• Title/Summary/Keyword: clustering modeling

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Environmental Survey Data Modeling Using K-means Clustering Techniques

  • Park, Hee-Chang;Cho, Kwang-Hyun
    • Journal of the Korean Data and Information Science Society
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    • v.16 no.3
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    • pp.557-566
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    • 2005
  • Clustering is the process of grouping the data into clusters so that objects within a cluster have high similarity in comparison to one another. In this paper we used k-means clustering of several clustering techniques. The k-means Clustering Is classified as a partitional clustering method. We analyze 2002 Gyeongnam social indicator survey data using k-means clustering techniques for environmental information. We can use these outputs given by k-means clustering for environmental preservation and environmental improvement.

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Enhanced Extraction of Traversable Region by Combining Scene Clustering with 3D World Modeling based on CCD/IR Image (CCD/IR 영상 기반의 3D 월드모델링과 클러스터링의 통합을 통한 주행영역 추출 성능 개선)

  • Kim, Jun
    • Journal of the Korea Institute of Military Science and Technology
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    • v.11 no.4
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    • pp.107-115
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    • 2008
  • Accurate extraction of traversable region is a critical issue for autonomous navigation of unmanned ground vehicle(UGV). This paper introduces enhanced extraction of traversable region by combining scene clustering with 3D world modeling using CCD(Charge-Coupled Device)/IR(Infra Red) image. Scene clustering is developed with K-means algorithm based on CCD and IR image. 3D world modeling is developed by fusing CCD and IR stereo image. Enhanced extraction of traversable regions is obtained by combining feature of extraction with a clustering method and a geometric characteristic of terrain derived by 3D world modeling.

Fuzzy Modeling based on FCM Clustering Algorithm (FCM 클러스터링 알고리즘에 기초한 퍼지 모델링)

  • 윤기찬;오성권
    • 제어로봇시스템학회:학술대회논문집
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    • 2000.10a
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    • pp.373-373
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    • 2000
  • In this paper, we propose a fuzzy modeling algorithm which divides the input space more efficiently than convention methods by taking into consideration correlations between components of sample data. The proposed fuzzy modeling algorithm consists of two steps: coarse tuning, which determines consequent parameters approximately using FCRM clustering method, and fine tuning, which adjusts the premise and consequent parameters more precisely by gradient descent algorithm. To evaluate the performance of the proposed fuzzy mode, we use the numerical data of nonlinear function.

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An Survey on the Power System Modeling using a Clustering Algorithm (클러스터링 기법을 적용한 전력시스템 모델링에 관한 사례 조사)

  • Park, Young-Soo;Kim, Jin-Ho
    • Proceedings of the KIEE Conference
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    • 2006.07a
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    • pp.410-411
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    • 2006
  • This paper is focused on the survey on the power system modeling using a clustering algorithm. In electricity markets, clustering method is a efficient tool to model the power system. It can be seen that electricity markets can also be classified into several groups which show similar patterns and that the fundamental characteristics of power systems can be widely applicable to other technical problems in power system such as generation scheduling, power flow analysis, short-term load forecasting, and so on. There are several researches on the power system modeling using a clustering algorithm. We specially surveyed their own clustering methods to model the power system.

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A New Learning Algorithm for Neuro-Fuzzy Modeling Using Self-Constructed Clustering

  • Kim, Sung-Suk;Kwak, Keun-Chang;Kim, Sung-Soo;Ryu, Jeong-Woong
    • 제어로봇시스템학회:학술대회논문집
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    • 2005.06a
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    • pp.1254-1259
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    • 2005
  • In this paper, we proposed a learning algorithm for the neuro-fuzzy modeling using a learning rule to adapt clustering. The proposed algorithm includes the data partition, assigning the rule into the process of partition, and optimizing the parameters using predetermined threshold value in self-constructing algorithm. In order to improve the clustering, the learning method of neuro-fuzzy model is extended and the learning scheme has been modified such that the learning of overall model is extended based on the error-derivative learning. The effect of the proposed method is presented using simulation compare with previous ones.

