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arXiv 2025年

作者： Seran, Carl Errol Tan, Myles Joshua Toledo Karim, Hezerul Abdul AlDahoul, Nouar Biology Program College of Arts and Sciences University of St. La Salle Bacolod Philippines Department of Natural Sciences College of Arts and Sciences University of St. La Salle Bacolod Philippines Faculty of Education University of the Philippines Open University Los Baños Philippines Department of Electrical and Computer Engineering Herbert Wertheim College of Engineering University of Florida GainesvilleFL United States Department of Epidemiology College of Public Health and Health Professions College of Medicine University of Florida GainesvilleFL United States Department of Chemical Engineering College of Engineering and Technology University of St. La Salle Bacolod Philippines Department of Electronics Engineering College of Engineering and Technology University of St. La Salle Bacolod Philippines Yo-Vivo Corporation Bacolod Philippines Faculty of Engineering Multimedia University Persiaran Multimedia Cyberjaya Malaysia Department of Computer Science Division of Science New York University Abu Dhabi Abu Dhabi United Arab Emirates

The integration of Generative Artificial Intelligence (GenAI) intouniversity-level academic writing presents both opportunities and challenges,particularly in relation to cognitive dissonance (CD). This work explores howGenAI serves as both a trigger and amplifier of CD, as students navigateethical concerns, academic integrity, and self-efficacy in their writingpractices. By synthesizing empirical evidence and theoretical insights, weintroduce a hypothetical construct of GenAI-induced CD, illustrating thepsychological tension between AI-driven efficiency and the principles oforiginality, effort, and intellectual ownership. We further discuss strategiesto mitigate this dissonance, including reflective pedagogy, AI literacyprograms, transparency in GenAI use, and discipline-specific task *** approaches reinforce critical engagement with AI, fostering a balancedperspective that integrates technological advancements while safeguarding humancreativity and learning. Our findings contribute to ongoing discussions on AIin education, self-regulated learning, and ethical AI use, offering aconceptual framework for institutions to develop guidelines that align AIadoption with academic values. © 2025, CC BY.

关键词： Generative adversarial networks

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A conceptual framework of deploying a trained CNN model for mobile breast self-screening 18

A conceptual framework of deploying a trained CNN model for ...

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18th International Conference on electrical engineering/Electronics, Computer, Telecommunications and Information Technology, ECTI-CON 2021

作者： Roslidar Muchamad, Masduki Khamdan Arnia, Fitri Syukri, Maimun Munadi, Khairul Universitas Syiah Kuala Doctoral Program School of Engineering Post Graduate Program Banda Aceh23111 Indonesia Universitas Syiah Kuala Department of Electrical and Computer Engineering Banda Aceh23111 Indonesia Universitas Syiah Kuala Banda Aceh23111 Indonesia Universitas Syiah Kuala Medical Faculty Banda Aceh23111 Indonesia Universitas Syiah Kuala Banda Aceh23111 Indonesia

ISBN: (纸本)9780738111278

In this article, we propose a conceptual framework of mobile self-screening to detect breast cancer at the initial stage using temperature characteristics. A trained convolutional neural network (CNN) is implemented to extract and classify the breast thermogram. Moreover, a histogram of temperature distribution is also provided to support a recommendation to the user. As we consider the user's data privacy, reliability, low latency, and low cost, we also suggest some recommendations in inferencing the CNN model on the mobile device. Finally, we describe the application overview to demonstrate the feature function. © 2021 IEEE.

