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第三十八届中国控制会议

作者： Hao Liu Wanbing Zhao Frank L.Lewis Zhong-Ping Jiang Hamidreza Modares School of Astronautics Beihang University Key Laboratory of Spacecraft Design Optimization and Dynamic Simulation Technologies of Ministry of Education Beihang University the University of Texas at Arlington Research Institute University of Texas at Arlington the Control and Networks Lab Department of Electrical and Computer Engineering Tandon School of Engineering New York University the University of Texas at Arlington Research Institute

In this paper, a reinforcement learning based control law is proposed to solve the attitude synchronization problem of the leader-following multi-quadrotor systems. The overall system is composed of a team of quadrotors, modeled with highly nonlinear and coupled dynamics. An optimal control solution is obtained by solving an augmented HamiltonJacobi-Bellman equation. A reinforcement learning approach is used to learn the optimal control law. Simulation results are provided to verify the effectiveness of the proposed controller.

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Energetic electron precipitation driven by electromagnetic ion cyclotron waves from ELFIN’s low altitude perspective

arXiv

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arXiv 2022年

作者： Angelopoulos, V. Zhang, X.-J. Artemyev, A.V. Mourenas, D. Tsai, E. Wilkins, C. Runov, A. Liu, J. Turner, D.L. Li, W. Khurana, K. Wirz, R.E. Sergeev, V.A. Meng, X. Wu, J. Hartinger, M.D. Raita, T. Shen, Y. An, X. Shi, X. Bashir, M.F. Shen, X. Gan, L. Qin, M. Capannolo, L. Ma, Q. Russell, C.L. Masongsong, E.V. Caron, R. He, I. Iglesias, L. Jha, S. King, J. Kumar, S. Le, K. Mao, J. McDermott, A. Nguyen, K. Norris, A. Palla, A. Roosnovo Tam, J. Xie, E. Yap, R.C. Ye, S. Young, C. Adair, L.A. Shaffer, C. Chung, M. Cruce, P. Lawson, M. Leneman, D. Allen, M. Anderson, M. Arreola-Zamora, M. Artinger, J. Asher, J. Branchevsky, D. Cliffe, M. Colton, K. Costello, C. Depe, D. Domae, B.W. Eldin, S. Fitzgibbon, L. Flemming, A. Frederick, D.M. Gilbert, A. Hesford, B. Krieger, R. Lian, K. McKinney, E. Miller, J.P. Pedersen, C. Qu, Z. Rozario, R. Rubly, M. Seaton, R. Subramanian, A. Sundin, S.R. Tan, A. Thomlinson, D. Turner, W. Wing, G. Wong, C. Zarifian, A. Earth Planetary and Space Sciences Department Institute of Geophysics and Planetary Physics University of California Los Angeles Los AngelesCA90095 United States University of Texas at Dallas RichardsonTX75080 United States CEA DAM DIF Arpajon France Atmospheric and Oceanic Sciences Departments University of California Los AngelesCA United States Johns Hopkins University Applied Physics Laboratory LaurelMD United States Department of Astronomy Center for Space Physics Boston University BostonMA United States Mechanical and Aerospace Engineering Department Henry Samueli School of Engineering University of California Los AngelesCA90095 United States School of Mechanical Industrial Manufacturing Engineering Oregon State University CorvallisOR97331 United States University of St. Petersburg St. Petersburg Russia Jet Propulsion Laboratory California Institute of Technology PasadenaCA91109 United States Space Science Institute BoulderCO80301 United States Sodankylä Geophysical Observatory University of Oulu Sodankylä Finland Materials Science and Engineering Department Henry Samueli School of Engineering University of California Los AngelesCA90095 United States Deloitte Consulting New YorkNY10112 United States Computer Science Department Henry Samueli School of Engineering University of California Los AngelesCA90095 United States Microsoft RedmondWA98052 United States Physics and Astronomy Department University of California Los AngelesCA90095 United States Department of Astronomy and Astrophysics The University of Chicago ChicagoIL60637 United States Raybeam Inc. Mountain ViewCA94041 United States SpaceX HawthorneCA90250 United States Reliable Robotics Corporation Mountain ViewCA94043 United States Los Alamos National Laboratory Los AlamosNM87545 United States Mathematics Department University of California Los AngelesCA90095 United States Planet Labs PBC San FranciscoCA94107 United States KSAT Inc. De

