A location-aware GIServices quality prediction model via collaborative filtering

The quality of GIServices (QoGIS) is an important consideration for services sharing and interoperation. However, QoGIS is a complex concept and difficult to be evaluated reasonably. Most of the current studies have focused on static and non-scalable evaluation methods but have ignored location sensitivity subsequently resulting in the inaccurate QoGIS values. For intensive geodata and computation, GIServices are more sensitive to the location factor than general services. This paper proposes a location-aware GIServices quality prediction model via collaborative filtering (LAGCF). The model uses a mixed CF method based on time zone feature from the perspectives of both user and GIServices. Time zone is taken as the location factor and mapped into the prediction process. A time zone-adjusted Pearson correlation coefficient algorithm was designed to measure the similarity between the GIServices and the target, helping to identify highly similar GIServices. By adopting a coefficient of confidence in the final generation phase, the value of the QoGIS most similar to the target services will play a dominant role in the comprehensive result. Two series of experiments on large-scale QoGIS data were implemented to verify the effectivity of LAGCF. The results showed that LAGCF can improve the accuracy of QoGIS prediction significantly.     

Novel fusion approach on automatic object extraction from spatial data: case study Worldview-2 and TOPO5000

The automatic extraction of information content from remotely sensed data is always challenging. We suggest a novel fusion approach to improve the extraction of this information from mono-satellite images. A Worldview-2 (WV-2) pan-sharpened image and a 1/5000-scaled topographic vector map (TOPO5000) were used as the sample data. Firstly, the buildings and roads were manually extracted from WV-2 to point out the maximum extractable information content. Subsequently, object-based automatic extractions were performed. After achieving two-dimensional results, a normalized digital surface model (nDSM) was generated from the underlying digital aerial photos of TOPO5000, and the automatic extraction was repeated by fusion with the nDSM to include individual object heights as an additional band for classification. The contribution was tested by precision, completeness and overall quality. Novel fusion technique increased the success of automatic extraction by 7% for the number of buildings and by 23% for the length of roads.     

磁法三维建模在龙王沟磁铁矿勘探中的应用

以龙王沟磁铁矿高精度磁测数据为基础,采用小波多尺度分解方法,对化极后的磁法数据进行多尺度分解,通过功率谱法计算小波多尺度分解后的各阶细节异常的场源深度。结合地形、地质及钻孔资料,采用2.5D人机交互反演对磁测剖面上的磁铁矿体进行拟合,利用Golden Software公司开发的Voxler软件,在虚拟三维环境下实现二维小波多尺度分解数据的可视化,建立三维地质体模型,直观的预测磁铁矿体的空间范围和分布形态,实现地质、物探和地上、地下信息一体化集成展示,进行专业分析评价和辅助决策,可以有效的提高地质找矿效果。     

CINRAD-SA双偏振雷达资料在降水估测中的应用初探

对基于水平反射率ZH和差分传播相移率K_(DP)的降水估测综合法R(C)进行了改进,并对广州S波段双偏振雷达2016年2次飑线和2次台风降水过程的Φ_(DP)使用小波分析进行滤波处理,在此基础上使用变距最小二乘法拟合得到K_(DP)的值。分别使用R(C)和R(Z_H)法对2次飑线和2次台风降水过程进行降水估算,将估算结果和雨量计小时雨量进行了对比,并将两种方法的评估结果进行了对比。结果表明:(1)对于飑线类型降水,R(C)法对5 mm·h~(-1)以上的降水估测精度要好于R(Z_H)法,且降水率越大,R(C)法优势越明显,当降水率≥20 mm·h~(-1)时,两次过程R(C)法比R(Z_H)法的平均相对误差(RE)降低了17. 2%,平均绝对误差(AE)减少了1.89 mm,平均均方根误差(RMSE)减少了1.66 mm;(2)对于台风类型降水,R(C)法对5 mm·h~(-1)以上的降水估测精度也好于R(Z_H)法,当降水率≥20 mm·h~(-1)时,两次过程R(C)法比R(Z_H)法的平均RE降低了33. 19%,平均AE减少了3. 95 mm,平均RMSE减少了4.05 mm;(3)对于飑线和台风两种类型降水R(C)法都明显改善了降水率较大时的R(Z_H)法低估问题,但R(C)法在降水率10 mm·h~(-1)时也存在低估,可能是由雨滴谱资料观测误差导致拟合的系数偏小或雷达硬件造成的观测偏差等造成的。     

