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

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Chinese Physics C 2024年第6期48卷 169-178页

作者：曹臻 F.Aharonian 安琪阿西克古白云翔包逸炜 D.Bastieri 毕效军毕玉江蔡金庭曹晴曹文羽曹喆常进常劲帆陈尚明陈恩生陈亮陈林陈龙陈明君陈玛丽陈起辉陈素弘陈天禄陈阳程宁程耀东崔明阳崔树旺崔晓红崔昱东戴本忠代洪亮戴子高单增罗布 D.della Volpe 董绪强段凯凯樊军辉范一中方军方堃冯存峰封莉冯少辉丰晓婷冯有亮 S.Gabici 高博高川东高林青高启高卫高伟康葛茂茂耿利斯 G.Giacinti 龚光华苟全补顾旻皓郭福来郭晓磊郭义庆郭莹莹韩毅昂何会海贺昊宁何佳银何新波何钰 M.heller 贺远强侯博文侯超侯贤胡红波胡铨胡世聪黄代绘黄天奇黄文俊黄性涛黄晓渊黄勇黄志成季筱璐贾焕玉贾康江琨姜晓巍姜泽军金敏康明铭柯通 D.Kuleshov.K.Kurinov 李兵兵李澄李骢李丹李飞李海波李会财李华阳李军李剑李捷李凯李文龙李文莲李秀荣李昕李一卓李哲黎卓梁恩维梁云峰林苏杰刘冰刘成刘栋刘虎刘海东刘佳刘江来刘金艳刘茂元柳若愚刘四明刘伟刘怡刘以农鲁睿罗晴吕洪魁马伯强马玲玲马欣华毛基荣闵振 W.Mitthumsiri 穆慧君南云程 A.Neronov 区子维庞彬宇 P.Pattarakijwanich 裴致远齐孟尧祁业情乔冰强秦家军 D.Ruffolo A.Sáiz D.Semikoz 邵澄宇邵琅 O.Shchegolev 盛祥东舒富文宋慧超 Yu.V.Stenkin V.Stepanov 苏扬孙秦宁孙晓娜孙志斌谭柏轩唐庆文唐泽波田文武王超王昌贝王广威王洪光王惠惠王建成汪凯王利苹王玲玉王培汉王冉王为王祥高王祥玉王阳王玉东王岩谨王忠海王仲翔王振王铮韦大明魏俊杰魏永健文韬吴超勇吴含荣武莎吴雪峰吴雨生席邵强夏捷夏君集项光漫肖迪泫肖刚辛广广辛玉良邢祎熊峥徐东莲徐仁峰徐仁新徐伟立薛良闫大海颜景志颜田杨朝文杨帆杨冯帆杨何文杨佳盈杨莉莉杨明洁杨睿智杨深邦姚玉华姚志国叶一锰尹丽巧尹娜游晓浩游智勇于艳红袁强岳华曾厚敦曾婷轩曾玮查敏张彬彬张丰张海明张恒英张建立张丽霞张力张鹏飞张佩佩张瑞张少博张少如张寿山张潇张笑鹏张云峰张毅张勇赵兵赵静赵雷赵立志赵世平郑福周斌周浩周佳能周猛周平周荣周勋秀祝成光祝凤荣朱辉朱科军左雄 LhAASO Collaboration Key Laboratory of Particle Astrophyics&Experimental Physics Division&Computing Center Institute of High Energy PhysicsChinese Academy of SciencesBeijing 100049China University of Chinese Academy of Sciences Beijing 100049China Tianfu Cosmic Ray Research Center Chengdu 610000China Dublin Institute for Advanced Studies 31 Fitzwilliam Place2 DublinIreland Max-Planck-Institut for Nuclear Physics P.O.Box 10398069029 HeidelbergGermany State Key Laboratory of Particle Detection and Electronics China University of Science and Technology of China Hefei 230026China School of Physical Science and Technology&School of Information Science and Technology Southwest Jiaotong UniversityChengdu 610031China School of Astronomy and Space Science Nanjing UniversityNanjing 210023China Center for Astrophysics Guangzhou UniversityGuangzhou 510006China hebei Normal University Shijiazhuang 050024China Key Laboratory of Dark Matter and Space Astronomy&Key Laboratory of Radio Astronomy Purple Mountain ObservatoryChinese Academy of SciencesNanjing 210023China Tsung-Dao Lee Institute&School of Physics and Astronomy Shanghai Jiao Tong UniversityShanghai 200240China Key Laboratory for Research in Galaxies and Cosmology Shanghai Astronomical ObservatoryChinese Academy of SciencesShanghai 200030China Key Laboratory of Cosmic Rays(Tibet University) Ministry of EducationLhasa 850000TibetChina National Astronomical Observatories Chinese Academy of SciencesBeijing 100101China School of Physics and Astronomy(Zhuhai)&School of Physics(Guangzhou)&Sino-French Institute of Nuclear Engineering and Technology(Zhuhai) Sun Yat-sen UniversityZhuhai 519000&Guangzhou 510275China School of Physics and Astronomy Yunnan UniversityKunming 650091China Département de Physique Nucléaire et Corpusculaire Facultéde SciencesUniversitéde Genève24 Quai Ernest Ansermet1211 GenevaSwitzerland Institute of Frontier and Interdisciplinary Science Shandong UniversityQingdao 266237China APC Universit'e Paris Cit'eCNRS/IN2P3CEA/IRFUObservat

