检索结果-内蒙古大学图书馆

arXiv 2025年

作者： Cao, Zhen Aharonian, F. Bai, Y.X. Bao, Y.W. Bastieri, D. Bi, X.J. Bi, Y.J. Bian, W. Bukevich, A.V. Cai, C.M. Cao, W.Y. Cao, Zhe Chang, J. Chang, J.F. Chen, A.M. Chen, E.S. Chen, H.X. Chen, Liang Chen, Long Chen, M.J. Chen, M.L. Chen, Q.H. Chen, S. Chen, S.H. Chen, S.Z. Chen, T.L. Chen, X.B. Chen, X.J. Chen, Y. Cheng, N. Cheng, Y.D. Chu, M.C. Cui, M.Y. Cui, S.W. Cui, X.H. Cui, Y.D. Dai, B.Z. Dai, H.L. Dai, Z.G. Danzengluobu Diao, Y.X. Dong, X.Q. Duan, K.K. Fan, J.H. Fan, Y.Z. Fang, J. Fang, J.H. Fang, K. Feng, C.F. Feng, H. Feng, L. Feng, S.H. Feng, X.T. Feng, Y. Feng, Y.L. Gabici, S. Gao, B. Gao, C.D. Gao, Q. Gao, W. Gao, W.K. Ge, M.M. Ge, T.T. Geng, L.S. Giacinti, G. Gong, G.H. Gou, Q.B. Gu, M.H. Guo, F.L. Guo, J. Guo, X.L. Guo, Y.Q. Guo, Y.Y. Han, Y.A. Hannuksela, O.A. Hasan, M. He, H.H. He, H.N. He, J.Y. He, X.Y. He, Y. Hernández-Cadena, S. Hor, Y.K. Hou, B.W. Hou, C. Hou, X. Hu, H.B. Hu, S.C. Huang, C. Huang, D.H. Huang, J.J. Huang, T.Q. Huang, W.J. Huang, X.T. Huang, X.Y. Huang, Y. Huang, Y.Y. Ji, X.L. Jia, H.Y. Jia, K. Jiang, H.B. Jiang, K. Jiang, X.W. Jiang, Z.J. Jin, M. Kaci, S. Kang, M.M. Karpikov, I. Khangulyan, D. Kuleshov, D. Kurinov, K. Li, B.B. Li, Cheng Li, Cong Li, D. Li, F. Li, H.B. Li, H.C. Li, Jian Li, Jie Li, K. Li, L. Li, R.L. Li, S.D. Li, T.Y. Li, W.L. Li, X.R. Li, Xin Li, Y.Z. Li, Zhe Li, Zhuo Liang, E.W. Liang, Y.F. Lin, S.J. Liu, B. Liu, C. Liu, D. Liu, D.B. Liu, H. Liu, H.D. Liu, J. Liu, J.L. Liu, J.R. Liu, M.Y. Liu, R.Y. Liu, S.M. Liu, W. Liu, X. Liu, Y. Liu, Y. Liu, Y.N. Lou, Y.Q. Luo, Q. Luo, Y. Lv, H.K. Ma, B.Q. Ma, L.L. Ma, X.H. Mao, J.R. Min, Z. Mitthumsiri, W. Mou, G.B. Mu, H.J. Nan, Y.C. Neronov, A. Ng, K.C.Y. Ni, M.Y. Nie, L. Ou, L.J. Pattarakijwanich, P. Pei, Z.Y. Qi, J.C. Key Laboratory of Particle Experimental Physics Division Computing Center Institute of High Energy Physics Chinese Academy of Sciences Beijing100049 China University of Chinese Academy of Sciences Beijing100049 China TIANFU Cosmic Ray Research Center Sichuan Chengdu China University of Science and Technology of China Anhui Hefei230026 China Yerevan State University 1 Alek Manukyan Street Yerevan0025 Armenia Max-Planck-Institut for Nuclear Physics P.O. Box 103980 Heidelberg69029 Germany Tsung-Dao Lee Institute School of Physics and Astronomy Shanghai Jiao Tong University Shanghai200240 China Center for Astrophysics Guangzhou University Guangdong Guangzhou510006 China Institute for Nuclear Research of Russian Academy of Sciences Moscow117312 Russia School of Physical Science and Technology School of Information Science and Technology Southwest Jiaotong University Sichuan Chengdu610031 China State Key Laboratory of Particle Detection and Electronics China Key Laboratory of Dark Matter and Space Astronomy Key Laboratory of Radio Astronomy Purple Mountain Observatory Chinese Academy of Sciences Jiangsu Nanjing210023 China Research Center for Astronomical Computing Zhejiang Laboratory Zhejiang Hangzhou311121 China Shanghai Astronomical Observatory Chinese Academy of Sciences Shanghai200030 China School of Physics and Astronomy Yunnan University Yunnan Kunming650091 China Key Laboratory of Cosmic Rays Tibet University Ministry of Education Tibet Lhasa850000 China School of Astronomy and Space Science Nanjing University Jiangsu Nanjing210023 China Department of Physics The Chinese University of Hong Kong New Territories Shatin Hong Kong Hebei Normal University Hebei Shijiazhuang050024 China Key Laboratory of Radio Astronomy and Technology National Astronomical Observatories Chinese Academy of Sciences Beijing100101 China Sun Yat-sen University Guangdong Zhuhai519000 China Sun Yat-sen University Guangdong Guangzhou510275

