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arXiv 2020年

作者： Wang, Jingxian Wang, Tianye Wang, Wei Dong, Xiwang Wang, Yandong School of Physics Peking University Beijing100871 China Department of Urban Studies and Planning Massachusetts Institute of Technology CambridgeMA02139 United States Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology CambridgeMA02139 United States School of Automation Science and Electrical Engineering Beihang University Beijing100191 China

Distributed localization is essential in many robotic collective tasks such as shape formation and self-assembly. Inspired by the statistical mechanics of energy transition, this paper presents a fully distributed localization algorithm named as virtual particle exchange (VPE) localization algorithm, where each robot repetitively exchanges virtual particles (VPs) with neighbors and eventually obtains its relative position from the virtual particle (VP) amount it owns. Using custom-designed hardware and protocol, VPE localization algorithm allows robots to achieve localization using sensor readings only, avoiding direct communication with neighbors and keeping anonymity. Moreover, VPE localization algorithm determines the swarm center automatically, thereby eliminating the requirement of fixed beacons to embody the origin of coordinates. Theoretical analysis proves that the VPE localization algorithm can always converge to the same result regardless of initial state and has low asymptotic time and memory complexity. Extensive localization simulations with up to 10000 robots and experiments with 52 low-cost robots are carried out, which verify that VPE localization algorithm is scalable, accurate and robust to sensor noises. Based on the VPE localization algorithm, shape formations are further achieved in both simulations and experiments with 52 robots, illustrating that the algorithm can be directly applied to support swarm collaborative tasks. Copyright © 2020, The Authors. All rights reserved.

关键词： Statistical mechanics

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Achieve Global Renewable Energy and Low Carbon Goal through Constructing Human 'Faults' in Electric Grids

SSRN

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SSRN 2020年

作者： Li, Canbing Liu, Xubin Xu, Zhenci Zhou, Bin Shahidehpour, Mohammad Cao, Yijia Liu, Jianguo Sun, Kai Wu, Qiuwei Liu, Hui Chen, Xinyu Zhang, Yongjun Chen, Chen Chen, Dawei Li, Yingjie Hatziargyriou, Nikos Huang, Wentao Zhang, Cong Liu, Yakun Department of Electrical Engineering Shanghai Jiao Tong University Shanghai200240 China College of Electrical and Information Engineering Hunan University Changsha410082 China Center for Systems Integration and Sustainability Michigan State University East LansingMI48823 United States Electrical and Computer Engineering Department Illinois Institute of Technology Chicago United States School of Electrical Engineering and Information Changsha University of Science and Technology Changsha410114 China State Key Laboratory of Power Systems Department of Electrical Engineering Tsinghua University Beijing100084 China Department of Electrical Engineering Technical University of Denmark Elektrovej Kgs.Lyngby2800 Denmark College of Electrical Engineering Guangxi University Nanning China School of Engineering and Applied Sciences Harvard University CambridgeMA United States School of Electric Power South China University of Technology Guangzhou510640 China School of Electrical Engineering Xi’an Jiaotong University Xi’an710049 China School of Electrical and Computer Engineering National Technical University of Athens Athens15773 Greece Michigan State University United States

The current 25% of global renewable energy among electric grids generation is far from the stated goal of 60% in 2040 and 100% in 2050. One of the biggest dilemma to cost-effectively increase this percentage, through grids interconnection, is that the traditional fault blocking methodology has reached its safety protection ceiling. In contrary to the traditional wisdom that any form of faults must be avoided, here we proposed a fault dredging methodology by constructing human “faults” to create artificial fault current release channels to solve high-level fault current bottleneck during grids interconnection. Using actual region electric grids from globally representative countries across six continents for demonstration, our results show that human “faults” can enable the percentages of renewable energy in USA, UK, China, Brazil, Australia, Nigeria and IEEE RTS system increase from 19.2%~47.2% to over 90%, which are much higher than all of conventional technologies. Correspondingly, the emissions of CO2, NOX and SOX in these systems respectively decrease by 67.84%~88.07%, 61.17%~88.24% and 58.25%~88.33%. Our findings suggest a new approach to reach the global renewable energy goal and promote the progress toward achieving carbon-free energy system. © 2020, The Authors. All rights reserved.

