Ensemble Bayesian method for parameter distribution inference:application to reactor physics

The estimation of model parameters is an important subject in engineering.In this area of work,the prevailing approach is to estimate or calculate these as deterministic parameters.In this study,we consider the model parameters from the perspective of random variables and describe the general form of the parameter distribution inference problem.Under this framework,we propose an ensemble Bayesian method by introducing Bayesian inference and the Markov chain Monte Carlo(MCMC)method.Experiments on a finite cylindrical reactor and a 2D IAEA benchmark problem show that the proposed method converges quickly and can estimate parameters effectively,even for several correlated parameters simultaneously.Our experiments include cases of engineering software calls,demonstrating that the method can be applied to engineering,such as nuclear reactor engineering.

An Adaptive Learning Method for the Generation of Fuzzy Inference System from Data

Designing a fuzzy inference system(FIS)from data can be divided into two main phases:structure identification and parameter optimization.First,starting from a simple initial topology,the membership functions and system rules are defined as specific structures.Second,to speed up the convergence of the learning algorithm and lighten the oscillation,an improved descent method for FIS generation is developed.Furthermore, the convergence and the oscillation of the algorithm are system- atically analyzed.Third,using the information obtained from the previous phase,it can be decided in which region of the in- put space the density of fuzzy rules should be enhanced and for which variable the number of fuzzy sets that used to partition the domain must be increased.Consequently,this produces a new and more appropriate structure.Finally,the proposed method is applied to the problem of nonlinear function approximation.

Causal inference and related statistical methods

Statistical approaches for evaluating causal effects and for discovering causal networks are discussed in this paper.A causal relation between two variables is different from an association or correlation between them.An association measurement between two variables and may be changed dramatically from positive to negative by omitting a third variable,which is called Yule-Simpson paradox.We shall discuss how to evaluate the causal effect of a treatment or exposure on an outcome to avoid the phenomena of Yule-Simpson paradox. Surrogates and intermediate variables are often used to reduce measurement costs or duration when measurement of endpoint variables is expensive,inconvenient,infeasible or unobservable in practice.There have been many criteria for surrogates.However,it is possible that for a surrogate satisfying these criteria,a treatment has a positive effect on the surrogate,which in turn has a positive effect on the outcome,but the treatment has a negative effect on the outcome,which is called the surrogate paradox.We shall discuss criteria for surrogates to avoid the phenomena of the surrogate paradox. Causal networks which describe the causal relationships among a large number of variables have been applied to many research fields.It is important to discover structures of causal networks from observed data.We propose a recursive approach for discovering a causal network in which a structural learning of a large network is decomposed recursively into learning of small networks.Further to discover causal relationships,we present an active learning approach in terms of external interventions on some variables.When we focus on the causes of an interest outcome, instead of discovering a whole network,we propose a local learning approach to discover these causes that affect the outcome.

The Method for Inferring a Buried Fault from Resistivity Tomograms and Its Typical Electrical Features

Electrical resistivity tomography (ERT) has been used to experimentally detect shallow buried faults in urban areas in the past a few years, with some progress and experience obtained. According to the results from Olympic Park, Beijing, Shandong Province, Gansu Province and Shanxi Province, we have generalized the method and procedure for inferring the discontinuity of electrical structures (DES) indicating a buried fault in urban areas from resistivity tomograms and its typical electrical features. In general, the layered feature of the electrical structure is first analyzed to preliminarily define whether or not a DES exists in the target area. Resistivity contours in resistivity tomograms are then analyzed from the deep to the shallow. If they extend upward from the deep to the shallow and shape into an integral dislocation, sharp flexure (convergence) or gradient zone, it is inferred that the DES exists, indicating a buried fault. Finally, horizontal tracing is be carried out to define the trend of the DES. The DES can be divided into three types-type AB, ABA and AC. In the present paper, the Zhangdian-Renhe fault system in Zibo city is used as an example to illustrate how to use the method to infer the location and spatial extension of a target fault. Geologic drilling holes are placed based on our research results, and the drilling logs testify that our results are correct. However, the method of this paper is not exclusive and inflexible. It is expected to provide reference and assistance for inferring the shallow buried faults in urban areas from resistivity tomograms in the future.

