• Title/Summary/Keyword: dimensional similarity

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SOM-Based $R^{*}-Tree$ for Similarity Retrieval (자기 조직화 맵 기반 유사 검색 시스템)

  • O, Chang-Yun;Im, Dong-Ju;O, Gun-Seok;Bae, Sang-Hyeon
    • The KIPS Transactions:PartD
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    • v.8D no.5
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    • pp.507-512
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    • 2001
  • Feature-based similarity has become an important research issue in multimedia database systems. The features of multimedia data are useful for discriminating between multimedia objects. the performance of conventional multidimensional data structures tends to deteriorate as the number of dimensions of feature vectors increase. The $R^{*}-Tree$ is the most successful variant of the R-Tree. In this paper, we propose a SOM-based $R^{*}-Tree$ as a new indexing method for high-dimensional feature vectors. The SOM-based $R^{*}-Tree$ combines SOM and $R^{*}-Tree$ to achieve search performance more scalable to high-dimensionalties. Self-Organizingf Maps (SOMs) provide mapping from high-dimensional feature vectors onto a two-dimensional space. The map is called a topological feature map, and preserves the mutual relationships (similarity) in the feature spaces of input data, clustering mutually similar feature vectors in neighboring nodes. Each node of the topological feature map holds a codebook vector. We experimentally compare the retrieval time cost of a SOM-based $R^{*}-Tree$ with of an SOM and $R^{*}-Tree$ using color feature vectors extracted from 40,000 images. The results show that the SOM-based $R^{*}-Tree$ outperform both the SOM and $R^{*}-Tree$ due to reduction of the number of nodes to build $R^{*}-Tree$ and retrieval time cost.

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The application for predictive similarity measures of binary data in association rule mining (이분형 예측 유사성 측도의 연관성 평가 기준 적용 방안)

  • Park, Hee-Chang
    • Journal of the Korean Data and Information Science Society
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    • v.22 no.3
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    • pp.495-503
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    • 2011
  • The most widely used data mining technique is to find association rules. Association rule mining is the method to quantify the relationship between each set of items in very huge database based on the association thresholds. There are some basic association thresholds to explore meaningful association rules ; support, confidence, lift, etc. Among them, confidence is the most frequently used, but it has the drawback that it can not determine the direction of the association. The net confidence and the attributably pure confidence were developed to compensate for this drawback, but they have other drawbacks.In this paper we consider some predictive similarity measures for binary data in cluster analysis and multi-dimensional analysis as association threshold to compensate for these drawbacks. The comparative studies with net confidence, attributably pure confidence, and some predictive similarity measures are shown by numerical example.

Acoustical Similarity for Small Cooling Fans Revisited (소형 송풍기 소음의 음향학적 상사성에 관한 연구)

  • 김용철;진성훈;이승배
    • Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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    • 1995.04a
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    • pp.196-201
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    • 1995
  • The broadband and discrete sources of sound in small cooling fans of propeller type and centrifugal type were investigated to understand the turbulent vortex structures from many bladed fans using ANSI test plenum for small air-moving devices (AMDs). The noise measurement method uses the plenum as a test apparatus to determine the acoustic source spectral density function at each operating conditions similar to real engineering applications based on acoustic similarity laws. The characteristics of fans including the head rise vs. volumetric flow rate performance were measured using a performance test facility. The sound power spectrum is decomposed into two non-dimensional functions: an acoustic source spectral distribution function F(St,.phi.) and an acoustic system response function G(He,.phi.) where St, He, and .phi. are the Strouhal number, the Helmholtz number, and the volumetric flow rate coefficient, respectively. The autospectra of radiated noise measurements for the fan operating at several volumetric flow rates,.phi., are analyzed using acoustical similarity. The rotating stall in the small propeller fan with a bell-mouth guided is mainly due to a leading edge separation. It creates a blockage in the passage and the reduction in the flow rate. The sound power levels with respect to the rotational speeds were measured to reveal the mechanisms of stall and/or surge for different loading conditions and geometries, for example, fans installed with a impinging plate. Lee and Meecham (1993) studied the effect of the large-scale motions like impinging normally on a flat plate using Large-Eddy Simulation(LES) and Lighthill's analogy.[ASME Winter Annual Meeting 1993, 93-WA/NCA-22]. The dipole and quadrupole sources in the fans tested are shown closely related to the vortex structures involved using cross-correlations of the hot-wire and microphone signals.

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SIFT based Image Similarity Search using an Edge Image Pyramid and an Interesting Region Detection (윤곽선 이미지 피라미드와 관심영역 검출을 이용한 SIFT 기반 이미지 유사성 검색)

  • Yu, Seung-Hoon;Kim, Deok-Hwan;Lee, Seok-Lyong;Chung, Chin-Wan;Kim, Sang-Hee
    • Journal of KIISE:Databases
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    • v.35 no.4
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    • pp.345-355
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    • 2008
  • SIFT is popularly used in computer vision application such as object recognition, motion tracking, and 3D reconstruction among various shape descriptors. However, it is not easy to apply SIFT into the image similarity search as it is since it uses many high dimensional keypoint vectors. In this paper, we present a SIFT based image similarity search method using an edge image pyramid and an interesting region detection. The proposed method extracts keypoints, which is invariant to contrast, scale, and rotation of image, by using the edge image pyramid and removes many unnecessary keypoints from the image by using the hough transform. The proposed hough transform can detect objects of ellipse type so that it can be used to find interesting regions. Experimental results demonstrate that the retrieval performance of the proposed method is about 20% better than that of traditional SIFT in average recall.

