共查询到19条相似文献，搜索用时 171 毫秒
1.
基于时序模拟的配电系统可靠性评估中,采用状态持续时间抽样,各元件的状态时间段是随机产生的,属于不确定性问题。求解不确定性问题的可靠性指标的收敛判据不同于常规数值迭代算法,需要运用概率知识描述。针对配电系统的随机时序模拟问题,研究了独立事件的选取,提出了以随机初始种子,相同循环次数作为广义独立事件;分析了在给定置信概率下总体均值的置信区间,其置信区间长度与样本均值的绝对误差有关,从而得出置信区间长度与给定计算精度的不等关系式,提出了以绝对误差作为计算精度的实用收敛判据。通过对RTBS测试系统的分析验证了该方法的合理性与有效性。 相似文献
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基于时序模拟的配电系统可靠性评估中,由于抽样的随机性导致求解结果的不确定性,为此,求解过程需要运用概率知识描述,可靠性指标的收敛判据也不同于常规数值迭代算法.针对配电系统的时序模拟的不确定性问题,研究了独立事件的选取,构造了独立广义事件;分析了在给定置信概率下总体均值的置信区间,得出置信区间长度与样本均值的绝对误差相关的结论,进而提出了用绝对误差描述的实用收敛判据,并且给出该置信概率下置信区间长度与给定计算精度的不等关系式.通过对RTBS测试系统的实例分析,验证了该方法的合理性与有效性. 相似文献
3.
针对贝叶斯网络在具体应用时需根据实际应用问题来建立相应的贝叶斯网络模型的问题，采用Java语言设计并开发了一套贝叶斯网络预测诊断系统，用来实现因果推理和诊断推理。重点讨论了系统的网络数字化、网络学习、网络推理等关键问题。利用该平台建立了过度训练的诊断网络，通过试验证明得出，此套系统简单易用、通用性强，可应用于不同的研究领域。 相似文献
4.
现存农作物单产预测方法往往是在历史数据完备的前提下进行精确数值预测,未能真实体现农作物单产系统的不确作定性,而且无法对既成现实进行诊断分析.在决策需求仅仅要求产量等级水平的假设下,本文把不确定性信息处理方法Credal网络模型引入到农作物单产预测系统,通过分析农作物单产系统各影响因素之间的关系,提出类要素和影响因子的概念,构建了进行农作物单产预测的通用Credal网络模型.把农作物单产量的等级水平状态作为概率事件,通过Credal网络前向推理功能预测其状态发生的概率,把高概率事件发生的等级状态作为其单产预测等级,实现了在已知部分事实发生情况下的知识推理.此外,利用Credal网络的后向推理功能,实现对农作物低产事实下的诊断分析,找出影响低产的关键类要素和影响因子.最后结合算例,采用基于扩展关系模型的近似推理算法阐述了利用该模型对农作物单产进行预测和诊断的应用过程,推理结果合理,表明该方法是可行的,且具有可释性,为农业生产科学决策提供了方法指导. 相似文献
5.
