Construction and realization of the knowledge base and inference engine of an IDSS model for air-conditioning cooling/heating sources selection

The knowledge representation mode and inference control strategy were analyzed according to the specialties of air-conditioning cooling/heating sources selection. The constructing idea and working procedure for knowledge base and inference engine were proposed while the realization technique of the C language was discussed. An intelligent decision support system (IDSS) model based on such knowledge representation and inference mechanism was developed by domain engineers. The model was verified to have a small kernel and powerful capability in list processing and data driving, which was successfully used in the design of a cooling/heating sources system for a large-sized office building.

Construction of the Inference Engine of Blast Furnace Expert System

The expert system for judging blast furnace condition is built to realize automatic judgment of blast furnace conditions and standardization of blast furnace operation. This paper discusses the design and implementation of the inference engine of blast furnace expert system. The satisfactory simulation results have been obtained.

Design of an Inference Engine Based on BP Network and LabVIEW

In the field of engineering, LabVIEW and MATLAB are the languages most commonly used by program developers. Howev-er, they have their respective advantages and disadvantages. The combination of the two will undoubtedly facilitate program develop-ment. Design of inference engine is the key point and difficulty in the design of fault diagnosis expert system. This paper combinesLabVIEW and MATLAB to design the inference engine of fault diagnosis expert system based on the advantages of the graphical pro-gramming environment and signal analysis toolkit of LabVIEW without the defect of neural network toolkit. In addition, it introducesthe implementation methods and precautions for combination of LabVIEW and BP network so as to make LabVIWE amd BP benefitfrom each other, which is of great pragmatic value.

GRADE-LIFE PROGNOSTIC MODEL OF AIRCRAFT ENGINE BEARING

Research on practical and verifiable prediction methods for the service life of bearings plays a critical role in improving the reliability and safety of aircraft engines. The concept of grade-life （GL） is introduced to de- scribe the service life of bearings. A GL prognostic model for aircraft engine bearings is proposed based on sup- port vector machine （SVM） and fuzzy logic inference. Firstly, the mathematical model is discussed to predict the physics-based GL （PGL）. Then, the diagnostic estimation model based on SVM is presented in detail to predict the empirical GL （EPL）. Thirdly, a fuzzy logic inference is adopted to fuse two GL predicted results. Finally, the GL prognostic model is verified by the run-to-failure data acquired from an accelerated life test of an aircraft bearing. The results show that the model provides a more practical and reliable prediction for the service life of bearings.

Updating the models and uncertainty of mechanical parameters for rock tunnels using Bayesian inference

Rock mechanical parameters and their uncertainties are critical to rock stability analysis,engineering design,and safe construction in rock mechanics and engineering.The back analysis is widely adopted in rock engineering to determine the mechanical parameters of the surrounding rock mass,but this does not consider the uncertainty.This problem is addressed here by the proposed approach by developing a system of Bayesian inferences for updating mechanical parameters and their statistical properties using monitored field data,then integrating the monitored data,prior knowledge of geotechnical parameters,and a mechanical model of a rock tunnel using Markov chain Monte Carlo(MCMC)simulation.The proposed approach is illustrated by a circular tunnel with an analytical solution,which was then applied to an experimental tunnel in Goupitan Hydropower Station,China.The mechanical properties and strength parameters of the surrounding rock mass were modeled as random variables.The displacement was predicted with the aid of the parameters updated by Bayesian inferences and agreed closely with monitored displacements.It indicates that Bayesian inferences combined the monitored data into the tunnel model to update its parameters dynamically.Further study indicated that the performance of Bayesian inferences is improved greatly by regularly supplementing field monitoring data.Bayesian inference is a significant and new approach for determining the mechanical parameters of the surrounding rock mass in a tunnel model and contributes to safe construction in rock engineering.

On-Line Real Time Realization and Application of Adaptive Fuzzy Inference Neural Network

In this paper, a modeling algorithm developed by transferring the adaptive fuzzy inference neural network into an on-line real time algorithm, combining the algorithm with conventional system identification method and applying them to separate identification of nonlinear multi-variable systems is introduced and discussed.

A reliability analysis framework coupled with statistical uncertainty characterization for geotechnical engineering

Reliability analysis plays an important role in the risk management of geotechnical engineering.For the random field-based method,it is expected that the uncertainty characterization of geo-material parameters and the realization of random field can be integrated effectively.Moreover,as the increase in measured data size is generally difficult in the field investigation of geotechnical engineering due to limitation of budget and time etc.,the statistical uncertainty resulting from sparse data should be paid great attention.Therefore,taking the determination of hyper-parameters for Bayesian-based conditional random field as the breakthrough,this study proposed a reliability analysis framework to achieve the expectation above.In this proposed reliability analysis framework,the present characterization method of statistical uncertainty is improved by setting the lognormal distribution as the prior distribution of scale of fluctuation(SOF).Subsequently,the performance of statistical uncertainty characterization method is tested by a set of unconfined compressive strength(UCS)database about rocks.Then,a case study about the stability analysis of slope is employed to demonstrate the beneficial effect of the proposed reliability analysis framework.It is found that the uncertainty in both the realization of random field and the reliability analysis results can be significantly mitigated by the proposed reliability analysis framework.

