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Measurement of the integrated Luminosities of cross-section scan data samples around theψ(3770)mass region

Chinese Physics C 2018年第6期42卷 1-8页

作者： M. Ablikim M. N. Achasov S. Ahmed M. Albrecht M. Alekseev A. Amoroso F. F. An q. An Y. Bai O. Bakina R. Baldini Ferroli Y. Ban K. Begzsuren D. W. Bennett J. V. Bennett N. Berger M. Bertani D. Bettoni F. Bianchi E. Boger I. Boyko R. A. Briere H. Cai X. Cai O. Cakir A. Calcaterra G. F. Cao S. A. Cetin J. Chai J. F. Chang W. L. Chang G. Chelkov G. Chen H. S. Chen J. C. Chen M. L. Chen P. L. Chen S. J. Chen X. R. Chen Y. B. Chen X. K. Chu G. Cibinetto F. Cossio H. a. Dai, J. F. Dai A. Dbeyssi D. Dedovich Z. Y. Deng A. Denig I. Denysenko M. Destefanis F. De Mori Y. Ding C. Dong J. Dong L. Y. Dong M. Y. Dong Z. L. Dou S. X. Du P. F. nuan J. Fang S. S. Fang Y. Fang R. Farinelli L. Fava S. Fegan F. Feldbauer G. Felici C. q. Feng E. Fioravanti M. Fritsch C. D. Fu q. Gao X. L. Gao Y. Gao, Y. G. Gao Z. Gao B. Garillon I. Garzia A. Gilman K. Goetzen L. Gong W. X. Gong W. Gradl M. Greco L. M. Gu M. H. Gu Y. T. Gu A. q. Guo L. B. Guo R. P. Guo Y. F. Guo A. Guskov Z. Haddadi S. Han X. q. Hao F. A. Harris Z. L. He X. O. He F. H. Heinsius T. Held Y. K. Heng T. Holtmann Z. L. Hou H. M. Hu J. F. gu T. Hu Y. Hu G. S. Huang J. S. Huang X. T. Huang X. Z. Huang Z. L. guang T. Hussain W. Ikegami Andersson M Irshad q. ji q. P. ji X. B. ji X. L. ji X. S. jiang X. Y. jiang J. B. jiao Z. jiao D. P. jin S. jin Y. jin T. Johansson A. Julin N. Kalantar-Nayestanaki X. S. Kang M. Kavatsyuk B. C. Ke T. Khan A. Khoukaz P. Kiese R. Kliemt L. Koch O. B. Kolcu B. Kopf M. Kornicer M. Kuemmel M. Kuessner A. Kupsc M. Kurth W. Kiihn J. S. Lange M. Lara P. Larin L. Lavezzi S. Leiber H. Leithofi C. Li Cheng Li D. M. Li F. Li F. Y. Li G. Li H. B. Li H. J. Li J. C. Li J. W. Li K. J. Li Kang Li Ke Li Lei Li P. L. Li P. R. Li q. Y. Li T. Li W. D. Li W. G. Li X. L. Li X. N. Li X. q. Li Z. B. Li H. Liang Y. F. Liang Y. T. Liang G. R. Liao L. Z. Liao J. Libby C. X. Lin D. X. Lin S. Liu B. J. Liu C. X. Liu D. Liu D. Y. Liu F. H. Liu Fang Liu Feng Liu H. B. Liu H. L Liu H. M. Liu Huanhuan Liu Huihui Liu J. B. Liu J. Y. Liu K. Liu K. Y. Liu Ke Liu L. D. Liu q. Liu S B. Y. Zhang144 C. C. Zhang1D. H. Zhang1 H. H. Zhang45H. Y. Zhang144 J. Zhang151 J. L. Zhang65 J. Q. Zhang4 J. W. Zhang1 J. Y. Zhang1 J. Z. Zhang151 K. Zhang151 L. Zhang46 S. F. Zhang35 T. J. Zhang3975 X. Y. Zhang38 Y. Zhang4457 Y. H. Zhang144 Y. T. Zhang4557 Yang Zhang1 Yao Zhang1 Yu Zhang51 Z. H. Zhang6 Z. P. Zhan57Z. Y. Zhang64 G. Zhao1J. W. Zhao144 J. Y. Zhao151 J. Z. Zhao144 Lei Zhao4457 Ling Zhao1 M. G. Zhao35 Q. Zhao1 S. J. Zhao67 T. C. Zhao1 Y. B. Zhao144 Z. G. Zhao4457 A. Zhemehugov2869 B. Zheng58J. P. Zheng144 W. J. Zheng38 Y. H. Zheng51B. Zhong33 L. Zhou144 Q. Zhou151 X. Zhou64 X. K. Zhou4457 X. R. Zhou4457 X. Y. Zhou1 A. N. Zhu151 J. Zhu35 J. Zhu45 K. Zhu1 K. J. Zhu1 S. Zhu1 S. H. Zhu56 X. L. Zhu46 Y. C. Zhu4457 Y. S. Zhu151 Z. A. Zhu151 J. Zhuang144B. S. Zou1 J. H. Zou1 （ 1 Institute of High Energy Physics Beijing 100049 China Beihang University Beijing 100191 China Beijing Institute of Petrochemical Technology Beijing 102617 China Bochum Ruhr-University D-44780 Bochum Germany Carnegie Mellon University Pittsburgh Pennsylvania 15213 USA Central China Normal University Wuhan 430079 China China Center of Advanced Science and Technology Beijing 100190 China COMSATS Institute of Information Technology Lahore Defence Road Off Raiwind Road 54000 Lahore Pakistan G.I. Budker Institute of Nuclear Physics SB RAS （BINP） Novosibirsk 630090 Russia GSI Helmholtzcentre for Heavy Ion Research GmbH D-64291 Darmstadt Germany Guangxi Normal University Guilin 541004 China Guangxi University Nanning 530004 China Hangzhou Normal University Hangzhou 310036 China Helmholtz Institute Mainz JohannwJoachim-Becher-Weg 45 D-55099 Mainz Germany Henan Normal University Xinxiang 453007 China Henan University of Science and Technology Luoyang 471003 China Huangshan College Huangshan 245000 China Hunan Normal University Changsha 410081 China Hunan University Changsha 410082 China Indian Institute of Tec

