• Title/Summary/Keyword: 가우시안 거리 함수

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Human Face Recognition using Feature Extraction Based on HOLA(Higher Order Local Autocorrelation) and BP Neural Networks (HOLA 기반 특징추출과 BP 신경망을 이용한 얼굴 인식)

  • 최광미;서요한;정채영
    • Proceedings of the Korean Information Science Society Conference
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    • 2002.10d
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    • pp.541-543
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    • 2002
  • 본 논문에서는 HOLA(고차국소자동상관계수)를 이용한 특징추출과 BP(Backpropagation Network) 알고리즘을 이용하여 얼굴을 인식하는 방법을 제안한다. 이를 위해 동일한 환경, 즉 일정한 조도 하에서 카메라로부터 동일거리에 있는 영상을 256$\times$256 크기의 그레이 스케일(Gray Scale)로 취득하여 영상내의 잡음을 가우시안(Gaussian) 필터를 이용하여 제거한다. 차영상을 이용하여 얼굴영역을 분리한 후 얼굴영역의 특징벡터를 구하기 위하여 HOLA(고차 국소 자동 상관함수)를 사용한다. 계산된 특징벡터는 BP 신경망의 학습을 통하여 얼굴인식을 위한 데이터로 사용된다. 시뮬레이션을 통해 제안된 알고리즘에 의한 인식률향상과 속도 향상을 입증한다.

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Development of Range-Dependent Ray Model for Sonar Simulator (소나 시뮬레이터용 거리 종속 음선 모델 개발)

  • Jung, Young-Cheol;Lee, Keunhwa;Seong, Woojae;Kim, Hyoung-Rok
    • The Journal of the Acoustical Society of Korea
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    • v.33 no.3
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    • pp.163-173
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    • 2014
  • Sound propagation algorithm for a sonar simulator is required to run in real-time and should be able to model the range and depth dependence of the Korean ocean environments. Ray model satisfies these requirements and we developed an algorithm for range-dependent ocean environments. In this algorithm, we considered depth-dependence of sound speed through rays based on a rectangular cell method and layer method. Range-dependence of sound speed was implemented based on a split-step method in the range direction. Eigen-ray is calculated through an interpolation of ray bundles and Gaussian interpolation function was used. The received time signal of sonar was simulated by Fourier transform of eigen-ray solution in the frequency domain. Finally, for the verification of proposed algorithm, we compared the results of transmission loss with other validated models such as BELLHOP, SNUPE, KRAKEN and OASES, for the Pekeris waveguide, wedge, and deep ocean environments. As a result, we obtained satisfactory agreements among them.

Characteristics of Hydrodynamic Dispersion Using a Natural Gradient Tracer Test in a Fractured Rock at the Jwacheon-dong, Busan City (부산시 좌천동 단열암반층에서 자연구배 추적자시험을 이용한 수리분산특성 연구)

  • Chung Sang-Yong;Kang Dong-Hwan;Kim Byung-Woo
    • The Journal of Engineering Geology
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    • v.16 no.3 s.49
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    • pp.245-254
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    • 2006
  • Using a natural gradient tracer test, the characteristics of hydrodynamic dispersion according to each depth of a fractured rock were studied, and the effective porosity and longitudinal dispersivity of the fractured rock were estimated. The difference of vertical hydrodynamic dispersion was identified by concentration breakthrough curves linear regression analyses of bromide concentrations according to depths versus time, and hydraulic fracture characteristics at two intervals of the monitoring well. Higher concentration and faster arrival time at GL- 18 m depth (RQD 13%, average joint spacing 2 cm, TCR 100%) than at GL- 25 m depth (RQD 41%, average joint spacing 7 cm, TCR 100%) resulted from shorter distance and more fractures. Tracer was transported through the 1 st fractures until the arrival of its peak concentration and through the 2nd fractures or matrix diffusion after the arrival of its peak concentration. The increase/decrease slopes of bromide concentration versus time were 3.46/-1.57 at GL-18 m depth and 3.l9/-0.47 at GL- 25 m depth of the monitoring well. So the faster bromide transport was confirmed at GL- 18 m depth with more fractures. The concentration increment of bromide was fitted by a Gaussian function and the concentration decrement of bromide was fitted by an exponential function. Effective porosity and longitudinal dispersivity estimated by CATTI code were 10.50% and 0.85 m, respectively.