关键词： Free energy

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Stacked auto encoder based Deep Reinforcement Learning for online resource scheduling in large-scale MEC networks

arXiv

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arXiv 2020年

作者： Jiang, Feibo Wang, Kezhi Dong, Li Pan, Cunhua Yang, Kun Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha China Department of Computer and Information Sciences Northumbria University United Kingdom Key Laboratory of Hunan Province for New Retail Virtual Reality Technology Hunan University of Commerce Changsha China School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom School of Computer Sciences and Electrical Engineering University of Essex ColchesterCO4 3SQ United Kingdom University of Electronic Science and Technology of China Chengdu China

An online resource scheduling framework is proposed for minimizing the sum of weighted task latency for all the mobile users, by optimizing offloading decision, transmission power, and resource allocation in the mobile edge computing (MEC) system. Towards this end, a deep reinforcement learning (DRL) method is proposed to obtain an online resource scheduling policy. Firstly, a related and regularized stacked auto encoder (2r-SAE) with unsupervised learning is proposed to perform data compression and representation for high dimensional channel quality information (CQI) data, which can reduce the state space for DRL. Secondly, we present an adaptive simulated annealing based approach (ASA) as the action search method of DRL, in which an adaptive h-mutation is used to guide the search direction and an adaptive iteration is proposed to enhance the search efficiency during the DRL process. Thirdly, a preserved and prioritized experience replay (2p-ER) is introduced to assist the DRL to train the policy network and find the optimal offloading policy. Numerical results are provided to demonstrate that the proposed algorithm can achieve near-optimal performance while significantly decreasing the computational time compared with existing benchmarks. It also shows that the proposed framework is suitable for resource scheduling problem in large-scale MEC networks, especially in the dynamic environment. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep learning

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Interval Markov decision processes with multiple objectives: From Robust strategies to Pareto curves

Interval Markov decision processes with multiple objectives:...

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作者： Hahn, Ernst Moritz Hashemi, Vahid Hermanns, Holger Lahijanian, Morteza Turrini, Andrea School of Electronics Electrical Engineering and Computer Science Queen's University Belfast Belfast United Kingdom State Key Laboratory of Computer Science Institute of Software CAS Beijing China Department of Information Technology Audi AG Ingolstadt Germany Saarland University Saarland Informatics Campus Saarbrücken Germany Institute of Intelligent Software Guangzhou Guangzhou China Department of Smead Aerospace Engineering and Sciences University of Colorado BoulderCO United States

Accurate Modelling of a real-world system with probabilistic behaviour is a difficult task. Sensor noise and statistical estimations, among other imprecisions, make the exact probability values impossible to obtain. In this article, we consider Interval Markov decision processes (IMDPs), which generalise classical MDPs by having interval-valued transition probabilities. They provide a powerful modelling tool for probabilistic systems with an additional variation or uncertainty that prevents the knowledge of the exact transition probabilities. We investigate the problem of robust multi-objective synthesis for IMDPs and Pareto curve analysis of multi-objective queries on IMDPs. We study how to find a robust (randomised) strategy that satisfies multiple objectives involving rewards, reachability, and more general ω-regular properties against all possible resolutions of the transition probability uncertainties, as well as to generate an approximate Pareto curve providing an explicit view of the trade-offs between multiple objectives. We show that the multi-objective synthesis problem is PSPACE-hard and provide a value iteration-based decision algorithm to approximate the Pareto set of achievable points. We finally demonstrate the practical effectiveness of our proposed approaches by applying them on several case studies using a prototype tool. © 2019 Copyright held by the owner/author(s).