泥石流地区铁路线路系统可靠性分析 总被引：1，自引：0，他引：1
该文探讨了泥石流地区铁路线路系统可靠性评估方法，首先提出了线路系统可靠性的基本概念和度量指标，然后介绍了用计算机模拟方法计算可靠性的过程，最后讨论了马尔可夫模型在系统可靠性状态预测中的应用。 相似文献
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小麦叶形空间分布的模拟模型及推理系统 总被引：10，自引：3，他引：10
对不同类型冬小麦品种叶形空间分布参数进行了定量描述,建立了叶片形状、叶面积指数随生长期的变化、叶面积随高度的分布及叶倾角分布(LAD)等叶形空间分布的模拟模型及推理系统,提出了叶片形状因子和长势模拟模型,单叶空间最高点、高度层间隔内累积投影叶面积及各组分的空间取向与叶倾角分布解析模型系统及其主要设计算法。上述模型为具有普适意义的冬小麦冠层结构机理模型,其推理系统可以精确计算任意高度层次的投影叶面积分布特性,是评价作物株形结构优劣判断群体结构合理与否的有力工具,同时对于解析和利用多角度遥感数据监测作物长势等也具有重要参考价值 相似文献
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含分布式发电接入的农村电网多目标规划 总被引：3，自引：2，他引：1
分布式发电（distributed generation,DG）接入对农村电网损耗、电压及可靠性有很大影响,为了充分发挥DG接入的经济技术效益,提出了一种多目标分布式电源规划方法。该方法以设备投资成本、系统有功损耗、停电损失及购电费用4个指标最小为目标函数,利用判断矩阵获得各目标函数权重,通过加权将多目标优化转化成单目标优化问题,采用改进遗传算法实现了DG位置及容量的优化配置。为了避免因DG可选布点太多、导致算法计算速度慢的问题,根据配电网损耗、电压及可靠性指标改善效果,提出了一种实用的确定DG候选位置的方法。IEEE33节点系统仿真结果表明,本文提出的DG规划模型和求解方法能够有效提高农村电网的投资效益和性能指标。 相似文献
10.
玉米果穗的表型参数是玉米生长状态的重要表征,生长状况的好坏直接影响玉米产量和质量。为方便无人巡检机器人视觉系统高通量、自动化获取玉米表型参数,该研究基于YOLACT（you only look at coefficients）提出一种高精度-速度平衡的玉米果穗分割模型SwinT-YOLACT。首先使用Swin-Transformer作为模型主干特征提取网络,以提高模型的特征提取能力;然后在特征金字塔网络之前引入有效通道注意力机制,剔除冗余特征信息,以加强对关键特征的融合;最后使用平滑性更好的Mish激活函数替换模型原始激活函数Relu,使模型在保持原有速度的同时进一步提升精度。基于自建玉米果穗数据集训练和测试该模型,试验结果表明,SwinT-YOLACT的掩膜均值平均精度为79.43%,推理速度为35.44帧/s,相较于原始YOLACT和其改进算法YOLACT++,掩膜均值平均精度分别提升了3.51和3.38个百分点;相较于YOLACT、YOLACT++和Mask R-CNN模型,推理速度分别提升了3.39、2.58和28.64帧/s。该模型对玉米果穗有较为优秀的分割效果,适于部署在无人巡检机器人视觉系统上,为玉米生长状态监测提供技术支撑。 相似文献
11.
James C. Russell Ephraim M. Hanks Murali Haran 《Journal of Agricultural, Biological & Environmental Statistics》2016,21(1):22-40
When analyzing animal movement, it is important to account for interactions between individuals. However, statistical models for incorporating interaction behavior in movement models are limited. We propose an approach that models dependent movement by augmenting a dynamic marginal movement model with a spatial point process interaction function within a weighted distribution framework. The approach is flexible, as marginal movement behavior and interaction behavior can be modeled independently. Inference for model parameters is complicated by intractable normalizing constants. We develop a double Metropolis–Hastings algorithm to perform Bayesian inference. We illustrate our approach through the analysis of movement tracks of guppies (Poecilia reticulata). 相似文献
12.
Aki Niemi Carmen Fernández 《Journal of Agricultural, Biological & Environmental Statistics》2010,15(3):327-345
This paper develops a Bayesian approach for spatial inference on animal density from line transect survey data. We model the
spatial distribution of animals within a geographical area of interest by an inhomogeneous Poisson process whose intensity
function incorporates both covariate effects and spatial smoothing of residual variation. Independently thinning the animal
locations according to their estimated detection probabilities results into another spatial Poisson process for the sightings
(the observations). Prior distributions are elicited for all unknown model parameters. Due to the sparsity of data in the
application we consider, eliciting sensible prior distributions is important in order to get meaningful estimation results.
