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2019 International Conference on Intelligent computing, Automation and Systems, ICICAS 2019

作者： Xia, Yuwei Fu, Min Zheng, Xi Wu, Binqing Ke, Zhihao School of Economic Information Engineering Southwestern University of Finance and Economics Chengdu China Alibaba Group Hangzhou China Department of Computing Macquarie University Sydney Australia Department of Computer Science Leeds Joint School Southwest Jiaotong University Chengdu China State Key Laboratory of Traction Power Southwest Jiaotong University Chengdu China

ISBN: (纸本)9781728161068

Nowadays, making investments in trading areas around organizations is becoming increasingly pervasive since more and more organizations (e.g. universities) have been relocated in suburban-districts far from downtown. It generally requires a fairly long time for investors to make decisions and establish mature trading areas around organizations. Existing researches that focus on optimal location selection ignore the latent relationship between organizations and their surrounding business types. Therefore, this paper proposes a machine-learning based methodology of determining business types and trading areas, named TrORF, to help investors select business types and build trading areas around organizations. We implement our approach and evaluate it by using 13 real-world universities as our case studies. We compare the business type recommendation accuracy of the 13 real-world universities between our method and the other four existing methods. The experiment results indicate that our approach has strong ability to build the suitable business types around each university's trading area since its average recommendation accuracy is 98.23%, and it improves on other approaches by at least 13%. © 2019 IEEE.

关键词： Machine learning

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Characterization of Mo-6Ta alloy targets and its magnetron sputtering deposited thin film

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International Journal of Refractory Metals and Hard Materials 2022年 103卷

作者： Jiwen Li Zhenhua Fu Liu Wei Guoshang Zhang Shizhong Wei Liujie Xu Kunming Pan Jie Shen Jinghong Yang Material Science & Engineering School Henan University of Science & Technology Luoyang 471023 China Material Science & Engineering School Zhengzhou University Zhengzhou 450001 China Henan Key Laboratory of High-temperature Structure and Functional Materials Luoyang 471003 China Department of Computer& information Science University of Michigan-Dearborn Dearborn 48128 USA Achemetal Tungsten & Molybdenum Co. LTD. Luoyang 471822 China

In this study, pure Mo and Mo-6Ta alloy targets were produced by a manufacturing method, which is called die pressing with a combination of cold isostatic pressing and hot-pressing sintering technology, and molybdenum thin films were deposited on corning glass substrates using a DC magnetron sputtering system. The effect of alloying with tantalum on the phase structure, grain size, orientation, thin film surface morphologies and electrochemical properties has been investigated by a variety of techniques such as X-ray diffraction (XRD), field emission scanning electron microscopy (FE-SEM) with electron back-scattered diffraction (EBSD), atomic force microscopy (AFM), four-probe resistivity tester, and electrochemical analyzer. The observation of the Mo targets reveals that this manufacturing technology with alloying of tantalum can reduce the target's grain size to lower than 50 μm with random grain orientation. The microstructure of the deposited Mo thin films indicates that the alloyed films have the higher deposition velocity with a dense fine grain structure and smoother surface. The sheet resistivity of the alloyed Mo film increases continuously with the increasing of the sputtering time, and eventually becomes higher than that of the pure Mo film. In electrochemical tests, both of the Mo films show capacitive reactance, and the capacitive arc radius of the alloyed Mo films are obviously larger than that of pure Mo films. The result of alloying with tantalum shows an increase in the corrosion potential of the Mo film from −0.5 V to −0.2 V, indicating improvement of the corrosion resistance of Mo films.

关键词： Mo targets Alloying with tantalum Crystal orientation Magnetron sputtering thin film Electrochemical properties

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SOUA: Towards intelligent recommendation for applying for overseas universities

SOUA: Towards intelligent recommendation for applying for ov...

