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arXiv 2019年

作者： Liu, Shenglan Yu, Yang Liu, Yang Qiao, Hong Feng, Lin Feng, Jiashi School of Computer Science and Technology Faculty of Electronic Information and Electrical Engineering Dalian University of Technology Dalian Liaoning116024 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China Department of Electrical and Computer Engineering National University of Singapore Singapore Singapore

Manifold learning now plays a very important role in machine learning and many relevant applications. Although its superior performance in dealing with nonlinear data distribution, data sparsity is always a thorny knot. There are few researches to well handle it in manifold learning. In this paper, we propose Hierarchic Neighbors Embedding (HNE), which enhance local connection by the hierarchic combination of neighbors. After further analyzing topological connection and reconstruction performance, three different versions of HNE are given. The experimental results show that our methods work well on both synthetic data and high-dimensional real-world tasks. HNE develops the outstanding advantages in dealing with general data. Furthermore, comparing with other popular manifold learning methods, the performance on sparse samples and weak-connected manifolds is better for HNE. Copyright © 2019, The Authors. All rights reserved.

关键词： Topology

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Strong Converse Magnetoelectric Effect in a Composite of Weakly Ferromagnetic Iron Borate and Ferroelectric Lead Zirconate Titanate

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Physical Review Applied 2020年第3期14卷 034039-034039页

作者： M. Popov Y. Liu V.L. Safonov I.V. Zavislyak V. Moiseienko P. Zhou Jiayu Fu Wei Zhang Jitao Zhang Y. Qi Tianjin Zhang T. Zhou P.J. Shah M.E. McConney M.R. Page G. Srinivasan Physics Department Oakland University Rochester Michigan 48309 USA Faculty of Radiophysics Electronics and Computer Systems Taras Shevchenko National University of Kyiv Kyiv 01601 Ukraine Department of Materials Science and Engineering Hubei University Wuhan 430062 People’s Republic of China Materials and Manufacturing Directorate Air Force Research Laboratory Wright-Patterson Air Force Base Dayton Ohio 45433 USA College of Electronics and Information Hangzhou Dianzi University Hangzhou 310018 People’s Republic of China College of Electrical and Information Engineering Zhengzhou University of Light Industry Zhengzhou 450002 People’s Republic of China

This report is on a model and experiment on the nature of mechanical-strain-mediated converse magnetoelectric (CME) effect in a composite of single-crystal iron borate, a canted antiferromagnet with a weak ferromagnetic moment, and ferroelectric lead zirconate titanate (PZT). The piezoelectric strain generated in PZT by an electric field E manifested as a shift in the quasiferromagnetic resonance (FMR) field in iron borate due to strong magnetoelastic interactions. The CME interaction strength determined from data on field shift in FMR versus E is 46–54 MHz cm/kV at 5.5–6.5 GHz. The strength of the CME is comparable to values reported for composites of ferrimagnetic oxides and PZT. A model that considers the effect of piezoelectric deformation on magnetic order parameters and magnetic resonance in iron borate is proposed for the CMEs in the composite and estimated ME coupling coefficients are in good agreement with data. The E tunability of the high-frequency AFMR mode at about 300 GHz is estimated to be on the order of 1.7 MHz kV/cm and is very small relative to the quasi-FMR mode. Composites of iron borate and ferroelectrics are very attractive for use in dual electric field and magnetic field tunable signal processing devices due to strong CME interactions and the need for a rather small bias magnetic field compared with traditional ferrimagnetic oxide based devices.

关键词： Magnetic coupling Magnetoelectric effect Multiferroics Strain Antiferromagnets Bilayer films Ferroelectrics Transition metal oxides

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Searching for GEMS: TOI-6383Ab, a giant planet transiting an M3-dwarf star in a binary system

