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International Conference on Control, Mechatronics and Automation (ICCMA)

作者： Sarah B. Fahmy Silvana E. Guirguis Omar M. Shehata Elsayed I. Morgan Multi-Robot Systems (MRS) Research Group Cairo Egypt Faculty of Engineering and Materials Science German University in Cairo Egypt Faculty of Engineering Ain Shams University Cairo Egypt

ISBN: (数字)9781728192109

ISBN: (纸本)9781728192116

The world is shifting from conventional vehicles to Electric Vehicles (EVs) since their operation has a low cost and is environmentally friendly. However, there are various obstacles affecting EVs development, such as the vehicle range, charging process and infrastructure, etc. This paper focuses on the charging infrastructure of EVs investigating their optimal charging cycles in a Smart-Grid environment in order to improve the profitability of charging stations and decrease the high energy demand from the grid. Accordingly, a charging station was studied equipped with Photo-voltaic rooftop allowing vehicles to sell energy to the grid forming a Vehicle-to-Grid strategy. A priority-based algorithm was implemented to schedule EVs charging/discharging. Two optimization techniques; Particle Swarm Optimization (PSO) and Genetic Algorithm (GA), were used to optimize the installation and operation of the station to maximize the station's profit. Accordingly, several cases were studied to analyze the influence of the mentioned capabilities. The results revealed that the maximum parking profit was obtained by utilizing the photo-voltaic rooftop with vehicle-to-Grid capability. Moreover, the performance of the two optimization techniques is compared showing similar results regarding the optimization effectiveness. However, GA takes longer computational time.

关键词： State of charge Vehicle-to-grid Optimization Batteries Particle swarm optimization Genetic algorithms Electric vehicle charging

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The CMS Statistical Analysis and Combination Tool: Combine

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Computing and Software for Big science 2024年第1期8卷 1-45页

作者： Zhokin, A. Zhizhin, I. Zarubin, A. Yuldashev, B.S. Voytishin, N. Vorotnikov, G. Vorobyev, A. Volkov, P. Uzunian, A. Uvarov, L. Toropin, A. Tlisova, I. Teryaev, O. Terkulov, A. Tcherniaev, E. Sulimov, V. Sosnov, D. Smirnov, V. Slabospitskii, S. Skovpen, Y. Shulha, S. Shmatov, S. Shalaev, V. Savrin, V. Savina, M. Radchenko, O. Popov, V. Polikarpov, S. Perfilov, M. Perelygin, V. Palichik, V. Oreshkin, V. Obraztsov, S. Nikitenko, A. Murzin, V. Matveev, V. Malakhov, A. Makarenko, V. Lychkovskaya, N. Levchenko, P. Lanev, A. Krasnikov, N. Kozyrev, A. Korenkov, V. Konstantinov, D. Kodolova, O. Klyukhin, V. Kirsanov, M. Kirpichnikov, D. Kirakosyan, M. Kim, V. Karneyeu, A. Karjavine, V. Kachanov, V. Ivanov, Y. Gribushin, A. Gorbunov, I. Golutvin, I. Golubev, N. Golovtcov, V. Gninenko, S. Gavrilov, V. Gavrilov, G. Dudko, L. Dubinin, M. Druzhkin, D. Dimova, T. Dermenev, A. Chistov, R. Chekhovsky, V. Chadeeva, M. Bunichev, V. Budkouski, D. Borshch, V. Boos, E. Blinov, V. Babaev, A. Azarkin, M. Aushev, T. Andreev, Yu. Andreev, V. Alexakhin, V. Afanasiev, S. Warden, A. Vetens, W. Tsoi, H.F. Teague, D. Smith, W.H. Sharma, V. Shang, V. Savin, A. Pinna, D. Pétré, L. Parida, G. Mondal, S. Mohammadi, A. Mallampalli, A. Sreekala, J. Madhusudanan Loveless, R. Lanaro, A. Koraka, C.K. Herve, A. Herndon, M. He, H. Galloni, C. Everaerts, P. De Bruyn, I. Dasu, S. Bose, T. Black, K. Banerjee, S. Aravind, A. Karchin, P.E. Bhattacharya, S. Neu, C. Ledovskoy, A. Hirosky, R. Hakala, J. Cox, B. Cardwell, B. Viinikainen, J. Velkovska, J. Tuo, S. Sheldon, P. Romeo, F. Melo, A. Elayavalli, R. Kunnawalkam Johns, W. Gurrola, A. Greene, S. Chen, Y. Appelt, E. Volobouev, I. Peltola, T. Mankel, A. Lee, S.W. Lamichhane, K. Kazhykarim, Y. Hussain, A. Hegde, V. Gogate, N. Damgov, J. Akchurin, N. Safonov, A. Rathjens, D. Overton, D. Mueller, R. Luo, S. Kim, H. Kamon, T. Huang, T. Gilmore, J. Eusebi, R. Bouhali, O. Akhter, T. Ahmad, M. Aebi, D. Spanier, S. Nibigira, E. Lee, L. Karunarathna, N. Kanuganti, A.R. Holmes, T. Higginbotham, S. Fiorendi, S. Delannoy Yerevan Physics Institute Yerevan Armenia Institut für Hochenergiephysik Vienna Austria Universiteit Antwerpen Antwerpen Belgium Vrije Universiteit Brussel Brussel Belgium Université Libre de Bruxelles Bruxelles Belgium Ghent University Ghent Belgium Université Catholique de Louvain Louvain-la-Neuve Belgium Centro Brasileiro de Pesquisas Fisicas Rio de Janeiro Brazil Universidade do Estado do Rio de Janeiro Rio de Janeiro Brazil Universidade Estadual Paulista Universidade Federal do ABC São Paulo Brazil Institute for Nuclear Research and Nuclear Energy Bulgarian Academy of Sciences Sofia Bulgaria University of Sofia Sofia Bulgaria Instituto De Alta Investigación Universidad de Tarapacá Casilla 7 D Arica Chile Beihang University Beijing China Department of Physics Tsinghua University Beijing China Institute of High Energy Physics Beijing China State Key Laboratory of Nuclear Physics and Technology Peking University Beijing China Guangdong Provincial Key Laboratory of Nuclear Science and Guangdong-Hong Kong Joint Laboratory of Quantum Matter South China Normal University Guangzhou China Sun Yat-Sen University Guangzhou China University of Science and Technology of China Hefei China Nanjing Normal University Nanjing China Key Laboratory of Nuclear Physics and Ion-beam Application (MOE) Institute of Modern Physics Fudan University Shanghai China Zhejiang University Hangzhou Zhejiang China Universidad de Los Andes Bogota Venezuela Universidad de Antioquia Medellin Colombia Faculty of Electrical Engineering Mechanical Engineering and Naval Architecture University of Split Split Croatia Faculty of Science University of Split Split Croatia Institute Rudjer Boskovic Zagreb Croatia University of Cyprus Nicosia Cyprus Charles University Prague Czech Republic Universidad San Francisco de Quito Quito Ecuador Egyptian Network of High Energy Physics Academy of Scientific Research and Technology of the Arab Republic of Egypt Cairo Egypt Center for High

