• Title/Summary/Keyword: Divergence Measure

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A Transitional Behavior of a Premixed Flame and a Triple Flame in a Lifted Flame(II) (부상화염에서 예혼합화염과 삼지화염의 천이적 거동(II))

  • Jang Jun Young;Kim Tae Kwon
    • Transactions of the Korean Society of Mechanical Engineers B
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    • v.29 no.3 s.234
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    • pp.376-383
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    • 2005
  • In the paper we investigate characteristics of a transitional behavior from a premixed flame to a triple flame in a lifted flame according to the change of equivalence ratio. In previous study, we showed that the stabilized laminar lifted flame regime is categorized by regimes of premixed flame, triple flame and critical flame. A gas-chromatograph is used to measure concentration field, a smoke-wire system is used to measure streak line, and a PIV system is used to measure velocity field in lifted flame. In the visualization experiment of smoke wire, the flow divergence and redirection reappeared in premixed flame as well as triple flame. Thus we cannot express the flame front of lifted flame has a behavior of triple flame with only flow divergence and redirection. In PIV measurement, flow velocity for those three flames has minimum value at the tip of flame front. To differentiate triple flame and premixed flame, $\Phi$ value of partially premixed fraction is employed. The partially premixed fraction $\Phi$ was constant in premixed flame. In critical flame small gradient appears over the whole regime. In triple flame, typical diffusion flame shape is obtained as parabolic distribution type due to diffusion flame trailing.

A Hill-Sliding Strategy for Initialization of Gaussian Clusters in the Multidimensional Space

  • Park, J.Kyoungyoon;Chen, Yung-H.;Simons, Daryl-B.;Miller, Lee-D.
    • Korean Journal of Remote Sensing
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    • v.1 no.1
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    • pp.5-27
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    • 1985
  • A hill-sliding technique was devised to extract Gaussian clusters from the multivariate probability density estimates of sample data for the first step of iterative unsupervised classification. The underlying assumption in this approach was that each cluster possessed a unimodal normal distribution. The key idea was that a clustering function proposed could distinguish elements of a cluster under formation from the rest in the feature space. Initial clusters were extracted one by one according to the hill-sliding tactics. A dimensionless cluster compactness parameter was proposed as a universal measure of cluster goodness and used satisfactorily in test runs with Landsat multispectral scanner (MSS) data. The normalized divergence, defined by the cluster divergence divided by the entropy of the entire sample data, was utilized as a general separability measure between clusters. An overall clustering objective function was set forth in terms of cluster covariance matrices, from which the cluster compactness measure could be deduced. Minimal improvement of initial data partitioning was evaluated by this objective function in eliminating scattered sparse data points. The hill-sliding clustering technique developed herein has the potential applicability to decomposition of any multivariate mixture distribution into a number of unimodal distributions when an appropriate diatribution function to the data set is employed.

On an Information Theoretic Diagnostic Measure for Detecting Influential Observations in LDA

  • Kim, Hea-Jung
    • Journal of the Korean Statistical Society
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    • v.25 no.2
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    • pp.289-301
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    • 1996
  • This paper suggests a new diagnostic measure for detecting influential observations in two group linear discriminant analysis(LDA). It is developed from an information theoretic point of view using the minimum discrimination information(MDI) methodology. MDI estimator of symmetric divergence by Kullback(l967) is taken as a measure of the power of discrimination in LDA. It is shown that the effect of an observation over the power of discrimination is fully explained by the diagnostic measure. Asymptotic distribution of the proposed measure is derived as a function of independent chi-squared and standard normal variables. By means of the distributions, a couple of methods are suggested for detecting the influential observations in LDA. Performance of the suggested methods are examined through a simulation study.

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Image Processing of Pseudo-rate-distortion Function Based on MSSSIM and KL-Divergence, Using Multiple Video Processing Filters for Video Compression (MSSSIM 및 쿨백-라이블러 발산 기반 의사 율-왜곡 평가 함수와 복수개의 영상처리 필터를 이용한 동영상 전처리 방법)

  • Seok, Jinwuk;Cho, Seunghyun;Kim, Hui Yong;Choi, Jin Soo
    • Journal of Broadcast Engineering
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    • v.23 no.6
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    • pp.768-779
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    • 2018
  • In this paper, we propose a novel video quality function for video processing based on MSSSIM to select an appropriate video processing filter and to accommodate multiple processing filters to each pixel block in a picture frame by a mathematical selection law so as to maintain video quality and to reduce the bitrate of compressed video. In viewpoint of video compression, since the properties of video quality and bitrate is different for each picture of video frames and for each areas in the same frame, it is difficult for the video filter with single property to satisfy the object of increasing video quality and decreasing bitrate. Consequently, to maintain the subjective video quality in spite of decreasing bitrate, we propose the methodology about the MSSSIM as the measure of subjective video quality, the KL-Divergence as the measure of bitrate, and the combination method of those two measurements. Moreover, using the proposed combinatorial measurement, when we use the multiple image filters with mutually different properties as a pre-processing filter for video, we can verify that it is possible to compress video with maintaining the video quality under decreasing the bitrate, as possible.

