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| WoS | SCOPUS | Document Type | Document Title | Abstract | Authors | Affiliation | ResearcherID (WoS) | AuthorsID (SCOPUS) | Author Email(s) | Journal Name | JCR Abbreviation | ISSN | eISSN | Volume | Issue | WoS Edition | WoS Category | JCR Year | IF | JCR (%) | FWCI | FWCI Update Date | WoS Citation | SCOPUS Citation | Keywords (WoS) | KeywordsPlus (WoS) | Keywords (SCOPUS) | KeywordsPlus (SCOPUS) | Language | Publication Stage | Publication Year | Publication Date | DOI | JCR Link | DOI Link | WOS Link | SCOPUS Link |
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| ○ | Conference paper | Numerical Investigation of Thermo-Mechanical Behaviour of an Aluminum-Silicon Alloy Piston in an IC Engine | Increasing the power to volume density of an engine has been a significant research concern. A turbocharger is usually utilized to boost volumetric efficiency, but it also increases the in-cylinder temperature and pressure, compromising piston life. Thermal barrier coatings have emerged as a potential solution to minimize heat flow toward the piston skirt. Moreover, recent developments in ceramic-based coated pistons have shown promising outcomes. Zirconia, for instance, enables high-temperature operations of the machine component by reducing heat loss and protecting the engine parts from high thermo-mechanical stresses. In this study, actual engine-like thermal and structural loads are considered in a Finite Element Method-based numerical model to evaluate the structural behavior in an IC engine. Temperature distributions and thermo-mechanical stresses are determined. Thus, this article aims to develop a numerically model to observe the thermo-mechanical response. Results show thermal load as the primary contributor toward structural deformations compared with the structural loads. Such a model can effectively evaluate the thermo-mechanical response of a coated piston. Encouraging thermo-mechanical trends were also observed for a coated piston utilizing the developed model. © 2022 IEEE. | Khan, Shah Nawaz; Usman, Ali; Mourad, Abdel-Hamid Ismail; Park, Cheol Woo; Liwicki, Marcus; Almqvist, Andreas | Comsats University Islamabad, Department of Mechanical Engineering, Wah Cantt, Pakistan; University of Technology Luleå, Eislab Machine Learning Luleå, Sweden; United Arab Emirates University, Mechanical & Aerospace Engineering Department, Al Ain, United Arab Emirates; Kyungpook National University, School of Mechanical Engineering, Daegu, South Korea; University of Technology Luleå, Eislab Machine Learning Luleå, Sweden; Luleå University of Technology, Division of Machine Element, Luleå, Sweden | 57203224284; 42062484500; 57215576604; 7408416474; 14021418000; 8367337400 | 2022 13th International Conference on Mechanical and Aerospace Engineering, ICMAE 2022 | 1.39 | 2025-06-25 | 2 | ceramic coating; diesel engine; numerical analysis; piston; thermal stress | Aluminum alloys; Diesel engines; Engine pistons; Integrated circuits; Silicon alloys; Thermal barrier coatings; Timing circuits; Zirconia; Aluminum -Silicon alloys; I.C. engine; IC engines; Numerical investigations; Power; Thermo-mechanical behaviors; Thermo-mechanical response; Thermo-mechanical stress; Volume density; Volumetric efficiency; Numerical methods | English | Final | 2022 | 10.1109/icmae56000.2022.9852899 | 바로가기 | 바로가기 | |||||||||||||||||||
| ○ | Book chapter | Numerical modeling of nanofluids’ flow and heat transfer | The performance evaluation of the nanofluids through numerical methods is much more affordable compared to time-consuming experiments involving complex and expensive processes and materials. The accurate modeling of the nanofluid flow and hydrothermal characteristics requires a comprehensive realization of the underlying physical mechanisms determining the heat transfer attributes of the nanofluids. This chapter summarizes the potential physical mechanisms contributing to the thermal activity of the nanofluids. The thermal performance enhancement of the nanofluids is regarded as a relative change of their thermophysical properties, therefore a brief discussion of the thermophysical characteristics of the nanofluids is presented along with their property correlations. The single-phase and multiphase (Eulerian–Eulerian and Eulerian–Lagrangian models) mathematical models used for simulating nanofluids’ flow and thermal characterizes are discussed in detail. Finally, a brief overview of the macro-, micro-, and mesoscale computational fluid dynamic techniques to solve the respective governing equations is presented in this chapter. © 2022 Elsevier Inc. All rights reserved. | Ambreen, Tehmina; Saleem, Arslan; Park, Cheol Woo | School of Mechanical Engineering, Kyungpook National University, Bukgu Daegu, South Korea; School of Engineering, Cardiff University, Cardiff, United Kingdom; School of Mechanical Engineering, Kyungpook National University, Bukgu Daegu, South Korea | 57195420431; 57194776354; 7408416474 | Advances in Nanofluid Heat Transfer | 0.98 | 2025-06-25 | 2 | Eulerian–Eulerian; Eulerian–Lagrangian; heat transfer enhancement mechanisms; Nanofluid mathematical models; numerical techniques; single-phase | English | Final | 2022 | 10.1016/b978-0-323-88656-7.00015-5 | 바로가기 | 바로가기 | ||||||||||||||||||||
| ○ | Conference paper | Numerical Prediction of Failure in Single Point Incremental Forming Using a New Yield Criterion for Sheet Metal | A new yield function depends on the second stress invariant J2 and the third stress invariant J3 is proposed to describe the elastoplastic behavior of sheet metals. Additionally, a series of basic fracture testing covering a wide range of stress state and different material orientations for aluminum alloy is carried out. The ductile fracture of the aluminum alloy is investigated using a hybrid experimental–numerical approach. Besides, a new uncoupled ductile fracture that is concerned with the micro-mechanisms of voids is introduced to predict the failure of material. The new yield criterion and fracture model are implemented into the ABAQUS/Explicit code to predict the fracture in different stress states. The incremental sheet-forming tests are performed to verify the efficiency of the proposed yield criterion and fracture criterion. The proposed