This paper describes the Combine software package used for statistical analyses by the CMS Collaboration. The package, originally designed to perform searches for a Higgs boson and the combined analysis of those searches, has evolved to become the statistical analysis tool presently used in the majority of measurements and searches performed by the CMS Collaboration. It is not specific to the CMS experiment, and this paper is intended to serve as a reference for users outside of the CMS Collaboration, providing an outline of the most salient features and capabilities. Readers are provided with the possibility to run Combine and reproduce examples provided in this paper using a publicly available container image. Since the package is constantly evolving to meet the demands of ever-increasing data sets and analysis sophistication, this paper cannot cover all details of Combine. However, the online documentation referenced within this paper provides an up-to-date and complete user guide. © CERN, for the benefit of the CMS Collaboration 2024.

关键词： CMS Combine Higgs Higgs boson Statistics

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Decentralized Intersection Management of Autonomous Vehicles Using Nonlinear MPC

Decentralized Intersection Management of Autonomous Vehicles...

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Novel Intelligent and Leading Emerging sciences Conference (NILES)

作者： Hussein Ali Samaa Khaled Omar M. Shehata Elsayed I. Morgan Multi-Robot Systems (MRS) Research Group Cairo Egypt Faculty of Engineering and Materials Science German University in Cairo Egypt Faculty of Engineering Ain Shams University Cairo Egypt

ISBN: (数字)9781728182261

ISBN: (纸本)9781728182278

The rapid population growth and increase in vehicle numbers over the last few decades have caused traffic congestion worldwide, as intersections significantly impact the efficiency of traffic networks in urban areas, this paper focuses on intelligent traffic management at intersections by integrating the technologies of Intelligent Transportation Systems (ITS) and Autonomous Vehicles (AV). In this framework, a physical model represents each vehicle taking into account the dynamic limits. A decentralized Nonlinear Model Predictive Control (NMPC) is proposed to help coordinate the traffic flow of the AV at the intersection. The controller solves a quadratic cost function for the vehicle to ensure a smooth trajectory and minimum energy consumption, while avoiding collisions that is guaranteed using linear constraints and two developed priority assignment methods, Predicted Arrival Time (PAT) and First-Come First-Served (FCFS). The two methods are tested using a developed simulation environment on MATLAB/Simulink and the results are compared, the predicted arrival time method outperformed the FCFS method.