A Combined Method of Rule Induction Learning and Instance-Based Learning (귀납법칙 학습과 개체위주 학습의 결합방법)

  • Lee, Chang-Hwan
    • The Transactions of the Korea Information Processing Society
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    • v.4 no.9
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    • pp.2299-2308
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    • 1997
  • While most machine learning research has been primarily concerned with the development of systems that implement one type of learning strategy, we use a multistrategy approach which integrates rule induction learning and instance-based learning, and show how this marriage allows for overall better performance. In the rule induction learning phase, we derive an entropy function, based on Hellinger divergence, which can measure the amount of information each inductive rule contains, and show how well the Hellinger divergence measures the importance of each rule. We also propose some heuristics to reduce the computational complexity by analyzing the characteristics of the Hellinger measure. In the instance-based learning phase, we improve the current instance-based learning method in a number of ways. The system has been implemented and tested on a number of well-known machine learning data sets. The performance of the system has been compared with that of other classification learning technique.

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A New Importance Measure of Association Rules Using Information Theory (정보이론에 기반한 연관 규칙들의 새로운 중요도 측정 방법)

  • Lee, Chang-Hwan;Bae, Joohyun
    • KIPS Transactions on Software and Data Engineering
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    • v.3 no.1
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    • pp.37-42
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    • 2014
  • The abstract should concisely state what was done, how it was done, principal results, and their significance. It should be less than 300 words for all forms of publication. The abstract should be written as one paragraph and should not contain tabular material or numbered references. At the end of abstract, keywords should be given in 3 to 5 words or phrases.

Empirical analysis of strategy selection for the technology leading and technology catch-up in the IT industry

  • Byung-Sun Cho;Sang-Sup Cho;Sung-Sik Shin;Gang-hoon Kim
    • ETRI Journal
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    • v.45 no.2
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    • pp.267-276
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    • 2023
  • R&D strategies of companies with low and high technological levels are discussed based on the concept of technology convergence and divergence. However, empirically detecting enterprise technology convergence in the distribution of enterprise technology (total productivity increase) over time and identifying key change factors are challenging. This study used a novel statistical indicator that captures the internal technology distribution change with a single number to clearly measure the technology distribution peak as a change in critical bandwidth for enterprise technology convergence and presented it as evidence of each technology convergence or divergence. Furthermore, this study applied the quantitative technology convergence identification method. Technology convergence appeared from the separation of total corporate productivity distribution of 69 IT companies in Korea in 2019-2020 rather than in 2015-2016. Results indicated that when the total technological level was separated from the technology leading and technology catch-up, IT companies were found to be pursuing R&D strategies for technology catch-up.

A Kullback-Leibler divergence based comparison of approximate Bayesian estimations of ARMA models

  • Amin, Ayman A
    • Communications for Statistical Applications and Methods
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    • v.29 no.4
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    • pp.471-486
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    • 2022
  • Autoregressive moving average (ARMA) models involve nonlinearity in the model coefficients because of unobserved lagged errors, which complicates the likelihood function and makes the posterior density analytically intractable. In order to overcome this problem of posterior analysis, some approximation methods have been proposed in literature. In this paper we first review the main analytic approximations proposed to approximate the posterior density of ARMA models to be analytically tractable, which include Newbold, Zellner-Reynolds, and Broemeling-Shaarawy approximations. We then use the Kullback-Leibler divergence to study the relation between these three analytic approximations and to measure the distance between their derived approximate posteriors for ARMA models. In addition, we evaluate the impact of the approximate posteriors distance in Bayesian estimates of mean and precision of the model coefficients by generating a large number of Monte Carlo simulations from the approximate posteriors. Simulation study results show that the approximate posteriors of Newbold and Zellner-Reynolds are very close to each other, and their estimates have higher precision compared to those of Broemeling-Shaarawy approximation. Same results are obtained from the application to real-world time series datasets.

Effect of fringe divergence in fluid acceleration measurement using LDA (레이저 도플러 원리를 이용한 유체 가속도 측정)

  • Chun, Se-Jong;Nobach, Holger;Tropea, Cam;Sung, Hyung-Jin
    • Proceedings of the KSME Conference
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    • 2004.11a
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    • pp.1546-1551
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    • 2004
  • The laser Doppler technique is well-established as a velocity measurement technique of high precision for flow velocity. Recently, the laser Doppler technique has also been used to measure acceleration of fluid particles. Acceleration is interesting from a fluid mechanics point of view, since the Navier Stokes equations, specifically the left-hand-side, are formulated in terms of fluid acceleration. Further, there are several avenues to estimating the dissipation rate using the acceleration. However such measurements place additional demands on the design of the optical system; in particular fringe non-uniformity must be held below about 0.0001 to avoid systematic errors. Relations expressing fringe divergence as a function of the optical parameters of the system have been given in the literature; however, direct use of these formulae to minimize fringe divergence lead either to very large measurement volumes or to extremely high intersection angles. This dilemma can be resolved by using an off-axis receiving arrangement, in which the measurement volume is truncated by a pinhole in front of the detection plane. In the present study an optical design study is performed for optimizing laser Doppler systems for fluid acceleration measurements. This is followed by laboratory validation using a round free jet and a stagnation flow, two flows in which either fluid acceleration has been previously measured or in which the acceleration is known analytically. A 90 degree off-axis receiving angle is used with a pinhole or a slit.

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ANOTHER COMPLETE DECOMPOSITION OF A SELF-SIMILAR CANTOR SET

  • Baek, In Soo
    • Korean Journal of Mathematics
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    • v.16 no.2
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    • pp.157-163
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    • 2008
  • Using informations of subsets of divergence points and the relation between members of spectral classes, we give another complete decomposition of spectral classes generated by lower(upper) local dimensions of a self-similar measure on a self-similar Cantor set with full information of their dimensions. We note that it is a complete refinement of the earlier complete decomposition of the spectral classes. Further we study the packing dimension of some uncountable union of distribution sets.

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