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2019 International Conference on Intelligent computing, Automation and Systems, ICICAS 2019

作者： Wu, Binqing Ke, Zhihao Fu, Min Xia, Yuwei Department of Computer Science Leeds Joint School Southwest Jiaotong University Chengdu China State Key Laboratory of Traction Power Southwest Jiaotong University Chengdu China Alibaba Group Hangzhou China Department of Computing Macquarie University Sydney Australia School of Economic Information Engineering Southwestern University of Finance and Economics Chengdu China

ISBN: (纸本)9781728161068

In China, to pursue a master's degree in foreign countries is becoming an increasingly popular choice for undergraduate students. However, predicting the results of university applications is mostly done by applying empirical rules and referring to previous admission cases similar to the applicant's conditions, which is unreliable. In this paper, we propose an intelligent recommendation method, called SOUA, to make recommendations for students who apply for the admissions of overseas universities to study abroad. Our method is proposed based on a dataset obtained by us, which is about Chinese undergraduate students' applying for master's degrees in computer science-related majors of American universities. We apply data mining techniques on this dataset. A new data augmentation method named ILGA is proposed to expand the size of the training set. In the experiments, machine learning models such as LR, KNN, DT, SVM, RF, GBDT and MLP are investigated using this dataset. The experimental results show that RF achieving a weighed f1-score of 71.2% on the test set outperforms other considered machine learning models. And RF produces the optimal university recommendation accuracy of 80.9%. © 2019 IEEE.

关键词： Data mining

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The performance of the LHAASO-KM2A tested by the cosmic-ray Moon shadow 37

The performance of the LHAASO-KM2A tested by the cosmic-ray ...

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37th International Cosmic Ray Conference, ICRC 2021

作者： Nan, Yuncheng Li, Zhe Chen, Songzhan Feng, Cunfeng Wu, Sha Li, Yizhuo Cao, Zhen Aharonian, F. An, Q. Axikegu Bai, L.X. Bai, Y.X. Bai, L.X. Bai, Y.X. Bao, Y.W. Bastieri, D. Bi, X.J. Bi, Y.J. Cai, H. Cai, J.T. Cao, Zhe Chang, J. Chang, J.F. Chen, B.M. Chen, E.S. Chen, J. Chen, Liang Chen, Liang Chen, Long Chen, M.J. Chen, M.L. Chen, Q.H. Chen, S.H. Chen, S.Z. Chen, T.L. Chen, X.L. Chen, Y. Cheng, N. Cheng, Y.D. Cui, S.W. Cui, X.H. Cui, Y.D. D’Ettorre Piazzoli, B. Dai, B.Z. Dai, H.L. Dai, Z.G. Danzengluobu della Volpe, D. Dong, X.J. 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, Y.L. Gao, B. Gao, C.D. Gao, L.Q. Gao, Q. Gao, W. 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, J.C. He, S.L. He, X.B. He, Y. Heller, M. Hor, Y.K. Hou, C. Hu, H.B. Hu, S. Hu, S.C. Hu, X.J. Huang, D.H. Huang, Q.L. Huang, W.H. Huang, X.T. Huang, X.Y. Huang, Z.C. Ji, F. Ji, X.L. Jia, H.Y. Jiang, K. Jiang, Z.J. Jin, C. Ke, T. Kuleshov, D. Levochkin, K. Li, B.B. Li, Cheng Li, Cong Li, F. Li, H.B. Li, H.C. Li, H.Y. Li, J. Li, K. Li, W.L. Li, X.R. Li, Xin Li, Xin Li, Y. 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. Liu, Z.X. Long, W.J. Lu, R. Lv, H.K. Ma, B.Q. Ma, L.L. Ma, X.H. Mao, J.R. Masood, A. Min, Z. Mitthumsiri, W. Montaruli, T. Nan, Y.C. Pang, B.Y. Pattarakijwanich, P. Pei, Z.Y. Qi, M.Y. Qi, Y.Q. Qiao, B.Q. Qin, J.J. Ruffolo, D. Rulev, V. Sáiz, A. 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. Shandong University Institute of Frontier and Interdisciplinary Science 72 Binhai Road Jimo Shandong Qingdao China Key Laboratory of Particle Astrophysics Institute of High Energy Physics CAS 19-yi Yuquan Road Shijingshan District Beijing China 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 School of Physics and Technology Wuhan University Hubei Wuhan430072 China Key Laboratory of Dark Matter and Space Astronomy Purple Mountain Observatory Chinese Academy of Sciences Jiangsu Nanjing210023 China Hebei Normal University Hebei Shijiazhuang050024 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 Sun Yat-Sen University Guangdong Zhuhai519000 China Dipartimento di Fisica Università di Napoli `‘Federico II" Complesso Universitario di Monte Sant’Angelo via Cinthia Napoli80126 Italy School of Physics and Astronomy Yu