关键词： Neural network models

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Time-domain methods for quantifying dynamic cerebral blood flow autoregulation: Review and recommendations. A white paper from the Cerebrovascular Research Network (CARNet)

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Journal of Cerebral Blood Flow and Metabolism 2024年第9期44卷 1480-1514页

作者： Kostoglou, Kyriaki Bello-Robles, Felipe Brassard, Patrice Chacon, Max Claassen, Jurgen AHR Czosnyka, Marek Elting, Jan-Willem Hu, Kun Labrecque, Lawrence Liu, Jia Marmarelis, Vasilis Z Payne, Stephen J Shin, Dae Cheol Simpson, David Smirl, Jonathan Panerai, Ronney B Mitsis, Georgios D Department of Electrical and Computer Engineering McGill University Montreal QC Canada Institute of Neural Engineering Graz University of Technology Graz Austria Departamento de Ingeniería Informática Universidad de Santiago de Chile Santiago Chile Department of Kinesiology Faculty of Medicine Université Laval QC Canada Research Center of the Institut universitaire de cardiologie et de pneumologie de Québec Quebec QC Canada Department of Geriatrics Radboud University Medical Center Research Institute for Medical Innovation and Donders Institute Nijmegen Netherlands Cerebral Haemodynamics in Ageing and Stroke Medicine (CHiASM) Department of Cardiovascular Sciences University of Leicester Leicester United Kingdom Department of Clinical Neurosciences Neurosurgery Department University of Cambridge Cambridge United Kingdom Department of Neurology and Clinical Neurophysiology University Medical Center Groningen Groningen Netherlands Medical Biodynamics Program Division of Sleep and Circadian Disorders Brigham and Women's Hospital Harvard Medical School Boston MA United States Division of Sleep Medicine Harvard Medical School Boston MA United States Laboratory for Engineering and Scientific Computing Institute of Advanced Computing and Digital Engineering Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Shenzhen China Department Biomedical Engineering Viterbi School of Engineering University of Southern California Los Angeles CA United States Institute of Applied Mechanics National Taiwan University Taipei Taiwan Institute of Sound and Vibration Research University of Southampton Southampton United Kingdom Cerebrovascular Concussion Laboratory Faculty of Kinesiology University of Calgary Calgary AB Canada Sport Injury Prevention Research Centre Faculty of Kinesiology University of Calgary Calgary AB Canada Hotchkiss Brain Institute University of Calgary Calgary AB Canada NIHR Leicester Biomedical Research Centre Bri

Cerebral Autoregulation (CA) is an important physiological mechanism stabilizing cerebral blood flow (CBF) in response to changes in cerebral perfusion pressure (CPP). By maintaining an adequate, relatively constant supply of blood flow, CA plays a critical role in brain function. Quantifying CA under different physiological and pathological states is crucial for understanding its implications. This knowledge may serve as a foundation for informed clinical decision-making, particularly in cases where CA may become impaired. The quantification of CA functionality typically involves constructing models that capture the relationship between CPP (or arterial blood pressure) and experimental measures of CBF. Besides describing normal CA function, these models provide a means to detect possible deviations from the latter. In this context, a recent white paper from the Cerebrovascular Research Network focused on Transfer Function Analysis (TFA), which obtains frequency domain estimates of dynamic CA. In the present paper, we consider the use of time-domain techniques as an alternative approach. Due to their increased flexibility, time-domain methods enable the mitigation of measurement/physiological noise and the incorporation of nonlinearities and time variations in CA dynamics. Here, we provide practical recommendations and guidelines to support researchers and clinicians in effectively utilizing these techniques to study CA. © The Author(s) 2024.

关键词： CARNet cerebral autoregulation cerebral blood flow time-domain methods white paper

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Density Based Spatial Clustering of Applications with Noise and Sentence Bert Embedding for Indonesian Utterance Clustering

Density Based Spatial Clustering of Applications with Noise ...

