典型航天装备试验鉴定总体策略研究

航天装备具有面向任务、科研性强、全系统的技术状态难以固定,以及小子样等特点,在全寿命周期内的试验策略与常规武器装备的差异很大。本文在介绍装备试验鉴定模式改革的基础上,分析了航天装备试验鉴定按照新模式进行的相应调整,重点给出了卫星系统、运载火箭和航天测控装备等典型航天装备的全寿命周期模型,设计了新体制下全寿命周期内的试验阶段和关键时间节点、主要试验类型、决策支持依据、试验方式方法,以及与原试验要求的继承关系等总体策略;同时,鉴于航天装备试验鉴定数据量小等特殊性,给出了有别于常规装备的数据采信原则。该试验鉴定总体策略和数据采信原则可为航天装备试验鉴定顶层设计、组织实施、数据分析和决策支持提供参考。     

高中生成就动机和一般自我效能感及其关系的研究

本文采用《一般自我效能感量表》(GSES)和《成就动机量表》(AMS)对253名高中生进行了测量,以探讨高中生成就动机与一般自我效能感之间的关系。其结果为:1.高中生成就动机性别差异与年级差异均不显著(P>0.05);2.高中生一般自我效能感的性别差异与年级差异也不显著(P>0.05)。     

人体综合电阻测试研究

采用恒压比较法设计了人体综合电阻测试系统,电路结构为两路双限比较器,符合国标、军标的防静电测试要求,具有声光提示功能,可广泛应用于弹药库、电子产品及火工品生产车间等防静电工作区。     

基于改进广义成本函数的可靠性分配优化

可靠性分配优化问题的难点在于影响分配的各类因素与可靠性指标间关系的描述与表达.从可靠性成本与各类影响因素的内在关系人手,通过改进广义成本函数进行各类影响因素的融合处理,构建得出新的可靠性分配优化模型实现可靠性指标的综合求解.     

minimax问题的广义共轭向量法

本文按照共轭向量法的理论,针对非奇异对称矩阵提出了广义共轭向量的概念,并论述了它的三个重要性质(线性独立性、可寻驻点和可扩展性)。在此基础上,构造了极小极大(minimax)问题的广义共轭向量法。理论分析表明,该算法具有二次终止性质,且在每一维搜索时具有确定的寻优特征(求极小或求极大)。该算法为用拉格朗日乘子法求解数学规划提供了一个可能的途径。计算实例表明该算法是有效的。     

舰船成本预测系统中的自变量约简方法

针对舰船成本预测系统中自变量众多且相互关系错综复杂的特点,提出了用模糊粗糙集方法对舰船成本预测系统中的自变量进行约简。在模糊粗糙集方法的框架下,给出了模糊不可分辨关系和相对约简的定义,构建了基于模糊粗糙集的舰船成本预测系统自变量约简模型,并且针对实际的舰船成本预测系统一般是混合数据系统的问题,对广义差别矩阵的定义进行了改进,设计了相应的启发式约简算法。实例分析结果表明:文中所提的方法不仅能使约简的自变量大为减少,而且约简的效果较传统方法更好,且计算过程也更简单。     

通用军械装备维修备件合同商保障方式研究

提出了备件合同商保障的概念,给出了备件合同商保障内容和维度,分析了备件合同商保障的特征,着重从备件的存储方式和供应方式两个方面进行了定性分析,为实施备件合同商保障奠定了理论基础．     

基于本体的上下文建模与应用研究

为了在开放、动态的环境中建立上下文感知系统,首先需要定义一个适合于知识共享的上下文描述,即上下文建模．提出了一种基于本体的通用上下文模型,由顶层上下文本体和6种特定本体构成．结合2008年汶川地震救援应用本体,描述了应用本体与通用上下文模型之间的关系．最后结合Web服务技术,提出了上下文感知的Web服务发现方法,对上下文感知计算的应用作了初步探索．     

Deterministic and stochastic scheduling with teamwork tasks

We study a class of new scheduling problems which involve types of teamwork tasks. Each teamwork task consists of several components, and requires a team of processors to complete, with each team member to process a particular component of the task. Once the processor completes its work on the task, it will be available immediately to work on the next task regardless of whether the other components of the last task have been completed or not. Thus, the processors in a team neither have to start, nor have to finish, at the same time as they process a task. A task is completed only when all of its components have been processed. The problem is to find an optimal schedule to process all tasks, under a given objective measure. We consider both deterministic and stochastic models. For the deterministic model, we find that the optimal schedule exhibits the pattern that all processors must adopt the same sequence to process the tasks, even under a general objective function GC = F(f₁(C₁), f₂(C₂), … , f_n(C_n)), where f_i(C_i) is a general, nondecreasing function of the completion time C_i of task i. We show that the optimal sequence to minimize the maximum cost MC = max f_i(C_i) can be derived by a simple rule if there exists an order f₁(t) ≤ … ≤ f_n(t) for all t between the functions {f_i(t)}. We further show that the optimal sequence to minimize the total cost TC = ∑ f_i(C_i) can be constructed by a dynamic programming algorithm. For the stochastic model, we study three optimization criteria: (A) almost sure minimization; (B) stochastic ordering; and (C) expected cost minimization. For criterion (A), we show that the results for the corresponding deterministic model can be easily generalized. However, stochastic problems with criteria (B) and (C) become quite difficult. Conditions under which the optimal solutions can be found for these two criteria are derived. © 2004 Wiley Periodicals, Inc. Naval Research Logistics, 2004     

Planning gamma accelerated degradation tests with two accelerating variables

Gamma accelerated degradation tests (ADT) are widely used to assess timely lifetime information of highly reliable products with degradation paths that follow a gamma process. In the existing literature, there is interest in addressing the problem of deciding how to conduct an efficient, ADT that includes determinations of higher stress‐testing levels and their corresponding sample‐size allocations. The existing results mainly focused on the case of a single accelerating variable. However, this may not be practical when the quality characteristics of the product have slow degradation rates. To overcome this difficulty, we propose an analytical approach to address this decision‐making problem using the case of two accelerating variables. Specifically, based on the criterion of minimizing the asymptotic variance of the estimated q quantile of lifetime distribution of the product, we analytically show that the optimal stress levels and sample‐size allocations can be simultaneously obtained via a general equivalence theorem. In addition, we use a practical example to illustrate the proposed procedure.