• Title/Summary/Keyword: Topological Orders

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Quantum Spin Hall Effect And Topological Insulator

  • Lee, Ilyoung;Yu, Hwan Joo;Lee, Won Tae
    • Proceeding of EDISON Challenge
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    • 2014.03a
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    • pp.516-520
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    • 2014
  • Fractional quantum Hall Effect (FQSH) is one of most fundamental issues in condensed matter physics, and the Topological insulator becomes its prominent applications. This article reviews the general frameworks of these development and the physical properties. FQSH states and topological insulators are supposed to be topologically invariant under the minor change of geometrical shape or internal impurities. The phase transitions involved in this phenomena are known not to be explained in terms of symmetry breaking or Landau-Ginsburg theory. The new type of phase transitions related to topological invariants has acquired new name - topological phase transition. The intuitive concepts and the other area having same type of phase transitions are discussed.

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Symmetry and depth-dependent orders of subsurface defects in Mn-doped Sb(111) studied by using STM

  • Cho, Doo-Hee;Kim, Min-Seong;Lyo, In-Whan
    • Proceedings of the Korean Vacuum Society Conference
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    • 2010.08a
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    • pp.57-57
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    • 2010
  • Sb(111) is a spin textured surface due to the strong spin-orbit coupling, often viewed as a proto-type topological insulator. We used scanning tunneling microscopy (STM) to characterize various Mn-induced subsurface defects existing at the surface of Mn-doped Sb at 50 K. Our STM images show that every defect exhibits 3-fold symmetry with a single rotational orientation and can be categorized by their shapes and sizes. We found more than 10 types of subsurface defects with distinctive orders, which allows the resolution of the vertical positions of the magnetic dopants lying more than 10 layers down from the surface. We will discuss about our findings in comparison with theoretical results.

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Fuzzy Syntopogenous Structures and Orders

  • 정세화;주무홍
    • Journal of the Korean Institute of Intelligent Systems
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    • v.7 no.3
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    • pp.102-106
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    • 1997
  • We introduce the category [PFSyn] of saturated fuzzy syntopogenous preordered spaces and continuous isotones ans show that the category [PFSyn] is topological and cotopological. Furthermore, to consdier a compatibility between order structures and fuzzy syntopogenous structure, we introduce a category [IPFSyn] of increasing saturated fuzzy syntopogenous spaces and its dual category [IPFSyn] of decreasing fuzzy syntopogenous spaces, and show that [IPFSyn] and [DPFSyn] are both bireflective in the category [PFSyn].

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Design and Evaluation of a parallel EMG Signal Identifier using Trsnsputers (트랜스퓨터를 이용한 병렬 근신호 인식기의 설계 및 평가)

  • 김종원;김성환
    • Journal of Biomedical Engineering Research
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    • v.17 no.4
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    • pp.459-468
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    • 1996
  • This paper considers the problem of realising a parallel EMG identifier used in FES (functional electrical stimulation) system on a fixed dimension transputer array. This involves using an identifiestion algorithm in the wavelet transform domain. This algorithm have suggested by the authors in a previous paper(6). The transputer serial links permit higtlly varied and economic network-type connections and the structure enables rapid topological reconfiguration. Analysing the results Showed that the Speed-UPS ranged from 1.82 to 3.44 With 2-4 transputers for corresponding model order, and from 1.82 to 3.97 with increasing the model orders when two and four transputers are used respectively.

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Big Data Astronomy: Large-scale Graph Analyses of Five Different Multiverses

  • Hong, Sungryong
    • The Bulletin of The Korean Astronomical Society
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    • v.43 no.2
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    • pp.36.3-37
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    • 2018
  • By utilizing large-scale graph analytic tools in the modern Big Data platform, Apache Spark, we investigate the topological structures of five different multiverses produced by cosmological n-body simulations with various cosmological initial conditions: (1) one standard universe, (2) two different dark energy states, and (3) two different dark matter densities. For the Big Data calculations, we use a custom build of stand-alone Spark cluster at KIAS and Dataproc Compute Engine in Google Cloud Platform with the sample sizes ranging from 7 millions to 200 millions. Among many graph statistics, we find that three simple graph measurements, denoted by (1) $n_\k$, (2) $\tau_\Delta$, and (3) $n_{S\ge5}$, can efficiently discern different topology in discrete point distributions. We denote this set of three graph diagnostics by kT5+. These kT5+ statistics provide a quick look of various orders of n-points correlation functions in a computationally cheap way: (1) $n = 2$ by $n_k$, (2) $n = 3$ by $\tau_\Delta$, and (3) $n \ge 5$ by $n_{S\ge5}$.

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