• Title/Summary/Keyword: 근사적 분산

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Motion-Compensated Noise Estimation for Effective Video Processing (효과적인 동영상 처리를 위한 움직임 보상 기반 잡음 예측)

  • Song, Byung-Cheol
    • Journal of the Institute of Electronics Engineers of Korea SP
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    • v.46 no.5
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    • pp.120-125
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    • 2009
  • For effective noise removal prior to video processing, noise power or noise variance of an input video sequence needs to be found exactly, but it is actually a very difficult process. This paper presents an accurate noise variance estimation algorithm based on motion compensation between two adjacent noisy pictures. Firstly, motion estimation is performed for each block in a picture, and the residue variance of the best motion-compensated block is calculated. Then, a noise variance estimate of the picture is obtained by adaptively averaging and properly scaling the variances close to the best variance. The simulation results show that the proposed noise estimation algorithm is very accurate and stable irrespective of noise level.

Distributed and Range-Free Acoustic Source Localization Techniques in Wireless Sensor Networks (센서네트워크에서 Range-free 기반의 분산 음원위치 판별 기법)

  • You, Young-Bin;Cha, Ho-Jung
    • Proceedings of the Korean Information Science Society Conference
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    • 2005.11b
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    • pp.571-573
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    • 2005
  • 본 논문에서는 수동형 위치 판별 시스템의 대표적인 음원위치 판별 알고리즘을 제안한다. 제안하는 알고리즘은 무선 센서 네트워크에 최적화 되어있으며, 일반적인 무선 센서 네트워크에서 사용되는 노드와 마이크만 요구되며 추가적인 장비를 필요로 하지 않는다. 제안하는 시스템은 동일한 노드에 분산된 알고리즘을 이용하여 각 노드는 이벤트 발생시에 동적으로 추정 Grid를 생성한 후 이 Grid를 이용하여 추정치를 산정하고 이를 종합하여 최종적으로 2차원 평면에서의 음원의 위치를 판별한다. 제안하는 시스템의 위치판별 알고리즘은 Range-free방식으로 생성된 Grid를 각 노드가 음파를 감지한 시각을 바탕으로 영역별로 근사한다. 시스템은 실제 MicaZ 노드에 구현되었으며 제한된 하드웨어와 자원만을 바탕으로 높은 복잡도를 지니는 음원탐지시스템을 구축하였다.

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A Prediction Method for Sabot-Trajectory of Projectile by using High Speed Camera Data Analysis (고속카메라 데이터 분석을 통한 발사체 지지대 분산 궤적의 근사적 예측 방법)

  • Park, Yunho;Woo, Hokil
    • Journal of the Korea Institute of Military Science and Technology
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    • v.21 no.1
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    • pp.1-9
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    • 2018
  • In this paper, we have proposed a prediction method for sabot-trajectory of projectile using high speed camera data analysis. Through analyzing trajectory of sabot with high speed camera data, we can extract its real velocity and acceleration including effects of friction force, pressure of flume, etc. Using these data, we suggest a prediction method for sabot-trajectory of projectile having variable acceleration, especially for minimum and maximum acceleration, by using interpolation method for velocity and acceleration data of sabot. Also we perform the projectile launching tests to achieve the trajectory of sabot in case of minimum and maximum thrust. Simulation results show that they are similar to real tests data, for example velocity, acceleration and the trajectory of sabot.

A Theoretical Study on the Dispersion of Elastic Waves in Particulate Composites (입자복합재료 내부의 탄성파 분산에 관한 이론적 연구)

  • 김진연;이정권
    • Transactions of the Korean Society of Mechanical Engineers
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    • v.18 no.7
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    • pp.1697-1704
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    • 1994
  • Elastic wave propagation in discrete random medium studies to predict dynamic effective properties of composite materials containing spherical inclusions. A self-consistent method is proposed which is analogous to the well-known coherent potential approximation. Three conditions that must be satisfied by two effective elastic moduli and effective density are derived for the time without limit of frequency. The derived self-consistency conditions have the physical meaning that the scattering of coherent wave by the constituents in effective medium is vanished on the average. The frequency-dependent complex effective wave speed and coherent attenuation can be obtained by solving the derived self-consistency conditions numerically. The wave speed and attenuation obtained from present theory are shown to be in the better agreements with previous experimental observations than the previous theory.

Max k-Cut based Clustering Algorithm for Wireless Sensor Networks (무선 센서 네트워크에서의 Max k-Cut기반의 클러스터링 알고리즘)

  • Kim, Jae-Hwan;Chang, Hyeong-Soo
    • Journal of KIISE:Information Networking
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    • v.36 no.2
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    • pp.98-107
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    • 2009
  • In this paper, we propose a novel centralized energy-efficient clustering algorithm, called "MCCA : Max k-Cut based Clustering Algorithm for Wireless Sensor Networks." The algorithm does not use location information and constructs clusters via a distributive Max k-Cut based cluster-head election method, where only relative and approximate distance information with neighbor nodes is used and nodes, not having enough energy, are excluded for cluster-heads for a specific period. We show that the energy efficiency performance of MCCA is better than that of LEACH, EECS and similar to BCDCP's by simulation studies.

