casimir interaction between ultrathin films from the perspective of force distribution over real frequencies for the first time. Pronounced repulsive contributions to the force are found due to the highly confined and nearly dispersion-free epsilon-near-zero (ENZ) modes that only exist in ultrathin films. These contributions persistently occur around the ENZ frequency of the film irrespective of the interfilm separation. We further associate the ENZ modes with a striking thickness dependence of a proposed figure of merit (FOM) for conductive thin films, suggesting that the motion of objects induced by Casimir interactions is boosted for deeply nanoscale sizes. Our results shed light on the correlation between special electromagnetic modes and the vacuum fluctuation-induced force as well as the resulting mechanical properties of ultrathin ENZ materials, which may create new opportunities for engineering the motion of ultrasmall objects in nanomechanical systems.

关键词： Casimir effect Electromagnetic interactions Light-matter interaction Metamaterials Optical materials & elements

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Examining the development of attitude scales using Large Language Models (LLMs)

arXiv

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arXiv 2024年

作者： Symeonaki, Maria Stamou, Giorgos Kazani, Aggeliki Tsouparopoulou, Eva Stamatopoulou, Glykeria School of Political Sciences Department of Social Policy Panteion University of Social and Political Sciences Greece School of Electrical and Computer Engineering National Technical University of Athens Greece

For nearly a century, social researchers and psychologists have debated the efficacy of psychometric scales for attitude measurement, focusing on Thurstone’s equal appearing interval scales and Likert’s summated rating scales. Thurstone scales fell out of favour due to the labour-intensive process of gathering judges’ opinions on the initial items. However, advancements in technology have mitigated these challenges, nullifying the simplicity advantage of Likert scales, which have their own methodological issues. This study explores a methodological experiment to develop a Thurstone scale for assessing attitudes towards individuals living with AIDS. An electronic questionnaire was distributed to a group of judges, including undergraduate, postgraduate, and PhD students from disciplines such as social policy, law, medicine, and computer engineering, alongside established social researchers, and their responses were statistically analysed. The primary innovation of this study is the incorporation of an Artificial Intelligence (AI) Large Language Model (LLM) to evaluate the initial 63 items, comparing its assessments with those of the human judges. Interestingly, the AI provided also detailed explanations for its categorisation. Results showed no significant difference between AI and human judges for 35 items, minor differences for 23 items, and major differences for 5 items. This experiment demonstrates the potential of integrating AI with traditional psychometric methods to enhance the development of attitude measurement scales. © 2024, CC BY-NC-SA.

关键词： Students

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Overcoming Hurdles: A Comprehensive Analysis of Renewable Energy in Bangladesh

Overcoming Hurdles: A Comprehensive Analysis of Renewable En...

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Computational Intelligence for Green and Sustainable Technologies (ICCIGST), International Conference on

作者： Md Ashraful Islam Khusbu Rahman M M Naushad Ali Mashfiq Ahasan Hridoy Suribabu Golla Taha Selim Ustun Valisher Sapayev Odilbek Uglu Abdullayev Abdulla Fayzulla Ugli Ahmed Al Mansur Department of Electrical and Electronic Engineering Green University of Bangladesh Bangladesh Doermer School of Business Purdue University Fort Wayne USA Centre for Policy Dialogue (CPD) Dhaka Bangladesh Civil Engineering Department SRKR Engineering College India Department of Electrical and Computer Engineering Carnegie Mellon University United States Department of social-humanitarian and exact specific sciences Tashkent state University of Economy Uzbekistan

ISBN: (数字)9798350381887

ISBN: (纸本)9798350381894

This study addresses the critical aspects of renewable energy development in Bangladesh, identifying challenges and offering practical solutions. It recommends revising and enhancing renewable energy policies to establish realistic and inclusive targets over short, medium, and long terms. Financial support is advocated through the introduction of incentives and subsidies, particularly to overcome economic barriers hindering widespread solar technology adoption, with a focus on rural areas. The proposal includes combining feedin tariff and net metering, removing the limit in net metering, and offering tax exemptions for early-stage independent power producers. Additionally, the study underscores the need for integrated institutional and capacity development, emphasizing the establishment of an institute for skill building and technology innovation. Collaboration is a key theme, urging synergies between academia, government, and industries to bridge the gap between expertise, policies, and applications, ultimately contributing to a sustainable energy future in Bangladesh.

