通用教练机选型的熵权TOPSIS多层次综合评价

针对飞行航校通用教练机飞机选型评价方法存在的不足,根据系统工程的基本原则和思想,以教练机性能、经济、安全、维护、财务、环境和供应商选择为基本评价对象,建立通用教练机飞机选型的多层次评价体系,并针对评价指标结果的不确定性特点,提出了基于三参数区间的通用教练机飞机选型的熵权理想解法(TOPSIS法),并构建通用教练机飞机选型评价数学模型.实例计算结果表明,方法能够有效利用评价指标信息,且具有更好的可操作性和实用性,对科学合理选择通用教练机飞机型号具有重要的参考价值.     

灰色模糊聚类理论在飞机方案优选中的应用研究

将灰色系统理论应用到飞机设计方案评价中,研究了一种含有方案评价指标体系和考虑评价指标权重以及灰关联分析的飞机方案优选灰色模糊聚类决策理论,这种决策理论利用指标权重将评价指标联系在一起,在评价方案与理想方案灰关联度的基础上进行灰色模糊聚类,实现方案优选.最后以一个短距/垂直起降(V/STOL)飞机设计实例证实此方法的科学性与可行性.     

考虑信息熵模型的道路交通安全态势模型

分析道路交通事故统计中的交通事故数、死亡人数和受伤人数,并且将这三种统计数据和某地区的机动车辆数和机动车驾驶人数相结合,分别得到万车交通事故数和万人死亡人数和万人受伤人数,建立信息熵模型(information entropy model)对以上数据进行分析研究,从而得出评价交通安全程度的指标.这种方法突破了以往仅停留在万车死亡人数的指标上,且以往的研究方法还需采用专家评价法或德尔菲等定性评价方法,此评价方法对道路交通安全的分析更客观.最后以北京和天津近三年的交通事故统计数据为案例进行分析,研究结果表明:2012年至2014年间北京道路交通安全评价指标主要以交通事故起数认定所在年的道路交通安全状况,天津道路交通安全评价指标主以交通事故造成的受伤人数认定所在年的道路交通安全状况,且北京三个指标的G_i均高于天津,因此北京交通安全水平要低于天津.     

基于GAHP和云模型的军事组织安全文化评价方法

安全文化评价是军事组织安全文化建设进行协调、控制的重要环节.为实现军事组织安全文化的定量评价,分析了军事组织安全文化的特点,根据SMART原则建立了科学的评价指标体系,提出了一种基于群组层次分析法(GAHP)和云模型的安全文化评价方法.综合多名专家意见,利用群组层次分析法来确定各级指标权重,得出各指标的重要性度量,基于云模型实现评价主体的模糊语义性评价与定量评分的双向映射,并通过聚合计算得到综合评价结果.实例分析表明,所提算法具有较好的实用性和适用性,为客观评估军事组织安全文化建设水平提供了可行手段.     

山体隧道结构安全模糊可拓评估模型

为完善山体隧道结构安全评价方法,提高运营期保障可靠度.首先在分析隧道结构固有属性和动态影响因素的基础上,构建了能够全面反映山体隧道结构安全的评价指标体系,并对不同等级下指标值进行无量纲化处理.其次将区间模糊数与可拓理论相结合提出山体隧道结构安全评价模型,关联度由不确定型可拓初等关联函数确定,指标权重由区间模糊熵权法求解,并采用集结算子进行综合评价.最后,以霞浦洋坪隧道为例,按照模型步骤对其结构安全进行了系统评价,同时与其它已有方法进行了对比分析.研究成果不仅丰富山体隧道安全评价模型,也为交通部门决策提供了参考.     

基于熵权层次分析法的飞行学员安全状态模糊综合评价

飞行安全状态评估是飞行员选拔和资质审查的重要环节。通过对飞行事故/事故征候致因统计数据分析,结合专家调查法,构建飞行学员安全状态综合评价指标体系.针对评价指标模糊难以客观量化特征,采用熵权层次分析法组合赋权法确定指标权重,,建立飞行学员安全状态模糊综合评价模型,并通过对飞行学员安全状态评价进行了算例验证,结论为飞行员选拔和培养提供理论参考.     

规定安全间隔的CNS性能环境研究

对平行航路下规定安全间隔的CNS性能环境评估问题进行了研究.首先分析了CNS性能环境对飞机碰撞风险的影响,并结合Reich模型和概率论方法建立了CNS性能环境下飞机纵向、侧向和垂直方向上的碰撞风险模型;其次对碰撞风险模型进行分析转化得到了规定的安全间隔和安全目标水平下CNS性能环境的评估计算方法.最后对平行航路的CNS性能环境进行了评估计算,得到符合航路安全目标水平1.5×10~(-8)的CNS性能环境为RNP10、RCP400、RSP20.     