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A New Learning Algorithm of Neuro-Fuzzy Modeling Using Self-Constructed Clustering

  • Ryu, Jeong-Woong;Song, Chang-Kyu;Kim, Sung-Suk;Kim, Sung-Soo
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • v.5 no.2
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    • pp.95-101
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    • 2005
  • In this paper, we proposed a learning algorithm for the neuro-fuzzy modeling using a learning rule to adapt clustering. The proposed algorithm includes the data partition, assigning the rule into the process of partition, and optimizing the parameters using predetermined threshold value in self-constructing algorithm. In order to improve the clustering, the learning method of neuro-fuzzy model is extended and the learning scheme has been modified such that the learning of overall model is extended based on the error-derivative learning. The effect of the proposed method is presented using simulation compare with previous ones.

Efficient Continuous Vocabulary Clustering Modeling for Tying Model Recognition Performance Improvement (공유모델 인식 성능 향상을 위한 효율적인 연속 어휘 군집화 모델링)

  • Ahn, Chan-Shik;Oh, Sang-Yeob
    • Journal of the Korea Society of Computer and Information
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    • v.15 no.1
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    • pp.177-183
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    • 2010
  • In continuous vocabulary recognition system by statistical method vocabulary recognition to be performed using probability distribution it also modeling using phoneme clustering for based sample probability parameter presume. When vocabulary search that low recognition rate problem happened in express vocabulary result from presumed probability parameter by not defined phoneme and insert phoneme and it has it's bad points of gaussian model the accuracy unsecure for one clustering modeling. To improve suggested probability distribution mixed gaussian model to optimized for based resemble Euclidean and Bhattacharyya distance measurement method mixed clustering modeling that system modeling for be searching phoneme probability model in clustered model. System performance as a result of represent vocabulary dependence recognition rate of 98.63%, vocabulary independence recognition rate of 97.91%.

Environmental Survey Data Modeling using K-means Clustering Techniques

  • Park, Hee-Chang;Cho, Kwang-Hyun
    • 한국데이터정보과학회:학술대회논문집
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    • 2004.10a
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    • pp.77-86
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    • 2004
  • Clustering is the process of grouping the data into clusters so that objects within a cluster have high similarity in comparison to one another. In this paper we used k-means clustering of several clustering techniques. The k-means Clustering is classified as a partitional clustering method. We analyze 2002 Gyeongnam social indicator survey data using k-means clustering techniques for environmental information. We can use these outputs given by k-means clustering for environmental preservation and environmental improvement.

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Nonlinear structural finite element model updating with a focus on model uncertainty

  • Mehrdad, Ebrahimi;Reza Karami, Mohammadi;Elnaz, Nobahar;Ehsan Noroozinejad, Farsangi
    • Earthquakes and Structures
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    • v.23 no.6
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    • pp.549-580
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    • 2022
  • This paper assesses the influences of modeling assumptions and uncertainties on the performance of the non-linear finite element (FE) model updating procedure and model clustering method. The results of a shaking table test on a four-story steel moment-resisting frame are employed for both calibrations and clustering of the FE models. In the first part, simple to detailed non-linear FE models of the test frame is calibrated to minimize the difference between the various data features of the models and the structure. To investigate the effect of the specified data feature, four of which include the acceleration, displacement, hysteretic energy, and instantaneous features of responses, have been considered. In the last part of the work, a model-based clustering approach to group models of a four-story frame with similar behavior is introduced to detect abnormal ones. The approach is a composition of property derivation, outlier removal based on k-Nearest neighbors, and a K-means clustering approach using specified data features. The clustering results showed correlations among similar models. Moreover, it also helped to detect the best strategy for modeling different structural components.

Design of improved Mulit-FNN for Nonlinear Process modeling

  • Park, Hosung;Sungkwun Oh
    • 제어로봇시스템학회:학술대회논문집
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    • 2002.10a
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    • pp.102.2-102
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    • 2002
  • In this paper, the improved Multi-FNN (Fuzzy-Neural Networks) model is identified and optimized using HCM (Hard C-Means) clustering method and optimization algorithms. The proposed Multi-FNN is based on FNN and use simplified and linear inference as fuzzy inference method and error back propagation algorithm as learning rules. We use a HCM clustering and genetic algorithms (GAs) to identify both the structure and the parameters of a Multi-FNN model. Here, HCM clustering method, which is carried out for the process data preprocessing of system modeling, is utilized to determine the structure of Multi-FNN according to the divisions of input-output space using I/O process data. Also, the parame...

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