yield criterion and fracture model can be utilized for predicting plastic deformation and initial fracture in sheet metal forming. © 2022, The Minerals, Metals & Materials Society. | Quach, H.; Xiao, X.; Kim, J.J.; Kim, Y.S. | Graduate School of Mechanical Engineering, Kyungpook National University, 80, Daehakro, Bukgu, Daegu, 41566, South Korea; Graduate School of Mechanical Engineering, Kyungpook National University, 80, Daehakro, Bukgu, Daegu, 41566, South Korea; Graduate School of Mechanical Engineering, Kyungpook National University, 80, Daehakro, Bukgu, Daegu, 41566, South Korea; Schoool of Mechanical Engineering, Kyungpook National University, 80, Daehakro, Bukgu, Daegu, 41566, South Korea | 57211711094; 57195394085; 57209555877; 36065820800 | caekim@knu.ac.kr; | Minerals, Metals and Materials Series | 2367-1181 | 0 | 2025-06-25 | 0 | Aluminum alloys; Forecasting; Fracture testing; Metal forming; Metal testing; Sheet metal; Elasto-plastic behaviours; Fracture model; Material orientation; Numerical predictions; Prediction of failures; Single point incremental forming; Stress invariants; Stress state; Yield criterion; Yield function; Ductile fracture | English | Final | 2022 | 10.1007/978-3-031-06212-4_11 | 바로가기 | 바로가기 | ||||||||||||||||||
| ○ | Article | NUMERICAL RADIUS POINTS OF L(ml∞n : l∞n ) | For n ≥ 2 and a real Banach space E, L(nE : E) denotes the space of all continuous n-linear mappings from E to itself. Let (Fomula Presented) where v(T) denotes the numerical radius of T. T is called numerical radius peak mapping if there is (Fomula Presented) that satisfies Nrad (Fomula Presented) . In this paper we classify Nrad(T) for every (Fomula Presented) in connection with the set of the norm attaining points of T. We also characterize all numerical radius peak mappings in (Fomula Presented) with the supremum norm. © 2022 Authors. All rights reserved. | Kim, Sung Guen | Department of Mathematics, Kyungpook National University, Daegu, 702-701, South Korea | 34769667700 | sgk317@knu.ac.kr; | New Zealand Journal of Mathematics | 1179-4984 | 53 | 0.96 | 2025-06-25 | 4 | Norming points; numerical radius; numerical radius attaining mappings; numerical radius peak multilinear mappings; numerical radius points | English | Final | 2022 | 10.53733/179 | 바로가기 | 바로가기 | |||||||||||||||||
| ○ | Conference paper | Observation of Z>2 trapped nuclei by AMS on ISS | The Alpha Magnetic Spectrometer (AMS-02) is a high energy particle physics experiment operating continuously aboard the International Space Station (ISS) since the 19th of May of 2011. A component of trapped Z>2 ions located in the South Atlantic Anomaly (SAA) has been detected traversing the instrument both in down-going and up-going directions. © Copyright owned by the author(s) under the terms of the Creative Commons. | Giovacchini, Francesca; Oliva, Alberto; Valencia-Otero, Martha; Aguilar, M.; Ali Cavasonza, L.; Allen, M.S.; Alpat, B.; Ambrosi, G.; Arruda, L.; Attig, N.; Barao, F.; Barrin, L.; Bartoloni, A.; Başeğmez-Du Pree, S.; Battiston, R.; Behlmann, M.; Beranek, B.; Berdugo, J.; Bertucci, B.; Bindi, V.; Bollweg, K.; Borgia, B.; Boschini, M.J.; Bourquin, M.; Bueno, E.F.; Burger, J.; Burger, W.J.; Burmeister, S.; Cai, X.D.; Capell, M.; Casaus, J.; Castellini, G.; Cervelli, F.; Chang, Y.H.; Chen, G.M.; Chen, G.R.; Chen, H.S.; Chen, Y.; Cheng, L.; Chou, H.Y.; Chouridou, S.; Choutko, V.; Chung, C.H.; Clark, C.; Coignet, G.; Consolandi, C.; Contin, A.; Corti, C.; Cui, Z.; Dadzie, K.; Delgado, C.; Della Torre, S.; Demirköz, M.B.; Derome, L.; Di Falco, S.; Di Felice, V.; Díaz, C.; Dimiccoli, F.; von Doetinchem, P.; Dong, F.; Donnini, F.; Duranti, M.; Egorov, A.; Eline, A.; Feng, J.; Fiandrini, E.; Fisher, P.; Formato, V.; Freeman, C.; Galaktionov, Y.; Gámez, C.; García-López, R.J.; Gargiulo, C.; Gast, H.; Gervasi, M.; Gómez-Coral, D.M.; Gong, J.; Goy, C.; Grabski, V.; Grandi, D.; Graziani, M.; Haino, S.; Han, K.C.; Hashmani, R.K.; He, Z.H.; Heber, B.; Hsieh, T.H.; Hu, J.Y.; Incagli, M.; Jang, W.Y.; Jia, Yi; Jinchi, H.; Kanishev, K.; Khiali, B.; Kim, G.N.; Kirn, Th.; Konyushikhin, M.; Kounina, O.; Kounine, A.; Koutsenko, V.; Kuhlman, A.; Kulemzin, A.; La Vacca, G.; Laudi, E.; Laurenti, G.; Lazzizzera, I.; Lebedev, A.; Lee, H.T.; Lee, S.C.; Li, J.Q.; Li, M.; Li, Q.; Li, S.; Li, J.H.; Li, Z.H.; Liang, J.; Light, C.; Lin, C.H.; Lippert, T.; Liu, J.H.; Liu, Z.; Lu, S.Q.; Lu, Y.S.; Luebelsmeyer, K.; Luo, J.Z.; Luo, Xi; Lyu, S.S.; Machate, F.; Mañá, C.; Marín, J.; Marquardt, J.; Martin, T.; Martínez, G.; Masi, N.; Maurin, D.; Menchaca-Rocha, A.; Meng, Q.; Mikhailov, V.V.; Mo, D.C.; Molero, M.; Mott, P.; Mussolin, L.; Negrete, J.; Nikonov, N.; Nozzoli, F.; Orcinha, M.; Palermo, M.; Palmonari, F.; Paniccia, M.; Pashnin, A.; Pauluzzi, M.; Pensotti, S.; Phan, H.D.; Piandani, R.; Plyaskin, V.; Poluianov, S.; Qin, X.; Qu, Z.Y.; Quadrani, L.; Rancoita, P.G.; Rapin, D.; Reina Conde, A.; Robyn, E.; Rosier-Lees, S.; Rozhkov, A.; Rozza, D.; Sagdeev, R.; Schael, S.; Schulz von Dratzig, A.; Schwering, G.; Seo, E.S.; Shakfa, Z.; Shan, B.S.; Siedenburg, T.; Song, J.W.; Song, X.J.; Sonnabend, R.; Strigari, L.; Su, T.; Sun, Q.; Sun, Z.T.; Tacconi, M.; Tang, X.W.; Tang, Z.C.; Tian, J.; Ting, Samuel C.C.; Ting, S.M.; Tomassetti, N.; Torsti, J.; Tüysüz, C.; Urban, T.; Usoskin, I.; Vagelli, V.; Vainio, R.; Valente, E.; Valtonen, E.; Vázquez Acosta, M.; Vecchi, M.; Velasco, M.; Vialle, J.P.; Wang, C.X.; Wang, L.; Wang, L.Q.; Wang, N.H.; Wang, Q.L.; Wang, S.; Wang, X.; Wang, Yu; Wang, Z.M.; Wei, J.; Weng, Z.L.; Wu, H.; Xiong, R.Q.; Xu, W.; Yan, Q.; Yang, Y.; Yashin, I.I.; Yi, H.; Yu, Y.M.; Yu, Z.Q.; Zannoni, M.; Zhang, C.; Zhang, F.; Zhang, F.Z.; Zhang, J.H.; Zhang, Z.; Zhao, F.; Zheng, C.; Zheng, Z.M.; Zhuang, H.L.; Zhukov, V.; Zichichi, A.; Zuccon, P. | Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas, Av. 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Physics Institute, JARA-FAME, RWTH Aachen University, Aachen, 52056, Germany; Centro de Investigaciones Energetica, Medioambientales y Tecnológicas (CIEMAT), Madrid, 28040, Spain; INFN Sezione di Perugia, Perugia, 06100, Italy, Universitá di Perugia, Perugia, 06100, Italy; Physics and Astronomy Department, University of Hawaii, Honolulu, 96822, HI, United States; National Aeronautics and Space Administration Johnson Space Center (JSC), Houston, 77058, TX, United States; INFN Sezione di Roma 1, Roma, 00185, Italy, Universitá di Roma La Sapienza, Roma, 00185, Italy; INFN Sezione di Milano-Bicocca, Milano, 20126, Italy; DPNC, Université de Genève, Genève 4, 1211, Switzerland; Kapteyn Astronomical Institute, University of Groningen, P.O. 