• 제목/요약/키워드: Bayesian Method

검색결과 1,146건 처리시간 0.027초

Genetic relationship between purebred and synthetic pigs for growth performance using single step method

  • Hong, Joon Ki;Cho, Kyu Ho;Kim, Young Sin;Chung, Hak Jae;Baek, Sun Young;Cho, Eun Seok;Sa, Soo Jin
    • Animal Bioscience
    • /
    • 제34권6호
    • /
    • pp.967-974
    • /
    • 2021
  • Objective: The objective of this study was to estimate the genetic correlation (rpc) of growth performance between purebred (Duroc and Korean native) and synthetic (WooriHeukDon) pigs using a single-step method. Methods: Phenotypes of 15,902 pigs with genotyped data from 1,792 pigs from a nucleus farm were used for this study. We estimated the rpc of several performance traits between WooriHeukDon and purebred pigs: day of target weight (DAY), backfat thickness (BF), feed conversion rate (FCR), and residual feed intake (RFI). The variances and covariances of the studied traits were estimated by an animal multi-trait model that applied the Bayesian inference. Results: rpc within traits was lower than 0.1 for DAY and BF, but high for FCR and RFI; in particular, rpc for RFI between Duroc and WooriHeukDon pigs was nearly 1. Comparison between different traits revealed that RFI in Duroc pigs was associated with different traits in WooriHeukDon pigs. However, the most of rpc between different traits were estimated with low or with high standard deviation. Conclusion: The results indicated that there were substantial differences in rpc of traits in the synthetic WooriHeukDon pigs, which could be caused by these pigs having a more complex origin than other crossbred pigs. RFI was strongly correlated between Duroc and WooriHeukDon pigs, and these breeds might have similar single nucleotide polymorphism effects that control RFI. RFI is more essential for metabolism than other growth traits and these metabolic characteristics in purebred pigs, such as nutrient utilization, could significantly affect those in synthetic pigs. The findings of this study can be used to elucidate the genetic architecture of crossbred pigs and help develop new breeds with target traits.

실시간 뇌파 특성 분석을 위한 저잡음 스펙트럼 추정 알고리즘 (Low Noise Time-Frequency Analysis Algorithm for Real-Time Spectral Estimation)

  • 김연수;박범수;김성은
    • 전기전자학회논문지
    • /
    • 제23권3호
    • /
    • pp.805-810
    • /
    • 2019
  • 본 연구에서는 multitaper 기반의 스펙트럼 추정기법과 상태-공간 모델링 기반의 변수 추정 기법을 통합한 시간-주파수 분석 알고리즘을 제안한다. 일반적으로 시간-주파수 분석에는 불확실성 원리에 의해 시간 해상도와 주파수 해상도 사이에 트레이드오프 문제가 발생한다. 트레이드오프 문제를 최적화하기 위해서 short-time Fourier transform(STFT)와 wavelet 기반의 알고리즘들이 제안되었다. 본 논문의 저자는 다른 대안으로 상태-공간 프레임워크를 기반으로 한 새로운 multitaper 스펙트럼 추정 방법을 제안하였다. 그러나 기존의 방법은 스펙트럼이 시간에 따라 변하지 않는 경우에 잘 동작하지만, dynamic하게 변할 경우 제대로 추정하지 못하는 문제점이 있다. 그래서 본 논문에서는 상태-공간 모델에 사용되는 상태 노이즈와 관찰 노이즈를 주기적으로 업데이트 하는 방법을 제안하고자 한다. 우리는 제안 알고리즘을 시뮬레이션 데이터를 사용하여 테스트 하였고, 시간에 따라 변하는 스펙트럼에 대해서도 잘 동작하는 것을 확인하였다.

전투 시스템의 신뢰성 분석을 위한 FTA와 BBN을 이용한 2계층 접근에 관한 연구 (Two-Layer Approach Using FTA and BBN for Reliability Analysis of Combat Systems)

  • 강지원;이장세
    • 한국정보통신학회논문지
    • /
    • 제23권3호
    • /
    • pp.333-340
    • /
    • 2019
  • 전투 시스템은 다양한 적대적 환경에서 주어진 임무를 수행한다. 주어진 임무를 수행하는 능력을 높이기 위하여 전투 시스템의 신뢰성을 분석하는 연구가 중요하다. 대부분의 기존 연구에서는 위협을 고려하지 않거나 하나의 위협을 고려하며 구성 요소간의 종속적 관계를 고려하고 있지 않다. 본 논문에서는 전투 시스템의 기능에 대한 상실 확률을 도출하며, 이를 이용하여 신뢰성 분석을 진행한다. 제안하는 방법은 하위, 상위의 두 계층으로 나누어 분석한다. 하위 계층에서는 다양한 위협을 동시에 고려하기 위하여 FTA 기법을 이용하여 구성 요소별 고장 확률을 도출한다. 상위 계층에서는 하위 계층에서 도출된 구성 요소의 고장 확률을 이용하며 구성 요소간의 종속적 관계를 고려하기 위해 BBN을 이용하여 기능의 상실 확률을 분석한다. 제안하는 방법을 이용하면 다양한 위협을 동시에 고려하면서 구성 요소간의 종속적 관계에 대한 분석이 가능하다.