The KM2A is a major ground-based array for the researches on cosmic rays around the "knee" region and the ultra high energy gamma-ray astronomy, which depends heavily on its performance. The half array of the KM2A has been operated from December 27, 2019 to November 30, 2020. The cosmic ray Moon shadow, which moves between ±25◦ of declination and is observed with significance up to -25 standard deviation each month, is used as an unique and powerful source to test the array’s performance. Through the observation of the characteristics of the Moon shadow, including the position displacement, shape, deficit, and their variation with the time and energy, we test the pointing error, angular resolution, long-term stability of the KM2A and the absolute energy scale of the primary cosmic-ray particles. In particular, the pointing errors for showers from different declinations are discussed in this work. © Copyright owned by the author(s).

关键词： Cosmic rays

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作者： Yang, Peng Zhang, Ning Zhang, Shan Yu, Li Zhang, Junshan Shen, Xuemin School of Electronic Information and Communications Huazhong University of Science and Technology Wuhan 430074 China Department of Computing Sciences Texas AandM University-Corpus Christi Corpus Christi TX 78412 United States Beijing Key Laboratory of Computer Networks School of Computer Science and Engineering Beihang University Beijing 100191 China School of Electrical Computer and Energy Engineering Arizona State University Tempe AZ 85287 United States Department of Electrical and Computer Engineering University of Waterloo Waterloo ON N2L 3G1 Canada

Mobile edge caching aims to enable content delivery within the radio access network, which effectively alleviates the backhaul burden and reduces response time. To fully exploit edge storage resources, the most popular contents should be identified and cached. Observing that user demands on certain contents vary greatly at different locations, this paper devises location-customized caching schemes to maximize the total content hit rate. Specifically, a linear model is used to estimate the future content hit rate. For the case with zero-mean noise, a ridge regression-based online algorithm with positive perturbation is proposed. Regret analysis indicates that the hit rate achieved by the proposed algorithm asymptotically approaches that of the optimal caching strategy in the long run. When the noise structure is unknown, an H∞ filter-based online algorithm is devised by taking a prescribed threshold as input, which guarantees prediction accuracy even under the worst-case noise process. Both online algorithms require no training phases and, hence, are robust to the time-varying user demands. The estimation errors of both algorithms are numerically analyzed. Moreover, extensive experiments using real-world datasets are conducted to validate the applicability of the proposed algorithms. It is demonstrated that those algorithms can be applied to scenarios with different noise features, and are able to make adaptive caching decisions, achieving a content hit rate that is comparable to that via the hindsight optimal strategy. © 1999-2012 IEEE.

关键词： Mobile edge computing

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Constraints on Ultra Heavy Dark Matter Properties from Dwarf Spheroidal Galaxies with LHAASO Observations

