鱼雷寿命周期费用数据的采集与灰色处理方法

鱼雷寿命周期费用数据的采集与处理是进行鱼雷寿命周期费用建模的基础.简要介绍了鱼雷寿命周期费用的概念,讨论了鱼雷寿命周期费用数据采集的基本要求和内容,同时应用灰色系统理论对鱼雷寿命周期费用数据进行处理,即:在数据采集时收集了尽可能多的数据,须判断和费用有关的因素中是否有若干个因素关系十分密切,以致既能够用这些因素综合平均指标或用其中的某一个因素来代表这几个因素,又使信息不受严重损失.选择灰色系统理论的灰关联聚类方法进行分析.结果表明,提出的寿命周期费用数据的灰色处理方法,有利于建模时决定因素的取舍,为鱼雷寿命周期费用数据处理提供了有效的方法.     

基于灰色聚类的汽车健康状态评估

文中基于汽车健康管理的概念提出对汽车健康状态进行评估。汽车健康状态评估作为健康管理的重要内容具有重要的现实研究意义。汽车健康状态评估的结果可以为车主提供维修提醒,为汽车维修人员提供初步维修决策,其满足汽车健康管理的需求。汽车系统是一个灰色系统,文中采用灰色理论与聚类分析相结合的灰色聚类方法进行汽车健康状态评估,根据灰色聚类决策方法的原理,分析了汽车健康状态评估指标体系,然后使用模糊层次分析法确定各指标权重,最后利用灰色聚类方法对汽车健康状态等级进行评估。实例表明,基于灰色聚类理论建立的汽车健康状态评估模型科学合理、方便快捷,并具有很好的实用性。     

基于灰色聚类的群决策方法研究

采用非线性变换对区间灰数形式的决策信息进行规范化处理,并由决策者问差异度最小构建优化模型,确定指标权重.考虑到在决策者较多的情况下对决策者进行聚类处理可以提高决策质量,给出了决策者决策信息的灰色关联度的计算方法,并基于灰色关联度提出了对决策者进行分类的灰色聚类算法.在此基础上,提出了对聚类后各决策者类的决策信息集结的决策模型.最后通过一个实例说明了所提出方法的可行性和有效性.     

基于灰色决策理论的声诱饵作战效能评估

针对声诱饵型号多样、评价指标复杂且现有效能评估方法无法全面进行评估,现运用灰色决策理论,综合声诱饵在反鱼雷作战时的技术和使用因素,建立了完整的声诱饵反鱼雷作战效能评估指标体系,提出了改进灰色白化权函数的声诱饵效能灰色决策评估方法。不但易于工程实现,有效提高评估的客观性,同时引入效能综合评估值使结果更直观。最后以四型声诱饵仿真评估实验为例,演示了评估的全过程,结果显示新型拖曳式与自航式声诱饵的作战效能较其第一代分别提高了59％和64％。通过与现有的只考虑使用因素的LMBP神经网络方法进行对比分析,验证了所提方法的有效性和合理性。     

鱼雷寿命周期费用模型敏感参量的识别

介绍了鱼雷寿命周期费用的概念和研究寿命周期费用的重要性,并对影响寿命周期费用的敏感参量进行识别.影响鱼雷寿命周期费用的参量很多,各参量对寿命周期费用的作用不完全相同,影响大的参量是鱼雷寿命周期费用的敏感参量.信息论中的相对熵可用于度量两个参量符合的程度,符合的程度越大,相对熵越小.结合实例对鱼雷寿命周期费用的敏感参量进行了识别,结果令人满意.利用相对熵理论识别鱼雷寿命周期费用模型敏感参量,对影响寿命周期费用的因素按重要性进行分类,便于建立模型时对参量的选择.     

改进灰色聚类法的飞机液压能源系统效能评估

由于飞机液压能源系统缺乏完善的效能评估指标体系和效能评估方法,构建了一个包含液压能源系统关键部件和参数的两级效能评估指标体系.结合层次分析法和熵权法对指标权重的主观性进行修正,提出了基于改进灰色聚类法的综合评估模型,同时将指标划分为优秀、良好、中等、较差四个灰类,最后综合各指标所属灰类的白化权函数和权重得到系统的聚类系数,确定系统的效能等级.以某型飞机液压能源系统为例,得到了切合实际的效能评估结果,验证了方法在液压系统效能评估中的有效性.     

