Institution
Nanjing University of Science and Technology
Education•Nanjing, China•
About: Nanjing University of Science and Technology is a education organization based out in Nanjing, China. It is known for research contribution in the topics: Control theory & Catalysis. The organization has 31581 authors who have published 36390 publications receiving 525474 citations. The organization is also known as: Nánjīng Lǐgōng Dàxué & Nánlǐgōng.
Papers published on a yearly basis
Papers
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TL;DR: A novel autofocusing technique is developed for random translational motion compensation in inverse synthetic aperture radar (ISAR) imaging of objects based on an entropy minimization principle and validated via a nonparametric estimation method.
Abstract: A novel autofocusing technique is developed for random translational motion compensation in inverse synthetic aperture radar (ISAR) imaging of objects. This technique is based on an entropy minimization principle and validated via a nonparametric estimation method. Images of a simulation and a real flying aircraft are used for illustration. Images of encouraging quality confirm the feasibility of autofocusing the radar images by just the requirement of minimizing the image entropy.
550 citations
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TL;DR: In this article, the authors investigated the research development, current trends and intellectual structure of topic modeling based on Latent Dirichlet Allocation (LDA), and summarized challenges and introduced famous tools and datasets in topic modelling based on LDA.
Abstract: Topic modeling is one of the most powerful techniques in text mining for data mining, latent data discovery, and finding relationships among data, text documents. Researchers have published many articles in the field of topic modeling and applied in various fields such as software engineering, political science, medical and linguistic science, etc. There are various methods for topic modeling, which Latent Dirichlet allocation (LDA) is one of the most popular methods in this field. Researchers have proposed various models based on the LDA in topic modeling. According to previous work, this paper can be very useful and valuable for introducing LDA approaches in topic modeling. In this paper, we investigated scholarly articles highly (between 2003 to 2016) related to Topic Modeling based on LDA to discover the research development, current trends and intellectual structure of topic modeling. Also, we summarize challenges and introduce famous tools and datasets in topic modeling based on LDA.
546 citations
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TL;DR: A review of carbon nanotube reinforced composite (CNTRC) materials can be found in this article, where the concept of functionally graded (FG) pattern of reinforcement has been applied for functionally graded carbon nanite reinforced composite materials.
541 citations
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TL;DR: This note provides an improved asymptotic stability condition for time-delay systems in terms of a strict linear matrix inequality that avoids bounding certain cross terms which often leads to conservatism.
Abstract: This note provides an improved asymptotic stability condition for time-delay systems in terms of a strict linear matrix inequality. Unlike previous methods, the mathematical development avoids bounding certain cross terms which often leads to conservatism. When time-varying norm-bounded uncertainties appear in a delay system, an improved robust delay-dependent stability condition is also given. Examples are provided to demonstrate the reduced conservatism of the proposed conditions.
536 citations
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University of Savoy1, Université Paris-Saclay2, CERN3, Czech Technical University in Prague4, Technische Universität München5, University of Belgrade6, University of Santiago de Compostela7, University of Tokyo8, École des mines de Nantes9, Nanjing University of Science and Technology10, University of Cape Town11, Saint Petersburg State University12, Federico Santa María Technical University13, Utrecht University14, Duke University15, University of Bergen16, University of Auvergne17, Texas A&M University18, Iowa State University19, Bielefeld University20, Heidelberg University21, University of Grenoble22, Tata Institute of Fundamental Research23, Kent State University24, University of Lyon25, Goethe University Frankfurt26, Los Alamos National Laboratory27, University of California, Davis28, Lawrence Livermore National Laboratory29, Central China Normal University30, Tsinghua University31
TL;DR: In this paper, the authors review the study of open heavy-flavour and quarkonium production in high-energy hadronic collisions, as tools to investigate fundamental aspects of Quantum Chromodynamics, from the proton and nucleus structure at high energy to deconfinement and the properties of the Quark-Gluon Plasma.
Abstract: This report reviews the study of open heavy-flavour and quarkonium production in high-energy hadronic collisions, as tools to investigate fundamental aspects of Quantum Chromodynamics, from the proton and nucleus structure at high energy to deconfinement and the properties of the Quark–Gluon Plasma. Emphasis is given to the lessons learnt from LHC Run 1 results, which are reviewed in a global picture with the results from SPS and RHIC at lower energies, as well as to the questions to be addressed in the future. The report covers heavy flavour and quarkonium production in proton–proton, proton–nucleus and nucleus–nucleus collisions. This includes discussion of the effects of hot and cold strongly interacting matter, quarkonium photoproduction in nucleus–nucleus collisions and perspectives on the study of heavy flavour and quarkonium with upgrades of existing experiments and new experiments. The report results from the activity of the SaporeGravis network of the I3 Hadron Physics programme of the European Union 7
$$\mathrm{th}$$
Framework Programme.
535 citations
Authors
Showing all 31818 results
Name | H-index | Papers | Citations |
---|---|---|---|
Jian Yang | 142 | 1818 | 111166 |
Liming Dai | 141 | 781 | 82937 |
Hui Li | 135 | 2982 | 105903 |
Jian Zhou | 128 | 3007 | 91402 |
Shuicheng Yan | 123 | 810 | 66192 |
Zidong Wang | 122 | 914 | 50717 |
Xin Wang | 121 | 1503 | 64930 |
Xuan Zhang | 119 | 1530 | 65398 |
Zhenyu Zhang | 118 | 1167 | 64887 |
Xin Li | 114 | 2778 | 71389 |
Zeshui Xu | 113 | 752 | 48543 |
Xiaoming Li | 113 | 1932 | 72445 |
Chunhai Fan | 112 | 702 | 51735 |
H. Vincent Poor | 109 | 2116 | 67723 |
Qian Wang | 108 | 2148 | 65557 |