arXiv

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arXiv 2024年

作者： Cao, Zhen Aharonian, F. An, Q. Axikegu Bai, Y.X. Bao, Y.W. Bastieri, D. Bi, X.J. Bi, Y.J. Cai, J.T. Cao, Q. Cao, W.Y. Cao, Zhe Chang, J. Chang, J.F. Chen, A.M. Chen, E.S. Chen, Liang Chen, Lin Chen, Long Chen, M.J. Chen, M.L. Chen, Q.H. Chen, S.H. Chen, S.Z. Chen, T.L. Chen, Y. Cheng, N. Cheng, Y.D. Cui, M.Y. Cui, S.W. Cui, X.H. Cui, Y.D. Dai, B.Z. Dai, H.L. Dai, Z.G. Danzengluobu della Volpe, D. Dong, X.Q. Duan, K.K. Fan, J.H. Fan, Y.Z. Fang, J. Fang, K. Feng, C.F. Feng, L. Feng, S.H. Feng, X.T. Feng, Y.L. Gabici, S. Gao, B. Gao, C.D. Gao, L.Q. Gao, Q. Gao, W. Gao, W.K. Ge, M.M. Geng, L.S. Giacinti, G. Gong, G.H. Gou, Q.B. Gu, M.H. Guo, F.L. Guo, X.L. Guo, Y.Q. Guo, Y.Y. Han, Y.A. He, H.H. He, H.N. He, J.Y. He, X.B. He, Y. Heller, M. Hor, Y.K. Hou, B.W. Hou, C. Hou, X. Hu, H.B. Hu, Q. Hu, S.C. Huang, D.H. Huang, T.Q. Huang, W.J. Huang, X.T. Huang, X.Y. Huang, Y. Huang, Z.C. Ji, X.L. Jia, H.Y. Jia, K. Jiang, K. Jiang, X.W. Jiang, Z.J. Jin, M. Kang, M.M. Ke, T. Kuleshov, D. Kurinov, K. Li, B.B. Li, Cheng Li, Cong Li, D. Li, F. Li, H.B. Li, H.C. Li, H.Y. Li, J. Li, Jian Li, Jie Li, K. Li, W.L. 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, H. Liu, H.D. Liu, J. Liu, J.L. Liu, J.Y. Liu, M.Y. Liu, R.Y. Liu, S.M. Liu, W. Liu, Y. Liu, Y.N. Lu, R. Luo, Q. Lv, H.K. Ma, B.Q. Ma, L.L. Ma, X.H. Mao, J.R. Min, Z. Mitthumsiri, W. Mu, H.J. Nan, Y.C. Neronov, A. 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. Semikoz, D. Shao, C.Y. Shao, L. Shchegolev, O. Sheng, X.D. Shu, F.W. Song, H.C. Stenkin, Yu.V. Stepanov, V. Su, Y. Sun, Q.N. Sun, X.N. Sun, Z.B. Key Laboratory of Particle Astrophysics 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 Chengdu610000 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 School of Astronomy and Space Science Nanjing University Jiangsu Nanjing210023 China Center for Astrophysics Guangzhou University Guangdong Guangzhou510006 China Hebei Normal University Hebei Shijiazhuang050024 China Key Laboratory of Dark Matter and Space Astronomy Key Laboratory of Radio Astronomy Purple Mountain Observatory Chinese Academy of Sciences Jiangsu Nanjing210023 China Tsung-Dao Lee Institute School of Physics and Astronomy Shanghai Jiao Tong University Shanghai200240 China Key Laboratory for Research in Galaxies and Cosmology Shanghai Astronomical Observatory Chinese Academy of Sciences Shanghai200030 China Key Laboratory of Cosmic Rays Tibet University Ministry of Education Tibet Lhasa850000 China National Astronomical Observatories Chinese Academy of Sciences Beijing100101 China Sun Yat-sen University Guangdong Zhuhai519000 China Guangdong Guangzhou510275 China Sun Yat-sen University Guangdong Zhuhai519000 China School of Physics and Astronomy Yunnan University Yunnan Kunming650091 China Département de Physique Nucléaire et Corpusculaire Faculté de Sciences Université de Genève 24 Quai Ernest Ansermet Geneva1211 Switzerland Institute of Frontier and Interdisciplinary Science Shandon

In this work we try to search for signals generated by ultra-heavy dark matter at the Large high Altitude Air Shower Observatory (LHAASO) data. We look for possible gamma-ray by dark matter annihilation or decay from 16 dwarf spheroidal galaxies in the field of view of LHAASO. Dwarf spheroidal galaxies are among the most promising targets for indirect detection of dark matter which have low fluxes of astrophysical γ-ray background while large amount of dark matter. By analyzing more than 700 days observational data at LHAASO, no significant dark matter signal from 1 TeV to 1 EeV is detected. Accordingly we derive the most stringent constraints on the ultra-heavy dark matter annihilation cross-section up to EeV. The constraints on the lifetime of dark matter in decay mode are also derived. © 2024, CC BY.

关键词： Galaxies

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Adaptive Fuzzy Sliding Mode Control for Active Front Steering System

Adaptive Fuzzy Sliding Mode Control for Active Front Steerin...

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Chinese Control Conference (CCC)

作者： Tian Tian Liandi Fang Shihong Ding Wei Xing Zheng School of Electrical and Information Engineering Jiangsu University Zhenjiang P. R. China College of Mathematics and Computer Science Tongling University Tongling P. R. China High-tech Key Laboratory of Agricultural Equipment and Intelligence of Jiangsu Province Zhenjiang P. R. China School of Computing Engineering and Mathematics Western Sydney University Sydney NSW Australia

ISBN: (数字)9789881563903

ISBN: (纸本)9781728165233

In order to alleviate the adverse effects of uncertainty in the active front steering (AFS) system, a new type of observer is designed in this paper that can simultaneously estimate the sideslip angle and disturbance, which provides the controller with accurate compensation and simplifies the system model. In addition, an active front steering controller based on adaptive fuzzy sliding mode (AFSM) is proposed, which uses adaptive algorithms to adjust and update fuzzy system parameters in real time. The stability of the closed-loop AFS system based on the Lyapunov stability theory is analyzed. Compared with traditional PI control, the simulation results show the effectiveness of the proposed AFSM control method.

