Probabilistic learning on manifolds (PLoM) with partition

The probabilistic learning on manifolds (PLoM) introduced in 2016 has solved difficult supervised problems for the “small data” limit where the number N of points in the training set is small. Many extensions have since been proposed, making it possible to deal with increasingly complex cases. However, the performance limit has been observed and explained for applications for which N is very small and for which the dimension of the diffusion-map basis is close to N. For these cases, we propose a novel extension based on the introduction of a partition in independent random vectors. We take advantage of this development to present improvements of the PLoM such as a simplified algorithm for constructing the diffusion-map basis and a new mathematical result for quantifying the concentration of the probability measure in terms of a probability upper bound. The analysis of the efficiency of this extension is presented through two applications.     

多堆厂址始发事件分析探讨

多堆厂址一级概率安全评价（PSA）研究中，机组数目的增加使得建模工作量剧增，给整个核电厂的风险评估带来困难。结合已有基础，本文研究了多堆厂址始发事件分析的筛选方法，提出利用堆芯损伤频率（CDF）上下限值评估方法，分析厂址内不同机组数对厂址CDF的影响。结果表明，双机组厂址适合优先进行具体分析。针对双机组核电站，对多堆厂址内各始发事件进行筛选。结果表明，丧失厂外电、丧失热阱等事件适合建模分析，并对其他筛选结果给出后续分析建议，为多堆厂址一级PSA后续事故序列建模工作提供了重要基础。     

Integration of imitation learning using GAIL and reinforcement learning using task-achievement rewards via probabilistic graphical model

The integration of reinforcement learning (RL) and imitation learning (IL) is an important problem that has long been studied in the field of intelligent robotics. RL optimizes policies to maximize the cumulative reward, whereas IL attempts to extract general knowledge about the trajectories demonstrated by experts, i.e, demonstrators. Because each has its own drawbacks, many methods combining them and compensating for each set of drawbacks have been explored thus far. However, many of these methods are heuristic and do not have a solid theoretical basis. This paper presents a new theory for integrating RL and IL by extending the probabilistic graphical model (PGM) framework for RL, control as inference. We develop a new PGM for RL with multiple types of rewards, called probabilistic graphical model for Markov decision processes with multiple optimality emissions (pMDP-MO). Furthermore, we demonstrate that the integrated learning method of RL and IL can be formulated as a probabilistic inference of policies on pMDP-MO by considering the discriminator in generative adversarial imitation learning (GAIL) as an additional optimality emission. We adapt the GAIL and task-achievement reward to our proposed framework, achieving significantly better performance than policies trained with baseline methods.     

Deterministic and probabilistic ship pitch prediction using a multi-predictor integration model based on hybrid data preprocessing,reinforcement learning and improved QRNN

The deterministic and probabilistic prediction of ship motion is important for safe navigation and stable real-time operational control of ships at sea. However, the volatility and randomness of ship motion, the non-adaptive nature of single predictors and the poor coverage of quantile regression pose serious challenges to uncertainty prediction, making research in this field limited. In this paper, a multi-predictor integration model based on hybrid data preprocessing, reinforcement learning and improved quantile regression neural network (QRNN) is proposed to explore the deterministic and probabilistic prediction of ship pitch motion. To validate the performance of the proposed multi-predictor integrated prediction model, an experimental study is conducted with three sets of actual ship longitudinal motions during sea trials in the South China Sea. The experimental results indicate that the root mean square errors (RMSEs) of the proposed model of deterministic prediction are 0.0254°, 0.0359°, and 0.0188°, respectively. Taking series #2 as an example, the prediction interval coverage probabilities (PICPs) of the proposed model of probability predictions at 90%, 95%, and 99% confidence levels (CLs) are 0.9400, 0.9800, and 1.0000, respectively. This study signifies that the proposed model can provide trusted deterministic predictions and can effectively quantify the uncertainty of ship pitch motion, which has the potential to provide practical support for ship early warning systems.     

Partial safety factors for laminated glass

Glass plies can be bonded together by polymeric interlayers to form laminated glass. Thanks to the capacity in finding alternative stress paths after partial breakage, it is commonly accepted that the more the layers are, the higher the safety level is. However, a negative aspect is that the tensile strength of glass carries a size effect in terms of surface area, which increases with lamination. Here we evaluate these competing factors by calibrating the partial safety factors to be used in semi-probabilistic (level-I) design, through comparison with probabilistic (level-III) methods in paradigmatic case-studies under wind, snow, and dead weight. Starting from a two-parameter Weibull distribution for glass strength, the “failure modes” approach determines the statistical distribution of strength for a multi-laminate as a function of the number of plies, which interferes with the statistics for actions. As a function of the target probability of failure for the assigned class of consequence, we introduce a new coefficient in the verification formula of level-I, to account for the effects of lamination with dependence upon the number of plies. We find that there is strong gain when passing from a monolith to a two-ply laminate, but the advantage fades by increasing the number of layers. Verification formulas of this type could avoid overconservative design.     

Pore-scale modeling of competition and cooperation of multispecies biofilms for nutrients in changing environments

In this article, we developed a pore-scale model of integrated lattice Boltzmann method and cellular automata to investigate competitive growth of aerobic nitrite and ammonium oxidizers in a bioreactor. The results showed that inlet nutrient concentrations have significant effects on maximum biofilm concentration, ratio of microorganisms' concentrations, growth pattern, and time. The local availability of oxygen could control the competition, resulting in different growth patterns. The coexistence of ammonium and nitrite in same inlet zone increased not only the biofilm concentration (7%) but also the ratio of microorganisms' concentrations (36%). Although this coexistence decreased the total biofilm concentration in some cases, it increased the growth rate about 25%. Changes of the maximum biomass concentration could change biofilm concentration of about 40% and microorganisms' concentrations ratio of about 30%. This framework provides a powerful tool to improve our understanding of dynamic interdependency of many complex microbial consortia systems with environments.     

轧制速度对AZ31镁合金管材冷轧成形的影响

轧制速度是三辊式冷轧成形过程中关键的工艺参数，决定其力学特征及温升情况。基于此，本文以冷轧AZ31镁合金管材为研究对象，通过全流程数值仿真计算，对比分析不同轧制速度在各特征变形段对等效应力、等效塑性应变及节点温度的影响规律。结果表明，等效应力、等效塑性应变及节点温度均随轧制速度的增大而增大。通过元胞自动机模型及实验等手段，探明了晶粒在轧制过程中产生连续再结晶并细化的初步组织演变规律；对比分析实验与模拟结果并结合多方面因素，得到800mm/s的轧制速度可以更好的满足工艺要求的结果，为冷轧镁合金管材轧制速度的选择提供依据。     

元胞自动机模型在航空发动机故障诊断中的应用

航空发动机一般在高温、高压和高速转动的状态下工作，因此很难获取其全生命周期试验数据。针对无完整生命周期数据的小样本集合进行设计，提出一种基于元胞自动机的航空发动机故障诊断方法，该方法在获取发动机故障特征信息之后，利用元胞的扩散机制获取故障模式的分类边界。其优势在于:在给定的数据集前提下,可以利用较少的运行时间来约减给定的规则样本;可以利用积累或迭代的方式来分步获得原给定样本集的一致性子集。同时，算法的可积累性、运算时间可控等特点，使得该算法能连续应用在航空发动机试验样本数据集由小样本持续增加到大样本的过程中。该方法的应用对发动机的故障诊断的研究具有重要的指导意义。