关键词： Vehicle dynamics Engines Mathematical model Force Aerodynamics Wheels Torque

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A New Pairwise Deep Learning Feature For Environmental Microorganism Image Analysis

arXiv

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arXiv 2021年

作者： Kulwa, Frank Li, Chen Zhang, Jinghua Shirahama, Kimiaki Kosov, Sergey Zhao, Xin Sun, Hongzan Jiang, Tao Grzegorzek, Marcin Microscopic Image and Medical Image Analysis Group College of Medicine and Biological Information Engineering Northeastern University Shenyang110169 China Faculty of Science and Engineering Kindai University Kindai Japan Faculty of Data Engineering Jacobs University Bremen Bremen Germany Shengjing Hospital of China Medical University Shenyang China Control Engineering College Chengdu University of Information Technology Chengdu China Institute for Medical Informatics University of Lubeck Ratzeburger Allee 160 Lubeck23538 Germany

Environmental microorganism (EM) offers a high-efficient, harmless, and low-cost solution to environmental pollution. They are used in sanitation, monitoring, and decomposition of environmental pollutants. However, this depends on the proper identification of suitable microorganisms. In order to fasten, low the cost, increase consistency and accuracy of identification, we propose the novel pairwise deep learning features to analyze microorganisms. The pairwise deep learning features technique combines the capability of handcrafted and deep learning features. In this technique we, leverage the Shi and Tomasi interest points by extracting deep learning features from patches which are centered at interest points’ locations. Then, to increase the number of potential features that have intermediate spatial characteristics between nearby interest points, we use Delaunay triangulation theorem and straight-line geometric theorem to pair the nearby deep learning features. The potential of pairwise features is justified on the classification of EMs using SVMs, k-NN, and Random Forest classifier. The pairwise features obtain outstanding results of 99.17%, 91.34%, 91.32%, 91.48%, and 99.56%, which are the increase of about 5.95%, 62.40%, 62.37%, 61.84%, and 3.23% in accuracy, F1-score, recall, precision, and specificity respectively, compared to non-paired deep learning features. Copyright © 2021, The Authors. All rights reserved.

关键词： Microorganisms

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Behavior Evaluation of Vehicle Platoon via Different Fuzzy-X Tuned Controllers

Behavior Evaluation of Vehicle Platoon via Different Fuzzy-X...

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International Conference on Control, Mechatronics and Automation (ICCMA)

作者： Mohamed A. Olwan AbdELRahman A. Mostafa Youssef M. AbdELAty Dalia M. Mahfouz Omar M. Shehata Elsayed I. Morgan Multi-Robot Systems (MRS) Research Group Cairo Egypt Faculty of Engineering and Materials Science German University in Cairo Egypt Faculty of Engineering Ain Shams University Cairo Egypt

ISBN: (数字)9781728192109

ISBN: (纸本)9781728192116

Over the past few decades, Intelligent Transportation System (ITS) has become a topic of considerable interest for transportation development. One of the significant applications of ITS is vehicle platooning, which is described as a string of fully or partly automated vehicles traveling together at closely controlled inter-vehicular distances while preserving a set speed without violating safety restrictions. Control studies of a platoon of vehicles moving in a single dimension has recently attracted extensive research interest. The platoon control challenge relies on the adaptation of the predefined vehicle's speed while keeping a safe gap between each two successive vehicles. This study investigates intelligent unidirectional decentralized control approach based on Fuzzy Logic Control (FLC). The performance of the controller is tuned using three different techniques through the hybridization of FLC with two approaches; Genetic Algorithm (GA) and Proportional-Integral-Derivative (PID) and the adaptation of FLC with Neural Networks (NN) forming Fuzzy-X tuned controllers to control the follower vehicles to achieve their objective. In order to accomplish the goal of the study, each vehicle is represented through a longitudinal vehicle dynamic model. A reference velocity trajectory is built for the vehicles to follow. Simulations are conducted to evaluate the performance of each controller in terms of spacing error convergence and desired velocity tracking. Results show that the desired tracking performance and gap control are achieved by all the controllers with certain limitations that are discussed through out the study.

