离心叶轮气动阻尼的数值分析

为进行运动边界下离心叶轮流场的数值分析,独立开发了网格变形程序和非定常流动分析程序,实现了流场中振动离心叶轮的气动阻尼计算。采用紧支撑径向基函数法进行结构到气动表面变形的数据传递,应用二叉树技术进行壁面距离的计算,大幅提高了网格变形和流场分析中距离搜索的计算效率。通过振动叶栅和离心叶轮的算例,验证了程序应用于运动边界流场计算和叶轮流场模拟的正确性。以某离心叶轮为对象,展开其模态气动阻尼比的计算分析。结果表明:考察的两个模态下气动阻尼比均为正值,小幅振动下模态气动阻尼比与振幅无关,轮盘振动模态下叶轮的气动阻尼比随工况接近失速而逐渐减小。      

基于框架域的随机游走全色锐化方法

针对多光谱图像与全色图像的融合问题,提出了一种新的全色锐化方法。该方法首先通过亮度、色调、饱和度(IHS)变换与非下采样框架变换将原图像从空间域变换到框架域,然后利用基于图论的随机游走,建立高频框架系数的统计融合模型。此模型根据高频框架系数的邻域相关性与尺度相关性构造新的随机游走协调函数,将高频框架系数融合权重的估计转化为随机游走标记问题的求解。实验结果表明,该方法有利于保持图像的光谱信息和边缘轮廓信息,可以在降低融合图像光谱误差的同时提高空间分辨率,并且优于一些主流全色锐化方法。     

复合材料层合结构频率约束的可靠性优化设计(英文)

The reliability based optimization （RBO） issue of composite laminates trader fundamental frequency constraint is studied. Considering the tmcertainties of material properties, the frequency constraint reliability of the structure is evaluated by the combination of response surface method （RSM） and finite element method. An optimization algorithm is developed based on the mechanism of laminate frequency characteristics, to optimize the laminate in terms of the ply amount and orientation angles. Numerical examples of composite laminates and cylindrical shell illustrate the advantages of the present optimization algorithm on the efficiency and applicability respects. The optimal solutions of RBO are obviously different from the deterministic optimization results, and the necessity of considering material property uncertainties in the composite structural frequency constraint optimization is revealed.     

结构与纯度结合的新型决策树分裂准则

决策树（Desision tree， DT）生长关键步骤的分裂或分叉准则通常根据纯度和误分类误差等实现，分裂生长分为轴平行和非轴平行方式。这些分裂准则一般与数据内在结构（如类别是否是多簇或单簇组成）无关。为了弥补这一缺失，本文提出了两种混合分裂准则，分别用加权和两步法将同类内的节点间距（Between-node margin within the same class，BNM）和同一节点内的类紧性（Within-class compactness and between-class separation in the same inner node，CSN）与纯度度量相结合。由于传统决策树以贪婪方式生长，仅能确定出当前的一个局部最优分裂点，为改善这个缺点，本文首先根据纯度确定出前k个候选分裂点，然后通过最大化BNM和最小化CSN确定最终的分裂点，不仅缓和了纯度上的局部最优性，而且引入了数据结构的全局性，因此能较大程度地改进后代节点的分裂，增强树的泛化性和可解释性。将上述两种分裂准则组合还可以进一步提升性能。在21个标准验证数据集上的比较结果表明：新准则下的决策树不仅提高了预测性能、降低了复杂性，而且相比于其他采用混合分裂准则的DTs更具竞争力。     

Multi-seasonal evaluation of hybrid poplar (P. Deltoides) plantations using Worldview-3 imagery and State-Of-The-Art ensemble learning algorithms

Forest resources are the primary components of the ecosystem environment. Poplars (Populus sp.), a member of the fast-growing trees, are one of the most productive forest tree species for industrial production thanks to their desirable traits comprising rapid growth, hybridization ability, and ease of propagation. Determining poplar cultivated areas and mapping their geographical distributions is critical for planners and decision-makers to increase the ecological and economic benefits of poplar trees. Due to the biodiversity of each geographical region and seasonal vegetation variations, classification based on remotely sensed imagery is essential for cropland monitoring. The main goal of this study is to evaluate the potential of high-resolution multi-temporal (growing season and end of the growing season) Worldview-3 imagery in mapping poplar plantations in the Akyaz? district of Sakarya, Turkey. For this purpose, pixel- and object-based image analysis with up-to-date ensemble learning algorithms, namely random forest (RF), categorical boosting (CB), and extreme gradient boosting tree (XGB), were employed for mapping poplar fields. Results indicated that the object-based classification approach provided statistically significant improvements in map-level (about 4%) and class-level accuracy (e.g., approximately 7% and %2 for poplar and young poplar classes, respectively) than pixel-based classification. While the CB performed superior classification performance for the object-based approach (92.56%), the highest classification performance was obtained with the XGB algorithm for the pixel-based approach (90.42%) for the end of the growing season data. McNemar’s statistical test also confirmed that the performances of CB and XGB algorithms were statistically similar in pixel-based classification. Finally, analysis of multi-season images revealed that sensitivity of the vegetation phenology and seasonal effects considerably affect the separability of poplar tree species.     