Galaxy clusters act as reservoirs of high-energy cosmic rays (CRs). As CRs propagate through the intracluster medium, they generate diffuse γ-rays detectable by arrays such as LHAASO. These γ-rays result from proton-proton (pp) collisions of very high-energy cosmic rays (VHECRs) or inverse Compton (IC) scattering of positron-electron pairs created by pγ interactions of ultra-high-energy cosmic rays (UHECRs). We analyzed diffuse γ-ray emission from the Coma, Perseus, and Virgo clusters using LHAASO data. Diffuse emission was modeled as a disk of radius R500 for each cluster while accounting for point sources. No significant diffuse emission was detected, yielding 95% confidence level (C.L.) upper limits on the γ-ray flux: for WCDA (1–25 TeV) and KM2A (>25 TeV), less than (49.4, 13.7, 54.0) and (1.34, 1.14, 0.40) ×10−14 ph cm−2 s−1 for Coma, Perseus, and Virgo, respectively. The γ-ray upper limits can be used to derive model-independent constraints on the integral energy of CRp above 10 TeV (corresponding to the LHAASO observational range > 1 TeV under the pp scenario) to be less than (1.96, 0.59, 0.08) × 1061 erg. The absence of detectable annuli/ring-like structures, indicative of cluster accretion or merging shocks, imposes further constraints on models in which the UHECRs are accelerated in the merging shocks of galaxy clusters. Copyright © 2025, The Authors. All rights reserved.

关键词： Cosmic rays

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Variational quantum circuits for quantum state tomography

arXiv

引用

arXiv 2019年

作者： Liu, Yong Wang, Dongyang Xue, Shichuan Huang, Anqi Fu, Xiang Qiang, Xiaogang Xu, Ping Huang, He-Liang Deng, Mingtang Guo, Chu Yang, Xuejun Wu, Junjie Institute for Quantum Information & State Key Laboratory of High Performance Computing College of Computer National University of Defense Technology Changsha410073 China National Innovation Institute of Defense Technology AMS Beijing100071 China Henan Key Laboratory of Quantum Information and Cryptography IEU Zhengzhou450001 China Hefei National Laboratory for Physical Sciences at Microscale Department of Modern Physics University of Science and Technology of China HefeiAnhui230026 China CAS Centre for Excellence Synergetic Innovation Centre in Quantum Information and Quantum Physics University of Science and Technology of China Hefei Anhui230026 China Quantum Intelligence Lab Supremacy Future Technologies Guangzhou511340 China

We propose a hybrid quantum-classical algorithm for quantum state tomography. Given an unknown quantum state, a quantum machine learning algorithm is used to maximize the fidelity between the output of a variational quantum circuit and this state. The number of parameters of the variational quantum circuit grows linearly with the number of qubits and the circuit depth. After that, a subsequent classical algorithm is used to reconstruct the unknown quantum state. We demonstrate our method by performing numerical simulations to reconstruct the ground state of a one-dimensional quantum spin chain, using a variational quantum circuit simulator. Our method is suitable for near-term quantum computing platforms, and could be used for relatively large-scale quantum state tomography for experimentally relevant quantum states. Copyright © 2019, The Authors. All rights reserved.