基于灰色聚类决策的图像分割性能评价

针对图像分割的性能分析问题,运用灰色聚类决策技术建立一种多层次的综合评估方法。该方法通过图像多个分割指标的特点,得到合适的白化函数,再通过白化函数和灰色聚类权值的分析计算,将收集到的分散信息生成灰色聚类矩阵,以此对图像分割性能进行分类;其评估结果的离散性高,便于区分不同分割算法的性能。应用实例表明,该方法很好地克服了以往评估方法的不灵敏性和奇异性,从而使评判更容易进行。     

基于决策粗糙集的多属性灰色关联聚类方法

针对多属性灰色关联聚类的阈值确定问题,利用决策粗糙集方法,通过引入两个阈值参数定义决策对象间的可能关系和集合;将其代替基于灰色关联聚类的非此即彼关系,构建基于决策粗糙集的多属性灰色关联聚类方法,并采用贝叶斯推理探讨多属性灰色关联聚类的阈值计算机理;最后以案例验证所提方法的有效性和合理性.结果表明,所提出的方法是经典灰色关联聚类的拓展和泛化,能够客观、科学地确定多属性灰色关联聚类阈值.     

基于白化权函数聚类法的航电系统效能评估

航空电子综合系统是作战飞机的神经中枢,准确评估航电系统的效能对我国航空电子技术的发展具有重要意义。为了用灰色白化权函数聚类法对基于1553B总线的航电系统进行效能评估。首先将1553B总线负载、延迟时间、总线使用效率三项指标分为优、良、中、差四个灰类,综合各指标的白化权函数和其隶属于不同灰类的权重得到航电系统聚类系数向量,然后由该向量中的最大聚类系数所属灰类来确定该航电系统的灰类等级。灰色白化权函数聚类方法为航电系统的设计和效能评估提供了重要方法和理论依据。     

信息化条件下潜艇鱼雷武器系统作战效能评估

该文首先对信息化条件进行了说明,然后分析了作战效能评估的量度方式和模型选取,进而从信息化条件下潜艇鱼雷武器系统的系统组成出发,通过分析潜艇鱼雷武器系统的作战过程得出信息化条件下影响系统作战效能的主要因素,然后采用WSEIAC效能评估模型对系统作战效能进行评估,并给出模型各参数以及参数组成因子的详细确定方法,最后,文章通过给出的一个算例来表明评估结果,即在信息化条件下,潜艇鱼雷武器系统的作战效能得到大大提高.     

基于Vague集的鱼雷研制阶段年度投资分配模型

鱼雷的研制费用在鱼雷整个寿命周期费用中所占比系不大，但研制阶段决定了鱼雷寿命周期费用的绝大部分，闪此对鱼雷研制费用的研究可供鱼雷武器管理和决策者作经费计划综合平衡时参考。文章基于Vague集，提出了Vague集模糊熵的构造方法，进而提出了运用Vague集模糊熵进行鱼雷研制阶段年度投资强度分配的新模型。实例表明，该方法具有实用价值。     

Modeling project time–cost trade-off in fuzzy random environment

In real projects, the trade-off between the project cost and the project completion time, and the environmental uncertainty are aspects of considerable importance for managers. For complex environment with more than one type of uncertainty, this paper presents three types of time–cost trade-off models, in which the project environment is described via introducing the fuzzy random theory. The expected value and the chance measure of fuzzy random variable are introduced for modeling the problem under different decision-making criteria. After that, this paper is devoted to designing a searching method integrating the technique of fuzzy random simulations and genetic algorithm for searching the quasi-optimal schedules. Finally, some numerical examples are given to demonstrate the effectiveness of the designed method for solving the proposed models.     