The full array of the Large high Altitude Air Shower Observatory(LhAASO)has been in operation since July *** its kilometer-square array(KM2A),we optimized the selection criteria for very high and ultrahigh energyγ-rays using data collected from August 2021 to August 2022,resulting in an improvement in significance of the detection in the Crab Nebula of approximately 15%,compared with that of previous *** the implementation of these new selection criteria,the angular resolution was also significantly improved by approximately 10%at tens of *** aspects of the full KM2A array performance,such as the pointing error,were also calibrated using the Crab *** resulting energy spectrum of the Crab Nebula in the energy range of 10-1000 TeV are well fitted by a log-parabola model,which is consistent with the previous results from LhAASO and other experiments.

关键词： γ-ray Crab Nebula significance

LhAASO观测发现W51区域粒子能量加速至PeV的证据（英文）

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Science Bulletin 2024年第18期 2833-2841页

作者： Zhen Cao F.Aharonian Axikegu Y.X.Bai Y.W.Bao D.Bastieri X.J.Bi Y.J.Bi W.Bian A.V.Bukevich Q.Cao W.Y.Cao Zhe Cao J.Chang J.F.Chang A.M.Chen E.S.Chen h.X.Chen Liang Chen Lin Chen Long Chen M.J.Chen M.L.Chen Q.h.Chen S.Chen S.h.Chen S.Z.Chen T.L.Chen Y.Chen N.Cheng Y.D.Cheng M.Y.Cui S.W.Cui X.h.Cui Y.D.Cui B.Z.Dai h.L.Dai Z.G.Dai Danzengluobu X.Q.Dong K.K.Duan J.h.Fan Y.Z.Fan J.Fang J.h.Fang Kun Fang C.F.Feng h.Feng L.Feng S.h.Feng X.T.Feng Y.Feng Y.L.Feng S.Gabici B.Gao C.D.Gao Q.Gao W.Gao W.K.Gao M.M.Ge L.S.Geng G.Giacinti G.h.Gong Q.B.Gou M.h.Gu F.L.Guo X.L.Guo Y.Q.Guo Y.Y.Guo Y.A.han M.hasan h.h.he h.N.he J.Y.he Y.he Y.K.hor B.W.hou C.hou X.hou h.B.hu Q.hu S.C.hu D.h.huang T.Q.huang W.J.huang X.T.huang X.Y.huang Y.huang X.L.Ji h.Y.Jia K.Jia K.Jiang X.W.Jiang Z.J.Jiang M.Jin M.M.Kang I.Karpikov D.Kuleshov K.Kurinov B.B.Li C.M.Li Cheng Li Cong Li D.Li F.Li h.B.Li h.C.Li Jian Li Jie Li K.Li S.D.Li W.L.Li W.L.Li X.R.Li Xin Li Y.Z.Li Zhe Li Zhuo Li E.W.Liang Y.F.Liang S.J.Lin B.Liu Cheng Liu D.Liu D.B.Liu h.Liu h.D.Liu J.Liu J.L.Liu M.Y.Liu R.Y.Liu S.M.Liu W.Liu Y.Liu Y.N.Liu Q.Luo Y.Luo h.K.Lv B.Q.Ma L.L.Ma X.h.Ma J.R.Mao Z.Min W.Mitthumsiri h.J.Mu Y.C.Nan A.Neronov L.J.Ou P.Pattarakijwanich Z.Y.Pei J.C.Qi M.Y.Qi B.Q.Qiao J.J.Qin A.Raza D.Ruffolo A.Sáz M.Saeed D.Semikoz L.Shao O.Shchegolev X.D.Sheng F.W.Shu h.C.Song Yu.V.Stenkin V.Stepanov Y.Su D.X.Sun Q.N.Sun X.N.Sun Z.B.Sun J.Takata P.h.T.Tam Q.W.Tang R.Tang Z.B.Tang W.W.Tian C.wang