arXiv

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arXiv 2024年

作者： Bernabò, Lia Marta Kanodia, Shubham CaÑas, Caleb I. Cochran, William D. Csizmadia, Szilárd Mahadevan, Suvrath StefÁnsson, Gudhmundur Gupta, Arvind F. Monson, Andrew Kobulnicky, Henry A. Larsen, Alexander K. Cotter, Ethan G. Birkholz, Alexina Swaby, Tera N. Zeimann, Gregory Bender, Chad F. Diddams, Scott A. Libby-Roberts, Jessica E. Lin, Andrea S.J. Ninan, Joe P. Rauer, Heike Reji, Varghese Robertson, Paul Roy, Arpita Schwab, Christian Rutherfordstrasse 2 Berlin12489 Germany McDonald Observatory Department of Astronomy The University of Texas Austin United States Earth and Planets Laboratory Carnegie Science 5241 Broad Branch Road NW WashingtonDC20015 United States NASA Goddard Space Flight Center 8800 Greenbelt Road GreenbeltMD20771 United States Center for Planetary Systems Habitability The University of Texas Austin United States ELKH-SZTE Stellar Astrophysics Research Group Szegedi út Kt. 766 BajaH-6500 Hungary Department of Astronomy & Astrophysics 525 Davey Laboratory The Pennsylvania State University University ParkPA16802 United States Center for Exoplanets and Habitable Worlds 525 Davey Laboratory The Pennsylvania State University University ParkPA16802 United States ETH Zurich Institute for Particle Physics & Astrophysics Switzerland Anton Pannekoek Institute for Astronomy University of Amsterdam Science Park 904 Amsterdam1098 XH Netherlands U.S. National Science Foundation National Optical-Infrared Astronomy Research Laboratory 950 N. Cherry Ave. TucsonAZ85719 United States Steward Observatory The University of Arizona 933 N. Cherry Avenue TucsonAZ85721 United States Department of Physics & Astronomy University of Wyoming LaramieWY82070 United States Hobby Eberly Telescope University of Texas AustinTX78712 United States Electrical Computer & Energy Engineering University of Colorado 425 UCB BoulderCO80309 United States Department of Physics University of Colorado 2000 Colorado Avenue BoulderCO80309 United States Department of Astronomy and Astrophysics Tata Institute of Fundamental Research Homi Bhabha Road Colaba Mumbai400005 India Institut für Geologische Wissenschaften Freie Universität Berlin Berlin12249 Germany Department of Physics & Astronomy University of California Irvine IrvineCA92697 United States Astrophysics & Space Institute Schmidt Sciences New YorkNY10011 United States School of Mathematical and Physical Sciences Macq

We report on the discovery of a transiting giant planet around the 3500 K M3-dwarf star TOI-6383A located 172 pc from Earth. It was detected by the Transiting Exoplanet Survey Satellite (TESS) and confirmed by a combination of ground-based follow-up photometry and precise radial velocity measurements. This planet has an orbital period of ∼1.791 days, mass of 1.040±0.094 MJ and a radius of (Equation presented), resulting in a mean bulk density of (Equation presented). TOI-6383A has an M-dwarf companion star, TOI-6383B, which has a stellar effective temperature Teff ∼ 3100 K and a projected orbital separation of 3100 AU. TOI-6383A is a low-mass dwarf star hosting a giant planet and is an intriguing object for planetary evolution studies due to its high planet-to-star mass ratio. This discovery is part of the Searching for Giant Exoplanets around M-dwarf Stars (GEMS) Survey, intending to provide robust and accurate estimates of the occurrence of GEMS and the statistics on their physical and orbital parameters. This paper presents an interesting addition to the small number of confirmed GEMS, particularly notable since its formation necessitates massive, dust-rich protoplanetary discs and high accretion efficiency (> 10%). © 2024, CC BY.

关键词： Orbits

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Blockchain Technology Based Decentralized Energy Trading for Multiple-Microgrid systems

Blockchain Technology Based Decentralized Energy Trading for...

引用

IEEE Conference on Energy Internet and Energy System Integration (EI2)

作者： Ting Li Wei Zhang Ning Chen Minhui Qian Ying Xu School of Electrical Engineering and Automation Harbin Institute of Technology Harbin China State Key Laboratory of Operation and Control of Renewable Energy & Storage Systems China EPRI Nanjing China Department of Electrical Engineering & Computer Science University of Central Florida Orlando USA

ISBN: (数字)9781728131375

ISBN: (纸本)9781728131382

With the emerging technology for distributed generation and urge of improving quality of service of power supply for energy users, more and more Microgrids (MGs) are integrated into the distributed networks to serve the energy users. These Microgrids are gradually formulating a Multi-Microgrid System (MMGS, Multi-Microgrid System), which will play an important role for the future energy supply. Building a centralized control center not only increases the expense of investment, but also brings the issues of maintaining fairness among energy users. To address these problems, this paper proposes a peer-to-peer method for energy trading of MMGS, based on the idea of decentralized trading. An auction-based trading mechanism suitable for peer-to-peer energy trading is proposed first. For this mechanism, the MGs firstly declare their energy buying bids or selling quotations. Then, the market-clearing price is determined by using a unified weight clearing algorithm in a decentralized manner. By applying the edge technology of Blockchain, the implementation of the proposed peer-to-peer energy trading method, including the architecture, procedures, security check, etc., is also discussed. The proposed Blockchain-based energy trading platform can realize the decentralized and autonomous energy trading of MGs within an MMGS. A case study with an MMGS with 10 MG units is provided to demonstrate the effectiveness of the proposed approach.