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.

自适应神经模糊推理系统优化的快速上肢评估方法

传统方法对工作相关肌肉骨骼疾病风险评估的输入变量变化敏感性较低,导致风险评估输出结果的精确性和可靠性不足。为更加准确地进行人因工程风险评估,提出了基于自适应神经模糊推理系统的快速上肢评估方法(RULA)。首先,基于卷积神经网络对视频中人体工作姿势的关键点进行检测及识别,并计算关节角度;其次,基于自适应神经模糊推理系统对快速上肢评估方法进行改进,搭建工作相关肌肉骨骼疾病风险评估架构以解决评估不同姿势时获得相同评分的问题;再次,随机选取不同工作姿势的关节角度数据对网络进行训练和检测,调整基于自适应神经模糊推理系统和快速上肢评估方法的工作相关肌肉骨骼疾病风险预测模型的最佳参数;最后,选取关节角度数据集里的前15个工作姿势进行相关性验证,将结果与原始快速上肢评估方法的结果进行比较,应用树枝修剪工具的操作过程进行案例分析以实现风险得分的实时动态评估。结果表明,优化后的快速上肢评估方法比原始方法更敏感,验证了利用自适应神经模糊推理系统能够有效改进快速上肢评估方法并实时预测风险得分。

基于异同移动平均线指标的氨氮厌氧工艺诊断研究

合理的诊断方法和控制策略是实现厌氧处理工艺安全性和稳定性的必要手段。为了对氨氮厌氧工艺的处理过程进行准确的诊断与控制,基于在线监测系统对氨氮扰动冲击情境下上流式厌氧污泥床(Up-flow Anaerobic Sludge Blanket,UASB)反应器的气液相参数进行实时监测,并根据指数平滑异同移动平均算法,建立各参数相对应的异同移动平均线指标。根据各参数的指标响应特征与反应器之间的状态关系,通过建立诊断规则,构建氨氮厌氧工艺诊断推理数据库。诊断推理数据库结果显示,气相参数指标整体上比液相参数指标响应快,适合作为识别氨氮扰动抑制发生的敏感性指标;综合气相和液相多参数异同移动平均线指标的响应特征,不仅能够对扰动抑制程度和厌氧生化系统的恢复情况做出准确、全面的诊断,还能够提供快速的预警和有效的控制策略。

基于CBR RBR的推理机在机械比能分析模型中的研究

为了解决录井储层物性评价时由于环境复杂,人员耗费周期长、缺少对数据的全面分析与利用、机械比能分析模型的不确定性的问题,引入知识库的概念,利用CBR RBR推理决策,结合录井专业知识,针对不同情况下的勘探情况,分别建立案例库和规则库,利用混合的推理方法,检索出与当前情况相似度较高的案例库中的案例,并进行推理得出高效的机械比能模型,使之取代人工选择模型,节约人力资源,利用知识驱动概念使模型更加高效,可根据现场人员的需求设计推理结构,可以提高模型的效率,极大地满足储层物性评价需要。

一种基于Agent的任务自生成方法

任务自生成是基于预先设定的业务工作流完成任务执行流程制定的一个过程,任务执行流程制定主要包括任务执行步骤选取与任务执行步骤相应的解决方案选取。提出一种基于Agent的任务自生成方法来解决基于传统的业务工作流的流程制定机制,在面对有差异性需求的不同任务时,因其不能做出任务执行流程制定的自适应性改变而在处理任务时出现处理速度慢、结果不准确等问题。利用Agent的环境感知能力实现对任务需求信息和任务自生成系统环境的感知,利用Agent的自主行为决策能力结合感知的信息实现对任务执行流程制定工作的自主管理,在真实应用场景测试中,通过一系列比较实验验证了这种方法在处理速度和准确度方面具有明显优势。

模糊推理系统在内河通航风险评价中的应用--以长江干线为例

为评估内河通航风险,对408件长江干线历史事故进行致因分析,构建分类分级的内河通航风险评价指标体系;在模糊推理系统(Fuzzy Inference System,FIS)中引入突变理论(Catastrophe Theory,CT),构建基于CT FIS的风险评价模型;对59个历史案例进行实证分析。结果显示:在59个样本中,中等风险及以上的样本数分数为88.1%;对各风险评价指标进一步分析发现,人的因素是影响长江干线通航风险的首要因素,货物和船舶因素相对于船员、管理和环境因素具有相对较低的影响;将人为可控的影响因素控制在安全状态时,系统的总风险可显著降低。