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Physics Institute, JARA-FAME, RWTH Aachen University, Aachen, 52056, Germany; INFN Sezione di Milano-Bicocca, Milano, 20126, Italy, Universitá di Milano-Bicocca, Milano, 20126, Italy; Physics and Astronomy Department, University of Hawaii, Honolulu, 96822, HI, United States; Southeast University (SEU), Nanjing, 210096, China; Université Grenoble Alpes, Université Savoie Mont Blanc, CNRS, LAPP-IN2P3, Annecy, 74000, France; Instituto de Física, Universidad Nacional Autónoma de México (UNAM), Ciudad de México, 01000, Mexico; INFN Sezione di Milano-Bicocca, Milano, 20126, Italy, Universitá di Milano-Bicocca, Milano, 20126, Italy; INFN Sezione di Perugia, Perugia, 06100, Italy, Universitá di Perugia, Perugia, 06100, Italy; Institute of Physics, Academia Sinica, Nankang, Taipei, 11529, Taiwan; National Chung-Shan Institute of Science and Technology (NCSIST), Tao Yuan, Longtan, 32546, Taiwan; Department of Physics, Middle East Technical University (METU), Ankara, 06800, Turkey; Sun Yat-Sen University (SYSU), Guangzhou, 510275, China; Institut für Experimentelle und Angewandte Physik, Christian-Alberts-Universität zu Kiel, Kiel, 24118, Germany; Massachusetts Institute of Technology (MIT), Cambridge, 02139, MA, United States; Institute of High Energy Physics (IHEP), Chinese Academy of Sciences, Beijing, 100049, China, University of Chinese Academy of Sciences (UCAS), Beijing, 100049, China; INFN Sezione di Pisa, Pisa, 56100, Italy; CHEP, Kyungpook National University, Daegu, 41566, South Korea; Massachusetts Institute of Technology (MIT), Cambridge, 02139, MA, United States; National Chung-Shan Institute of Science and Technology (NCSIST), Tao Yuan, Longtan, 32546, Taiwan; INFN TIFPA, Povo, Trento, 38123, Italy; INFN Sezione di Roma Tor Vergata, Roma, 00133, Italy; CHEP, Kyungpook National University, Daegu, 41566, South Korea; I. Physics Institute, JARA-FAME, RWTH Aachen University, Aachen, 52056, Germany; Massachusetts Institute of Technology (MIT), Cambridge, 02139, MA, United States; Massachusetts Institute of Technology (MIT), Cambridge, 02139, MA, United States; Massachusetts Institute of Technology (MIT), Cambridge, 02139, MA, United States; Massachusetts Institute of Technology (MIT), Cambridge, 02139, MA, United States; Physics and Astronomy Department, University of Hawaii, Honolulu, 96822, HI, United States; Massachusetts Institute of Technology (MIT), Cambridge, 02139, MA, United States; INFN Sezione di Milano-Bicocca, Milano, 20126, Italy, Universitá di Milano-Bicocca, Milano, 20126, Italy; European Organization for Nuclear Research (CERN), Geneva 23, 1211, Switzerland; INFN Sezione di Bologna, Bologna, 40126, Italy; INFN TIFPA, Povo, Trento, 38123, Italy, Universitá di Trento, Trento, Povo, 38123, Italy; Massachusetts Institute of Technology (MIT), Cambridge, 02139, MA, United States; Academia Sinica Grid Center (ASGC), Nankang, Taipei, 11529, Taiwan; Institute of Physics, Academia Sinica, Nankang, Taipei, 11529, Taiwan; Southeast University (SEU), Nanjing, 210096, China; I. 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Physics Institute, JARA-FAME, RWTH Aachen University, Aachen, 52056, Germany; Shandong University (SDU), Shandong, Jinan, 250100, China; Institute of High Energy Physics (IHEP), Chinese Academy of Sciences, Beijing, 100049, China, University of Chinese Academy of Sciences (UCAS), Beijing, 100049, China; Shandong University (SDU), Shandong, Jinan, 250100, China; Physics and Astronomy Department, University of Hawaii, Honolulu, 96822, HI, United States; Institute of Physics, Academia Sinica, Nankang, Taipei, 11529, Taiwan; Jülich Supercomputing Centre, JARA-FAME, Research Centre Jülich, Jülich, 52425, Germany; Institute of Electrical Engineering (IEE), Chinese Academy of Sciences, Beijing, 100190, China; DPNC, Université de Genève, Genève 4, 1211, Switzerland; Institute of Physics, Academia Sinica, Nankang, Taipei, 11529, Taiwan; Institute of High Energy Physics (IHEP), Chinese Academy of Sciences, Beijing, 100049, China; I. Physics Institute, JARA-FAME, RWTH Aachen University, Aachen, 52056, Germany; Southeast University (SEU), Nanjing, 210096, China; Shandong Institute of Advanced Technology (SDIAT), Shandong, Jinan, 250100, China; Sun Yat-Sen University (SYSU), Guangzhou, 510275, China; I. Physics Institute, JARA-FAME, RWTH Aachen University, Aachen, 52056, Germany; Centro de Investigaciones Energetica, Medioambientales y Tecnológicas (CIEMAT), Madrid, 28040, Spain; Centro de Investigaciones Energetica, Medioambientales y Tecnológicas (CIEMAT), Madrid, 28040, Spain; Massachusetts Institute of Technology (MIT), Cambridge, 02139, MA, United States, National Aeronautics and Space Administration Johnson Space Center (JSC), Houston, 77058, TX, United States; Centro de Investigaciones Energetica, Medioambientales y Tecnológicas (CIEMAT), Madrid, 28040, Spain; INFN Sezione di Bologna, Bologna, 40126, Italy, Universitá di Bologna, Bologna, 40126, Italy; Université Grenoble Alpes, CNRS, Grenoble INP, LPSC-IN2P3, Grenoble, 38000, France; Instituto de Física, Universidad Nacional Autónoma de México (UNAM), Ciudad de México, 01000, Mexico; Southeast University (SEU), Nanjing, 210096, China; NRNU MEPhI (Moscow Engineering Physics Institute), Moscow, 115409, Russian Federation; Sun Yat-Sen University (SYSU), Guangzhou, 510275, China; Centro de Investigaciones Energetica, Medioambientales y Tecnológicas (CIEMAT), Madrid, 28040, Spain; Massachusetts Institute of Technology (MIT), Cambridge, 02139, MA, United States, National Aeronautics and Space Administration Johnson Space Center (JSC), Houston, 77058, TX, United States; INFN Sezione di Perugia, Perugia, 06100, Italy, Universitá di Perugia, Perugia, 06100, Italy; Physics and Astronomy Department, University of Hawaii, Honolulu, 96822, HI, United States; I. Physics Institute, JARA-FAME, RWTH Aachen University, Aachen, 52056, Germany; INFN TIFPA, Povo, Trento, 38123, Italy; Laboratório de Instrumentação