关键词： Peer-to-peer computing Microgrids Blockchain Distributed power generation Electronic commerce Power distribution economics Pricing

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Simulating emergent properties of human driving behavior using multi-agent reward augmented imitation learning

arXiv

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arXiv 2019年

作者： Bhattacharyya, Raunak P. Phillips, Derek J. Liu, Changliu Gupta, Jayesh K. Driggs-Campbell, Katherine Kochenderfer, Mykel J. Stanford Intelligent Systems Laboratory Department of Aeronautics and Astronautics Stanford University StanfordCA94305 United States Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign UrbanaIL61801 United States

Recent developments in multi-agent imitation learning have shown promising results for modeling the behavior of human drivers. However, it is challenging to capture emergent traffic behaviors that are observed in real-world datasets. Such behaviors arise due to the many local interactions between agents that are not commonly accounted for in imitation learning. This paper proposes Reward Augmented Imitation Learning (RAIL), which integrates reward augmentation into the multi-agent imitation learning framework and allows the designer to specify prior knowledge in a principled fashion. We prove that convergence guarantees for the imitation learning process are preserved under the application of reward augmentation. This method is validated in a driving scenario, where an entire traffic scene is controlled by driving policies learned using our proposed algorithm. Further, we demonstrate improved performance in comparison to traditional imitation learning algorithms both in terms of the local actions of a single agent and the behavior of emergent properties in complex, multi-agent settings. Copyright © 2019, The Authors. All rights reserved.

关键词： Multi agent systems

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Are state-of-the-art visual place recognition techniques any good for aerial robotics?

arXiv

引用

arXiv 2019年

作者： Zaffar, Mubariz Khaliq, Ahmad Ehsan, Shoaib Milford, Michael Alexis, Kostas McDonald-Maier, Klaus Embedded and Intelligent Systems Laboratory in Computer Science Electronic Engineering Department University of Essex Colchester United Kingdom Australian Centre for Robotic Vision and School of Electrical Engineering and Computer Science Queensland University of Technology BrisbaneQLD Australia Autonomous Robots Lab University of Nevada United States

Visual Place Recognition (VPR) has seen significant advances at the frontiers of matching performance and computational superiority over the past few years. However, these evaluations are performed for ground-based mobile platforms and cannot be generalized to aerial platforms. The degree of viewpoint variation experienced by aerial robots is complex, with their processing power and on-board memory limited by payload size and battery ratings. Therefore, in this paper, we collect 8 state-of-the-art VPR techniques that have been previously evaluated for ground-based platforms and compare them on 2 recently proposed aerial place recognition datasets with three prime focuses: a) Matching performance b) Processing power consumption c) Projected memory requirements. This gives a birds-eye view of the applicability of contemporary VPR research to aerial robotics and lays down the the nature of challenges for aerial-VPR. Copyright © 2019, The Authors. All rights reserved.

关键词： Antennas

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Transfer learning-based online multiperson tracking with Gaussian process regression

Transfer learning-based online multiperson tracking with Gau...

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作者： Zhang, Baobing Li, Siguang Huang, Zhengwen Rahi, Babak H. Wang, Qicong Li, Maozhen Department of Electronic and Computer Engineering Brunel University London London United Kingdom The Key Laboratory of Embedded Systems and Service Computing Tongji University Shanghai China College of Electrical Engineering Binzhou University Binzhou China Department of Computer Science Xiamen University Xiamen China

Most existing tracking-by-detection approaches are affected by abrupt pedestrian pose changes, lighting conditions, scale changes, and real-time processing, which leads to issues such as detection errors and drifts. To deal with these issues, we present a novel multi-person tracking framework by introducing a new Gaussian Process Regression based observation model, which learns in a semi-supervised manner. The background information is taken into consideration to build the discriminative tracker, training samples are re-weighted appropriately to ease the impact of the potential sample misalignment and noisy during model updating. Unlabeled samples from the current frame provide rich information, which is used for enhancing the tracking inference. Experimental results show that the proposed approach outperforms a number of state-of-the-art methods on some benchmark datasets. © 2018 John Wiley & Sons, Ltd.