关键词： Stability analysis Observers Fuzzy logic Atmospheric modeling Load modeling Sliding mode control Vehicle dynamics

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Optimization of performance of the KM2A full array using the Crab Nebula

arXiv

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arXiv 2024年

作者： Cao, Zhen Aharonian, F. An, Q. Axikegu Bai, Y.X. Bao, Y.W. Bastieri, D. Bi, X.J. Bi, Y.J. Cai, J.T. Cao, Q. Cao, W.Y. Cao, Zhe Chang, J. Chang, J.F. Chen, A.M. Chen, E.S. Chen, Liang Chen, Lin Chen, Long Chen, M.J. Chen, M.L. Chen, Q.H. Chen, S.H. Chen, S.Z. Chen, T.L. Chen, Y. Cheng, N. Cheng, Y.D. Cui, M.Y. Cui, S.W. Cui, X.H. Cui, Y.D. Dai, B.Z. Dai, H.L. Dai, Z.G. Danzengluobu della Volpe, D. Dong, X.Q. Duan, K.K. Fan, J.H. Fan, Y.Z. Fang, J. Fang, K. Feng, C.F. Feng, L. Feng, S.H. Feng, X.T. Feng, Y.L. Gabici, S. Gao, B. Gao, C.D. Gao, L.Q. Gao, Q. Gao, W. Gao, W.K. Ge, M.M. Geng, L.S. Giacinti, G. Gong, G.H. Gou, Q.B. Gu, M.H. Guo, F.L. Guo, X.L. Guo, Y.Q. Guo, Y.Y. Han, Y.A. He, H.H. He, H.N. He, J.Y. He, X.B. He, Y. Heller, M. Hor, Y.K. Hou, B.W. Hou, C. Hou, X. Hu, H.B. Hu, Q. Hu, S.C. Huang, D.H. Huang, T.Q. Huang, W.J. Huang, X.T. Huang, X.Y. Huang, Y. Huang, Z.C. Ji, X.L. Jia, H.Y. Jia, K. Jiang, K. Jiang, X.W. Jiang, Z.J. Jin, M. Kang, M.M. Ke, T. Kuleshov, D. Kurinov, K. Li, B.B. Li, Cheng Li, Cong Li, D. Li, F. Li, H.B. Li, H.C. Li, H.Y. Li, J. Li, Jian Li, Jie Li, K. Li, W.L. 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, H. Liu, H.D. Liu, J. Liu, J.L. Liu, J.Y. Liu, M.Y. Liu, R.Y. Liu, S.M. Liu, W. Liu, Y. Liu, Y.N. Lu, R. Luo, Q. Lv, H.K. Ma, B.Q. Ma, L.L. Ma, X.H. Mao, J.R. Min, Z. Mitthumsiri, W. Mu, H.J. Nan, Y.C. Neronov, A. 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. Semikoz, D. Shao, C.Y. Shao, L. Shchegolev, O. Sheng, X.D. Shu, F.W. Song, H.C. Stenkin, Yu.V. Stepanov, V. Su, Y. Sun, Q.N. Sun, X.N. Sun, Z.B. Tam, P.H.T. Tang, Q.W. Tang, Z.B. 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 Chengdu610000 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 School of Astronomy and Space Science Nanjing University Jiangsu Nanjing210023 China Center for Astrophysics Guangzhou University Guangdong Guangzhou510006 China Hebei Normal University Hebei Shijiazhuang050024 China Key Laboratory of Dark Matter and Space Astronomy Key Laboratory of Radio Astronomy Purple Mountain Observatory Chinese Academy of Sciences Jiangsu Nanjing210023 China Tsung-Dao Lee Institute School of Physics and Astronomy Shanghai Jiao Tong University Shanghai200240 China Key Laboratory for Research in Galaxies and Cosmology Shanghai Astronomical Observatory Chinese Academy of Sciences Shanghai200030 China Key Laboratory of Cosmic Rays Tibet University Ministry of Education Tibet Lhasa850000 China National Astronomical Observatories Chinese Academy of Sciences Beijing100101 China Sun Yat-sen University Zhuhai519000 China Guangdong Guangzhou510275 China School of Physics and Astronomy Yunnan University Yunnan Kunming650091 China Département de Physique Nucléaire et Corpusculaire Faculté de Sciences Université de Genève 24 Quai Ernest Ansermet Geneva1211 Switzerland Institute of Frontier and Interdisciplinary Science Shandong University Shandong Qingdao266237 China APC Universit'e Paris Cit