To investigate the nature of the Ψ（3770） resonance and to measure the cross section for e^＋e^-→DD, a cross-section scan data sample, distributed among 41 center-of-mass energy points from 3.73 to 3.89 GeV, was taken with the BESIII detector operated at the BEPCII collider in the year 2010. By analyzing the large angle Bhabha scattering events, we measure the integrated luminosity of the data sample at each center-of-mass energy point. The total integrated luminosity of the data sample is 76.16±0.04±0.61 pb^-1, where the first uncertainty is statistical and the second systematic.

关键词： Bhabha scattering events integrated luminosity BESⅢ

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Reducing the anisotropy of a Brazilian disc generated in a bonded-particle model

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Acta Mechanica Sinica 2018年第4期34卷 716-727页

作者： q.Zhang X.P.Zhang P.q.ji The Key Laboratory of Safety for Geotechnical and Structural Engineering of Hubei Province School of Civil EngineeringWuhan University

The Brazilian test is a widely used method for determining the tensile strength of rocks and for calibrating parameters in bondedparticle models（BPMs）. In previous studies, the Brazilian disc has typically been trimmed from a compacted rectangular specimen. The present study shows that different tensile strength values are obtained depending on the compressive loading direction. Several measures are proposed to reduce the anisotropy of the disc. The results reveal that the anisotropy of the disc is significantly influenced by the compactibility of the specimen from which it is trimmed. A new method is proposed in which the Brazilian disc is directly generated with a particle boundary, effectively reducing the anisotropy. The stiffness（particle and bond） and strength（bond） of the boundary are set at less than and greater than those of the disc assembly, respectively,which significantly decreases the stress concentration at the boundary contacts and prevents breakage of the boundary particle bonds. This leads to a significant reduction in the anisotropy of the disc and the discreteness of the tensile strength. This method is more suitable for carrying out a realistic Brazilian test for homogeneous rock-like material in the BPM.