关键词： Numerical methods

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Adjustable propagating plasmons in α − T3 lattice-based armchair nanoribbons

arXiv

引用

arXiv 2021年

作者： Iurov, Andrii Zhemchuzhna, Liubov Fekete, Paula Gumbs, Godfrey Huang, Danhong Anwar, Farhana Dahal, Dipendra Weekes, Nicholas Department of Physics and Computer Science Medgar Evers College City University of New York BrooklynNY11225 United States Department of Physics and Astronomy Hunter College The City University of New York 695 Park Avenue New YorkNY10065 United States US Military Academy at West Point 606 Thayer Road West PointNY10996 United States P de Manuel Lardizabal 4 Basque Country San Sebastian20018 Spain Air Force Research Laboratory Space Vehicles Directorate Kirtland Air Force Base NM87117 United States Center for High Technology Materials University of New Mexico 1313 Goddard SE AlbuquerqueNM87106 United States Lawrence Berkeley National Laboratory 1 Cyclotron Rd BerkeleyCA94720 United States Texas Center for Superconductivity Department of Physics University of Houston HoustonTX77204 United States Department of Information Systems and Cybersecurity Grove School of Engineering The City College of New York 275 Convent Avenue New YorkNY10031 United States

We have obtained and analyzed the electronic states, polarization function and the plasmon excitations for α−T3-based nanoribbons with armchair termination. The calculated plasmon dispersions strongly depend on the number of the atomic rows across the ribbon, and the presence of the energy gap between the valence and conduction bands which is also determined by the nanoribbon geometry. The bandgap was proven to have the strongest effect on both the plasmon dispersions and their Landau damping. We have also demonstrated that for a small electron doping the plasmon dispersions do not depend on the relative hopping parameter α of the considered α − T3 material in the long-wave limit and investigated the conditions when α becomes an important factor which strongly affects the plasmons. We believe that our new uncovered electronic and collective properties of nano-size α − T3 ribbons will find their applications in the field of modern electronics and nanodevices. © 2021, CC BY.

关键词： Nanoribbons

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Tailoring plasmon excitations in α − T3 armchair nanoribbons

Research Square

引用

Research Square 2021年

作者： Iurov, Andrii Zhemchuzhna, Liubov Gumbs, Godfrey Huang, Danhong Fekete, Paula Anwar, Farhana Dahal, Dipendra Weekes, Nicholas Department of Physics and Computer Science Medgar Evers College of the City University of New York BrooklynNY11225 United States Department of Physics and Astronomy Hunter College of the City University of New York 695 Park Avenue New YorkNY10065 United States P de Manuel Lardizabal 4 San Sebastian Basque Country 20018 Spain Air Force Research Laboratory Space Vehicles Directorate Kirtland Air Force Base NM87117 United States Center for High Technology Materials University of New Mexico 1313 Goddard SE AlbuquerqueNM87106 United States US Military Academy at West Point 606 Thayer Road West PointNY10996 United States Lawrence Berkeley National Laboratory 1 Cyclotron Road BerkeleyCA94720 United States Texas Center for Superconductivity and Department of Physics University of Houston HoustonTX77204 United States Department of Information Systems and Cybersecurity Grove School of Engineering The City College of New York 275 Convent Avenue New YorkNY10031 United States

We have calculated and investigated the electronic states, dynamical polarization function and the plasmon excitations for α−T3 nanoribbons with armchair-edge termination. The obtained plasmon dispersions are found to depend significantly on the number of atomic rows across the ribbon and the energy gap which is also determined by the nanoribbon geometry. The bandgap appears to have the strongest effect on both the plasmon dispersions and their Landau damping. We have determined the conditions when relative hopping parameter α of an α − T3 lattice has a strong effect on the plasmons which makes our material distinguished from graphene nanoribbons. Our results for the electronic and collective properties of α−T3 nanoribbons are expected to find numerous applications in the development of the next-generation electronic, nano-optical and plasmonic devices. © 2021, CC BY.