Performance Enhancement of Algorithms based on Error Distributions under Impulsive Noise (충격성 잡음하에서 오차 분포에 기반한 알고리듬의 성능향상)

  • Kim, Namyong;Lee, Gyoo-yeong
    • Journal of Internet Computing and Services
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    • v.19 no.3
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    • pp.49-56
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    • 2018
  • Euclidean distance (ED) between error distribution and Dirac delta function has been used as an efficient performance criterion in impulsive noise environmentsdue to the outlier-cutting effect of Gaussian kernel for error signal. The gradient of ED for its minimization has two components; $A_k$ for kernel function of error pairs and the other $B_k$ for kernel function of errors. In this paper, it is analyzed that the first component is to govern gathering close together error samples, and the other one $B_k$ is to conduct error-sample concentration on zero. Based upon this analysis, it is proposed to normalize $A_k$ and $B_k$ with power of inputs which are modified by kernelled error pairs or errors for the purpose of reinforcing their roles of narrowing error-gap and drawing error samples to zero. Through comparison of fluctuation of steady state MSE and value of minimum MSE in the results of simulation of multipath equalization under impulsive noise, their roles and efficiency of the proposed normalization method are verified.

Centroid Neural Network with Bhattacharyya Kernel (Bhattacharyya 커널을 적용한 Centroid Neural Network)

  • Lee, Song-Jae;Park, Dong-Chul
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.32 no.9C
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    • pp.861-866
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    • 2007
  • A clustering algorithm for Gaussian Probability Distribution Function (GPDF) data called Centroid Neural Network with a Bhattacharyya Kernel (BK-CNN) is proposed in this paper. The proposed BK-CNN is based on the unsupervised competitive Centroid Neural Network (CNN) and employs a kernel method for data projection. The kernel method adopted in the proposed BK-CNN is used to project data from the low dimensional input feature space into higher dimensional feature space so as the nonlinear problems associated with input space can be solved linearly in the feature space. In order to cluster the GPDF data, the Bhattacharyya kernel is used to measure the distance between two probability distributions for data projection. With the incorporation of the kernel method, the proposed BK-CNN is capable of dealing with nonlinear separation boundaries and can successfully allocate more code vector in the region that GPDF data are densely distributed. When applied to GPDF data in an image classification probleml, the experiment results show that the proposed BK-CNN algorithm gives 1.7%-4.3% improvements in average classification accuracy over other conventional algorithm such as k-means, Self-Organizing Map (SOM) and CNN algorithms with a Bhattacharyya distance, classed as Bk-Means, B-SOM, B-CNN algorithms.

A Appropriate Flux Generating Conditions for Semiconductor Etching Simulation (반도체 식각 전산모사에 적합한 플럭스 생성 조건)

  • Jeong, Seunghan;Gwun, Oubong;Shin, Seongsik
    • Journal of the Institute of Electronics and Information Engineers
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    • v.52 no.3
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    • pp.105-115
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    • 2015
  • In semiconductor etching simulation, The source modeling for generating plasma species is required. In this paper, we modeled the source of plasma etching process with probability distribution and the feature profile with simple geometry objects, then got the flux on the feature profile. The distance between the source and the cell on the modeling parameters of the source, there are a number of particles to be emitted from a source, there is a number (area of the cell) of the cell on the profile with additional parameters to give the calculation of flux. The flux error ratio on both gaussian(Incident Flux) and cosine probability distribution(Incident Neutral Flux) is much decreased as the number of ray is increased but the processing time is more increased than that. The increase of the number of cell and distance makes increase the flux error ratio and the processing time moderately. In view of the processing time through the experimental results in this paper, it is possible to analogize the calculation of appropriate fluxes.

