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The economic end of life of electrochemical energy storage

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arXiv 2018年

作者： He, Guannan Ciez, Rebecca Chen, Qixin Moutis, Panayiotis Kar, Soummya Whitacre, Jay Department of Engineering and Public Policy Carnegie Mellon University PittsburghPA15213 United States Wilton E. Scott Institute for Energy Innovation Carnegie Mellon University PittsburghPA15213 United States Andlinger Center for Energy and the Environment Princeton University PrincetonNJ08544 United States Department of Electrical Engineering Tsinghua University Beijing100084 China Department of Electrical Engineering Carnegie Mellon University PittsburghPA15213 United States Department of Materials Science and Engineering Carnegie Mellon University PittsburghPA15213 United States

The useful life of electrochemical energy storage (EES) is a critical factor to EES planning, operation, and economic assessment. Today, systems commonly assume a physical end-of-life criterion, retiring EES when the remaining capacity reaches a threshold below which the EES is of little use because of functionality degradation. Here, we propose an economic end of life criterion, where EES is retired when it cannot earn positive net economic benefit in its intended application. This criterion depends on the use case and degradation characteristics of the EES, but is independent of initial capital cost. Using an intertemporal operational framework to consider functionality and profitability degradation, our case study shows that the economic end of life could occur significantly faster than the physical end of life. We argue that both criteria should be applied in EES system planning and assessment. We also analyze how R&D efforts should consider cycling capability and calendar degradation rate when considering the economic end-of-life of EES. Copyright © 2018, arXiv, All rights reserved.

关键词： Degradation

All models are wrong, but many are useful: Learning a variable's importance by studying an entire class of prediction models simultaneously

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arXiv 2018年

作者： Fisher, Aaron Rudin, Cynthia Dominici, Francesca Takeda Pharmaceuticals CambridgeMA02139 United States Departments of Computer Science and Electrical and Computer Engineering Duke University DurhamNC27708 United States Department of Biostatistics Harvard T.H. Chan School of Public Health BostonMA02115 United States

Variable importance (VI) tools describe how much covariates contribute to a prediction model's accuracy. However, important variables for one well-performing model (for example, a linear model f(x) = xTβ with a fixed coefficient vector β) may be unimportant for another model. In this paper, we propose model class reliance (MCR) as the range of VI values across all well-performing model in a prespecified class. Thus, MCR gives a more comprehensive description of importance by accounting for the fact that many prediction models, possibly of different parametric forms, may fit the data well. In the process of deriving MCR, we show several informative results for permutation-based VI estimates, based on the VI measures used in Random Forests. Specifically, we derive connections between permutation importance estimates for a single prediction model, U-statistics, conditional variable importance, conditional causal effects, and linear model coefficients. We then give probabilistic bounds for MCR, using a novel, generalizable technique. We apply MCR to a public data set of Broward County criminal records to study the reliance of recidivism prediction models on sex and race. In this application, MCR can be used to help inform VI for unknown, proprietary models. Copyright © 2018, The Authors. All rights reserved.

关键词： Forecasting

Detection of paroxysmal atrial fibrillation using attention-based bidirectional recurrent neural networks

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arXiv 2018年

作者： Shashikumar, Supreeth P. Shah, Amit J. Clifford, Gari D. Nemati, Shamim School of Electrical and Computer Engineering Georgia Institute of Technology AtlantaGA United States Department of Epidemiology Rollins School of Public Health Emory University AtlantaGA United States Department of Biomedical Informatics Emory University Department of Biomedical Engineering Georgia Institute of Technology AtlantaGA United States Department of Biomedical Informatics Emory University AtlantaGA United States

Detection of atrial fibrillation (AF), a type of cardiac arrhythmia, is difficult since many cases of AF are usually clinically silent and undiagnosed. In particular paroxysmal AF is a form of AF that occurs occasionally, and has a higher probability of being undetected. In this work, we present an attention based deep learning framework for detection of paroxysmal AF episodes from a sequence of windows. Time-frequency representation of 30 seconds recording windows, over a 10 minute data segment, are fed sequentially into a deep convolutional neural network for image-based feature extraction, which are then presented to a bidirectional recurrent neural network with an attention layer for AF detection. To demonstrate the effectiveness of the proposed framework for transient AF detection, we use a database of 24 hour Holter Electrocardiogram (ECG) recordings acquired from 2850 patients at the University of Virginia heart station. The algorithm achieves an AUC of 0.94 on the testing set, which exceeds the performance of baseline models. We also demonstrate the cross-domain generalizablity of the approach by adapting the learned model parameters from one recording modality (ECG) to another (photoplethysmogram) with improved AF detection performance. The proposed high accuracy, low false alarm algorithm for detecting paroxysmal AF has potential applications in long-term monitoring using wearable sensors. Copyright © 2018, The Authors. All rights reserved.

