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Distributed control of DC grids: Integrating prosumers motives

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arXiv 2019年

作者： Cucuzzella, Michele Bouman, Thijs Kosaraju, Krishna Chaitanya Schuitema, Geertje Lemmen, N.H. Johnson-Zawadzki, Steph Fischione, Carlo Steg, Linda Scherpen, Jacquelien M.A. Department of Electrical Computer and Biomedical Engineering University of Pavia Pavia27100 Italy Jan C. Willems Center for Systems and Control ENTEG University of Groningen Nijenborgh 4 Groningen9747 AG Netherlands University of Groningen Grote Kruisstraat 2/1 Groningen9712 TS Netherlands Department of Electrical Engineering University of Notre Dame Notre DameIN46556 United States University College Dublin College of Business Carysfort Avenue Blackrock Dublin Ireland The School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm114 28 Sweden

In this paper, a novel distributed control strategy addressing a (feasible) psycho-social-physical welfare problem in islanded Direct Current (DC) smart grids is proposed. Firstly, we formulate a (convex) optimization problem that allows prosumers to share current with each other, taking into account the technical and physical aspects and constraints of the grid (e.g., stability, safety), as well as psycho-social factors (i.e., prosumers’ personal values). Secondly, we design a controller whose (unforced) dynamics represent the continuous time primal-dual dynamics of the considered optimization problem. Thirdly, a passive interconnection between the physical grid and the controller is presented. Global asymptotic convergence of the closed-loop system to the desired steady-state is proved and simulations based on collected data on psycho-social aspects illustrate and confirm the theoretical results. Copyright © 2019, The Authors. All rights reserved.

关键词： Controllers

Secondary concentration plateau and formation of flow stagnation by ion concentration polarization in microchannels

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arXiv 2019年

作者： Kim, Junsuk Huh, Keon Mani, Ali Kim, Sung Jae Department of Electrical and Computer Engineering Seoul National University Seoul08826 Korea Republic of Department of Mechanical Engineering Stanford University Stanford94305 United States Nano Systems Institute Seoul National University Seoul08826 Korea Republic of Inter-university Semiconductor Research Center Seoul National University Seoul08826 Korea Republic of

Ion transport in perm-selective media has been extensively investigated in recent years owing to its applications in advancements of fundamental understanding of nanoscale electrokinetics as well as innovative engineering applications. A key phenomenon in this context is ion concentration polarization (ICP) that occurs near perm-selective nanoporous membranes or nanochannels under dc bias. In classical settings, involving voltage-driven transport of ions through perm-selective membranes, concentration polarization is well understood as formation of steep concentration drop to reach near zero concentration (depletion layer) at the anodic side of cation-selective membranes. In contrast to this classical description, we demonstrate here that when ICP is driven in a microchannel and coupled with electroosmotic flows a long-tailed fluorescent layer in front of diffusion layer is formed, which can be characterized as an additional plateau in the concentration profile. Using a micro/nanofluidic platform, in situ visualizations, and local concentration measurements by microelectrode arrays we quantify that this plateau layer has a concentration of 60 % of bulk electrolyte concentration, regardless of the bulk concentration itself. Using a mathematical analysis of conservation laws for flow, charge, and salt, we demonstrate that this plateau region is related to the formation of a flow stagnation point due to the competition between electro-osmotic and induced pressure driven flows. Consistent with the experiments, this analysis predicts the plateau concentration to be 60% of the bulk electrolyte. Thus, this plateau concentration region completes the picture of ICP in microchannels which has drawn significant attention from the fields of electrokinetics and micro/nanofluidics. Copyright © 2019, The Authors. All rights reserved.

关键词： Microchannels

Phaseless Passive Synthetic Aperture Radar Imaging via Wirtinger Flow 52

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Phaseless Passive Synthetic Aperture Radar Imaging via Wirti...

