THE DEFENCE SPENDING–EXTERNAL DEBT NEXUS IN ETHIOPIA

The aim of this paper is to examine the impact of defence spending and income on the evolution of Ethiopia’s external debt over the period 1970–2005. Using the bounds test approach to cointegration and Granger causality tests, we find a long run and a causal relationship between external debt, defence spending and income. Defence spending had a positive and a significant impact on the stock of external debt while income had a negative and a statistically significant impact on external debt. Our findings suggest that an increase in defence spending contributes to the accumulation of Ethiopia’s external debt, while an increase in economic growth helps Ethiopia to reduce its external debt.     

基于时滞分割法的具有leakage项时滞的离散型神经网络状态估计

摘要：研究一类具有leakage时滞的离散型神经网络的状态估计问题．通过构造新的Lyapunov泛函得到保证估计误差全局渐近稳定的充分条件,并通过求解一个线性矩阵不等式（LMI）得到状态估计器的增益矩阵．采用一种新的时滞分割方法将变时滞区间分割为多个子区间,使该结果在获得更小的保守性同时也降低了计算的复杂度．     

基于奇异值分解的潜艇磁场推算

针对潜艇磁性防护,基于磁偶极子阵列模型,建立了潜艇磁性目标高精度磁场计算模型。提出了利用奇异值分解的方法对模型进行求解,利用该求解算法进行了模型计算与实测数据比对,结果表明,该计算方法能够有效适用于计算高精度潜艇磁场。基于奇异分解的方法计算了潜艇高空磁场,计算结果可供潜艇防护磁性探测参考。     

多站时差定位中的病态性分析及其改善方法

在多站时差定位过程中,传统定位算法在传感器布站不合理的情况下通常会表现出明显的病态性,其导致定位结果不稳定、精度低。针对此问题,首先分析了时差定位过程中病态性产生的物理场景,并通过奇异值分解揭示了病态性的本质;其次根据时差定位观测矩阵的特征值分布特点,提出了一种改进的多站时差定位改善方法,该方法基于LS估计信息矩阵的条件指标进行奇异值修正,兼顾了目标定位的分辨率与方差,能显著提高多站时差目标定位的精度和稳定性,且没有增加系统和算法复杂度;最后将该算法与传统算法进行了仿真比较,结果证明了该算法优越性。     

Sandwich structure for enhancing the interface reaction of hexanitrohexaazaisowurtzitane and nanoporous carbon scaffolds film to improve the thermal decomposition performance

Improving the thermal decomposition performance of hexanitrohexaazaisowurtzitane (CL-20) by appropriate methods is helpful to promote the combustion performance of CL-20-based solid propellants. In this study, we synthesized a sandwich structure of CL-20 and nanoporous carbon scaffolds film (NCS) and emphatically studied the thermal decomposition performance of the composite structure. Thermogravimetric analysis and differential scanning calorimetry were used to measure the thermal decomposition process of the composite structure. The kinetic parameters of thermal decomposition were calculated by the thermal dynamic analysis software AKTS. These results showed that the thermal decomposition performance of the sandwich structure of CL-20 and NCS was better than CL-20. Among the tested samples, NCS with a pore size of 15 nm had the best catalytic activity for the thermal decomposition of CL-20. Moreover, the thermal decomposition curve of the composite structure at the heating rate of 1 K/min was deconvoluted by mathematical method to study the thermal decomposition process. And a possible catalytic mechanism was proposed. The excellent thermal decomposition performance is due to the sandwich structure enhances the interface reaction of CL-20 and NCS. This work may promote the extensive use of CL-20 in the field of solid rocket propellant.     

一种新的动态信道化接收机设计方法

针对复杂电磁环境中存在多个非均匀分布的、不同带宽的信号的情况,提出一种新的动态信道化接收机设计方法。设计一个半带滤波器,并通过内插运算压缩它的频率响应,形成周期频谱;对输入信号进行滤波,同时使原信号经过一定延迟后减去滤波后的信号,形成两路在频谱上互补的信号;用均匀分析滤波器组对得到的两路信号分别进行滤波分解处理,实现监视频段的均匀信道化;根据能量检测结果将属于同一信号的相邻子信道信号作为下一步重构处理的输入,并设计出相应的综合滤波器组提取信号。理论分析和仿真结果验证了新方法的有效性。     

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。     

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.     

A multi‐stage stochastic programming approach for network capacity expansion with multiple sources of capacity

In networks, there are often more than one sources of capacity. The capacities can be permanently or temporarily owned by the decision maker. Depending on the nature of sources, we identify the permanent capacity, spot market capacity, and contract capacity. We use a scenario tree to model the uncertainty, and build a multi‐stage stochastic integer program that can incorporate multiple sources and multiple types of capacities in a general network. We propose two solution methodologies for the problem. Firstly, we design an asymptotically convergent approximation algorithm. Secondly, we design a cutting plane algorithm based on Benders decomposition to find tight bounds for the problem. The numerical experiments show superb performance of the proposed algorithms compared with commercial software. © 2016 Wiley Periodicals, Inc. Naval Research Logistics 63: 600–614, 2017     

Military expenditure and income distribution in South Korea

This paper attempts to investigate the long-run and the causal relationship between military expenditure and income distribution in South Korea for the period 1965–2011. Applying the bounds test approach to cointegration, we found a long-run relationship between military expenditure and the Gini coefficient with military expenditure having a positive and a statistically significant impact on income inequality. A 1% rise in military expenditure increased the Gini coefficient by 0.38%. Application of the lag-augmented causality test also reveals a unidirectional causality running from military expenditure to income inequality. The evidence seems to suggest that devoting more resources to the military sector may further worsen income inequality in South Korea.