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281.
《防务技术》2022,18(9):1622-1642
Steel-tube-confined concrete (STCC) targets are provided with excellent anti-penetration performance over semi-infinite concrete (SIC) targets since the steel tube imposes passive restraint on the in-filled concrete during the penetration process. Grid STCC system with square steel tubes is a potential solution to protective structures. In this paper, experiments of 9-cell grid STCC targets penetrated by 12.7 mm Armor Piercing Projectile (APP) were performed. The influence of side length and thickness of steel tube, steel ratio and impact velocity on anti-penetration performance were taken into account. Additionally, single-cell square STCC targets were also designed and tested for comparison with the 9-cell grid STCC targets. Damage modes and parameters of the tested targets were measured and discussed. Moreover, the stiffness of radial confinement of grid STCC targets is achieved according to the elastic solution of infinite cylindrical shell in Winkler medium. Furthermore, the penetration resistance and depth of penetration (DOP) for grid STCC targets are obtained on the basis of the dynamic finite spherical cavity-expansion (FSCE) models including radial confinement effect. It is shown that the 9-cell grid STCC targets with optimal dimension match of thickness and side length of steel tube can reduce the DOP by about 17 % and 23 % in comparison with the SIC targets and single-cell square STCC targets, respectively, due to both the confinement of square steel tube to concrete in the impacted cell and the additional confinement of the surrounding cells to the impacted cell; the penetration resistance and DOP of the grid and cellular STCC targets with similar steel ratio is close, and thus the grid STCC targets with simpler manufacturing process and excellent in-plane expandability are preferred in engineering practice; moreover, the predicted results of DOP model based on the FSCE models agree well with the tested results with the maximum disparity less than 12 % and the proposed model is more applicable to the grid and cellular STCC targets with high radial confinement. 相似文献
282.
《防务技术》2022,18(9):1589-1601
Infrared (IR) small target detection is one of the key technologies of infrared search and track (IRST) systems. Existing methods have some limitations in detection performance, especially when the target size is irregular or the background is complex. In this paper, we propose a pixel-level local contrast measure (PLLCM), which can subdivide small targets and backgrounds at pixel level simultaneously. With pixel-level segmentation, the difference between the target and the background becomes more obvious, which helps to improve the detection performance. First, we design a multiscale sliding window to quickly extract candidate target pixels. Then, a local window based on random walker (RW) is designed for pixel-level target segmentation. After that, PLLCM incorporating probability weights and scale constraints is proposed to accurately measure local contrast and suppress various types of background interference. Finally, an adaptive threshold operation is applied to separate the target from the PLLCM enhanced map. Experimental results show that the proposed method has a higher detection rate and a lower false alarm rate than the baseline algorithms, while achieving a high speed. 相似文献
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针对相干和非相干信源同时存在的情况,结合斜投影理论和互相关矢量Toeplitz矩阵重构(CVTR)的方法,提出一种新的信源DOA分步估计方法。该方法把相干信源和非相干信源分开分辨,对相干信源用CVTR方法来恢复为满秩,避免了常规平滑算法阵列孔径损失大、运算量大的缺点;用斜投影算子而非差分方法对信源进行分离,不受相关矩阵须为Toeplitz结构的限制,使得算法可适用于任意阵列结构形式。这种分步分辨思路可有效增强信源过载能力,同时在互相关矢量Toeplitz矩阵重构过程中,可把非平稳噪声协方差矩阵转换成白噪声结构,使得算法对非平稳噪声有较好的适应能力。 相似文献
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提出了一种基于最小最大决策的三站时差定位布阵优化方法,以提高系统对目标区域的整体定位精度。应用基于最小最大决策的最优化理论,建立布阵优化问题的数学模型。该模型以三站坐标为决策变量,以目标区域的最大水平定位误差最小为目标函数,然后运用基于最小最大决策的最优化算法,求解模型的最优解,并将此最优解作为三站时差定位系统的最优布阵。仿真结果与理论计算一致,验证了这种布阵优化结果的最优性。 相似文献
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车辆典型部件结构的有限元模型修正方法 总被引:1,自引:0,他引:1
以车身结构中简化的典型结构形式为研究对象,利用有限元软件中的焊点模拟单元,建立典型结构的有限元模型并进行模态分析。在参数灵敏度分析的基础上,结合模态试验测得的数据,利用广义简约梯度算法,对结构的有限元模型进行修正和模型验证,深入探讨了车辆典型部件结构的有限元模型修正方法。通过模型修正,有限元模型的精度得到了明显的提高,为进一步研究车辆结构部件的振动特性奠定了基础。 相似文献
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The Annealing Adaptive Search (AAS) algorithm for global optimization searches the solution space by sampling from a sequence of Boltzmann distributions. For a class of optimization problems, it has been shown that the complexity of AAS increases at most linearly in the problem dimension. However, despite its desirable property, sampling from a Boltzmann distribution at each iteration of the algorithm remains a practical challenge. Prior work to address this issue has focused on embedding Markov chain‐based sampling techniques within the AAS framework. In this article, based on ideas from the recent Cross‐Entropy method and Model Reference Adaptive Search, we propose an algorithm, called Model‐based Annealing Random Search (MARS), that complements prior work by sampling solutions from a sequence of surrogate distributions that iteratively approximate the target Boltzmann distributions. We establish a novel connection between MARS and the well‐known Stochastic Approximation method. By exploiting this connection, we prove the global convergence of MARS and characterize its asymptotic convergence rate behavior. Our empirical results indicate promising performance of the algorithm in comparison with some of the existing methods. © 2011 Wiley Periodicals, Inc. Naval Research Logistics, 2011 相似文献