基于统计学特征的密集型气液雾化随机模型优化方法研究

在高气液动量比的空气雾化流场中，液滴和液丝从液核剥离的过程具有高自由度、分布密集的特点，传统理论模型难以对其准确预测。本文对结合大涡模拟方法的随机雾化模型进行优化，在初始雾化过程，提出使用液滴统计平均温度来表征液滴碰撞统计学特性的改进方法。液滴的统计平均温度分别采用气液相对动能模型的粒子追踪法和亚网格动能模型的粒子追踪法。研究表明，使用改进的气液雾化随机模型预测密集型空气雾化流场，大幅改善了传统雾化随机模型在初始雾化区域过预测的缺陷，平均动能的相对误差为15.5%，平均索特尔直径的相对误差为7.2%，与未改进前的模拟结果相比，误差降低了41.1%和15.0%。此外，本文还探究了喷雾张角模型对雾化流场预测结果的影响，分别将实验所得经验公式法、相界面气液动量平衡所得模拟法与亚网格动能模型的粒子追踪法结合。结果表明喷雾张角经验公式预测结果更为准确，在平均索特尔直径预测方面准确性提高了17.3%。     

基于多层次特征的高分影像海岸带地物分类

针对高分辨率影像地物光谱丰富复杂的问题，文章提出了一种结合像元-对象-空间格局特征的水域、盐田等海岸带精细地物分类方法。首先基于像元和空间格局特征分别利用遗传算法优化随机森林参数进行分类；其次将两种分类结果采用D-S证据理论融合；然后基于不同地物斑块的空间上下文信息，采用粒子群算法优化支持向量机（Support Vector Machine，SVM）参数实现面向对象的二次分类。为验证方法的有效性，收集山东省莱州湾海岸带附近区域的“高分二号”卫星影像，开展了水域及水利设施用地、盐田、工矿用地、交通运输用地、住宅用地、沿海滩涂、海域与耕地8类地物的分类实验，总体精度达到97.21%，Kappa系数为0.964 2。实验结果表明，文章提出的高分影像海岸带地物分类方法兼顾像元、对象及空间格局特征优点，能够实现多层次细致分类，并同时兼具较短的训练时长与较高的分类精度。该研究成果可为中国海岸带生态环境监测与土地利用规划提供一定技术支撑。     

Enhancement on parallel unstructured overset grid method for complex aerospace engineering applications

In the present study, an efficient overset grid method by means of parallel implicit hole-cutting is proposed for the sake of simulating unsteady flows in aerospace engineering involving multiple bodies in relative movement. In view of the degraded computational efficiency and robustness for conventional overset grid assembly, several innovative techniques are developed within the overset grid assembly process, viz., a bookkeeping alternative digital tree method to speed up the donor-cell search...     

基于 PDPC-EMV模型的装备研制方案优化设计

将全面质量管理的过程决策程序图法(PDPC)与决策分析技术的期望值法(EMV)有机结合,构建了PDPC-EMV理论模型,并将该模型引入到武器装备研制方案的设计环节中,用风险评估的思维解决全面质量管理的问题,为武器装备研制方案的设计工作开辟了一种先期质量控制思路。     

STABC-IR: An air target intention recognition method based on bidirectional gated recurrent unit and conditional random field with space-time attention mechanism

The battlefield environment is changing rapidly, and fast and accurate identification of the tactical intention of enemy targets is an important condition for gaining a decision-making advantage. The current Intention Recognition (IR) method for air targets has shortcomings in temporality, interpretability and back-and-forth dependency of intentions. To address these problems, this paper designs a novel air target intention recognition method named STABC-IR, which is based on Bidirectional Gated Recurrent Unit (BiGRU) and Conditional Random Field (CRF) with Space-Time Attention mechanism (STA). First, the problem of intention recognition of air targets is described and analyzed in detail. Then, a temporal network based on BiGRU is constructed to achieve the temporal requirement. Subsequently, STA is proposed to focus on the key parts of the features and timing information to meet certain interpretability requirements while strengthening the timing requirements. Finally, an intention transformation network based on CRF is proposed to solve the back-and-forth dependency and transformation problem by jointly modeling the tactical intention of the target at each moment. The experimental results show that the recognition accuracy of the jointly trained STABC-IR model can reach 95.7%, which is higher than other latest intention recognition methods. STABC-IR solves the problem of intention transformation for the first time and considers both temporality and interpretability, which is important for improving the tactical intention recognition capability and has reference value for the construction of command and control auxiliary decision-making system.