• Title/Summary/Keyword: Cluster estimation

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Box Feature Estimation from LiDAR Point Cluster using Maximum Likelihood Method (최대우도법을 이용한 라이다 포인트군집의 박스특징 추정)

  • Kim, Jongho;Yi, Kyongsu
    • Journal of Auto-vehicle Safety Association
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    • v.13 no.4
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    • pp.123-128
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    • 2021
  • This paper present box feature estimation from LiDAR point cluster using maximum likelihood Method. Previous LiDAR tracking method for autonomous driving shows high accuracy about velocity and heading of point cluster. However, Assuming the average position of a point cluster as the vehicle position has a lower accuracy than ground truth. Therefore, the box feature estimation algorithm to improve position accuracy of autonomous driving perception consists of two procedures. Firstly, proposed algorithm calculates vehicle candidate position based on relative position of point cluster. Secondly, to reflect the features of the point cluster in estimation, the likelihood of the particle scattered around the candidate position is used. The proposed estimation method has been implemented in robot operating system (ROS) environment, and investigated via simulation and actual vehicle test. The test result show that proposed cluster position estimation enhances perception and path planning performance in autonomous driving.

Unbiased Balanced Half-Sample Variance Estimation in Stratified Two-stage Sampling

  • Kim, Kyu-Seong
    • Journal of the Korean Statistical Society
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    • v.27 no.4
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    • pp.459-469
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    • 1998
  • Balanced half sample method is a simple variance estimation method for complex sampling designs. Since it is simple and flexible, it has been widely used in large scale sample surveys. However, the usual BHS method overestimate the true variance in without replacement sampling and two-stage cluster sampling. Focusing on this point , we proposed an unbiased BHS variance estimator in a stratified two-stage cluster sampling and then described an implementation method of the proposed estimator. Finally, partially BHS design is explained as a tool of reducing the number of replications of the proposed estimator.

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Estimation of Market Size and Value Added by Embedded SW Industry Cluster (임베디드 S/W 산업 클러스터별 시장 규모 및 부가가치 추정)

  • Yang, Hae-Bong;Moon, Jung-Hyun;Jeong, Min-A
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.35 no.8B
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    • pp.1211-1216
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    • 2010
  • There is no reference grasp only embedded SW market because embedded SW is built in SW production. In this paper, In order to know only embedded SW market, we used estimation method size of the amount of production. We draw suitable industry cluster structure of embedded SW market estimation. As we estimated size of embedded SW market by industry cluster. And, We calculated importance of embedded SW by industry cluster and finally we estimated size of embedded SW market. Result of estimation, added values of embedded SW estimated about 27 trillion.

Dirichlet Process Mixtures of Linear Mixed Regressions

  • Kyung, Minjung
    • Communications for Statistical Applications and Methods
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    • v.22 no.6
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    • pp.625-637
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    • 2015
  • We develop a Bayesian clustering procedure based on a Dirichlet process prior with cluster specific random effects. Gibbs sampling of a normal mixture of linear mixed regressions with a Dirichlet process was implemented to calculate posterior probabilities when the number of clusters was unknown. Our approach (unlike its counterparts) provides simultaneous partitioning and parameter estimation with the computation of the classification probabilities. A Monte Carlo study of curve estimation results showed that the model was useful for function estimation. We find that the proposed Dirichlet process mixture model with cluster specific random effects detects clusters sensitively by combining vague edges into different clusters. Examples are given to show how these models perform on real data.

Power System State Estimation Using Parallel PSO Algorithm based on PC cluster (PC 클러스터 기반 병렬 PSO 알고리즘을 이용한 전력계통의 상태추정)

  • Jeong, Hee-Myung;Park, June-Ho;Lee, Hwa-Seok
    • Proceedings of the KIEE Conference
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    • 2008.07a
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    • pp.303-304
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    • 2008
  • For the state estimation problem, the weighted least squares (WLS) method and the fast decoupled method are widely used at present. However, these algorithms can converge to local optimal solutions. Recently, modern heuristic optimization methods such as Particle Swarm Optimization (PSO) have been introduced to overcome the disadvantage of the classical optimization problem. However, heuristic optimization methods based on populations require a lengthy computing time to find an optimal solution. In this paper, we used PSO to search for the optimal solution of state estimation in power systems. To overcome the shortcoming of heuristic optimization methods, we proposed parallel processing of the PSO algorithm based on the PC cluster system. the proposed approach was tested with the IEEE-118 bus systems. From the simulation results, we found that the parallel PSO based on the PC cluster system can be applicable for power system state estimation.

