Aggregation operators and CRITIC-VIKOR method for confidence complex q-rung orthopair normal fuzzy information and their applications

Supply chain management is an essential part of an organisation's sustainable programme.Understanding the concentration of natural environment,public,and economic influence and feasibility of your suppliers and purchasers is becoming progressively familiar as all industries are moving towards a massive sustainable potential.To handle such sort of developments in supply chain management the involvement of fuzzy settings and their generalisations is playing an important role.Keeping in mind this role,the aim of this study is to analyse the role and involvement of complex q-rung orthopair normal fuzzy(CQRONF)information in supply chain management.The major impact of this theory is to analyse the notion of confidence CQRONF weighted averaging,confidence CQRONF ordered weighted averaging,confidence CQRONF hybrid averaging,confidence CQRONF weighted geometric,confidence CQRONF ordered weighted geometric,confidence CQRONF hybrid geometric operators and try to diagnose various properties and results.Furthermore,with the help of the CRITIC and VIKOR models,we diagnosed the novel theory of the CQRONF-CRITIC-VIKOR model to check the sensitivity analysis of the initiated method.Moreover,in the availability of diagnosed operators,we constructed a multi-attribute decision-making tool for finding a beneficial sustainable supplier to handle complex dilemmas.Finally,the initiated operator's efficiency is proved by comparative analysis.

Research on fault time prediction method for high speed rail BTM unit based on multi method interactive validation

The Balise Transmission Module(BTM)unit of the on-board train control system is a crucial component.Due to its unique installation position and complex environment,this unit has a higher fault rate within the on-board train control system.To conduct fault prediction for the BTM unit based on actual fault data,this study proposes a prediction method combining reliability statistics and machine learning,and achieves the fusion of prediction results from different dimensions through multi-method interactive validation.Firstly,a method for predicting equipment fault time targeting batch equipment is introduced.This method utilizes reliability statistics to construct a model of the remaining faultless operating time distribution considering uncertainty,thereby predicting the remaining faultless operating probability of the BTM unit.Secondly,considering the complexity of the BTM unit’s fault mechanism,the small sample size of fault cases,and the potential presence of multiple fault features in fault text records,an individual-oriented fault prediction method based on Bayesian-optimized Gradient Boosting Regression Tree(Bayes-GBRT)is proposed.This method achieves better prediction results compared to linear regression algorithms and random forest regression algorithms,with an average absolute error of only 0.224 years for predicting the fault time of this type of equipment.Finally,a multi-method interactive validation approach is proposed,enabling the fusion and validation of multi-dimensional results.The results indicate that the predicted fault time and the actual fault time conform to a log-normal distribution,and the parameter estimation results are basically consistent,verifying the accuracy and effectiveness of the prediction results.The above research findings can provide technical support for the maintenance and modification of BTM units,effectively reducing maintenance costs and ensuring the safe operation of high-speed railway,thus having practical engineering value for preventive maintenance.

Monte Carlo simulation of sequential structure control of AN-MA-IA aqueous copolymerization by different operation modes

The regulation of polyacrylonitrile(PAN)copolymer composition and sequence structure is the precondition for producing high-quality carbon fiber high quality.In this work,the sequential structure control of acrylonitrile(AN),methyl acrylate(MA)and itaconic acid(IA)aqueous copolymerization was investigated by Monte Carlo(MC)simulation.The parameters used in Monte Carlo were optimized via machine learning(ML)and genetic algorithms(GA)using the experimental data from batch copolymerization.The results reveal that it is difficult to control the aqueous copolymerization to obtain PAN copolymer with uniform sequence structure by batch polymerization with one-time feeding.By contrary,it is found that the PAN copolymer with uniform composition and sequence structure can be obtained by adjusting IA feeding quantity in each reactor of a train of five CSTRs.Hopefully,the results obtained in this work can provide valuable information for the understanding and optimization of AN copolymerization process to obtain high-quality PAN copolymer precursor.

