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CEPC Technical Design Report

第25届中国控制会议

作者： yu fei, lv dongmei, liu ximei college of automation and electronic engineering, qingdao university of science and technology, qingdao 266042

＜正＞The broad applications of multi-agent systems in many areas have stimulated a great deal of interests in studying consensus problems. This paper investigates an optimal distributed consensus and analysis the application in small world network by reducing the RH problem to 1 -dimension and with the penalty F（xi,X（i）） tracing the fast signal to reach consensus. We prove the algebraic connectivity of a graph is the second smallest eigenvalue of its Laplacian matrix and a measure of speed of solving consensus problems in networks. We find that the convergence speed of the consensus algorithm on a regular lattice can be greatly enhanced by just randomly rewiring a very small number of links in the network. By increasing the algebraic connectivity λ2 （G） and the Laplacian matrix λ 2 （L）,we can improve the convergent speed.

关键词： Optimal distributed consensus WS algebraic connectivity randomly rewire penalty function

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Radiation Detection technology and Methods 2024年第1期8卷 I0003-I0016,1-1091页

作者： Waleed Abdallah Tiago Carlos Adorno de Freitas Konstantin Afanaciev Shakeel Ahmad Ijaz Ahmed Xiaocong Ai Abid Aleem Wolfgang Altmannshofer Fabio Alves Weiming An Rui An Daniele Paolo Anderle Stefan Antusch Yasuo Arai Andrej Arbuzov Abdesslam Arhrib Mustafa Ashry Sha Bai yu Bai Yang Bai Vipul Bairathi Csaba Balazs Philip Bambade Yong Ban Tripamo Bandyopadhyay Shou-Shan Bao Desmond P.Barber Ayse Bat Varvara Batozskaya Subash Chandra Behera Alexander Belyaev Michele Bertucci Xiao-Jun Bi yuanjie Bi Tianjian Bian Fabrizio Bianchi Thomas Biekotter Michela Biglietti Shalva Bilanishvili Deng Binglin Denis Bodrov Anton Bogomyagkov Serge Bondarenko Stewart Boogert Maarten Boonekamp Marcello Borri Angelo Bosotti Vincent Boudry Mohammed Boukidi Igor Boyko Ivanka Bozovic Giuseppe Bozzi Jean-Claude Brient Anastasiia Budzinskaya Masroor Bukhari Vladimir Bytev Giacomo Cacciapaglia Hua Cai Wenyong Cai Wujun Cai Yijian Cai Yizhou Cai yuchen Cai Haiying Cai Huacheng Cai Lorenzo Calibbi Junsong Cang Guofu Cao Jianshe Cao Antoine Chance Xuejun Chang yue Chang Zhe Chang Xinyuan Chang Wei Chao Auttakit Chatrabhuti Yimin Che yuzhi Che Bin Chen Danping Chen Fuqing Chen Fusan Chen Gang Chen Guoming Chen Hua-Xing Chen Huirun Chen Jinhui Chen Ji-yuan Chen Kai Chen Mali Chen Mingjun Chen Mingshui Chen Ning Chen Shanhong Chen Shanzhen Chen Shao-Long Chen Shaomin Chen Shiqiang Chen Tianlu Chen Wei Chen Xiang Chen Xiaoyu Chen Xin Chen Xun Chen Xurong Chen Ye Chen Ying Chen yukai Chen Zelin Chen Zilin Chen Gang Chen Boping Chen Chunhui Chen Hok Chuen Cheng Huajie Cheng Shan Cheng Tongguang Cheng yunlong Chi Pietro Chimenti Wen Han Chiu Guk Cho Ming-Chung Chu Xiaotong Chu Ziliang Chu Guglielmo Coloretti Andreas Crivellin Hanhua Cui Xiaohao Cui Zhaoyuan Cui Brunella D'Anzi Ling-yun Dai Xinchen Dai Xuwen Dai Antonio De Maria Nicola De Filippis Christophe De La Taille Francesca De Mori Chiara De Sio Elisa Del Core Shuangxue Deng Wei-Tian Deng Zhi Deng Ziyan Deng Bhupal Dev Tang Dewen Biagio Di Micco Ran Ding Siqin Dingl Yadong Ding Haiyi Dong Jianin AGH university of science and technology Faculty of Physics and Applied Computer ScienceKrakow Anhui university HefeiAnhui Applied Physics Department Federal Urdu University of ArtsScience and TechnologyIslamabad Banaras Hindu university Institute of ScienceVaranasi Bandirma Onyedi Eylul university Bandirma Beihang university Beijing Beijing Computational science Research Center Beijing Beijing Conveyi LTD Beijing Beijing Glass Research Institute Co. LtdBeijing Beijing Institute of technology Beijing Beijing Normal university Beijing Brown university ProvidenceRI Budker Institute of Nuclear Physics Novosibirsk Cadi Ayyad university Laboratory of Fundamental and Applied PhysicsPolydisciplinary FacultyMarrakech Cairo university Giza Center for Theory and Computation National Tsing Hua UniversityHsinchu Central china Normal university WuhanHubei Central South university ChangshaHunan Centro de Fisica Teorica de Particulas Universidade Tecnica de LisboaLisboa Chengdu KaitengSifang Digital Radio&TV Equipment Co. LtdChengduSichuan china academy of engineering physics MianyangSichuan china building materials academy Beijing china Center of Advanced science and technology Beijing china Institute of Atomic Energy Beijing china Jiliang university HangzhouZhejiang china Nuclear Instrument CO. LTDBeijing china university of Geosciences Beijing china university of Geosciences WuhanHubei china university of Mining and technology XuzhouJiangsu china West Normal university NanchongSichuan Chongqing university Department of PhysicsChongqing Chulalongkorn university Bangkok City university of Hong Kong Department of PhysicsHong Kong Cockcroft Institute Daresbury Cornell university IthacaNY CPPM(AMU/IPhU&CNRS/INP) Marseille Dalian Minzu university DalianLiaoning Dalian university of technology Department of PhysicsDalian Deutsches Elektronen-Synchrotron DESY Hamburg Dzhelepov Laboratory of Nuclear Problems of Joint Institute for Nuclear Research(DLNP JINR) Dubna East china university Of science And Technol

