如何在防化科研试验中加强安全保密工作

针对我院科研试验的特点和要求,分析了安全保密工作的艰巨性和重要性,明确了安全保密总要求,提出了把安全保密工作落到实处的具体措施。     

热力设备可靠性分析的问题与对策

以热力设备为研究对象 ,从可靠性分析特点出发 ,对应用故障统计方法所遇到的典型问题作了探讨 ,就分析方法、建模技术等方面论述了作者的观点     

基于锥形量热法的油池火相似性及点火特性模拟实验

针对油料池火灾相似性及点火特性研究需要,构建了一种基于耗氧原理的油池火锥形量热法模拟实验系统,引入火灾尺度相似性概念,研究了实验室尺度油池火与真实尺度油池火的相似性关系,探讨了实验室尺度油池火预测真实尺度油池火的可行性,基于实验室尺度锥形量热法研究了油池火点火特性,得到了辐射强度、辐射距离对油料点燃时间的影响规律以及点燃时间与锥照射强度之间的拟合公式。     

The (s,Q) inventory model with Erlang lead time and deterministic demand

We develop a simple, approximately optimal solution to a model with Erlang lead time and deterministic demand. The method is robust to misspecification of the lead time and has good accuracy. We compare our approximate solution to the optimal for the case where we have prior information on the lead‐time distribution, and another where we have no information, except for computer‐generated sample data. It turns out that our solution is as easy as the EOQ's, with an accuracy rate of 99.41% when prior information on the lead‐time distribution is available and 97.54–99.09% when only computer‐generated sample information is available. Apart from supplying the inventory practitioner with an easy heuristic, we gain insights into the efficacy of stochastic lead time models and how these could be used to find the cost and a near‐optimal policy for the general model, where both demand rate and lead time are stochastic. © 2004 Wiley Periodicals, Inc. Naval Research Logistics, 2004     

Robust cubature Kalman filter method for the nonlinear alignment of SINS

Nonlinear initial alignment is a significant research topic for strapdown inertial navigation system(SINS).Cubature Kalman filter(CKF)is a popular tool for nonlinear initial alignment.Standard CKF assumes that the statics of the observation noise are pre-given before the filtering process.Therefore,any unpredicted outliers in observation noise will decrease the stability of the filter.In view of this problem,improved CKF method with robustness is proposed.Multiple fading factors are introduced to rescale the obser-vation noise covariance.Then the update stage of the filter can be autonomously tuned,and if there are outliers exist in the observations,the update should be less weighted.Under the Gaussian assumption of KF,the Mahalanobis distance of the innovation vector is supposed to be Chi-square distributed.Therefore a judging index based on Chi-square test is designed to detect the noise outliers,determining whether the fading tune are required.The proposed method is applied in the nonlinear alignment of SINS,and vehicle experiment proves the effective of the proposed method.