e Física Experimental de Partículas (LIP), Lisboa, 1649-003, Portugal; Physics and Astronomy Department, University of Hawaii, Honolulu, 96822, HI, United States; INFN Sezione di Bologna, Bologna, 40126, Italy, Universitá di Bologna, Bologna, 40126, Italy; DPNC, Université de Genève, Genève 4, 1211, Switzerland; Massachusetts Institute of Technology (MIT), Cambridge, 02139, MA, United States; INFN Sezione di Perugia, Perugia, 06100, Italy, Universitá di Perugia, Perugia, 06100, Italy; INFN Sezione di Milano-Bicocca, Milano, 20126, Italy, Universitá di Milano-Bicocca, Milano, 20126, Italy; Massachusetts Institute of Technology (MIT), Cambridge, 02139, MA, United States; Institut für Experimentelle Teilchenphysik, Karlsruhe Institute of Technology (KIT), Karlsruhe, 76131, Germany; Massachusetts Institute of Technology (MIT), Cambridge, 02139, MA, United States; Sodankylä Geophysical Observatory and Space Physics and Astronomy Research Unit, University of Oulu, Oulu, 90014, Finland; Massachusetts Institute of Technology (MIT), Cambridge, 02139, MA, United States; Institute of Physics, Academia Sinica, Nankang, Taipei, 11529, Taiwan; INFN Sezione di Bologna, Bologna, 40126, Italy, Universitá di Bologna, Bologna, 40126, Italy; INFN Sezione di Milano-Bicocca, Milano, 20126, Italy; DPNC, Université de Genève, Genève 4, 1211, Switzerland; DPNC, Université de Genève, Genève 4, 1211, Switzerland; Université Grenoble Alpes, Université Savoie Mont Blanc, CNRS, LAPP-IN2P3, Annecy, 74000, France; Massachusetts Institute of Technology (MIT), Cambridge, 02139, MA, United States; INFN Sezione di Milano-Bicocca, Milano, 20126, Italy, Universitá di Milano-Bicocca, Milano, 20126, Italy; East-West Center for Space Science, University of Maryland, College Park, 20742, MD, United States; I. Physics Institute, JARA-FAME, RWTH Aachen University, Aachen, 52056, Germany; I. Physics Institute, JARA-FAME, RWTH Aachen University, Aachen, 52056, Germany; I. Physics Institute, JARA-FAME, RWTH Aachen University, Aachen, 52056, Germany; IPST, University of Maryland, College Park, 20742, MD, United States; Department of Physics, Middle East Technical University (METU), Ankara, 06800, Turkey; Beihang University (BUAA), Beijing, 100191, China; I. Physics Institute, JARA-FAME, RWTH Aachen University, Aachen, 52056, Germany; Shandong University (SDU), Shandong, Jinan, 250100, China; Shandong Institute of Advanced Technology (SDIAT), Shandong, Jinan, 250100, China; I. Physics Institute, JARA-FAME, RWTH Aachen University, Aachen, 52056, Germany; INFN Sezione di Roma 1, Roma, 00185, Italy; Shandong Institute of Advanced Technology (SDIAT), Shandong, Jinan, 250100, China; Shandong University (SDU), Shandong, Jinan, 250100, China; Institute of High Energy Physics (IHEP), Chinese Academy of Sciences, Beijing, 100049, China, University of Chinese Academy of Sciences (UCAS), Beijing, 100049, China; INFN Sezione di Milano-Bicocca, Milano, 20126, Italy, Universitá di Milano-Bicocca, Milano, 20126, Italy; Institute of High Energy Physics (IHEP), Chinese Academy of Sciences, Beijing, 100049, China; Institute of High Energy Physics (IHEP), Chinese Academy of Sciences, Beijing, 100049, China; INFN Sezione di Perugia, Perugia, 06100, Italy, Universitá di Perugia, Perugia, 06100, Italy; Massachusetts Institute of Technology (MIT), Cambridge, 02139, MA, United States, European Organization for Nuclear Research (CERN), Geneva 23, 1211, Switzerland; Massachusetts Institute of Technology (MIT), Cambridge, 02139, MA, United States; INFN Sezione di Perugia, Perugia, 06100, Italy, Universitá di Perugia, Perugia, 06100, Italy; Space Research Laboratory, Department of Physics and Astronomy, University of Turku, Turku, 20014, Finland; Department of Physics, Middle East Technical University (METU), Ankara, 06800, Turkey; Massachusetts Institute of Technology (MIT), Cambridge, 02139, MA, United States, National Aeronautics and Space Administration Johnson Space Center (JSC), Houston, 77058, TX, United States; Sodankylä Geophysical Observatory and Space Physics and Astronomy Research Unit, University of Oulu, Oulu, 90014, Finland; INFN Sezione di Perugia, Perugia, 06100, Italy, Agenzia Spaziale Italiana (ASI), Roma, 00133, Italy; Space Research Laboratory, Department of Physics and Astronomy, University of Turku, Turku, 20014, Finland; INFN Sezione di Roma 1, Roma, 00185, Italy, Universitá di Roma La Sapienza, Roma, 00185, Italy; Space Research Laboratory, Department of Physics and Astronomy, University of Turku, Turku, 20014, Finland; Kapteyn Astronomical Institute, University of Groningen, P.O. Box 800, Groningen, 9700 AV, Netherlands; Centro de Investigaciones Energetica, Medioambientales y Tecnológicas (CIEMAT), Madrid, 28040, Spain; Université Grenoble Alpes, Université Savoie Mont Blanc, CNRS, LAPP-IN2P3, Annecy, 74000, France; Shandong University (SDU), Shandong, Jinan, 250100, China; Institute of Electrical Engineering (IEE), Chinese Academy of Sciences, Beijing, 100190, China; Shandong University (SDU), Shandong, Jinan, 250100, China; Shandong University (SDU), Shandong, Jinan, 250100, China; Institute of Electrical Engineering (IEE), Chinese Academy of Sciences, Beijing, 100190, China; Physics and Astronomy Department, University of Hawaii, Honolulu, 96822, HI, United States; Massachusetts Institute of Technology (MIT), Cambridge, 02139, MA, United States; Shandong University (SDU), Shandong, Jinan, 250100, China; Shandong Institute of Advanced Technology (SDIAT), Shandong, Jinan, 250100, China; DPNC, Université de Genève, Genève 4, 1211, Switzerland; Massachusetts Institute of Technology (MIT), Cambridge, 02139, MA, United States; Southeast University (SEU), Nanjing, 210096, China; Southeast University (SEU), Nanjing, 210096, China; Shandong University (SDU), Shandong, Jinan, 250100, China, Shandong Institute of Advanced Technology (SDIAT), Shandong, Jinan, 250100, China; Massachusetts Institute of Technology (MIT), Cambridge, 02139, MA, United States; National Cheng Kung University, Tainan, 70101, Taiwan; NRNU MEPhI (Moscow Engineering Physics Institute), Moscow, 115409, Russian Federation; Southeast University (SEU), Nanjing, 210096, China; Sun Yat-Sen University (SYSU), Guangzhou, 510275, China; Institute of High Energy Physics (IHEP), Chinese Academy of