• 제목/요약/키워드: kernels

검색결과 561건 처리시간 0.028초

ON A CLASS OF GENERALIZED FUNCTIONS FOR SOME INTEGRAL TRANSFORM ENFOLDING KERNELS OF MEIJER G FUNCTION TYPE

  • Al-Omari, Shrideh Khalaf
    • 대한수학회논문집
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    • 제33권2호
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    • pp.515-525
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    • 2018
  • In this paper, we investigate a modified $G^2$ transform on a class of Boehmians. We prove the axioms which are necessary for establishing the $G^2$ class of Boehmians. Addition, scalar multiplication, convolution, differentiation and convergence in the derived spaces have been defined. The extended $G^2$ transform of a Boehmian is given as a one-to-one onto mapping that is continuous with respect to certain convergence in the defined spaces. The inverse problem is also discussed.

Global Feature Extraction and Recognition from Matrices of Gabor Feature Faces

  • Odoyo, Wilfred O.;Cho, Beom-Joon
    • Journal of information and communication convergence engineering
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    • 제9권2호
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    • pp.207-211
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    • 2011
  • This paper presents a method for facial feature representation and recognition from the Covariance Matrices of the Gabor-filtered images. Gabor filters are a very powerful tool for processing images that respond to different local orientations and wave numbers around points of interest, especially on the local features on the face. This is a very unique attribute needed to extract special features around the facial components like eyebrows, eyes, mouth and nose. The Covariance matrices computed on Gabor filtered faces are adopted as the feature representation for face recognition. Geodesic distance measure is used as a matching measure and is preferred for its global consistency over other methods. Geodesic measure takes into consideration the position of the data points in addition to the geometric structure of given face images. The proposed method is invariant and robust under rotation, pose, or boundary distortion. Tests run on random images and also on publicly available JAFFE and FRAV3D face recognition databases provide impressively high percentage of recognition.

A Local Linear Kernel Estimator for Sparse Multinomial Data

  • Baek, Jangsun
    • Journal of the Korean Statistical Society
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    • 제27권4호
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    • pp.515-529
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    • 1998
  • Burman (1987) and Hall and Titterington (1987) studied kernel smoothing for sparse multinomial data in detail. Both of their estimators for cell probabilities are sparse asymptotic consistent under some restrictive conditions on the true cell probabilities. Dong and Simonoff (1994) adopted boundary kernels to relieve the restrictive conditions. We propose a local linear kernel estimator which is popular in nonparametric regression to estimate cell probabilities. No boundary adjustment is necessary for this estimator since it adapts automatically to estimation at the boundaries. It is shown that our estimator attains the optimal rate of convergence in mean sum of squared error under sparseness. Some simulation results and a real data application are presented to see the performance of the estimator.

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A note on nonparametric density deconvolution by weighted kernel estimators

  • Lee, Sungho
    • Journal of the Korean Data and Information Science Society
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    • 제25권4호
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    • pp.951-959
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    • 2014
  • Recently Hazelton and Turlach (2009) proposed a weighted kernel density estimator for the deconvolution problem. In the case of Gaussian kernels and measurement error, they argued that the weighted kernel density estimator is a competitive estimator over the classical deconvolution kernel estimator. In this paper we consider weighted kernel density estimators when sample observations are contaminated by double exponentially distributed errors. The performance of the weighted kernel density estimators is compared over the classical deconvolution kernel estimator and the kernel density estimator based on the support vector regression method by means of a simulation study. The weighted density estimator with the Gaussian kernel shows numerical instability in practical implementation of optimization function. However the weighted density estimates with the double exponential kernel has very similar patterns to the classical kernel density estimates in the simulations, but the shape is less satisfactory than the classical kernel density estimator with the Gaussian kernel.

복수 해상도 시스템의 Pattern Kernels에 의한 Lip Print 인식에 관한 연구 (A Study on Lip Print Recognition by using Pattern Kernels in Multi-Resolution Architecture)

  • 백경석;정진현
    • 정보처리학회논문지B
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    • 제8B권2호
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    • pp.189-194
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    • 2001
  • 본 논문에서는 개인 식별을 위하여 복수 해상도 구조를 제시하였고 이 방법으로 구순문 인식을 구현하였다. 구순문 인식은 지문, 음성 패턴, 홍채 패턴과 얼굴 인식과 같은 신체적 특징에 비하여 상대적으로 연구가 많이 이루어지지 않은 신체적 특징이다. 구순문은 CCD 카메라를 이용할 경우 홍채나 얼굴 패턴 같은 다른 특징 요소와 연결하여 인식 시스템을 구축할 수 있는 장점을 가지고 있다. 구순문 인식을 위해 pattern kernels를 이용한 새로운 방법을 제시하였다. Pattern kernels는 여러 개의 local lip print mask들로 구성된 함수이며, lip print의 정보를 디지털 데이터로 전환시켜 준다. 복수 해상도를 가지는 인식 시스템은 단일 해상도의 시스템보다 더욱 신뢰적이며 인식률도 높다.