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Computer Science, Information Technology and engineering (ICCoSITE), International Conference on

作者： Muhammad Fikri Hasani Yaya Heryadi Yulyani Arifin Lukas Wayan Suparta Computer Science Department BINUS Graduate Program Computer Science School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program Computer Science Bina Nusantara University Jakarta Indonesia Cognitive Engineering Research Group (CERG) Universitas Katolik Indonesia Atma Jaya Jakarta Indonesia Department of Electrical Engineering Faculty of Industrial Technology Institut Teknologi Nasional Yogyakarta Yogyakarta Indonesia

Task oriented chatbots are a sub-topic related to chatbots, where chatbots will perform certain tasks with specific goals. One part of creating a task-oriented chatbot is doing intent classification. Intent classification is a task of text classification. As in general text classification, the required dataset requires a label to carry out the classification process. To speed up and help the utterance analysis process, there is already a method, namely clustering, and Density-based clustering is a part of clustering that can determine cluster patterns based on arbitrary data, with DBScan as one of its algorithms. This research used 10000 client utterance data of awhatsapp based e-commerce conversation. SentenceBert also used as a state of art sentence embedding. This research yield silhouette score of 0.327 as the best result from eps of 0.1 and MinPts of 95. However, based on the cluster result, sentences labelled as noise can be further clustered. Text Preprocessing, text augmentation and sentence embedding techniques can be explored to increase the cluster performance.

关键词： Computer science Art Text categorization Clustering algorithms Oral communication Chatbots Classification algorithms

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EEG-Based Multi-Class Classification for Recognizing Pedaling Velocities: A Promising Approach for Brain-Computer Interface-Enhanced Lower-Limb Robotic Rehabilitation

EEG-Based Multi-Class Classification for Recognizing Pedalin...

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IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob)

作者： A.X. Gonzalez-Cely C.F. Blanco-Diaz D. Delisle-Rodriguez Teodiano Bastos-Filho Faculty of Electrical Engineering Federal University of Espirito Santo Vitoria-ES Brazil Department of Excellence and AI The BioRobotics Institute Scuola Superiore Sant'Anna Pisa Italy Postgraduate Program in Neuroengineering Edmond and Lily Safra International Institute of Neuroscience Santos Dumont Institute 1560- Zona Rural Macaiba-RN Brazil

ISBN: (数字)9798350386523

ISBN: (纸本)9798350386530

Lower-limb robotic rehabilitation devices have shown promising results in restoring functional activities of individuals with motor impairments. Motorized Mini-Exercise Bikes (MMEBs) are robotic devices that provide functional and repetitive flexion/extension exercises through Passive Pedaling (PP), improving muscular activity, coordination, and functional independence. These technologies activated by Brain-Computer Interfaces (BCIs) can be useful during therapeutic interventions for individuals who cannot voluntarily perform a movement. This study proposes a multi-class classification strategy for recognizing different pedaling velocities using electroencephalography signals. To provide a meaningful task, we conducted a protocol with ten able-bodied subjects who received PP from an MMEB configured at 30, 45, and 60 rpm. We provide a practical demonstration by implementing a feature extraction stage based on Common Spatial Patterns, Power Spectral Density, and a classification system based on four Machine Learning techniques: Linear Discriminant Analysis, Support Vector Machine, k-nearest Neighbors, and Decision Tree. Our strategy achieved a mean accuracy of 0.86, false positive rate of 0.13, and kappa index of 0.64, we concluded that the system has potential to be used in the design of robust controllers for robotic devices. Further research will be centered on real-time evaluation of our strategy to implement an MMEB-based BCI for personalized rehabilitation of people with lower-limb limitations, such as stroke or spinal cord injury survivors.

关键词： Support vector machines Protocols Accuracy Robot kinematics Stroke (medical condition) Feature extraction Real-time systems Electroencephalography Brain-computer interfaces Spinal cord injury

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Design for Reliability (DFR) of DC-Link Capacitors for Multilevel Inverter Topologies Operating in Hostile Environments

Design for Reliability (DFR) of DC-Link Capacitors for Multi...