52nd Asilomar Conference on Signals, systems and computers, ACSSC 2018

作者： Yonel, Bariscan Mason, Eric Yazici, Birsen Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute 110 8th Street TroyNY12180 United States Tactical Electronic Warfare Division Naval Research Laboratory WashingtonDC20375 United States

ISBN: (纸本)9781538692189

In this paper, we present a novel method for passive imaging of stationary targets using phaseless measurements. One of the main benefits of the proposed approach is increased robustness to uncertainties inherent in passive imaging. Furthermore, unlike most passive radar imaging methods, our approach circumvents the need for two receivers by auto-correlating the received bistatic signal. Since the resulting measurements are phaseless, coherent imaging algorithms previously used in radar are no longer applicable. Thus, we propose an optimization based approach using the Wirtinger Flow algorithm. This is a non-convex approach, offering computational efficiency not realizable by convex alternatives. Through numerical simulations we demonstrate the effectiveness of this approach. © 2018 IEEE.

关键词： Computational efficiency

The Brain Tumor Sequence Registration (BraTS-Reg) Challenge: Establishing Correspondence Between Pre-Operative and Follow-up MRI Scans of Diffuse Glioma Patients

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arXiv 2021年

作者： Baheti, Bhakti Chakrabarty, Satrajit Akbari, Hamed Bilello, Michel Wiestler, Benedikt Schwarting, Julian Calabrese, Evan Rudie, Jeffrey Abidi, Syed Mousa, Mina Villanueva-Meyer, Javier Fields, Brandon K.K. Kofler, Florian Shinohara, Russell Takeshi Iglesias, Juan Eugenio Mok, Tony C.W. Chung, Albert C.S. Wodzinski, Marek Jurgas, Artur Marini, Niccolò Atzori, Manfredo Müller, Henning Grosbröhmer, Christoph Siebert, Hanna Hansen, Lasse Heinrich, Mattias P. Canalini, Luca Klein, Jan Gerken, Annika Heldmann, Stefan Hering, Alessa Hahn, Horst K. Meng, Mingyuan Bi, Lei Feng, Dagan Kim, Jinman Zeineldinal, Ramy A. Karar, Mohamed E. Mathis-Ullrich, Franziska Burgert, Oliver Abderezaei, Javid Pionteck, Aymeric Chopra, Agamdeep Kurt, Mehmet Yan, Kewei Yan, Yonghong Tang, Zhe Ma, Jianqiang Nasser, Sahar Almahfouz Kurian, Nikhil Cherian Meena, Mohit Shamsi, Saqib Sethi, Amit Tustison, Nicholas J. Avants, Brian B. Cook, Philip Gee, James C. Tian, Lin Greer, Hastings Niethammer, Marc Hoopesar, Andrew Hoffmannas, Malte Dalcaar, Adrian V. Christodoulidis, Stergios Estiene, Théo Vakalopoulou, Maria Paragios, Nikos Marcus, Daniel S. Davatzikos, Christos Sotiras, Aristeidis Menze, Bjoern Bakas, Spyridon Waldmannstetter, Diana Division of Computational Pathology Department of Pathology and Laboratory Medicine Indiana University School of Medicine IndianapolisIN United States Department of Electrical and Systems Engineering Washington University in St. Louis St. LouisMO United States GE HealthCare San RamonCA United States University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Mallinckrodt Institute of Radiology Washington University School of Medicine St. LouisMO United States Institute for Informatics Data Science & Biostatistics Washington University School of Medicine St. LouisMO United States Department of Bioengineering Santa Clara University Santa ClaraCA United States Department of Quantitative Biomedicine University of Zurich Zurich Switzerland Department of Computer Science TUM School of Computation Information and Technology Technical University of Munich Munich Germany Department of Diagnostic and Interventional Neuroradiology School of Medicine Klinikum rechts der Isar Technical University of Munich Munich Germany Department of Nuclear Medicine University Hospital Regensburg Regensburg Germany University Hospital Regensburg Regensburg Germany Department of Radiology & Biomedical Imaging University of California San FranciscoCA United States Department of Radiology Duke University Medical Center DurhamNC United States Department of Radiology University of California San DiegoCA United States Helmholtz AI Helmholtz Munich Neuherberg Germany Penn Statistics in Imaging and Visualization Center Department of Biostatistics Epidemiology and Informatics University of Pennsylvania Philadelphia United States Massachusetts General Hospital Harvard Medical School United States Centre for Medical Imaging Computing University College London United Kingdom The Hong Kong University of Science and Technology Hong Kong University of Applied Sciences West

