Bayesian network-based extraction of lunar impact craters from optical images and DEM data

Impact craters are among the most noticeable geomorphological features on the planetary surface and yield significant information about terrain evolution and the history of the solar system. Thus, the recognition of impact craters is an important branch of modern planetary studies. Aiming at addressing problems associated with the insufficient and inaccurate detection of lunar impact craters, a decision fusion method within the Bayesian network (BN) framework is developed in this paper to handle multi-source information from both optical images and associated digital elevation model (DEM) data. First, we implement the edge-based method for efficiently searching crater candidates which are the image patches that can potentially contain impact craters. Secondly, the multi-source representations of an impact crater derived from both optical images and DEM data are proposed and constructed to quantitatively describe the two-dimensional (2D) and three-dimensional (3D) morphology, consisting of Histogram of Oriented Gradient (HOG), Histogram of Multi-scale Slope (HMS) and Histogram of Multi-scale Aspect (HMA). Finally, a BN-based framework integrates the multi-source representations of impact craters, which can provide reductant and complementary information, for distinguishing craters from non-craters. To evaluate the effectiveness and robustness of the proposed method, experiments were conducted on three lunar scenes using both orthoimages from the Lunar Reconnaissance Orbiter (LRO) and DEM data acquired by the Lunar Orbiter Laser Altimeter (LOLA). Experimental results demonstrate that integrating optical images with DEM data significantly decreases the number of false positives compared with using optical images alone, with F₁-score of 84.8% on average. Moreover, compared with other existing fusion methods, our proposed method was quite advantageous especially for the detection of small-scale craters with diameters less than 1000 m.     

Application of a PCA-DBN-based surrogate model to robust aerodynamic design optimization

An efficient method employing a Principal Component Analysis (PCA)-Deep Belief Network (DBN)-based surrogate model is developed for robust aerodynamic design optimization in this study. In order to reduce the number of design variables for aerodynamic optimizations, the PCA technique is implemented to the geometric parameters obtained by parameterization method. For the purpose of predicting aerodynamic parameters, the DBN model is established with the reduced design variables as input and the aerodynamic parameters as output, and it is trained using the k-step contrastive divergence algorithm. The established PCA-DBN-based surrogate model is validated through predicting lift-to-drag ratios of a set of airfoils, and the results indicate that the PCA-DBN-based surrogate model is reliable and obtains more accurate predictions than three other surrogate models. Then the efficient optimization method is established by embedding the PCA-DBN-based surrogate model into an improved Particle Swarm Optimization (PSO) framework, and applied to the robust aerodynamic design optimizations of Natural Laminar Flow (NLF) airfoil and transonic wing. The optimization results indicate that the PCA-DBN-based surrogate model works very well as a prediction model in the robust optimization processes of both NLF airfoil and transonic wing. By employing the PCA-DBN-based surrogate model, the developed efficient method improves the optimization efficiency obviously.     

Neural network-based fault diagnosis for spacecraft with single-gimbal control moment gyros

This paper proposes a neural network-based fault diagnosis scheme to address the problem of fault isolation and estimation for the Single-Gimbal Control Moment Gyroscopes(SGCMGs) of spacecraft in a periodic orbit. To this end, a disturbance observer based on neural network is developed for active anti-disturbance, so as to improve the accuracy of fault diagnosis.The periodic disturbance on orbit can be decoupled with fault by resorting to the fitting and memory ability of neural network. Subsequ...     

计算机生成兵力

介绍分布交互仿真（ＤＩＳ）环境中计算机生成兵力（ＣＧＦ）的概念,分析ＣＧＦ系统的原理、结构、特点和主要研究内容。介绍现有的ＣＧＦ系统,分析ＣＧＦ系统与人工智能以及ＣＧＦ实体与ＤＩＳ的３种仿真类型之间的关系。     

贝叶斯景象匹配技术

景象匹配技术以其极高的末制导精度在导航系统中起着关键性的作用。经典的景象匹配定位方法只能提供象素级的定位精度,为了获得更高的精度可采用ＦＦＴ匹配算法、最小二乘匹配算法等。该文首次提出了一种新的贝叶斯景象匹配算法。其基本思想在于：匹配定位位置可看作为随机量,先利用匹配图像间的信息求其条件概率分布,再利用图像中的大量样本来求解其贝叶斯估计值。同时研究了基于贝叶斯估计的景象匹配技术的原理及算法,并将其与     

车间生产计划与加工过程的集成系统MFAS

在自动化程度低的传统机械加工车间,由于生产计划与加工过程之间缺乏紧密的联系,使得计划对生产现场的管理与控制比较困难。目前开发的物流自动化系统ＭＦＡＳ（Ｍａｔｅ－ｒｉａｌＦｌｏｗｉｎｇＡｕｔｏｍａｔｉｃＳｙｓｔｅｍ）,以层次式的计划管理方法为基础,利用计算机网络和在线式立体仓库,将车间生产计划与加工过程有效地集成为一个整体。     

自适应DR算法研究

首先介绍了 DR算法的基本思想 ,并分析了现有的 DR算法的不足之处。然后提出了一种自适应 DR算法。最后 ,将提出的这种自适应 DR算法与普通 DR算法进行了比较研究。结果表明 ,自适应 DR算法是十分有效的     

一种基于贝叶斯估计的高分辨目标定向新方法

提出一种高分辨方位估计新方法。它是建立在阵列输出信号和噪声参数联合后验概率密度基础上的空间谱估计。仿真结果表明,该方法具有以下几个显著的优点：①极高的空间分辨率和估计精度;②可解相干源;③低信噪比、少快拍数的情况下很有潜力;④可以估计不同方向目标源的能量;⑤具有较好的工程应用前景。该方法和ＭＵＳＩＣ方法的性能进行了比较并给出计算机仿真结果。     

通用航空机队设备可靠性动态识别模型

通用航空机队设备可靠性是通航单位安全运行的前提条件,是影响运营单位经济效益的重要因素之一。根据通航单位机队设备可靠性数据统计、分析及实际使用情况,采用ATA100章节名称作为指标源。综合可变模糊识别方法和权重阶梯朴素贝叶斯分类器模型的优势,构建了通用航空机队设备可靠性动态识别模型。为了避免主观给定指标权重导致的不合理,应用熵权法客观获取指标权重。最后利用实例测试样本验证了权重阶梯朴素贝叶斯分类器的合理性,并基于该方法对待识别样本进行了可靠性状态识别。实例分析表明：基于权重阶梯朴素贝叶斯分类器的通用航空机队设备可靠性状态识别模型具有较强的可行性和合理性,为通用航空机队设备可靠性状态提供了一种科学的识别方法。     

基于连续蚁群算法的Bayesian方位估计快速方法

针对蚁群算法(ACO)在解决高维非线性搜索问题方面的有效性,提出了基于蚁群优化算法的Bayesian最大后验概率方位估计(ACO-Bayesian)快速方法.该方法将Bayesian最大后验概率函数作为蚁群算法的目标函数,选取若干一维高斯函数的加权和作为连续蚁群算法中信息量概率分布函数,经过有限次迭代得到Bayesian方法的非线性全局最优解.仿真结果表明,ACO-Bayesian方法在保持Bayesian方法优良性能的同时,将Bayesian方法的计算量减少到原来的1/14.水池实验结果验证了ACO-Bayesian方法的正确性和有效性,为其工程应用奠定了基础.