GreenPro-I: a risk-based life cycle assessment and decision-making methodology for process plant design

In recent years, significant attention and emphasis has been given to cleaner and greener technologies in processes and product manufacturing. This is recognized as a key element in pollution prevention (P2) and development of sustainable strategies. Life cycle assessment (LCA) is a systematic approach that enables implementation of cleaner and greener product and process concepts in industry. In recent times substantial progress has been made in the use of LCA for product evaluation and selection. However, its use in cleaner and greener process design and decision-making has not been explored to a great extent.Process design and decision-making are challenging activities that involve trade-off of conflicting objectives, namely costs, technical feasibility and environmental impacts. These conflicting objectives can be analysed at the early design and decision-making stage by considering the full life cycle of a process or a product. A cleaner and greener process is the one that is cost optimal, technically feasible, and environmentally benign. To obtain these results LCA requires various tools and techniques in a systematic methodology. This paper proposes a holistic and integrated methodology GreenPro-I for process/product design by combining the traditional LCA approach with multi-criteria decision-making methods. This methodology is simple and applicable at the early design stage and is more robust against uncertainty in the data. Application of the methodology has been demonstrated in the paper through a urea production case study.     

基于频率响应分析的机械液压装置建模方法研究

为提高机械液压装置设计效率,降低生产成本,建立其全生命周期管理平台,需构建机械液压装置数学模型;常规的机械液压装置大多是线性定常高阶系统,时域分析方法在建模过程中存在较多局限性;文章采用频响分析方法,以某机械液压装置控制回路为研究对象,通过频响分析仪,进行机械液压装置建模,并对获得的模型精度进行仿真验证;将频域辨识模型与实物试验数据进行对比,验证了模型的精度,证明了频域建模的有效性,为后续机械液压装置控制系统全数字仿真与硬件在回路仿真提供了基础.     

参数估算法在无人机费用估算中的应用

无人侦察机是重要的空中侦察装备,可以在目标区域上空进行连续的侦察监视,截获和收集目标区域的完整情报,在空中武器装备中占有重要的地位。为更好地使该系统在运用中得以发展,对其进行费用效能分析估算是关键问题。文章简述了武器装备系统的全寿命周期费用（LCC）的内涵,介绍了费用估算的基本方法及分析了参数估算法在无人侦察机的研制费用估算中的应用,为机关决策提供了先决条件。     

神经网络用于复杂产品成本估算方法的研究

讨论了传统成本估算方法在成本管理中的弊端,总结了成本估算方法的研究现状和神经网络应用于成本估算方法的国内外研究现状,提出了应用神经网络在产品全生命周期的早期估算复杂产品成本的方法,给予了案例数据,并分析了实验输出的回归分析结果,实验证明,神经网络方法更有效,精度更高。     

A study on customer involvement in final assembly for DIY products

The objective of this paper is to investigate the degree of the customer involvement in final assembly (CIFA) in establishing a do-it-yourself (DIY) product design in order to achieve cost minimization of the incumbent firm in a competitive environment. Such a decision-making problem is addressed with consideration of the competitors and the learning curve effect within the three hypothetical stages of a product life cycle (PLC). Accordingly, a Bayesian decision model that incorporates expert opinions with available information is proposed to determine the optimal CIFA ratio for a DIY product. Finally, a numerical example is used to illustrate the effectiveness of the proposed model, and sensitivity analyses are also performed in light of the numerical example.     

A novel Long-term degradation trends predicting method for Multi-Formulation Li-ion batteries based on deep reinforcement learning

In the design phase of Li-ion batteries for electric vehicles, battery manufacturers need to carry out cycle life tests on a large number of formulations to get the best one that meets customer demands. However, such tests take considerable time and money due to the long cycle life of power Li-ion batteries. Aiming at reducing the cost of cycle life tests, we propose a prediction method that can learn historical degradation data and extrapolate to predict the remaining degradation trend of the current formulation sample taking the initial stage of partial cycle life test results as input. Compared with existing methods, the proposed deep reinforcement learning based method is able to learn degradation trends with different formulations and predict long-term degradation trends. Based on the deep deterministic policy gradient algorithm, the proposed method builds a degradation trend prediction model. Meanwhile, an interactive environment is designed for the model to explore and learn in the training phase. The proposed method is verified with real test data from battery manufacturers under three different temperature conditions in the formulation design stage. The comparisons indicate that the proposed method is superior to traditional degradation trend prediction methods in both accuracy and stability.