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2018 IEEE Wireless Communications and Networking Conference, WCNC 2018

作者： Iran, M.B. Lima, F. Rafael M. Maciel, Tarcisio. F. Cavalcanti, F. Rodrigo P. Postgraduate Program in Electrical and Computer Engineering Federal University of Ceará Sobral Ceará Brazil Wireless Telecom Research Group Federal University of Ceará Fortaleza Ceará Brazil

ISBN: (纸本)9781538617342

In this article we deal with total transmit power minimization problem in SC-FDMA system constrained to Quality of Service (QoS) and user satisfaction constraints. Through algebraic manipulations we were able to convert an integer nonlinear optimization problem in an Integer Linear Problem (ILP) so as to obtain the optimal solution. Motivated by the high computational to obtain the optimal solution, we propose a low computational complexity suboptimal solution. The results obtained through computational simulations show that the proposed solution presents satisfactory results regarding both outage rate and energy consumption. © 2018 IEEE.

关键词： Quality of service

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A Novel Ensemble XGBoost and Deep Q-Network for Pregnancy Risk Prediction on Multi-Class Imbalanced Datasets

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ICT Express 2025年

作者： Kurnianingsih Sou Nobukawa Melyana Nurul Widyawati Cipta Pramana Nurseno Bayu Aji Afandi Nur Aziz Thohari Dwiana Hendrawati Eri Sato-Shimokawara Naoyuki Kubota Department of Electrical Engineering Politeknik Negeri Semarang H. Soedarto Tembalang Semarang 50275 Central Java Indonesia Department of Computer Science Chiba Institute of Technology 2-17-1 Tsudanuma Narashino 275-0016 Chiba Japan Department of Postgraduate Program Poltekkes Kemenkes Semarang Jl. Tirto Agung Pedalangan Kecamatan Banyumanik Semarang 50268 Central Java Indonesia Department of Obstetrics and Gynecology KRMT Wongsonegoro Hospital Jl. Tembalang Semarang 50272 Central Java Indonesia Department of Mechanical Engineering Politeknik Negeri Semarang H. Soedarto Tembalang Semarang 50275 Central Java Indonesia Faculty of Systems Design Tokyo Metropolitan University Tokyo 1910065 Japan

Addressing imbalanced data is essential for accurate prediction. We propose a novel ensemble method of XGBoost and deep Q-learning networks (DQN) for pregnancy risk prediction. First, we train the majority class utilizing XGBoost. We subsequently utilize DQN to train the minority class into binary classifications. Finally, we use the trained models from DQN and XGBoost in ensemble learning to generate the final classification results. The XGBoost-DQN model achieves high performance with 0.9819 in precision, recall, F1-score, and accuracy, outperforming several baseline classifiers on private data from 5,313 pregnant women in Indonesia and showing superior results on public datasets.

关键词： Imbalanced dataset ensemble learning XGBoost Tree Deep Q-Network pregnancy risk

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Single-stage grid tied converter to improve solar PV array performance and power quality in microgeneration-consumers units 13

Single-stage grid tied converter to improve solar PV array p...

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13th IEEE International Conference on Industry Applications, INDUSCON 2018

作者： Costa, Paulo Robson Melo Pereira, Larissa Souza Pontes, Yury Castro, Marcus Rogerio De Machado, Isaac Rocha Postgraduate Program in Electrical and Computer Engineering Federal University of Ceara Sobral Brazil

ISBN: (纸本)9781538679951

The main purpose of this paper is to present the combination of the renewable energy generation and power conditioners benefits in microgeneration systems. This is done through a Voltage Source Converter (VSC) used to maximize the efficiency of a photovoltaic (PV) solar array and, at the same time, filtering the harmonic currents of non-linear loads present in consumer units equipped with distributed microgeneration. In this way, the VSC is used to implement the Maximum Power Point Tracking (MPPT) algorithm and act as a Shunt Active Filter (SAF), forming a so-called PV-SAF system. The main strategy of the proposed system is to maximize the operating range of the DC bus voltage to perform current harmonic filtering and enable maximum power tracking satisfactorily using the p-v curve over a wide operating range. The proposed PV-SAF system uses a three-phase single-stage topology and can track the maximum power point (MPP) even in partial shading condition (PSC). The PSIM software is used to perform the simulations of the proposed PV-SAF system, the simulation results validate the control strategy that is capable of performing the reactive power compensation, current harmonics filtering and active power management produced by a PV array. © 2018 IEEE.