A reversible jump Markov Chain Monte Carlo (MCMC) algorithm for simulation of the posterior distribution is developed. We
present results for simulated data and a real data set of minke whale pods from Antarctic waters. The main advantages of our
method compared to design-based analyses are that it can use data arising from sources other than specifically designed surveys
and its ability to link covariate effects to variation of animal density. The Bayesian paradigm provides a coherent framework
for quantifying uncertainty in estimation results. 相似文献
13.
Brian J. Reich Benjamin A. Shaby Daniel Cooley 《Journal of Agricultural, Biological & Environmental Statistics》2014,19(1):119-135
Heat waves take a major toll on human populations, with negative impacts on the economy, agriculture, and human health. As a result, there is great interest in studying the changes over time in the probability and magnitude of heat waves. In this paper we propose a hierarchical Bayesian model for serially-dependent extreme temperatures. We assume the marginal temperature distribution follows the generalized Pareto distribution (GPD) above a location-specific threshold, and capture dependence between subsequent days using a transformed max-stable process. Our model allows both the parameters in the marginal GPD and the temporal dependence function to change over time. This allows Bayesian inference on the change in likelihood of a heat wave. We apply this methodology to daily high temperatures in nine cities in the western US for 1979–2010. Our analysis reveals increases in the probability of a heat wave in several US cities. This article has supplementary material online. 相似文献
14.
Megan D. Higgs William A. Link Gary C. White Mark A. Haroldson Daniel D. Bjornlie 《Journal of Agricultural, Biological & Environmental Statistics》2013,18(4):556-577
Mark-resight designs for estimation of population abundance are common and attractive to researchers. However, inference from such designs is very limited when faced with sparse data, either from a low number of marked animals, a low probability of detection, or both. In the Greater Yellowstone Ecosystem, yearly mark-resight data are collected for female grizzly bears with cubs-of-the-year (FCOY), and inference suffers from both limitations. To overcome difficulties due to sparseness, we assume homogeneity in sighting probabilities over 16 years of bi-annual aerial surveys. We model counts of marked and unmarked animals as multinomial random variables, using the capture frequencies of marked animals for inference about the latent multinomial frequencies for unmarked animals. We discuss undesirable behavior of the commonly used discrete uniform prior distribution on the population size parameter and provide OpenBUGS code for fitting such models. The application provides valuable insights into subtleties of implementing Bayesian inference for latent multinomial models. We tie the discussion to our application, though the insights are broadly useful for applications of the latent multinomial model. 相似文献
15.
Bas Engel Jantien Backer Willem Buist 《Journal of Agricultural, Biological & Environmental Statistics》2010,15(1):83-100
A model is presented to evaluate the accuracy of diagnostic tests from data from individuals that are repeatedly tested in
time. Repeated measurements from three diagnostic tests for foot-and-mouth disease, applied to vaccinated and experimentally
infected cattle, were analyzed. At any time the true disease status of the individuals was unknown, i.e., no gold standard
was available. The model allows for correlation between repeated test results, in consequence of the underlying structure
for the unknown true disease status, but also by the distribution of the test results conditional upon true disease status.
The model also allows for dependence between the different diagnostic tests conditional upon true disease status. Prior information
about the structure of the prevalence and the specificity of the tests was incorporated in a Bayesian analysis. Posterior
inference was carried out with Markov chain Monte Carlo. Simulated data were analyzed to gain insight into the performance
of the posterior Bayesian inference. The simulated data are typical for the expensive and, therefore, modestly sized infection
experiments that are conducted under controlled conditions. 相似文献
16.
