• Title/Summary/Keyword: sequential Monte Carlo

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P2P Ranging-Based Cooperative Localization Method for a Cluster of Mobile Nodes Containing IR-UWB PHY

  • Cho, Seong Yun;Kim, Joo Young;Enkhtur, Munkhzul
    • ETRI Journal
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    • v.35 no.6
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    • pp.1084-1093
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    • 2013
  • problem of pedestrian localization using mobile nodes containing impulse radio ultra wideband (IR-UWB) is considered. IEEE 802.15.4a-based IR-UWB can achieve accurate ranging. However, the coverage is as short as 30 m, owing to the restricted transmit power. This factor may cause a poor geometric relationship among the mobile nodes and anchor nodes in certain environments. To localize a group of pedestrians accurately, an enhanced cooperative localization method is proposed. We describe a sequential algorithm and define problems that may occur in the implementation of the algorithm. To solve these problems, a batch algorithm is proposed. The batch algorithm can be carried out after performing the sequential algorithm to linearize the nonlinear range equation. When a sequential algorithm cannot be performed due to a poor geometric relationship among nodes, a batch algorithm can be carried out directly. Herein, Monte Carlo simulations are presented to illustrate the proposed method and verify its performance.

The inference and estimation for latent discrete outcomes with a small sample

  • Choi, Hyung;Chung, Hwan
    • Communications for Statistical Applications and Methods
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    • v.23 no.2
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    • pp.131-146
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    • 2016
  • In research on behavioral studies, significant attention has been paid to the stage-sequential process for longitudinal data. Latent class profile analysis (LCPA) is an useful method to study sequential patterns of the behavioral development by the two-step identification process: identifying a small number of latent classes at each measurement occasion and two or more homogeneous subgroups in which individuals exhibit a similar sequence of latent class membership over time. Maximum likelihood (ML) estimates for LCPA are easily obtained by expectation-maximization (EM) algorithm, and Bayesian inference can be implemented via Markov chain Monte Carlo (MCMC). However, unusual properties in the likelihood of LCPA can cause difficulties in ML and Bayesian inference as well as estimation in small samples. This article describes and addresses erratic problems that involve conventional ML and Bayesian estimates for LCPA with small samples. We argue that these problems can be alleviated with a small amount of prior input. This study evaluates the performance of likelihood and MCMC-based estimates with the proposed prior in drawing inference over repeated sampling. Our simulation shows that estimates from the proposed methods perform better than those from the conventional ML and Bayesian method.

Parametric Sequential Test Procedure to Find the Minimum Effective Dose (최소 효과 용량을 정하는 축차 검정법)

  • Park, Su-Jin;Kim, Dong-Jae
    • The Korean Journal of Applied Statistics
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    • v.22 no.5
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    • pp.1033-1046
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    • 2009
  • In new drug development studies or clinical trials, zero-dose control is needed in general to determine the lowest dose level for a new drug which can act with our bodies. When the lowest dose level compared with zero-dose control has significant difference in effect, it is referred as minimum effective dose(MED). We propose, in this paper, parametric sequential test using updated control to identify the minimum effective dose(MED) level. Monte Carlo Simulation is adapted to examine the power and experimental significance levels of the proposed method with other methods.

Parallelism Test of Slope in a Several Simple Linear Regression Model based on a Sequential Slope (여러개의 단순 선형 회귀모형에서 순차기울기를 이용한 평행성 검정)

  • Kim, Juhie;Kim, Dongjae
    • The Korean Journal of Applied Statistics
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    • v.26 no.6
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    • pp.1009-1018
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    • 2013
  • Regression analysis is useful to understand the relationship of variables; however, we need to test if the slope of each regression lines is the same when comparing several populations. This paper suggests a new parallelism test for several linear regression lines. We use F-test of ANOVA and Kruskal-Wallis (1952) tests after obtaining slope estimator from a sequential slope. In addition, a Monte Carlo simulation study is adapted to compare the power of the proposed methods with those of Park and Kim (2009).

A sequential outlier detecting method using a clustering algorithm (군집 알고리즘을 이용한 순차적 이상치 탐지법)

  • Seo, Han Son;Yoon, Min
    • The Korean Journal of Applied Statistics
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    • v.29 no.4
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    • pp.699-706
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    • 2016
  • Outlier detection methods without performing a test often do not succeed in detecting multiple outliers because they are structurally vulnerable to a masking effect or a swamping effect. This paper considers testing procedures supplemented to a clustering-based method of identifying the group with a minority of the observations as outliers. One of general steps is performing a variety of t-test on individual outlier-candidates. This paper proposes a sequential procedure for searching for outliers by changing cutoff values on a cluster tree and performing a test on a set of outlier-candidates. The proposed method is illustrated and compared to existing methods by an example and Monte Carlo studies.

Unlicensed Band Traffic and Fairness Maximization Approach Based on Rate-Splitting Multiple Access (전송률 분할 다중 접속 기술을 활용한 비면허 대역의 트래픽과 공정성 최대화 기법)

  • Jeon Zang Woo;Kim Sung Wook
    • KIPS Transactions on Computer and Communication Systems
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    • v.12 no.10
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    • pp.299-308
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    • 2023
  • As the spectrum shortage problem has accelerated by the emergence of various services, New Radio-Unlicensed (NR-U) has appeared, allowing users who communicated in licensed bands to communicate in unlicensed bands. However, NR-U network users reduce the performance of Wi-Fi network users who communicate in the same unlicensed band. In this paper, we aim to simultaneously maximize the fairness and throughput of the unlicensed band, where the NR-U network users and the WiFi network users coexist. First, we propose an optimal power allocation scheme based on Monte Carlo Policy Gradient of reinforcement learning to maximize the sum of rates of NR-U networks utilizing rate-splitting multiple access in unlicensed bands. Then, we propose a channel occupancy time division algorithm based on sequential Raiffa bargaining solution of game theory that can simultaneously maximize system throughput and fairness for the coexistence of NR-U and WiFi networks in the same unlicensed band. Simulation results show that the rate splitting multiple access shows better performance than the conventional multiple access technology by comparing the sum-rate when the result value is finally converged under the same transmission power. In addition, we compare the data transfer amount and fairness of NR-U network users, WiFi network users, and total system, and prove that the channel occupancy time division algorithm based on sequential Raiffa bargaining solution of this paper satisfies throughput and fairness at the same time than other algorithms.

