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Large Expansion of Operating Voltage Window in Polymer Based Flexible Solid State Supercapacitor

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Large Expansion of Operating Voltage Window in Polymer Based...

MRS Advances

作者： White, Curtis Skinner, Tristan Santiago, Kevin Pradhan, Sangram K. Bahoura, Messaoud Center for Materials Science and Research Norfolk State University VA23504 United States Electrical and Computer Engineering Dept. Tennessee State University TN37209 United States

Specific demand of lightweight and high efficient flexible energy unit is increased day by day for its integration into bendable electronics devices. Super-capacitor is one of the promising power unit to meet the current requirement. Flexible metal oxide and polypyrrole based flexible electrode materials are prepared using electrodeposition. The calculated specific capacitances of the devices shows 0.5 mill farad per gram. The super-capacitor is ultra-flexible, stable with operational voltage window expands from 0.8 to 2.5 V which can help to reduce the number of super-capacitor in series connection to obtain the same output. In this study, a conductive polymer can be coupled with MnO2 to improve capacitance and conductivity of a hybrid structure based on MnO2. © 2018 Materials research Society.

关键词： Metals

Real-Time Monitoring and Control of a Microgrid - Pilot Project: Hardware and Software

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Real-Time Monitoring and Control of a Microgrid - Pilot Proj...

Australasian Universities Power engineering Conference, AUPEC

作者： Nasser Hosseinzadeh Ali Mousavi Abbas Teirab Shahriar Varzandeh Amer Al-Hinai Smart Power Eng. Research Group School of Engineering Deakin University Geelong Victoria Australia Sustainable Energy Research Center Sultan Qaboos University Oman Dept. of Electrical and Computer Eng. College of Engineering Sultan Qaboos University Oman

ISBN: (数字)9781728150437

ISBN: (纸本)9781728150444

This paper explains the development of a small-scale microgrid, which is equipped with a dedicated wireless communication network and a real-time monitoring and control system. The microgrid was formed by combining four renewable energy systems (RESs) located in different parts of the university campus. The aim of the project was to collect data from embedded sensors in each of these RESs, and design and implement an on-line near real-time monitoring system to enable observation of their performances simultaneously. Challenges that have been addressed are 1) security of information going through a dedicated communication channel; 2) time synchronisation of the data packets, which were transmitted at the same time, but received at the access point at slightly different times; 3) capability of the system for the interoperabilty of different types of inverters.

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

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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-dept. 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

Surface Plasmons on a Au Waveguide Electrode Open New Redox Channels Associated with the Transfer of Energetic Carriers

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arXiv 2021年

作者： Hirbodvash, Zohreh Krupin, Oleksiy Northfield, Howard Olivieri, Anthony Baranova, Elena A. Berini, Pierre Dept. of Physics University of Ottawa 150 Louis Pasteur OttawaONK1N 6N5 Canada Center for Research in Photonics University of Ottawa 25 Templeton St. OttawaONK1N 6N5 Canada Department of Chemical and Biological Engineering University of Ottawa 161 Louis-Pasteur OttawaONK1N 6N5 Canada Centre for Catalysis Research and Innovation University of Ottawa 161 Louis-Pasteur OttawaONK1N 6N5 Canada School of Electrical Engineering and Computer Science University of Ottawa 800 King Edward Ave. OttawaONK1N 6N5 Canada

Plasmonic catalysis holds significant promise for opening new reaction pathways inaccessible thermally, or for improving the efficiency of chemical processes. However, challenges persist in distinguishing photothermal effects from effects due to energetic electrons and/or holes excited in the metal comprising a plasmonic structure. Here we use a Au stripe waveguide along which surface plasmon polaritons (SPPs) propagate, operating simultaneously as a working electrode in a 3-electrode electrochemical cell. Cyclic voltammograms were obtained under SPP excitation as a function of incident optical power and wavelength (λ0 ~1350 nm), enabling processes involving energetic holes to be separated from processes involving energetic electrons by investigating oxidation and reduction reactions separately. Redox current densities increase by 10× under SPP excitation. The oxidation, reduction and equilibrium potentials drop by as much as 2× and split beyond a clear threshold with SPP power in correlation with the photon energy. The temperature of the working electrode under SPP excitation is monitored in situ and independent control experiments isolate thermal effects. Chronoamperometry measurements with on-off modulated SPPs at 600 Hz, yield a diffusion-limited rapid current response modulated at the same frequency, ruling out thermally-enhanced mass transport. Our observations are attributed to the opening of optically controlled non-equilibrium redox channels associated with energetic carrier transfer to the redox species. © 2021, CC BY-NC-ND.