C.B.wang G.W.wang h.G.wang h.h.wang J.C.wang Kai wang Kai wang L.P.wang L.Y.wang P.h.wang R.wang W.wang X.G.wang X.Y.wang Y.wang Y.D.wang Y.J.wang Z.h.wang Z.X.wang Zhen wang Zheng wang D.M.Wei J.J.Wei Y.J.Wei T.Wen C.Y.Wu h.R.Wu Q.W.Wu S.Wu X.F.Wu Y.S.Wu S.Q.Xi J.Xia G.M.Xiang D.X.Xiao G.Xiao Y.L.Xin Y.Xing D.R.Xiong Z.Xiong D.L.Xu R.F.Xu R.X.Xu W.L.Xu L.Xue D.h.Yan J.Z.Yan T.Yan C.W.Yang C.Y.Yang F.Yang F.F.Yang L.L.Yang M.J.Yang R.Z.Yang W.X.Yang Y.h.Yao Z.G.Yao L.Q.Yin N.Yin X.h.You Z.Y.You Y.h.Yu Q.Yuan h.Yue h.D.Zeng T.X.Zeng W.Zeng M.Zha B.B.Zhang F.Zhang h.Zhang h.M.Zhang h.Y.Z LhAASO合作组 Key Laboratory of Particle Astrophysics & Experimental Physics Division & Computing Center Institute of high Energy Physics Chinese Academy of Sciences University of Chinese Academy of Sciences TIANFU Cosmic Ray Research Center Dublin Institute for Advanced Studies Max-Planck-Institut for Nuclear Physics School of Physical Science and Technology & School of Information Science and Technology Southwest Jiaotong University School of Astronomy and Space Science Nanjing University Center for Astrophysics Guangzhou University Tsung-Dao Lee Institute & School of Physics and Astronomy Shanghai Jiao Tong University Institute for Nuclear Research of Russian Academy of Sciences hebei Normal University University of Science and Technology of 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 Research Center for Astronomical Computing Zhejiang Laboratory Key Laboratory for Research in Galaxies and Cosmology Shanghai Astronomical Observatory Chinese Academy of Sciences School of Physics and Astronomy Yunnan University Key Laboratory of Cosmic Rays (Tibet University) Ministry of Education National Astronomical Observatories Chinese Academy of Sciences School of Physics and Astronomy (Zhuhai) & School of Physics(Guangzhou) & Sino-French Institute of Nuclear Engineering and Technology (Zhuhai) Sun Yat-sen University Institute of Frontier and Interdisciplinary Science Shandong University APC UniversitéParis Cité CNRS/IN2P3 CEA/IRFU Observatoire de Paris Department of Engineering Physics Tsinghua University School of Physics and Microelectronics Zhengzhou University Yunnan Observatories Chinese Academy of Sciences China Center of Advanced Science and Technology College of Physics Sichuan University School of Physics Peking University Guangxi Key Laboratory for Relativistic Astrophysics School of Physical Science and Technology Guangxi University Department of Ph