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Energy/Distance Estimation-based and Distributed Selection/Migration of Cluster Heads in Wireless Sensor Networks (센서 네트워크의 에너지 및 거리 추정 기반 분산 클러스터 헤드 선정과 이주 방법)

  • Kim, Dong-Woo;Park, Jong-Ho;Lee, Tae-Jin
    • Journal of the Institute of Electronics Engineers of Korea TC
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    • v.44 no.3 s.357
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    • pp.18-25
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    • 2007
  • In sensor networks, sensor nodes have limited computational capacity, power and memory. Thus energy efficiency is one of the most important requirements. How to extend the lifetime of wireless sensor networks has been widely discussed in recent years. However, one of the most effective approaches to cope with power conservation, network scalability, and load balancing is clustering technique. The function of a cluster head is to collect and route messages of all the nodes within its cluster. Cluster heads must be changed periodically for low energy consumption and load distribution. In this paper, we propose an energy-aware cluster head selection algorithm and Distance Estimation-based distributed Clustering Algorithm (DECA) in wireless sensor networks, which exchanges cluster heads for less energy consumption by distance estimation. Our simulation result shows that DECA can improve the system lifetime of sensor networks up to three times compared to the conventional scheme.

Cluster Sampling in Sampling Inspection: Bayes Estimation

  • Juyoung Lee
    • Communications for Statistical Applications and Methods
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    • v.6 no.1
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    • pp.107-116
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    • 1999
  • We propose a sample design which minimize Bayes risk for cluster smpling in sampling inspection. We treat a pilot sample and an additional sample size as random variable. In addition we compute an appropriate cluster size for handling over-dispersion.

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An Algorithm of Optimal Training Sequence for Effective 1-D Cluster-Based Sequence Equalizer (효율적인 1차원 클러스터 기반의 시퀀스 등화기를 위한 최적의 훈련 시퀀스 구성 알고리즘)

  • Kang Jee-Hye;Kim Sung-Soo
    • The Journal of Korean Institute of Electromagnetic Engineering and Science
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    • v.15 no.10 s.89
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    • pp.996-1004
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    • 2004
  • 1-Dimensional Cluster-Based Sequence Equalizer(1-D CBSE) lessens computational load, compared with the classic maximum likelihood sequence estimation(MLSE) equalizers, and has the superiority in the nonlinear channels. In this paper, we proposed an algorithm of searching for optimal training sequence that estimates the cluster centers instead of time-varying multipath fading channel estimation. The proposed equalizer not only resolved the problems in 1-D CBSE but also improved the bandwidth efficiency using the shorten length of taming sequence to improve bandwidth efficiency. In experiments, the superiority of the new method is demonstrated by comparing conventional 1-D CBSE and related analysis.

A Technique of Angle-of-Arrival Estimation in an Ultra wide Band(UWB) Indoor Wireless Communication (초광대역 옥내 무선 통신에서 신호 도착 방향 추정 기법)

  • Lee Yong-Up;Seo Young-Jun;Cho Gin-Kyu
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.31 no.3C
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    • pp.279-285
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    • 2006
  • In this study, a new signal model suitable for UWB indoor environments with random angle spread is proposed to estimate the angle-of-arrivals(AOAs) of UWB cluster signals in an UWB wireless communication. A subspace based estimation technique adopted for this model is investigated and the estimates of the AOA and distribution parameter on the received UWB cluster signals are obtained. The proposed model and estimation technique are verified using computer simulations, and the performance of the estimation error is analyzed.

Cluster-Based Quantization and Estimation for Distributed Systems

  • Kim, Yoon Hak
    • Journal of information and communication convergence engineering
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    • v.14 no.4
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    • pp.215-221
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    • 2016
  • We consider a design of a combined quantizer and estimator for distributed systems wherein each node quantizes its measurement without any communication among the nodes and transmits it to a fusion node for estimation. Noting that the quantization partitions minimizing the estimation error are not independently encoded at nodes, we focus on the parameter regions created by the partitions and propose a cluster-based quantization algorithm that iteratively finds a given number of clusters of parameter regions with each region being closer to the corresponding codeword than to the other codewords. We introduce a new metric to determine the distance between codewords and parameter regions. We also discuss that the fusion node can perform an efficient estimation by finding the intersection of the clusters sent from the nodes. We demonstrate through experiments that the proposed design achieves a significant performance gain with a low complexity as compared to the previous designs.