关键词： Recurrent neural networks

Author Correction: Federated learning enables big data for rare cancer boundary detection

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Nature communications 2023年第1期14卷 436页

作者： Sarthak Pati Ujjwal Baid Brandon Edwards Micah Sheller Shih-Han Wang G Anthony Reina Patrick Foley Alexey Gruzdev Deepthi Karkada Christos Davatzikos Chiharu Sako Satyam Ghodasara Michel Bilello Suyash Mohan Philipp Vollmuth Gianluca Brugnara Chandrakanth J Preetha Felix Sahm Klaus Maier-Hein Maximilian Zenk Martin Bendszus Wolfgang Wick Evan Calabrese Jeffrey Rudie Javier Villanueva-Meyer Soonmee Cha Madhura Ingalhalikar Manali Jadhav Umang Pandey Jitender Saini John Garrett Matthew Larson Robert Jeraj Stuart Currie Russell Frood Kavi Fatania Raymond Y Huang Ken Chang Carmen Balaña Jaume Capellades Josep Puig Johannes Trenkler Josef Pichler Georg Necker Andreas Haunschmidt Stephan Meckel Gaurav Shukla Spencer Liem Gregory S Alexander Joseph Lombardo Joshua D Palmer Adam E Flanders Adam P Dicker Haris I Sair Craig K Jones Archana Venkataraman Meirui Jiang Tiffany Y So Cheng Chen Pheng Ann Heng Qi Dou Michal Kozubek Filip Lux Jan Michálek Petr Matula Miloš Keřkovský Tereza Kopřivová Marek Dostál Václav Vybíhal Michael A Vogelbaum J Ross Mitchell Joaquim Farinhas Joseph A Maldjian Chandan Ganesh Bangalore Yogananda Marco C Pinho Divya Reddy James Holcomb Benjamin C Wagner Benjamin M Ellingson Timothy F Cloughesy Catalina Raymond Talia Oughourlian Akifumi Hagiwara Chencai Wang Minh-Son To Sargam Bhardwaj Chee Chong Marc Agzarian Alexandre Xavier Falcão Samuel B Martins Bernardo C A Teixeira Flávia Sprenger David Menotti Diego R Lucio Pamela LaMontagne Daniel Marcus Benedikt Wiestler Florian Kofler Ivan Ezhov Marie Metz Rajan Jain Matthew Lee Yvonne W Lui Richard McKinley Johannes Slotboom Piotr Radojewski Raphael Meier Roland Wiest Derrick Murcia Eric Fu Rourke Haas John Thompson David Ryan Ormond Chaitra Badve Andrew E Sloan Vachan Vadmal Kristin Waite Rivka R Colen Linmin Pei Murat Ak Ashok Srinivasan J Rajiv Bapuraj Arvind Rao Nicholas Wang Ota Yoshiaki Toshio Moritani Sevcan Turk Joonsang Lee Snehal Prabhudesai Fanny Morón Jacob Mandel Konstantinos Kamnitsas Ben Glocker Luke V M Dixon Matthew Williams Peter Zamp Center for Biomedical Image Computing and Analytics (CBICA) University of Pennsylvania Philadelphia PA USA. Department of Radiology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA. Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania Philadelphia PA USA. Department of Informatics Technical University of Munich Munich Bavaria Germany. Intel Corporation Santa Clara CA USA. Department of Neuroradiology Heidelberg University Hospital Heidelberg Germany. Clinical Cooperation Unit Neuropathology German Cancer Consortium (DKTK) within the German Cancer Research Center (DKFZ) Heidelberg Germany. Department of Neuropathology Heidelberg University Hospital Heidelberg Germany. Division of Medical Image Computing German Cancer Research Center Heidelberg Germany. Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Heidelberg Germany. Neurology Clinic Heidelberg University Hospital Heidelberg Germany. Department of Radiology & Biomedical Imaging University of California San Francisco San Francisco CA USA. Symbiosis Center for Medical Image Analysis Symbiosis International University Pune Maharashtra India. Department of Neuroimaging and Interventional Radiology National Institute of Mental Health and Neurosciences Bangalore Karnataka India. Department of Radiology School of Medicine and Public Health University of Wisconsin Madison WI USA. Department of Medical Physics School of Medicine and Public Health University of Wisconsin Madison WI USA. Leeds Teaching Hospitals Trust Department of Radiology Leeds UK. Department of Radiology Brigham and Women's Hospital Harvard Medical School Boston MA USA. Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital Charlestown MA USA. Catalan Institute of Oncology Badalona Spain. Consorci MAR Parc de Salut de Barcelona Catalonia Spain. Department of Radiology (IDI

Learning from learning machines: A new generation of AI technology to meet the needs of science