关键词： Power quality

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Improved Cross-Spectral Iris Matching Using Multi-Scale Weberface and Gabor Local Binary Pattern

Improved Cross-Spectral Iris Matching Using Multi-Scale Webe...

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International Conference on Electronics, computers and Artificial Intelligence (ECAI)

作者： Maulisa Oktiana Khairun Saddami Fitri Arnia Yuwaldi Away Khairul Munadi Postgraduate Program in Engineering Syiah Kuala University Banda Aceh Indonesia Department of Electrical and computer Engineering

ISBN: (纸本)9781538649022;9781538649015

Iris matching is widely used in various biometric systems, such as for person identification and verification. Cross-spectral iris matching is defined as the matching between two iris images acquired in different electromagnetic spectra. One of the problems in cross-spectral iris matching is the modality gap between the images. Therefore, feature descriptors that are invariant to illumination and generate higher accuracy are required. This paper proposes an improved cross-spectral iris matching framework using Multi-Scale Weberface (MSW). In the proposed method, MSW is integrated with the Gabor Local Binary Pattern (GLBP), and it is abbreviated as MGLBP. Experimental results demonstrate that the proposed framework outperforms the previous method with higher accuracy. By using MGLBP, the recognition rate increases by 40% and 9% compared with the LBP and GLBP, respectively.

关键词： Iris recognition Hamming distance Lighting Image segmentation Feature extraction Image recognition Gabor filters

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FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

