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Max-Min Fair Sensor Scheduling: Game-Theoretic Perspective and Algorithmic Solution

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arXiv 2019年

作者： Wu, Shuang Ren, Xiaoqiang Hong, Yiguang Shi, Ling Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong Access Linnaeus Center School of Electrical Engineering Kth Royal Institute of Technology Stockholm114 28 Sweden Key Laboratory of Systems and Control Institute of Systems Science Chinese Academy of Sciences Beijing100080 China

We consider the design of a fair sensor schedule for a number of sensors monitoring different linear time-invariant processes. The largest average remote estimation error among all processes is to be minimized. We first consider a general setup for the max-min fair allocation problem. By reformulating the problem as its equivalent form, we transform the fair resource allocation problem into a zero-sum game between a "judge" and a resource allocator. We propose an equilibrium seeking procedure and show that there exists a unique Nash equilibrium in pure strategy for this game. We then apply the result to the sensor scheduling problem and show that the max-min fair sensor scheduling policy can be achieved. Copyright © 2019, The Authors. All rights reserved.

关键词： Game theory

AN INFEASIBLE-START FRAMEWORK FOR CONVEX QUADRATIC OPTIMIZATION, WITH APPLICATION TO CONSTRAINT-REDUCED INTERIOR-POINT METHODS ∗

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arXiv 2019年

作者： Paul Laiu, M. Tits, André L. Computational and Applied Mathematics Group Computer Science and Mathematics Division Oak Ridge National Laboratory Oak RidgeTN37831 United States Department of Electrical and Computer Engineering Institute for Systems Research University of Maryland College ParkMD20742 United States

A framework is proposed for solving general convex quadratic programs (CQPs) from an infeasible starting point by invoking an existing feasible-start algorithm tailored for inequalityconstrained CQPs. The central tool is an exact penalty function scheme equipped with a penalty-parameter updating rule. The feasible-start algorithm merely has to satisfy certain general requirements, and so is the updating rule. Under mild assumptions, the framework is proved to converge on CQPs with both inequality and equality constraints and, at a negligible additional cost per iteration, produces an infeasibility certificate, together with a feasible point for an (approximately) `1-least relaxed feasible problem when the given problem does not have a feasible solution. The framework is applied to a feasible-start constraint-reduced interior-point algorithm previously proved to be highly performant on problems with many more constraints than variables ("imbalanced"). Numerical comparison with popular codes (SDPT3, SeDuMi, MOSEK) is reported on both randomly generated problems and support-vector machine classifier training problems. The results show that the former typically outperforms the latter on imbalanced problems. Copyright © 2019, The Authors. All rights reserved.