改进贝叶斯网络模型在起重作业人机交互差错风险分析中的应用

为量化分析起重作业人机交互差错风险,根据安全工效学原理及安全技术规范将起重作业人、机、环相关影响因素作为根节点,按照事故致因层次关联关系确定子节点,构建起重作业人机交互差错的3层级贝叶斯网络模型(Bayesian Network, BN);基于模糊集理论,采用认知可靠性与失误分析方法(Cognitive Reliability and Error Analysis Method, CREAM),厘定贝叶斯网络父节点失效概率以及中间节点条件概率;利用逆向推理仿真技术分析起重作业人机交互差错发生的因果链,探究起重伤害事故发生的人机交互差错风险。结果表明:起重作业人机交互差错最可能致因链为起重设备安全检查不到位→管理人员失误→人员操作失误→起重伤害事故发生;单因素失效条件下,起重作业人机交互差错风险概率呈线性增长趋势;在多因素失效条件下,一级节点因素失效概率愈大则人机交互差错效应愈显著,且呈现非线性增长态势。

一种基于机器学习识别医疗设备异常运行状态方法的建立

由于传统识别医疗设备异常运行状态方法是通过手工预定义的关键字与设备响应数据的字段相匹配完成设备异常识别,易导致医疗设备识别结果不精确。基于此,本研究提出基于机器学习的医疗设备异常运行状态识别方法。基于机器学习的医疗设备异常运行状态识别方法通过传感器采集医疗设备的电压、电流、工作温度等异常特征数据,对多特征数据进行叠加融合,引入GRU网络构建机器学习识别模型,输入数据训练模型完成医疗设备异常运行状态识别。实验结果表明,基于机器学习的医疗设备异常运行状态识别方法可识别医疗设备不同类型的异常运行状态,误报率为4.43%,识别异常运行状态时间较短,准确度更高。

基于改进SSI法的加工机器人工作模态在线辨识

为提高串联式机器人的加工能力,提出一种基于改进SSI法的加工机器人工作模态在线辨识方法,该方法首先在传统SSI法的基础上进行改进,利用NExT法、模态置信因子及模态保证准则3种手段来提高加工机器人工作模态参数辨识精度。其次,利用加工机器人铣削加工振动数据对其走刀路径上关键点位出的模态频率和阻尼比进行在线辨识。最终,通过机器人切削加工实验验证该方法,实验结果表明,相比于锤击实验结果,改进SSI法模态参数辨识误差在7%以内,且相比于传统SSI法,改进SSI法模态参数辨识精度更高。因此,若仅关心模态频率及阻尼比,该方法可实现加工机器人走刀过程中模态参数的在线辨识,可为后续机器人进行工艺规划和优化提供输入条件。

自动飞行系统飞行模式操作验证方法研究

现代民用飞机的自动飞行控制系统是主要的人机交互系统之一,其飞行模式操作控制功能由两大物理部分组成:FMCP硬件与自动飞行控制软件。对于复杂的人机交互功能的验证,需要采用面向开发过程的模型验证方法,首先由系统功能层级到功能模块层级逐级分解飞行模式操作控制功能开发架构,进而从功能模块层级到系统功能层级自下而上设计飞行模式操作控制功能验证架构,最后针对功能验证架构中的集成模型测试阶段,以FMCP速度视窗功能为例,通过对所有相关的人机操作进行分类定义,分别定义了速度目标值的状态转换与动作响应,进而针对定义梳理出完整的速度目标值状态转换与动作响应矩阵,矩阵中的每一个表格均对应着速度目标值的状态转换或者动作操作及其对应的响应情况。为自动飞行模式操作控制的集成模型验证提供了参考。

Hybrid method integrating machine learning and particle swarm optimization for smart chemical process operations

Modeling and optimization is crucial to smart chemical process operations.However,a large number of nonlinearities must be considered in a typical chemical process according to complex unit operations,chemical reactions and separations.This leads to a great challenge of implementing mechanistic models into industrial-scale problems due to the resulting computational complexity.Thus,this paper presents an efficient hybrid framework of integrating machine learning and particle swarm optimization to overcome the aforementioned difficulties.An industrial propane dehydrogenation process was carried out to demonstrate the validity and efficiency of our method.Firstly,a data set was generated based on process mechanistic simulation validated by industrial data,which provides sufficient and reasonable samples for model training and testing.Secondly,four well-known machine learning methods,namely,K-nearest neighbors,decision tree,support vector machine,and artificial neural network,were compared and used to obtain the prediction models of the processes operation.All of these methods achieved highly accurate model by adjusting model parameters on the basis of high-coverage data and properly features.Finally,optimal process operations were obtained by using the particle swarm optimization approach.