arXiv

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arXiv 2023年

作者： Lekadir, Karim Feragen, Aasa Fofanah, Abdul Joseph Frangi, Alejandro F. Buyx, Alena Emelie, Anais Lara, Andrea Porras, Antonio R. Chan, An-Wen Navarro, Arcadi Glocker, Ben Botwe, Benard O. Khanal, Bishesh Beger, Brigit Wu, Carol C. Cintas, Celia Langlotz, Curtis P. Rueckert, Daniel Mzurikwao, Deogratias Fotiadis, Dimitrios I. Zhussupov, Doszhan Ferrante, Enzo Meijering, Erik Weicken, Eva González, Fabio A. Asselbergs, Folkert W. Prior, Fred Krestin, Gabriel P. Collins, Gary S. Tegenaw, Geletaw S. Kaissis, Georgios Misuraca, Gianluca Tsakou, Gianna Dwivedi, Girish Kondylakis, Haridimos Jayakody, Harsha Woodruf, Henry C. Mayer, Horst Joachim Aerts, Hugo JWL Walsh, Ian Chouvarda, Ioanna Buvat, Irène Tributsch, Isabell Rekik, Islem Duncan, James Kalpathy-Cramer, Jayashree Zahir, Jihad Park, Jinah Mongan, John Gichoya, Judy W. Schnabel, Julia A. Kushibar, Kaisar Riklund, Katrine Mori, Kensaku Marias, Kostas Amugongo, Lameck M. Fromont, Lauren A. Maier-Hein, Lena Alberich, Leonor Cerdá Rittner, Leticia Phiri, Lighton Marrakchi-Kacem, Linda Donoso-Bach, Lluís Martí-Bonmatí, Luis Cardoso, M. Jorge Bobowicz, Maciej Shabani, Mahsa Tsiknakis, Manolis Zuluaga, Maria A. Bielikova, Maria Fritzsche, Marie-Christine Camacho, Marina Linguraru, Marius George Wenzel, Markus De Bruijne, Marleen Tolsgaard, Martin G. Ghassemi, Marzyeh Ashrafuzzaman, Md Goisauf, Melanie Yaqub, Mohammad Abadía, Mónica Cano Mahmoud, Mukhtar M.E. Elattar, Mustafa Rieke, Nicola Papanikolaou, Nikolaos Lazrak, Noussair Díaz, Oliver Salvado, Olivier Pujol, Oriol Sall, Ousmane Guevara, Pamela Gordebeke, Peter Lambin, Philippe Brown, Pieta Abolmaesumi, Purang Dou, Qi Lu, Qinghua Osuala, Richard Nakasi, Rose Zhou, S. Kevin Napel, Sandy Colantonio, Sara Albarqouni, Shadi Joshi, Smriti Carter, Stacy Klein, Stefan Petersen, Steffen E. Aussó, Susanna Awate, Suyash Raviv, Tammy Riklin Cook, Tessa Mutsvangwa, Tinashe E.M. Rogers, Wendy A. Niessen, Wiro J. Puig-Bosch, Xènia Zeng, Yi Mohammed, Yunusa G. Aquino, Yves Saint James Salahuddin, Zohaib Starmans, Martijn P.A. Department de Matemàtiques i Informàtica Universitat de Barcelona Barcelona Spain Barcelona Spain DTU Compute Technical University of Denmark Kgs Lyngby Denmark Department of Mathematics and Computer Science Faculty of Science and Technology Milton Margai Technical University Freetown Sierra Leone Center for Computational Imaging & Simulation Technologies in Biomedicine Schools of Computing and Medicine University of Leeds Leeds United Kingdom Cardiovascular Science and Electronic Engineering Departments KU Leuven Leuven Belgium Institute of History and Ethics in Medicine Technical University of Munich Munich Germany Faculty of Engineering of Systems Informatics and Sciences of Computing Galileo University Guatemala City Guatemala Department of Biostatistics and Informatics Colorado School of Public Health University of Colorado Anschutz Medical Campus AuroraCO United States Department of Medicine Women’s College Research Institute University of Toronto Toronto Canada Universitat Pompeu Fabra BarcelonaBeta Brain Research Center Barcelona Spain Department of Computing Imperial College London London United Kingdom School of Biomedical & Allied Health Sciences University of Ghana Accra Ghana Department of Midwifery & Radiography School of Health & Psychological Sciences City University of London United Kingdom Kathmandu Nepal European Heart Network Brussels Belgium Department of Thoracic Imaging University of Texas MD Anderson Cancer Center Houston United States IBM Research Africa Nairobi Kenya Departments of Radiology Medicine and Biomedical Data Science Stanford University School of Medicine Stanford United States Institute for AI and Informatics in Medicine Klinikum rechts der Isar Technical University Munich Munich Germany Department of Computing Imperial College London London United Kingdom Muhimbili University of Health and Allied Sciences Dar es Salaam Tanzania United Republic of Ioannina Greece Almaty AI Lab Almaty Kazakhstan

Background: Despite major advances in artificial intelligence (AI) research for healthcare, the deployment and adoption of AI technologies remain limited in clinical practice. In recent years, concerns have been raised about the clinical, technical, ethical and legal risks associated with healthcare AI. To increase adoption in the real world, it is essential that AI technologies are trusted and accepted by patients, clinicians, health organisations and authorities. This paper describes the FUTURE-AI framework as the first international consensus guideline for trustworthy AI in healthcare. Methods: The FUTURE-AI consortium was founded in 2021 and now comprises 117 interdisciplinary experts from 50 countries representing all continents, including AI scientists, clinical researchers, biomedical ethicists, and social scientists. Over a two-year period, the consortium established guiding principles and best practices for trustworthy and deployable AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. Findings: The FUTURE-AI framework was established based on six guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 30 best practices were defined, addressing technical, clinical, socio-ethical and legal dimensions of trustworthy AI. The recommendations cover the entire lifecycle of healthcare AI, from design, development and validation to regulation, deployment, and monitoring. Interpretation: FUTURE-AI is a structured, risk-informed framework which provides guidance for constructing healthcare AI tools that will be trusted, deployed and adopted in real-world clinical practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of healthcare AI. Copyright © 2023, The Authors. All rights