An R package UnifiedDoseFinding for continuous and ordinal outcomes in Phase I dose-finding trials

  • Pan, Haitao;Mu, Rongji;Hsu, Chia-Wei;Zhou, Shouhao
    • Communications for Statistical Applications and Methods
    • /
    • 제29권4호
    • /
    • pp.421-439
    • /
    • 2022
  • Phase I dose-finding trials are essential in drug development. By finding the maximum tolerated dose (MTD) of a new drug or treatment, a Phase I trial establishes the recommended doses for later-phase testing. The primary toxicity endpoint of interest is often a binary variable, which describes an event of a patient who experiences dose-limiting toxicity. However, there is a growing interest in dose-finding studies regarding non-binary outcomes, defined by either the weighted sum of rates of various toxicity grades or a continuous outcome. Although several novel methods have been proposed in the literature, accessible software is still lacking to implement these methods. This study introduces a newly developed R package, UnifiedDoseFinding, which implements three phase I dose-finding methods with non-binary outcomes (Quasi- and Robust Quasi-CRM designs by Yuan et al. (2007) and Pan et al. (2014), gBOIN design by Mu et al. (2019), and by a method by Ivanova and Kim (2009)). For each of the methods, UnifiedDoseFinding provides corresponding functions that begin with next that determines the dose for the next cohort of patients, select, which selects the MTD defined by the non-binary toxicity endpoint when the trial is completed, and get oc, which obtains the operating characteristics. Three real examples are provided to help practitioners use these methods. The R package UnifiedDoseFinding, which is accessible in R CRAN, provides a user-friendly tool to facilitate the implementation of innovative dose-finding studies with nonbinary outcomes.

도심 자율주행을 위한 라이다 정지 장애물 지도 기반 위치 보정 알고리즘 (LiDAR Static Obstacle Map based Position Correction Algorithm for Urban Autonomous Driving)

  • 노한석;이현성;이경수
    • 자동차안전학회지
    • /
    • 제14권2호
    • /
    • pp.39-44
    • /
    • 2022
  • This paper presents LiDAR static obstacle map based vehicle position correction algorithm for urban autonomous driving. Real Time Kinematic (RTK) GPS is commonly used in highway automated vehicle systems. For urban automated vehicle systems, RTK GPS have some trouble in shaded area. Therefore, this paper represents a method to estimate the position of the host vehicle using AVM camera, front camera, LiDAR and low-cost GPS based on Extended Kalman Filter (EKF). Static obstacle map (STOM) is constructed only with static object based on Bayesian rule. To run the algorithm, HD map and Static obstacle reference map (STORM) must be prepared in advance. STORM is constructed by accumulating and voxelizing the static obstacle map (STOM). The algorithm consists of three main process. The first process is to acquire sensor data from low-cost GPS, AVM camera, front camera, and LiDAR. Second, low-cost GPS data is used to define initial point. Third, AVM camera, front camera, LiDAR point cloud matching to HD map and STORM is conducted using Normal Distribution Transformation (NDT) method. Third, position of the host vehicle position is corrected based on the Extended Kalman Filter (EKF).The proposed algorithm is implemented in the Linux Robot Operating System (ROS) environment and showed better performance than only lane-detection algorithm. It is expected to be more robust and accurate than raw lidar point cloud matching algorithm in autonomous driving.