关键词： Photons

Triboelectric Backgrounds to Radio-based Polar UHE Neutrino Experiments

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arXiv 2021年

作者： Aguilar, J.A. Anker, A. Allison, P. Archambault, S. Baldi, P. Barwick, S.W. Beatty, J.J. Beise, J. Besson, Dave Bishop, A. Bondarev, E. Botner, O. Bouma, S. Buitink, S. Cataldo, M. Chen, C.C. Chen, C.H. Chen, P. Chen, Y.C. Choi, T. Clark, B.A. Clay, W. Curtis-Ginsberg, Z. Connolly, A. Cremonesi, L. Dasgupta, P. Davies, J. De Kockere, S. De Vries, K.D. Deaconu, C. DuVernois, M.A. Flaherty, J. Friedman, E. Gaior, R. Gaswint, G. Glaser, C. Hallgren, A. Hallmann, S. Ham, Y.-B. Hanson, J.C. Harty, N. Hendricks, B. Hoffman, K.D. Hong, E. Hornhuber, C. Hsu, S.Y. Hu, L. Huang, J.J. Huang, M.-H. Hughes, K. Ishihara, A. Jee, G. Jung, J. Karle, A. Kelley, J.L. Klein, S.R. Kleinfelder, S.A. Kim, J. Kim, K.-C. Kim, M.-C. Kravchenko, I. Krebs, R. Ku, Y. Kuo, C.Y. Kurusu, K. Kwon, Hyuck-Jin Lahmann, R. Landsman, H. Latif, U. Lee, C. Leung, C.-H. Li, C.-J. Liu, J. Liu, T.-C. Lu, M.-Y. Madison, K. Mammo, J. Mase, K. McAleer, S. Meures, T. Meyers, Z.S. Michaels, K. Mikhailova, M. Mulrey, K. Nam, J. Nichol, R.J. Nir, G. Nelles, A. Novikov, A. Nozdrina, A. Oberla, E. Oeyen, B. Osborn, J. Pan, Y. Pandya, H. Paul, M.P. Persichilli, C. Pfendner, C. Plaisier, I. Punsuebsay, N. Pyras, L. Rice-Smith, R. Roth, J. Ryckbosch, D. Scholten, O. Seckel, D. Seikh, M.F.H. Shiao, Y.-S. Shin, B.-K. Shultz, A. Smith, D. Southall, D. Tatar, J. Torres, J. Toscano, S. Tosi, D. Touart, J. Van Den Broeck, D.J. van Eijndhoven, N. Varner, G.S. Vieregg, A.G. Wang, M.-Z. Wang, S.-H. Wang, Y.H. Welling, C. Williams, D.R. Wissel, S. Xie, C. Yoshida, S. Young, R. Zhao, L. Zink, A. Universite Libre de Bruxelles Science Faculty CP230 BrusselsB-1050 Belgium Department of Physics and Astronomy University of California IrvineCA92697 United States Dept. of Physics Center for Cosmology and AstroParticle Physics The Ohio State University ColumbusOH43210 United States Dept. of Physics Chiba University Chiba Japan Department of Information and Computer Science University of California IrvineCA92697 United States Uppsala University Department of Physics and Astronomy UppsalaSE-752 37 Sweden University of Kansas Department of Physics and Astronomy LawrenceKS66045 United States Dept. of Physics University of Wisconsin-Madison MadisonWI53706 United States ECAP Friedrich-Alexander Universität Erlangen-Nürnberg Erlangen91058 Germany Vrije Universiteit Brussel Astrophysical Institute Pleinlaan 2 Brussels1050 Belgium Dept. of Physics Grad. Inst. of Astrophys. Leung Center for Cosmology and Particle Astrophysics National Taiwan University Taipei Taiwan Department of Polar Science Korea University of Science and Technology Daejeon Korea Republic of Division of Atmospheric Sciences Korea Polar Research Institute Incheon Korea Republic of Department of Astronomy Space Science and Geology Chungnam National University Daejeon34134 Korea Republic of Dept. of Physics and Astronomy Michigan State University East LansingMI48824 United States Dept. of Physics Enrico Fermi Institue Kavli Institute for Cosmological Physics University of Chicago ChicagoIL60637 United States Dept. of Physics and Astronomy University College London London United Kingdom Vrije Universiteit Brussel Dienst ELEM Brussels Belgium Dept. of Physics University of Maryland College ParkMD20742 United States Whittier College Department of Physics WhittierCA90602 United States Dept. of Physics University of Delaware NewarkDE19716 United States Dept. of Physics Pennsylvania State University University ParkPA16802 United States Center for Multi-Messenger