关键词： Bonded-particle model Brazilian disc Anisotropy Compactibility Particle boundary

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Self-Assembly and Photocatalysis of Mesoporous TiO2 Nanocrystal Clusters

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Nano Research 2011年第1期4卷 103-114页

作者： q.ao Zhang ji-Bong Joo Zhenda Lu Michael Dahl Diana q. L. Oliveira Miaomiao Ye Yadong Yin Department of Chemistry University of California Riverside California 92521 USA

Mesoporous nanocrystal clusters of anatase TiO2 with large surface area and enhanced photocatalytic activity have been successfully synthesized. The synthesis involves the self-assembly of hydrophobic TiO2 nanocrystals into submicron clusters, coating of these clusters with a silica layer, thermal treatment to remove organic ligands and improve the crystallinity of the clusters, and finally removing silica to expose the mesoporous catalysts. With the help of the silica coating, the clusters not only maintain their small grain size but also keep their mesoporous structure after calcination at high temperatures （with BET surface area as high as 277 m2/g）. The etching of SiO2 also results in the clusters having high dispersity in water. We have been able to identify the optimal calcination temperature to produce TiO2 nanocrystal clusters that possess both high crystallinity and large surface area, and therefore show excellent catalytic efficiency in the decomposition of organic molecules under illumination by UV light. Convenient doping with nitrogen converts these nanocrystal clusters into active photocatalysts in both visible light and natural sunlight. The strategy of forming well-defined mesoporous clusters using nanocrystals promises a versatile and useful method for designing photocatalysts with enhanced activity and stability.

关键词： Mesoporous titanium dioxide photocatalysis self-assembl~ nitrogen doping nanocrystals

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Fault Identification in Power Network Based on Deep Reinforcement Learning

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CSEE Journal of Power and Energy Systems 2022年第3期8卷 721-731页

作者： Mengshi Li Huanming Zhang Tianyao ji q.H.Wu School of Electric Power Engineering South China University of TechnologyGuangzhou510641China

With the integration of alternative energy and renewables,the issue of stability and resilience of the power network has received considerable *** basic necessity for fault diagnosis and isolation is fault identification and *** conventional intelligent fault identification method needs supervision,manual labelling of characteristics,and req.ires large amounts of labelled *** enhance the ability of intelligent methods and get rid of the dependence on a large amount of labelled data,a novel fault identification method based on deep reinforcement learning(DRL),which has not received enough attention in the field of fault identification,is investigated in this *** proposed method uses different faults as parameters of the model to expand the scope of fault *** addition,the DRL algorithm can intelligently modify the fault parameters according to the observations obtained from the power network environment,rather than req.iring manual and mechanical tuning of *** methodology was tested on the IEEE 14 bus for several scenarios and the performance of the proposed method was compared with that of population-based optimization methods and supervised learning *** obtained results have confirmed the feasibility and effectiveness of the proposed method.