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Industrial Applications Society Annual Conference on Petroleum and Chemical Industry

作者： Caio Eduardo Silva AndréM. Alzamora Marco T. A. Êvo Hélder De Paula Grad. Program in Elec. Eng. sFederal Univ. of Uberlândia (UFU) Uberlândia MG Brazil Electrical Engineering Department (DEPEL) Federal Univ. of São João del-Rei (UFSJ) São João del-Rei MG Brazil Electrical Engineering Faculty (FEELT) Federal Univ. of Uberlândia (UFU) Uberlândia MG Brazi

Especially in the mining industry, Pulse Width Modulated Voltage Source Converters (PWM-VSC) driving induction motors at long distances from the inverter is common. Due to the long cable used for the converter-to-motor connection, several unwanted high-frequency phenomena occur. A new configuration for these motor drive systems was previously proposed to address this situation. The new configuration involves keeping the rectifier in its original location, while the inverter bridge is installed close to the motor terminals, and long cables are used as a DC transmission line. The environment in which the inverter will now be located is more hostile and can compromise the inverter’s lifetime. This situation creates new concerns about the system’s reliability, despite the various advantages of this new configuration. This paper presents a study on the reliability of the DC-Link capacitors for Three Level Neutral Point Clamped (3L-NPC) and Three Level Active Neutral Point Clamped (3L-ANPC) inverter topologies when operating in aggressive environments. For this purpose, methodologies based on Physics of Failure (PoF) and Design for Reliability (DfR) were used. In this sense, an investigation was conducted to find the configurations best suited to improve the solution regarding reliability, cost, energy, and volume.

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AI-Empowered Data Offloading in MEC-Enabled IoV Networks

arXiv

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arXiv 2022年

作者： Fontes, Afonso de Ribeiro, Igor L. Muhammad, Khan Gandomi, Amir H. Gay, Gregory de Albuquerque, Victor Hugo C. Chalmers The University of Gothenburg Gothenburg Sweden University of Fortaleza CE Fortaleza Brazil Department of Software Sejong University Seoul Korea Republic of Faculty of Engineering & Information Technology University of Technology Sydney Australia Graduate Program on Teleinformatics Engineering Federal University of Ceará Fortaleza CE Fortaleza Brazil Graduate Program on Electrical Engineering Federal University of Ceará CE Fortaleza Brazil

Advancements in smart vehicle design have enabled the creation of Internet of Vehicle (IoV) technologies that can utilize the information provided by various sensors and wireless communication to perform complex functionality. Many of these functionalities rely on high computational power and low latency. Mobile Edge Computing (MEC) technologies have been proposed as a way to meet these requirements, as their proximity and decentralization provide unique benefits for networks like real-time communication, higher throughput, and flexibility. Diverse challenges to the process of offloading data in a MEC-enabled IoV network have emerged, such as offloading reliability in highly mobile environments, security for users within the same network, and energy management to keep users from being disincentivized to participate in the network. This article surveys research studies that use AI as part of the data offloading process, categorized based on four main issues: reliability, security, energy management, and service seller profit. Afterward, this article discusses challenges and future perspectives for IoV technologies. © 2022, CC BY-SA.

关键词： Mobile edge computing

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Probabilistic Power Flow Analysis Based on Low Rank Approximation and Principle Component Analysis

Probabilistic Power Flow Analysis Based on Low Rank Approxim...

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Conference on Power, Energy and Innovations (ICPEI)

作者： Jirasak Laowanitwattana Sermsak Uatrongjit PhD’s Degree Program in Electrical Engineering Faculty of Engineering Chiang Mai University Chiang Mai Thailand Faculty of Engineering Chiang Mai University Chiang Mai Thailand

Probabilistic power flow (PPF) analysis is usually applied for evaluating the effects of uncertain parameters on power system performances. This paper presents a technique to enhance the arbitrary polynomial chaos expansion (aPCE) based PPF analysis technique when applying to system with many uncertain parameters. The proposed method represents a power system response as low rank approximation (LRA). In addition, the principle component analysis (PCA) is applied to reduce the number of uncertain parameters and also de-correlate them. This combination enables the proposed method to perform PPF of the power system having large number of uncertain parameters. Based on preliminary numerical results on the modified IEEE 57-bus system, it can be noticed that the proposed modified method is able to find accurate statistical characteristics of the responses but uses less computation time compared to the MCS based PPF analysis.