A Watermarking Method Based on the Informed Coding and Embedding Using Trellis Code and Entropy Masking (Trellis 부호 및 엔트로피 마스킹을 이용한 정보부호화 기반 워터마킹)

  • Lee, Jeong-Hwan
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.13 no.12
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    • pp.2677-2684
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    • 2009
  • In this paper, we study a watermarking method based on the informed coding and embedding by means of trellis code and entropy masking. An image is divided as $8{\times}8$ block with no overlapping and the discrete cosine transform(DCT) is applied to each block. Then the 16 medium-frequency AC terms of each block are extracted. Next it is compared with gaussian random vectors having zero mean and unit variance. As these processing, the embedding vectors with minimum value of linear combination between linear correlation and Watson distance can be obtained by Viterbi algorithm at each stage of trellis coding. For considering the image characteristics, we apply different weight value between the linear correlation and the Watson distance using the entropy masking. To evaluate the performance of proposed method, the average bit error rate of watermark message is calculated from different several images. By the experiments the proposed method is improved in terms of the average bit error rate.

Low-Frequency Normal Mode Reverberation Model (저주파수 정상모드 잔향음 모델)

  • Oh, Suntaek;Cho, Sungho;Kang, Donhyug;Park, Kyoungju
    • The Journal of the Acoustical Society of Korea
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    • v.34 no.3
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    • pp.184-191
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    • 2015
  • In this paper, a normal mode reverberation model for a range-independent environment of shallow water is proposed to calculate the reverberation level in the low-frequency range. Normal mode is used to calculate the acoustic energy propagating from the source to the scattering area and from the scattering area to the receiver. Each mode is decomposed into up and down going waves to consider scattering strength at the scattering area. The scattering functional form combines Lambert's law with a Gaussian-like term near the specular direction based on Kirchhoff approximation considering bottom condition. For verification of the suggested model, the result is relatively compared to several solutions of the problem XI and XV in the Reverberation Modeling Workshop I sponsored by the US Office of Naval Research.

Identification of Fuzzy-Radial Basis Function Neural Network Based on Mountain Clustering (Mountain Clustering 기반 퍼지 RBF 뉴럴네트워크의 동정)

  • Choi, Jeoung-Nae;Oh, Sung-Kwun;Kim, Hyun-Ki
    • The Journal of Korea Institute of Information, Electronics, and Communication Technology
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    • v.1 no.3
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    • pp.69-76
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    • 2008
  • This paper concerns Fuzzy Radial Basis Function Neural Network (FRBFNN) and automatic rule generation of extraction of the FRBFNN by means of mountain clustering. In the proposed network, the membership functions of the premise part of fuzzy rules do not assume any explicit functional forms such as Gaussian, ellipsoidal, triangular, etc., so its resulting fitness values (degree of membership) directly rely on the computation of the relevant distance between data points. Also, we consider high-order polynomial as the consequent part of fuzzy rules which represent input-output characteristic of sup-space. The number of clusters and the centers of clusters are automatically generated by using mountain clustering method based on the density of data. The centers of cluster which are obtained by using mountain clustering are used to determine a degree of membership and weighted least square estimator (WLSE) is adopted to estimate the coefficients of the consequent polynomial of fuzzy rules. The effectiveness of the proposed model have been investigated and analyzed in detail for the representative nonlinear function.

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A Study on the Effects of Droplets Characteristics of Water Mist on the Spray Density on the Floor (미분무 액적특성이 살수밀도에 미치는 영향 연구)

  • Kim, Jong-Hoon;Park, Won-Hee;Kim, Woon-Hyung;Myoung, Sang-Yup
    • Journal of the Society of Disaster Information
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    • v.17 no.1
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    • pp.120-127
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    • 2021
  • Purpose: In this study, the effect of changes in the variables related to water droplets on the spray density on the floor in the analysis of the water mist fire protection system using FDS was analyzed. Method: When the spray of the water mist nozzle was analyzed in FDS, Particles Per Seconds, Particle Velocity, Size Distribution, and Spray Pattern Shape that can be set in relation to droplets were input to review the analyzed results. Result: In the analysis results, when the number of particles per second was set above a certain value, the spray density of the floor was similar. In the case of Particle Velocity, as the velocity decreases, the spray density of the central portion increases but decreases at a distance of 0.15m or more. From the analysis of the change in the size distribution function, it was found that an increase in the 𝛾 value increases the spray density of the central part, but the value at a remote location decreases. Compared to the result of applying the Gaussian distribution, the median value decreases dramatically when the uniform distribution is applied, but the value at the adjacent position increases. Conclusion: Variables related to the droplet properties of the FDS affect the spray density of the floor. Therefore, in order to increase the reliability of results before performing analyses such as fire suppression or cooling, a sufficient review of input variables is required.