关键词： Nanoribbons

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Efficient verification of continuous-variable quantum states and devices without assuming identical and independent operations

arXiv

引用

arXiv 2020年

作者： Wu, Ya-Dong Bai, Ge Chiribella, Giulio Liu, Nana QICI Quantum Information and Computation Initiative Department of Computer Science The University of Hong Kong Pokfulam Road Hong Kong The University of Hong Kong Shenzhen Institute of Research and Innovation 5/F Key Laboratory Platform Building No.6 Yuexing 2nd Rd. Nanshan Shenzhen518057 China Department of Computer Science Parks Road OxfordOX1 3QD United Kingdom Perimeter Institute for Theoretical Physics WaterlooONN2L 2Y5 Canada Institute of Natural Sciences Shanghai Jiao Tong University Shanghai200240 China Ministry of Education Key Laboratory in Scientific and Engineering Computing Shanghai Jiao Tong University Shanghai200240 China University of Michigan Shanghai Jiao Tong University Joint Institute Shanghai200240 China

Continuous-variable quantum information, encoded into infinite-dimensional quantum systems, is a promising platform for the realization of many quantum information protocols, including quantum computation, quantum metrology, quantum cryptography, and quantum communication. To successfully demonstrate these protocols, an essential step is the certification of multimode continuous-variable quantum states and quantum devices. This problem is well studied under the assumption that multiple uses of the same device result in identical and independently distributed (i.i.d.) operations. However, in realistic scenarios, identical and independent state preparation and calls to the quantum devices cannot be generally guaranteed. Important instances include adversarial scenarios and instances of time-dependent and correlated noise. In this paper, we propose the first set of reliable protocols for verifying multimode continuous-variable entangled states and devices in these non-i.i.d scenarios. Although not fully universal, these protocols are applicable to Gaussian quantum states, non-Gaussian hypergraph states, as well as amplification, attenuation, and purification of noisy coherent states. Copyright © 2020, The Authors. All rights reserved.

关键词： Quantum optics

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Vision-based Semantic Mapping and Localization for Autonomous Indoor Parking

Vision-based Semantic Mapping and Localization for Autonomou...

引用

2018 IEEE Intelligent Vehicles Symposium, IV 2018

作者： Huang, Yewei Zhao, Junqiao He, Xudong Zhang, Shaoming Feng, Tiantian Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai China Department of Computer Science and Technology School of Electronics and Information Engineering Tongji University Shanghai China School of Surveying and Geo-Informatics Tongji University Shanghai China

ISBN: (纸本)9781538644522

In this paper, we proposed a novel and practical solution for the real-time indoor localization of autonomous driving in parking lots. high-level landmarks, the parking slots, are extracted and enriched with labels to avoid the aliasing of low-level visual features. We then proposed a robust method for detecting incorrect data associations between parking slots and further extended the optimization framework by dynamically eliminating suboptimal data associations. Visual fiducial markers are introduced to improve the overall precision. As a result, a semantic map of the parking lot can be established fully automatically and robustly. We experimented the performance of real-time localization based on the map using our autonomous driving platform TiEV, and the average accuracy of 0.3m track tracing can be achieved at a speed of 10kph. © 2018 IEEE.