关键词： Multiobjective optimization

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Imaging with Nanometer Resolution Using Optically Active Defects in Silicon Carbide

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Physical Review Applied 2020年第3期14卷 034021-034021页

作者： Stefania Castelletto Martina Barbiero Mirren Charnley Alberto Boretti Min Gu School of Engineering RMIT University Melbourne Victoria Australia Swinburne Nanotechnology Facility Optical Sciences Centre Swinburne University of Technology Hawthorn Victoria Australia Laboratory of Artificial-Intelligence Nanophotonics School of Science RMIT University Melbourne Victoria Australia Division of Cancer Biology The Institute of Cancer Research London UK Optical Sciences Centre Faculty of Science Engineering and Technology Swinburne University of Technology P.O. Box 218 Hawthorn Victoria Australia Immune Signalling Laboratory Peter MacCallum Cancer Centre Parkville Victoria Australia College of Engineering Prince Mohammad Bin Fahd University Al Khobar Kingdom of Saudi Arabia Centre for Artificial-Intelligence Nanophotonics School of Optical-Electrical and Computer Engineering the University of Shanghai for Science and Technology Shanghai China

Nanostructured and bulk silicon carbide (SiC) materials are relevant for electronics, nano- and micromechanical systems, and biosensing applications. SiC has recently emerged as an alternative platform for nanophotonics and quantum applications due to its intra-band-gap point defects, emitting from the visible to the near-infrared, which are ideal for photoluminescent probes. Here, we use a single-molecule localization microscope to study the photoluminescence (PL) properties of SiC point defects in bulk, quantum dots, and nanoparticles of different sizes in the 3C and 4H−SiC polytypes using intra-band-gap excitation. We study the PL dynamics by using different excitation wavelengths, and we use the point-defect PL intermittency to achieve superresolved images, with a resolution of 20 nm and a minimum distance between emitters of 40 nm. We observe that, while 561 nm is an ideal excitation wavelength to obtain a sufficient blinking behavior, 638 nm is mostly quenching the PL. We further incubate 4H−SiC nanoparticles with MCF10A cells in vitro and observe superresolved images of the nanoparticles in cells by combining 561 and 638 nm excitation. This approach is very promising for the application of SiC fluorescent nanocrystals and their quantum dots, hosting a combination of intra-band-gap color centers and surface defects, for quantum nanophotonics, magnetic sensing, and biomedical imaging, paving the way for single-particle tracking combined with spin sensing within a cellular environment using near-infrared emission.

关键词： Band gap Color centers Nanophotonics Point defects Quantum information with solid state qubits Quantum sensing Semiconductor compounds Optical nanoscopy

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Nanoelectromechanical Systems: Straining and Tuning Atomic Layer Nanoelectromechanical Resonators via Comb-Drive MEMS Actuators (Adv. Mater. Technol. 2/2021)

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Advanced Materials Technologies 2021年第2期6卷

作者： Yong Xie Jaesung Lee Yanan Wang Philip X.-L. Feng Department of Electrical Engineering & Computer Science Case School of Engineering Case Western Reserve University Cleveland OH 44106 USA Key Laboratory of Wide Band-Gap Semiconductor Technology School of Advanced Materials and Nanotechnology Xidian University Xi'an 710071 China Department of Electrical & Computer Engineering Herbert Wertheim College of Engineering University of Florida Gainesville FL 32611 USA

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State estimation for a class of linear systems with quadratic output

arXiv

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arXiv 2020年

作者： Theodosis, Dionysios Berkane, Soulaimane Dimarogonas, Dimos V. Dynamic Systems and Simulation Laboratory School of Production Engineering and Management Technical University of Crete Greece Département d’informatique et d’ingénierie Université du Québec en Outaouis GatineauQC Canada Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden

This paper deals with the problem of state estimation for a class of linear time-invariant systems with quadratic output measurements. An immersion-type approach is presented that transforms the system into a state-affine system by adding a finite number of states to the original system. Under suitable persistence of excitation conditions on the input and its higher derivatives, global state estimation is exhibited by means of a Kalman-type observer. A numerical example is provided to illustrate the applicability of the proposed observer design for the problem of position and velocity estimation for a vehicle navigating in the n−dimensional Euclidean space using a single position range measurement. Copyright © 2020, The Authors. All rights reserved.