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고차원 데이터의 분류를 위한 서포트 벡터 머신을 이용한 피처 감소 기법 (Feature reduction for classifying high dimensional data sets using support vector machine)

  • 고석하;이현주
    • 대한전자공학회:학술대회논문집
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    • 대한전자공학회 2008년도 하계종합학술대회
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    • pp.877-878
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    • 2008
  • We suggest a feature reduction method to classify mouse function data sets, which integrate several biological data sets represented as high dimensional vectors. To increase classification accuracy and decrease computational overhead, it is important to reduce the dimension of features. To do this, we employed Hybrid Huberized Support Vector Machine with kernels used for a kernel logistic regression method. When compared to support vector machine, this a pproach shows the better accuracy with useful features for each mouse function.

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Geometrical Distortion-Resilient Watermarking Based on Image Features

  • Shim, Hiuk-Jae;Byeungwoo Jeon;Kim, Rin-Chul
    • 대한전자공학회:학술대회논문집
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    • 대한전자공학회 2002년도 ITC-CSCC -2
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    • pp.1268-1271
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    • 2002
  • The major threat of geometric manipulations is that they change the positions of watermarks, therefore the detection process fails to extract watermark properly. Since they cause the same effects on the host image as watermarks simultaneously, evaluating the distorted host image can be helpful to measure the nature of distortions. In this paper, we propose a geometrical distortion-resilient watermarking algorithm based on this property. Firstly we evaluate the orientation of a host image by filtering it with directional Gabor kernels, then we insert embedding pattern aligned to the estimated orientation. In its detection step, we evaluate the orientation again by Gabor filtering, then simply project and average the projected value to obtain a 1-D projection average pattern. Finally, auto-correlation function of the 1-D projection average pattern identifies periodic peaks. Analysed are experimental results against geometrical attacks including aspect ratio changes.

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A Bright H${\alpha}$ kernel Observed Using the FISS

  • 조규현;채종철;임은경
    • 천문학회보
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    • 제37권1호
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    • pp.87.2-87.2
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    • 2012
  • H${\alpha}$ transient bright kernels may be an important diagnostic of energy conversion processes occurring in the choromosphere during flares. We observed an H${\alpha}$ kernel that occurred in AR 11263 in associated with a small flare on 2011 Autust 5th using the Fast Imaging Solar Spectrograph installed at the 1.6m New Solar Telescope of Big Bear Solar Observatory. We find that both the H${\alpha}$ line and the CaII 8542${\AA}$ line appear in emission, with a red asymmetry in that they display red wings of enhanced emission. The red asymmetry shows 5-30 km/s downward motion for 8 minutes. We determine some physical parameters by adopting the Cloud mode and discuss the physical meaning of these results.

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슈퍼컴퓨팅 응용기술 개발 및 성과 (DEVELOPMENT OF SUPERCOMPUTING APPLICATION TECHNOLOGY AND ITS ACHIEVEMENTS)

  • 김정호
    • 한국전산유체공학회:학술대회논문집
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    • 한국전산유체공학회 2006년도 추계 학술대회논문집
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    • pp.207-207
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    • 2006
  • Hardware technologies for high-performance computing has been developing continuously. However, actual performance of software cannot keep up with the speed of development in hardware technologies, because hardware architectures become more and more complicated and hardware scales become larger. So, software technique to utilize high-performance computing systems more efficiently plays more important role in realizing high-performance computing for computational science. In this paper, the effort to enhance software performance on large and complex high-performance computing systems such as performance optimization and parallelization will be presented. Our effort to serve high-performance computational kernels such as high-performance sparse solvers and the achievements through this effort also will be introduced.

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의사 솔리드 모델의 캐비티 및 코어판 생성 (Generation of Cavity and Core Plates of an Injection Mold for a Pseudo-Solid Part Model)

  • 장진우;이상헌;임성락
    • 한국정밀공학회:학술대회논문집
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    • 한국정밀공학회 2003년도 춘계학술대회 논문집
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    • pp.1601-1604
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    • 2003
  • This paper describes a split operation for generation of core and cavity plates of an injection mold for a pseudo-solid model of a plastic part. Here, a pseudo-solid model means a sheet model that looks like a solid model. but whose boundary is not closed. When a solid model created in a different CAD system is imported through standard data exchange format, a pseudo-solid model is created in most cases as tolerance or some other problems make sewing operation failed. As most existing mold design system based on solid modeling kernels require a complete part solid model, mold designers have to do time-consuming healing operations to convert a pseudo-solid to solid. The essential capability of mold design system is the split operation for generation of core and cavity plates. Thus. we developed a split operation for pseudo-solid part model to eliminate or reduce healing preprocessing for mold design.

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