• Title/Summary/Keyword: 2차 MAP

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A Secondary MAP Scheme for Decreasing a Handover Delay and Packet Loss in an HMIPv6 (HMIPv6에서 핸드오버 지연 및 패킷 손실 감소를 위한 2차 MAP 이용 기법)

  • Jang Seong Sik;Lee Won Yeoul;Park Sun Young;Byun Tae Young;Han Ki Jun
    • Journal of the Institute of Electronics Engineers of Korea TC
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    • v.42 no.2 s.332
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    • pp.39-48
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    • 2005
  • An HMIPv6 provides micro mobility management using MAP for decreasing handover delay and network load in a mobile IP networks. An HMIPv6 uses distance based algorithm for MAP selection when a mobile host enters a new network domain. However, since every mobile hosts select a farthest router as a MAP, a handover delay and packet loss will be increased. A new MAP selection scheme is herein proposed to solve the problems caused by the distance based MAP selection algorithm by using secondary MAP. We executed the performance evaluation by simulation about handover delay and packet loss of an HMIPv6 and our proposed scheme. The simulation results show that the performance of our proposed scheme is better than that of HMIPv6.

Discussions on the Distribution and Genesis of Mountain Ranges in the Korean Peninsular (III): Proposing a New Mountain Range Map (한국 산맥론(III): 새로운 산맥도의 제안)

  • Park, Soo-Jin;Son, Ill
    • Journal of the Korean Geographical Society
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    • v.43 no.3
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    • pp.276-295
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    • 2008
  • Recent disputes on mountain ranges in Korea have partially been derived from the discordance of the spatial distribution and the extent of mountain ranges presented by different researchers and school textbooks. The lack of consensus on the definition and genesis of mountain ranges adds further confusion. In order to overcome these problems, it is necessary to provide genetically classified mountain range maps for different usages, map scales and educational purposes. This paper first argues that mountain ranges and mountain ridges should separately be used as different conceptual frameworks to explain complex spatial distribution of mountains in Korea. The new mountain range map (sanmaekdo) proposed in this research puts strong emphasis on tectonic movement and denudational processes to explain the spatial distribution of mountains. The new mountain range map has 15 mountain ranges (sanmaek: in total, which are further divided into 7 primary and 8 secondary mountain ranges. The new mountain range map eliminates Jeogyuryeongsanmaek, Myohyangsanmaek, Myeoraksanmaek, and Masingnyeongsanmaek from the existing map, since these have a vague definition and obscure spatial distribution. On the contrary, few new primary mountain ranges (Gilju-Myeongcheonsanmaek, Yangsansanmaek, Jirisanmaek) and secondary mountain ranges (Wolchulsanmaek and Buksubaeksanmaek) are added to the new mountain range map. Other mountain ranges also show a large difference both in their spatial distribution and the extent of mountain ranges, compared with the previous map. This is especially the case for Nangnimsanmaek, Hamgyeongsanmaek, Taebaeksanmaek, and Sobaeksanmaek. A few new names are also assigned to Macheollyeongsanmaek (Baekdusanmaek), Gwangjusanmaek (Hwaaksanmaek), Charyeongsanmaek (Chiaksanmaek), and Horyeongsanmaek (Naejangsanmaek), even though they show similar spatial distribution patterns with the ones in the existing map.

A Two-Stage Document Page Segmentation Method using Morphological Distance Map and RBF Network (거리 사상 함수 및 RBF 네트워크의 2단계 알고리즘을 적용한 서류 레이아웃 분할 방법)

  • Shin, Hyun-Kyung
    • Journal of KIISE:Software and Applications
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    • v.35 no.9
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    • pp.547-553
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    • 2008
  • We propose a two-stage document layout segmentation method. At the first stage, as top-down segmentation, morphological distance map algorithm extracts a collection of rectangular regions from a given input image. This preliminary result from the first stage is employed as input parameters for the process of next stage. At the second stage, a machine-learning algorithm is adopted RBF network, one of neural networks based on statistical model, is selected. In order for constructing the hidden layer of RBF network, a data clustering technique bared on the self-organizing property of Kohonen network is utilized. We present a result showing that the supervised neural network, trained by 300 number of sample data, improves the preliminary results of the first stage.

An Implementation of Integrated System for Topographic and Cadastral Data (지형 및 지적자료의 통합체계 구축)

  • 유복모;김갑진
    • Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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    • v.18 no.2
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    • pp.143-155
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    • 2000
  • With the increasing needs for the integrated use of topographic and cadastral data in order to build an efficient geo-spatial information system. it is urgently necessary to research into its solution. The intention of this study is to detect error types of data and to propose adjustment methods for solving the problems caused by integrating topographic and cadastral data. For this purpose a primary integrated data model is created to link attribute data(land management system) and graphic data within cadastral information in the first step. In next, a secondary integrated data model based on the improved method is formed to coincide the graphic data of cadastral map with that of topographic map. At the first, because a numerous error types md sources caused by separate management of graphic and attribute data are easily checked, it is possible to suggest an improved method to correct these errors using the primary integrated data model. In addition, the accuracy in position and area with coordinate transformation method based on multi-block adjustment is more efficient than rubber-sheeting method. As a result, the secondary integrated data model could be built by harmonizing cadastral map with topographic map using the improved solution.