We review comprehensive observations of electromagnetic ion cyclotron (EMIC) wave-driven energetic electron precipitation using data from the energetic electron detector on the Electron Losses and Fields InvestigatioN (ELFIN) mission, two polar-orbiting low-altitude spinning CubeSats, measuring 50-5000 keV electrons with good pitch-angle and energy resolution. EMIC wave-driven precipitation exhibits a distinct signature in energy-spectrograms of the precipitating-to-trapped flux ratio: peaks at >0.5 MeV which are abrupt (bursty) (lasting ∼17s, or ∆L ∼ 0.56) with significant substructure (occasionally down to sub-second timescale). We attribute the bursty nature of the precipitation to the spatial extent and structuredness of the wave field at the equator. Multiple ELFIN passes over the same MLT sector allow us to study the spatial and temporal evolution of the EMIC wave - electron interaction region. Case studies employing conjugate ground-based or equatorial observations of the EMIC waves reveal that the energy of moderate and strong precipitation at ELFIN approximately agrees with theoretical expectations for cyclotron resonant interactions in a cold plasma. Using 2 years of ELFIN data uniformly distributed in local time, we assemble a statistical database of ∼50 events of strong EMIC wave-driven precipitation. Most reside at L ∼ 5 − 7 at dusk, while a smaller subset exists at L ∼ 8 − 12 at post-midnight. The energies of the peak-precipitation ratio and of the half-peak precipitation ratio (our proxy for the minimum resonance energy) exhibit an L-shell dependence in good agreement with theoretical estimates based on prior statistical observations of EMIC wave power spectra. The precipitation ratio’s spectral shape for the most intense events has an exponential falloff away from the peak (i.e., on either side of ∼ 1.45 MeV). It too agrees well with quasi-linear diffusion theory based on prior statistics of wave spectra. It should be noted though that this diffusive

关键词： Radiation belts

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An Improved Non-Geometrical Stochastic Model for Non-WSSUS Vehicle-to-Vehicle Channels

An Improved Non-Geometrical Stochastic Model for Non-WSSUS V...

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2018中国信息通信大会(CICC2018)

作者： Ziwei Huang Xiang Cheng State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Engineering and Computer Science Peking University

In this paper,we propose a novel non-geometrical stochastic model(NGSM)for non-wide sense stationary uncorrelated scattering(non-WSSUS)vehicle-to-vehicle(V2V)*** proposed model is based on a conventional NGSM and employs a more accurate method to reproduce the realistic characteristics of V2V channels,which successfully extends the existing NGSM to include the line-of-sight(LoS)***,the statistical properties of the proposed model in different scenarios,including Doppler power spectral density(PSD),power delay profile(PDP),and the tap correlation coefficient matrix are simulated and compared with those of the existing ***,the simulation results demonstrate not only the utility of the proposed model,but also the correctness of our theoretical derivations.

关键词： vehicle-to-vehicle non-WSSUS channels non-geometrical stochastic model LoS component statistical properties

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On Parameter Tuning in Meta-Learning for computer Vision

On Parameter Tuning in Meta-Learning for Computer Vision

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International Conference on Computational Science and Computational Intelligence (CSCI)

作者： Farid Ghareh Mohammadi Hamid R. Arabnia M. Hadi Amini University of Georgia Athens GA USA Department of Computer Science Franklin College of Arts and Sciences University of Georgia Athens Georgia School of Computing and Information Sciences College of Engineering and Computing Sustainability Optimization and Learning for InterDependent networks laboratory (solid lab) Florida International University Miami FL

ISBN: (数字)9781728155845

ISBN: (纸本)9781728155852

Learning to learn plays a pivotal role in meta-learning (MTL) to obtain an optimal learning model. In this paper, we investigate image recognition for unseen categories of a given dataset with limited training information. We deploy a zero-shot learning (ZSL) algorithm to achieve this goal. We also explore the effect of parameter tuning on performance of semantic auto-encoder (SAE). We further address the parameter tuning problem for meta-learning, especially focusing on zero-shot learning. By combining different embedded parameters, we improved the accuracy of tuned-SAE. Advantages and disadvantages of parameter tuning and its application in image classification are also explored.

