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13th IEEE International Conference on Industry Applications, INDUSCON 2018

作者： Fogli, Gabriel A. De Fernandes, Marcelo C. De Faria, Jose A. T. Barbosa, Pedro G. De Almeida, Pedro M. Department of Electrical Engineering Federal University of Ouro Preto João Monlevade MG Brazil Power Electronics and Automation Group Electrical Engineering Program Federal University of Juiz de Fora MG Brazil Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute TroyNY United States Flextronics Institute of Technology Sorocaba SP Brazil

ISBN: (纸本)9781538679951

This paper presents design steps of a distributed generation system with load power compensation. A single-phase full-bridge converter is used to connect the alternative source to the mains. Besides injecting active power, the distributed generation also provides load power compensation capability. In this context, a current reference strategy based on single-phase circuit theory is proposed for grid-connected multi-task converters. The main idea is to synthesize the signal reference for control loop generating orthogonal signals from grid voltage and current load. A Third Order Sinusoidal Integrator is used to extract fundamental component and generate the orthogonal waveforms of grid voltage and load current. Current controller loop for interface converter is developed based on Linear Quadratic Regulator theory. Simulation results are presented to verify the system behavior. © 2018 IEEE.

关键词： Distributed power generation

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Building a quantum engineering undergraduate program

arXiv

引用

arXiv 2021年

作者： Asfaw, Abraham Blais, Alexandre Brown, Kenneth R. Candelaria, Jonathan Cantwell, Christopher Carr, Lincoln D. Combes, Joshua Debroy, Dripto M. Donohue, John M. Economou, Sophia E. Edwards, Emily Fox, Michael F.J. Girvin, Steven M. Ho, Alan Hurst, Hilary M. Jacob, Zubin Johnson, Blake R. Johnston-Halperin, Ezekiel Joynt, Robert Kapit, Eliot Klein-Seetharaman, Judith Laforest, Martin Lewandowski, H.J. Lynn, Theresa W. McRae, Corey Rae H. Merzbacher, Celia Michalakis, Spyridon Narang, Prineha Oliver, William D. Palsberg, Jens Pappas, David P. Raymer, Michael G. Reilly, David J. Saffman, Mark Searles, Thomas A. Shapiro, Jeffrey H. Singh, Chandralekha IBM Quantum IBM T. J. Watson Research Center Yorktown HeightsNY United States Institut Quantique Département de Physique Université de Sherbrooke SherbrookeQCJ1K 2R1 Canada Canadian Institute for Advanced Research TorontoONM5G 1M1 Canada The Duke Quantum Center Department of Physics Department of Electrical and Computer Engineering Department of Chemistry Duke University DurhamNC27708 United States SystemX Program Department of Electrical Engineering Stanford University StanfordCA94305 United States Department of Physics and Astronomy University of Southern California Los AngelesCA90089 United States The Quantum Engineering Program Department of Physics Colorado School of Mines GoldenCO80401 United States The Department of Electrical Computer and Energy Engineering University of Colorado Boulder BoulderCO80309 United States The Institute for Quantum Computing University of Waterloo WaterlooONN2L 3G1 Canada The Department of Physics Virginia Tech BlacksburgVA24061 United States The IQUIST University of Illinois Urbana-Champaign UrbanaIL61801 United States JILA National Institute of Standards and Technology The University of Colorado BoulderCO80309 United States Department of Physics University of Colorado Boulder BoulderCO80309 United States The Department of Physics Imperial College London Prince Consort Road LondonSW7 2AZ United Kingdom The Yale Quantum Institute Department of Physics Yale University New HavenCT06520 United States Google Research VeniceCA90291 United States The Department of Physics & Astronomy San José State University San JoséCA95192 United States The School of Electrical and Computer Engineering Purdue University Indiana West Lafayatte47907 United States The Department of Physics The Ohio State University ColumbusOH43210 United States The Department of Physics University of Wisconsin-Madison 1150 University Avenue MadisonWI53706 United States The Quantitative Biosciences and Engineering