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A Study on Speaker Adaptation of Large Continuous Spoken Language Using back-off bigram (Back-off bigram을 이랑한 대용량 연속어의 화자적응에 관한 연구)

  • 최학윤
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.28 no.9C
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    • pp.884-890
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    • 2003
  • In this paper, we studied the speaker adaptation methods that improve the speaker independent recognition system. For the independent speakers, we compared the results between bigram and back-off bigram, MAP and MLLR. Cause back-off bigram applys unigram and back-off weighted value as bigram probability value, it has the effect adding little weighted value to bigram probability value. We did an experiment using total 39-feature vectors as featuring voice parameter with 12-MFCC, log energy and their delta and delta-delta parameter. For this recognition experiment, We constructed a system made by CHMM and tri-phones recognition unit and bigram and back-off bigrams language model.

Turbo Trellis Coded Modulation with Multiple Symbol Detection (다중심벌 검파를 사용한 터보 트렐리스 부호화 변조)

  • Kim Chong Il
    • Journal of the Institute of Convergence Signal Processing
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    • v.1 no.2
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    • pp.105-114
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    • 2000
  • In this paper, we propose a bandwidth-efficient channel coding scheme using the turbo trellis-coded modulation with multiple symbol detection. The turbo code can achieve good bit error rates (BER) at low SNR. That comprises two binary component codes and an interleaver. TCM codes combine modulation and coding by optimizing the euclidean distance between codewords. This can be decoded with the Viterbi or the symbol-by- symbol MAP algorithm. But we present the MAP algorithm with branch metrics of the Euclidean distance of the first phase difference as well as the Lth phase difference. The study shows that the turbo trellis-coded modulation with multiple symbol detection can improve the BER performance at the same SNR.

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Supervised Kohonen Feature Map Using Higher Order Neuron (고차 뉴런을 이용한 KOHONEN의 자기 조직화 맵)

  • Jung, Jong-Soo;Hagiwara, Massfume
    • Proceedings of the KIEE Conference
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    • 2001.07d
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    • pp.2656-2659
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    • 2001
  • 본 논문은 교사 있는 학습기의 Kohonen Feature Map에 고차 뉴런을 도입, 고차 뉴런을 이용한 Kohonen의 자기 조직화 맵을 제안한다. 일반적인 Kohonen Feature Map의 특징은 입력신호를 받아 출력 면(Kohonen Feature Map) 내의 특정한 위치 주위에 집중하는 메커니즘으로 즉, 국소집중 반응을 구하는 구조이다. 본 논문에서는 종래형의 Kohonen Feature Map의 특징을 보유하며 교사 있는 학습기의 Kohonen Feature Map에 고차 뉴런을 도입하여 국소집중반응 및 특징 축출이 용이하도록 네트워크 구조를 개선한 것이다. 특히, 일차 뉴런의 문제점인 비선형 분리 문제에 대하여 교사 있는 학습기의 Kohonen Feature Map의 입력층에 고차 뉴런을 도입함으로 비선형 분리 가능한 형태의 네트워크 구조로 형성하였다. 그러나, 일반적인 고차 뉴런의 문제점을 보안하기 위해 본 논문에서는 오직 2차 뉴런만을 생성하였으며 중복되는 뉴런을 최대한 억제하였다. 본 제안 모델의 특성을 살펴보기 위해 XOR문제와 20개의 Alphabet을 식별하는 패턴인식 시뮬레이션을 했으며, 본 제안 모델의 범화능력을 알아보기 위하여 Mirror Symmetry를 사용하여 계산기 시뮬레이션을 했다. 그 결과, 본 제안 모델이 종래형의 네트워크 구조보다 뛰어난 인식률을 얻을 수 있었다.

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우리도 PATENTMAP를 만들자

  • 남개영
    • 발명특허
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    • v.13 no.4 s.146
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    • pp.50-53
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    • 1988
  • -Patent map는 수집된 정보를 종합$\cdot$정리$\cdot$해석함으로써 2차정보와 새로운 전략을 창출하게 된다.-

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우리도 PATENTMAP를 만들자(3)

  • 남계영
    • 발명특허
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    • v.13 no.6 s.148
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    • pp.39-43
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    • 1988
  • -Patent map는 수집된 정보를 종합$\cdot$정리$\cdot$해석함으로써 2차정보와 새로운 전략을 창출하게 된다.-

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우리도 PATENTMAP를 만들자 (완)

  • 남계영
    • 발명특허
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    • v.13 no.7 s.149
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    • pp.23-27
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    • 1988
  • -Patent Map는 수집된 정보를 종합$\cdot$정리$\cdot$해석함으로써 2차정보와 새로운 전략을 창출하게 된다.-

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