关键词： State estimation

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arXiv

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arXiv 2023年

作者： Bilgic, Berkin Costagli, Mauro Chan, Kwok-Shing Duyn, Jeff Langkammer, Christian Lee, Jongho Li, Xu Liu, Chunlei Marques, José P. Milovic, Carlos Robinson, Simon Schweser, Ferdinand Shmueli, Karin Spincemaille, Pascal Straub, Sina van Zijl, Peter Wang, Yi Martinos Center for Biomedical Imaging Massachusetts General Hospital Harvard Medical School CharlestownMA United States University of Genoa Genoa Italy Laboratory of Medical Physics and Magnetic Resonance IRCCS Stella Maris Pisa Italy Donders Institute for Brain Cognition and Behaviour Radboud University Nijmegen Netherlands Advanced MRI Section NINDS National Institutes of Health BethesdaMD United States Department of Neurology Medical University of Graz Graz Austria Department of Electrical and Computer Engineering Seoul National University Seoul Korea Republic of Russell H. Morgan Department of Radiology and Radiological Science Johns Hopkins University School of Medicine BaltimoreMD United States F.M. Kirby Research Center for Functional Brain Imaging Kennedy Krieger Institute BaltimoreMD United States Department of Electrical Engineering and Computer Sciences University of California BerkeleyCA United States Helen Wills Neuroscience Institute University of California BerkeleyCA United States Pontificia Universidad Catolica de Valparaiso Valparaiso Chile High Field MR Centre Department of Biomedical Imaging and Image-Guided Therapy Medical University of Vienna Austria Buffalo Neuroimaging Analysis Center Department of Neurology Jacobs School of Medicine and Biomedical Sciences the University at Buffalo BuffaloNY United States Center for Biomedical Imaging Clinical and Translational Science Institute the University at Buffalo BuffaloNY United States Department of Medical Physics and Biomedical Engineering University College London London United Kingdom MRI Research Institute Department of Radiology Weill Cornell Medicine New YorkNY United States Department of Radiology Mayo Clinic JacksonvilleFL United States MRI Research Institute Departments of Radiology and Biomedical Engineering Cornell University New YorkNY United States

This article provides recommendations for implementing quantitative susceptibility mapping (QSM) for clinical brain research. It is a consensus of the ISMRM Electro-Magnetic Tissue Properties Study Group. While QSM technical development continues to advance rapidly, the current QSM methods have been demonstrated to be repeatable and reproducible for generating quantitative tissue magnetic susceptibility maps in the brain. However, the many QSM approaches available give rise to the need in the neuroimaging community for guidelines on implementation. This article describes relevant considerations and provides specific implementation recommendations for all steps in QSM data acquisition, processing, analysis, and presentation in scientific publications. We recommend that data be acquired using a monopolar 3D multi-echo GRE sequence, that phase images be saved and exported in DICOM format and unwrapped using an exact unwrapping approach. Multi-echo images should be combined before background removal, and a brain mask created using a brain extraction tool with the incorporation of phase-quality-based masking. Background fields should be removed within the brain mask using a technique based on SHARP or PDF, and the optimization approach to dipole inversion should be employed with a sparsity-based regularization. Susceptibility values should be measured relative to a specified reference, including the common reference region of whole brain as a region of interest in the analysis, and QSM results should be reported with – as a minimum – the acquisition and processing specifications listed in the last section of the article. These recommendations should facilitate clinical QSM research and lead to increased harmonization in data acquisition, analysis, and reporting. © 2023, CC BY.