The rapidly growing quantum information science and engineering (QISE) industry will require both quantum-aware and quantum-proficient engineers at the bachelor's level. We provide a roadmap for building a quantum engineering education program to satisfy this need. For quantum-aware engineers, we describe how to design a first quantum engineering course accessible to all STEM students. For the education and training of quantum-proficient engineers, we detail both a quantum engineering minor accessible to all STEM majors, and a quantum track directly integrated into individual engineering majors. We propose that such programs typically require only three or four newly developed courses that complement existing engineering and science classes available on most larger campuses. We describe a conceptual quantum information science course for implementation at any post-secondary institution, including community colleges and military schools. QISE presents extraordinary opportunities to work towards rectifying issues of inclusivity and equity that continue to be pervasive within engineering. We present a plan to do so and describe how quantum engineering education presents an excellent set of education research opportunities. Finally, we outline a hands-on training plan on quantum hardware, a key component of any quantum engineering program, with a variety of technologies including optics, atoms and ions, cryogenic and solid-state technologies, nanofabrication, and control and readout electronics. Our recommendations provide a flexible framework that can be tailored for academic institutions ranging from teaching and undergraduate-focused two- and four-year colleges to research-intensive universities. © 2021, CC BY.

关键词： engineering education

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Unifying femtosecond and picosecond single-pulse magnetic switching in GdFeCo

arXiv

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arXiv 2020年

作者： Jakobs, F. Ostler, T.A. Lambert, C.-H. Yang, Y. Salahuddin, S. Wilson, R.B. Gorchon, J. Bokor, J. Atxitia, U. Dahlem Center for Complex Quantum Systems and Fachbereich Physik Freie Universität Berlin Berlin14195 Germany LiégeB-4000 Belgium College of Business Technology and Engineering Sheffield Hallam University Howard Street SheffieldS1 1WB United Kingdom Department of Electrical Engineering and Computer Sciences University of California BerkeleyCA94720 United States Department of Materials Science and Engineering University of California BerkeleyCA94720 United States Lawrence Berkeley National Laboratory 1 Cyclotron Road BerkeleyCA94720 United States Department of Mechanical Engineering and Materials Science and Engineering Program University of California RiversideCA92521 United States ETH Zurich Hönggerbergring 64 Zürich8093 Switzerland Portland Technology Development Department Intel Corp. HillsboroOR97006 United States Universite de Lorraine CNRS IJL NancyF-54000 France

Many questions are still open regarding the physical mechanisms behind the magnetic switching in GdFeCo alloys by single optical pulses. Phenomenological models suggest a femtosecond scale exchange relaxation between sublattice magnetization as the driving mechanism for switching. The recent observation of thermally induced switching in GdFeCo by using both several picosecond optical laser pulse as well as electric current pulses has questioned this previous understanding. This has raised the question of whether or not the same switching mechanics are acting at the femoand picosecond scales. In this work, we aim at filling this gap in the understanding of the switching mechanisms behind thermal single-pulse switching. To that end, we have studied experimentally thermal single-pulse switching in GdFeCo alloys, for a wide range of system parameters, such as composition, laser power and pulse duration. We provide a quantitative description of the switching dynamics using atomistic spin dynamics methods with excellent agreement between the model and our experiments across a wide range of parameters and timescales, ranging from femtoseconds to picoseconds. Furthermore, we find distinct element-specific damping parameters as a key ingredient for switching with long picosecond pulses and argue, that switching with pulse durations as long as 15 picoseconds is possible due to a low damping constant of Gd. Our findings can be easily extended to speed up dynamics in other contexts where ferrimagnetic GdFeCo alloys have been already demonstrated to show fast and energy-efficient processes, e.g. domain-wall motion in a track and spin-orbit torque switching in spintronics devices. Copyright © 2020, The Authors. All rights reserved.

关键词： Ternary alloys

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On Stability of Event-Triggered systems With Sliding Mode Control

On Stability of Event-Triggered Systems With Sliding Mode Co...

引用

International Workshop on Variable Structure systems, VSS

作者： Abhisek K. Behera Bijnan Bandyopadhyay Department of Electrical and Computer Engineering Seoul National University Seoul South Korea Interdisciplinary Program in Systems and Control Engineering Indian Institute of Technology Bombay Mumbai India

In this paper, we investigate the stability of a controlled system in the event-triggering framework. By designing the triggering scheme, the control signals are updated when it is demanded subject to some satisfactory system performance. However, the stability of the system is not yet fully investigated with respect to the triggering conditions. So, the main purpose of the paper is to analyse the stability of the system with respect to the proposed triggering scheme. In this paper, sliding mode control (SMC) is used to design the event-triggered control. Our analysis proposes a new notion called bounded event property for the event which guarantees the triggering instants are generated in finite-time for all possible values of the triggering parameter. It is shown that the event-triggered SMC ensures possess the bounded event property and moreover the proposed triggering mechanism also guarantees the stability of the closed loop system stability.