关键词： Chaos Technological innovation Probabilistic logic Power systems Principal component analysis Load flow analysis

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Groundwater quality assessment using few-shot learning with prototypical, Siamese, and matching networks

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Journal of Water Process engineering 2025年 75卷

作者： Derdour, Abdessamed Baz, Mohammed Alzaed, Ali Bojer, Amanuel Kumsa Ghoneim, Sherif S.M. Artificial Intelligence Laboratory for Mechanical and Civil Structures and Soil University Center of Naama P.O. Box 66 Naama 45000 Algeria Laboratory for the Sustainable Management of Natural Resources in Arid and Semi-Arid Zones University Center of Naama Naama 45000 Algeria Department of Computer Engineering College of Computers and Information Technology Taif University Taif 21944 Saudi Arabia Architectural Engineering Program - Civil Engineering Department Faculty of Engineering Taif University Taif 21944 Saudi Arabia Ethiopian Artificial Intelligence Institute PO Box 40782 Addis Ababa Ethiopia Department of Electrical Engineering College of Engineering Taif University Taif 21944 Saudi Arabia

Groundwater quality assessment is critical for sustainable water resource management, especially in hyperarid regions like Adrar, Algeria, where data scarcity and environmental challenges hinder traditional monitoring methods. This study explores the application of three Few-Shot Learning (FSL) algorithms: Prototypical Networks, Siamese Networks, and Matching Networks, for groundwater quality classification using limited datasets. The dataset comprises 166 groundwater samples from the Adrar region, characterized by five quality classes: “Excellent,” “Very Good,” “Good,” “Satisfactory,” and “Unsatisfactory.” Results demonstrate that Prototypical Networks outperform other FSL algorithms, achieving 93 % accuracy with 10 support samples per class, while Siamese and Matching Networks achieve 90 % and 88 % accuracy, respectively. The study highlights the potential of FSL in addressing data scarcity, offering a cost-effective and efficient approach for groundwater quality assessment in data-scarce regions. The findings underscore the importance of FSL in complementing traditional methods, particularly in hyper arid areas where data collection is challenging. © 2025 The Authors

关键词： Accuracy evaluation Classification algorithms Data scarcity Hyperarid regions Water resource management

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Preliminary Design for Reference tracking feedback for Medical Cyber-Physical Pharmacokinetic Systems

Preliminary Design for Reference tracking feedback for Medic...

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IEEE International Conference on Automatica (ICA-ACCA)

作者： Pedro M. Oliveira Daniel Bustos Rodolfo Lobo Márcio J. Lacerda Jonathan M. Palma Graduate Program in Electrical Engineering Federal University of Minas Gerais Belo Horizonte Brazil Dept. of Translational Medicine Universidad Católica del Maule Talca Chile Ingenieria en Telecomunicaciones e Informática Universidad Diego Portales Santiago Chile Centre for Communications Technology School of Computing and Digital Media London Metropolitan University London UK Faculty of Engineering Universidad de Talca Curicó Chile

ISBN: (数字)9798350378115

ISBN: (纸本)9798350378122

This paper addresses the reference tracking control problem for Medical Cyber-Physical Systems (MCPS). The control theory is employed to guarantee the suitable concentration of drugs in the body of patients to guarantee a safe treatment. The MCPS is modeled as a switched system, and the modes consider the different scenarios for the problem. A discrete-time model is utilized for the pharmacokinetic process, and the zero input control strategy is employed to design state-feedback controllers with a guaranteed exponential convergence rate. A numerical experiment is presented to illustrate the validity and effectiveness of our method.

关键词： Drugs Switched systems Design methodology Process control Medical services Cyber-physical systems Numerical models Control theory Pharmacokinetics Convergence

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