关键词： Clinical research

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DHJ: A database of handwritten Jawi for recognition research

DHJ: A database of handwritten Jawi for recognition research

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International Conference on electrical engineering and Informatics (ICELTICs)

作者： Khairun Saddami Khairul Munadi Yuwaldi Away Fitri Arnia Postgraduate Program in Engineering Department of Electrical and Computer Engineering Syiah Kuala University Banda Aceh Indonesia

This paper proposes a database of handwritten Jawi (DHJ) for conducting the research in this area. Previously, a printed Jawi dataset has been proposed and this handwritten dataset is aim to extend the dataset collection in Jawi database. The DHJ database contains handwritten Jawi character, handwritten Jawi word, and handwritten Jawi sentence. This dataset was recorded from ten writers with different age, gender, and educational background. The participants were asked to fill a form, which had three parts: character part, word part, and sentence part. The DHJ was stored in jpeg digital image format and each of image has three different sizes. Totally, we have 3,810 handwritten Jawi characters, 300 handwritten Jawi words, and 40 handwritten Jawi sentences.

关键词： Databases Character recognition Handwriting recognition electrical engineering Informatics Digital images Writing

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Predicting the effect of variations in ambient temperature and operating power on the response of a microwave filter

Predicting the effect of variations in ambient temperature a...

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Antennas and Propagation Conference, LAPC, Loughborough

作者： L. M. de la Torre Rodríguez L. L. Bravo Roger Jose Pissolato Filho Yuso Iano H.E. Hernández Figueroa Miguel A. Sanchez-Soriano A. A. C. Alves School of Electrical and Computer Engineering University of Campinas Campinas SP Brazil Fellow of the Students-Agreement Postgraduate Program - PEC-PG CAPES/CNPq Brazil Polytechnic University College University of Alicante Alicante Alicante Spain Institute of Electrical Systems and Energy Federal University of Itajubá Itajubá MG Brazil

ISBN: (纸本)9781509007844

A multiphysics study of the electromagnetic behavior of a filter designed for use in high-power microwave systems is presented. In the past, the methodology used to design filters generally assumed ideal working conditions for these devices. However, when such filters are used in a real environment, factors such as temperature and working power affect their performance. Here, we use modern simulation tools to evaluate the electromagnetic response of a filter at different temperatures and under different operating conditions.

关键词： Electromagnetics Dielectric constant Radio frequency Microwave filters Performance evaluation Substrates Radar

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An impedance control modification guaranteeing compliance strictly within preselected spatial limits

An impedance control modification guaranteeing compliance st...

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IEEE/RSJ International Conference on Intelligent Robots and Systems

作者： A. Theodorakopoulos G. A. Rovithakis Z. Doulgeri IKY Fellowships of Excellence for Postgraduate Studies in Greece - Siemens Program Greek State Scholarships Foundation Dept. of Electrical & Computer Engineering Aristotle University of Thessaloniki Greece

In this work a modification of a Cartesian impedance controller is presented. The proposed controller guarantees compliance of the operated manipulator strictly within preselected spatial limits. Specifically, by introducing a nonlinear stiffness term into the control design, the manipulator is prevented from colliding with an explicitly defined hard boundary, like a human being or a fragile object. When operating in spatial regions sufficiently away from the boundary the manipulator behaves as if it were operated via a linear stiffness impedance controller, thus preserving in these regions its compliance and convergence characteristics. A comparative simulation with a linear stiffness impedance controller is performed to verify and clarify the proposed approach.

关键词： Impedance Manipulator dynamics Robot kinematics Force Aerospace electronics

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Detecting and locating cell phone signals from avalanche victims using unmanned aerial vehicles

Detecting and locating cell phone signals from avalanche vic...