关键词： Autonomous vehicles

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Flux Variations of Cosmic Ray Air Showers Detected by LHAASO-KM2A During a Thunderstorm on 10 June 2021

arXiv

引用

arXiv 2022年

作者： Aharonian, F. An, Q. Axikegu Bai, L.X. Bai, Y.X. Bao, Y.W. Bastieri, D. Bi, X.J. Bi, Y.J. Cai, J.T. Cao, Zhe Cao, Zhen Chang, J. Chang, J.F. Chen, E.S. Chen, Liang Chen, Liang Chen, Long Chen, M.J. Chen, M.L. Chen, S.H. Chen, S.Z. Chen, T.L. Chen, X.J. Chen, Y. Cheng, H.L. Cheng, N. Cheng, Y.D. Cui, S.W. Cui, X.H. Cui, Y.D. Dai, B.Z. Dai, H.L. Dai, Z.G. Danzengluobu della Volpe, D. Duan, K.K. Fan, J.H. Fan, Y.Z. Fan, Z.X. Fang, J. Fang, K. Feng, C.F. Feng, L. Feng, S.H. Feng, X.T. Feng, Y.L. Gao, B. Gao, C.D. Gao, L.Q. Gao, Q. Gao, W. Gao, W.K. Ge, M.M. Geng, L.S. Gong, G.H. Gou, Q.B. Gu, M.H. Guo, F.L. Guo, J.G. Guo, X.L. Guo, Y.Q. Guo, Y.Y. Han, Y.A. He, H.H. He, H.N. He, S.L. He, X.B. He, Y. Heller, M. Hor, Y.K. Hou, C. Hou, X. Hu, H.B. Hu, Q. Hu, S. Hu, S.C. Hu, X.J. Huang, D.H. Huang, W.H. Huang, X.T. Huang, X.Y. Huang, Y. Huang, Z.C. Ji, X.L. Jia, H.Y. Jia, K. Jiang, K. Jiang, Z.J. Jin, M. Kang, M.M. Ke, T. Kuleshov, D. Li, B.B. Li, Cheng Li, Cong Li, F. Li, H.B. Li, H.C. Li, H.Y. Li, J. Li, Jian Li, Jie Li, K. Li, W.L. Li, X.R. Li, Xin Li, Xin Li, Y.Z. Li, Zhe Li, Zhuo Liang, E.W. Liang, Y.F. Lin, S.J. Liu, B. Liu, C. Liu, D. Liu, H. Liu, H.D. Liu, J. Liu, J.L. Liu, J.S. Liu, J.Y. Liu, M.Y. Liu, R.Y. Liu, S.M. Liu, W. Liu, Y. Liu, Y.N. Long, W.J. Lu, R. Luo, Q. Lv, H.K. Ma, B.Q. Ma, L.L. Ma, X.H. Mao, J.R. Masood, A. Min, Z. Mitthumsiri, W. Nan, Y.C. Ou, Z.W. Pang, B.Y. Pattarakijwanich, P. Pei, Z.Y. Qi, M.Y. Qi, Y.Q. Qiao, B.Q. Qin, J.J. Ruffolo, D. Sáiz, A. Shao, C.Y. Shao, L. Shchegolev, O. Sheng, X.D. Shi, J.Y. Song, H.C. Stenkin, Yu.V. Stepanov, V. Su, Y. Sun, Q.N. Sun, X.N. Sun, Z.B. Tam, P.H.T. Tang, Z.B. Tian, W.W. Wang, B.D. Wang, C. Wang, H. Wang, H.G. Wang, J.C. Wang, J.S. Key Laboratory of Particle Astrophyics & Experimental Physics Division & Computing Center Institute of High Energy Physics Chinese Academy of Sciences Beijing100049 China University of Chinese Academy of Sciences Beijing100049 China TIANFU Cosmic Ray Research Center Sichuan Chengdu China Dublin Institute for Advanced Studies 31 Fitzwilliam Place 2 Dublin Ireland Max-Planck-Institut for Nuclear Physics P.O. Box 103980 Heidelberg69029 Germany State Key Laboratory of Particle Detection and Electronics China University of Science and Technology of China Anhui Hefei230026 China School of Physical Science and Technology School of Information Science and Technology Southwest Jiaotong University Sichuan Chengdu610031 China College of Physics Sichuan University Sichuan Chengdu610065 China School of Astronomy and Space Science Nanjing University Jiangsu Nanjing210023 China Center for Astrophysics Guangzhou University Guangdong Guangzhou510006 China Key Laboratory of Dark Matter and Space Astronomy Key Laboratory of Radio Astronomy Purple Mountain Observatory Chinese Academy of Sciences Jiangsu Nanjing210023 China Key Laboratory for Research in Galaxies and Cosmology Shanghai Astronomical Observatory Chinese Academy of Sciences Shanghai200030 China Ministry of Education Tibet Lhasa850000 China National Astronomical Observatories Chinese Academy of Sciences Beijing100101 China Hebei Normal University Hebei Shijiazhuang050024 China Sun Yat-sen University Guangdong Zhuhai519000 China Sun Yat-sen University Guangdong Guangzhou510275 China School of Physics and Astronomy Yunnan University Yunnan Kunming650091 China Département de Physique Nucléaire et Corpusculaire Facultéde Sciences Universitéde Genève 24 Quai Ernest Ansermet Geneva1211 Switzerland Institute of Frontier and Interdisciplinary Science Shandong University Shandong Qingdao266237 China Department of Engineering Physics Tsinghua University Beijing100084 China School

