Uncertainty quantification via bayesian inference using sequential monte carlo methods for CO2 adsorption process

This work presents the uncertainty quantification, which includes parametric inference along with uncertainty propagation, for CO₂ adsorption in a hollow fiber sorbent, a complex dynamic chemical process. Parametric inference via Bayesian approach is performed using Sequential Monte Carlo, a completely parallel algorithm, and the predictions are obtained by propagating the posterior distribution through the model. The presence of residual variability in the observed data and model inadequacy often present a significant challenge in performing the parametric inference. In this work, residual variability in the observed data is handled by three different approaches: (a) by performing inference with isolated data sets, (b) by increasing the uncertainty in model parameters, and finally, (c) by using a model discrepancy term to account for the uncertainty. The pros and cons of each of the three approaches are illustrated along with the predicted distributions of CO₂ breakthrough capacity for a scaled‐up process. © 2016 American Institute of Chemical Engineers AIChE J, 62: 3352–3368, 2016     

Semiparametric Inference in Seasonal and Cyclical Long Memory Processes

Several semiparametric estimates of the memory parameter in standard long memory time series are now available. They consider only local behaviour of the spectrum near zero frequency, about which the spectrum is symmetric. However long-range dependence can appear as a spectral pole at any Nyqvist frequency (reflecting seasonal or cyclical long-memory), where the spectrum need display no such symmetry. We introduce Seasonal/Cyclical Asymmetric Long Memory (SCALM) processes that allow differing rates of increase on either side of such a pole. To estimate the two consequent memory parameters we extend two semiparametric methods that were proposed for the standard case of a spectrum diverging at the origin, namely the log-periodogram and Gaussian or Whittle methods. We also provide three tests of symmetry. Monte Carlo analysis of finite sample behaviour and an empirical application to UK inflation data are included. Our models and methods allow also for the possibility of negative dependence, described by a possibly asymmetric spectral zero.     

THE EXACT ERROR IN ESTIMATING THE SPECTRAL DENSITY AT THE ORIGIN

Abstract. This paper derives expressions for the exact bias and variance of a general class of spectral density estimators at the zero frequency, building on the work of Neave (The exact error in spectrum estimates. Ann. Math. Statist. 42 (1971), 961–75) who studied the case where the mean of the series is assumed known. These expressions are evaluated for 15 different windows and for a wide variety of stationary time series. The exact error of the estimators is found to depend on whether the sample mean has to be estimated, and some windows are noticeably inferior at certain values of the bandwidth. A response surface analysis reveals that the finite sample relationships between the bandwidth and the exact error are quite different from the ones suggested by asymptotic theory.     

基于形状文法和ABAQUS的客家土楼木制拼装玩具设计及结构性能分析

目的 客家土楼造型丰富、独特,特点鲜明。分析、提取客家土楼核心元素,在现代木制拼装玩具中传承、应用,推陈出新、展现创新力量。通过将客家土楼精髓元素融入木制拼装玩具设计中,能够让传统文化焕发时代新生命力,创造集教育、娱乐、安全性于一体的新产品。方法 以形状文法及ABAQUS为工具,以客家土楼为分析对象,以木制拼装产品为设计对象,创造独特、安全的木制拼装产品。结果 深入剖析客家土楼传统居民建筑的特征,从中提取其独特的造型和装饰元素,并将其进行融合设计,再对其拼装结构进行合理性验证,最终形成独具魅力的设计成果。结论 通过形状文法对客家土楼文化特征元素进行推演,保证木制拼装玩具外观,既具有客家土楼元素形体特征,又符合现代审美特点;再通过ABAQUS拼装结构进行力学试验,验证了拼装产品的安全性与合理性。该研究达成了对地域文化进行传承和创新的目的。     

基于自适应切片辅助推理的航拍图像目标检测方法

针对无人机航拍图像中目标尺度不一、密度不一、细节不清,尤其小目标众多所导致的漏检和误检问题,提出一种基于自适应切片辅助推理的目标检测新方法。该方法首先将航拍图像输入目标检测网络进行初次推理,设计一种窗口得分机制来根据初次推理结果定位输入图像中的不确定目标,并自动选择有效的图像区域进行切片以适应不同尺度和密度的目标。接着将切片图像送入目标检测网络进行二次推理。最后对两次推理结果执行改进的非极大值抑制处理得到最终检测结果。在典型的VisDrone2019和AI-TOD数据集上的实验结果表明,本文方法提升了包括YOLOv7-tiny、YOLOv8n、YOLOv8s及YOLOv9-C在内的典型轻量级目标检测模型的mAP指标,有效提高了航拍图像目标检测性能。     

基于模糊理论和三段论推理的电影情感分类

人类情感反应具有模糊属性,针对直接应用视频底层特征识别情感类型难以提高精确度的问题,本研究在情感关联底层特征的基础上引入了模糊隶属函数,采用最大隶属度模糊化原则对底层特征进行处理,最终得到一种蕴含情感信息的特征来描述视频片段,为随后采用三段论推理实现电影情感分类提供了合适的特征,这种特征能缩短情感鸿沟。三类基本情感的分类精确度都超过84％,与现有方法相比,分类精确度平均提高了9.33％。因此本研究所提出的算法能有效地提高电影情感分类的精确度。