关键词： Torque Genetic algorithms Mathematical model Drag Vehicle dynamics Tuning Roads

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Measurement of the double-differential inclusive jet cross section in proton-proton collisions at s = 5.02 TeV

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Journal of High Energy Physics 2025年第1期2025卷 1-49页

作者： Hayrapetyan, A. Tumasyan, A. Adam, W. Andrejkovic, J.W. Bergauer, T. Chatterjee, S. Damanakis, K. Dragicevic, M. Escalante Del Valle, A. Hussain, P.S. Jeitler, M. Krammer, N. Lechner, L. Liko, D. Mikulec, I. Schieck, J. Schöfbeck, R. Schwarz, D. Sonawane, M. Templ, S. Waltenberger, W. Wulz, C.-E. Darwish, M.R. Janssen, T. Kello, T. Van Mechelen, P. Bols, E.S. D’Hondt, J. De Moor, A. Delcourt, M. El Faham, H. Lowette, S. Makarenko, I. Morton, A. Müller, D. Sahasransu, A.R. Tavernier, S. Van Putte, S. Vannerom, D. Clerbaux, B. Dansana, S. De Lentdecker, G. Favart, L. Hohov, D. Jaramillo, J. Lee, K. Mahdavikhorrami, M. Malara, A. Paredes, S. Pétré, L. Postiau, N. Thomas, L. Vanden Bemden, M. Vander Velde, C. Vanlaer, P. De Coen, M. Dobur, D. Knolle, J. Lambrecht, L. Mestdach, G. Rendón, C. Samalan, A. Skovpen, K. Tytgat, M. Van Den Bossche, N. Vermassen, B. Wezenbeek, L. Benecke, A. Bruno, G. Bury, F. Caputo, C. Delaere, C. Donertas, I.S. Giammanco, A. Jaffel, K. Jain, Sa. Lemaitre, V. Lidrych, J. Mastrapasqua, P. Mondal, K. Tran, T.T. Vischia, P. Wertz, S. Alves, G.A. Coelho, E. Hensel, C. Moraes, A. Rebello Teles, P. Aldá Júnior, W.L. Alves Gallo Pereira, M. Barroso Ferreira Filho, M. Brandao Malbouisson, H. Carvalho, W. Chinellato, J. Da Costa, E.M. Da Silveira, G.G. De Jesus Damiao, D. Dos Santos Sousa, V. Fonseca De Souza, S. Martins, J. Mora Herrera, C. Mota Amarilo, K. Mundim, L. Nogima, H. Santoro, A. Silva Do Amaral, S.M. Sznajder, A. Thiel, M. Vilela Pereira, A. Bernardes, C.A. Calligaris, L. Fernandez Perez Tomei, T.R. Gregores, E.M. Mercadante, P.G. Novaes, S.F. Orzari, B. Padula, Sandra S. Aleksandrov, A. Antchev, G. Hadjiiska, R. Iaydjiev, P. Misheva, M. Shopova, M. Sultanov, G. Dimitrov, A. Ivanov, T. Litov, L. Pavlov, B. Petkov, P. Petrov, A. Shumka, E. Keshri, S. Thakur, S. Cheng, T. Guo, Q. Javaid, T. Mittal, M. Yuan, L. Bauer, G. Hu, Z. Lezki, S. Yi, K. Chen, G.M. Chen, H.S. Chen, M. Iemmi, F. Jiang, C.H. Kapoor, A. Liao, H. Liu, Z.-A. Monti, F. Sharma, R. Song, J.N. Tao, J. Wang, J. Zhang, H. Aga Yerevan Physics Institute Yerevan Armenia Institut für Hochenergiephysik Vienna Austria Universiteit Antwerpen Antwerpen Belgium Vrije Universiteit Brussel Brussel Belgium Université Libre de Bruxelles Bruxelles Belgium Ghent University Ghent Belgium Université Catholique de Louvain Louvain-la-Neuve Belgium Centro Brasileiro de Pesquisas Fisicas Rio de Janeiro Brazil Universidade do Estado do Rio de Janeiro Rio de Janeiro Brazil Universidade Estadual Paulista Universidade Federal do ABC São Paulo Brazil Institute for Nuclear Research and Nuclear Energy Bulgarian Academy of Sciences Sofia Bulgaria University of Sofia Sofia Bulgaria Instituto De Alta Investigación Universidad de Tarapacá Casilla 7 D Arica Chile Beihang University Beijing China Department of Physics Tsinghua University Beijing China Institute of High Energy Physics Beijing China State Key Laboratory of Nuclear Physics and Technology Peking University Beijing China Sun Yat-Sen University Guangzhou China University of Science and Technology of China Hefei China Institute of Modern Physics and Key Laboratory of Nuclear Physics and Ion-beam Application (MOE) — Fudan University Shanghai China Zhejiang University Zhejiang Hangzhou China Universidad de Los Andes Bogota Venezuela Universidad de Antioquia Medellin Colombia University of Split Faculty of Electrical Engineering Mechanical Engineering and Naval Architecture Split Croatia University of Split Faculty of Science Split Croatia Institute Rudjer Boskovic Zagreb Croatia University of Cyprus Nicosia Cyprus Charles University Prague Czech Republic Escuela Politecnica Nacional Quito Ecuador Universidad San Francisco de Quito Quito Ecuador Academy of Scientific Research and Technology of the Arab Republic of Egypt Egyptian Network of High Energy Physics Cairo Egypt Center for High Energy Physics (CHEP-FU) Fayoum University El-Fayoum Egypt National Institute of Chemical Physics and Biophysics Tallinn Estonia Department of Physics