关键词： Artificial intelligence deep q.network deep reinforcement learning fault diagnosis fault identification parameter identification power network

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A Hybrid Signal Processing Method Combining Mathematical Morphology and Walsh Theory for Power q.ality Disturbance Detection and Classification

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CSEE Journal of Power and Energy Systems 2024年第2期10卷 584-592页

作者： Zhi Ding Tianyao ji Mengshi Li q.H.Wu School of Electric Power Engineering South China University of Technology(SCUT)Guangzhou 510641China

In this paper, a novel signal processing method combining mathematical morphology (MM) and Walsh theory is proposed, which uses Walsh functions to control the structuring element (SE) and MM operators. Based on the Walsh-MM method, a scheme for power q.ality disturbances detection and classification is developed, which involves three steps: denoising, feature extraction and morphological clustering. First, various evolution rules of Walsh function are used to generate groups of SEs for the multiscale Walsh-ordered morphological operation, so the original signal can be denoised. Next, the fundamental wave of the denoised signal is suppressed by Hadamard matrix;thus, disturbances can be extracted. Finally, the Walsh power spectrum of the waveform extracted in the previous step is calculated, and the parameters of which are taken by morphological clustering to classify the disturbances. Simulation results reveal the proposed scheme can effectively detect and classify disturbances, and the Walsh-MM method is less affected by noise and only involves simple calculation, which has a potential to be implemented in hardware and more suitable for real-time application.

关键词： Hadamard matrix mathematical morphology morphological clustering power q.ality disturbance Walsh theory

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Fault Location in Distribution System Using Convolutional Neural Network Based on Domain Transformation

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CSEE Journal of Power and Energy Systems 2021年第3期7卷 472-484页

作者： Yang Yu Mengshi Li Tianyao ji q.H.Wu School of Electric Power Engineering South China University of TechnologyGuangzhou 510640China

Distribution lines are integral parts of the modern power system,which can affect the security and stability of power supply *** effective power system protection scheme should be able to detect all occurring faults as soon as *** are two tasks in fault *** is the fault classification,where high accuracy rates have already ***,this paper focuses on the other task,*** *** by Fourier transform,this paper proposes an online data-driven method,which transforms signals from time domain to image domain through signal-to-image(SIG)algorithm and then process the transformed images with framework based on convolutional neural network(CNN).On the one hand,we can extract more crucial characteristic and information from image *** the other hand,the CNN-based structure is much smaller than *** needs less memory space and would be easier to be transplanted to hardware ***,the proposed algorithm does not req.ire synchronous *** numerical comparison shows that the proposed SIG-CNN fault location model achieves robust and accurate results compared with other data-driven algorithms.

关键词： Convolutional neural network distribution lines domain transformation fault location

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Stochastic Economic Dispatch Considering the Dependence of Multiple Wind Farms Using Multivariate Gaussian Kernel Copula

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CSEE Journal of Power and Energy Systems 2022年第5期8卷 1352-1362页

作者： Yantai Lin Tianyao ji Yuzi jiang q.H.Wu School of Electric Power Engineering South China University of TechnologyGuangzhou 510641China

Wind farms usually cluster in areas with abundant wind ***,spatial dependence of wind speeds among nearby wind farms should be taken into account when modeling a power system with large-scale wind power *** paper proposes a novel non-parametric copula method,multivariate Gaussian kernel copula(MGKC),to describe the dependence structure of wind speeds among multiple wind *** speed scenarios considering the dependence among different wind farms are sampled from the MGKC by the q.asi-Monte Carlo(q.C)method,so as to solve the stochastic economic dispatch(SED)problem,for which an improved meanvariance(MV)model is established,which targets at minimizing the expectation and risk of fuel cost *** this model,confidence interval is applied in the wind speed to obtain more practical dispatch solutions by excluding extreme scenarios,for which the q.antile-copula is proposed to construct the confidence interval *** studies are carried out on a modified IEEE 30-bus power system with wind farms integrated in two areas,and the results prove the superiority of the MGKC in formulating the dependence among different wind farms and the superiority of the improved MV model based on q.antilecopula in determining a better dispatch solution.