基于Theil指数的复合土钉墙安全灰色评价

针对复合土钉墙的安全评价问题,以复合土钉墙基坑支护技术规范为依据构建了安全评价指标体系,并在运用Theil指数确定安全评价指标权重的基础上,对复合土钉墙安全进行灰色综合评价,得出安全评价等级.为复合土钉墙安全评价提供了新思路.     

基于序关系法的飞行员安全行为评价指标权重系数的确定

飞行员操作行为的表现直接影响民航飞行安全.通过对飞行员不安全行为事故致因数据分析,运用德尔菲(Delphi)调查方法,从安全素养、心理因素、飞行技术及生理因素4个方面确定影响飞行员安全行为的指标体系构成.以飞行员行为评价实例,运用序关系法并结合专家调查确定各评价指标的权重系数,分析了职业安全意识、飞行情景意识、特情处理能力以及机组资源管理能力等17个指标对飞行安全行为的显著性影响程度,结论为飞行员安全行为风险分析提供参考.     

基于模糊C均值聚类的煤矿区生态安全评价

煤炭区的生态安全评价对促进煤矿区可持续发展具有重要的意义.以压力-状态-响应模型作为框架选取企业员工人数、原煤产量、工业废水排放等15个具有代表性的生态安全评价指标,运用模糊C均值聚类方法构建煤矿区生态安全评价模型.以郑州某煤炭企业2007-2013年的煤矿生态安全数据为例,对矿区生态安全进行科学分析与评价,并针对评价结果提出相应的生态改进对策.     

基于不确定信息处理的语言群决策方法

研究了具有语言偏好信息的语言群决策问题。首先提出了一种基于不确定信息处理的二元语义混合加权平均（T—HWA）算子，并对该算子的性质进行了分析，然后给出了一种基于T—HWA算子的语言群决策（TGDM）方法。最后，通过算例对新方法的有效性进行了验证。     

Integrated decision support for aviation safety inspectors

This paper presents a systems viewpoint for developing an advanced decision support system for aircraft safety inspectors. Research results from a Federal Aviation Administration (FAA) sponsored project to use neural network and expert systems technology to analyze aircraft maintenance databases are summarized. One of the main objectives of this research is to define more refined “alert” indicators for national comparison purposes that can signal potential problem areas by aircraft type for safety inspector consideration.

Integration aspects are addressed on two levels: (1) integration of the various technical components of the decision support system, and (2) integration of the decision support system with individual behavior, management systems and organizational structure, as well as corporate culture across both formal and informal dimensions. The paper summarizes the creation of strategic “inspection profiles” for aging aircraft and reliability curve fitting for structural components both based upon using neural network technology. Also, the potential use of a model-based expert system to facilitate field inspection diagnostics is presented. Finally, a framework for developing an intelligent decision system to support aircraft safety inspections is proposed that links expert systems, neural networks, as well as a paradigm of the decision making process typically used in unstructured situations.     

基于二维二元语义和模糊AHP-TODIM的低碳供应商选择方法

针对专家给出二维语言评价信息的低碳供应商评选问题,提出一种基于二维二元语义和模糊AHP-TODIM的方法。该方法首先提出改进的二元语义模型,基于此定义二维二元语义及其加权平均算子。接着构建低碳供应商评价的指标体系。最后,将专家给出的二维语言评价信息转化为二维二元语义,使用模糊AHP法计算指标权重,并利用二维二元语义加权平均算子集结信息,在此基础上将TODIM方法扩展到二维二元语义环境以获取低碳供应商排序。案例分析说明了所提出的方法的有效性。     

Numerical scales generated individually for analytic hierarchy process

Because individual interpretations of the analytic hierarchy process (AHP) linguistic scale vary for each user, this study proposes a novel framework that AHP decision makers can use to generate numerical scales individually, based on the 2-tuple linguistic modeling of AHP scale problems. By using the concept of transitive calibration, individual characteristics in understanding the AHP linguistic scale are first defined. An algorithm is then proposed for detecting the individual characteristics from the linguistic pairwise comparison data that is associated with each of the AHP individual decision makers. Finally, a nonlinear programming model is proposed to generate individual numerical scales that optimally match the obtained individual characteristics. Two well-known numerical examples are re-examined using the proposed framework to demonstrate its validity.     

