线目标间拓扑关系的细化计算方法

拓扑关系的细化计算是生产实践中提出的新的需求,代表了当今空间关系理论研究的趋势。本文从1∶5万数据更新质量检查的实际应用需求出发,研究提出线目标间复杂拓扑关系的分解-组合计算思路;讨论直线段拓扑关系计算的改进平面扫描计算方法;建立根据直线段拓扑关系推断局部拓扑关系的基本规则;给出由局部拓扑关系组合描述线目标间全局拓扑关系的拓扑链模型;将该方法应用于国家1∶5万地图数据库更新中,取得良好的效果。     

基于ERSM涡轮盘径向变形的非线性动态概率分析

为了准确设计高压涡轮盘和叶尖间隙,从概率的角度进行了涡轮盘径向变形的分析。介绍了高精度高效率的非线性动态概率分析的极值响应面方法(Extremum Response Surface Method, ERSM),并建立了其数学模型。考虑材料属性和边界条件的非线性,以及热载荷和离心载荷的动态性,基于ERSM对涡轮盘径向变形进行了非线性动态概率分析,得到了输入输出参数的分布特征和影响涡轮盘径向动态变形的主要因素。最后,通过方法比较,验证了ERSM在保证计算精度的前提下能大大提高计算速度,节约计算时间,改善计算效率。为进行更有效的涡轮盘设计和优化,改善叶尖间隙设计和控制的合理性提供了有效依据。      

Equation of state, elasticity, and shear strength of pyrite under high pressure

 Physical properties including the equation of state, elasticity, and shear strength of pyrite have been measured by a series of X-ray diffraction in diamond-anvil cells at pressures up to 50 GPa. A Birch–Murnaghan equation of state fit to the quasihydrostatic pressure–volume data obtained from laboratory X-ray source/film techniques yields a quasihydrostatic bulk modulus K _0T =133.5 (±5.2) GPa and bulk modulus first pressure derivative K ^′ _0T =5.73 (±0.58). The apparent equation of state is found to be strongly dependent on the stress conditions in the sample. The stress dependency of the high-pressure properties is examined with anisotropic elasticity theory from subsequent measurements of energy-dispersive radial diffraction experiments in the diamond-anvil cell. The calculated values of K _0T depend largely upon the angle ψ between the diffracting plane normal and the maximum stress axis. The uniaxial stress component in the sample, t=σ₃−σ₁, varies with pressure as t=−3.11+0.43P between 10 and 30 GPa. The pressure derivatives of the elastic moduli dC ₁₁/dP=5.76 (±0.15), dC ₁₂/dP=1.41 (±0.11) and dC ₄₄/dP=1.92 (±0.06) are obtained from the diffraction data assuming previously reported zero-pressure ultrasonic data (C ₁₁=382 GPa, C ₁₂=31 GPa, and C ₄₄=109 GPa). Received: 21 December 2000 / Accepted: 11 July 2001     

“5．10”强对流天气雷达产品特征及临近预报

本文运用多普勒雷达的基本反射率（R）、基本径向速度（V）、垂直积分液态含水量（VIL）等产品对2005年5月10日邯郸发生的强对流天气进行分析，总结了雷达产品特征，得出了一些具有实用价值的结果，为预报员快速判断强对流的潜势提供参考。     

台风"碧利斯"影响庐山降水的探测资料分析

通过分析台风“碧利斯“影响江西期间的自动气象站资料和雷达回波特征,探讨了造成庐山过程强降水较周围雨量明显偏多的原因.研究结果发现,“碧利斯“过境时,雷达回波强度在庐山会有加强的现象,径向速度场表现为有风向和风速的切变存在;零等速度线的走向变化和降水的变化有关,且零等速度线的变化往往先于降水的变化,能预示降水的变化趋势.除“碧利斯“本身携带的大量水汽外,庐山迎风坡的地形抬升作用,以及鄱阳湖水域充沛的水汽供应,是造成这种现象的根源;中小尺度辐合在庐山较长时间的维持,是造成庐山降水普遍大于周边地区的根本原因.     

安顺市2015—2019年冰雹时空特征 及雷达临近预警指标研究

利用安顺市2015-2019年降雹个例、Micaps常规观测资料以及贵阳雷达资料,分析总结了安顺市冰雹时空分布特征及雷达临近预警指标。结果表明：安顺市冰雹以直径20 mm以下的小冰雹为主,冰雹站次呈北多南少特征;冰雹天气主要发生在春季,4月最多,5月次之;2015—2019年期间,2019年安顺市冰雹站次最多,2017年最少。直径10 mm以上与直径10mm以下冰雹对应的雹云相比,其强回波值更强、降雹概率更高、强回波中心平均升降次数更多、上升幅度及最高上升高度更高（须在0 ℃层高度以上）、对应的径向速度场有明显特征（逆风区、辐合/辐散、旋转）的比例更高、径向速度特征（逆风区、辐合/辐散、旋转）出现时间较降雹时间提前量更多。此外,强回波中心上升高度（0 ℃层高度以上）越高,冰雹直径越大。以上雷达回波特征均可作为安顺市冰雹预警指标,有利于提高安顺市冰雹预警准确率及提前量。     

A hybrid machine learning ensemble approach based on a Radial Basis Function neural network and Rotation Forest for landslide susceptibility modeling: A case study in the Himalayan area,India

In this paper, a hybrid machine learning ensemble approach namely the Rotation Forest based Radial Basis Function (RFRBF) neural network is proposed for spatial prediction of landslides in part of the Himalayan area (India). The proposed approach is an integration of the Radial Basis Function (RBF) neural network classifier and Rotation Forest ensemble, which are state-of-the art machine learning algorithms for classification problems. For this purpose, a spatial database of the study area was established that consists of 930 landslide locations and fifteen influencing parameters (slope angle, road density, curvature, land use, distance to road, plan curvature, lineament density, distance to lineaments, rainfall, distance to river, profile curvature, elevation, slope aspect, river density, and soil type). Using the database, training and validation datasets were generated for constructing and validating the model. Performance of the model was assessed using the Receiver Operating Characteristic (ROC) curve, area under the ROC curve (AUC), statistical analysis methods, and the Chi square test. In addition, Logistic Regression (LR), Multi-layer Perceptron Neural Networks (MLP Neural Nets), Naïve Bayes (NB), and the hybrid model of Rotation Forest and Decision Trees (RFDT) were selected for comparison. The results show that the proposed RFRBF model has the highest prediction capability in comparison to the other models (LR, MLP Neural Nets, NB, and RFDT); therefore, the proposed RFRBF model is promising and should be used as an alternative technique for landslide susceptibility modeling.     

Wood anatomical variation of Neobuxbaumia tetetzo: A columnar Cactaceae

Anatomical variation in radial and longitudinal wood in Neobuxbaumia tetetzo was studied in four monopodic individuals. Vascular bundles in the radial direction from the pith to the vascular cambium and in the longitudinal direction from the base to the stem apex were analysed. Except for vessel density in the radial direction, the anatomical characters studied showed a pattern of cambial maturation similar to those reported for other angiosperms. The patterns of fiber anatomical variation show higher mechanical strength in the lower part of the stem, while the increase in vessel diameter towards the vascular cambium produces a positive relationship between plant water demands and larger size.