信息计量学的创新之道:近年优秀国际期刊论文的启示

[目的/意义]在智联网时代,信息计量学的研究环境、研究对象、研究内容和研究方法都发生了深刻变化。揭示近年信息计量学研究的创新之道,有助于了解信息计量学领域的前沿知识,为信息计量学的高质量发展提供参考。[方法/过程]文章系统梳理了2019—2022年的国际信息计量学研究,归纳出三类创新之道。[结果/结论]一是通过引入计算机科学、数学、经济学和社会学等领域的先进知识,实现交叉融合创新;二是辩证思考经典的计量方法、计量指标和计量模型,实现分析方法创新;三是顺应智能预测和因果推断的时代潮流,实现研究范式创新。结论表明,为适应新时代的发展需要,信息计量学研究者必须积极汲取多学科知识,拓展分析方法的适用范围,不断突破面向国家需求的战略问题。

寒门何以出贵子:农村低收入群体收入跃升的机会不平等

机会平等是实现低收入群体收入跃升的重要前提。本文基于2015—2021年中国社会状况综合调查数据,将“环境—努力”机会均等分析框架引入农村低收入群体收入跃升的影响因素研究中,采用相异指数衡量农村低收入群体陷入低收入状态的收入机会不平等程度,运用非参数条件推断树方法测算农村低收入群体陷入低收入状态的机会不平等系数,并进一步讨论了环境因素对农村低收入群体陷入低收入状态的机会不平等的直接影响和间接影响。研究发现:2015—2021年,农村低收入群体陷入低收入状态的机会不平等系数为0.2356—0.2925,说明农村居民收入跃升的机会不平等程度较高;农村低收入群体性别差异对机会不平等的影响最大,其次是地区和年龄。随着时间的变化,户口迁移和父母务农的农村低收入群体,机会差异对农村低收入群体陷入低收入状态的影响逐渐增加;环境因素通过教育渠道和就业渠道间接影响农村低收入群体陷入低收入状态的收入机会不平等的贡献度为43.34%—55.65%,其中,教育渠道的贡献度为34.34%—43.87%,就业渠道的贡献度仅有6.07%—14.26%。本文的研究结论为更好地保障农村低收入群体发展机会平等,助力农村低收入群体收入跃升提供了理论依据。

基于真实世界数据的观察性因果推断研究新框架(目标试验模拟)及其在中医药领域中的应用展望

“目标试验模拟”(target trial emulation)作为一种新的真实世界研究框架在近年来被正式确立,其可用于指导基于真实世界数据开展观察性因果推断研究,继而进行医疗干预有效性和安全性评价。该框架的核心思想是参照随机对照试验的原则,利用真实世界数据模拟相应的随机对照试验,进而得出干预措施与临床结局间因果关联的结论。其主要实施要点可总结为“3⁃7⁃2”,即声明因果问题、制订模拟方案和模拟目标研究3个实施步骤,纳排标准、治疗策略、干预分配、随访期、结局指标、因果比较和分析计划7个设计要素,对永恒时间偏倚和现用药者偏倚2个关键偏倚的控制。本文通过对该框架的历史与现状、实施要点、经典案例、优势与局限性及其在中医药领域中的应用前景进行概述,以期帮助中医药学者开展中医药真实世界研究,并为构建具有中医药特色的临床评价体系提供新思路。