关键词： Semantics Training computer vision Tuning Machine learning Testing Image recognition

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Tunable multi-wavelength EDF laser based on Sagnac interferometer with weakly-coupled FMF delay line

Tunable multi-wavelength EDF laser based on Sagnac interfero...

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Optical Fiber Communication Conference, OFC 2018

作者： Zhou, Muqing Ren, Fang Li, Juhao Ge, Dawei Zhang, Yichi Chen, Zhangyuan He, Yongqi State Key Laboratory of Advanced Optical Communication Systems and Networks Peking University Beijing100871 China School of Computer and Communication Engineering University of Science and Technology Beijing Beijing100083 China Wuhan Research Institute of Posts and Telecommunications Wuhan430074 China

We propose an EDF laser based on Sagnac interferometer with weakly-coupled FMF delay line. We experimentally demonstrate that it has high stability, an OSNR higher than 30-dB and a tuning range over 30-nm. © OSA ... 详细信息

ISBN: (纸本)9781943580385

关键词： Interferometers

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Is AI Robust Enough for Scientific Research?

arXiv

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arXiv 2024年

作者： Zhang, Jun-Jie Song, Jiahao Wang, Xiu-Cheng Li, Fu-Peng Liu, Zehan Chen, Jian-Nan Dang, Haoning Wang, Shiyao Zhang, Yiyan Xu, Jianhui Shi, Chunxiang Wang, Fei Pang, Long-Gang Cheng, Nan Zhang, Weiwei Zhang, Duo Meng, Deyu Northwest Institute of Nuclear Technology No. 28 Pingyu Road Shaanxi Xi’an710024 China School of Telecommunications Engineering Xidian University No. 2 South Taibai Road Shaanxi Xi’an710071 China State Key Laboratory of ISN No. 2 South Taibai Road Shaanxi Xi’an710071 China Institute of Particle Physics Central China Normal University No. 152 Luoyu Road Hubei Wuhan30079 China School of Computer Science and Technology Xi’an Jiaotong University No. 28 Xianning West Road Shaanxi Xi’an710049 China Ministry of Education Key Lab of Intelligent Networks and Network Security Xi’an Jiaotong University No. 28 Xianning West Road Shaanxi Xi’an710049 China Guangzhou Institute of Geography Academy of Sciences No. 100 Xianlie Road Guangdong Guangzhou510070 China National Meteorological Information Center Beijing100044 China MDX Research Center for Element Strategy Institute of Integrated Research Institute of Science Tokyo Midori-ku Yokohama226-8503 Japan School of Physics and Information Technology Shaanxi Normal University No. 620 West Chang’an Avenue Shaanxi Xi’an710119 China School of Aeronautics Northwestern Polytechnical University No. 127 West Youyi Road Shaanxi Xi’an710072 China AI for Science Institute Beijing100080 China DP Technology Beijing100080 China Academy for Advanced Interdisciplinary Studies Peking University Beijing100871 China School of Mathematics and Statistics Xi’an Jiaotong University No. 28 Xianning West Road Shaanxi Xi’an710049 China

We uncover a phenomenon largely overlooked by the scientific community utilizing AI: neural networks exhibit high susceptibility to minute perturbations, resulting in significant deviations in their outputs. Through an analysis of five diverse application areas—weather forecasting, chemical energy and force calculations, fluid dynamics, quantum chromodynamics, and wireless communication—we demonstrate that this vulnerability is a broad and general characteristic of AI systems. This revelation exposes a hidden risk in relying on neural networks for essential scientific computations, calling further studies on their reliability and security. © 2024, CC0.