The Circular Electron Positron Collider(CEPC)is a large scientific project initiated and hosted by china,fostered through extensive collaboration with international *** complex comprises four accelerators:a 30 GeV Linac,a 1.1 GeV Damping Ring,a Booster capable of achieving energies up to 180 GeV,and a Collider operating at varying energy modes(Z,W,H,and tt).The Linac and Damping Ring are situated on the surface,while the subterranean Booster and Collider are housed in a 100 km circumference underground tunnel,strategically accommodating future expansion with provisions for a potential Super Proton Proton Collider(SPPC).The CEPC primarily serves as a Higgs *** its baseline design with synchrotron radiation(SR)power of 30 MW per beam,it can achieve a luminosity of 5×10^(34)cm^(-2)s^(-1)per interaction point(IP),resulting in an integrated luminosity of 13 ab^(-1)for two IPs over a decade,producing 2.6 million Higgs *** the SR power to 50 MW per beam expands the CEPC's capability to generate 4.3 million Higgs bosons,facilitating precise measurements of Higgs coupling at sub-percent levels,exceeding the precision expected from the HL-LHC by an order of *** Technical Design Report(TDR)follows the Preliminary Conceptual Design Report(Pre-CDR,2015)and the Conceptual Design Report(CDR,2018),comprehensively detailing the machine's layout,performance metrics,physical design and analysis,technical systems design,R&D and prototyping efforts,and associated civil engineering ***,it includes a cost estimate and a preliminary construction timeline,establishing a framework for forthcoming engineering design phase and site selection *** is anticipated to begin around 2027-2028,pending government approval,with an estimated duration of 8 *** commencement of experiments and data collection could potentially be initiated in the mid-2030s.

关键词： initiated exceeding precise

基于等效短线路解耦法的风电场模型分割仿真研究

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电力系统保护与控制 2023年第19期51卷 164-172页

作者：于飞董乐李梅航张倩刘喜梅青岛科技大学自动化与电子工程学院山东青岛266061

为了加快大型风电场的仿真速率,提出了一种基于等效短线路解耦的模型分割方案。首先对于机组类型单一的大型风电场,采用输出倍乘与集电线路等值的方法进行简化建模。在此基础上,针对风电场内线路较短,难以采用长输电线路自然解耦来并行... 详细信息

为了加快大型风电场的仿真速率,提出了一种基于等效短线路解耦的模型分割方案。首先对于机组类型单一的大型风电场,采用输出倍乘与集电线路等值的方法进行简化建模。在此基础上,针对风电场内线路较短,难以采用长输电线路自然解耦来并行运算的缺点,提出对等值后的机组连接线与连接升压站的长汇集线之间进行参数补偿,从而满足输电线路在一个步长上的解耦判据。在Matlab/Simulink搭建仿真模型,对模型分割前后进行了对比。仿真结果验证了所提方案的可行性。在此基础上采用状态空间节点(state space node,SSN)法对风电机组内部划分群组,最终在RT-LAB平台上实现了大型海上风电场的实时化仿真。