基于条件变分自编码器的熔铸炸药成型缺陷快速模拟和预测

为了实现凝固缺陷的快速模拟和预测,提出了一种基于条件变分自编码器(CVAE)的熔铸炸药成型缺陷预测模型;以注液温度、冒口预热温度等工艺参数为条件,通过条件变分自编码器建立工艺参数与熔铸炸药缺陷的条件概率模型;采用多层神经网络和变分推断方法结合进行模型训练,实现了RHT和DNP基熔铸炸药凝固成型缺陷预测。结果表明,成功构建了熔铸炸药凝固过程数值模拟的条件概率分布,实现了基于仿真数据的RHT和DNP基熔铸炸药凝固缺陷预测;与有限元直接数值计算结果比较,CVAE算法计算缺陷位置的准确率可达到99%,计算时间小于2 s;CVAE在熔铸炸药缺陷概率分布建模上具有性能高、泛化性强的特点,能有效实现熔铸炸药成型缺陷的智能预测。

龙羊峡冷水鱼养殖气象服务效益评估

针对冷水鱼养殖中的气象服务,效益的评估是判断预报能力的技术之一。为提高冷水鱼养殖业的生产效益和应对气象灾害的能力,以龙羊峡冷水养殖为研究对象,提出冷水鱼养殖气象服务效益评估方法,综合分析冷水鱼养殖区域实际情况与气象服务关键影响因素,从气象服务质量与气象服务绩效两方面出发,选取气象数据准确性以及冷水鱼养殖产量增减等8个评估指标,构建冷水鱼养殖气象服务效益评估指标体系。利用属性层次分析模型计算评估指标权重,采用逆推法构建效益评估模型,并确定无法避免损失因子、气象服务质量和绩效指标参数,获取气象服务效益评估值。结果显示该方法能够有效计算出气象服务效益评估结果,得出冷水鱼养殖业的投入产出比,在冷水鱼养殖中具有一定的实用价值。

基于分位数处理效应的面板数据政策评价方法

学者们在估计不同群体受到同一政策的影响时,通常需要使用协变量或者工具变量。为了在缺少协变量和工具变量的情况下估计一项政策对不同群体产生的影响,文章将分位数处理效应与面板数据政策评价方法相结合,得到一种新的政策影响效应估计方法——基于分位数处理效应的面板数据政策评价方法,并进一步研究了政策影响估计量的方差缩减和分解方法。新方法得到的政策影响估计量具有一致性和渐近正态性,交叉验证方法能够缩减估计量的方差,政策影响估计量可以分解为共有影响和特有影响。新的政策影响估计方法为学者们在缺少工具变量和协变量的情况下研究一项政策对不同群体产生的差异化影响提供了帮助。

Differently implicational α-universal triple I restriction method of (1, 2, 2) type

From the viewpoints of both fuzzy system and fuzzy reasoning, a new fuzzy reasoning method which contains the α- triple I restriction method as its particular case is proposed. The previous α-triple I restriction principles are improved, and then the optimal restriction solutions of this new method are achieved, especially for seven familiar implications. As its special case, the corresponding results of α-triple I restriction method are obtained and improved. Lastly, it is found by examples that this new method is more reasonable than the α-triple I restriction method.

A precise tidal prediction mechanism based on the combination of harmonic analysis and adaptive network-based fuzzy inference system model