The γ-ray emission from the W51 complex is widely acknowledged to be attributed to the interaction between the cosmic rays （CRs） accelerated by the shock of supernova remnant （SNR） W51C and the dense molecular clouds in the adjacent star-forming region,***,the maximum acceleration capability of W51C for CRs remains *** on observations conducted with the Large high Altitude Air Shower Observatory （LhAASO）,we report a significant detection of γ rays emanating from the W51 complex,with energies from 2 to 200 *** LhAASO measurements,for the first time,extend the γ-ray emission from the W51 complex beyond 100 TeV and reveal a significant spectrum bending at tens of *** combining the"π0-decay bump"featured data from Fermi-LAT,the broadband γ-ray spectrum of the W51 region can be well-characterized by a simple pp-collision *** observed spectral bending feature suggests an exponential cutoff at～400 TeV or a power-law break at～200 TeV in the CR proton spectrum,most likely providing the first evidence of SNRs serving as CR accelerators approaching the PeV ***,two young star clusters within W51B could also be theoretically viable to produce the most energetic γ rays observed by *** findings strongly support the presence of extreme CR accelerators within the W51 complex and provide new insights into the origin of Galactic CRs.

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Connections Between Induction Generator Effect and Open-loop Modal Proximity to Cause Sub-synchronous Oscillations in Series-compensated Power System

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CSEE Journal of Power and Energy Systems 2024年第3期10卷 963-973页

作者： Wenjuan Du Yang wang h.F.wang Tao Zhao Tim Littler College of Electrical Engineering Sichuan UniversityChengduChina School of Electrical Engineering the Queen’s University of BelfastBelfastUK IEEE

The induction generator effect(IGE)and the openloop modal proximity(OLMP)are two different reasons why subsynchronous oscillations(SSOs)in a series-compensated power system(SCPS)may *** IGE attributes the growing SSOs to negative resistance,while the OLMP explains the SSO mechanism from the standpoint of modal *** this paper,we investigate the connections between the IGE and the OLMP through equivalent RLC circuit and open-loop modal *** investigation is conducted for two types of seriescompensated power systems where either a synchronous generator or a DFIG is connected at the sending *** investigation reveals the conditions,in which the IGE and the OLMP may jointly cause the growing SSOs,i.e.,both the IGE and the OLMP can explain why the growing SSOs ***,the investigation indicates that the IGE and the OLMP may be totally irrelevant and lead to growing SSOs *** implies that it is possible that in a SCPS,the growing SSOs are only due to the IGE,and the OLMP is non-existent,and vice ***,when the growing SSOs occurs in a SCPS,examination based on both the IGE and the OLMP should be carefully conducted in order to find if the oscillatory instability is due to the IGE,or the OLMP,or both of them.

关键词： Induction generator effect open-loop modal proximity series compensation sub-synchronous oscillations

Integralinput-to-statestability ystemswithsmalldelays Integral input-to-state stability of systems with small delays

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Control Theory and Technology 2024年第3期22卷 455-467页

作者： R.h.harsha Nawarathna Yuandan Lin Yuan wang Department of Mathematical Sciences Florida Atlantic UniversityBoca RatonFL 33431USA

We consider how small delays affect the integral-input-to-state stability(iss)property for a *** result is similar to the input-to-state stability(Iss)result obtained in[1]:the iss property will be preserved in a practical and semi-global manner if the delay interval is small ***,since the iss quantifies the robust stability in terms of a generalized Li norm of the inputs instead of a generalized Loo norm of the inputs for the Iss case,the techniques and proofs for the Iss case do not apply to the iss case *** the proofs in[1]are based on the Lyapunov-Razumikhin approach,our proofs are based on the iss-Lyapunov functions for the zero-delay *** addition to the interest by its own in showing how the iss property is affected by small delays,the result also serves to the study of the iss property for singularly perturbed systems.