关键词： Multivariate Gaussian kernel copula q.asi-Monte Carlo q.antile-copula stochastic economic dispatch

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BFA BASED NEURAL NETWORK FOR IMAGE COMPRESSION

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Journal of Electronics(China) 2008年第3期25卷 405-408页

作者： Chu Ying Mi Hua ji Zhen Shao Zibo q. H. Wu TI DSPs Lab Faculty of Information Engineering Shenzhen University Shenzhen 518060 China Department of Electronic ＆ Electrical Engineering University College London London WCIE 7JE UK Department of Electrical Engineering ＆ Electronics The University of Liverpool Liverpool L69 3GJ Ut

A novel Bacterial Foraging Algorithm (BFA) based neural network is presented for image compression. To improve the q.ality of the decompressed images, the concepts of reproduction, elimination and dispersal in BFA are firstly introduced into neural network in the proposed algorithm. Extensive experiments are conducted on standard testing images and the results show that the pro- posed method can improve the q.ality of the reconstructed images significantly.

关键词： Bacterial Foraging Algorithm (BFA) Artificial Neural Network (ANN) Back Propagation(BP) Image compression

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A novel friction stir diffusion bonding process using convex-vortex pin tools

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Journal of Materials Science & Technology 2020年第13期47卷 23-30页

作者： S.D.ji q.Wen Z.W.Li College of Aerospace Engineering Shenyang Aerospace UniversityShenyang 110136China State Key Laboratory of Advanced Welding and Joining Harbin Institute of TechnologyHarbin 150001China

In this work,a novel friction stir diffusion bonding(FSDB)method was proposed to eliminate hook when joining thin *** tools,with several convex-vortex pins were innovatively designed to improve the diffusion bonding effect of the lap *** showed that sound joints,excellently bonded at the lap interface and without hook,were *** lap interfaces on the joints welded using the T-tool and F-tool almost remained intact,thereby indicating that they were formed by diffusion *** material flow behavior was also simulated by FLUENT software and the results showed that the material flow along thickness was significantly enhanced by increasing the pin *** joint welded by the T-tool showed shear fracture *** bonding was formed at the lap interface when using the F-tool and thus the joint showed tensile fracture.

关键词： Friction stir diffusion bonding Convex-vortex pin Hook Material flow Failure

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Two-Stage Multi-Objective Optimization and Decision-Making Method for Integrated Energy System Under Wind Generation Disturbances

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CSEE Journal of Power and Energy Systems 2024年第6期10卷 2564-2576页

作者： Bin Deng Xiaosheng Xu Mengshi Li Tianyao ji q. H. Wu School of Electric Power Engineering South China University of Technology Guangzhou China

Although integrated energy systems (IES) are currently modest in size, their scheduling faces strong challenges, stemming from both wind generation disturbances and the system's complexity, including intrinsic heterogeneity and pronounced non-linearity. For this reason, a two-stage algorithm called the Multi-Objective Group Search Optimizer with Pre-Exploration (MOGSOPE) is proposed to efficiently achieve the optimal solution under wind generation disturbances. The optimizer has an embedded trainable surrogate model, Deep Neural Networks (DNNs), to explore the common features of the multi-scenario search space in advance, guiding the population toward a more efficient search in each scenario. Furthermore, a multi-scenario Multi-Attribute Decision Making (MADM) approach is proposed to make the final decision from all alternatives in different wind scenarios. It reflects not only the decision-maker's (DM) interests in other indicators of IES but also their risk preference for wind generation disturbances. A case study conducted in Barry Island shows the superior convergence and diversity of MOGSOPE in comparison to other optimization algorithms. With respect to numerical performance metrics HV, IGD, and SI, the proposed optimizer exhibits improvements of 3.1036%, 4.8740%, and 4.2443% over MOGSO, and 4.2435%, 6.2479%, and 52.9230% over NSGAII, respectively. What's more, the effectiveness of the multi-scenario MADM in making final decisions under uncertainty is demonstrated, particularly in optimal scheduling of IES under wind generation disturbances.

关键词： Optimization Heating systems Uncertainty Resistance heating Temperature measurement Stochastic processes Generators

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