关键词： Support vector machines

QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Results

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arXiv 2021年

作者： Mehta, Raghav Filos, Angelos Baid, Ujjwal Sako, Chiharu McKinley, Richard Rebsamen, Michael Dätwyler, Katrin Meier, Raphael Radojewski, Piotr Murugesan, Gowtham Krishnan Nalawade, Sahil Ganesh, Chandan Wagner, Ben Yu, Fang F. Fei, Baowei Madhuranthakam, Ananth J. Maldjian, Joseph A. Daza, Laura Gómez, Catalina Arbeláez, Pablo Dai, Chengliang Wang, Shuo Reynaud, Hadrien Mo, Yuanhan Angelini, Elsa Guo, Yike Bai, Wenjia Banerjee, Subhashis Pei, Linmin Murat, A.K. Rosas-González, Sarahi Zemmoura, Ilyess Tauber, Clovis Vu, Minh H. Nyholm, Tufve Löfstedt, Tommy Ballestar, Laura Mora Vilaplana, Veronica McHugh, Hugh Talou, Gonzalo Maso Wang, Alan Patel, Jay Chang, Ken Hoebel, Katharina Gidwani, Mishka Arun, Nishanth Gupta, Sharut Aggarwal, Mehak Singh, Praveer Gerstner, Elizabeth R. Kalpathy-Cramer, Jayashree Boutry, Nicolas Huard, Alexis Vidyaratne, Lasitha Rahman, Md Monibor Iftekharuddin, Khan M. Chazalon, Joseph Puybareau, Elodie Tochon, Guillaume Ma, Jun Cabezas, Mariano Llado, Xavier Oliver, Arnau Valencia, Liliana Valverde, Sergi Amian, Mehdi Soltaninejad, Mohammadreza Myronenko, Andriy Hatamizadeh, Ali Feng, Xue Dou, Quan Tustison, Nicholas Meyer, Craig Shah, Nisarg A. Talbar, Sanjay Weber, Marc-André Mahajan, Abhishek Jakab, Andras Wiest, Roland Fathallah-Shaykh, Hassan M. Nazeri, Arash Milchenko, Mikhail Marcus, Daniel Kotrotsou, Aikaterini Colen, Rivka Freymann, John Kirby, Justin Davatzikos, Christos Menze, Bjoern Bakas, Spyridon Gal, Yarin Arbel, Tal McGill University MontrealQC Canada Group University of Oxford Oxford United Kingdom University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine The University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States University Institute of Diagnostic and Interventional Neuroradiology University of Bern Inselspital Bern University Hospital Bern Switzerland Department of Radiology University of Texas Southwestern Medical Center DallasTX United States Department of Bioengineering University of Texas DallasTX United States Advanced Imaging Research Center University of Texas Southwestern Medical Center DallasTX United States Universidad de los Andes Bogotá Colombia Data Science Institute Imperial College London London United Kingdom NIHR Imperial BRC ITMAT Data Science Group Imperial College London London United Kingdom Department of Brain Sciences Imperial College London London United Kingdom Machine Intelligence Unit Indian Statistical Institute Kolkata India Department of CSE University of Calcutta Kolkata India Department of Information Technology Uppsala University Uppsala Sweden Department of Diagnostic Radiology The University of Pittsburgh Medical Center PittsburghPA United States UMR U1253 iBrain Université de Tours Inserm Tours France Department of Radiation Sciences Umeå University Umeå Sweden Department of Computing Science Umeå University Umeå Sweden Signal Theory and Communications Department Universitat Politècnica de Catalunya Barcelona Tech Barcelona Spain Faculty of Medical and Health Sciences University of Auckland Auckland New Zealand Radiology Department Auckland City Hospital Auckland New Zealand Auckland Bioengineering Institute University of Auckland New Zealand Athinoula A. Martinos Center for Biomedical Imaging Department of Radiology

Deep learning (DL) models have provided state-of-the-art performance in various medical imaging benchmarking challenges, including the Brain Tumor Segmentation (BraTS) challenges. However, the task of focal pathology multi-compartment segmentation (e.g., tumor and lesion sub-regions) is particularly challenging, and potential errors hinder translating DL models into clinical workflows. Quantifying the reliability of DL model predictions in the form of uncertainties could enable clinical review of the most uncertain regions, thereby building trust and paving the way toward clinical translation. Several uncertainty estimation methods have recently been introduced for DL medical image segmentation tasks. Developing scores to evaluate and compare the performance of uncertainty measures will assist the end-user in making more informed decisions. In this study, we explore and evaluate a score developed during the BraTS 2019 and BraTS 2020 task on uncertainty quantification (QU-BraTS) and designed to assess and rank uncertainty estimates for brain tumor multi-compartment segmentation. This score (1) rewards uncertainty estimates that produce high confidence in correct assertions and those that assign low confidence levels at incorrect assertions, and (2) penalizes uncertainty measures that lead to a higher percentage of under-confident correct assertions. We further benchmark the segmentation uncertainties generated by 14 independent participating teams of QU-BraTS 2020, all of which also participated in the main BraTS segmentation task. Overall, our findings confirm the importance and complementary value that uncertainty estimates provide to segmentation algorithms, highlighting the need for uncertainty quantification in medical image analyses. Finally, in favor of transparency and reproducibility, our evaluation code is made publicly available at https://***/RagMeh11/QU-BraTS. © 2021, CC BY.

关键词： Benchmarking

Factored pose estimation of articulated objects using efficient nonparametric belief propagation

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arXiv 2018年

作者： Desingh, Karthik Lu, Shiyang Opipari, Anthony Jenkins, Odest Chadwicke Department of Electrical Engineering and Computer Science Robotics Institute University of Michigan Ann Arbor United States

Robots working in human environments often encounter a wide range of articulated objects, such as tools, cabinets, and other jointed objects. Such articulated objects can take an infinite number of possible poses, as a point in a potentially high-dimensional continuous space. A robot must perceive this continuous pose to manipulate the object to a desired pose. This problem of perception and manipulation of articulated objects remains a challenge due to its high dimensionality and multi-modal uncertainty. In this paper, we propose a factored approach to estimate the poses of articulated objects using an efficient nonparametric belief propagation algorithm. We consider inputs as geometrical models with articulation constraints, and observed RGBD sensor data. The proposed framework produces object-part pose beliefs iteratively. The problem is formulated as a pairwise Markov Random Field (MRF) where each hidden node (continuous pose variable) is an observed object-part’s pose and the edges denote the articulation constraints between the parts. We propose articulated pose estimation by Pull Message Passing algorithm for Nonparametric Belief Propagation (PMPNBP) and evaluate its convergence properties over scenes with articulated objects. Copyright © 2018, The Authors. All rights reserved.