关键词： Integral-input-to-state stability Nonlinear systems Small delays Lyapunov method

Data-Driven Fault Detection of Multiple Open-Circuit Faults for MMC Systems Based on Long Short-Term Memory Networks

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CSEE Journal of Power and Energy Systems 2024年第4期10卷 1563-1574页

作者： Chenxi Fan Kaishun Xiahou Lei wang Q.h.Wu School of Electric Power Engineering South China University of TechnologyGuangzhou 510641China

This paper presents a long short-term memory(LSTM)-based fault detection method to detect the multiple open-circuit switch faults of modular multilevel converter(MMC)systems with full-bridge sub-modules(FB-SMs).Eighteen sensor signals of grid voltages,grid currents and capacitance voltages of MMC for single and multi-switch faults are collected as sampling *** output signal characteristics of four types of single switch faults of FB-SM,as well as double switch faults in the same and different phases of MMC,are analyzed under the conditions of load variations and control command changes.A multi-layer LSTM network is devised to deeply extract the fault characteristics of MMC under different faults and operation conditions,and a Softmax layer detects the fault *** results have confirmed that the proposed LSTM-based method has better detection performance compared with three other methods:K-nearest neighbor(KNN),naive bayes(NB)and recurrent neural network(RNN).In addition,it is highly robust to model uncertainties and Gaussian *** validity of the proposed method is further demonstrated by experiment studies conducted on a hardware-in-the-loop(hIL)testing platform.

关键词： Fault detection long short-term memory(LSTM) modular multilevel converter(MMC) open circuit fault

Enhancing Log Anomaly Detection with Semantic Embedding and Integrated Neural Network Innovations

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Computers, Materials and Continua 2024年第3期80卷 3991-4015页

作者： Xu Z. wang Z. Xu J. Shi h. Zhao h. School of Computer and Software Nanjing University of Information Science and Technology Nanjing 210044 China

System logs, serving as a pivotal data source for performance monitoring and anomaly detection, play an indispensable role in assuring service stability and reliability. Despite this, the majority of existing log-based anomaly detection methodologies predominantly depend on the sequence or quantity attributes of logs, utilizing solely a single Recurrent Neural Network (RNN) and its variant sequence models for detection. These approaches have not thoroughly exploited the semantic information embedded in logs, exhibit limited adaptability to novel logs, and a single model struggles to fully unearth the potential features within the log sequence. Addressing these challenges, this article proposes a hybrid architecture based on a multiscale convolutional neural network, efficient channel attention and mogrifier gated recurrent unit networks (LogCEM), which amalgamates multiple neural network technologies. Capitalizing on the superior performance of robustly optimized BERT approach (RoBERTa) in the realm of natural language processing, we employ RoBERTa to extract the original word vectors from each word in the log template. In conjunction with the enhanced Smooth Inverse Frequency (SIF) algorithm, we generate more precise log sentence vectors, thereby achieving an in-depth representation of log semantics. Subsequently, these log vector sequences are fed into a hybrid neural network, which fuses 1D Multi-Scale Convolutional Neural Network (MSCNN), Efficient Channel Attention Mechanism (ECA), and Mogrifier Gated Recurrent Unit (GRU). This amalgamation enables the model to concurrently capture the local and global dependencies of the log sequence and autonomously learn the significance of different log sequences, thereby markedly enhancing the efficacy of log anomaly detection. To validate the effectiveness of the LogCEM model, we conducted evaluations on two authoritative open-source datasets. The experimental results demonstrate that LogCEM not only exhibits excellent ac

关键词： anomaly detection Deep learning log analysis natural language processing

hWD-YOLO: A New Vision-Based helmet Wearing Detection Method

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Computers, Materials and Continua 2024年第3期80卷 4543-4560页

作者： Sun L. Li h. wang L. College of Information Science and Technology Beijing University of Technology Beijing 100124 China Chinese Institute of Coal Science Beijing 100013 China State Key Laboratory for Intelligent Coal Mining and Strata Control Beijing 100013 China Engineering Research Center of Digital Community of Ministry of Education Beijing 100124 China