关键词： Industries Renewable energy sources Technological innovation Green buildings Government Buildings Collaboration Tariffs Propulsion Proposals

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Adapting a Self-Supervised Speech Representation for Noisy Speech Emotion Recognition by Using Contrastive Teacher-Student Learning 48

Adapting a Self-Supervised Speech Representation for Noisy S...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Leem, Seong-Gyun Fulford, Daniel Onnela, Jukka-Pekka Gard, David Busso, Carlos University of Texas at Dallas Department of Electrical and Computer Engineering United States Boston University Occupational Therapy and Psychological & Brain Sciences United States Harvard University Harvard T.H. Chan School of Public Health Department of Biostatistics United States San Francisco State University Psychology Department United States

ISBN: (纸本)9781728163277

Studies have shown high performance in the speech emotion recognition (SER) task by fine-tuning a self-supervised speech representation model. Although this model can provide emotionally discriminative embedding in clean conditions, adapting it to a noisy target environment is still required when deployed on real-world applications. For adaptation, it is essential to balance between acquiring new knowledge from noisy speech and keeping the previous knowledge acquired during the pre-training and fine-tuning of the model. Therefore, we propose a contrastive teacher-student learning framework to retrain a self-supervised speech representation model for noisy SER. To keep the knowledge of the original model, we minimize the root mean square error between the clean embeddings from the original SER model and the noisy embeddings from the retrained model. To acquire the discriminative knowledge in the target noisy condition, we also minimize the InfoNCE loss by selecting the corresponding clean embedding as a positive sample and other noisy embeddings with different emotional labels as negative samples. Our experiment with the clean and noisy version of the MSP-Podcast corpus demonstrates that the contrastive teacher-student learning framework can significantly improve the performance of the model only trained with the clean speech in the target noisy condition for all the emotional attributes. © 2023 IEEE.

关键词： Embeddings

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Enhanced Multiclass Brain Tumor Detection in MRI Using Similarity-Navigated Graph Neural Networks with Banyan Tree Growth Optimization

Enhanced Multiclass Brain Tumor Detection in MRI Using Simil...

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Ubiquitous Computing and Intelligent Information Systems (ICUIS), International Conference on

作者： Babu Rao K Pallavi Singh Gargi Shankar Verma L. Mohana Kannan Swaraj Kadam Department of Computer Science and Engineering Mangalore Institute of Technology & Engineering Mijar Karnataka India Department of Public Health Dentistry Saraswati Dental College & Hospital Lucknow Uttar Pradesh India Department of Computer Science and Engineering Krishna's Vikash Institute of Technology Raipur Chhattisgarh India Department of Biomedical Engineering Erode Sengunthar Engineering College Perundurai Tamil Nadu India Department of Electrical Engineering Dr. D Y Patil Institute of Technology Pune Maharashtra India

ISBN: (数字)9798331529635

ISBN: (纸本)9798331529642

The diagnosis of brain tumors is admittedly difficult because of their varied and complicated nature. Therefore, one has to get the right diagnosis and categorization for it to be treated well. This is a situation where identification of multiple classes such as; meningioma, pituitary, glioma and no tumor is crucial in taking the right course of action. Therefore, a new deep learning architecture is suggested in this work to increase the specificity between different types of tumors by detecting brain tumors with high accuracy in MRI scans. The proposed methodology starts with the Cumulative Curve Fitting Approximation (CCFA) preprocessing algorithm to improve the quality of MRI images. Preprocessed images are used as inputs for feature extraction based on geometric algebra transformers. These transformers include geometric relationships within the data, ensuring more distinctive selection. The categorization is further carried out with assistance of a Similarity-Navigated Graph Neural Network (SNGNN) incorporating the Banyan Tree Growth Optimizer (BTGO) model, which is constructed for efficient detection of the subtlest patterns and their connection between classes of brain tumors. It integrates very well in terms of the accuracy shown by its performance metrics. It shows effectiveness in a multiclass scenario of detecting brain tumors, which helps in the precise and early diagnosis and even tailors proper treatment plans.

关键词： Accuracy Algebra Magnetic resonance imaging Brain tumors Brain modeling Transformers Feature extraction Approximation algorithms Graph neural networks Curve fitting

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