关键词： Magnetic resonance imaging

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Theoretical modeling of tunable vibrations of three-dimensional serpentine structures for simultaneous measurement of adherent cell mass and modulus

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MRS Bulletin 2020年

作者： Zhao, Jianzhong Li, Weican Guo, Xingming Wang, Heling Rogers, John A. Huang, Yonggang Shanghai Institute of Applied Mathematics and Mechanics Shanghai Key Laboratory of Mechanics in Energy Engineering School of Mechanics and Engineering Science Shanghai University China Departments of Civil and Environmental Engineering Mechanical Engineering and Materials Science and Engineering Northwestern University Illinois United States School of Engineering Brown University Rhode Island United States Shanghai Institute of Applied Mathematics and Mechanics Shanghai Key Laboratory of Mechanics in Energy Engineering School of Mechanics and Engineering Science Shanghai University China Departments of Civil and Environmental Engineering Mechanical Engineering and Materials Science and Engineering Northwestern University Illinois United States Departments of Materials Science and Engineering Biomedical Engineering Chemistry Mechanical Engineering Electrical Engineering and Computer Science and Neurological Surgery Northwestern University Illinois United States Querrey-Simpson Institute for Bioelectronics Northwestern University Illinois United States Departments of Civil and Environmental Engineering Mechanical Engineering and Materials Science and Engineering Northwestern University Illinois United States Querrey-Simpson Institute for Bioelectronics Northwestern University Illinois United States

Vibration-based methods can be used effectively to characterize the physical properties of biological materials, with an increasing interest focused on the mechanics of individual, living cells. Real-time measurements of cell properties, such as mass and Young's modulus, can yield important insights into many aspects of cell growth and metabolism as well as the interaction of cells with external stimuli (e.g., drugs). Vibrational test structures designed for the study of such cell properties often use fixed configurations and operational modes, with associated limitations in determining multiple characteristics of the cell, simultaneously. Recent development of mechanics-guided techniques for deterministic assembly of three-dimensional (3D) microstructures provides a route to vibrational frameworks that offer tunable configurations, vibration modes, and resonant frequencies. Here we propose a method that exploits such tunable vibrational structures to simultaneously determine the mass and modulus of a single adherent cell, or of other biological materials or small-scale living systems (e.g., organoids), through theoretical modeling and finite element analysis. The idea involves a 3D architecture that supports two different vibrational structures and can be converted from one to the other through application of strain to an elastomeric substrate. Specifically, tailored designs for serpentine ribbons in these systems enable a decoupling of the dependence of the resonant frequencies of the two structures to the cell mass and modulus, with an associated ability to measure these two properties accurately and independently. These same concepts can be scaled to apply to various types of cells, as well as to organoids (3D clusters of cells) and other biological materials with small geometries, across a range of values of mass and modulus. This method could serve as the foundation for microelectromechanical systems capable of monitoring mass and modulus in real time for use

关键词： Serpentine

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Image Restoration for Terahertz Image Based on Complex-Valued Deconvolution 8

Image Restoration for Terahertz Image Based on Complex-Value...

引用

8th Asia-Pacific Conference on Antennas and Propagation, APCAP 2019

作者： Ning, Wei Qi, Feng Wang, Jinkuan Northeastern University School of Computer Science and Engineering Shenyang110169 China Chinese Academy of Sciences Shenyang Institute of Automation Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110016 China Key Laboratory of Opto-Electronic Information Processing Chinese Academy of Sciences Shenyang110016 China Key Lab of Image Understanding and Computer Vision Liaoning Province Shenyang110016 China

ISBN: (纸本)9781665400541

According to the unique characteristics of terahertz (THz) waves, THz imaging has become a hot topic in widely application areas. However, the imaging resolution is constrained by its long wavelength. Generally, the deconvolution is always used to reconstruct the object function. In this paper, a method that combining the classic deconvolution algorithm with the complex-value data processing is investigated. It is posed that the proposed complex-number deconvolution achieves better performance compared to the real-valued deconvolution. © 2019 IEEE.

关键词： Deconvolution

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