关键词： Stability analysis Trajectory Closed loop systems Manifolds Steady-state Sliding mode control System performance

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BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets

引用

Nature methods 2023年第6期20卷 824-835页

作者： Linus Manubens-Gil Zhi Zhou Hanbo Chen Arvind Ramanathan Xiaoxiao Liu Yufeng Liu Alessandro Bria Todd Gillette Zongcai Ruan Jian Yang Miroslav Radojević Ting Zhao Li Cheng Lei Qu Siqi Liu Kristofer E Bouchard Lin Gu Weidong Cai Shuiwang Ji Badrinath Roysam Ching-Wei Wang Hongchuan Yu Amos Sironi Daniel Maxim Iascone Jie Zhou Erhan Bas Eduardo Conde-Sousa Paulo Aguiar Xiang Li Yujie Li Sumit Nanda Yuan Wang Leila Muresan Pascal Fua Bing Ye Hai-Yan He Jochen F Staiger Manuel Peter Daniel N Cox Michel Simonneau Marcel Oberlaender Gregory Jefferis Kei Ito Paloma Gonzalez-Bellido Jinhyun Kim Edwin Rubel Hollis T Cline Hongkui Zeng Aljoscha Nern Ann-Shyn Chiang Jianhua Yao Jane Roskams Rick Livesey Janine Stevens Tianming Liu Chinh Dang Yike Guo Ning Zhong Georgia Tourassi Sean Hill Michael Hawrylycz Christof Koch Erik Meijering Giorgio A Ascoli Hanchuan Peng Institute for Brain and Intelligence Southeast University Nanjing China. Microsoft Corporation Redmond WA USA. Tencent AI Lab Bellevue WA USA. Computing Environment and Life Sciences Directorate Argonne National Laboratory Lemont IL USA. Kaya Medical Seattle WA USA. University of Cassino and Southern Lazio Cassino Italy. Center for Neural Informatics Structures and Plasticity Krasnow Institute for Advanced Study George Mason University Fairfax VA USA. Faculty of Information Technology Beijing University of Technology Beijing China. Beijing International Collaboration Base on Brain Informatics and Wisdom Services Beijing China. Nuctech Netherlands Rotterdam the Netherlands. Janelia Research Campus Howard Hughes Medical Institute Ashburn VA USA. Department of Electrical and Computer Engineering University of Alberta Edmonton Alberta Canada. Ministry of Education Key Laboratory of Intelligent Computation and Signal Processing Anhui University Hefei China. Paige AI New York NY USA. Scientific Data Division and Biological Systems and Engineering Division Lawrence Berkeley National Lab Berkeley CA USA. Helen Wills Neuroscience Institute and Redwood Center for Theoretical Neuroscience UC Berkeley Berkeley CA USA. RIKEN AIP Tokyo Japan. Research Center for Advanced Science and Technology (RCAST) The University of Tokyo Tokyo Japan. School of Computer Science University of Sydney Sydney New South Wales Australia. Texas A&M University College Station TX USA. Cullen College of Engineering University of Houston Houston TX USA. Graduate Institute of Biomedical Engineering National Taiwan University of Science and Technology Taipei Taiwan. National Centre for Computer Animation Bournemouth University Poole UK. PROPHESEE Paris France. Department of Neuroscience Columbia University New York NY USA. Mortimer B. Zuckerman Mind Brain Behavior Institute Columbia University New York NY USA. Department of Computer Science Northern Illinois Universit

BigNeuron is an open community bench-testing platform with the goal of setting open standards for accurate and fast automatic neuron tracing. We gathered a diverse set of image volumes across several species that is representative of the data obtained in many neuroscience laboratories interested in neuron tracing. Here, we report generated gold standard manual annotations for a subset of the available imaging datasets and quantified tracing quality for 35 automatic tracing algorithms. The goal of generating such a hand-curated diverse dataset is to advance the development of tracing algorithms and enable generalizable benchmarking. Together with image quality features, we pooled the data in an interactive web application that enables users and developers to perform principal component analysis, t-distributed stochastic neighbor embedding, correlation and clustering, visualization of imaging and tracing data, and benchmarking of automatic tracing algorithms in user-defined data subsets. The image quality metrics explain most of the variance in the data, followed by neuromorphological features related to neuron size. We observed that diverse algorithms can provide complementary information to obtain accurate results and developed a method to iteratively combine methods and generate consensus reconstructions. The consensus trees obtained provide estimates of the neuron structure ground truth that typically outperform single algorithms in noisy datasets. However, specific algorithms may outperform the consensus tree strategy in specific imaging conditions. Finally, to aid users in predicting the most accurate automatic tracing results without manual annotations for comparison, we used support vector machine regression to predict reconstruction quality given an image volume and a set of automatic tracings.

