• Title/Summary/Keyword: Local clustering

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Galaxy Clusters at High Redshift

  • Im, Myungshin
    • The Bulletin of The Korean Astronomical Society
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    • v.40 no.1
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    • pp.41.1-41.1
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    • 2015
  • Hierarchical galaxy formation models under LCDM cosmology predict that the most massive structures such as galaxy clusters (M > $10^{14}M_{\odot}$) appear late (z < 1) in the history of the universe through hierarchical clustering of small objects. Galaxy formation is also expected to be accelerated in overdense environments, with the star formation rate-density relation to be established at z ~ 2. In this talk, we present our search of massive structures of galaxies at 0.7 < z < 4, using the data from GOODS survey and our own imaging survey, Infrared Medium-deep Survey (IMS). From these studies, we find that there are excess of massive structures of galaxies at z > 2 in comparison to the Millennium simulation data. At 1 < z < 2, the number density of massive structures is consistent with the simulation data, but the star formation history is more or less identical between field and cluster. The star formation quenching process is dominated by internal process (stellar mass). The environmental effect becomes important only at z < 1, which contributes to create the well known star formation-density relation in the local universe. Our results suggest that galaxy formation models under LCDM cosmology may require further refinements to match the observation.

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Shot Boundary Detection Using Global Information (전역적 정보를 이용한 샷 경계 검출)

  • Shin, Seong-Yoon;Shin, Kwang-Sung;Lee, Hyun-Chang;Jin, Chan-Yong;Rhee, Yang-Won
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • 2012.05a
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    • pp.149-150
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    • 2012
  • This paper presents a shot boundary detection method based on the global decision tree that allows for extraction of boundaries of high variations occurring due to camera breaks from frame difference values. For a start, difference values between frames are calculated through local X2-histogram and normalization. Next, the distances between difference values are calculated through normalization.

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A Research of Obstacle Detection and Path Planning for Lane Change of Autonomous Vehicle in Urban Environment (자율주행 자동차의 실 도로 차선 변경을 위한 장애물 검출 및 경로 계획에 관한 연구)

  • Oh, Jae-Saek;Lim, Kyung-Il;Kim, Jung-Ha
    • Journal of Institute of Control, Robotics and Systems
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    • v.21 no.2
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    • pp.115-120
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    • 2015
  • Recently, in automotive technology area, intelligent safety systems have been actively accomplished for drivers, passengers, and pedestrians. Also, many researches are focused on development of autonomous vehicles. This paper propose the application of LiDAR sensors, which takes major role in perceiving environment, terrain classification, obstacle data clustering method, and local map building for autonomous driving. Finally, based on these results, planning for lane change path that vehicle tracking possible were created and the reliability of path generation were experimented.

The Study of System Security Technique for Mobile Ad Hoc Network (Mobile Ad Hoc Network에서 시스템 보안 기법에 관한 연구)

  • Yang, Hwan-Seok
    • Journal of Digital Contents Society
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    • v.9 no.1
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    • pp.33-39
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    • 2008
  • Mobile Ad Hoc Network is easy to be attacked because nodes are distributed not network based infrastructure. Intrusion detection system perceives the trust values of neighboring nodes and receives inspection on local security of nodes and observation ability. This study applied clustering mechanism to reduce overhead in intrusion detection. And, in order to measure the trust values, it associates the trust information cluster head received from member nodes with its own value and evaluates the trust of neighboring nodes. Secure data transmission is received by proposed concept because the trust of nodes on network is achieved accurately.

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A Possibilistic C-Means Approach to the Hough Transform for Line Detection

  • Frank Chung-HoonRhee;Shim, Eun-A
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • 2003.09a
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    • pp.476-479
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    • 2003
  • The Rough transform (HT) is often used for extracting global features in binary images, for example curve and line segments, from local features such as single pixels. The HT is useful due to its insensitivity to missing edge points and occlusions, and robustness in noisy images. However, it possesses some disadvantages, such as time and memory consumption due to the number of input data and the selection of an optimal and efficient resolution of the accumulator space can be difficult. Another problem of the HT is in the difficulty of peak detection due to the discrete nature of the image space and the round off in estimation. In order to resolve the problem mentioned above, a possibilistic C-means approach to clustering [1] is used to cluster neighboring peaks. Several experimental results are given.

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Classification of Foreign Trade Ports using Fuzzy Clustering (퍼지 클러스터링에 의한 항만의 분류)

  • 양원재;금종수
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • 2000.11a
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    • pp.129-132
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    • 2000
  • Grouping ports in certain region by their characteristics could be used as the principal informations to establish national policy for port development or investment and also to analyze the competitiveness between ports. Currently Korean ports are divided into two groups such as the local port and the designated port containing foreign trade port and coastal port under the Korean port law. These divisions seem to be used for port administration as the matter of convenience but some qualitative grouping is needed for research of port problems. In this paper, 28 major Korean ports were clustered by the similar characteristics using Fuzzy C-Means and found to be classified 8 qualitative groups.