The Large high Altitude Air Shower Observatory (LHAASO) has three sub-arrays, KM2A, WCDA and WFCTA. The flux variations of cosmic ray air showers were studied by analyzing the KM2A data during the thunderstorm on 10 June 2021. The number of shower events that meet the trigger conditions increases significantly in atmospheric electric fields, with maximum fractional increase of 20%. The variations of trigger rates (increases or decreases) are found to be strongly dependent on the primary zenith angle. The flux of secondary particles increases significantly, following a similar trend with that of the shower events. To better understand the observed behavior, Monte Carlo simulations are performed with CORSIKA and G4KM2A (a code based on GEANT4). We find that the experimental data (in saturated negative fields) are in good agreement with simulations, assuming the presence of a uniform electric field of -700 V/cm with a thickness of 1500 m in the atmosphere above the observation level. Due to the acceleration/deceleration by the atmospheric electric field, the number of secondary particles with energy above the detector threshold is modified, resulting in the changes in shower detection rate. © 2022, CC BY-NC-SA.

关键词： Cosmic rays

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Fast stochastic ordinal embedding with variance reduction and adaptive step size

arXiv

引用

arXiv 2019年

作者： Ma, Ke Zeng, Jinshan Xiong, Jiechao Xu, Qianqian Cao, Xiaochun Liu, Wei Yao, Yuan School of Computer Science and Technology University of Chinese Academy of Sciences Beijing100049 China Artificial Intelligence Research Center Peng Cheng Laboratory Shenzhen518055 China School of Computer Information Engineering Jiangxi Normal University NanchangJiangxi330022 China Tencent AI Lab Shenzhen Guangdong China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China Cyberspace Security Research Center Peng Cheng Laboratory Shenzhen518055 China School of Cyber Security University of Chinese Academy of Sciences Beijing100049 China Department of Mathematics and by courtesy Department of Computer Science and Engineering Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong

—Learning representation from relative similarity comparisons, often called ordinal embedding, gains rising attention in recent years. Most of the existing methods are based on semi-definite programming (SDP), which is generally time-consuming and degrades the scalability, especially confronting large-scale data. To overcome this challenge, we propose a stochastic algorithm called SVRG-SBB, which has the following features: i) achieving good scalability via dropping positive semi-definite (PSD) constraints as serving a fast algorithm, i.e., stochastic variance reduced gradient (SVRG) method, and ii) adaptive learning via introducing a new, adaptive step size called the stabilized Barzilai-Borwein (SBB) step size. Theoretically, under some natural assumptions, we show the O(T1 ) rate of convergence to a stationary point of the proposed algorithm, where T is the number of total iterations. Under the further Polyak-Lojasiewicz assumption, we can show the global linear convergence (i.e., exponentially fast converging to a global optimum) of the proposed algorithm. Numerous simulations and real-world data experiments are conducted to show the effectiveness of the proposed algorithm by comparing with the state-of-the-art methods, notably, much lower computational cost with good prediction performance. Copyright © 2019, The Authors. All rights reserved.