The proposed IceCube-Gen2 seeks to instrument approximately 500 square kilometers of Antarctic ice near the geographic South Pole with radio antennas, in the hopes of observing the highest-energy neutrinos (E>1 EeV) populating the Universe. For this purpose IceCube-Gen2 will exploit the impulsive radio emission produced by neutrino interactions in the polar ice cap. In such polar-sited radio-frequency neutrino search experiments, rare single event candidates must be unambiguously identified above backgrounds. Background rejection strategies to date primarily target thermal noise fluctuations and also impulsive radio-frequency signals of anthropogenic origin. In this paper, we consider the possibility that ‘fake’ neutrino signals may also be generated naturally via the ‘triboelectric effect.’ This broadly describes any process in which force applied at a boundary layer results in displacement of surface charge, leading to the production of an electrostatic potential difference ∆V. Wind blowing over granular surfaces such as snow can induce such a potential difference, with subsequent coronal discharge. Discharges over timescales as short as nanoseconds can then lead to radio-frequency emissions at characteristic MHz – GHz frequencies. Using data from various past and current neutrino experiments, we find that such backgrounds are observed by all radio neutrino experiments and are generally characterized by: a) a threshold wind velocity which likely depends on the experimental signal trigger threshold and layout;for the experiments considered herein, this value is typically O(10 m/s), b) frequency spectra generally shifted to the low-end of the frequency regime to which current radio experiments are typically sensitive (100-200 MHz), c) for the strongest background signals, an apparent preference for discharges from above-surface structures, although the presence of more isotropic, lower amplitude triboelectric discharges cannot be excluded. Copyright © 2021, The Auth

关键词： Boundary layers

Impact of narrow lanes on arterial road vehicle crashes: A machine learning approach

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arXiv 2019年

作者： Elhenawy, Mohammed Jahangiri, Arash Rakha, Hesham Center for Sustainable Mobility Virginia Tech Transportation Institute 3500 Transportation Research Plaza BlacksburgVA24061 United States San Diego State University 5500 Campanile Dr San DiegoCA92182 United States Samuel Reynolds Pritchard Professor of Engineering Dept. of Civil and Environmental Engineering Bradley Dept. of Electrical and Computer Engineering

In this paper we adopted state-of-the-art machine learning algorithms, namely: Random forest (RF) and least squares boosting, to model crash data and identify the optimum model to study the impact of narrow lanes on the safety of arterial roads. Using a ten-year crash dataset in four cities in Nebraska, two machine learning models were assessed based on the prediction error. The RF model was identified as the best model. The RF was used to compute the importance of the lane width predictors in our regression model based on two different measures. Subsequently, the RF model was used to simulate the crash rate for different lane widths. The Kruskal-Wallis test, was then conducted to determine if simulated values from the four lane width groups have equal means. The test null hypothesis of equal means for simulated values from the four lane width groups was rejected. Consequently, it was concluded that the crash rates from at least one lane width group was statistically different from the others. Finally, the results from the pairwise comparisons using the Tukey and Kramer test showed that the changes in crash rates between any two lane width conditions were statistically significant. Copyright © 2019, The Authors. All rights reserved.

关键词： Decision trees

Seethevoice: Learning from Music to Visual Storytelling of Shots

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Seethevoice: Learning from Music to Visual Storytelling of S...

2018 IEEE International Conference on Multimedia and Expo, ICME 2018

作者： Wei, Wen-Li Lin, Jen-Chun Liu, Tyng-Luh Yang, Yi-Hsuan Wang, Hsin-Min Tyan, Hsiao-Rong Mark Liao, Hong-Yuan Institute of Information Science United States Research Center for IT Innovation Academia Sinica Taiwan Department of Electrical Engineering Yuan Ze University Taiwan Dept. of Information and Computer Engineering Chung Yuan Christian Univ. Taiwan