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ELM vs. WiSARD: A performance comparison 25

ELM vs. WiSARD: A performance comparison

引用

25th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2017

作者： Oliveira, Luiz Fernando R. França, Felipe M.G. Systems Engineering and Computer Science Program COPPE Universidade Federal Do Rio de Janeiro Brazil

ISBN: (纸本)9782875870391

The extreme learning machine (ELM) is known for being a fast learning neural model. This work presents a performance comparison between ELM and the WiSARD weightless neural network model, regarding training and testing times, and classification accuracy as well. The two models were implemented in the same programming language and experiments were carried out on the same hardware environment. By using a group of datasets from the public repositories UCI and Statlog, experimental results shows that the WiSARD presented training times approximately one order of magnitude smaller than ELM, while classification accuracy varied according the number of classes involved. However, while WiSARD's architecture setups were not exhaustively searched, architecture setups for ELM were kept the same as the ones found in the literature as the best for each given dataset. © ESANN 2017 - Proceedings, 25th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. All rights reserved.

关键词： Classification (of information)

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Data quality measures and data cleansing for research information systems

arXiv

引用

arXiv 2019年

作者： Azeroual, Otmane Saake, Gunter Abuosba, Mohammad Berlin10117 Germany Otto-Von-Guericke University Magdeburg Department of Computer Science Institute for Technical and Business Information Systems Database Research Group P.O. Box 4120 Magdeburg39106 Germany University of Applied Sciences Htw Berlin Study Program Computational Science and Engineering Wilhelminenhofstrasse 75a Berlin12459 Germany

The collection, transfer and integration of research information into different research information systems can result in different data errors that can have a variety of negative effects on data quality. In order to detect errors at an early stage and treat them efficiently, it is necessary to determine the clean-up measures and the new techniques of data cleansing for quality improvement in research institutions. Thereby an adequate and reliable basis for decision-making using an RIS is provided, and confidence in a given dataset increased. In this paper, possible measures and the new techniques of data cleansing for improving and increasing the data quality in research information systems will be presented and how these are to be applied to the research information. Copyright © 2019, The Authors. All rights reserved.

关键词： Standardization

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Design and Implementation of OLED Driving and OPD Readout Circuitry for an Optical Vibration Sensor 17

Design and Implementation of OLED Driving and OPD Readout Ci...

引用

17th IEEE SENSORS Conference, SENSORS 2018

作者： Wu, Yi-Cheng Kao, Yung-Hua Chao, Paul C.-P. Wey, Chin-Long Sauter, Thilo Eka, Fitrah P. Pandey, Rajeev Department of Electrical Engineering National Chiao Tung University Hsinchu300 Taiwan Institute of Electrical and Control Engineering National Chiao Tung University Hsinchu City Taiwan EECS International Graduate Program National Chiao Tung University Hsinchu City Taiwan Department for Integrated Sensor Systems Danube University Krems Austria Department of Electrical and Computer Engineering National Chiao Tung University Hsinchu City Taiwan

ISBN: (纸本)9781538647073

This study presents an organic light-emitting diode (OLED) drive circuit and an organic photo detector (OPD) readout circuits for a vibration sensor. Vibration sensor consist of micro-electro-mechanical systems (MEMS), which are OLED and OPD needs to be integrated for many applications in automation or consumer electronics. The light flux is generated by an OLED located on top of the structure and the modulated light flux is measured by an OPD. The OLED drive circuit contains pulse width modulation (PWM) driver to control PWM output for optimization of voltage operation is designed in order to minimize power consumption efficiency of device. The readout circuit includes a trans-impedance amplifier (TIA) to convert incident light of OPD to voltage output, a 14-bit analog-to-digital converter (ADC) to extract amplitude of vibration level, circuit to generate clock for time-to-digital converter (TDC) which detect frequency of vibration sensor. The designed circuit has been successfully fabricated by using TSMC 0.18 μm CMOS process, the chip area is 1.012 mm2. The power consumption of chip is about 6.2 mW. The measured frequency of PWM is about 200 kHz with 50% duty cycle. The frequency range of the vibration can be sensed well as about 10 Hz1.5 kHz, and resulting in high SNR at frequencies under 60 Hz. © 2018 IEEE.