基于小波长短期记忆网络的风电功率超短期概率预测

随着大规模的风电并网,风电所具有的间歇性与随机性对电力系统的稳定性产生了很大的影响,风电功率预测成为当前解决该问题重要的方式之一.本文利用长短期记忆（LSTM）网络良好的时序记忆特性,将小波分解技术与LSTM深度网络结合,提出基于小波长短期记忆网络的风电功率超短期概率预测模型.首先通过小波分解技术将原始时间序列进行平稳化处理,再建立各子序列样本的LSTM网络预测模型,借助最大似然估计法估计预测误差的高斯分布函数,最终实现对未来4 h时刻的风电功率概率区间预测.最后,采用中国东北某风电场数据对所提方法进行算例分析,结果表明,将小波分解与深度学习方法结合可以较好地提高预测的精度,提高概率预测的区间可靠性.     

基于阈值可调切换系统的多翅膀混沌吸引子设计及电路仿真

本文提出了一种通过调节一类状态依赖切换系统的阈值来生成多翅膀混沌吸引子的设计方案.首先,设计了两个类Lorenz子系统;然后,基于这两个子系统构造了一个状态依赖于阈值的切换策略,从而构造了一类状态依赖于阈值的切换系统;最后,通过调整切换系统的阈值来改变鞍焦平衡点的个数与分布,使得该切换系统能够产生4-6-8翅膀的混沌吸引子.此外,根据切换系统的状态方程设计了该系统的模拟电路,电路仿真结果与数值仿真结果一致.实验结果证实：该设计方案可以在不增加系统维数或无需设计复杂非线性函数的情况下生成多翅膀混沌吸引子.     

基于模糊熵的GLLE熵阈值分割方法

图像分割是计算机视觉中基础且重要的一个问题.熵阈值图像分割作为一种有效的分割方法,被广泛应用于模式识别和图像处理中.传统的图像分割方法并不能获得足够有效的图像特征.为解决这个问题且进一步探究熵阈值在图像分割中的应用,引入一种GLLE（Gray Level and Local Entropy）二维直方图改进熵阈值图像分割模型,并提出了基于模糊熵的方法计算所建立的二维直方图模型.通过标准实验数据集上的对比实验表明,基于模糊熵的GLLE熵阈值分割方法可以得到更加准确的阈值,提高了分割精度.同时在处理不同类型图像的表现上优于往常的算法,具有更强的鲁棒性.     

基于小波分析的场地波速测量方法

在场地波速测量中,由于噪声等因素的影响很难准确识别P、S波的初至时刻,致使波速结果存在很大的误差。本文给出了一种基于小波变换的波速测量的新方法。该方法利用波动信号的小波变换与弹性波群速度的关系准确识别弹性波初至时刻。弹性波小波变换的峰值时刻代表着以群速度传播的弹性波的初至时刻,使P波、S波的初至时刻的确定具有明确的物理意义,波速的结果准确、可靠、稳定。此外,波动信号的小波多尺度分析还可以确定地层中传播的弹性波的频散特性。最后,该方法在场地波速测量的实测信号的应用表明该方法可准确确定P、S波速。     

Morlet 小波用于环境激励下的模态参数识别研究

本文分别从卷积和Parseval定理的角度推导了非正交小波变换系数的实用计算方法。在环境激励下以互相关函数代替系统的自由响应数据,给出了基于Morlet小波变换的频率、阻尼比的参数识别方法,重点介绍了基于最小二乘法的振型识别技术。采用2层楼仿真算例和潮白河桥应用实例验证本算法,识别结果表明基于Morlet小波变换的模态参数识别技术能够有效地识别出环境激励下系统的模态参数。     

地磁Z分量低点时间季节性变化研究

通过小波分析和频谱分析,发现Z分量低点时间的小波变换结果曲线(α=6)明显地随节气而变化;对地磁Z分量低点时间季节性变化进行了初步的比较分析.