Dombi-Normalized Weighted Bonferroni Mean Operators with Novel Multiple-Valued Complex Neutrosophic Uncertain Linguistic Sets and Their Application in Decision Making

Although fuzzy set concepts have evolved,neutrosophic sets are attractingmore attention due to the greater power of the structure of neutrosophic sets.The ability to account for components that are true,false or neither true nor false is useful in the resolution of real-life problems.However,simultaneous variations render neutrosophic sets unsuitable in specific circumstances.To enable the management of these sorts of issues,we combine the principle of multi-valued neutrosophic uncertain linguistic sets and complex fuzzy sets to develop the principle of multivalued complex neutrosophic uncertain linguistic sets.Multi-valued complex neutrosophic uncertain linguistic sets can contain grades of truth,abstinence,and falsity,and uncertain linguistic terms,which are expressed as complex numbers whose real and imaginary parts are limited to the unit interval.Some important Dombi laws are elaborated along with Bonferroni mean operators,which offer a flexible general structure with modifiable factors.Bonferroni means aggregation operators perform a significant role in conveying the magnitude level of options and characteristics.To determine relationships among any number of attributes,we develop multi-valued complex neutrosophic uncertain linguistic Dombi-normalized weighted Bonferroni mean operators and discuss their important properties with some special cases.By using these laws,we can deploy themulti-attribute decisionmaking(MADM)technique using the novel principle of multi-valued complex neutrosophic uncertain linguistic sets.To determine the power and flexibility of the elaborated approach,we resolve some numerical examples based on the proposed operator.Finally,the work is validated with the help of comparative analysis,a discussion of its advantages,and geometric expressions of the elaborated theories.

温室草莓不同喷药方式作业质量研究

为筛选出适用于温室草莓病虫害防治植保作业机具和喷药方式,在广泛调研国内外植保机械与施药技术基础上,选出一种进口新型温室草莓植保机械,该机配有静电喷枪和垄上草莓仿形喷杆两种喷雾装置,并将该机与目前草莓温室常用喷枪作业性能进行对比测试。测试两种机型三种喷雾装备在不同喷药方式下的雾滴沉积分布、温室草莓冠层雾滴沉积分布规律、药液沉积量、农药有效利用率及地面流失数据。结果表明,在流失率和农药利用率方面,使用静电喷枪、仿形喷杆、传统喷枪施药的行间地面的药液流失率依次增大。使用静电喷枪,不同作业方式施药的农药利用率在40%~65%之间,使用仿形喷杆施药的农药利用率为29.87%,使用传统喷枪的农药利用率最小,为8.18%。静电喷枪的施药效果优于仿形喷杆施药和传统喷枪;静电喷枪的不同作业方式有其各自的优点,其中直线直喷的作业方式沉积率最高,直线直喷的作业方式更适合温室草莓病虫害的防治。该研究为草莓植保机械的选型和作业方式提供数据支撑。

基于复合信号的挖掘机工作状态智能识别方法

针对液压挖掘机作业循环状态直接识别准确率较低的问题,提出了一种基于复合信号、支持向量机(SVM)的挖掘机工作状态智能识别方法。首先,对液压挖掘机循环作业各个工作阶段进行了划分,以各个工作阶段开始或结束波形作为分段标志,对循环作业数据进行了分段标志提取;然后,对各个提取的分段标志进行了数据预处理,采用支持向量机库(LIBSVM)多分类方法,基于主泵压力与先导压力复合信号,建立了液压挖掘机工作状态识别模型,设定了分类可信度阈值;最后,引入了智能校验系统,对工作状态直接识别结果进行了矫正,完成了挖掘机运行工作状态识别的目标。研究结果表明:相较于主泵压力识别方法,采用复合信号识别方法,挖掘机工作状态的直接识别准确率由54%提升至了78%,最终识别准确率在95%以上;复合信号识别方法能够用于有效识别液压挖掘机各工作阶段,对解决液压挖掘机作业循环状态直接识别准确率低的问题有明显效果。

