Asymmetric information and third-party intervention in civil wars

I study a two-period model of conflict with two combatants and a third party who is an ally of one of the combatants. The third party is fully informed about the type of her ally but not about the type of her ally’s enemy. In a signaling game, I find that if the third party is unable to give a sufficiently high assistance to her ally, then there exists a unique separating equilibrium in which the third party’s expected intervention causes her ally’s enemy to exert more effort than in the absence of third-party intervention; this worsens the conflict.     

考虑抗拉强度的含外倾结构面岩质边坡稳定性分析

当前工程设计都是将边坡失稳破坏看成整体的剪切破坏即M-C剪切破坏,基本不考虑岩土体本身抗拉强度.而岩质边坡真实的破坏应该是张拉-剪切复合破坏,不应是传统不考虑抗拉强度的单一剪切破坏模式.利用FLAC3D软件,基于张拉-剪切复合破坏准则对岩体和结构面强度参数同时进行强度折减,与只考虑抗剪强度的情况进行比较来研究抗拉强度对...     

基于空间子区一致性的异源图像匹配方法

提出一种基于空间子区一致性的异源图像匹配方法.该方法中,将图像划分为许多大小相同的子区;分别计算邻近子区间的灰度直方图距离,得到一种图像结构特征描述--似邻矩阵;利用似邻矩阵计算图像间的子区一致性;采用改进的搜索策略,快速获得匹配结果.实验结果表明,该方法在正确率和计算时间方面均优于传统方法.     

基于目的地预测的多相似加权目标编群方法

针对目标编群中单一算法存在的适用范围小、误分率高的问题,提出一种新的态势估计中目标编群的处理方法。首先应用Hop fie ld神经网络对态势中目标的目的地做出判断,然后采用多相似性加权策略计算出目标间的相关系数,再根据最大相关系数层次聚类算法实现编群。仿真结果表明方法能在一定程度上减小错误编群的概率,同时适用范围也得到了扩展。     

侦察无人机作战效能评估准则研究*

在对有人驾驶飞机作战效能评估方法的分析研究基础上,提出了完整的侦察无人机作战效能评估指标体系。建立了作战效能的评估准则与数学模型,并对影响侦察效能的参数进行了分析。运用该方法对“全球鹰”和“捕食者”两种无人机的侦察效能指数进行了定量评估,结果验证了该评估准则的可用性。     

基于结构相似度的SAR干扰效果评估方法

传统的合成孔径雷达(SAR)图像质量评价指标没有考虑像素点间的相关性和人眼视觉系统感知特性,导致主客观不一致的现象时有发生。提出了一种主客观相结合评估方法,该方法不仅巧妙地利用了结构相似度(SSIM)作为主客观评价的连接纽带,而且结合局部方差相似度与图像高频信息相对应的特点,通过非线性合并得到了结构相似度的修正模型。仿真结果表明,该方法较之传统评估手段具有明显的优势,不仅能够更好地反映出干扰对图像细节的影响,而且能够与人类视觉系统保持高度一致,进一步提升了整套评估系统的效率和准确率。     

基于内容检索中算法的层次化体系组织

基于内容的检索中,检索算法的常规实现是面向单个特征完成特征提取和相似性匹配,再对单特征检索结果加权完成多特征检索,这种方法计算量大,缺乏灵活性。针对于此,提出了一种检索算法的层次化组织体系,能够灵活组织面向不同特征的检索算法,通过传输因子、优先级因子和复杂度因子的赋值,获得逐步精确的检索结果。实验证明,在查全率和查准率基本一致的前提下,与常规检索体系相比,新检索体系能够减少近40%的检索时间。     

效力准则的电子战干扰效果度量的研究

探讨了舰载电子战系统干扰效果的度量准则,分析了压制干扰对反舰导弹的单发命中概率的影响,从研究系统射击效力指标的角度出发,提出一种新的评定舰载电子战干扰效果的准则——效力准则。并通过算例得出典型情况下系统施放压制性干扰时机与反舰导弹单发命中概率之间的关系。     

专题式Web信息检索系统的设计与实现

主要介绍WIRSOS (Webinformationretrievalsystemforordnancesupport)系统设计与实现的关键技术。本系统采用搜索引擎技术 ,利用网络机器人 (WebRobot)采集WWW上信息 ,通过对整篇文档的分词处理和相似性计算 ,针对某一专题信息进行收集 ,从而提高检索效率和精度     

A novel noise reduction technique for underwater acoustic signals based on complete ensemble empirical mode decomposition with adaptive noise,minimum mean square variance criterion and least mean square adaptive filter

Underwater acoustic signal processing is one of the research hotspots in underwater acoustics. Noise reduction of underwater acoustic signals is the key to underwater acoustic signal processing. Owing to the complexity of marine environment and the particularity of underwater acoustic channel, noise reduction of underwater acoustic signals has always been a difficult challenge in the field of underwater acoustic signal processing. In order to solve the dilemma, we proposed a novel noise reduction technique for underwater acoustic signals based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), minimum mean square variance criterion (MMSVC) and least mean square adaptive filter (LMSAF). This noise reduction technique, named CEEMDAN-MMSVC-LMSAF, has three main advantages: (i) as an improved algorithm of empirical mode decomposition (EMD) and ensemble EMD (EEMD), CEEMDAN can better suppress mode mixing, and can avoid selecting the number of decomposition in variational mode decomposition (VMD); (ii) MMSVC can identify noisy intrinsic mode function (IMF), and can avoid selecting thresholds of different permutation entropies; (iii) for noise reduction of noisy IMFs, LMSAF overcomes the selection of decomposition number and basis function for wavelet noise reduction. Firstly, CEEMDAN decomposes the original signal into IMFs, which can be divided into noisy IMFs and real IMFs. Then, MMSVC and LMSAF are used to detect identify noisy IMFs and remove noise components from noisy IMFs. Finally, both denoised noisy IMFs and real IMFs are reconstructed and the final denoised signal is obtained. Compared with other noise reduction techniques, the validity of CEEMDAN-MMSVC-LMSAF can be proved by the analysis of simulation signals and real underwater acoustic signals, which has the better noise reduction effect and has practical application value. CEEMDAN-MMSVC-LMSAF also provides a reliable basis for the detection, feature extraction, classification and recognition of underwater acoustic signals.