진동 아날로그 신호 기반의 이상상황 탐지를 위한 기계학습 모형의 성능지표 향상 (Improving the Performance of Machine Learning Models for Anomaly Detection based on Vibration Analog Signals)

  • 김재훈;엄상천;박철순
    • 산업경영시스템학회지
    • /
    • 제47권2호
    • /
    • pp.1-9
    • /
    • 2024
  • New motor development requires high-speed load testing using dynamo equipment to calculate the efficiency of the motor. Abnormal noise and vibration may occur in the test equipment rotating at high speed due to misalignment of the connecting shaft or looseness of the fixation, which may lead to safety accidents. In this study, three single-axis vibration sensors for X, Y, and Z axes were attached on the surface of the test motor to measure the vibration value of vibration. Analog data collected from these sensors was used in classification models for anomaly detection. Since the classification accuracy was around only 93%, commonly used hyperparameter optimization techniques such as Grid search, Random search, and Bayesian Optimization were applied to increase accuracy. In addition, Response Surface Method based on Design of Experiment was also used for hyperparameter optimization. However, it was found that there were limits to improving accuracy with these methods. The reason is that the sampling data from an analog signal does not reflect the patterns hidden in the signal. Therefore, in order to find pattern information of the sampling data, we obtained descriptive statistics such as mean, variance, skewness, kurtosis, and percentiles of the analog data, and applied them to the classification models. Classification models using descriptive statistics showed excellent performance improvement. The developed model can be used as a monitoring system that detects abnormal conditions of the motor test.

델파이 기법을 이용한 원전사고의 종합적인 경제적 리스크 평가 (A Study on the Overall Economic Risks of a Hypothetical Severe Accident in Nuclear Power Plant Using the Delphi Method)

  • 장한기;김주연;이재기
    • Journal of Radiation Protection and Research
    • /
    • 제33권4호
    • /
    • pp.127-134
    • /
    • 2008
  • 울진원전 3,4 호기의 가상적 중대사고로 인한 종합적인 경제적 리스크를 평가하였다. 이 연구의 목적을 위해 방사능 구름이 내륙을 향하는 것으로 가정하였다. 평가과정에서 불확실한 인자의 정량화에는 전문가 판단 및 의견도출에 유용한 것으로 알려진 델파이 기법을 이용하였다. 종합적인 경제적 리스크는 직접영향 비용과 간접영향 비용으로 구분되므로, 먼저 직접영향에 대한 비용을 평가하고, 예측된 가중치들 이용하여 직접영향 대비 간접영향 비용을 평가하였다. 행동학적 접근방법인 델파이 문제점을 보완하기 위해 수학적 접근방법인 베이지안 기법을 자료처리 과정으로 하는 모형을 적용하여 간접영향에 대한 경제적 충격량을 예측하였다. 1D 몬테칼로분석(MCA)으로 평가한 간접피해에 대한 가중치는 평균 2.59, 중앙값 2.08로 OECD/NEA에서 제시하는 가중치 1.25보다 높게 나타났다. 작은 국토나 방사선에 민감한 대중 성향과 같은 인지들이 패널의 판단에 영향을 미쳤을 수 있다. 직접피해 평가모델의 모수를 U형과 V형으로 구분하고 2D MCA를 사용한 종합적 경제적 리스크는 중앙값의 50%ile을 기준으로 2006년 국내총생산의 3.9%에 해당되었으며, 직접피해 영향이 가장 큰 자산 및 전력손실 비용을 제외하면 총 경제적 리스크는 국내총생산의 2.2% 수준이었다. 이 결과는 원전 비상계획과 대응태세 준비에 대한 투자 정당화에 참조 자료로 이용될 수 있다.

한우에 있어서 유전체 육종가 추정 (Prediction of genomic breeding values of carcass traits using whole genome SNP data in Hanwoo (Korean cattle))

  • 이승환;김형철;임다정;당창권;조용민;김시동;이학교;이준헌;양보석;오성종;홍성구;장원경
    • 농업과학연구
    • /
    • 제39권3호
    • /
    • pp.357-364
    • /
    • 2012
  • Genomic breeding value (GEBV) has recently become available in the beef cattle industry. Genomic selection methods are exceptionally valuable for selecting traits, such as marbling, that are difficult to measure until later in life. One method to utilize information from sparse marker panels is the Bayesian model selection method with RJMCMC. The accuracy of prediction varies between a multiple SNP model with RJMCMC (0.47 to 0.73) and a least squares method (0.11 to 0.41) when using SNP information, while the accuracy of prediction increases in the multiple SNP (0.56 to 0.90) and least square methods (0.21 to 0.63) when including a polygenic effect. In the multiple SNP model with RJMCMC model selection method, the accuracy ($r^2$) of GEBV for marbling predicted based only on SNP effects was 0.47, while the $r^2$ of GEBV predicted by SNP plus polygenic effect was 0.56. The accuracies of GEBV predicted using only SNP information were 0.62, 0.68 and 0.73 for CWT, EMA and BF, respectively. However, when polygenic effects were included, the accuracies of GEBV were increased to 0.89, 0.90 and 0.89 for CWT, EMA and BF, respectively. Our data demonstrate that SNP information alone is missing genetic variation information that contributes to phenotypes for carcass traits, and that polygenic effects compensate genetic variation that whole genome SNP data do not explain. Overall, the multiple SNP model with the RJMCMC model selection method provides a better prediction of GEBV than does the least squares method (single marker regression).