Jagadeesh B. Yeluripati Marcel van Oijen A. Neftel W.J. Parton 《Soil biology & biochemistry》2009,41(12):2579-321
The most widely applied soil carbon models partition the soil organic carbon into two or more kinetically defined conceptual pools. The initial distribution of soil organic matter between these pools influences the simulations. Like many other soil organic carbon models, the DAYCENT model is initialised by assuming equilibrium at the beginning of the simulation. However, as we show here, the initial distribution of soil organic matter between the different pools has an appreciable influence on simulations, and the appropriate distribution is dependent on the climate and management at the site before the onset of a simulated experiment. If the soil is not in equilibrium, the only way to initialise the model is to simulate the pre-experimental period of the site. Most often, the site history, in terms of land use and land management is often poorly defined at site level, and entirely unknown at regional level. Our objective was to identify a method that can be applied to initialise a model when the soil is not in equilibrium and historic data are not available, and which quantifies the uncertainty associated with initial soil carbon distribution. We demonstrate a method that uses Bayesian calibration by means of the Accept-Reject algorithm, and use this method to calibrate the initial distribution of soil organic carbon pools against observed soil respiration measurements. It was shown that, even in short-term simulations, model initialisation can have a major influence on the simulated results. The Bayesian calibration method quantified and reduced the uncertainties in initial carbon distribution. 相似文献
17.
We present a Bayesian mark-recapture method for explicitly communicating uncertainty about the size of a closed population
where capture probabilities vary across both individuals and sampling occasions. Heterogeneity is modeled hierarchically using
a continuous logistic-Normal model to specify the capture probabilities for both individuals that are captured on at least
one occasion and individuals that are never captured and so remain undetected. Inference about how many undetected individuals
to include in the model is accomplished through a Bayesian model selection procedure using MCMC, applied to a product space
of possible models for different numbers of undetected individuals. Setting the estimation problem in a fixed dimensional
parameter space enables the model selection procedure to be performed using the freely available WinBUGS software. The outcome
of inference is a full “posterior” probability distribution for the population size parameter. We demonstrate this method
through an example involving real mark-recapture data. 相似文献
18.
Patrick L. McDermott Christopher K. Wikle Joshua Millspaugh 《Journal of Agricultural, Biological & Environmental Statistics》2017,22(3):294-312
Modeling complex collective animal movement presents distinct challenges. In particular, modeling the interactions between animals and the nonlinear behaviors associated with these interactions, while accounting for uncertainty in data, model, and parameters, requires a flexible modeling framework. To address these challenges, we propose a general hierarchical framework for modeling collective movement behavior with multiple stages. Each of these stages can be thought of as processes that are flexible enough to model a variety of complex behaviors. For example, self-propelled particle (SPP) models (e.g., Vicsek et al. in Phys Rev Lett 75:1226–1229, 1995) represent collective behavior and are often applied in the physics and biology literature. To date, the study and application of these models has almost exclusively focused on simulation studies, with less attention given to rigorously quantifying the uncertainty. Here, we demonstrate our general framework with a hierarchical version of the SPP model applied to collective animal movement. This structure allows us to make inference on potential covariates (e.g., habitat) that describe the behavior of agents and rigorously quantify uncertainty. Further, this framework allows for the discrete time prediction of animal locations in the presence of missing observations. Due to the computational challenges associated with the proposed model, we develop an approximate Bayesian computation algorithm for estimation. We illustrate the hierarchical SPP methodology with a simulation study and by modeling the movement of guppies.Supplementary materials accompanying this paper appear online. 相似文献
19.
J. Andrew Royle Robert M. Dorazio 《Journal of Agricultural, Biological & Environmental Statistics》2006,11(3):249-263
Much of animal ecology is devoted to studies of abundance and occurrence of species, based on surveys of spatially referenced
sample units. These surveys frequently yield sparse counts that are contaminated by imperfect detection, making direct inference
about abundance or occurrence based on observational data infeasible. This article describes a flexible hierarchical modeling
framework for estimation and inference about animal abundance and occurrence from survey data that are subject to imperfect
detection. Within this framework, we specify models of abundance and detectability of animals at the level of the local populations
defined by the sample units. Information at the level of the local population is aggregated by specifying models that describe
variation in abundance and detection among sites. We describe likelihood-based and Bayesian methods for estimation and inference
under the resulting hierarchical model. We provide two examples of the application of hierarchical models to animal survey
data, the first based on removal counts of stream fish and the second based on avian quadrat counts. For both examples, we
provide a Bayesian analysis of the models using the software WinBUGS. 相似文献