关键词： Iterative methods

A Gd@C82-based single molecular electret device

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arXiv 2020年

作者： Zhang, Kangkang Wang, Cong Zhang, Minhao Bai, Zhanbin Xie, Fangfang Tan, Yuanzhi Guo, Yilv Hu, Kuo-Juei Cao, Lu Zhang, Shuai Tu, Xuecou Kang, Lin Chen, Jian Wu, Peiheng Wang, Xuefeng Wang, Jinlan Liu, Junming Wang, Baigeng Wang, Guanghou Xie, Suyuan Ji, Wei Shi, Su-Fei Reed, M.A. Song, Fengqi National Laboratory of Solid State Microstructures Collaborative Innovation Center of Advanced Microstructures School of Physics Nanjing University Nanjing210093 China Beijing Key Laboratory of Optoelectronic Functional Materials & Micro-Nano Devices Department of Physics Renmin University of China Beijing100872 China State Key Laboratory of Physical Chemistry of Solid Surfaces Department of Chemistry College of Chemistry and Chemical Engineering Xiamen University Xiamen361005 China School of Physics Southeast University Nanjing211189 China School of Electronic Science and Engineering and Collaborative Innovation Center of Advanced Microstructures Nanjing University Nanjing210023 China Department of Chemical and Biological Engineering Rensselaer Polytechnic Institute TroyNY12180 United States Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute TroyNY12180 United States Departments of Applied Physics and Electrical Engineering Yale University New HavenCT06520 United States Nanjing211805 China

Single molecular electrets exhibiting single‐molecule electric polarization switching have been long desired as a platform for extremely small non-volatile storage devices, although it is controversial because of the poor stability of single molecular electric dipoles. Here we study the single molecular device of Gd@C82, where the encapsulated Gd atom forms a charge center, and we have observed a gate-controlled switching behavior between two sets of single-electron-transport stability diagrams. The switching is operated in a hysteresis loop with a coercive gate field of around 0.5 V/nm. Theoretical calculations have assigned the two conductance diagrams to corresponding energy levels of two states that the Gd atom is trapped at two different sites of the C82 cage, which possess two different permanent electrical dipole orientations. The two dipole states are stabilized by the anisotropic energy and separated by a transition energy barrier of 70 meV. Such switching is then accessed to the electric field driven reorientation of individual dipole while overcoming the barriers by the coercive gate field, and demonstrates the creation of a single molecular electret. Copyright © 2020, The Authors. All rights reserved.

关键词： Switching

Exploration of Additive Manufacturing for HTS Cable Components for Electric Aircrafts

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Exploration of Additive Manufacturing for HTS Cable Componen...

2018 AIAA/IEEE Electric Aircraft Technologies Symposium, EATS 2018

作者： Cheetham, Peter Nowell, Robert Al-Taie, Aws McAulev, James Han Kim, Chul Graber, Lukas Pamidi, Sastry Center for Advanced Power Systems Florida State University TallahasseeFL32310 United States Innovation Hub Florida State University TallahasseeFL32304 United States FAMU-FSU College of Engineering Department of Electrical and Computer Engineering TallahasseeFL32310 United States Georgia Institute of Technology College of Engineering Department of Electrical and Computer Engineering AtlantaGA30332 United States

ISBN: (数字)9781624105722

ISBN: (纸本)9781624105722

High temperature Superconducting (HTS) technology has the potential to deliver the required power densities necessary for electric aircrafts. A promising HTS cable design for electric aircraft applications that we have been investigating is the Superconducting Gas-Insulated Transmission Line (S-GIL). The S-GIL requires insulator spacers that are compatible with cryogenic temperatures as well as the high electrical and mechanical stresses. One of the major mechanical stresses currently limiting the design options is the compatibility of insulation materials and designs for cryogenic temperatures and associated thermal shocks. This paper discusses the investigation of using various 3D printed materials to fabricate prototype insulator spacer designs for the S-GIL and evaluate their compatibility with the required cryogenic operating conditions. The studies demonstrated that several materials and designs selected show suitability to operate at cryogenic temperature and we plan to proceed with further mechanical and electrical characterization of the 3D printed components. © 2018 AIAA.