Sciences, Beijing, 100049, China; INFN Sezione di Milano-Bicocca, Milano, 20126, Italy, Universitá di Milano-Bicocca, Milano, 20126, Italy; Institute of High Energy Physics (IHEP), Chinese Academy of Sciences, Beijing, 100049, China; Institute of High Energy Physics (IHEP), Chinese Academy of Sciences, Beijing, 100049, China; Institute of High Energy Physics (IHEP), Chinese Academy of Sciences, Beijing, 100049, China, University of Chinese Academy of Sciences (UCAS), Beijing, 100049, China; Southeast University (SEU), Nanjing, 210096, China; Massachusetts Institute of Technology (MIT), Cambridge, 02139, MA, United States; Institute of High Energy Physics (IHEP), Chinese Academy of Sciences, Beijing, 100049, China, University of Chinese Academy of Sciences (UCAS), Beijing, 100049, China; Shandong Institute of Advanced Technology (SDIAT), Shandong, Jinan, 250100, China; Beihang University (BUAA), Beijing, 100191, China; Institute of High Energy Physics (IHEP), Chinese Academy of Sciences, Beijing, 100049, China; I. Physics Institute, JARA-FAME, RWTH Aachen University, Aachen, 52056, Germany; INFN Sezione di Bologna, Bologna, 40126, Italy, Universitá di Bologna, Bologna, 40126, Italy; INFN TIFPA, Povo, Trento, 38123, Italy, Universitá di Trento, Trento, Povo, 38123, Italy | 7801414508; 57923509300; 57194199213; 22996439600; 57191410260; 55677088495; 7004466804; 7006385307; 7004947256; 57223376954; 35314473000; 55646582300; 35285626700; 29667446900; 7006819163; 55646746400; 57222348704; 57215449045; 35226994400; 57222995845; 57189869313; 7007048697; 57203921827; 35876701500; 57191419057; 35352197500; 7201366306; 56881766800; 35227001900; 35227003900; 35227047800; 7004686796; 7005849628; 36760341300; 56896268700; 57770857500; 59666635000; 56137098800; 59783845300; 56365790700; 59880360300; 57216843622; 7403613567; 26665276200; 7004915931; 57222996264; 57191631180; 56872614900; 57225771159; 36481146400; 55635066900; 55891367200; 15843124700; 57217206029; 10041262700; 23479571900; 56463148700; 56365795400; 57222997362; 57222997042; 56437611300; 35338969500; 9277807200; 35227188500; 56365757500; 7004110494; 34770275300; 38561219400; 57202653707; 7005152953; 57211714717; 7006562654; 6602129426; 22834585300; 7003542102; 57193066571; 57194348346; 7004647768; 12545785500; 8435313700; 55646269300; 6602286685; 56365702500; 57217054073; 9638950300; 8371200500; 57214622344; 57217061944; 7003367998; 59777356000; 57200080694; 55646334500; 14041902800; 56678677500; 35313854400; 7004426528; 22134943600; 55646570800; 6507961476; 35227523800; 12239763300; 16019713900; 25960147300; 26967798300; 7003952900; 35227498500; 7402203861; 56365458100; 55337748800; 59649303200; 57217055777; 59447556300; 59630035000; 57221810319; 57189355627; 56959673200; 57196712846; 37099295200; 57202385981; 56103245100; 57739250200; 56662601100; 57212185186; 6603714395; 56437050600; 44561341800; 57217660157; 57192255225; 35227644500; 7201368610; 56027602600; 57213093167; 7201759290; 54929862600; 23019068500; 56254267800; 59063266500; 8088744900; 23470418700; 57205664408; 57197414498; 57194701604; 58711964100; 38561788700; 6603891162; 56365718600; 57206439911; 7005895860; 56266617500; 57205658335; 35227915000; 7004136045; 57200079784; 24401662400; 35227897900; 55991195200; 58602560100; 56662705400; 9338192300; 57208587725; 7004095328; 57205657945; 57221808079; 15835400200; 57212750153; 55084788000; 57226207657; 56029998100; 55646351100; 35228049900; 57225453766; 57221799961; 55647041700; 14629476900; 56978396500; 57221805527; 57217054674; 59812536800; 57221800567; 59045426200; 55504541600; 54899147500; 35228080500; 57224667833; 57203395141; 35228106000; 35228151000; 25230473300; 7003546454; 58194973400; 57198326830; 7004683817; 55646766500; 7004090779; 35351623000; 35314156500; 35314150800; 7005558667; 57827162300; 24516496600; 57221808490; 59651331900; 56365823000; 57988997500; 7406915784; 58316262400; 57939847100; 35436531400; 59666826000; 59667047600; 36626819200; 59815832900; 36574222800; 57215821711; 57199874977; 59666671800; 6603852312; 57221300223; 57211439512; 57199729020; 7003655337; 56097029500; 59811515100; 57217058141; 56365642600; 57219233647; 57207165786; 57196415759; 56437095300; 57225700563; 7201458058; 57210687222; 23007073000 | Proceedings of Science | 1824-8039 | 395 | 1.66 | 2025-06-25 | 1 | Spectrometers; Trapped ions; Alpha magnetic spectrometers; High-energy particle physics; International Space stations; Particle physics experiments; South atlantic anomalies; Space stations | English | Final | 2022 | 바로가기 | ||||||||||||||||||||
| ○ | Article | Observational Evidence of Giant Cloud Condensation Nucleus Effects on the Precipitation Sensitivity in Marine Stratocumulus Clouds | Cloud-aerosol interactions are one of the paramount but least understood forcing factors in climate systems. Generally, an increase in the concentration of aerosols increases the concentration of cloud droplet numbers, implying that clouds tend to persist for longer than usual, suppressing precipitation in the warm boundary layer. The cloud lifetime effect has been the center of discussion in the scientific community, partly because of the lack of cloud life cycle observations and partly because of cloud problems. In this study, the precipitation susceptibility (S-o) matrix was employed to estimate the aerosols' effect on precipitation, while the non-aerosol effect is minimized. The S-o was calculated for the typical coupled, well-mixed maritime stratocumulus decks and giant cloud condensation nucleus (GCCN) seeded clouds. The GCCN-artificially introduced to the marine stratocumulus cloud decks-is shown to initiate precipitation and reduces S-o to approximately zero, demonstrating the cloud lifetime hypothesis. The results suggest that the response of precipitation to changes in GCCN must be considered for accurate prediction of aerosol-cloud-precipitation interaction by model studies | Jung, Eunsil | Kyungpook Natl Univ, Dept Adv Sci & Technol Convergence, Sangju 37224, South Korea | eunsil.jung@knu.ac.kr; | JOURNAL OF THE KOREAN EARTH SCIENCE SOCIETY | J KOR EARTH SCI SOC | 1225-6692 | 2287-4518 | 43 | 4 | ESCI | GEOSCIENCES, MULTIDISCIPLINARY | 2022 | 0.4 | 0 | cloud lifetime effect; aerosol; cloud seeding; precipitation susceptibility; precipitation sensitivity | SHALLOW CUMULUS; AEROSOL; SUSCEPTIBILITY; OCEAN; POLLUTION; IMPACT | English | 2022 | 2022-08 | 10.5467/jkess.2022.43.4.498 | 바로가기 | 바로가기 | 바로가기 | |||||||||||