The inclusive jet cross section is measured as a function of jet transverse momentum pT and rapidity y. The measurement is performed using proton-proton collision data at s = 5.02 TeV, recorded by the CMS experiment a... 详细信息

The inclusive jet cross section is measured as a function of jet transverse momentum p_T and rapidity y. The measurement is performed using proton-proton collision data at s = 5.02 TeV, recorded by the CMS experiment at the LHC, corresponding to an integrated luminosity of 27.4 pb⁻¹. The jets are reconstructed with the anti-k_T algorithm using a distance parameter of R = 0.4, within the rapidity interval |y| < 2, and across the kinematic range 0.06 < p_T< 1 TeV. The jet cross section is unfolded from detector to particle level using the determined jet response and resolution. The results are compared to predictions of perturbative quantum chromodynamics, calculated at both next-to-leading order and next-to-next-to-leading order. The predictions are corrected for nonperturbative effects, and presented for a variety of parton distribution functions and choices of the renormalization/factorization scales and the strong coupling α_S. © The Author(s) 2025.

关键词： Hadron-Hadron Scattering Jet Physics QCD

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3D Head Pointer: A manipulation method that enables the spatial localization for a wearable robot arm by head bobbing

3D Head Pointer: A manipulation method that enables the spat...

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IEEE International Symposium on Measurement and Control in robotics (ISMCR)

作者： Joy Oh Kozo Ando Shuhei Iizuka Lena Guinot Fumihiro Kato Hiroyasu Iwata Graduate School of Creative Science and Engineering Waseda University Tokyo Japan Waseda University Global Robot Academia Institute Tokyo Japan Faculty of Science and Engineering Waseda University Tokyo Japan

ISBN: (数字)9781665404792

ISBN: (纸本)9781665404808

This paper introduces a new type of interface “3D Head Pointer” for operation of a wearable robot arm in 3D space. The developed system is aimed at assisting the user in execution of daily chores (e.g. opening an umbrella, stirring a pot) while concurrently operating wearable robot arm. Previous researches have demonstrated the difficulty in simultaneously controlling a robot arm while uninterruptedly performing another task with one's own hands. The proposed method uses a combination of the orientation of the face and head bobbing (a forward and backward movement of the head) to manipulate spatial localization in a polar coordinate system. In a Virtual Reality (VR) environment, the proposed system and its efficiency at adapting its operating magnification (IDF) by relying on the incremental changes in head bobbing were evaluated through measurement of the instructional accuracy and time demand. Experiments were conducted with 12 participants. Results displayed an accuracy of 1.3 cm with a 4 seconds time span necessary to communicate an instruction. These results, along with the confirmed ability of an individual to multi-task while operating, suggested the effectiveness of the operation method and IDF.

关键词： Xenon 6G mobile communication

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Autoencoder-Based Anomaly Detection System for Online Data Quality Monitoring of the CMS Electromagnetic Calorimeter

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Computing and Software for Big science 2024年第1期8卷 1-19页