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International Conference on Unmanned Aircraft Systems (ICUAS)

作者： Victor Wolfe William Frobe Vineetha Shrinivasan Tsung-Yen Hsieh Dept. Naval Postgraduate School Electrical and Computer Engineering Monterey CA USA Lieutenant US Navy Program Executive Office C4I San Diego CA USA Univ. of Colorado USA

Scores of avalanche injuries and fatalities occur annually. This research presents an avalanche search and rescue (SAR) proof-of-concept option that utilizes a small unmanned aerial vehicle (UAV) helicopter with a payload capable of detecting and locating a buried victim's cell phone. Cell phones involved in this research were modern-day fourth generation (4G) Long Term Evolution (LTE) devices with an application specifically designed for potential SAR detection. This study focused primarily on radio frequency (RF) propagation modeling through snow and air. The analysis and associated field trials demonstrate that it is feasible to detect these cell phone devices through avalanche debris up to 7 feet deep. Alpine UAS inclement flight demonstrations were also conducted. Although many studies have been published on detecting the position of mobile cellular devices, this is the first of its kind to quantify the detection of a cell phone that is buried under the snow.

关键词： Snow Long Term Evolution Helicopters Streaming media Unmanned aerial vehicles Accuracy Smart phones

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Estimates, trends, and drivers of the global burden of type 2 diabetes attributable to PM_2·5 air pollution, 1990–2019: an analysis of data from the Global Burden of Disease Study 2019

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The Lancet Planetary Health 2022年第7期6卷 e586-e600页

作者： Burkart, Katrin Causey, Kate Cohen, Aaron J Wozniak, Sarah S Salvi, Devashri Digvijay Abbafati, Cristiana Adekanmbi, Victor Adsuar, Jose C Ahmadi, Keivan Alahdab, Fares Al-Aly, Ziyad Alipour, Vahid Alvis-Guzman, Nelson Amegah, Adeladza Kofi Andrei, Catalina Liliana Andrei, Tudorel Ansari, Fereshteh Arabloo, Jalal Aremu, Olatunde Aripov, Timur Babaee, Ebrahim Banach, MacIej Barnett, Anthony Bärnighausen, Till Winfried Bedi, Neeraj Behzadifar, Masoud Béjot, Yannick Bennett, Derrick A Bensenor, Isabela M Bernstein, Robert S Bhattacharyya, Krittika Bijani, Ali Biondi, Antonio Bohlouli, Somayeh Breitner, Susanne Brenner, Hermann Butt, Zahid A Cámera, Luis Alberto Cantu-Brito, Carlos Carvalho, Felix Cerin, Ester Chattu, Vijay Kumar Chauhan, Bal Govind Choi, Jee-Young Jasmine Chu, Dinh-Toi Dai, Xiaochen Dandona, Lalit Dandona, Rakhi Daryani, Ahmad Davletov, Kairat De Courten, Barbora Demeke, Feleke Mekonnen Denova-Gutiérrez, Edgar Dharmaratne, Samath Dhamminda Dhimal, Meghnath Diaz, Daniel Djalalinia, Shirin Duncan, Bruce B El Sayed Zaki, Maysaa Eskandarieh, Sharareh Fareed, Mohammad Farzadfar, Farshad Fattahi, Nazir Fazlzadeh, Mehdi Fernandes, Eduarda Filip, Irina Fischer, Florian Foigt, Nataliya A Freitas, Marisa Ghashghaee, Ahmad Gill, Paramjit Singh Ginawi, Ibrahim Abdelmageed Gopalani, Sameer Vali Guo, Yuming Gupta, Rajat Das Habtewold, Tesfa Dejenie Hamadeh, Randah R Hamidi, Samer Hankey, Graeme J Hasanpoor, Edris Hassen, Hamid Yimam Hay, Simon I Heibati, Behzad Hole, Michael K Hossain, Naznin Househ, Mowafa Irvani, Seyed Sina Naghibi Jaafari, Jalil Jakovljevic, Mihajlo Jha, Ravi Prakash Jonas, Jost B Jozwiak, Jacek Jerzy Kasaeian, Amir Kaydi, Neda Khader, Yousef Saleh Khafaie, Morteza Abdullatif Khan, Ejaz Ahmad Khan, Junaid Khan, Md Nuruzzaman Khatab, Khaled Khater, Amir M Kim, Yun Jin Kimokoti, Ruth W Kisa, Adnan Kivimäki, Mika Knibbs, Luke D Kosen, Soewarta Koul, Parvaiz A Koyanagi, Ai Defo, Barthelemy Kuate Kugbey, Nuworza Lauriola, Paolo Lee, Paul H Leili, Mostafa Lewycka, Sonia Li, Shanshan Lim, Lee-Ling Li Institute for Health Metrics and Evaluation University of Washington Seattle WA United States Department of Health Metrics Sciences School of Medicine University of Washington Seattle WA United States Division of Plastic and Reconstructive Surgery University of Washington Seattle WA United States Research Insights and Innovation Seattle WA United States Health Effects Institute Boston MA United States School of Public Health Boston University Boston MA United States Department of Juridical and Economic Studies La Sapienza University Rome Italy Department of Population Medicine Cardiff University Cardiff United Kingdom Sport Science Department University of Extremadura Badajoz Spain School of Public Health Imperial College London London United Kingdom Department of Primary Care and Public Health Imperial College London London United Kingdom Mayo Evidence-based Practice Center Mayo Clinic Foundation for Medical Education and Research Rochester MN United States John T. Milliken Department of Internal Medicine Washington University in St. Louis St. Louis MO United States Clinical Epidemiology Center US Department of Veterans Affairs (VA) St Louis MO United States Health Management and Economics Research Center Iran University of Medical Sciences Tehran Iran Department of Health Economics Iran University of Medical Sciences Tehran Iran Preventive Medicine and Public Health Research Center Iran University of Medical Sciences Tehran Iran Pars Advanced and Minimally Invasive Medical Manners Research Center Iran University of Medical Sciences Tehran Iran Department of Community and Family Medicine Iran University of Medical Sciences Tehran Iran Research Group in Hospital Management and Health Policies Universidad de la Costa (University of the Coast) Barranquilla Colombia Research Group in Health Economics University of Cartagena Cartagena Colombia Department of Biomedical Science University of Cape Coast Cape Coast Ghana Cardiology Department Ca