关键词： Weather forecasting

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Bit-length vs. dimension: Image formation of ghost imaging

Bit-length vs. dimension: Image formation of ghost imaging

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Frontiers in Optics, FIO 2018

作者： Ke, Meijing Li, Junhui Wu, Guohua Luo, Bin Yang, Dongyue Yin, Longfei Guo, Hong School of Electronic Engineering Beijing University of Posts and Telecommunications Beijing100876 China State Key Laboratory of Information Photonics and Optical Communications Beijing University of Posts and Telecommunications Beijing100876 China State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Engineering and Computer Science Center for Quantum Information Technology Peking University Beijing100871 China

Length-variant pooling according to conducted sample number excludes endeavor to expand image dimension from image formation process of ghost imaging, leaving bit-length growth alone. Experiment confirms positive corr... 详细信息

ISBN: (纸本)9781943580460

关键词： Ghost imaging Image processing Image quality Image quality assessment Image restoration Light fields

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Ciphertext-Policy Attribute Based Encryption Supporting Any Monotone Access Structures Without Escrow

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Chinese Journal of Electronics 2017年第3期26卷 640-646页

作者： ZHAO Qianqian ZHANG Yuqing ZHANG Guanghua WANG He State Key Laboratory of Integrated Services Networks Xidian University National Computer Network Intrusion Protection Center University of Chinese Academy of Sciences Beijing Key Laboratory of IOT Information Security Technology Institute of Information Engineering CAS

Ciphertext policy attribute-based encryption(CP-ABE)is becoming a new primitive for finegrained access *** neither produces multiple encrypted copies of the same data nor suffers from the severe burden of key distribution and *** escrow problem that the central authority could decrypt any ciphertexts addressed to all the specific users is still a challenge for CP-ABE *** new CP-ABE scheme without escrow is proposed,and furthermore the proposed scheme achieves fully security in the standard *** performance and security analysis results indicate that the proposed CP-ABE scheme is extremely appropriate for cloud storage system.

关键词： CP-ABE The escrow problem Fully security In the standard model

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Author Correction: Ultra-efficient frequency comb generation in AlGaAs-on-insulator microresonators

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Nature communications 2021年第1期12卷 1803页

作者： Lin Chang Weiqiang Xie Haowen Shu Qi-Fan Yang Boqiang Shen Andreas Boes Jon D Peters Warren Jin Chao Xiang Songtao Liu Gregory Moille Su-Peng Yu Xingjun Wang Kartik Srinivasan Scott B Papp Kerry Vahala John E Bowers Department of Electrical and Computer Engineering University of California Santa Barbara CA 93106 USA. Department of Electrical and Computer Engineering University of California Santa Barbara CA 93106 USA. weiqiangxie@ucsb.edu. State Key Laboratory of Advanced Optical Communications System and Networks Peking University Beijing 100871 China. T. J. Watson Laboratory of Applied Physics California Institute of Technology Pasadena CA 91125 USA. School of Engineering RMIT University Melbourne VIC 3000 Australia. Microsystems and Nanotechnology Division National Institute of Standards and Technology Gaithersburg MD 20899 USA. Time and Frequency Division National Institute of Standards and Technology Boulder CO 80305 USA. Department of Electrical and Computer Engineering University of California Santa Barbara CA 93106 USA. bowers@ece.ucsb.edu.

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Spatial resolution of compressive-sensing-based ghost imaging can approach theoretical limit

Spatial resolution of compressive-sensing-based ghost imagin...

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Frontiers in Optics, FIO 2018

作者： Du, Xieyu Li, Junhui Luo, Bin Han, Dongchu Yang, Dongyue Wu, Guohua Guo, Hong School of Electronic Engineering Beijing University of Posts and Telecommunications Beijing100876 China State Key Laboratory of Information Photonics and Optical Communications Beijing University of Posts and Telecommunications Beijing100876 China State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Engineering and Computer Science Center for Quantum Information Technology Peking University Beijing100871 China

We present a method for the spatial resolution of ghost imaging to approach its theoretical limit based on parameter optimization of compressive sensing This method shows robustness against additive noise in bucket de... 详细信息

ISBN: (纸本)9781943580460

关键词： Compressed sensing

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