关键词： Electric lines

Approximate dynamic programming with probabilistic temporal logic constraints

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arXiv 2018年

作者： Li, Lening Fu, Jie Robotics Engineering Program Department of Electrical and Computer Engineering Worcester Polytechnic Institute WorcesterMA01609 United States

In this paper, we develop approximate dynamic programming methods for stochastic systems modeled as Markov Decision Processes, given both soft performance criteria and hard constraints in a class of probabilistic temporal logic called Probabilistic Computation Tree Logic (PCTL). Our approach consists of two steps: First, we show how to transform a class of PCTL formulas into chance constraints that can be enforced during planning in stochastic systems. Second, by integrating randomized optimization and entropy-regulated dynamic programming, we devise a novel trajectory sampling-based approximate value iteration method to iteratively solve for an upper bound on the value function while ensuring the constraints that PCTL specifications are satisfied. Particularly, we show that by the on-policy sampling of the trajectories, a tight bound can be achieved between the upper bound given by the approximation and the true value function. The correctness and efficiency of the method are demonstrated using robotic motion planning examples. Copyright © 2018, The Authors. All rights reserved.

关键词： Stochastic systems

Control design considering magnetic flux characteristic uncertainty for magnetic levitation system with magnetic flux and current feedback

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IEEJ Transactions on Electronics, Information and Systems

IEEJ Transactions on Electronics, Information and systems 2019年第10期139卷 1159-1166页

作者： Urasaki, Shinpachiro Kobayashi, Yoshimitsu Kami, Yasushi Mitani, Yuichiroh Nobuyama, Eitaku Department of Electronic Control Engineering National Institute of Technology Gifu College 2236-2. Kamimakuwa. Motosu. Gifu 501-0495 Japan Department of Electrical and Computer Engineering National Institute of Technology Akashi College 679-3. Nishioka. Uozumi Akashi. Hyogo674-8501 Japan Department of Mechanical Engineering National Institute of Technology Numazu College 3600. Ooka. Numazu Shizuoka410-8501 Japan Department of Systems Innovation and Informatics Faculty of Computer Science and System Engineering Kyushu Institute of Technology 680-4.Kawazu. Iizuka Fukuoka820-8502 Japan

This paper describes control design considering magnetic flux characteristic uncertainty for magnetic levitation system with magnetic flux and current feedback. The position of the levitated object is calculated from the hall voltage and current of the electromagnet. We show the uncertainty for magnetic flux characteristic and design the stabilizing controller by proportional feedback with the hall voltage and current. Moreover, we tried to design servo controller by two-degree-of-freedom control by using a finite number of frequency response (FNFR) model including the uncertainty and confirmed the validity of the proposed method by experiment results. © 2019 The institute of Eleclrical Engineers of Japan.

关键词： Frequency response

Fast online reinforcement learning control using state-space dimensionality reduction

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arXiv 2019年

作者： Sadamoto, Tomonori Chakrabortty, Aranya Imura, Jun-Ichi Department of Mechanical and Intelligent Systems Engineering Graduate School of Informatics and Engineering University of Electro Communications 1-5-1 Chofugaoka Chofu Tokyo182-8585 Japan Electrical and Computer Engineering North Carolina State University RaleighNC27695 United States Department of Systems and Control Engineering Graduate School of Engineering Tokyo Institute of Technology Oh-Okayama Meguro Tokyo2-12-1 Japan

Reinforcement Learning (RL) is an effective way of designing model-free linear quadratic regulator (LQR) controller for linear time-invariant (LTI) networks with unknown state-space models. However, when the network size is large, conventional RL can result in unacceptably long learning time. In this paper we resolve this problem by developing an alternative approach for RL-based LQR that combines dimensionality reduction with RL theory. The approach is to construct a compressed state vector by projecting the measured state through a projective matrix. This matrix is constructed from the state measurements, and can be viewed as an empirical controllability gramian that captures the level of redundancy in the controllability of the open-loop network model. Next, a RL-controller is learned using the reduced-dimensional state instead of the original state such that the resultant cost is close to the optimal LQR cost. Numerical benefits as well as the cyber-physical implementation benefits of the proposed approach are verified using illustrative examples including an example of wide-area control of the IEEE benchmark power system. Copyright © 2019, The Authors. All rights reserved.

关键词： Controllers