A Study on the Numerical Wave Propagation Properties of the Finite Difference-Time Domain(FD-TD) Method for EM Wave Problems (전자파 문제에 대한 시간영역-유한차분법의 수치파 전파모델의 성질에 관한 연구)

  • 김인석
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.19 no.8
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    • pp.1595-1611
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    • 1994
  • In this paper, the numerical wave propagation properties of the finite difference-time domain(FD-TD) method is investigated as a discrete model describing electromagnetic(EM) wave propagation phenomena. The leap-frog approximation of Maxwell's curl equations in time-space simulates EM wave propagation in terms of the numerical characteristic and the domain of dependence. A geometrical interpretation of the FD-TD numerical procedure is presented. The numerical dispersion error due to the leap-frog approximation and its dependence on the stability factor are illustrated. The FD-TD method using the leap-frog approximation is inherently a descriptive model. Thus, not only any physical picture about EM wave propagation phenomena can be drawn through this model, but also physical or engineering parameters in the frequency domain can be extracted from descriptive results. E-plane filter characteristics in the WR-28 rectangular waveguide and reflection property of an inductive iris in the WR-90 rectangluar waveguide extracted from simulation of the FD-TD model is included.

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Sample Average Approximation Method for Task Assignment with Uncertainty (불확실성을 갖는 작업 할당 문제를 위한 표본 평균 근사법)

  • Gwang, Kim
    • Journal of Korea Society of Industrial Information Systems
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    • v.28 no.1
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    • pp.27-34
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    • 2023
  • The optimal assignment problem between agents and tasks is known as one of the representative problems of combinatorial optimization and an NP-hard problem. This paper covers multi agent-multi task assignment problems with uncertain completion probability. The completion probabilities are generally uncertain due to endogenous (agent or task) or exogenous factors in the system. Assignment decisions without considering uncertainty can be ineffective in a real situation that has volatility. To consider uncertain completion probability mathematically, a mathematical formulation with stochastic programming is illustrated. We also present an algorithm by using the sample average approximation method to solve the problem efficiently. The algorithm can obtain an assignment decision and the upper and lower bounds of the assignment problem. Through numerical experiments, we present the optimality gap and the variance of the gap to confirm the performances of the results. This shows the excellence and robustness of the assignment decisions obtained by the algorithm in the problem with uncertainty.

Structure of Data Fusion and Nonlinear Statistical Track Data Fusion in Cooperative Engagement Capability (협동교전능력을 위한 자료융합 구조와 비선형 통계적 트랙 융합 기법)

  • Jung, Hyoyoung;Byun, Jaeuk;Lee, Saewoom;Kim, Gi-Sung;Kim, Kiseon
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.39C no.1
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    • pp.17-27
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    • 2014
  • As the importance of Cooperative Engagement Capability and network-centric warfare has been dramatically increasing, it is necessary to develop distributed tracking systems. Under the development of distributed tracking systems, it requires tracking filters and data fusion theory for nonlinear systems. Therefore, in this paper, the problem of nonlinear track fusion, which is suitable for distributed networks, is formulated, four algorithms to solve the problem of nonlinear track fusion are introduced, and performance of introduced algorithms are analyzed. It is a main problem of nonlinear track fusion that cross-covarinaces among multiple platforms are unknown. Thus, in order to solve the problem, two techniques are introduced; a simplification technique and a approximation technique. The simplification technique that help to ignore cross-covariances includes two algorithms, i.e. the sample mean algorithm and the Millman formula algorithm, and the approximation technique to obtain approximated cross-covariances utilizes two approaches, by using analytical linearization and statistical linearization based on the sigma point approach. In simulations, BCS fusion is the most efficient scheme because it reduces RMSE by approximating cross-covariances with low complexity.

The Effective Training Method for the Statistical Classification of Remotely Sensed Imagery (위성영상의 통계적 분류를 위한 유효 트레이닝 기법에 관한 연구)

  • 이병길;김용일;어양담
    • Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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    • v.17 no.3
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    • pp.225-231
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    • 1999
  • In statistical analysis of remotely sensed data, means and variances of each classes are used as the basis of statistical similarity determination. Therefore, the overall accuracy of classification is affected by the training results. It is assumed that the ideal distributions of pixel values follow normal distributions, but practically they have some aggregations and biases. non anomalies of distribution can affect the classification results greatly as well as the variances of training results. In this study, relationships between the inferential variances of the training sets and the distributions of pixel values are examined. and the resulting changes of classification results are studied. Furthermore, the training method which minimizes the effect of underestimation of variances is proposed.

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Efficient k-Nearest Neighbor Query Processing Method for a Large Location Data (대용량 위치 데이터에서 효율적인 k-최근접 질의 처리 기법)

  • Choi, Dojin;Lim, Jongtae;Yoo, Seunghun;Bok, Kyoungsoo;Yoo, Jaesoo
    • The Journal of the Korea Contents Association
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    • v.17 no.8
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    • pp.619-630
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    • 2017
  • With the growing popularity of smart devices, various location based services have been providing to users. Recently, some location based social applications that combine social services and location based services have been emerged. The demands of a k-nearest neighbors(k-NN) query which finds k closest locations from a user location are increased in the location based social network services. In this paper, we propose an approximate k-NN query processing method for fast response time in a large number of users environments. The proposed method performs efficient stream processing using big data distributed processing technologies. In this paper, we also propose a modified grid index method for indexing a large amount of location data. The proposed query processing method first retrieves the related cells by considering a user movement. By doing so, it can make an approximate k results set. In order to show the superiority of the proposed method, we conduct various performance evaluations with the existing method.