It is crucial to ensure workers wear safety helmets when working at a workplace with a high risk of safety accidents, such as construction sites and mine tunnels. Although existing methods can achieve helmet detection in images, their accuracy and speed still need improvements since complex, cluttered, and large-scale scenes of real workplaces cause server occlusion, illumination change, scale variation, and perspective distortion. So, a new safety helmet-wearing detection method based on deep learning is proposed. Firstly, a new multi-scale contextual aggregation module is proposed to aggregate multi-scale feature information globally and highlight the details of concerned objects in the backbone part of the deep neural network. Secondly, a new detection block combining the dilate convolution and attention mechanism is proposed and introduced into the prediction part. This block can effectively extract deep features while retaining information on fine-grained details, such as edges and small objects. Moreover, some newly emerged modules are incorporated into the proposed network to improve safety helmet-wearing detection performance further. Extensive experiments on open dataset validate the proposed method. It reaches better performance on helmet-wearing detection and even outperforms the state-of-the-art method. To be more specific, the mAP increases by 3.4%, and the speed increases from 17 to 33 fps in comparison with the baseline, You Only Look Once (YOLO) version 5X, and the mean average precision increases by 1.0% and the speed increases by 7 fps in comparison with the YOLO version 7. The generalization ability and portability experiment results show that the proposed improvements could serve as a springboard for deep neural network design to improve object detection performance in complex scenarios. Copyright © 2024 The Authors. Published by Tech Science Press.

关键词： attention mechanism deep learning feature extraction Object detection safety helmet wearing detection

Photoluminescence and ratiometric thermo-response of Eu^(2+)and Eu^(3+)in BaAl_(2)B_(2)O_(7):Eu^(2+),Eu^(3+)phosphor materials

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Journal of Rare Earths 2024年第7期42卷 1240-1249,I0002页

作者： Ziyao wang haitao Fu Xiufeng Zhan Bo Tong Guofeng Ma h.T.(Bert)hintzen Institute of Innovative Science and Technology Shenyang UniversityShenyang110044China College of Mechanical Engineering Shenyang UniversityShenyang110044China Faculty of Applied Sciences Delft University of Technology2629 JBDelftthe Netherlands

A novel Eu^(2+)-Eu^(3+)co-activated ratiometric thermo-sensitive phosphor was developed and synthesized by solid-state *** valence state of Eu,photoluminescence and thermo-sensitive performance of the phosphor prepared either in ambient air or carbothermally were investigated and *** phosphor shows high sensitivity(Sa=0.0173 K^(-1),S_(r)=0.461%/K)and superior signal discriminability(Δν∼10380 cm^(−1)).The thermo-sensitive performance is subject to the dual effects of different thermo-responses by Eu^(2+)versus Eu^(3+)combined with energy transfer from Eu^(2+)to Eu^(3+),so that the sensitivity of the phosphor in the temperature range presents a non-monotonic *** development of the BaAl_(2)B_(2)O_(7):Eu^(2+),Eu^(3+)phosphor is not only expected to be relevant for application in the field of temperature sensing,but also of reference significance for improving the sensitivity by means of energy transfer between co-activator ions over a wider temperature range of Eu^(2+)-Eu^(3+)co-activated ratiometric thermo-sensitive phosphors.

关键词： Photoluminescence Ratiometric thermo-response Phosphor Eu^(2+)/Eu^(3+) Rare earths

Evaluation of E-sail parameters on central spacecraft attitude stability using a high-fidelity rigid–flexible coupling model

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Astrodynamics 2024年第2期8卷 271-284页

作者： Chonggang Du Zheng h.Zhu Changqing wang Aijun Li Tuanjie Li School of Electro-Mechanical Engineering Xidian UniversityXi’an 710071China Department of Mechanical Engineering York University4700 Keele StreetTorontoOntario M3J 1P3Canada School of Automation Northwestern Polytechnical UniversityXi’an 710129China

This study examines the impact of electric solar wind sail(E-sail)parameters on the attitude stability of E-sail’s central spacecraft by using a comprehensive rigid–flexible coupling dynamic *** this model,the nodal position finite element method is used to model the elastic deformation of the tethers through interconnected two-node tensile *** attitude dynamics of the central spacecraft is described using a natural coordinate *** rigid–flexible coupling between the central spacecraft and its flexible tethers is established using Lagrange *** research reveals the significant influences of parameters such as tether numbers,tether’s electric potential,and solar wind velocity on attitude ***,solar wind fluctuations and the distribution of electric potential on the main tethers considerably affect the attitude stability of the *** consistent management,the angular velocities of the spacecraft must remain at target ***,the attitude stability of a spacecraft has a pronounced dependence on the geometrical configuration of the E-sail,with axisymmetric E-sails proving to be more stable.

关键词： electric solar wind sail(E-sail) space tether nodal position finite element method(NPFEM) natural coordinate formulation (NCF) attitude stability