基于机器视觉的小尺寸外螺纹关键参数检测方法

针对小尺寸外螺纹测量难题,提出一种基于机器视觉的螺纹关键参数测量方法,获取外螺纹牙型边缘的精确信息,解决了扫描探针针尖过大无法测量小尺寸螺纹的问题。首先通过中值滤波进行预处理,去除图像中包含的椒盐噪声和脉冲干扰,并利用Otsu选取最佳阈值对图像二值化。通过改进的高斯滤波和自适应阈值Canny算子提取螺纹的轮廓边缘。同时设计了测量区域的最小矩形拟合算法对螺纹图像进行倾角校正。最后通过最小二乘法直线拟合计算螺纹的尺寸参数。实验结果表明,螺纹的螺距、中径的测量精度达到0.0014mm和0.0027mm,实现了螺纹关键参数的高精度测量。

基于熵权法优化组合的PSO-SVR-NGM边坡位移预测

基于边坡监测数据建立数学模型,是边坡变形和稳定性分析的重要方法。但是单一预测模型的形式和应用范围具有一定的确定性,不同模型对数据的利用程度也有所差别,往往不能充分运用已知信息,导致模型精度不高,适用性不强。针对单一预测模型存在的问题,提出一种基于熵权法的PSO-SVR-NGM优化组合模型。该模型结合高精度变权缓冲NGM(1,1,k,c)模型和PSO-SVR模型,能够减小单一预测模型的误差,大幅度提高预测精度。首先通过引入变权缓冲算子λ和背景值权重系数η、κ改进无偏NGM(1,1,k,c)模型,构建新的3参数变权缓冲NGM(1,1,k,c)模型。结合最大灰色关联度和最小平均相对拟合误差重新构造粒子群算法的适应度函数,利用改进的粒子群算法对提出的变权缓冲模型进行搜索寻优,确定最佳的参数组合。然后通过熵权法对改进的变权缓冲NGM(1,1,k,c)模型和PSO-SVR模型进行赋权建立优化组合模型。最后,将该组合模型应用于3个不同变形特征的边坡工程中,并与其他单一模型进行对比分析。结果表明,相对于单一模型,本文所提出的组合模型的拟合和预测误差较小,与原始位移数据的相关性较好,能够更真实地反映边坡变形规律,具有较强的工程适应性。同时组合模型的提出与发展也促进了单一模型的优化改进,为解决实际工程问题提供了良好的思路。

基于机器视觉的螺钉外形尺寸测量系统

针对螺钉头部正六边外形几何特征人工测量精度低、不利于自动测量的问题,提出了基于机器视觉的螺钉外形尺寸测量系统。首先对原始图像进行滤波、增强预处理解决成像图像细节信息不突出、对比度低的问题;其次,采用基于类间方差最大度量法的图像分割算法处理目标与背景灰度差异不明显的图像,改善算法鲁棒性;最后,结合改进Canny算子和最小二乘法拟合出最优六边形参考边缘,并设计基于质心与参考边缘的坐标系定位策略,在此基础上进行螺钉头部正六边形边缘查找及几何尺寸测量。进行了螺钉头部正六边形测量实验,结果显示该系统尺寸测量误差不超0.09%,满足工程实际需求。

拉索方法对脉冲折流板萃取柱存留分数的预测

为了获得适用性好,且便于分析的脉冲折流板萃取柱内存留分数变化的预测方程,本文使用拉索回归方法拟合分析了通过体积置换法测试的脉冲折流板萃取柱中煤油-水体系下的脉冲强度、分散相流速和连续相流速3个操作参数对存留分数的影响。并对比分析了相关文献中的多组经验关系式重新拟合的方程与使用拉索回归方法拟合出的多元二次形式的预测方程。结果表明:拉索回归方法拟合出的预测方程能够很好的贴合实验数据,并且通过分析该方程对应3个操作参数的偏导数,定量地分析不同参数在具体的操作范围内对存留分数的影响大小。本文方法同时能够很好地适用于其他文献的数据当中,拟合结果与实验数据的平均相对误差都在20%以内,能够简单直观地分析各个参数对存留分数的作用大小。