Background: Experimental and epidemiological studies indicate an association between exposure to particulate matter (PM) air pollution and increased risk of type 2 diabetes. In view of the high and increasing prevalence of diabetes, we aimed to quantify the burden of type 2 diabetes attributable to PM_2·5 originating from ambient and household air pollution. Methods: We systematically compiled all relevant cohort and case-control studies assessing the effect of exposure to household and ambient fine particulate matter (PM_2·5) air pollution on type 2 diabetes incidence and mortality. We derived an exposure–response curve from the extracted relative risk estimates using the MR-BRT (meta-regression—Bayesian, regularised, trimmed) tool. The estimated curve was linked to ambient and household PM_2·5 exposures from the Global Burden of Diseases, Injuries, and Risk Factors Study 2019, and estimates of the attributable burden (population attributable fractions and rates per 100 000 population of deaths and disability-adjusted life-years) for 204 countries from 1990 to 2019 were calculated. We also assessed the role of changes in exposure, population size, age, and type 2 diabetes incidence in the observed trend in PM_2·5-attributable type 2 diabetes burden. All estimates are presented with 95% uncertainty intervals. Findings: In 2019, approximately a fifth of the global burden of type 2 diabetes was attributable to PM_2·5 exposure, with an estimated 3·78 (95% uncertainty interval 2·68–4·83) deaths per 100 000 population and 167 (117–223) disability-adjusted life-years (DALYs) per 100 000 population. Approximately 13·4% (9·49–17·5) of deaths and 13·6% (9·73–17·9) of DALYs due to type 2 diabetes were contributed by ambient PM2·5, and 6·50% (4·22–9·53) of deaths and 5·92% (3·81–8·64) of DALYs by household air pollution. High burdens, in terms of numbers as well as rates, were estimated in Asia, sub-Saharan Africa, and South Am

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