Registration of longitudinal brain Magnetic Resonance Imaging (MRI) scans containing pathologies is challenging due to dramatic changes in tissue appearance. Although there has been considerable progress in developing general-purpose medical image registration techniques, they have not yet attained the requisite precision and reliability for this task, highlighting its inherent complexity. Here we describe the Brain Tumor Sequence Registration (BraTS-Reg) challenge, as the first public benchmark environment for deformable registration algorithms focusing on estimating correspondences between pre-operative and follow-up scans of the same patient diagnosed with a diffuse brain glioma. The challenge was conducted in conjunction with both the IEEE International Symposium on Biomedical Imaging (ISBI) 2022 and the International Conference on Medical Image Computing and computer-Assisted Intervention (MICCAI) 2022. The BraTS-Reg data comprise de-identified multi-institutional multi-parametric MRI (mpMRI) scans, curated for size and resolution according to a canonical anatomical template, and divided into training, validation, and testing sets. Clinical experts annotated ground truth (GT) landmark points of anatomical locations distinct across the temporal domain. The training data with their GT annotations, were publicly released to enable the development of registration algorithms. The validation data, without their GT annotations, were also released to allow for algorithmic evaluation prior to the testing phase, which only allowed submission of containerized algorithms for evaluation on hidden hold-out testing data. Quantitative evaluation and ranking was based on the Median Euclidean Error (MEE), Robustness, and the determinant of the Jacobian of the displacement field. The top-ranked methodologies yielded similar performance across all evaluation metrics and shared several methodological commonalities, including pre-alignment, deep neural networks, inverse consistency

关键词： Magnetic resonance imaging

Output-Based Event-Triggered Model Predictive Control for Networked Control systems

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IFAC-PapersOnLine 2017年第1期50卷 8951-8956页

作者： Berkel F. Watkins B. Liu S. Görges D. Institute of Control Systems Department of Electrical and Computer Engineering University of Kaiserslautern Kaiserslautern Germany Juniorprofessorship for Electromobility Department of Electrical and Computer Engineering University of Kaiserslautern Kaiserslautern Germany

In this paper an output-based model predictive controller for linear time-invariant discrete-time networked control systems subject to constraints is studied. In the networked control system the control loop is closed over a communication network between sensor and controller as well as controller and actuator. An output-based controller using a Luenberger observer for reconstruction of the system state is designed. For both communication networks event-triggers are utilized to reduce the communicational burden. Recursive feasibility of the control law is provided by robust control methods. Moreover, asymptotic stability of the closed-loop system is guaranteed. A simulation example illustrates that the proposed method can reduce the network utilization significantly. © 2017

关键词： Event-triggered control Networked control systems Output-based model predictive control

Corrigendum to: Metabolic and Hemodynamic Resting-State Connectivity of the Human Brain: A High-Temporal Resolution Simultaneous BOLD-fMRI and FDG-fPET Multimodality Study

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Cerebral cortex (New York, N.Y. : 1991) 2021年第8期31卷 3973页

作者： Sharna D Jamadar Phillip G D Ward Emma X Liang Edwina R Orchard Zhaolin Chen Gary F Egan Monash Biomedical Imaging Melbourne Vic 3800 Australia. Turner Institute for Brain and Mental Health Monash University Melbourne Vic 3800 Australia. Australian Research Council Centre of Excellence for Integrative Brain Function Melbourne 3800 Australia. Department of Electrical and Computer Systems Engineering Monash University Melbourne Vic 3800 Australia.