关键词：大型风电场等效短线路解耦模型分割状态空间节点法 RT-LAB 实时仿真

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工业园区微网调度研究与仿真

电力系统保护与控制 2023年第18期51卷 179-187页

作者：于飞葛思伟李梅航张倩刘喜梅青岛科技大学自动化与电子工程学院山东青岛266061

为提高新能源消纳能力和供电经济性、可靠性,研究了含风光柴储、生活负荷、具有有限可控能力工业负荷的工业园区微电网,提出了一种基于日前和日内相结合的优化调度方案。为保证日前优化调度的经济性,提出了一种分段变异优化的改进粒子... 详细信息

为提高新能源消纳能力和供电经济性、可靠性,研究了含风光柴储、生活负荷、具有有限可控能力工业负荷的工业园区微电网,提出了一种基于日前和日内相结合的优化调度方案。为保证日前优化调度的经济性,提出了一种分段变异优化的改进粒子群算法求解调度模型。改进算法相较于传统粒子群算法收敛性速度更快,同时提高了经济性,减少了园区的运行成本。日内运行时,为了校正日前预测偏差,采用源网荷储共同参与调整,保证尽可能跟随日前经济结果,提高微网系统的可靠性。日内调整建立详细化的仿真模型,采用实时仿真技术。利用实时仿真软件RT-LAB和实时仿真机OP4510构建的实时仿真平台,解决模型仿真用时较长、求解困难的问题,验证了工业园区微网调度策略的可行性。

关键词：可控工业负荷园区微网调度粒子群算法实时仿真技术

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模块化多电平换流器常用调制策略的对比分析

青岛科技大学学报（自然科学版） 2020年第1期41卷 110-118页

作者：刘喜梅姜汇溪于飞青岛科技大学自动化与电子工程学院

模块化多电平换流器(MMC)常用调制方式包括载波移相调制(CPS-PWM)、载波层叠调制(CLS-PWM)以及最近电平逼近调制(NLM)。为提高MMC工作效率,方便针对不同应用条件选取合适的调制方式,本研究采用Matlab/Simulink虚拟仿真方法,对以上调制... 详细信息

模块化多电平换流器(MMC)常用调制方式包括载波移相调制(CPS-PWM)、载波层叠调制(CLS-PWM)以及最近电平逼近调制(NLM)。为提高MMC工作效率,方便针对不同应用条件选取合适的调制方式,本研究采用Matlab/Simulink虚拟仿真方法,对以上调制方式从开关频率、谐波分布、谐波畸变率等方面进行仿真,并对仿真结果进行比较,得到结论:CPS-PWM方式在MMC电平增多后更便于滤波,但算法也更加复杂,较为适用于十几电平或者二十几电平场合;CLS-PWM中的三种方式在电平增高后,谐波仍较为分散,但在十电平以下时就能达到较低的谐波畸变率,适合应用于电平数不多的场合;NLM开关损耗小,调制简单,但波形质量在电平数少的时候相对较差,在高电平时较为适用。

关键词：模块化多电平换流器载波移相载波层叠最近电平逼近开关频率谐波畸变率

新工科背景下机器人工程专业人才培养模式研究与探讨

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中国现代教育装备 2022年第13期 88-90页

作者：刘喜梅邢关生于飞李梅航李振伟青岛科技大学自动化与电子工程学院山东青岛266045

探讨了新工科背景下机器人工程专业人才培养模式,构建具有新工科特点的机器人工程专业多学科交叉融合、多方协同育人的人才培养体系。建立多学科融合的理论课程体系及以项目为驱动的多层次实践课程体系,并将学科竞赛融入科技创新实践环... 详细信息

探讨了新工科背景下机器人工程专业人才培养模式,构建具有新工科特点的机器人工程专业多学科交叉融合、多方协同育人的人才培养体系。建立多学科融合的理论课程体系及以项目为驱动的多层次实践课程体系,并将学科竞赛融入科技创新实践环节,全面提升人才培养质量。

关键词：机器人工程新工科人才培养

Epidemic Dynamics Analysis of COVID-19 Using a Modified SEIR Model with Symptom Classifications

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Epidemic Dynamics Analysis of COVID-19 Using a Modified SEIR...