| ○ | Article | ON FUNCTIONAL EQUATIONS OF DEGENERATE DILOGARITHM | Recently, the degenerate polylogarithm is introduced by Kim-Kim as a degenerate version of the polylogarithm. In this note, we derive some interesting functional equations related to degenerate dilogarithm. © 2022 Jangjeon Research Institute for Mathematical Sciences and Physics. All rights reserved. | Kim, Taekyun; Kim, Dae San; Lee, Hyunseok; Kwon, Jongkyum; Kim, Yunjae | Department of Mathematics, Kwangwoon University, Seoul, 139-701, South Korea; Department of Mathematics, Sogang University, Seoul, 121-742, South Korea; Department of Mathematics, Kwangwoon University, Seoul, 139-701, South Korea; Department of Mathematics Education, Gyeongsang National University, Jinju, 52828, South Korea; Department of Mathematics, Kyungpook National University, Daegu, 702-701, South Korea | 7407121103; 26643172900; 57211927254; 55000770500; 57204477406 | Proceedings of the Jangjeon Mathematical Society | 1598-7264 | 25 | 1 | 0.25 | 2025-06-25 | 1 | degenerate dilogarithm; degenerate polylogarithm; functional equation | English | Final | 2022 | 10.17777/pjms2022.25.1.89 | 바로가기 | 바로가기 | |||||||||||||||||
| ○ | Conference paper | ON LOANS IN KOREAN NEW WORD FORMATION AND IN LEXICOGRAPHY | This study examines a list of 3,413 neologisms containing one or more borrowed item, which was compiled using the databases built by the Korean Neologism Investigation Project. Etymological aspects and morphological aspects are taken into consideration to show that, besides the overwhelming prevalence of English-based neologisms, particular loans from particular languages play a significant role in the prolific formation of Korean neologisms. Aspects of the lexicographic inclusion of loan-based neologisms demonstrate the need for Korean neologism and lexicography research to broaden its scopes in terms of methodology and attitudes, while also providing a glimpse of changes. © 2022, European Association for Lexicography. All rights reserved. | Choi, Jun; Jung, Hae-Yun | Kyungpook National University, South Korea; Kyungpook National University, South Korea | 57205286798; 57205293023 | EURALEX Proceedings | 2521-7100 | 0.21 | 2025-06-25 | 3 | blending; clipping; lexicography; loans; Neologisms; word formation | English | Final | 2022 | 바로가기 | |||||||||||||||||||||
| ○ | ○ | Proceedings Paper | On the universality of drain-induced-barrier-lowering in field-effect transistors | In this paper, we revisit on the extraction of drain-induced-barrier-lowering (DIBL) in various types of field-effect transistors (FETs) with L-g ranging from several mu m to sub-30 nm and from planar to gate-all-around (GAA) architectures, aiming to unify the extraction methodology of DIBL. In doing so, we found that the values of DIBL extracted in the conventional manner were strongly dependent on the choice of V-DS in the linear regime, especially for V-DS < 4x(kT/q). To physically understand this, we constructed a first-order model for the threshold voltage (V-T), which explains the abnormal positive shift of V-T with V-DS for V-DS < 4x(kT/q). This additional positive shift of V-T in the linear regime resulted in overestimation of DIBL. In an effort to unify the extraction procedure of DIBL, we herein propose first how to accurately extract DIBL and then how to correct reported values of DIBL extracted in the conventional manner. Finally, we highlighted the importance of accurate extraction of DIBL from the viewpoint of its impact on virtual-source modeling, universal relationship between DIBL and aspect ratio, and projection of maximum oscillation frequency (f(max)). | Choi, Su-Min; Jo, Hyeon-Bhin; Yun, Do-Young; Kim, Jun-Gyu; Park, Wan-Soo; Baek, Ji-Min; Lee, In-Geun; Shin, Jang-Kyoo; Kwon, Hyuk-Min; Tsutsumi, Takuya; Sugiyama, Hiroki; Matsuzaki, Hideaki; Lee, Jae-Hak; Kim, Dae-Hyun | Kyungpook Natl Univ, Sch Elect & Elect Engn, Daegu, South Korea; Korea Polytech, Anseong, South Korea; NTT Corp, NTT Device Technol Labs, Yokohama, Kanagawa, Japan | Kim, Junghwan/AAQ-9204-2021; Jo, Hyeon Bhin/HLW-9536-2023 | 57825819100; 57202871742; 57202865358; 57203326312; 57222957219; 57189694750; 37016357200; 7402723873; 55549386600; 37007126500; 35417698400; 7202461821; 55690077600; 57212363794 | dae-hyun.kim@ee.knu.ac.kr; | 2022 INTERNATIONAL ELECTRON DEVICES MEETING, IEDM | 2380-9248 | 0.69 | 2025-06-25 | 1 | 1 | MODEL | Aspect ratio; Field effect transistors; Threshold voltage; Drain-induced barrier lowering; Extraction procedure; Field-effect transistor; First-order models; Gate-all-around; In-field; Linear regime; Positive shift; Source models; Virtual sources; Extraction | English | 2022 | 2022 | 10.1109/iedm45625.2022.10019358 | 바로가기 | 바로가기 | 바로가기 | |||||||||||||
| ○ | ○ | Proceedings Paper | ONSITE EARTHQUAKE ALERT AND SAFETY SERVICES USING LOW-COST MEMS SENSORS | Due to the increase of earthquakes in the Korean peninsular, much effort has been made to prepare for earthquakes. Our prior work introduced an earthquake alert device using a low-cost MEMS acceleration sensor that can detect an earthquake using a simple artificial neural network and then send out an alert to nearby smart devices. In this study, we further extend the earthquake alert device, which can connect with multiple other earthquake alert devices to build an onsite earthquake alert system covering a small range of areas. To see the effectiveness of our system, we constructed a testbed at several camping sites that do not have earthquake alert systems. Throughput the testbed operation, our system detected two small earthquakes and successfully issued one alarm. Based on our experiments and testbeds, we believe that our earthquake alert system can be effectively used for earthquake preparedness and response even in locations where computing and networking infrastructures are not present. | Kim, Seonhyeong; Lim, Kihwan; Kim, Jaeseon; Kwon, Young-Woo | Kyungpook Natl Univ, Sch Comp Sci & Engn, Daegu, South Korea; Natl Disaster Management Inst, Ulsan, South Korea | Kwon, Young-Woo/HGE-6607-2022 | 57256850100; 57937218300; 57208390380; 57208480210 | 2022 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS 2022) | 2153-6996 | 0.4 | 2025-06-25 | 1 | 1 | Earthquake; MEMS sensor; safety services; onsite alert | Earthquake; MEMS sensor; onsite alert; safety services | Costs; Neural networks; Testbeds; Acceleration sensors; Alert Devices; Alert service; Alert systems; Low-costs; MEMS sensors; Onsite alert; Safety services; Simple++; Smart devices; Earthquakes | English | 2022 | 2022 | 10.1109/igarss46834.2022.9884578 | 바로가기 | 바로가기 | 바로가기 | |||||||||||||
