• Title/Summary/Keyword: multidimensional data

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SINGULARITY FORMATION FOR A NONLINEAR VARIATIONAL SINE-GORDON EQUATION IN A MULTIDIMENSIONAL SPACE

  • Fengmei Qin;Kyungwoo Song;Qin Wang
    • Bulletin of the Korean Mathematical Society
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    • v.60 no.6
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    • pp.1697-1704
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    • 2023
  • We study a multidimensional nonlinear variational sine-Gordon equation, which can be used to describe long waves on a dipole chain in the continuum limit. By using the method of characteristics, we show that a solution of a nonlinear variational sine-Gordon equation with certain initial data in a multidimensional space has a singularity in finite time.

An Efficient Multidimensional Scaling Method based on CUDA and Divide-and-Conquer (CUDA 및 분할-정복 기반의 효율적인 다차원 척도법)

  • Park, Sung-In;Hwang, Kyu-Baek
    • Journal of KIISE:Computing Practices and Letters
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    • v.16 no.4
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    • pp.427-431
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    • 2010
  • Multidimensional scaling (MDS) is a widely used method for dimensionality reduction, of which purpose is to represent high-dimensional data in a low-dimensional space while preserving distances among objects as much as possible. MDS has mainly been applied to data visualization and feature selection. Among various MDS methods, the classical MDS is not readily applicable to data which has large numbers of objects, on normal desktop computers due to its computational complexity. More precisely, it needs to solve eigenpair problems on dissimilarity matrices based on Euclidean distance. Thus, running time and required memory of the classical MDS highly increase as n (the number of objects) grows up, restricting its use in large-scale domains. In this paper, we propose an efficient approximation algorithm for the classical MDS based on divide-and-conquer and CUDA. Through a set of experiments, we show that our approach is highly efficient and effective for analysis and visualization of data consisting of several thousands of objects.

Social media comparative analysis based on multidimensional scaling

  • Lee, Hanjun;Suh, Yongmoo
    • Journal of the Korean Data and Information Science Society
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    • v.25 no.3
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    • pp.665-676
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    • 2014
  • As social media draws attention as a business tool, organizations, large or small, are trying to exploit social media in their business. However, lack of understanding the characteristics of each social media led them to develop a naive strategy for dealing with social media. Thus, this study aims to deepen the understanding by comparatively analyzing how social media users perceive (the image of) each social media. Facebook, Twitter, YouTube, Blogs, Communities and Cyworld were chosen for our study and data from 132 respondents were analyzed using multidimensional scaling technique. The results show that there are meaningful differences in users' perception of social media attributes, which are grouped into four; information feature, motivation, promotion tool, usability. It is also analyzed whether such differences can be found between male and female users. (Such differences are also analyzed in both male and female users' perceptions.) Further, we discuss some implications of the research results for both practitioners and researchers.

The Comparison of Singular Value Decomposition and Spectral Decomposition

  • Shin, Yang-Gyu
    • Journal of the Korean Data and Information Science Society
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    • v.18 no.4
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    • pp.1135-1143
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    • 2007
  • The singular value decomposition and the spectral decomposition are the useful methods in the area of matrix computation for multivariate techniques such as principal component analysis and multidimensional scaling. These techniques aim to find a simpler geometric structure for the data points. The singular value decomposition and the spectral decomposition are the methods being used in these techniques for this purpose. In this paper, the singular value decomposition and the spectral decomposition are compared.

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Real-Time Visualization Techniques for Sensor Array Patterns Using PCA and Sammon Mapping Analysis (PCA와 Sammon Mapping 분석을 통한 센서 어레이 패턴들의 실시간 가시화 방법)

  • Byun, Hyung-Gi;Choi, Jang-Sik
    • Journal of Sensor Science and Technology
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    • v.23 no.2
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    • pp.99-104
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    • 2014
  • Sensor arrays based on chemical sensors produce multidimensional patterns of data that may be used discriminate between different chemicals. For the human observer, visualization of multidimensional data is difficult, since the eye and brain process visual information in two or three dimensions. To devise a simple means of data inspection from the response of sensor arrays, PCA (Principal Component Analysis) or Sammon's nonlinear mapping technique can be applied. The PCA, which is a well-known statistical method and widely used in data analysis, has disadvantages including data distortion and the axes for plotting the dimensionally reduced data have no physical meaning in terms of how different one cluster is from another. In this paper, we have investigated two techniques and proposed a combination technique of PCA and nonlinear Sammom mapping for visualization of multidimensional patterns to two dimensions using data sets from odor sensing system. We conclude the combination technique has shown more advantages comparing with the PCA and Sammon nonlinear technique individually.