Genetic Contribution of Indigenous Yakutian Cattle to Two Hybrid Populations, Revealed by Microsatellite Variation

  • Li, M.H.;Nogovitsina, E.;Ivanova, Z.;Erhardt, G.;Vilkki, J.;Popov, R.;Ammosov, I.;Kiselyova, T.;Kantanen, J.
    • Asian-Australasian Journal of Animal Sciences
    • /
    • 제18권5호
    • /
    • pp.613-619
    • /
    • 2005
  • Indigenous Yakutian cattle' adaptation to the hardest subarctic conditions makes them a valuable genetic resource for cattle breeding in the Siberian area. Since early last century, crossbreeding between native Yakutian cattle and imported Simmental and Kholmogory breeds has been widely adopted. In this study, variations at 22 polymorphic microsatellite loci in 5 populations of Yakutian, Kholmogory, Simmental, Yakutian-Kholmogory and Yakutian-Simmental cattle were analysed to estimate the genetic contribution of Yakutian cattle to the two hybrid populations. Three statistical approaches were used: the weighted least-squares (WLS) method which considers all allele frequencies; a recently developed implementation of a Markov chain Monte Carlo (MCMC) method called likelihood-based estimation of admixture (LEA); and a model-based Bayesian admixture analysis method (STRUCTURE). At population-level admixture analyses, the estimate based on the LEA was consistent with that obtained by the WLS method. Both methods showed that the genetic contribution of the indigenous Yakutian cattle in Yakutian-Kholmogory was small (9.6% by the LEA and 14.2% by the WLS method). In the Yakutian-Simmental population, the genetic contribution of the indigenous Yakutian cattle was considerably higher (62.8% by the LEA and 56.9% by the WLS method). Individual-level admixture analyses using STRUCTURE proved to be more informative than the multidimensional scaling analysis (MDSA) based on individual-based genetic distances. Of the 9 Yakutian-Simmental animals studied, 8 showed admixed origin, whereas of the 14 studied Yakutian-Kholmogory animals only 2 showed Yakutian ancestry (>5%). The mean posterior distributions of individual admixture coefficient (q) varied greatly among the samples in both hybrid populations. This study revealed a minor existing contribution of the Yakutian cattle in the Yakutian-Kholmogory hybrid population, but in the Yakutian-Simmental hybrid population, a major genetic contribution of the Yakutian cattle was seen. The results reflect the different crossbreeding patterns used in the development of the two hybrid populations. Additionally, molecular evidence for differences among individual admixture proportions was seen in both hybrid populations, resulting from the stochastic process in crossing over generations.

몬테카를로 방법과 ISO-GUM 방법의 불확도 평가 결과 비교 (Comparison of ISO-GUM and Monte Carlo Method for Evaluation of Measurement Uncertainty)

  • 하영철;허재영;이승준;이강진
    • 대한기계학회논문집B
    • /
    • 제38권7호
    • /
    • pp.647-656
    • /
    • 2014
  • 본 연구에서는 ISO GUM(불확도 표현 지침서)의 불확도 평가 방법을 보완하기 위해, 몬테카를로 방법(Monte Carlo Method, MCM)을 적용한 불확도 해석 프로그램을 개발하고, MCM과 GUM의 평가 결과를 비교하였다. 그 결과 다음과 같은 결과를 도출하였다. 첫째, 측정량의 확률 분포가 정규 분포가 아닌 때에도 MCM 방법은 정확한 포함 구간을 제공한다. 둘째, 정규 분포가 아닌 다른 분포들 몇몇 개가 합성되는 경우 그 확률 분포가 정규로 보이더라도 실제로는 정규가 아닌 경우가 있으며, 이의 판단은 합성 분산의 확률 분포로 할 수 있다. 셋째, 자유도가 낮은 A형 불확도가 불확도 평가에 포함된 경우 GUM은 포함 구간을 저평가하는 것을 알 수 있었고, 이러한 저평가 문제는 A형 표준 불확도에 t-분포의 표준 편차를 곱해주면 사라지는 것을 알 수 있었다. 이 경우 합성 분산의 유효 자유도는 확장 불확도 계산에 불필요하고, 신뢰의 수준 95 %의 포함 인자는 1.96이 적정한 것을 알 수 있었다.