An efficient and accurate prediction of a precise tidal level in estuaries and coastal areas is indispensable for the management and decision-making of human activity in the field wok of marine engineering. The variation of the tidal level is a time-varying process. The time-varying factors including interference from the external environment that cause the change of tides are fairly complicated. Furthermore, tidal variations are affected not only by periodic movement of celestial bodies but also by time-varying interference from the external environment. Consequently, for the efficient and precise tidal level prediction, a neuro-fuzzy hybrid technology based on the combination of harmonic analysis and adaptive network-based fuzzy inference system（ANFIS）model is utilized to construct a precise tidal level prediction system, which takes both advantages of the harmonic analysis method and the ANFIS network. The proposed prediction model is composed of two modules： the astronomical tide module caused by celestial bodies’ movement and the non-astronomical tide module caused by various meteorological and other environmental factors. To generate a fuzzy inference system（FIS） structure,three approaches which include grid partition（GP）, fuzzy c-means（FCM） and sub-clustering（SC） are used in the ANFIS network constructing process. Furthermore, to obtain the optimal ANFIS based prediction model, large numbers of simulation experiments are implemented for each FIS generating approach. In this tidal prediction study, the optimal ANFIS model is used to predict the non-astronomical tide module, while the conventional harmonic analysis model is used to predict the astronomical tide module. The final prediction result is performed by combining the estimation outputs of the harmonious analysis model and the optimal ANFIS model. To demonstrate the applicability and capability of the proposed novel prediction model, measured tidal level samples of Fort Pulaski tidal station are selected as the testing database. Simulation and experimental results confirm that the proposed prediction approach can achieve precise predictions for the tidal level with high accuracy, satisfactory convergence and stability.

Bayesian inference for ammunition demand based on Gompertz distribution

Aiming at the problem that the consumption data of new ammunition is less and the demand is difficult to predict,combined with the law of ammunition consumption under different damage grades,a Bayesian inference method for ammunition demand based on Gompertz distribution is proposed.The Bayesian inference model based on Gompertz distribution is constructed,and the system contribution degree is introduced to determine the weight of the multi-source information.In the case where the prior distribution is known and the distribution of the field data is unknown,the consistency test is performed on the prior information,and the consistency test problem is transformed into the goodness of the fit test problem.Then the Bayesian inference is solved by the Markov chain-Monte Carlo(MCMC)method,and the ammunition demand under different damage grades is gained.The example verifies the accuracy of this method and solves the problem of ammunition demand prediction in the case of insufficient samples.

Estimation of convergence of a high-speed railway tunnel in weak rocks using an adaptive neuro-fuzzy inference system(ANFIS) approach

Estimation of tunnel diameter convergence is a very important issue for tunneling construction,especially when the new Austrian tunneling method（NATM） is adopted.For this purpose,a systematic convergence measurement is usually implemented to adjust the design during the whole construction,and consequently deadly hazards can be prevented.In this study,a new fuzzy model capable of predicting the diameter convergences of a high-speed railway tunnel was developed on the basis of adaptive neuro-fuzzy inference system（ANFIS） approach.The proposed model used more than 1 000 datasets collected from two different tunnels,i.e.Daguan tunnel No.2 and Yaojia tunnel No.1,which are part of a tunnel located in Hunan Province,China.Six Takagi-Sugeno fuzzy inference systems were constructed by using subtractive clustering method.The data obtained from Daguan tunnel No.2 were used for model training,while the data from Yaojia tunnel No.1 were employed to evaluate the performance of the model.The input parameters include surrounding rock masses（SRM） rating index,ground engineering conditions（GEC） rating index,tunnel overburden（H）,rock density（？）,distance between monitoring station and working face（D）,and elapsed time（T）.The model’s performance was assessed by the variance account for（VAF）,root mean square error（RMSE）,mean absolute percentage error（MAPE） as well as the coefficient of determination（R2） between measured and predicted data as recommended by many researchers.The results showed excellent prediction accuracy and it was suggested that the proposed model can be used to estimate the tunnel convergence and convergence velocity.

THE DEVELOPING METHOD OF AI SOFTWARE FOR APPLICATION OF AERIAL PHOTOGRAPH(AIAAP)

This paper put forward a framework of artificial intelligence (AI) for app;ication of aerial photograph. The framwork contains structures of knowledge base and inference mechanism.The functions of framework about how to convert expert’s knowledge into logical rules and how to inference using the rules wer discussed. The regeneraion pltring of fired land in Dwi’an Mountains area was taken as an example to have a ditail discuss on the contation of knowledge base and the steps of inference mechaism. the program was written using the basic langUae and the running result proyed tha the software can take the place of expert’s work with high efficiellcy.