The full array of the Large high Altitude Air Shower Observatory (LHAASO) has been in operation since July 2021. For its kilometer-square array (KM2A), we have optimized the selection criteria for very high and ultra-high energy γ-rays, using the data collected from August 2021 to August 2022, resulting in an improvement on significance of about 15% compared with previous cuts. With the implementation of these new selection criteria, the angular resolution is also significantly improved by approximately 10% at tens of TeV. Other aspects of the full KM2A array performance, such as the pointing error are also calibrated using the Crab Nebula. The resulting energy spectrum of the Crab Nebula in the energy range of 10-1000 TeV can be well fitted by a log-parabola model, which is consistent with the previous results from LHAASO and other experiments. © 2024, CC BY.

关键词： Gamma rays

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Uplink-downlink duality between multiple-access and broadcast channels with compressing relays

arXiv

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arXiv 2020年

作者： Liu, Liang Liu, Ya-Feng Patil, Pratik Yu, Wei the Department of Electronic and Information Engineering The Hong Kong Polytechnic University Hong Kong the State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China The Edward S. Rogers Sr. Department of Electrical and Computer Engineering the University of Toronto the Department of Statistics and Data Science and the Machine Learning Department Carnegie Mellon University PittsburghPA15213 United States The Edward S. Rogers Sr. Department of Electrical and Computer Engineering University of Toronto 10 King’s College Road TorontoONM5S3G4 Canada

—Uplink-downlink duality refers to the fact that under a sum-power constraint, the capacity regions of a Gaussian multiple-access channel and a Gaussian broadcast channel with Hermitian transposed channel matrices are identical. This paper generalizes this result to a cooperative cellular network, in which remote access-points are deployed as relays in serving the users under the coordination of a central processor (CP). In this model, the users and the relays are connected over noisy wireless links, while the relays and the CP are connected over noiseless but rate-limited fronthaul links. Based on a Lagrangian technique, this paper establishes a duality relationship between such a multiple-access relay channel and broadcast relay channel, under the assumption that the relays use compression-based strategies. Specifically, we show that under the same total transmit power constraint and individual fronthaul rate constraints, the achievable rate regions of the Gaussian multiple-access and broadcast relay channels are identical, when either independent compression or Wyner-Ziv and multivariate compression strategies are used. The key observations are that if the beamforming vectors at the relays are fixed, the sum-power minimization problems under the achievable rate and fronthaul constraints in both the uplink and the downlink can be transformed into either a linear programming or a semidefinite programming problem depending on the compression technique, and that the uplink and downlink problems are Lagrangian duals of each other. Moreover, the dual variables corresponding to the downlink rate constraints become the uplink powers;the dual variables corresponding to the downlink fronthaul constraints become the uplink quantization noises. This duality relationship enables an efficient algorithm for optimizing the downlink transmission and relaying strategies based on the uplink. Copyright © 2020, The Authors. All rights reserved.

关键词： Linear programming

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Simulation Study on Scaler Mode in LHAASO-KM2A 37

Simulation Study on Scaler Mode in LHAASO-KM2A

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37th International Cosmic Ray Conference, ICRC 2021