作者： Zygal, L. Zhu, R.Y. Zhang, L. Zghiche, A. Zabi, A. Yu, S.S. Wadud, M.A. Wang, J. Wang, C. Wamorkar, T. Wachirapusitanand, V. Vlassov, E. Verma, P. Venditti, R. Varela, J. Vagnerini, A. Valsecchi, D. Usai, E. Tsai, L.S. Tramontano, R. Tornago, M. Tiwari, P.C. Tishelman-Charny, A. Tao, J. Succar, M. Steggemann, J. Steen, A. Stachon, K. Soffi, L. Smith, V.J. Skovpen, Y. Singovsky, A. Singla, A. Simkina, P. Shepherd-Themistocleous, C.H. Shahzad, M.A. Sculac, T. Sculac, A. Schroeder, N. Schneider, M. Sarkar, U. Saradhy, R. Santanastasio, F. Sandever, C. Sanchez, A. Sancar, O. Salvi, G. Rusack, R. Rovere, M. Rovelli, C. Rosowsky, A. Romanteau, T. Rogan, C. Riti, F. Reis, T. Teles, P. Rebello Razis, P.A. Rasteiro Da Silva, J.C. Rahatlou, S. Ragazzi, S. Quaranta, C. Prova, P.R. Pinolini, B.S. Pigazzini, S. Petyt, D. Pettinacci, V. Petraityte, E. Petkovic, A. Pauss, F. Paulini, M. Pastrone, N. Park, K. Paramatti, R. Pandolfi, F. Papagiannis, D. Paganis, E. Ovtin, I. Orlandi, F. Orimoto, T. Organtini, G. Obertino, M.M. Nigamova, A. Nicolaou, C. Nessi-Tedaldi, F. Mudholkar, T. Mousa, J. Moortgat, F. Monti, F. Monteno, M. Milosevic, J. Milenovic, P. Mijuskovic, J. Mijic, M. Meridiani, P. Mazza, G. Matveev, V. Massironi, A. Mascellani, A. Marzocchi, B. Martin, L. Marini, A.C. Marinelli, N. Marchese, L. Manzoni, R.A. Mandrik, P. Malcles, J. Maeshima, K. Lyon, A.M. Lutton, L. Lustermann, W. Lu, R.S. Loukas, N. Longo, E. Lin, W. Liang, Z. Li, Y.Y. Lelas, D. Legger, F. Ledovskoy, A. Lecoq, P. Lavizzari, G. Lambrecht, L. Kuo, C.M. Krishna, A. Kovac, M. Konstantinov, D. Kirilovas, A. King, J. Khurana, R. Kardapoltsev, L. Kao, Y.-W. Kaadze, K. Jourd‘hui, E. Joshi, B.M. Jimènez, R. Jessop, C. Javaid, T. Jain, Sh. Ingram, Q. Hou, X. Ho, K.W. Hirosky, R. Heath, H.F. Hashmi, R. Härringer, N. Harilal, A. de Monchenault, G. Hamel Hakala, J. Gras, P. Govoni, P. Golubev, N. Godinovic, N. Goadhouse, S. Gninenko, S. Ghose, P. Ghezzi, A. Gascon, S. Ganjour, S. Galli, M. Gajownik, J. Gadek, T. Funk, W. Frahm, E. Fiori, F. Finco, L. Ferri, F. Me Northeastern University Boston MA United States Florida State University Tallahassee FL United States VINCA Institute of Nuclear Sciences University of Belgrade Belgrade Serbia College Station Texas A & M University TX United States IRFU CEA Université Paris-Saclay Gif-sur-Yvette France Carnegie Mellon University Pittsburgh PA United States INFN Sezione di Torino Torino Italy Università di Torino Torino Italy Università del Piemonte Orientale Novara Italy CERN European Organization for Nuclear Research Geneva Switzerland Massachusetts Institute of Technology Cambridge MA United States University of Notre Dame Notre Dame IN United States INFN Sezione di Roma Roma Italy Sapienza Università di Roma Roma Italy Laboratório de Instrumentação e Física Experimental de Partículas Lisboa Portugal Rutherford Appleton Laboratory Didcot United Kingdom Authors affiliated with an institute or an international laboratory covered by a cooperation agreement with CERN Geneva Switzerland California Institute of Technology Pasadena CA United States INFN Sezione di Milano-Bicocca Milano Italy Università di Milano-Bicocca Milano Italy University of Minnesota Minneapolis MN United States Indian Institute of Technology Madras Madras India Panjab University Chandigarh India National Taiwan University (NTU) Taipei Taiwan University of Virginia Charlottesville VA United States ETH Zurich-Institute for Particle Physics and Astrophysics (IPA) Zurich Switzerland Università di Trieste Trieste Italy Institut de Physique des 1 Infinis de Lyon (IP2I) Villeurbanne France INFN Sezione di Bari Bari Italy Università di Bari Bari Italy Universidad de Oviedo Instituto Universitario de Ciencias y Tecnologías Espaciales de Asturias (ICTEA) Oviedo Spain University of Nebraska-Lincoln Lincoln NE United States INFN Sezione di Pisa Pisa Italy Università di Pisa Pisa Italy Scuola Normale Superiore di Pisa Pisa Italy Faculty of Electrical Engineering Mechanical Engineering and