Sub-percent precision measurement of neutrino oscillation parameters with JUNO

第40届中国控制会议

作者： Meng yuan Yi-Xuan Guo Xue-Yi liu Yi-Kang Wang Bao-Lin Zhang college of science China Jiliang University college of automation and electronic engineering Qingdao University of Science & Technology

The prevention and control of COVID-19 epidemic is a great challenge for human beings *** the battle against COVID-19,the hierarchical treatment measures based on symptom classifications have proved to be a particularly effective way to deal with the large-scale epidemic in the absence of adequate medical *** paper deals with the epidemic dynamic analyses of the COVID-19 based on a modified SEIR model with different ***,by taking symptom classifications and hierarchical treatments of patients into account,a modified SEIR model is ***,the proposed differential equations model is solved by using Runge-Kutta methods,and the parameters herein are estimated by least square principle based on the data released by the National Health *** results of the model show that the introduction of symptom classifications in the SEIR model can not only improve the fitting accuracy,but also precisely describe the evolution rules and mutual transfer rules of patients with different *** model can provide theoretical support for decision-making of the corresponding government departments,especially for the construction of mobile cabin hospitals and the reasonable preparation of important epidemic prevention resources.

关键词： COVID-19 Symptom classification SEIR model Epidemic prediction

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Chinese Physics C 2022年第12期46卷 28-49页

作者： Angel Abusleme Thomas Adam Shakeel Ahmad Rizwan Ahmed Sebastiano Aiello Muhammad Akram Abid Aleem Fengpeng An Qi An Giuseppe Andronico Nikolay Anfimov Vito Antonelli Tatiana Antoshkina Burin Asavapibhop João Pedro Athayde Marcondes de André Didier Auguste Weidong Bai Nikita Balashov Wander Baldini Andrea Barresi Davide Basilico Eric Baussan Marco Bellato Antonio Bergnoli Thilo Birkenfeld Sylvie Blin David Blum Simon Blyth Anastasia Bolshakova Mathieu Bongrand Clément Bordereau Dominique Breton Augusto Brigatti Riccardo Brugnera Riccardo Bruno Antonio Budano Jose Busto Ilya Butorov Anatael Cabrera Barbara Caccianiga Hao Cai Xiao Cai Yanke Cai Zhiyan Cai Riccardo Callegari Antonio Cammi Agustin Campeny Chuanya Cao Guofu Cao Jun Cao Rossella Caruso Cédric Cerna Chi Chan Jinfan Chang yun Chang Guoming Chen Pingping Chen Po-An Chen Shaomin Chen Xurong Chen Yixue Chen yu Chen Zhiyuan Chen Zikang Chen Jie Cheng Yaping Cheng yu Chin Cheng Alexey Chetverikov Davide Chiesa Pietro Chimenti Artem Chukanov Gérard Claverie Catia Clementi Barbara Clerbaux Selma Conforti Di Lorenzo Daniele Corti Flavio Dal Corso Olivia Dalager Christophe De La Taille Zhi Deng Ziyan Deng Wilfried Depnering Marco Diaz Xuefeng Ding Yayun Ding Bayu Dirgantara Sergey Dmitrievsky Tadeas Dohnal Dmitry Dolzhikov Georgy Donchenko Jianmeng Dong Evgeny Doroshkevich Marcos Dracos Frédéric Druillole Ran Du Shuxian Du Stefano Dusini Martin Dvorak Timo Enqvist Heike Enzmann Andrea Fabbri Donghua Fan Lei Fan Jian Fang Wenxing Fang Marco Fargetta Dmitry Fedoseev Zhengyong fei Li-Cheng Feng Qichun Feng Richard Ford Amélie Fournier Haonan Gan Feng Gao Alberto Garfagnini Arsenii Gavrikov Marco Giammarchi Nunzio Giudice Maxim Gonchar Guanghua Gong Hui Gong yuri Gornushkin Alexandre Göttel Marco Grassi Vasily Gromov Minghao Gu Xiaofei Gu yu Gu Mengyun Guan yuduo Guan Nunzio Guardone Cong Guo Jingyuan Guo Wanlei Guo Xinheng Guo yuhang Guo Paul Hackspacher Caren Hagner Ran Han Yang Han Miao He Wei He Tobias Heinz Patrick Hellmuth yuekun Heng Rafael Herrera yuenKeung Ho Yerevan Physics Institute YerevanArmenia UniversiéLibre de Bruxelles BrusselsBelgium Universidade Estadual de Londrina LondrinaBrazil Pontificia Universidade Catolica do Rio de Janeiro Rio de JaneiroBrazil Pontificia Universidad Católica de Chile SantiagoChile Universidad Tecnica Federico Santa Maria ValparaisoChile Beijing Institute of Spacecraft Environment engineering BeijingChina Beijing Normal university BeijingChina china Institute of Atomic Energy BeijingChina Institute of High Energy Physics BeijingChina North china Electric Power university BeijingChina School of Physics Peking UniversityBeijingChina Tsinghua university BeijingChina university of Chinese Academy of sciences BeijingChina Jilin university ChangchunChina college of electronic science and engineering National University of Defense TechnologyChangshaChina Chongqing university ChongqingChina Dongguan university of technology DongguanChina Jinan university GuangzhouChina Sun Yat-Sen university GuangzhouChina Harbin Institute of technology HarbinChina university of science and technology of china HefeiChina The Radiochemistry and Nuclear Chemistry Group in university of South china HengyangChina Wuyi university JiangmenChina shandong university JinanChinaand Key Laboratory of Particle Physics and Particle Irradiation of Ministry of EducationShandong UniversityQingdaoChina Institute of Modern Physics Chinese Academy of SciencesLanzhouChina Nanjing university NanjingChina Guangxi university NanningChina East china university of science and technology ShanghaiChina School of Physics and Astronomy Shanghai Jiao Tong UniversityShanghaiChina Tsung-Dao Lee Institute Shanghai Jiao Tong UniversityShanghaiChina Institute of Hydrogeology and Environmental Geology Chinese Academy of Geological SciencesShijiazhuangChina Nankai university TianjinChina Wuhan university WuhanChina Xi'an Jiaotong university Xi'anChina Xiamen university XiamenChina School of Physics and Microelectronics Zhengzhou UniversityZhengzhou