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arXiv 2021年

作者： Pion-Tonachini, Luca Bouchard, Kristofer Martin, Hector Garcia Peisert, Sean Holtz, W. Bradley Aswani, Anil Dwivedi, Dipankar Wainwright, Haruko Pilania, Ghanshyam Nachman, Benjamin Marrone, Babetta L. Falco, Nicola Prabhat Arnold, Daniel Wolf-Yadlin, Alejandro Powers, Sarah Climer, Sharlee Jackson, Quinn Carlson, Ty Sohn, Michael Zwart, Petrus Kumar, Neeraj Justice, Amy Tomlin, Claire Jacobson, Daniel Micklem, Gos Gkoutos, Georgios V. Bickel, Peter J. Cazier, Jean-Baptiste Müller, Juliane Webb-Robertson, Bobbie-Jo Stevens, Rick Anderson, Mark Kreutz-Delgado, Ken Mahoney, Michael W. Brown, James B. Pattern Computer Inc. Friday HarborWA98250 United States Biosciences Area Lawrence Berkeley National Lab BerkeleyCA94803 United States Computational Research Division Lawrence Berkeley National Lab BerkeleyCA94720 United States Helen Wils Neuroscience Institute Redwood Center for Theoretical Neuroscience Uc Berkeley BerkeleyCA94720 United States Doe Agile BioFoundry Lawrence Berkeley National Laboratory BerkeleyCA94803 United States Joint BioEnergy Institute Lawrence Berkeley National Laboratory BerkeleyCA94803 United States Bcam Basque Center for Applied Mathematics Bilbao48009 Spain Computer Science University of California Davis DavisCA95616 United States Cenic La MiradaCA90638 United States Health Informatics University of California Davis School of Medicine SacramentoCA95817 United States Berkeley Institute for Data Science University of California Berkeley BerkeleyCA94720 United States Industrial Engineering and Operations Research University of California Berkeley BerkeleyCA94720 United States Environmental & Earth Sciences Area Lawrence Berkeley National Laboratory BerkeleyCA94803 United States Nuclear Engineering University of California Berkeley BerkeleyCA94720 United States Materials Science and Technology Division Los Alamos National Laboratory Los AlamosNM87545 United States Physics Division Lawrence Berkeley National Lab BerkeleyCA94720 United States Bioscience Division Los Alamos National Laboratory Los AlamosNM87545 United States Earth and Environmental Sciences Area Lawrence Berkeley National Lab BerkeleyCA94803 United States Energy Technologies Area Lawrence Berkeley National Lab BerkeleyCA94803 United States Computing and Computational Sciences Oak Ridge National Laboratory Oak RidgeTN37831 United States Industrial & Systems Engineering The University of Tennessee KnoxvilleTN37996 United States Department of Computer Science University of Missouri-Saint Louis St. LouisMO63121 United

We outline emerging opportunities and challenges to enhance the utility of AI for scientific discovery. The distinct goals of AI for industry versus the goals of AI for science create tension between identifying patterns in data versus discovering patterns in the world from data. If we address the fundamental challenges associated with "bridging the gap" between domain-driven scientific models and data-driven AI learning machines, then we expect that these AI models can transform hypothesis generation, scientific discovery, and the scientific process itself. © 2021, CC BY.

关键词： Learning systems

Corrigendum: Functional vagotopy in the cervical vagus nerve of the domestic pig: implications for the study of vagus nerve stimulation (2020 J. Neural Eng. 17 026022)

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Journal of neural engineering 2021年第4期18卷

作者： Megan L Settell Nicole A Pelot Bruce E Knudsen Aaron M Dingle Andrea L McConico Evan N Nicolai James K Trevathan J Ashley Ezzell Erika K Ross Kenneth J Gustafson Andrew J Shoffstall Justin C Williams Weifeng Zeng Samuel O Poore Luis C Populin Aaron J Suminski Warren M Grill Kip A Ludwig Department of Biomedical Engineering University of Wisconsin-Madison Madison WI United States of America. Mayo Clinic Mayo Clinic Graduate School of Biomedical Sciences Rochester MN United States of America. Department of Biomedical Engineering Duke University Durham NC United States of America. Department of Neurologic Surgery Mayo Clinic Rochester MN United States of America. Division of Plastic Surgery Department of Surgery University of Wisconsin-Madison Madison WI United States of America. Histology Research Core University of North Carolina School of Medicine Chapel Hill NC United States of America. Department of Cell Biology and Physiology University of North Carolina School of Medicine Chapel Hill NC United States of America. Abbott Neuromodulation Plano TX United States of America. Department of Biomedical Engineering Case Western Reserve University Cleveland OH United States of America. Louis Stokes Cleveland VA Medical Center Cleveland OH United States of America. Department of Neurosurgery University of Wisconsin-Madison Madison WI United States of America. Department of Surgery University of Wisconsin-Madison Madison WI United States of America. University of Wisconsin School of Medicine and Public Health Madison WI United States of America. Department of Neuroscience University of Wisconsin-Madison Madison WI United States of America. Department of Electrical and Computer Engineering Duke University Durham NC United States of America. Department of Neurobiology Duke University Durham NC United States of America. Department of Neurosurgery Duke University Durham NC United States of America.