RESEARCH EFFORTS FOR THE RESOLUTION OF HYDROGEN RISK

  • HONG, SEONG-WAN;KIM, JONGTAE;KANG, HYUNG-SEOK;NA, YOUNG-SU;SONG, JINHO
    • Nuclear Engineering and Technology
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    • v.47 no.1
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    • pp.33-46
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    • 2015
  • During the past 10 years, the Korea Atomic Energy Research Institute (KAERI) has performed a study to control hydrogen gas in the containment of the nuclear power plants. Before the Fukushima accident, analytical activities for gas distribution analysis in experiments and plants were primarily conducted using a multidimensional code: the GASFLOW. After the Fukushima accident, the COM3D code, which can simulate a multidimensional hydrogen explosion, was introduced in 2013 to complete the multidimensional hydrogen analysis system. The code validation efforts of the multidimensional codes of the GASFLOW and the COM3D have continued to increase confidence in the use of codes using several international experimental data. The OpenFOAM has been preliminarily evaluated for APR1400 containment, based on experience from coded validation and the analysis of hydrogen distribution and explosion using the multidimensional codes, the GASFLOW and the COM3D. Hydrogen safety in nuclear power has become a much more important issue after the Fukushima event in which hydrogen explosions occurred. The KAERI is preparing a large-scale test that can be used to validate the performance of domestic passive autocatalytic recombiners (PARs) and can provide data for the validation of the severe accident code being developed in Korea.

Efficient Storage Techniques for Multidimensional Index Structures in Multi-Zoned Disk Environments (다중 존 디스크 환경에서 다차원 인덱스 구조의 효율적 저장 기법)

  • Yu, Byung-Gu;Kim, Seon-Ho;Chang, Jae-Young
    • Journal of KIISE:Databases
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    • v.34 no.4
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    • pp.315-327
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    • 2007
  • The performance of database applications with large sets of multidimensional data depends on the performance of its access methods and the underlying disk system. In modeling the disk system, even though modem disks are manufactured with multiple physical zones, conventional access methods have been developed based on a traditional disk model with many simplifying assumptions. Thus, there is a marked lack of investigation on how to enhance the performance of access methods given a zoned disk model. The paper proposes novel zoning techniques that can be applied to any multidimensional access methods, both static and dynamic, enhancing the effective data transfer rate of underlying disk system by fully utilizing its zone characteristics. Our zoning techniques include data placement algorithms for multidimensional index structures and accompanying localized query processing algorithms for range queries. The experimental results show that our zoning techniques significantly improve the query performance.

Improved Multidimensional Scaling Techniques Considering Cluster Analysis: Cluster-oriented Scaling (클러스터링을 고려한 다차원척도법의 개선: 군집 지향 척도법)

  • Lee, Jae-Yun
    • Journal of the Korean Society for information Management
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    • v.29 no.2
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    • pp.45-70
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    • 2012
  • There have been many methods and algorithms proposed for multidimensional scaling to mapping the relationships between data objects into low dimensional space. But traditional techniques, such as PROXSCAL or ALSCAL, were found not effective for visualizing the proximities between objects and the structure of clusters of large data sets have more than 50 objects. The CLUSCAL(CLUster-oriented SCALing) technique introduced in this paper differs from them especially in that it uses cluster structure of input data set. The CLUSCAL procedure was tested and evaluated on two data sets, one is 50 authors co-citation data and the other is 85 words co-occurrence data. The results can be regarded as promising the usefulness of CLUSCAL method especially in identifying clusters on MDS maps.

Improvement of Software Cost Estimation Guideline Using OLAP Multidimensional Model (OLAP 다차원 모델을 이용한 소프트웨어 사업대가기준의 개선)

  • Park, Hye-Ja;Hwang, In-Soo;Kwon, Ki-Tae
    • Journal of Information Technology Services
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    • v.11 no.1
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    • pp.197-210
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    • 2012
  • This paper presents the ways that can improve the Software Cost Estimation Guidelines in order to replace those that are expected to be abolished at February, 2012, and solve the problems that are being occurred in the current Software Cost Estimation Guidelines. By using multidimensional modeling of OLAP(On-Line Analytical Processing), this paper does three dimensional modeling that considers the product/service view, process view and skill view. Also, it presents the identification method of cost estimation data through the view of each dimension. Furthermore, it defines the software cost estimation process and adapts them into the bottom up estimation and the top down estimation. Finally, it proposes the access of cost estimation data by the multidimensional analysis of OLAP.

An Integer Programming-based Local Search for the Multiple-choice Multidimensional Knapsack Problem

  • Hwang, Junha
    • Journal of the Korea Society of Computer and Information
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    • v.23 no.12
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    • pp.1-9
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    • 2018
  • The multiple-choice multidimensional knapsack problem (MMKP) is a variant of the well known 0-1 knapsack problem, which is known as an NP-hard problem. This paper proposes a method for solving the MMKP using the integer programming-based local search (IPbLS). IPbLS is a kind of a local search and uses integer programming to generate a neighbor solution. The most important thing in IPbLS is the way to select items participating in the next integer programming step. In this paper, three ways to select items are introduced and compared on 37 well-known benchmark data instances. Experimental results shows that the method using linear programming is the best for the MMKP. It also shows that the proposed method can find the equal or better solutions than the best known solutions in 23 data instances, and the new better solutions in 13 instances.