干扰条件下末端防御武器系统作战效能评估

目前,关于雷达干扰对防空武器系统的作战效能影响的研究多集中于防空导弹武器系统,而末端防御武器系统火力拦截方式一般包括防空导弹和防空高炮,因而相关研究成果无法直接应用。针对此类问题,采用基于Agent的建模与仿真方法对末端防御武器系统在雷达干扰条件下的作战效能进行了研究。以典型的平原地区防空作战为背景,采用Anylogic软件对作战要素和作战流程进行了建模,使用有限状态机方法对末端防御车、防空导弹、防空高炮和敌机进行了建模。以雷达支援干扰为例,将雷达探测距离作为影响因素对拦截率进行了敏感度分析,从而达到评估目的。研究方法可作为末端防御武器系统在雷达干扰条件下的作战效能评估的新手段,对此类装备运用有一定的参考意义。

Review of application of artificial intelligence techniques in petroleum operations

In the last few years,the use of artificial intelligence(AI)and machine learning(ML)techniques have received considerable notice as trending technologies in the petroleum industry.The utilization of new tools and modern technologies creates huge volumes of structured and un-structured data.Organizing and processing of these information at faster pace for the performance assessment and forecasting for field development and management is continuously growing as an important field of investigation.Various difficulties which were faced in predicting the operative features by utilizing the conventional methods have directed the academia and industry toward investigations focusing on the applications of ML and data driven approaches in exploration and production operations to achieve more accurate predictions which improves decision-making processes.This research provides a review to examine the use cases and application of AI and ML techniques in petroleum industry for optimization of the upstream processes such as reservoir studies,drilling and production engineering.The challenges related to routine approaches for prognosis of operative parameters have been evaluated and the use cases of performance optimizations through employing data-driven approaches resulted in enhancement of decision-making workflows have been presented.Moreover,possible scenarios of the way that artificial intelligence will develop and influence the oil and gas industry and how it may change it in the future was discussed.

Selecting suitable key supplier for core components during smart complex equipment central-private enterprises collaborative development process:from two different forms of evaluation information and matching perspective

With the development of central-private enterprises integration,selecting suitable key suppliers are able to provide core components for smart complex equipment.We consider selecting suitable key suppliers from matching perspective,for it not only satisfies natural development of smart complex equipment,it is also a good implementation of equipment project in central-private enterprises integration context.In in this paper,we carry out two parts of research,one is evaluation attributes based on comprehensive analysis,and the other is matching process between key suppliers and core components based on the matching attribute.In practical analysis process,we employ comprehensive evaluated analysis methods to acquire relevant attributes for the matching process that follows.In the analysis process,we adopt entropy-maximum deviation method(MDM)-decision-making trial and evaluation laboratory(DEMATEL)-technique for order preference by similarity to an ideal solution(TOPSIS)to obtain a comprehensive analysis.The entropy-MDM is applied to get weight value,DEMATEL is utilized to obtain internal relations,and TOPSIS is adopted to get ideal evaluated solution.We consider aggregating two types of evaluation information according to similarities of smart complex equipment based on the combination between geometric mean and arithmetic mean.Moreover,based on the aforementioned attributes and generalized power Heronian mean operator,we aggregate preference information to acquire relevant satisfaction degree,then combine the constructed matching model to get suitable key supplier.Through comprehensive analysis of selecting suitable suppliers,we know that two-sided matching and information aggregation can provide more research perspectives for smart complex equipment.Through analysis for relevant factors,we find that leading role and service level are also significant for the smart complex equipment development process.

装备体系效能评估及支撑技术综述

效能评估用于评价装备完成特定作战任务目标的能力,目前已经广泛应用于各种武器装备论证中。本文在简要分析当前该领域的问题和挑战的基础上,分别介绍了基于多属性决策、复杂网络和机器学习算法3类装备体系效能评估方法,并分析了上述方向的研究现状;在装备体系效能评估支撑技术方面,总结了指标约简与优化方法的研究现状;最后,基于现有的研究进展,提出了未来装备体系效能评估可能的研究方向。

面向冷冻法常压进仓作业的盾构机选型研究

针对地下水流动性较强地层的盾构机进仓作业难题,提出通过冷冻法加固地层,以实现常压进仓作业。面向冷冻法常压进仓作业需求,从安全性、可靠性、经济性角度分析了常压刀盘的优势,对盾构机主机、刀盘刀具、盾体、人仓系统等进行了适应性的选型设计,并通过盾构实际开仓作业情况验证了所提出方法的正确性。