A 23.52µW / 0.7V Multi-stage Flip-flop Architecture Steered by a LECTOR-based Gated Clock

  • Bhattacharjee, Pritam;Majumder, Alak;Nath, Bipasha
    • IEIE Transactions on Smart Processing and Computing
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    • v.6 no.3
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    • pp.220-227
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    • 2017
  • Technology development is leading to the invention of more sophisticated electronics appliances that require long battery life. Therefore, saving power is a major concern in current-day scenarios. A notable source of power dissipation in sequential structures of integrated circuits is due to the continuous switching of high-frequency clock signals, which do not carry any information, and hence, their switching is eliminated by a method called clock gating. In this paper, we have incorporated a recent clock-gating style named Leakage Control Transistor (LECTOR)-based clock gating to drive a multi-stage sequential architectures, and we focus on its performance under three different process corners (fast-fast, slow-slow, typical-typical) through Monte Carlo simulation at 18 GHz clock with 90 nm technology. This gating is found to be one of the best gated approaches for multi-stage architectures in terms of total power consumption.

Reliability-Based Design Optimization Using Kriging Metamodel with Sequential Sampling Technique (순차적 샘플링과 크리깅 메타모델을 이용한 신뢰도 기반 최적설계)

  • Choi, Kyu-Seon;Lee, Gab-Seong;Choi, Dong-Hoon
    • Transactions of the Korean Society of Mechanical Engineers A
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    • v.33 no.12
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    • pp.1464-1470
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    • 2009
  • RBDO approach based on a sampling method with the Kriging metamodel and Constraint Boundary Sampling (CBS), which is sequential sampling method to generate metamodels is proposed. The major advantage of the proposed RBDO approach is that it does not require Most Probable failure Point (MPP) which is essential for First-Order Reliability Method (FORM)-based RBDO approach. The Monte Carlo Sampling (MCS), most well-known method of the sampling methods for the reliability analysis is used to assess the reliability of constraints. In addition, a Cumulative Distribution Function (CDF) of the constraints is approximated using Moving Least Square (MLS) method from empirical distribution function. It is possible to acquire a probability of failure and its analytic sensitivities by using an approximate function of the CDF for the constraints. Moreover, a concept of inactive design is adapted to improve a numerical efficiency of the proposed approach. Computational accuracy and efficiency of the proposed RBDO approach are demonstrated by numerical and engineering problems.

A study on the sequential algorithm for simultaneous estimation of TDOA and FDOA (TDOA/FDOA 동시 추정을 위한 순차적 알고리즘에 관한 연구)

  • 김창성;김중규
    • Journal of the Korean Institute of Telematics and Electronics S
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    • v.35S no.7
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    • pp.72-85
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    • 1998
  • In this paper, we propose a new method that sequentially estimates TDOA(Time Delay Of Arrival) and FDOA(Frequency Delay Of Arrival) for extracting the information about the bearing and relative velocity of a target in passive radar or sonar arrays. The objective is to efficiently estimate the TDOA and FDOA between two sensor signal measurements, corrupted by correlated Gaussian noise sources in an unknown way. The proposed method utilizes the one dimensional slice function of the third order cumulants between the two sensor measurements, by which the effect of correlated Gaussian measurement noises can be significantly suppressed for the estimation of TDOA. Because the proposed sequential algoritjhm uses the one dimensional complex ambiguity function based on the TDOA estimate from the first step, the amount of computations needed for accurate estimationof FDOA can be dramatically reduced, especially for the cases where high frequency resolution is required. It is demonstrated that the proposed algorithm outperforms existing TDOA/FDOA estimation algorithms based on the ML(maximum likelihood) criterionandthe complex ambiguity function of the third order cumulant as well, in the MSE(mean squared error) sense and computational burden. Various numerical resutls on the detection probability, MSE and the floatingpoint computational burden are presented via Monte-Carlo simulations for different types of noises, different lengths of data, and different signal-to-noise ratios.

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Determination of Incentive Level of Direct Load Control using Probabilistic Technique with Variance Reduction Technique (확률적 기법을 통한 직접부하제어의 제어지원금 산정)

  • Jeong Yun-Won;Park Jong-Bae;Shin Joong-Rin
    • Journal of Energy Engineering
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    • v.14 no.1
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    • pp.46-53
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    • 2005
  • This paper presents a new approach for determining an accurate incentive levels of Direct Load Control (DLC) program using probabilistic techniques. The economic analysis of DLC resources needs to identify the hourly-by-hourly expected energy-not-served resulting from the random outage characteristics of generators as well as to reflect the availability and duration of DLC resources, which results the computational explosion. Therefore, the conventional methods are based on the scenario approaches to reduce the computation time as well as to avoid the complexity of economic studies. In this paper, we have developed a new technique based on the sequential Monte Carlo simulation to evaluate the required expected load control amount in each hour and to decide the incentive level satisfying the economic constraints. In addition, we have applied the variance reduction technique to enhance the efficiency of the simulation. To show the efficiency and effectiveness of the suggested method, the numerical studies have been performed for the modified IEEE 24-bus reliability test system.