作者： Huang, Zhicheng Huang, Daihui Zhou, Xunxiu Zhao, Jing He, Huihai Chen, Songzhan Ma, Xinhua Liu, Dong Axi, Kegu Zhao, Bing Cao, Zhen Aharonian, F. An, Q. Axikegu Bai, L.X. Bai, Y.X. Bao, Y.W. Bastieri, D. Bi, X.J. Bi, Y.J. Cai, H. Cai, J.T. Cao, Zhe Chang, J. Chang, J.F. Chen, B.M. Chen, E.S. Chen, J. Chen, Liang Chen, Liang Chen, Long Chen, M.J. Chen, M.L. Chen, Q.H. Chen, S.H. Chen, S.Z. Chen, T.L. Chen, X.L. Chen, Y. Cheng, N. Cheng, Y.D. Cui, S.W. Cui, X.H. Cui, Y.D. D’Ettorre Piazzoli, B. Dai, B.Z. Dai, H.L. Dai, Z.G. Danzengluobu della Volpe, D. Dong, X.J. 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, Y.L. Gao, B. Gao, C.D. Gao, L.Q. Gao, Q. Gao, W. 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, J.C. He, S.L. He, X.B. He, Y. Heller, M. Hor, Y.K. Hou, C. Hu, H.B. Hu, S. Hu, S.C. Hu, X.J. Huang, D.H. Huang, Q.L. Huang, W.H. Huang, X.T. Huang, X.Y. Huang, Z.C. Ji, F. Ji, X.L. Jia, H.Y. Jiang, K. Jiang, Z.J. Jin, C. Ke, T. Kuleshov, D. Levochkin, K. Li, B.B. Li, Cheng Li, Cong Li, F. Li, H.B. Li, H.C. Li, H.Y. Li, J. Li, K. Li, W.L. Li, X.R. Li, Xin Li, Xin Li, Y. 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. Liu, Z.X. Long, W.J. Lu, R. Lv, H.K. Ma, B.Q. Ma, L.L. Ma, X.H. Mao, J.R. Masood, A. Min, Z. Mitthumsiri, W. Montaruli, T. Nan, Y.C. Pang, B.Y. Pattarakijwanich, P. Pei, Z.Y. Qi, M.Y. Qi, Y.Q. Qiao, B.Q. Qin, J.J. Ruffolo, D. Rulev, V. Saiz, A. Shao, L. Shchegolev, O. Sheng, X.D. Shi, J.Y. Song, H.C. Stenkin, Yu.V. Stepanov, V. Su, Y. Sun, Q.N. School of Physical Science and Technology Southwest Jiaotong University Chengdu610031 China Institute of High Energy Physics CAS Beijing100049 China Institute of Frontier and Interdisciplinary Science Shandong University Qingdao266237 China 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 School of Physics and Technology Wuhan University Hubei Wuhan430072 China Key Laboratory of Dark Matter and Space Astronomy Purple Mountain Observatory Chinese Academy of Sciences Jiangsu Nanjing210023 China Hebei Normal University Hebei Shijiazhuang050024 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 Sun Yat-Sen University Guangdong Zhuhai519000 China Dipartimento di Fisica Universit‘a di Napoli "Federico II" Complesso Universitario di Monte Sant’Angelo via Cinthia Napoli80126 Italy School of Physics and Astronomy Yunnan U

LHAASO, located at Daocheng in Sichuan province of China with an altitude up to 4410 m above the sea level, takes the function of hybrid technology to detect cosmic rays. As the major array of LHAASO, KM2A is composed of 5195 electromagnetic particle detectors (EDs) and 1188 muon detectors (MDs). In the ground-based experiments, there are two common independent data acquisition systems, corresponding to the scaler and shower operation modes. In order to learn more about the scaler mode in LHAASO-KM2A, we adopt the CORSIKA to study the shower development and employ the G4KM2A (based on Geant4) to simulate the detector responses. For one cluster (composed of 64 EDs) in the array of KM2A-ED, the event rates of showers having a number of fired EDs ≥ 1, 2, 3 and 4 (in a time coincidence of 100 ns) are recorded. The average rates of the four multiplicities are ∼ 88 kHz, ∼ 1.4 kHz, ∼ 210 Hz and ∼ 110 Hz, respectively. For the array of KM2A-MD, there are 16 MDs in one cluster. The average rates with multiplicities ≥ 1 and 2 are ∼ 84 kHz and ∼ 890 Hz, respectively. The corresponding primary energies are also given. According to our simulations, the energy threshold of the scaler mode can be lowered to ∼ 100 GeV. At the same time, the energy threshold of LHAASO-KM2A in shower mode is presented for comparison. The simulation results in this work are beneficial for the online trigger with scaler mode, and also be useful in understanding the experiment results in LHAASO-KM2A. © Copyright owned by the author(s).

关键词： Cosmic rays

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