Modeling and inferring 2.1D sketch with mixed Markov random field

This paper presents a method of computing a 2.1D sketch (i.e., layered image representation) from a single image with mixed Markov random field (MRF) under the Bayesian framework. Our model consists of three layers: the input image layer, the graphical representation layer of the computed 2D atomic regions and 3-degree junctions (such as T or arrow junctions), and the 2.1D sketch layer. There are two types of vertices in the graphical representation of the 2D entities: (i) regions, which act as the vertices found in traditional MRF, and (ii) address variables assigned to the terminators decomposed from the 3-degree junctions, which are a new type of vertices for the mixed MRF. We formulate the inference problem as computing the 2.1D sketch from the 2D graphical representation under the Bayesian framework, which consists of two components: (i) region layering/coloring based on the Swendsen-Wang cuts algorithm, which infers partial occluding order of regions, and (ii) address variable assignments based on Gibbs sampling, which completes the open bonds of the terminators of the 3-degree junctions. The proposed method is tested on the D-Order dataset, the Berkeley segmentation dataset and the Stanford 3D dataset. The experimental results show the efficiency and robustness of our approach. © 2017 Beijing Institute of Aerospace Information.

Records Method for the Natural Disasters Application to the Storm Events

Let {Tn } be a renewal process in R＋ representing the successive arrival times of some natural events. We studied this process by using a record process approach under the assumption that the interarrival times T,, = Tn, - Ta-1, n = 1, 2...are exponentially i.i.d （independent and identically distributed）. The goal is to test that the first observed events are sporadic events. For testing the hypothesis ＂sporadic＂ we used the non-parametric test based on the probability distribution of the statistic of the number of records N, among{Xx }k-1= where Xk = （ΔTk）-1. We showed that it is independent of the cumulative distribution of the observations and that it is exactly calculated for each n. We illustrated this statistic on a simulated trajectory and we compared it with descriptive smoothing methods. We studied an application to a data set as storms in France and US.

Adaptive Neuro-Fuzzy Inference System for Thermal Field Evaluation of Underground Cable System

The influence of thermal circuit parameters on a buried underground cable is investigated using an ANFIS （adaptive neuro-fuzzy inference system）. Finite element solution of the heat conduction equation is used, combined with artificial intelligence methods. The cable temperature depends on several parameters, such as the ambient temperature, the currents flowing through the conductor and the resistivity of the surrounding soil. In this paper, ANFIS is used to simulate the problem of the thermal field of underground cables under various parameters variation and climatic conditions. The developed model was trained using data generated from FEM （finite element method） for different configurations （training set） of the thermal field problem. After training, the system is tested for several scenarios, differing significantly from the training cases. It is shown that the proposed method is very time efficient and accurate in calculating the thermal fields compared to the relatively time consuming finite element method; thus ANFIS provides a potential computationally efficient and inexpensive predictive tool for more effective thermal design of underground cable systems.

Structural Reliability Analysis Method in Fuzzy Environment

In this paper, the fuzzy-set-based structural possibility theory is investigated, and this theory can be used to deal with the subjective uncertainties in the design of engineering structures. Furthermore, a comprehensive model of structural safety assessment, which can merge subjective uncertainties with objective uncertainties, is presented. In this model, the fuzziness of stress-strength inference model, safety margin functions of single or multiple limit-state, structural failure state and the final assessment result are taken into account. This continuous model can be transformed into an equivalent model of probability-based and solved by the present structural reliability analysis method and parallel algorithm. An example is given to show the main idea of the method presented in this paper.

Y形与Δ形电阻网络等效变换公式的推理记忆法

提出了电阻Y形网络和Δ形网络相互等效变换式的推理记忆法。具体推理步骤有量纲推理、电路结构类比推理和公平原则推理三个方面。特别在电路结构类比推理中指出电阻Y形网络和Δ形网络与简单电阻串联网络和并联网络具有对应相似性,有助于学生联想记忆。