Political Affiliation and Rooftop Solar Adoption in New York and Texas

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Political Affiliation and Rooftop Solar Adoption in New York...

2018 IEEE 7th World Conference on Photovoltaic Energy Conversion (WCPEC) (A Joint Conference of 45th IEEE PVSC, 28th PVSEC & 34th EU PVSEC)

作者： Deborah A. Sunter John Dees Sergio Castellanos Duncan Callaway Daniel M. Kammen Renewable and Appropriate Energy Laboratory University of California Berkeley CA USA Energy and Resources Group University of California Berkeley CA USA California Institute for Energy and Environment University of California Berkeley CA USA Department of Electrical Engineering and Computer Science University of California Berkeley CA USA Goldman School of Public Policy University of California Berkeley CA USA

Renewable energy is often presented as a partisan issue. However, solar incentives have made rooftop PV attractive to many consumers, regardless of political affiliation. This paper quantifies the role of political affiliation in rooftop PV adoption for the Democratic-leaning state of New York and Republican-leaning state of Texas. High resolution rooftop PV installation data from Google Project Sunroof is combined with election data from the Harvard Election Data Archive. We applied a bootstrapped LOWESS model to fit local linear relationships between rooftop PV adoption and both voting behavior and household income. In doing so, we find that Republican-majority communities are installing rooftop PV at equal or greater rates than Democraticmajority communities. While Republican politicians may be less likely than Democrats to support renewable energy, this partisan division is not found at the voter level.

关键词： Voting Renewable energy sources Portfolios Buildings Google Standards Indexes

Comparison study of displacement estimation methods for microwave ablation procedures using electrode displacement elastography

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Comparison study of displacement estimation methods for micr...

2017 IEEE International Ultrasonics Symposium, IUS 2017

作者： Pohlman, Robert Jiang, Jingfeng Yang, Wenjun Ziemlewicz, Timothy J. Alexander, Marci Wergin, Kelly Lubner, Meghan Hinshaw, James Louis Lee, Fred T. Varghese, Tomy Department of Medical Physics University of Wisconsin School of Medicine and Public Health MadisonWI United States Department of Electrical and Computer Engineering University of Wisconsin-Madison MadisonWI United States Department of Biomedical Engineering Michigan Technological University HoughtonMI United States Department of Radiology University of Wisconsin School of Medicine and Public Health MadisonWI United States

ISBN: (纸本)9781538633830

Minimally invasive procedures such as microwave ablation (MWA) are growing in popularity as a substitute for surgical resection of hepatocellular carcinomas. Ultrasound electrode displacement elastography (EDE) has demonstrated the potential to provide a feasible non-invasive and non-ionizing approach for monitoring the region of thermal necrosis. © 2017 IEEE.

关键词： Signal to noise ratio Estimation Strain Microwave imaging Microwave theory and techniques Electrodes Elastography

Zapping Zika with a Mosquito-Managing Drone: Computing Optimal Flight Patterns with Minimum Turn Cost 33

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Zapping Zika with a Mosquito-Managing Drone: Computing Optim...

33rd International Symposium on Computational Geometry, SoCG 2017

作者： Becker, Aaron T. Debboun, Mustapha Fekete, Sándor P. Krupke, Dominik Nguyen, An Department of Electrical and Computer Engineering University of Houston HoustonTX United States Mosquito and Vector Control Division Harris County Public Health HoustonTX United States Dept. of Computer Science TU Braunschweig Braunschweig Germany

ISBN: (纸本)9783959770385

We present results arising from the problem of sweeping a mosquito-infested area with an Unmanned Aerial Vehicle (UAV) equipped with an electrified metal grid. This is related to the Traveling Salesman Problem, the Lawn Mower Problem and, most closely, Milling with Turn Cost. Planning a good trajectory can be reduced to considering penalty and budget variants of covering a grid graph with minimum turn cost. On the theoretical side, we show the solution of a problem from The Open Problems Project that had been open for more than 15 years, and hint at approximation algorithms. On the practical side, we describe an exact method based on Integer Programming that is able to compute provably optimal instances with over 500 pixels. These solutions are actually used for practical trajectories, as demonstrated in the video. © Aaron T. Becker, Mustapha Debboun, Sándor P. Fekete, Dominik Krupke, An Nguyen.

关键词： Traveling salesman problem