| ○ | ○ | Article | Optical Properties according to BaO Addition for BaO-GeO2-La2O3-ZnO System | In this study, Barium Germanium glasses were prepared with a composition of xBaO-(72-x)GeO2-8La(2)O(3)-20ZnO where x = 16.0, 18.0, 20.0, 22.0 and 24.0 mol% respectively. Their physical and optical properties, such as refractiveness index, glass transition temperature (Tg), softening temperature (Ts), transmittance and Knoop hardness were studied. The results showed that refractive index, Tg, Ts and coefficient of thermal expansion (CTE) increased with increasing BaO concentration. The refractive index of all the prepared samples was observed between 1.7811 to 1.7881. The Abbe number was calculated by formula using nd (589.3 nm), nf (656.3 nm) and nc (486.1 nm) and observed to be between 38 to 40. The Abbe number of the prepared sample was similar to that of BaO and GeO2. The transmittance of the prepared glasses was observed to be between 80 similar to 82 % throughout the range from 200 nm to 800 nm. Knoop hardness divided into seven steps were measured 5 class (>= 450 similar to < 550) of all prepared samples. | Cho, Jaeyoung; Kim, Jinho; Kim, Sae-Hoon; Lee, Mijai | Korea Inst Ceram Engn & Technol, Display Mat Ctr, Jinju 52851, South Korea; Kyungpook Natl Univ, Dept Phys, Daegu 41566, South Korea; Gangneung Wonju Natl Univ, Dept Ceram Engn, Kangnung 25457, South Korea | Kim, Sae/AAR-3907-2020; Lee, Jung-Hyun/AAL-4128-2020 | 57204422573; 56032549400; 55133789500; 36019277000 | im1004@kicet.re.kr; | KOREAN JOURNAL OF MATERIALS RESEARCH | KOR J MATER RES | 1225-0562 | 2287-7258 | 32 | 9 | ESCI | MATERIALS SCIENCE, MULTIDISCIPLINARY | 2022 | 0.3 | 0 | 2025-06-25 | 0 | 0 | bao-geo(2) glass; tg; knoop hardness; abbe number | TEMPERATURE; GLASS | Abbe number; Bao-geo<sub>2</sub> glass; Knoop hardness; T<sub>g</sub> | Barium compounds; Germanium oxides; Glass transition; II-VI semiconductors; Lanthanum oxides; Rare earths; Refractive index; Thermal expansion; Zinc oxide; Abbe number; Bao-geo2 glass; Coefficient-of-thermal expansion; Glass transition temperature Tg; Knoop hardness; Softening temperature; Tg; Glass | Chinese | 2022 | 2022-09 | 10.3740/mrsk.2022.32.9.379 | 바로가기 | 바로가기 | 바로가기 | 바로가기 | ||
| ○ | Article | Optimal Distributed Generation Capacity Considering Energy Storages Connected to Renewable Resources as Reserve Supplier in Off-Grid Island | In this paper, we introduce a linear programming model to find optimal capacity for distributed power generating units and energy storage systems (ESSs) directly connected to solar photovoltaic systems or wind turbine generators. The ESSs are used to relieve the variability of renewable generators and provide operating reserves. The proposed optimization model minimizes the total annualized cost including construction and operation costs for generators and ESSs. A renewable portfolio standard and carbon dioxide emissions reduction target are applied to the optimization model as environmental policies. To examine the impacts of implementing the environmental policies and providing operating reserves by the ESSs, case studies are conducted, and results are compared. The optimal results obtained from the proposed model show that the total cost is reduced, and installed capacity of fossil-fired generators is smaller compared to when ESSs do not provide operating reserves. Copyright © The Korean Institute of Electrical Engineers. | Park, Sangwoo; Park, Heejung | Dept. of Construction, Environment and Energy Engineering, Kyungpook National University, South Korea; Dept. of Construction, Environment and Energy Engineering, Kyungpook National University, South Korea | 58040157200; 56484825400 | h.park@knu.ac.kr; | Transactions of the Korean Institute of Electrical Engineers | 1975-8359 | 71 | 12 | 0.16 | 2025-06-25 | 2 | Distributed Generation Capacity Planning; Energy Storage System; Renewable Energy; Reserve Deployment | Carbon dioxide; Emission control; Energy storage; Environmental protection; Global warming; Linear programming; Solar power generation; Turbogenerators; Wind Turbine Generators; Capacity planning; Distributed generation capacity planning; Energy storage system; Environmental policy; Generation capacity; Operating reserve; Optimization models; Renewable energies; Reserve deployment; Storage systems; Distributed power generation | Korean | Final | 2022 | 10.5370/kiee.2022.71.12.1731 | 바로가기 | 바로가기 | |||||||||||||||
| ○ | Conference paper | Optimal sensors placement in controlled environment agriculture using a reinforcement learning approach | Optimal placement of sensors in protected cultivation systems to maximize monitoring and control capabilities can guide effective decision-making toward achieving the highest productivity levels and other desirable outcomes. Unlike conventional machine learning methods such as supervised learning, Reinforcement learning does not require large, labeled datasets, thereby providing opportunities for more efficient and unbiased design optimization. A multi-arm bandit problem was formulated using the Beta distribution and solved by the Thompson sampling algorithm to determine the optimal locations of sensors in a protected cultivation system (greenhouse). A total of 56 two-in-one sensors designed to measure both internal air temperature and relative humidity were installed at a vertical distance of 1 m and a horizontal distance of 3m apart in a greenhouse used to cultivate strawberries. Data was collected over seven months covering four major seasons, February (winter), March, April, and May (spring), June and July (summer), and October (autumn), and analyzed separately. Results showed unique patterns for sensor selection for temperature and relative humidity during the other months. Furthermore, temperature and relative humidity each had different optimal location selections suggesting