The CMS detector is a general-purpose apparatus that detects high-energy collisions produced at the LHC. Online data quality monitoring of the CMS electromagnetic calorimeter is a vital operational tool that allows detector experts to quickly identify, localize, and diagnose a broad range of detector issues that could affect the quality of physics data. A real-time autoencoder-based anomaly detection system using semi-supervised machine learning is presented enabling the detection of anomalies in the CMS electromagnetic calorimeter data. A novel method is introduced which maximizes the anomaly detection performance by exploiting the time-dependent evolution of anomalies as well as spatial variations in the detector response. The autoencoder-based system is able to efficiently detect anomalies, while maintaining a very low false discovery rate. The performance of the system is validated with anomalies found in 2018 and 2022 LHC collision data. In addition, the first results from deploying the autoencoder-based system in the CMS online data quality monitoring workflow during the beginning of Run 3 of the LHC are presented, showing its ability to detect issues missed by the existing system. © The Author(s) 2024.

关键词： Anomaly detection Autoencoders Calorimeter Data quality monitoring Machine learning

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Research and Implementation of a Skyline Query Method for Hidden Web Database 16th

Research and Implementation of a Skyline Query Method for Hi...

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16th Web Information Systems and Applications Conference, WISA 2019

作者： Li, Zhengyu Li, Gui Han, Ziyang Sun, Ping Cao, Keyan Faculty of Information and Control Engineering Shenyang Jianzhu University Shenyang110168 China School of Information Science and Engineering Northeastern University Shenyang110004 China

ISBN: (纸本)9783030309510

Skyline discovery from a hidden web database can enable a wide variety of innovative applications over one or multiple web databases in web information integration area. Due to the finite number of web accesses one can issue through per-IP-address or per-API-key, no more than k tuples of all matching tuples one top-k query can return, the restricted web interface types, etc., it is difficult to utilize traditional database Skyline computation methods for discovering Skyline of a hidden Web database. In the paper, we first analyze the key intersecting characters of skyline tuples by parallel coordinate system, and next define a search decomposition tree for searching skyline tuples of a hidden web database, and then prove that the tree is guaranteed to discover all skyline tuples. In the end, we put forward heuristic algorithms for getting skyline tuples of a hidden web database based on the mixture attributes. Theoretical analysis and real-world online and offline experiments demonstrate the effectiveness of our proposed methods and the superiority over baseline solutions. © 2019, Springer Nature Switzerland AG.

关键词： Database systems

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Search for W′ bosons decaying to a top and a bottom quark in leptonic final states in proton-proton collisions at s = 13 TeV

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Journal of High Energy Physics 2024年第5期2024卷 46页