关键词： Stochastic systems

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high-efficiency broadband polarization-independent superscatterer using conformal metasurfaces

引用

Photonics Research 2018年第8期6卷 782-788页

作者： HE-XIU XU SHIWEI TANG CHEN SUN LIANLIN LI HAIWEN LIU XINMI YANG FANG YUAN YUNMING SUN Air and Missile Defense College Air Force Engineering University Department of Physics Faculty of Science Ningbo University School of Electronic Engineering and Computer Sciences Peking University School of Information Engineering East China Jiaotong University School of Electronics and Information Engineering Soochow University Advanced Technique Department Key Laboratory of Aeronautics Computing Technique

Safe detection of an arbitrarily shaped platform is critical for survivability, rescue, or navigation safety in a remote region. Metasurfaces afford great potential due to their strong electromagnetic(EM) wave control. However,studies have mainly focused on the physics and design of metasurfaces on planar plates, which does not satisfy the current requirements of aerodynamics and aesthetics. Herein, we propose a sophisticated strategy to design a metasurface that can wrap over arbitrarily shaped objects with moderate curvature on which optical aberrations are commonly introduced. By designing each meta-atom on the basis of the required position and phase compensation, exact EM wavefronts are restored. For verification, several conformal metasurfaces were designed and numerically studied on metallic cylinders at the microwave spectrum. A proof-of-concept device is fabricated and is experimentally characterized. The results demonstrate the availability of the desirable dual-beam superscatterer with strong backscattering enhancement toward two directions, thus indicating that the distortions induced by an arbitrary platform can be efficiently corrected. Our method affords an efficient alternative for designing highperformance multifunctional optoelectronic devices equipped on a moderately curved platform.

关键词：安全检测电磁波通讯技术发展现状

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Sleep Disorder Classification Method based on Logistic Regression with Apnea-ECG Dataset 2019

Sleep Disorder Classification Method based on Logistic Regre...

引用

2019 International Conference on Artificial Intelligence and Advanced Manufacturing, AIAM 2019

作者： Cai, Liangming Chen, Chunqiang Wang, Xiaoling Yang, Xiong Lin, Shurui Huang, Jiefeng Jiang, Jinping Datta, Rituparna Du, Min Jiang, Haiyan Zhu, Minchen Huang, Jingshan College of Physics and Information Engineering Fuzhou University Fuzhou China Zhicheng College University of South Alabama United States HaiXi Institutes Chinese Academy of Science Fuzhou China Zhicheng College Fuzhou University Fuzhou China Department of Respiratory Medicine First Affiliated Hospital of Fujian Medical University Fuzhou China College of Electrical Engineering and Automation Fuzhou University Fuzhou China School of Computing University of South Alabama Mobile United States Provincial Key Laboratory of Eco-industrial Green Technology Wuyi College Wuyishan China College of Mathematics and Computer Science Fuzhou University Fuzhou China Fuzhou University Fuzhou China University of South Alabama United States

ISBN: (纸本)9781450372022

Adequate sleep is significant for human to actively pursue daily activity. On the other hand, insomnia is directly proportional to aging and health deterioration. Sleep disorder classification is important for medical scientists as well as machine learning researchers. In the paper, we have developed a sleep disorder classification method for Electrocardiogram (ECG) data. The data set have two labels;sleep disorder or not, which can be categorized as binary output. As a result, logistic regression is used as classification technique. For initial experimentation, an existing data set is used. The data set consists of every 10 second data up to 6000 seconds for 35 patients. We have used 70% data for training, and 30% data for testing purpose. Our results from logistic regression show that the logistic regression method is efficient to detect sleep disorders. The prediction result is found to be 59%. The future research in this direction would be to take data from hospitals and use our developed algorithm for sleep disorder classification and prediction. © 2019 Association for computing Machinery.

关键词： Electrocardiograms

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