给定任务和模式下指挥体系节点重要性评估方法

针对一般节点重要度评估方法没有考虑指挥体系网络与外界的交互以及自身运行机制的问题,研究了指挥节点的重要度评估方法,在一般网络结构重要度分析基础上,综合作战任务和指挥模式对节点的需求,提出了作战任务对节点依赖度以及指挥模式对节点依赖度的计算方法,最后设计了评估算法并给出具体算法流程,案例分析结果证明该算法合理、有效。     

舰艇编队海上运输补给物资需求预测方法

按照是否与任务、事件相关,将物资需求分为两类,对于任务、事件相关物资需求的预测,将任务进行合理的分解,并根据物资消耗与任务、事件之间的关系,给出了预测的一般模型;对于与任务、事件联系不紧密的物资需求的预测,则根据历史经验及该物资固有的消耗规律,提出了经验预测模型。为了解决舰艇编队海上运输补给物资需求预测所存在的问题,利用案例推理的方法生成了预测所需的样本数据,以最小二乘向量机(LSSVM)模型为预测模型,并以岛屿进攻作战的防空弹药需求预测为例进行了实例分析。结果表明:案例推理生成的样本数据可用,选用LSSVM模型的预测结果与其他预测模型表现出了一致性,但LSSVM相对误差较小;该方法在某种程度上解决了样本数据有限的问题,适用于作战物资需求的预测问题。     

Preparation of ammonium nitrate-based solid composite propellants supplemented with polyurethane/nitrocellulose blends binder and their thermal decomposition behavior

To improve the performance of solid composite propellants (SCPs) supplemented with ammonium nitrate (AN) as an oxidizer, the incorporation of energetic ingredients such as explosives, energetic binders or catalysts is a common effective approach. For this purpose, polyurethane (PU), a typical inert binder, was mixed with nitrocellulose (NC) as an energetic polymer. Numerous composite solid propellant compositions based on AN and NC-modified polyurethane binder with different NC ratios were prepared. The prepared formulations were characterized using Fourier transform infrared spectroscopy (FTIR), RAMAN spectroscopy, X-ray diffraction (XRD), electron densimetry, thermogravimetric (TG) analysis, and differential scanning calorimetry (DSC). A kinetic study was then performed using the iterative Kissinger-Akahira-Sunose (It-KAS), Flynn-Wall-Ozawa (It-FWO), and non-linear Vyazovkin integral with compensation effect (VYA/CE) methods. The theoretical performances, such as theoretical specific impulse, adiabatic flame temperature, and ideal exhaust gaseous species, were also determined using the NASA Lewis Code, Chemical Equilibrium with Application (CEA). Spectroscopic examinations revealed the existence of NC and full polymerization of PU in the prepared propellants. According to density tests, the density of the propellant increases as the nitrocellulose component increases. According to the thermal analysis and kinetics study, the increase in NC content catalyzed the thermal decomposition of the AN-based composite solid propellants. Based on the theoretical study, increasing the amount of NC in the propellant increased the specific impulse and, as a result, the overall performance.     

一种新的基于特征值分解的双站无源定位算法

在自卫式噪声干扰的条件下,地面制导雷达将无法得到目标的斜距信息,无法实现对目标的定位,此时只有采用雷达组网方式,利用2个或多个雷达站的测角信息来对空间目标进行定位。提出一种基于特征值分解的新的定位跟踪算法,该算法具有计算简单、清晰,运算量小的优点,仿真也表明该算法有较好的精度。     

The effect of al particle size on thermal decomposition,mechanical strength and sensitivity of Al/ZrH2/PTFE composite

To study the thermal decomposition of Al/ZrH2/PTFE with different Al particle size as well as mechanical strength and impact sensitivity under medium and low strain rates, molding-vacuum sintering was adopted to prepare four groups of power materials and cylindrical specimens with different Al particle size. The active decomposition temperature of ZrH2 was obtained by TG-DSC, and the quasi-static me-chanics/reaction characteristics as well as the impact sensitivity of the specimen were studied respec-tively by quasi-static compression and drop-hammer test. The results show that the yield strength of the material decreased with the increase of the Al particle size, while the compressive strength, failure strain and toughness increased first and then decreased, which reached the maximum values of 116.61 MPa, 191％, and 119.9 MJ/m respectively when the Al particle size is 12—14μm because of particle size grading. The specimens with the highest strength and toughness formed circumferential open cracks and reacted partly when pressed. Those with developmental cracks formed inside did not react. It is considered that fracture of specimens first triggered initial reaction between Al and PTFE to release an amount of heat. Then ZrH2 was activated and decomposed, and participated in subsequent reaction to generate ZrC. The impact sensitivity of the specimens decreased with the increase of Al particle size.     