A photonic microwave and RF integrator based on a transversal filter

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TechRxiv

TechRxiv 2020年

作者： Xu, Xingyuan Tan, Mengxi Wu, Jiayang Boes, Andreas Corcoran, Bill Nguyen, Thach G. Chu, Sai T. Little, Brent E. Morandotti, Roberto Mitchell, Arnan Moss, David J. Centre for Micro-Photonics Swinburne University of Technology HawthornVIC3122 Australia The School of Engineering RMIT University MelbourneVIC3001 Australia Department of Electrical and Computer Systems Engineering Monash University ClaytonVIC3800 Australia Department of Physics City University of Hong Kong Tat Chee Avenue Hong Kong State Key Laboratory of Transient Optics and Photonics Xi'an Institute of Optics and Precision Mechanics Chinese Academy of Science Xi'an China INSR-Énergie Matériaux et Télécommunications 1650 Boulevard Lionel-Boulet VarennesQCJ3X 1S2 Canada

We demonstrate a photonic RF integrator based on an integrated soliton crystal micro-comb source. By multicasting and progressively delaying the input RF signal using a transversal structure, the input RF signal is integrated discretely. Up to 81 wavelengths are provided by the microcomb source, which enable a large integration time window of ~6.8 ns, together with a time resolution as fast as ~84 ps. We perform signal integration of a diverse range of input RF signals including Gaussian pulses with varying time widths, dual pulses with varying time intervals and a square waveform. The experimental results show good agreement with theory. These results verify our microcomb-based integrator as a competitive approach for RF signal integration with high performance and potentially lower cost and footprint. © 2020, CC BY.

关键词： Microwave photonics

Multilevel constructions: Coding, packing and geometric uniformity

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arXiv 2018年

作者： Bollauf, Maiara F. Zamir, Ram Costa, Sueli I.R. Institute of Mathematics Statistics and Computer Science University of Campinas Department of Electrical Engineering-Systems Tel Aviv University

Lattice and special nonlattice multilevel constellations constructed from binary codes, such as Construction C, have relevant applications in mathematics (sphere packing) and communication problems (multi-stage decoding and efficient vector quantization). In this work, we explore some properties of Construction C, in particular its geometric uniformity. We then propose a new multilevel construction, motivated by bit interleaved coded modulation, that we call Construction C?. We explore the geometric uniformity and minimum distance properties of Construction C?;and discuss its potential superior packing efficiency with respect to Construction C. Copyright © 2018, The Authors. All rights reserved.

关键词： Geometry

Approaching small molecule prioritization as a cross-modal information retrieval task through coordinated representation learning

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arXiv 2019年

作者： Finlayson, Samuel G. McDermott, Matthew B.A. Pickering, Alex V. Lipnick, Scott L. Yuan, William Kohane, Isaac S. Department of Systems Synthetic and Quantitative Biology Harvard Medical School BostonMA Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA United States Department of Biomedical Informatics Harvard Medical School BostonMA United States Department of Biological and Biomedical Sciences Harvard Medical School BostonMA United States

Modeling the relationship between chemical structure and molecular activity is a key task in drug development and precision medicine. In this paper, we utilize a novel deep learning architecture to jointly train coordinated embeddings of chemical structures and transcriptional signatures. We do so by training neural networks in a coordinated manner such that learned chemical representations correlate most highly with the encodings of the transcriptional patterns they induce. We then test this approach by using held-out gene expression signatures as queries into embedding space to recover their corresponding compounds. We evaluate these embeddings' utility for small molecule prioritization on this new benchmark task. Our method outperforms a series of baselines, successfully generalizing to unseen transcriptional experiments, but still struggles to generalize to entirely unseen chemical structures. Copyright © 2019, The Authors. All rights reserved.

关键词： Embeddings