一种新的SAR图像斜距多普勒定位模型的直接解法

斜距多普勒定位模型是实现SAR图像无控制点定位的基本模型,因此如何高效准确地获得RD方程组的解成为实现SAR图像无控制点几何定位的根本问题,也是实现SAR自动图像定位的关键。首先利用地固坐标系下目标速度为零的特性,简化RD模型并整理为一元四次方程,然后利用一元四次方程求根方法,推导出斜距多普勒定位模型的明确数学解析解,并与常用的数值迭代求解方法进行比较分析,仿真结果验证了推导的正确性。新解法具有无需设置初始值,易于编程实现,鲁棒性高的特点。     

OPEN XML文档数字签名的C#实现

针对电子文档的安全性问题,分析了文档的各种数字签名情况,设计了一种能够完成多种签名方法,且能全面验证签名的文档签名系统。系统采用C#编程,从而提高了电子文档的安全性。     

穷举法在程序设计中的应用

穷举法是程序设计中经常用到的一种算法,用来解决一些用常规的数学方法无法解决的问题.文章通过两个典型的例子对穷举法的思路和有关注意事项进行了分析,供编程学习者参考.     

光学遥感舰船目标识别方法

提出一种基于粗糙集理论和分层判别回归技术的光学遥感舰船目标识别方法。该方法首先提出新的光学遥感舰船识别特征———面积比编码,并与四类特征组合作为备选特征;然后基于粗糙集理论按同可区分度来计算各备选特征的重要性权值,自动选择出对正确识别贡献较大的特征组合;最后根据分层判别回归原理生成分类判决树来识别光学遥感舰船目标。实验结果表明,本文方法在识别精度和速度方面优于最近邻和支持向量机方法,且通用可行。     

基于多通路融合网络的高速公路雾天能见度等级识别

雾天是影响高速公路交通安全的重要因素。研究从监控图像进行高速公路雾天能见度的自动识别方法可以为交通管理部门的智能管理和决策提供技术支持。根据大气散射模型分析出与雾浓度相关的多个物理因素,提出了综合这些物理因素的多通路融合识别网络。该网络使用三个通路联合学习深度视觉特征、传输矩阵特征和场景深度特征,并设计注意力融合模块来自适应地融合这三类特征以进行能见度等级识别。同时构建了一个合成数据集和一个真实的高速公路场景数据集,用于网络参数学习和性能评估。实景数据集中的图像是从中国多条高速公路的监控视频中收集的。在这两个数据集上的实验表明,所提方法可以适应不同的监控拍摄场景,能够比现有方法更准确地识别能见度等级,有效提升了识别精度。     

基于Transformer实现文本导向的图像编辑

为方便非专业用户修图,提出一种基于Transformer的图像编辑模型TMGAN,使用户可通过自然语言描述自动修改图像属性。TMGAN整体框架采用生成对抗网络,生成器采用Transformer编码器结构提取全局上下文信息,解决生成图像不够真实的问题;判别器包含基于Transformer的多尺度判别器和词级判别器两部分,给生成器细粒度的反馈,生成符合文本描述的目标图像且保留原始图像中与文本描述无关的内容。实验表明,此模型在CUB Bird数据集上,IS（inception score）、FID（Fréchet inception distance）以及MP（manipulation precision）度量指标分别达到了9.07、8.64和0.081。提出的TMGAN模型对比现有模型效果更好,生成图像既满足了给定文本的属性要求又具有高语义性。     

An ET-Based Two-Phase Method for the Calibration and Application of Distributed Hydrological Models

Water Resources Management - Parameter calibration is a core process for the application of distributed hydrological models, which depends strongly on river runoff records. However, the sparse...     

组合隔板式鱼道水力特性数值模拟研究

鱼道是为鱼类提供洄游通道的一种重要的工程措施。基于Flow-3D采用标准RNG k-ε紊流模型和Tru-VOF追踪自由液面方法,对鱼道池室内流场进行三维紊流数值模拟,研究池室内水流流速、流态空间分布,分析鱼道的水力特性对目标鱼类洄游上溯的影响。结果表明:溢流堰与竖缝组合隔板式鱼道在设计流速v=0.75 m/s条件下,竖缝处平均流速为0.83 m/s,竖缝处流速分布范围为0.76~0.89 m/s;堰上流速为0.90~1.12 m/s,回流区流速分布范围为0.09~0.25 m/s,流速大小在鱼类的克流能力范围之内,但靠竖缝隔板处存在明显的主流贴壁现象。为此开展了池室优化方案设计,使鱼道池室流态满足鱼类上溯要求。     

Genetic algorithm hybridized with multilayer perceptron to have an economical slope stability design

The present work aimed to evaluate and optimize the design of an artificial neural network (ANN) combined with an optimization algorithm of genetic algorithm (GA) for the calculation of slope stability safety factors (SF) in a pure cohesive slope. To make datasets of training and testing for the developed predictive models, 630 finite element limit equilibrium (FELE) analyses were performed. Similar to many artificial intelligence-based solutions, the database was involved in 189 testing datasets (e.g., 30% of the entire database) and 441 training datasets; for example, a range of 70% of the total database. Moreover, variables of multilayer perceptron (MLP) algorithm (for example, number of nodes in any hidden layer) and the algorithm of GA like population size was optimized by utilizing a series of trial and error process. The parameters in input, which were used in the analysis, consist of slope angle (β), setback distance ratio (b/B), applied stresses on the slope (F_y) and undrained shear strength of the cohesive soil (C_u) where the output was taken SF. The obtained network outputs for both datasets from MLP and GA-MLP models are evaluated according to many statistical indices. A total of 72 MLP trial and error (e.g., parameter study) the optimal architecture of 4 × 8 × 1 were determined for the MLP structure. Both proposed techniques result in a proper performance; however, according to the statistical indices, the GA–MLP model can somewhat accomplish the least mean square error (MSE) when compared to MLP. In an optimized GA–MLP network, coefficient of determination (R²) and root mean square error (RMSE) values of (0.975, and 0.097) and (0.969, and 0.107) were found, respectively, to both of the normalized training and testing datasets.

     

Maximizing learning without sacrificing the fun: Stealth assessment,adaptivity and learning supports in educational games

In this study, we investigated the validity of a stealth assessment of physics understanding in an educational game, as well as the effectiveness of different game-level delivery methods and various in-game supports on learning. Using a game called Physics Playground, we randomly assigned 263 ninth- to eleventh-grade students into four groups: adaptive, linear, free choice and no-treatment control. Each condition had access to the same in-game learning supports during gameplay. Results showed that: (a) the stealth assessment estimates of physics understanding were valid—significantly correlating with the external physics test scores; (b) there was no significant effect of game-level delivery method on students' learning; and (c) physics animations were the most effective (among eight supports tested) in predicting both learning outcome and in-game performance (e.g. number of game levels solved). We included student enjoyment, gender and ethnicity in our analyses as moderators to further investigate the research questions.