Fuzzy logic modifications of the Analytic Hierarchy Process

The Analytic Hierarchy Process (AHP) is a measurement methodology based on pair-wise comparisons that relies on judgment to derive priority scales. During its implementation, one constructs hierarchies, then makes judgments or performs measurements on pairs of elements with respect to a criterion to derive preference scales, which are then synthesized throughout the structure to select the preferred alternative.One of the areas where the AHP finds application is in the subjective phases of risk assessment (RA), where it is used to structure and prioritize diverse risk factors, including the judgments of experts. Since fuzzy logic (FL) has been shown to be an effective tool for accommodating human experts and their communication of linguistic variables, there has been research aimed at modeling the fuzziness in the AHP (FAHP), and recently the focus of some of that modeling has been with respect to RA.The literature discusses more than one FAHP model, which raises the question as to which are the prominent models and what are their characteristics. In response to this question, we examine three of the most influential FAHP models. The article proceeds as follows. It begins with a brief overview of the AHP and its limitations when confronted with a fuzzy environment. This is followed by a discussion of FL modifications of the AHP. A RA-based likelihood score example is used throughout. The article ends with a commentary on the findings.     

A comparative study of the numerical scales and the prioritization methods in AHP

In the analytic hierarchy process (AHP), a decision maker first gives linguistic pairwise comparisons, then obtains numerical pairwise comparisons by selecting certain numerical scale to quantify them, and finally derives a priority vector from the numerical pairwise comparisons. In particular, the validity of this decision-making tool relies on the choice of numerical scale and the design of prioritization method. By introducing a set of concepts regarding the linguistic variables and linguistic pairwise comparison matrices (LPCMs), and by defining the deviation measures of LPCMs, we present two performance measure algorithms to evaluate the numerical scales and the prioritization methods. Using these performance measure algorithms, we compare the most common numerical scales (the Saaty scale, the geometrical scale, the Ma–Zheng scale and the Salo–Hämäläinen scale) and the prioritization methods (the eigenvalue method and the logarithmic least squares method). In addition, we also discuss the parameter of the geometrical scale, develop a new prioritization method, and construct an optimization model to select the appropriate numerical scales for the AHP decision makers. The findings in this paper can help the AHP decision makers select suitable numerical scales and prioritization methods.     

Multi-criteria decision making with fuzzy linguistic preference relations

Although the analytic hierarchy process (AHP) and the extent analysis method (EAM) of fuzzy AHP are extensively adopted in diverse fields, inconsistency increases as hierarchies of criteria or alternatives increase because AHP and EAM require rather complicated pairwise comparisons amongst elements (attributes or alternatives). Additionally, decision makers normally find that assigning linguistic variables to judgments is simpler and more intuitive than to fixed value judgments. Hence, Wang and Chen proposed fuzzy linguistic preference relations (Fuzzy LinPreRa) to address the above problem. This study adopts Fuzzy LinPreRa to re-examine three numerical examples. The re-examination is intended to compare our results with those obtained in earlier works and to demonstrate the advantages of Fuzzy LinPreRa. This study demonstrates that, in addition to reducing the number of pairwise comparisons, Fuzzy LinPreRa also increases decision making efficiency and accuracy.     

A hybrid deep computation model for feature learning on aero-engine data: applications to fault detection

Recently, the safety of aircraft has attracted much attention with some crashes occurring. Gas-path faults, as the most common faults of aircraft, pose a vast challenge for the safety of aircraft because of the complexity of the aero-engine structure. In this article, a hybrid deep computation model is proposed to effectively detect gas-path faults on the basis of the performance data. In detail, to capture the local spatial features of the gas-path performance data, an unfully connected convolutional neural network of one-dimensional kernels is used. Furthermore, to model the temporal patterns hidden in the gas-path faults, a recurrent computation architecture is introduced. Finally, extensive experiments are conducted on real aero-engine data. The results show that the proposed model can outperform the models with which it is compared.     

基于层次分析法的选股决策

应用层次分析法 ,通过建立递阶层次结构和判断矩阵 ,并结合定性评价和定量评价 ,对投资股票项目中的股票进行评价和排序 ,进而做出合理的选择决策 .通过实证表明 :层次分析法在股票选择中是一种实用、有效的方法 ,有较好的应用价值 .     

基于Elmeri指标体系的煤矿作业区安全风险模糊综合评价

2016年起我国推行煤矿安全风险分级管控与隐患排查治理双重预防机制,各煤矿积极探索自主管理之路,如何将作业区域的风险辨识与总体评价有机结合,建立综合评价模型是值得探讨的课题.本文给出一种便捷的安全风险模糊评价模型首先基于Elmeri指标体系构建了安全的行为、健康危险源、事故危险源、重大事故控制和应急准备5个一级指标和15个二级指标,在作业区进行现场观察,计算出安全指数作为风险管控的依据,采用层次分析法确定指标权重采用梯形分布确定隶属度函数,在总体评价中确定了好、较好、一般、差四个模糊评价等级,根据M(.,+)模型和最大隶属度原则确定作业区域安全风险等级,并在煤矿进行现场应用,该模型方法简单,将定量的风险辨识数据与综合评价有机结合起来,评价结果与实际相符,值得在国内外推广.