用谱分解法求解自适应因子值

在序贯平差自适应因子取值范围确定的情况下,以均方误差作为评判未知参数解优劣的标准,利用谱分解的方法求该范围内最佳的自适应因子值,并给出了相应的计算步骤和公式推导,为了说明该方法的正确性和合理性,将它与方差分量估计法进行计算和比较。结果证明利用该方法求解出的自适应因子值不仅使均方误差达到最小,而且使求解后得到的结果具有一定的实际意义。     

Ideal Case Study of Adaptive Localization in Storm-scale Ensemble Kalman Filter Assimilation

This study explores the use of the hierarchical ensemble filter to determine the localized influence of ob-servations in the Weather Research and Forecasting ensemble square root filtering (WRF-EnSRF) assimilation system. With error correlations between observations and background field state variables considered, the adaptive localization approach is applied to conduct a series of ideal storm-scale data assimilation experiments using simulated Doppler radar data. Comparisons between adaptive and empirical localization methods are made, and the feasibility of adaptive locali-zation for storm-scale ensemble Kalman filter assimilation is demonstrated. Unlike empirical localization, which relies on prior knowledge of distance between observations and background field, the hierarchical ensemble filter provides con-tinuously updating localization influence weights adaptively. The adaptive scheme improves assimilation quality during rapid storm development and enhances assimilation of reflectivity observations. The characteristics of both the observation type and the storm development stage should be considered when identifying the most appropriate localization method. Ultimately, combining empirical and adaptive methods can optimize assimilation quality.     

Adaptive Wind Gust and Associated Gust-factor Model for the Gust-producing Weather over the Northern South China Sea

Wind gusts are common environmental hazards that can damage buildings, bridges, aircraft, and cruise ships and interrupt electric power distribution, air traffic, waterway transport and port operations. Accurately predicting peak wind gusts in numerical models is essential for saving lives and preventing economic losses. This study investigates the climatology of peak wind gusts and their associated gust factors(GFs) using observations in the coastal and open ocean of the northern South China Se...     

肥城市城市规划区浅层地温能开发利用适宜性评价

在充分收集基础地质、水文地质、地热地质等资料基础上,结合钻探施工、现场热响应试验测试、井温测量、实验室测试等手段,对奥陶纪碳酸盐岩类裂隙岩溶水水位埋深、水温展布进行了测量,确定水位埋深在65～75m,埋深最大者位于白云山公园南、沙窝社区西南一带,埋深达约100m。平面地温最高温分布于研究区西部,西大封—朱庄—金槐—冉庄村一带,温度达到18℃以上。对肥城市城市规划区浅层地温能开发利用进行了适宜性评价,地下水地源热泵适宜中等区位于大面积地区,适宜差区位于泰肥铁路以北、白云山公园、龙山公园、母猪山一带。地埋管地源热泵适宜中等区位于大面积地区,适宜差区位于白云山公园、龙山公园一带的生态保护区。肥城市城市规划区浅层地温能资源丰富、开采条件便利,潜力巨大,具有良好的发展浅层地温能的基础条件。     

An adaptive dual clustering algorithm based on hierarchical structure: A case study of settlement zoning

Traditional dual clustering algorithms cannot adaptively perform clustering well without sufficient prior knowledge of the dataset. This article aims at accommodating both spatial and non‐spatial attributes in detecting clusters without the need to set parameters by default or prior knowledge. A novel adaptive dual clustering algorithm (ADC+) is proposed to obtain satisfactory clustering results considering the spatial proximity and attribute similarity with the presence of noise and barriers. In this algorithm, Delaunay triangulation is utilized to adaptively obtain spatial proximity and spatial homogenous patterns based on particle swarm optimization (PSO). Then, a hierarchical clustering method is employed to obtain clusters with similar attributes. The hierarchical clustering method adopts a discriminating coefficient to adaptively control the depth of the hierarchical architecture. The clustering results are further refined using an optimization approach. The advantages and practicability of the ADC+ algorithm are illustrated by experiments on both simulated datasets and real‐world applications. It is found that the proposed ADC+ algorithm can adaptively and accurately detect clusters with arbitrary shapes, similar attributes and densities under the consideration of barriers.     