基于描述逻辑的主体服务匹配

多主体系统中的服务匹配是智能主体和多主体系统等领域中的重要研究课题 .描述逻辑是知识表示和推理的形式化工具 ,它提供了可判定的和可靠的推理服务 .该文利用描述逻辑有效的推理功能 ,特别是它对概念包含关系的有效判断 ,把它与多主体系统的服务推理结合起来 .充分利用描述逻辑具有清晰模型 理论语义和有效的概念分层推理服务等功能 ,该文提出了基于描述逻辑的主体服务匹配算法 ,详细研究了如何利用描述逻辑的理论和推理机制来实现自动的服务分层及服务匹配 ,并提出了五种服务匹配算法 .这些方法都是基于语义的服务匹配 ,利用服务分层机制实现了有效和高效的多主体系统中的服务匹配 ,克服了基于语义距离进行服务匹配的不足 .

烧结法生产氧化铝生料浆的配料专家系统设计

基于烧结法生产氧化铝生料浆的配料机理和长期积累的专家经验知识,建立了生料浆质量预测模型,开发了适应于生料浆配料生产特点的专家优化配料系统。根据实际生产和配料机理,提出了产生式规则的知识表示形式,结合规则集分类原理,设计了生料浆配料的专家知识库。通过规则集之间的有知识搜索、规则集内部的前向推理、规则前提和结论之间的哈希算法的有机结合来实现推理机制,从而实现专家系统的优化配比计算。实际结果表明:该专家系统优化配比计算速度快、效率高,既提高了生料浆质量,又稳定了生产。

500kV双龙变电所操作票自动生成系统

介绍了 5 0 0 k V双龙变电所操作票自动生成系统。提出了一种采用模型化设备、领域知识库、拓扑路径算法和推理机的实用的专家系统构成方案。系统采用多重产生式系统来构造专家知识库。提出了二次设备模型化 ,并构造了相应的知识库 ,解决了二次设备操作票自动生成问题。通过在线拓扑分析算法 ,推理出操作路径 ,使该系统具有很好的跟踪电网运行方式的能力。所设计的解释机 ,能对推理过程和知识利用情况进行详细解释。该系统在线利用 SCADA实时信息 ,形成一在线智能生成与监视的闭环系统 ,并于 1999年 5月在金华 5 0 0 k

有限元网格划分方法应用研究

首先探讨了有限元网格划分的一些基本原则,然后从几何模型建立方法的角度对几种不同网格划分方法进行了深入讨论,最后在ANSYS中对这些网格划分方法进行了实例比较,给出了不同网格划分方法的适用范围及其应用特点。

飞行器机动动作风险定量评估模型

为了定量评估机动动作的风险,构建机动动作风险定量评估模型。提出使用关键点划分机动动作,并给出了不同风险等级的划分及其含义;由于关键点的风险等级与参数离差划分区间之间的不确定性,使用贝叶斯定理确定关键点处于不同风险等级的概率,并结合不同风险等级的权重得到关键点的风险值;由于专家在判断过程中的模糊性及不确定性,使用模糊层次分析法确定不同关键点的权重,最终实现了机动动作风险的定量评估。仿真实验部分以典型的机动动作斤斗为例进行研究,通过对斤斗动作实际数据的分析,验证了机动动作风险定量评估模型的有效性。

基于Mamdani模糊推理的山岭隧道围岩RMR14分级

岩体分级方法(RMR14)可充分评价岩体力学性质和结构面的条件,且可考虑初始地应力场和开挖方式等工程因素的影响,非常适用于山岭隧道围岩的评价。但RMR14评级方法的阶梯状评分会引起评分区间边界出现模糊不确定性,为更加准确地评价岩体质量,基于Mamdani模糊推理方法,引入模糊隶属函数来解决模糊不确定性问题。采用"if-then"推理法则实现多输入参数多推理规则的模糊推理,提出基于Mamdani模糊推理的RMR14岩体分级方法,并将此方法应用于独平高速漂里隧道的围岩质量评价。研究结果表明,基于Mamdani模糊推理的RMR14岩体质量分级方法既可借鉴既有工程经验,又可恰当处理评分边界的模糊不确定性问题;与直接采用RMR14分级方法相比,本文提出的模糊分级方法能更加准确评价围岩质量。

一个面向对象的推理机OOIE的设计与实现

本文提出了面向对象的推理机的设计思想，并在Ｗｉｎｄｏｗｓ环境下用ＢｏｒｌａｎｄＣ＋＋４．０编程实现．它提供多种推理方法和多种非精确性推理策略，具有定性推理与定量计算相结合的功能，已开发出多个实用专家系统．文中着重讨论了面向对象的推理模型、非精确性推理。