• Title/Summary/Keyword: Topological map

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Analysis of spatial mixing characteristics of water quality at the confluence using artificial intelligence (인공지능을 활용한 합류부에서 수질의 공간혼합 특성 분석)

  • Lee, Seo Gyeong;Kim, Dongsu;Kim, Kyungdong;Kim, Young Do;Lyu, Siwan
    • Proceedings of the Korea Water Resources Association Conference
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    • 2022.05a
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    • pp.482-482
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    • 2022
  • 하천의 합류부에서는 수질이 다른 유체가 혼합하여 합류 전과 다른 특성을 보인다. 하천의 합류부에서 수질을 효율적으로 관리하기 위해서는 수질의 공간적인 혼합 특성을 규명하는 것이 중요하다. 합류부에서 수질의 공간적인 혼합 특성을 분석하기 위해 본 연구에서는 토폴로지 데이터 분석(topological data analysis, TDA), 자기 조직화 지도(Self-Organizing Map, SOM), k-평균 알고리즘(K-means clustering algorithm) 세 가지 기법을 이용하였다. 세 가지 기법을 비교하여 어떤 알고리즘이 합류부의 수질 변화 특성을 더 뚜렷하게 나타내는지 분석하였다. 수질 변화 비교 인자들은 pH, chlorophyll, DO, Turbidity 등이 있고, 수질 인자들은 YSI를 활용해 측정하였다. 자료의 측정 지역은 낙동강과 황강이 합류하는 지역이며, 보트에 YSI 장비를 부착하고 횡단하여 측정하였다. 측정한 데이터를 R 프로그램을 통해 세 가지 기법을 적용시켜 수질 변화 비교를 분석한다. 토폴로지 데이터 분석(topological data analysis, TDA)은 거대하고 복잡한 데이터로부터 유의미한 정보를 추출하는 데 사용하고, 자기조직화지도(Self-Organizing Map, SOM) 기법은 차원 축소와 군집화를 동시에 수행한다. k-평균 알고리즘(K-means clustering algorithm) 기법은 주어진 데이터를 k개의 클러스터로 묶는 머신러닝 비지도학습에 속하는 알고리즘이다. 세 가지 방법들의 주목적은 클러스터링이다. 클러스터 분석(Cluster analysis)이란 주어진 데이터들의 특성을 고려해 동일한 성격을 가진 여러 개의 그룹으로 대상을 분류하는 데이터 마이닝의 한 방법이다. 군집화 방법들인 TDA, SOM, K-means를 이용해 합류 지역의 수질 특성들을 클러스터링하여 수질 패턴들을 분석해 하천 수질 오염을 방지할 수 있을 것이다. 본 연구에서는 토폴로지 데이터 분석(topological data analysis, TDA), 자기조직화지도(Self-Organizing Map, SOM), k-평균 알고리즘(K-means clustering algorithm) 세 가지 기법을 이용하여 합류부에서의 수질 특성을 비교하며 어떤 기법이 합류의 특성을 더욱 뚜렷하게 나타내는지 규명했다. 합류의 특성을 군집화 방법을 이용해 알게 된다면, 합류부의 수질 변화 패턴을 다른 합류 지역에서도 적용할 수 있을 것으로 기대된다.

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Fast Path Planning Algorithm for Mobile Robot Navigation (모바일 로봇의 네비게이션을 위한 빠른 경로 생성 알고리즘)

  • Park, Jung Kyu;Jeon, Heung Seok;Noh, Sam H.
    • IEMEK Journal of Embedded Systems and Applications
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    • v.9 no.2
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    • pp.101-107
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    • 2014
  • Mobile robots use an environment map of its workspace to complete the surveillance task. However grid-based maps that are commonly used map format for mobile robot navigation use a large size of memory for accurate representation of environment. In this reason, grid-based maps are not suitable for path planning of mobile robots using embedded board. In this paper, we present the path planning algorithm that produce a secure path rapidly. The proposed approach utilizes a hybrid map that uses less memory than grid map and has same efficiency of a topological map. Experimental results show that the fast path planning uses only 1.5% of the time that a grid map based path planning requires. And the results show a secure path for mobile robot.

A Systematic and Efficient Approach for Data Association in Topological Maps for Mobile Robot using Wavelet Transformation

  • Doh, N.L.;Lee, K.;Chung, W.K.
    • 제어로봇시스템학회:학술대회논문집
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    • 2004.08a
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    • pp.2017-2022
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    • 2004
  • Data association is a process that matches a recent observation with known data set, which is used for the localization of mobile robots. Edges in topological maps have rich information which can be used for the data association. However, no systematic approach on using the edge data for data association has been reported. This paper proposes a systematic way of utilizing the edge data for data association. First, we explain a Local Generalized Voronoi Angle(LGA) to represent the edge data in 1-dimension. Second, we suggest a key factor extraction procedure from the LGA to reduce the number by $2^7-2^8$ times, for computational efficiency using the wavelet transformation. Finally we propose a way of data association using the key factors of the LGA. Simulations show that the proposed data association algorithm yields higher probability for similar edges in computationally efficient manner.