大气可降水量预测模型的自适应Kalmam滤波改进

针对现有可降水量预报模型存在预报精度不高等问题,该文提出采用方差分量估计自适应卡尔曼滤波对可降水量数据进行预处理,用以提高径向基神经网络预测模型的预测精度,从而形成高精度预报模型。通过比较不同基站不同时间的数据,分析使用方法的预报精度。实验结果表明:将预测模型应用于全国7个测站进行实验,预测相对精度的平均值可达95%以上,预报残差在10-5左右,残差值小于0.001的占90%以上。在影响因素方面,使用较短时间作为模型原始数据进行预测会得到较好的预测结果。实验证明本预测方法在预报大气可降水量值方面具有较高的精度。     

顾及遥感影像场景类别信息的视觉单词优化分类

传统词包模型的视觉词典忽略了场景本身包含的类别信息,难以区分不同类别但外观相似的场景,针对这个问题,本文提出一种顾及场景类别信息的视觉单词优化方法,分别使用Boiman的分配策略和主成分分析对不同场景类别视觉单词的模糊性和单词冗余进行优化,增强视觉词典的辨识能力。本文算法通过计算不同视觉单词的影像频率,剔除视觉词典中影像频率较小的视觉单词,得到每种场景的类别视觉词典,计算类别直方图,将类别直方图和原始视觉直方图融合,得到不同类别场景的融合直方图,将其作为SVM分类器的输入向量进行训练和分类。选取遥感场景标准数据集,验证算法,实验结果表明:本算法能适应不同大小的视觉词典,在模型中增加场景类别信息,增强了词包模型的辨识能力,有效降低场景错分概率,总体分类精度高达89.5%,优于传统的基于金字塔匹配词包模型的遥感影像场景分类算法。     

集值系统的辨识、控制及应用

本文总结了近年来关于集值系统的研究工作:在辨识方面,从不同的系统结构、不同的噪声情况和不同的集值情形等方面进行了深入的研究,针对性地提出了参数解耦、比例满秩输入设计、联合可辨识、经验分布函数、递推投影辨识等有效的辨识方法,得到了一系列重要结果;在适应控制方面,都实现了一类集值增益系统的适应跟踪控制;在同步控制方面,提出了双时间尺度同步算法和递推投影同步算法,实现了二值量测下的多个体同步控制;在应用方面,分别将集值辨识应用到复杂疾病建模和雷达目标识别问题中,构造了新的、更为有效的统计验证方法,在低信噪比下将识别率提高了10%.     

顾及框架点坐标误差的三维基准转换严密模型

框架点坐标是由观测数据通过平差得到的,不可避免地受到观测误差的影响。针对原框架和目标框架坐标均存在误差、非公共点与公共点间存在相关性,以及转换系数矩阵中仅部分元素存在误差的实际情况,提出了同时考虑框架内误差以及转换点间相关性的基准转换严密模型,该模型将公共点和非公共点联合处理,同时计算坐标转换参数和所有点的坐标转换值,推导出了新的严格坐标转换公式,该公式为传统坐标转换公式基础上增加一改正量的形式;进一步,推导了原框架和目标框架坐标的方差不一致情况下的坐标转换模型的自适应解法;最后,利用"陆态网络工程"2000个区域站的实测坐标进行坐标转换验证,结果表明,这种严密模型较传统坐标转换模型具有更高的坐标转换精度。     

一种遥感数据自适应剖分分块渐进传输方法

针对当前遥感影像数据量巨大的特点,如何利用相对有限的带宽来保障遥感数据高效、灵活的传输与服务是遥感数据应用的一个关键问题。本文基于Geo SOT全球剖分与编码体系提出了一种对网络带宽、用户请求和元数据信息自适应的遥感数据剖分分块方法,并设计了基于优先级函数的面片渐进传输方法,实现根据目标的变化量信息和导航路径偏移信息估计面片对用户的重要性,从而渐进传输感兴趣数据的功能。通过模拟实验证明该方法可以满足遥感数据自适应剖分、灵活传输应用的需求。