JUNO is a multi-purpose neutrino observatory under construction in the south of *** publication presents new sensitivity estimates for the measurement of the △m_(31)^(2),△m_(21)^(2),sin^(2)θ_(12),and sin^(2)θ_(13) oscillation parameters using reactor antineutrinos,which is one of the primary physics goals of the *** sensitivities are obtained using the best knowledge available to date on the location and overburden of the experimental site,the nuclear reactors in the surrounding area and beyond,the detector response uncertainties,and the reactor antineutrino spectral shape constraints expected from the TAO satellite *** is found that the △m_(21)^(2) and sin^(2)θ_(12) oscillation parameters will be determined to 0.5%precision or better in six years of data *** the same period,the △m_(31)^(2) parameter will be determined to about 0.2%precision for each mass ordering *** new precision represents approximately an order of magnitude improvement over existing constraints for these three parameters.

关键词： neutrino oscillation reactor antineutrino precision measurement JUNO

基于贝叶斯加权LSTM网络的COVID-19多序列预测

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中国计量大学学报 2022年第3期33卷 379-387页

作者：袁梦郭奕瑄刘学艺王义康张宝琳中国计量大学理学院浙江杭州310018 青岛科技大学自动化与电子工程学院山东青岛266061

目的:基于贝叶斯优化方法构建COVID-19疫情的加权长短期记忆(LSTM)多序列预测模型,提高对COVID-19疫情的预测精度。方法:首先,通过引入输出权重系数,建立COVID-19的加权LSTM多序列预测模型;其次,提出一种基于贝叶斯优化的加权LSTM多序... 详细信息

目的:基于贝叶斯优化方法构建COVID-19疫情的加权长短期记忆(LSTM)多序列预测模型,提高对COVID-19疫情的预测精度。方法:首先,通过引入输出权重系数,建立COVID-19的加权LSTM多序列预测模型;其次,提出一种基于贝叶斯优化的加权LSTM多序列预测模型(Bayesian Weighted LSTM,BW-LSTM),并根据不同超参数间的关联特点,基于两步优化策略实现权重系数以及其它超参数的优化学习;最后,基于BW-LSTM模型实现COVID-19疫情的多序列预测。结果:与其它传统深度学习模型对比,基于BW-LSTM的COVID-19预测模型能获得更优的预测精度。结论:本文提出的加权LSTM多序列预测模型及其参数的贝叶斯优化学习算法,能够有效提升网络学习效率,提高模型预测精度。

关键词： COVID-19 贝叶斯优化 LSTM网络疫情预测