Analysis of a production system in a general configuration

We consider a production system comprising multiple stations (or workshops) such as an entry station, a set of work stations, a central station, and an exit station, which are arranged in a general configuration. A worker (or a vehicle tool) is assigned to each station, who sends a part from the station to the destination station according to the required process path of the part. Any part is allowed to visit a work station more than once if its process path requires. We propose a new control strategy with the push policy for instructing each worker to send a part and the kanban mechanism for controlling the work‐in‐process (WIP) in each work station. As all work stations have limited local buffers, the central station is used for storing blocked parts temporarily. Such a production system is modeled as an open queueing network in a general configuration with a Markovian part sending policy and a machine no blocking mechanism. The queueing network is analytically characterized. Some important performance measures are compared with other control strategies. A semi‐open decomposition approach is applied to the queueing network for computing the blocking probabilities when parts arrive at the work stations. An algorithm is developed based on the semi‐open decomposition approach. Numerical experiments show the quality of the solutions obtained by the algorithm as well as a property of a performance measure. © 2001 John Wiley & Sons, Inc. Naval Research Logistics 48: 128–143, 2001     

基于EMD、遗传算法及神经网络的柴油机故障诊断

研究了将经验模式分解(Empirical Mode Decom position,EMD)、遗传算法及BP神经网络相结合对柴油机振动信号进行故障诊断的方法。首先运用经验模式分解方法对柴油机缸盖表面振动信号进行分解并提取特征参数;然后利用遗传算法对得到的特征参数进行选择,找到对于故障诊断最为敏感的参数;最后建立了BP神经网络模型对柴油机典型故障进行诊断。通过对某型柴油机的验证,表明该方法能够准确识别柴油机供油系统的典型故障。     

有向无环图决策支持向量机和经验模式分解在轴承故障诊断中的应用

当旋转机械发生故障时,其振动信号常常表现出较为复杂的调制形式,经验模式分解能根据信号的真实物理意义完成自适应分解。支持向量机由于其出色的学习性能和良好的推广能力,使其在包括故障诊断在内的众多领域得到较为广泛的应用。利用经验模式分解结果提取频带能量特征向量,采用有向无环图决策支持向量机实现对轴承状态的判别,并基于留一法优化支持向量机的模型参数。最终的应用结果表明,基于EMD和有向无环图决策支持向量机方法可以有效实现对轴承的状态判别。     

Energy‐efficient scheduling for sustainable manufacturing systems with renewable energy resources

Environmentally friendly energy resources open a new opportunity to tackle the problem of energy security and climate change arising from wide use of fossil fuels. This paper focuses on optimizing the allocation of the energy generated by the renewable energy system to minimize the total electricity cost for sustainable manufacturing systems under time‐of‐use tariff by clipping the peak demand. A rolling horizon approach is adopted to handle the uncertainty caused by the weather change. A nonlinear mathematical programming model is established for each decision epoch based on the predicted energy generation and the probability distribution of power demand in the manufacturing plant. The objective function of the model is shown to be convex, Lipchitz‐continuous, and subdifferentiable. A generalized benders decomposition method based on the primal‐dual subgradient descent algorithm is proposed to solve the model. A series of numerical experiments is conducted to show the effectiveness of the solution approach and the significant benefits of using the renewable energy resources.     

面向癫痫脑电的简化深度学习模型

针对脑电信号随机性强、动态变化迅速等特点,提出了一种简化深度学习模型研究癫痫脑电识别问题。提出的模型以一维卷积神经网络为基础,在结构方面简化了卷积层、池化层等以提高模型效率,在整体框架方面应用了Keras框架,在训练优化算法方面采用RMSProp算法作为模型优化算法,通过预定义的目标函数来进行损失估计,模型设计上加入了批标准化层和全局均值池化层。基于所提模型,从三个方面研究了癫痫脑电识别问题,即:利用经验模态分解,分别选取前三阶、前五阶、前七阶、前八阶的本征模态函数分量,在简化模型上进行对比分析；利用提出模型所具备的深度学习特点,直接识别原始脑电信号而无须特征提取环节；增加了三种不同方法分别提取7类特征,对相同的脑电数据进行对比分析。性能分析结果表明:对于五类不同的脑电信号,前三阶的本征模态函数分量的识别率达到92.1%,比其他几种处理方式识别率高；前八阶的本征模态分量识别率不及原始信号,表明人工数据处理时会给数据带来噪声; 所提出的简化深度学习模型能高效处理癫痫脑电识别问题,具备较高效率和较好性能。