作者： Hayrapetyan, A. Tumasyan, A. Adam, W. Andrejkovic, J.W. Bergauer, T. Chatterjee, S. Damanakis, K. Dragicevic, M. Escalante Del Valle, A. Hussain, P.S. Jeitler, M. Krammer, N. Liko, D. Mikulec, I. Schieck, J. Schöfbeck, R. Schwarz, D. Sonawane, M. Templ, S. Waltenberger, W. Wulz, C.-E. Darwish, M.R. Janssen, T. Van Mechelen, P. Bols, E.S. D’hondt, J. Dansana, S. De Moor, A. Delcourt, M. El Faham, H. Lowette, S. Makarenko, I. Müller, D. Sahasransu, A.R. Tavernier, S. Tytgat, M. Van Putte, S. Vannerom, D. Clerbaux, B. De Lentdecker, G. Favart, L. Hohov, D. Jaramillo, J. Khalilzadeh, A. Lee, K. Mahdavikhorrami, M. Malara, A. Paredes, S. Pétré, L. Postiau, N. Thomas, L. Vanden Bemden, M. Vander Velde, C. Vanlaer, P. De Coen, M. Dobur, D. Hong, Y. Knolle, J. Lambrecht, L. Mestdach, G. Rendón, C. Samalan, A. Skovpen, K. Van Den Bossche, N. Wezenbeek, L. Benecke, A. Bruno, G. Caputo, C. Delaere, C. Donertas, I.S. Giammanco, A. Jaffel, K. Jain, Sa. Lemaitre, V. Lidrych, J. Mastrapasqua, P. Mondal, K. Tran, T.T. Wertz, S. Alves, G.A. Coelho, E. Hensel, C. Menezes De Oliveira, T. Moraes, A. Rebello Teles, P. Soeiro, M. Aldá Júnior, W.L. Alves Gallo Pereira, M. Barroso Ferreira Filho, M. Brandao Malbouisson, H. Carvalho, W. Chinellato, J. Da Costa, E.M. Da Silveira, G.G. De Jesus Damiao, D. Fonseca De Souza, S. Martins, J. Mora Herrera, C. Mota Amarilo, K. Mundim, L. Nogima, H. Santoro, A. Silva Do Amaral, S.M. Sznajder, A. Thiel, M. Vilela Pereira, A. Bernardes, C.A. Calligaris, L. Fernandez Perez Tomei, T.R. Gregores, E.M. Mercadante, P.G. Novaes, S.F. Orzari, B. Padula, Sandra S. Aleksandrov, A. Antchev, G. Hadjiiska, R. Iaydjiev, P. Misheva, M. Shopova, M. Sultanov, G. Dimitrov, A. Ivanov, T. Litov, L. Pavlov, B. Petkov, P. Petrov, A. Shumka, E. Keshri, S. Thakur, S. Cheng, T. Guo, Q. Javaid, T. Mittal, M. Yuan, L. Bauer, G. Hu, Z. Yi, K. Chen, G.M. Chen, H.S. Chen, M. Iemmi, F. Jiang, C.H. Kapoor, A. Liao, H. Liu, Z.-A. Monti, F. Sharma, R. Song, J.N. Tao, J. Wang, C. Wang, J. Wang, Z. Zhang, H. Agapitos, A. Ban, Y. Lev Yerevan Physics Institute Yerevan Armenia Institut für Hochenergiephysik Vienna Austria Universiteit Antwerpen Antwerpen Belgium Vrije Universiteit Brussel Brussel Belgium Université Libre de Bruxelles Bruxelles Belgium Ghent University Ghent Belgium Université Catholique de Louvain Louvain-la-Neuve Belgium Centro Brasileiro de Pesquisas Fisicas Rio de Janeiro Brazil Universidade do Estado do Rio de Janeiro Rio de Janeiro Brazil Universidade Estadual Paulista Universidade Federal do ABC São Paulo Brazil Institute for Nuclear Research and Nuclear Energy Bulgarian Academy of Sciences Sofia Bulgaria University of Sofia Sofia Bulgaria Instituto De Alta Investigación Universidad de Tarapacá Casilla 7 D Arica Chile Beihang University Beijing China Department of Physics Tsinghua University Beijing China Institute of High Energy Physics Beijing China State Key Laboratory of Nuclear Physics and Technology Peking University Beijing China Sun Yat-Sen University Guangzhou China University of Science and Technology of China Hefei China Institute of Modern Physics and Key Laboratory of Nuclear Physics and Ion-beam Application (MOE) — Fudan University Shanghai China Zhejiang University Zhejiang Hangzhou China Universidad de Los Andes Bogota Colombia Universidad de Antioquia Medellin Colombia University of Split Faculty of Electrical Engineering Mechanical Engineering and Naval Architecture Split Croatia University of Split Faculty of Science Split Croatia Institute Rudjer Boskovic Zagreb Croatia University of Cyprus Nicosia Cyprus Charles University Prague Czech Republic Escuela Politecnica Nacional Quito Ecuador Universidad San Francisco de Quito Quito Ecuador Academy of Scientific Research and Technology of the Arab Republic of Egypt Egyptian Network of High Energy Physics Cairo Egypt Center for High Energy Physics (CHEP-FU) Fayoum University El-Fayoum Egypt National Institute of Chemical Physics and Biophysics Tallinn Estonia Department of Physics

A search for W′ bosons decaying to a top and a bottom quark in final states including an electron or a muon is performed with the CMS detector at the LHC. The analyzed data correspond to an integrated luminosity of 138 fb⁻¹ of proton-proton collisions at a center-of-mass energy of 13 TeV. Good agreement with the standard model expectation is observed and no evidence for the existence of the W′ boson is found over the mass range examined. The largest observed deviation from the standard model expectation is found for a W′ boson mass (mW_′) hypothesis of 3.8 TeV with a relative decay width of 1%, with a local (global) significance of 2.6 (2.0) standard deviations. Upper limits on the production cross sections of W′ bosons decaying to a top and a bottom quark are set. Left- and right-handed W′ bosons with mW_′ below 3.9 and 4.3 TeV, respectively, are excluded at the 95% confidence level, under the assumption that the new particle has a narrow decay width. Limits are also set for relative decay widths up to 30%. © The Author(s) 2024.

关键词： Beyond Standard Model Hadron-Hadron Scattering Vector Boson Production

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