关键词： Gaussian noise (electronic)

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Designing non-Hermitian dynamics for conservative state evolution on the Bloch sphere

引用

Physical Review A 2018年第3期97卷 033805-033805页

作者： Sunkyu Yu Xianji Piao Namkyoo Park Photonic Systems Laboratory Department of Electrical and Computer Engineering Seoul National University Seoul 08826 South Korea

An evolution on the Bloch sphere is the fundamental state transition, including optical polarization controls and qubit operations. Conventional evolution of a polarization state or qubit is implemented within a closed system that automatically satisfies energy conservation from the Hermitian formalism. Although particular forms of static non-Hermitian Hamiltonians, such as parity-time-symmetric Hamiltonians, allow conservative states in an open system, the criteria for the energy conservation in a dynamical open system have not been fully explored. Here, we derive the condition of conservative state evolution in open-system dynamics and its inverse design method, by developing the non-Hermitian modification of the Larmor precession equation. We show that the geometrically designed locus on the Bloch sphere can be realized by different forms of dynamics, leading to the isolocus family of non-Hermitian dynamics. This increased degree of freedom allows the complementary phenomena of error-robust and highly sensitive evolutions on the Bloch sphere, which could be applicable to stable polarizers, quantum gates, and optimized sensors in dynamical open systems.

关键词： Polarization of light Quantum phase transitions Dynamical systems

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An Adaptive Strategy via Reinforcement Learning for the Prisoner＇s Dilemma Game

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IEEE/CAA Journal of Automatica Sinica 2018年第1期5卷 301-310页

作者： Lei Xue Changyin Sun Donald Wunsch Yingjiang Zhou Fang Yu Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education School of Automation Southeast University Nanjing 210096 China Department of Electrical and Computer Engineering Missouri University of Science and Technology Rolla MO 65409 USA College of Automation Nanjing University of Posts and Telecommunications Nanjing 210023 China Institute of Logistics Science and Engineering Shanghai Maritime University Shanghai 210096 China IEEE

The iterated prisoner's dilemma(IPD) is an ideal model for analyzing interactions between agents in complex networks. It has attracted wide interest in the development of novel strategies since the success of tit-for-tat in Axelrod's tournament. This paper studies a new adaptive strategy of IPD in different complex networks, where agents can learn and adapt their strategies through reinforcement learning method. A temporal difference learning method is applied for designing the adaptive strategy to optimize the decision making process of the agents. Previous studies indicated that mutual cooperation is hard to emerge in the IPD. Therefore, three examples which based on square lattice network and scale-free network are provided to show two features of the adaptive strategy. First, the mutual cooperation can be achieved by the group with adaptive agents under scale-free network, and once evolution has converged mutual cooperation, it is unlikely to shift. Secondly, the adaptive strategy can earn a better payoff compared with other strategies in the square network. The analytical properties are discussed for verifying evolutionary stability of the adaptive strategy.

关键词： Complex network prisoner’s dilemma reinforcement learning temporal differences learning

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Bohmian Photonics for Independent Control of the Phase and Amplitude of Waves

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Physical Review Letters 2018年第19期120卷 193902-193902页

作者： Sunkyu Yu Xianji Piao Namkyoo Park Photonic Systems Laboratory Department of Electrical and Computer Engineering Seoul National University Seoul 08826 Korea.

The de Broglie–Bohm theory is one of the nonstandard interpretations of quantum phenomena that focuses on reintroducing definite positions of particles, in contrast to the indeterminism of the Copenhagen interpretation. In spite of intense debate on its measurement and nonlocality, the de Broglie–Bohm theory based on the reformulation of the Schrödinger equation allows for the description of quantum phenomena as deterministic trajectories embodied in the modified Hamilton-Jacobi mechanics. Here, we apply the Bohmian reformulation to Maxwell’s equations to achieve the independent manipulation of optical phase evolution and energy confinement. After establishing the deterministic design method based on the Bohmian approach, we investigate the condition of optical materials enabling scattering-free light with bounded or random phase evolutions. We also demonstrate a unique form of optical confinement and annihilation that preserves the phase information of incident light. Our separate tailoring of wave information extends the notion and range of artificial materials.

关键词： Quantum description of light-matter interaction Transformation optics Optical materials & elements Random & disordered media

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