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Using Higher Order Neuron on the Supervised Learning Machine of Kohonen Feature Map (고차 뉴런을 이용한 교사 학습기의 Kohonen Feature Map)

  • Jung, Jong-Soo;Hagiwara, Masafumi
    • The Transactions of the Korean Institute of Electrical Engineers D
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    • v.52 no.5
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    • pp.277-282
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    • 2003
  • In this paper we propose Using Higher Order Neuron on the Supervised Learning Machine of the Kohonen Feature Map. The architecture of proposed model adopts the higher order neuron in the input layer of Kohonen Feature Map as a Supervised Learning Machine. It is able to estimate boundary on input pattern space because or the higher order neuron. However, it suffers from a problem that the number of neuron weight increases because of the higher order neuron in the input layer. In this time, we solved this problem by placing the second order neuron among the higher order neuron. The feature of the higher order neuron can be mapped similar inputs on the Kohonen Feature Map. It also is the network with topological mapping. We have simulated the proposed model in respect of the recognition rate by XOR problem, discrimination of 20 alphabet patterns, Mirror Symmetry problem, and numerical letters Pattern Problem.

Analysis of Very High Resolution Solar Energy Based on Solar-Meteorological Resources Map with 1km Spatial Resolution (1km 해상도 태양-기상자원지도 기반의 초고해상도 태양 에너지 분석)

  • Jee, JoonBum;Zo, Ilsung;Lee, Chaeyon;Choi, Youngjean;Kim, Kyurang;Lee, KyuTae
    • New & Renewable Energy
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    • v.9 no.2
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    • pp.15-22
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    • 2013
  • The solar energy are an infinite source of energy and a clean energy without secondary pollution. The global solar energy reaching the earth's surface can be calculated easily according to the change of latitude, altitude, and sloped surface depending on the amount of the actual state of the atmosphere and clouds. The high-resolution solar-meteorological resource map with 1km resolution was developed in 2011 based on GWNU (Gangneung-Wonju National University) solar radiation model with complex terrain. The very high resolution solar energy map can be calculated and analyzed in Seoul and Eunpyung with topological effect using by 1km solar-meteorological resources map, respectively. Seoul DEM (Digital Elevation Model) have 10m resolution from NGII (National Geographic Information Institute) and Eunpyeong new town DSM (Digital Surface Model) have 1m spatial resolution from lidar observations. The solar energy have small differences according to the local mountainous terrain and residential area. The maximum bias have up to 20% and 16% in Seoul and Eunpyung new town, respectively. Small differences are that limited area with resolutions. As a result, the solar energy can calculate precisely using solar radiation model with topological effect by digital elevation data and its results can be used as the basis data for the photovoltaic and solar thermal generation.

ENTROPY MAPS FOR MEASURE EXPANSIVE HOMEOMORPHISM

  • JEONG, JAEHYUN;JUNG, WOOCHUL
    • Journal of the Chungcheong Mathematical Society
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    • v.28 no.3
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    • pp.377-384
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    • 2015
  • It is well known that the entropy map is upper semi-continuous for expansive homeomorphisms on a compact metric space. Recently, Morales [3] introduced the notion of measure expansiveness which is general than that of expansiveness. In this paper, we prove that the entropy map is upper semi-continuous for measure expansive homeomorphisms.

FIXED POINTS OF BETTER ADMISSIBLE MAPS ON GENERALIZED CONVEX SPACES

  • Park, Se-Hie
    • Journal of the Korean Mathematical Society
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    • v.37 no.6
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    • pp.885-899
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    • 2000
  • We obtain generalized versions of the Fan-Browder fixed point theorem for G-convex spaces. We define the class B of better admissible multimaps on G-convex spaces and show that any closed compact map in b fro ma locally G-convex uniform space into itself has a fixed point.

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SOME RESULTS ON INTUITIONISTIC FUZZY TOPOLOGICAL SPACES DEFINED BY INTUITIONISTIC GRADATION OF OPENNESS

  • MIN WON KEUN;PARK CHUN-KEE
    • Communications of the Korean Mathematical Society
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    • v.20 no.4
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    • pp.791-801
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    • 2005
  • In this paper, we introduce the concepts of closure and interior defined by an intuitionistic gradation of openness. We also introduce the concepts of weakly gp-maps, gp-open maps and several types of compactness, and obtain some characterizations.

The reidemeister numbers on transformation groups

  • Ahn, Soo-Youp;Chung, In-Jae
    • Communications of the Korean Mathematical Society
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    • v.11 no.2
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    • pp.445-455
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    • 1996
  • In this paper we study the Reidemeister number $R(f_G)$ for a self-map $f_G : (X, G) \to (X, G)$ of the transformation group (X,G), as an extenstion of the Reidemeister number R(f) for a self-map $f : X \to X$ of a topological space X.

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