that two-in-one sensors might not be ideal in these cases. The use of reinforcement learning to design optimal sensor placement in this study aided in identifying 10 optimal sensor locations for monitoring and controlling temperature and relative humidity. © 2022 ASABE. All Rights Reserved. | Uyeh, Daniel Dooyum; Asem-Hiablie, Senorpe; Park, Tusan; Bassey, Blessing Itoro; Mallipeddi, Rammohan; Woo, Seungmin; Jang, Hoseung; Kwon, Minjeong; Kim, Yeongsu; Kang, Seokho; Park, Hyunggyu; Kim, Yonggik; Son, Jinho; Lim, Hyunseo; Hong, Jonggeun; Ha, Yushin | Kyungpook National University, Daegu, 41566, South Korea; The Pennsylvania State University, University Park, 16802, PA, United States; Kyungpook National University, Daegu, 41566, South Korea; African Institute for Mathematical Sciences, Kigali, KG590 ST, Rwanda; Kyungpook National University, Daegu, 41566, South Korea; Kyungpook National University, Daegu, 41566, South Korea; Kyungpook National University, Daegu, 41566, South Korea; Kyungpook National University, Daegu, 41566, South Korea; Kyungpook National University, Daegu, 41566, South Korea; Kyungpook National University, Daegu, 41566, South Korea; Kyungpook National University, Daegu, 41566, South Korea; Kyungpook National University, Daegu, 41566, South Korea; Kyungpook National University, Daegu, 41566, South Korea; Kyungpook National University, Daegu, 41566, South Korea; Kyungpook National University, Daegu, 41566, South Korea; Kyungpook National University, Daegu, 41566, South Korea | 57194449611; 36656958300; 57202780408; 57879869700; 25639919900; 57192074884; 57880320400; 57547402100; 57210594021; 57221791368; 57279183700; 58419351400; 57879932100; 57881104500; 57880320600; 57192072314 | 2022 ASABE Annual International Meeting | 0 | 2025-06-25 | 0 | Monitoring; Relative humidity; Sensor placement; Sensors; Temperature | Decision making; Greenhouses; Humidity control; Large dataset; Learning systems; Location; Reinforcement learning; Controlled environment agricultures; Monitoring and control; Optimal locations; Optimal placement of sensors; Optimal sensor placement; Reinforcement learning approach; Reinforcement learnings; Sensor placement; Temperature and relative humidity; Two in ones; Cultivation | English | Final | 2022 | 10.13031/aim.202200101 | 바로가기 | 바로가기 | |||||||||||||||||||
| ○ | Article | Optimization of a Multiple-Effect Evaporator Process by Simulation | Simulation is used to solve complex problems in the pulp and paper industry through process modeling based on the predicted values obtained to understand the characteristics and behaviors of the actual process of interest. The study aim was to evaluate a multiple-effect evaporator (MEE) model built using WinGEMS and determine the overall efficiency of the MEE based on different case studies of black liquor and steam feed arrangements. Efficiency was much better with the backward feed arrangement than with other models in terms of steam economy predicted. For more accurate simulation optimization in the MEE process, however, an actual dataset should be used to evaluate the mode efficiency. © 2022 Korean Technical Assoc. of the Pulp and Paper Industry. All rights reserved. | Han, Dong Gu; Lee, Jung Myoung | Department of Wood Science and Technology, Kyungpook National University, South Korea; Major in Wood Science and Technology, Department of Wood Science and Technology, School of Forestry, Science and Landscape Architecture, Agricultural Science and Technology Research Institute, Kyungpook National University, South Korea | 58041612900; 16197909600 | jmylee@knu.ac.kr; | Palpu Chongi Gisul/Journal of Korea Technical Association of the Pulp and Paper Industry | 0253-3200 | 54 | 3 | 0 | 2025-06-25 | 0 | feed arrangement; multiple effect evaporators (MEE); Simulation optimization; WinGEMS | Efficiency; Evaporators; Feeds; Interest; Processes; Pulp Industry; Simulation; Steam; Evaporators; Paper and pulp industry; Complex problems; Feed arrangement; Model-based OPC; Multiple effect evaporator; Multiple-effect evaporator; Optimisations; Pulp and paper industry; Simulation optimization; Through process modeling; WinGEMS; Efficiency | Korean | Final | 2022 | 10.7584/jktappi.2022.06.54.3.12 | 바로가기 | 바로가기 | |||||||||||||||
| ○ | Article | Optimization of Jamming Power and Trajectory Design for Cooperative UAV Jammer with Imperfect Channel State Information | With the recent advent of the Internet of Things (IoT) and the rapid spread of wireless communication devices, privacy and secure communication have been of interest due to the broadcast characteristics of wireless medium. The physical-layer security is an attractive technique for various security attacks, especially to resist the security risk of decryption of the conventional cryptographic protocol-based methods. In this paper, we consider the multi-cell scenario, each of which consists of a base station and a mobile user with the aid of a cooperative unmanned aerial vehicle (UAV) jammer to mitigate eavesdropping. In order to maximize the average secrecy rate of the entire network, we propose a joint optimization method of jamming power and trajectory of the multiple cooperative UAV jammers. For practicality, the location uncertainty of all the ground nodes, yielding the channel estimation errors, is assumed. To this end, the problem formulation is proposed, whose solution is developed based on Block Coordinate Descent (BCD) method. Via the simulation results, in comparison with either trajectory or jamming power optimization, the superiority of the proposed algorithm is verified. © 2022, Korean Institute of Communications and Information Sciences. All rights reserved. | Park, Ji-Young; Jeong, Seongah | Kyungpook National University, School of Electronic and Electrical Engineering Graduate School, South Korea; Kyungpook National University, School of Electronics Engineering, South Korea | 58968223000; 55210226900 | seongah@knu.ac.kr; | Journal of Korean Institute of Communications and Information Sciences | 1226-4717 | 47 | 3 | 0 | 2025-06-25 | 0 | Imperfect Channel State Information; Jammer; Physical Layer Security; Power Allocation; Trajectory; Unmanned Aerial Vehicle (UAV) | Korean | Final | 2022 | 10.7840/kics.2022.47.3.409 | 바로가기 | 바로가기 |
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