关键词： Electric power utilization

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Stakeholder Act as an IT and General Compliance Auditor

Stakeholder Act as an IT and General Compliance Auditor

引用

International Conference on Sustainable Information engineering and Technology (SIET)

作者： Surjandy Erick Fernando Meyliana Raymond Kosala Suhono Harso Supangkat Diani Dwiningrum Merry Marshela Angelica Renita Amalia Information Systems Audit Program Information Systems Department Bina Nusantara University Jakarta Indonesia Information Systems Departement Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Jakarta Indonesia School of Electrical Engineering and Informatics Institut Teknologi Bandung Bandung Indonesia

ISBN: (纸本)9781538674086;9781538674079

Recently, the Indonesian government requires auditors, the need for auditors around 40,000 auditors in 2016, currently has only about 10,800 auditors, however, the lack of the auditors impacting the risk of financial loss for the state. Referring to the problem, this experimental research was conducted. The experiment of the research was conducted two times and the audit topic is a compliance audit in the year 2017 and year 2018. The goal of this research is to know, can the stakeholder act as an IT and general compliance auditor? The experiment was conducted by 8 university students in 2017 and 19 university students in 2018, and the supporting tools of audit activities the university student use the smartphone to capture the object as evidence. The experiment results with descriptive method show university students can act as an auditor and found several pieces of evidence that might cause future impact to the institution.

关键词： Regulation Stakeholders Companies Libraries Finance

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Computationally designed proteins mimic antibody immune evasion in viral evolution

引用

Immunity 2025年第6期58卷 1411-1421.e6页

作者： Youssef, Noor Gurev, Sarah Ghantous, Fadi Brock, Kelly P. Jaimes, Javier A. Thadani, Nicole N. Dauphin, Ann Sherman, Amy C. Yurkovetskiy, Leonid Soto, Daria Estanboulieh, Ralph Kotzen, Ben Notin, Pascal Kollasch, Aaron W. Cohen, Alexander A. Dross, Sandra E. Erasmus, Jesse Fuller, Deborah H. Bjorkman, Pamela J. Lemieux, Jacob E. Luban, Jeremy Seaman, Michael S. Marks, Debora S. Department of Systems Biology Harvard Medical School Boston 02115 MA United States Broad Institute of Harvard and MIT Cambridge 02139 MA United States Department of Electrical Engineering and Computer Science MIT Cambridge 02139 MA United States Center for Virology and Vaccine Research Beth Israel Deaconess Medical Center Boston 02215 MA United States Program in Molecular Medicine University of Massachusetts Chan Medical School Worcester 01655 MA United States Division of Infectious Diseases Department of Medicine Brigham and Women's Hospital Boston 02115 MA United States Massachusetts General Hospital Boston 02114 MA United States Division of Biology and Biological Engineering California Institute of Technology Pasadena 91125 CA United States Department of Microbiology University of Washington Seattle 98195 WA United States National Primate Research Center Seattle 98109 WA United States HDT Bio Seattle 98109 WA United States Massachusetts Consortium on Pathogen Readiness Boston 02115 MA United States Ragon Institute of MGH MIT and Harvard Cambridge 02139 MA United States

Recurrent waves of viral infection necessitate vaccines and therapeutics that remain effective against emerging viruses. Our ability to evaluate interventions is currently limited to assessments against past or circulating variants, which likely differ in their immune escape potential compared with future variants. To address this, we developed EVE-Vax, a computational method for designing antigens that foreshadow immune escape observed in future viral variants. We designed 83 SARS-CoV-2 spike proteins that transduced ACE2-positive cells and displayed neutralization resistance comparable to variants that emerged up to 12 months later in the COVID-19 pandemic. Designed spikes foretold antibody escape from B.1-BA.4/5 bivalent booster sera seen in later variants. The designed constructs also highlighted the increased neutralization breadth elicited by nanoparticle-based, compared with mRNA-based, boosters in non-human primates. Our approach offers targeted panels of synthetic proteins that map the immune landscape for early vaccine and therapeutic evaluation against future viral strains. © 2025 The Authors

关键词： antibody escape generative protein models immune landscape machine learning protein design vaccine evaluation viral evolution

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