Distinction of Color Similarity for Clothes based on the LBG Algorithm (LBG 알고리즘 기반의 의상 색상 유사성 판별)

  • Ju, Hyung-Don;Hong, Min;Cho, We-Duke;Moon, Nam-Mee;Choi, Yoo-Joo
    • Journal of Internet Computing and Services
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    • v.9 no.5
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    • pp.117-130
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    • 2008
  • This paper proposes a stable and robust method to distinct the color similarity for clothes using the LBG algorithm under various light sources, Since the conventional methods, such as the histogram intersection and the accumulated histogram, are profoundly sensitive to the changing of light environments, the distinction of color similarity for the same cloth can be different due to the complicated light sources. To reduce the effects of the light sources, the properties of hue and saturation which consistently sustain the characteristic of the color under the various changes of light sources are analyzed to define the characteristic of the color distribution. In a two-dimensional space determined by the properties of hue and saturation, the LBG algorithm, a non-parametric clustering approach, is applied to examine the color distribution of images for each clothes. The color similarity of images is defined by the average of Euclidean distance between the mapping clusters which are calculated from the result of clustering of both images. To prove the stability of the proposed method, the results of the color similarity between our method and the traditional histogram analysis based methods are compared using a dozen of cloth examples that obtained under different light environments. Our method successively provides the classification between the same cloth image pair and the different cloth image pair and this classification of color similarity for clothe images obtains the 91.6% of success rate.

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ON THE CONVERGENCE OF FARIMA SEQUENCE TO FRACTIONAL GAUSSIAN NOISE

  • Kim, Joo-Mok
    • Journal of the Chungcheong Mathematical Society
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    • v.26 no.2
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    • pp.411-420
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    • 2013
  • We consider fractional Gussian noise and FARIMA sequence with Gaussian innovations and show that the suitably scaled distributions of the FARIMA sequences converge to fractional Gaussian noise in the sense of finite dimensional distributions. Finally, we figure out ACF function and estimate the self-similarity parameter H of FARIMA(0, $d$, 0) by using R/S method.

Application of FFT to orientation determination

  • Woo, Dong-Min;Park, Mignon
    • 제어로봇시스템학회:학술대회논문집
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    • 1991.10b
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    • pp.1730-1733
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    • 1991
  • In this paper, we two dimensional processing method is presented, which can accurately determine the orientation of an part. Matching bewteen the object and the object contour is decomposed to estimate the orientation of the object and to evaluate the similarity new approach is very robust with respect to noise and no preprocessing of the contour is required. Also, this method has many advantages over the converntional correlation technique. With only a few uniformly sampled points, this method can estimate the accurate orientation in an efficient manner even in a noisy environment.

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On the weyl spectrum of weight

  • Yang, Youngoh
    • Bulletin of the Korean Mathematical Society
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    • v.35 no.1
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    • pp.91-97
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    • 1998
  • In this paper we study the Weyl spectrum of weight $\alpha, \omega_\alpha(T)$, of an operator T acting on an infinite dimensional Hilbert space. Main results are as follows. Firstly, we show that the Weyll spectrum of weight $\alpha$ of a polynomially $\alpha$-compact operator is finite, and that similarity preserves polynomial $\alpha$-compactness and the $\alpha$-Weyl's theorem both. Secondly, we give a sufficient condition for an operator to be the sum of an unitary and a $\alpha$-compact operators.

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An Improved K-means Document Clustering using Concept Vectors

  • Shin, Yang-Kyu
    • Journal of the Korean Data and Information Science Society
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    • v.14 no.4
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    • pp.853-861
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    • 2003
  • An improved K-means document clustering method has been presented, where a concept vector is manipulated for each cluster on the basis of cosine similarity of text documents. The concept vectors are unit vectors that have been normalized on the n-dimensional sphere. Because the standard K-means method is sensitive to initial starting condition, our improvement focused on starting condition for estimating the modes of a distribution. The improved K-means clustering algorithm has been applied to a set of text documents, called Classic3, to test and prove efficiency and correctness of clustering result, and showed 7% improvements in its worst case.

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PARAMETER ESTIMATION AND SPECTRUM OF FRACTIONAL ARIMA PROCESS

  • Kim, Joo-Mok;Kim, Yun-Kyong
    • Journal of applied mathematics & informatics
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    • v.33 no.1_2
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    • pp.203-210
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    • 2015
  • We consider fractional Brownian motion and FARIMA process with Gaussian innovations and show that the suitably scaled distributions of the FARIMA processes converge to fractional Brownian motion in the sense of finite dimensional distributions. We figure out ACF function and estimate the self-similarity parameter H of FARIMA(0, d, 0) by using R/S method. Finally, we display power spectrum density of FARIMA process.