ISBN: (纸本)9781538617373

Types of shots in the language of film are considered the key elements used by a director for visual storytelling. In filming a musical performance, manipulating shots could stimulate desired effects such as manifesting the emotion or deepening the atmosphere. However, while the visual storytelling technique is often employed in creating professional recordings of a live concert, audience recordings of the same event often lack such sophisticated manipulations. Thus it would be useful to have a versatile system that can perform video mashup to create a refined video from such amateur clips. To this end, we propose to translate the music into a near-professional shot (type) sequence by learning the relation between music and visual storytelling of shots. The resulting shot sequence can then be used to better portray the visual storytelling of a song and guide the concert video mashup process. Our method introduces a novel probabilistic-based fusion approach, named as multi-resolution fused recurrent neural networks (MF-RNNs) with film-language, which integrates multi-resolution fused RNNs and a film-language model for boosting the translation performance. The results from objective and subjective experiments demonstrate that MF-RNNs with film-language can generate an appealing shot sequence with better viewing experience. © 2018 IEEE.

关键词： Recurrent neural networks

Reduction of thermal strain induced rate error for navigation grade fiber optic gyroscope

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Reduction of thermal strain induced rate error for navigatio...

Optical Fiber Sensors, OFS 2018

作者： Ogut, Serdar Osunluk, Berk Ozbay, Ekmel Dept. of Electrical and Electronics Engineering Bilkent University Ankara06800 Turkey Nanotechnology Research Center - NANOTAM Bilkent University Ankara06800 Turkey Aselsan Inc. Microelectronics Guidance and Electro-optics Sector Ankara06011 Turkey

ISBN: (纸本)9781943580507

This paper presents an 8-fold reduction of thermal sensitivity of a navigation grade fiber optic gyroscope (FOG) coil. We used an advanced thermal modeling of a fiber optic gyroscope coil to obtain the strain fields along the coil and calculate the rate error. Strain field calculations are validated by optical time domain reflectometer (OTDR) measurements of a laboratory fiber optic gyroscope setup. We separated the strain analysis into axial and radial gradients along the fiber coil. We designed a new fiber coil with reduced axial strain sensitivity and run simulations to calculate its strain coefficients and the rate error. Simulations show more than 8-fold reduction in rate error along with experimental results which show nearly 4-fold reduction. © OSA 2018 © 2018 The Author(s)

关键词： Errors

Automated Detection and Forecasting of COVID-19 using Deep Learning Techniques: A Review

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arXiv 2020年

作者： Shoeibi, Afshin Khodatars, Marjane Jafari, Mahboobeh Ghassemi, Navid Sadeghi, Delaram Moridian, Parisa Khadem, Ali Alizadehsani, Roohallah Hussain, Sadiq Zare, Assef Sani, Zahra Alizadeh Khozeimeh, Fahime Nahavandi, Saeid Rajendra Acharya, U. Gorriz, Juan M. The Data Science and Computational Intelligence Institute University of Granada Spain The Computer Engineering Department Ferdowsi University of Mashhad Mashhad Iran Deakin University VIC3217 Australia The Faculty of Electrical Engineering K. N. Toosi University of Technology Tehran Iran System Administrator at Dibrugarh University Assam786004 India Faculty of Electrical Engineering Gonabad Branch Islamic Azad University Gonabad Iran Rajaie Cardiovascular Medical and Research Center Iran University of Medical Sciences Tehran Iran The School of Mathematics Physics and Computing University of Southern Queensland Springfield Australia Dept. of Psychiatry University of Cambridge United Kingdom

Coronavirus, or COVID-19, is a hazardous disease that has endangered the health of many people around the world by directly affecting the lungs. COVID-19 is a medium-sized, coated virus with a single-stranded RNA, and also has one of the largest RNA genomes and is approximately 120 nm. The X-Ray and computed tomography (CT) imaging modalities are widely used to obtain a fast and accurate medical diagnosis. Identifying COVID-19 from these medical images is extremely challenging as it is time-consuming and prone to human errors. Hence, artificial intelligence (AI) methodologies can be used to obtain consistent high performance. Among the AI methods, deep learning (DL) networks have gained popularity recently compared to conventional machine learning (ML). Unlike ML, all stages of feature extraction, feature selection, and classification are accomplished automatically in DL models. In this paper, a complete survey of studies on the application of DL techniques for COVID-19 diagnostic and segmentation of lungs is discussed, concentrating on works that used X-Ray and CT images. Additionally, a review of papers on the forecasting of coronavirus prevalence in different parts of the world with DL is presented. Lastly, the challenges faced in the detection of COVID-19 using DL techniques and directions for future research are discussed. © 2020, CC BY-NC-ND.

关键词： COVID-19