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Posture Symmetry based Motion Capture System for Analysis of Lower -limbs Rehabilitation Training

  • Lee, Seok-Jun;Jung, Soon-Ki
    • Journal of Korea Multimedia Society
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    • v.14 no.12
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    • pp.1517-1527
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    • 2011
  • This paper presents a motion capture based rehabilitation training system for a lower-limb paretic patient. The system evaluates the rehabilitation status of the patient by using the bend posture of the knees and the weight balance of the body. The posture of both legs is captured with a single camera using the planar mirror. The weight distribution is obtained by the Wii Balance Board. Self-occlusion problem in the tracking of the legs is resolved by using k-nearest neighbor based clustering with body symmetry and local-linearity of the posture data. To do this, we present data normalization and its symmetric property in the normalized vector space.

Ultra-high-energy cosmic rays and filaments of galaxies in the northern sky

  • Kim, Jihyun;Ryu, Dongsu;Kim, Suk;Rey, Soo-Chang;Kang, Hyesung
    • The Bulletin of The Korean Astronomical Society
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    • v.42 no.1
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    • pp.36.3-36.3
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    • 2017
  • The Telescope Array (TA) experiment reported the arrival direction distribution of ultra-high-energy cosmic rays (UHECRs) with energies above $5.7{\times}10^{19}eV$ in the northern sky. A clustering of TA events, the so-called hotspot, was found; however, its nature has not yet been understood. To understand the origin of the TA hotspot, we examine the sky distributions of the TA UHECR arrival direction and filamentary structures of galaxies in the local universe. By statistical tests for anisotropy, we find a close correlation of the TA events with the filaments of galaxies connected to the Virgo cluster. We discuss our finding and its implications.

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An Efficient Clustering using the Genetic Algorithm (진화 알고리즘을 적용한 효율적 군집화 기법)

  • Lee, Soo-Jung;Kwon, Hye-Ryun;Kim, Eun-Ju;Lee, Yill-Byung
    • Annual Conference of KIPS
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    • 2001.04b
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    • pp.1017-1020
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    • 2001
  • 최근 들어 관심의 대상이 되고 있는 CRM, eCRM은 비즈니스 분야에 중요한 역할을 담당하고 있다. 이를 위해 여러 방법들이 사용되고 있으나, 그 중 데이터 마이닝은 핵심 기술이라 할 수 있다. 다양한 데이터 마이닝 기법가운데 군집화 기법은, 데이터 집합을 유사한 데이터 개체들의 군집들로 분할하여 데이터 속에 존재하는 의미 있는 정보를 얻는 과정이다. 그런데 기존의 군집화 알고리즘들은 사전에 군집의 개수를 미리 결정해져야 하며, 지역적 최적해(local minima)에 수렴할 수 있다는 문제점을 가지고 있다. 본 논문에서는 진화 알고리즘을 사용하여 자동적으로 적절한 군집의 개수를 결정하여 군집화 될 수 있도록 하고, 병렬 탐색을 통해 지역적 최적해에 수렴되는 문제점을 개선한 알고리즘과 적합도 함수를 제안한다.

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A Study on Fitness Function of Clustering Algorithm based on Genetic Algorithm (유전자 알고리즘을 이용한 군집화 기법의 적합도 함수에 관한 연구)

  • 이수정;권혜련;김은주;이일병
    • Proceedings of the Korean Information Science Society Conference
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    • 2001.04b
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    • pp.310-312
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    • 2001
  • 최근 관심의 대상이 되고 있는 CRM, eCRM에는 데이터 마이닝 기법이 핵심 기술로 이용되고 있다. 이러한 데이터 마이닝 기법가운데 가장 널리 사용되고 있는 군집화는, 데이터 집합을 유사한 데이터의 군집들로 분할하여 데이터 속에 존재하는 의미 있는 정보를 얻는 것이다. 그런데 기존의 군집화 알고리즘은 사전에 군집의 개수를 미리 결정해줘야 하고 잡음에 민감하여 지역적 최적해(local minima)에 수렴할 수 있다는 문제점을 가지고 있다. 이러한 문제점의 개선을 위해, 본 논문에서는 유사도 개념을 적합도 함수로 사용하는 유전자 알고리즘을 적용한 군집화 기법을 제안하다. 특히 적합도 하수에 사용된 군집의 대표값 개념은 요약 정보만을 이용하여 계산속도가 향상되기 때문에 대용량 데이터를 다루는 마이닝에 적합할 것을 기대된다.

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