Supervised classification of electric power transmission line nominal voltage from high-resolution aerial imagery     

Erik H. Schmidt  Budhendra L. Bhaduri  Nicholas Nagle  Bruce A. Ralston 《地理信息系统科学与遥感》2018,55(6):860-879

For many researchers, government agencies, and emergency responders, access to the geospatial data of US electric power infrastructure is invaluable for analysis, planning, and disaster recovery. Historically, however, access to high quality geospatial energy data has been limited to few agencies because of commercial licenses restrictions, and those resources which are widely accessible have been of poor quality, particularly with respect to reliability. Recent efforts to develop a highly reliable and publicly accessible alternative to the existing datasets were met with numerous challenges – not the least of which was filling the gaps in power transmission line voltage ratings. To address the line voltage rating problem, we developed and tested a basic methodology that fuses knowledge and techniques from power systems, geography, and machine learning domains. Specifically, we identified predictors of nominal voltage that could be extracted from aerial imagery and developed a tree-based classifier to classify nominal line voltage ratings. Overall, we found that line support height, support span, and conductor spacing are the best predictors of voltage ratings, and that the classifier built with these predictors had a reliable predictive accuracy (that is, within one voltage class for four out of the five classes sampled). We applied our approach to a study area in Minnesota.  相似文献   

一种基于流形正则化的半监督指纹定位算法     

朱顺涛  卢先领  于丹石 《测绘通报》2018,(2):11-15,20

针对传统指纹定位算法采集带标签训练数据成本高的问题,本文提出了一种基于流形正则化的半监督指纹定位算法。首先以流形假设为依据,利用批量输入的带标签数据与无标签数据之间的相似度构建图拉普拉斯算子;然后与极限学习机算法相结合,通过随机特征映射建立隐含层;最后在流形正则化框架下,求解隐含层和输出层之间的权值矩阵,从而建立位置估计模型。仿真结果表明,与INN、SVR、ELM 3种算法相比,该算法的训练和测试时间相对较短,且在带标签训练数据稀疏的前提下仍能保持较高的准确率与稳定性。  相似文献   

大型射电望远镜主反射面调整方法研究          下载免费PDF全文

孙正雄  王锦清  虞林峰  张志斌  王广利 《天文学报》2024,65(2):15

随着毫米波天文学和空间通信的重要性日益提高, 对天线性能提出了越来越高的要求, 而天线性能往往受到其反射器表面精度的限制. 微波全息技术是一种快速有效的检测反射面天线表面轮廓的测量技术. 通过微波全息测量得到天线口径场, 计算天马65m射电望远镜反射面与理想抛物面的偏差. 天马65m射电望远镜的主反射面板是放射状的, 有14圈. 面板的每个角都固定在面板下方促动器的螺栓上进行上下移动, 且相邻面板交点处的拐角共用一个促动器. 采用平面拟合的方法可以计算各块面板拐角处的调整值, 但是同一个促动器会得到4个不同的调整量. 通过平面拟合, 同时以天线照明函数为权重的平差计算方法得到相邻面板拐角的一个平差值, 即天马65m射电望远镜1104个促动器的最佳调整值. 通过多次调整和新算法的应用, 天马65m射电望远镜反射面的面形精度逐渐提高到了0.24mm.  相似文献   

Identification of Convective and Stratiform Clouds Based on the Improved DBSCAN Clustering Algorithm     

Yuanyuan ZUO  Zhiqun HU  Shujie YUAN  Jiafeng ZHENG  Xiaoyan YIN  Boyong LI 《大气科学进展》2022,39(12):2203-2212

A convective and stratiform cloud classification method for weather radar is proposed based on the density-based spatial clustering of applications with noise (DBSCAN) algorithm. To identify convective and stratiform clouds in different developmental phases, two-dimensional (2D) and three-dimensional (3D) models are proposed by applying reflectivity factors at 0.5° and at 0.5°, 1.5°, and 2.4° elevation angles, respectively. According to the thresholds of the algorithm, which include echo intensity, the echo top height of 35 dBZ (ET), density threshold, and ε neighborhood, cloud clusters can be marked into four types: deep-convective cloud (DCC), shallow-convective cloud (SCC), hybrid convective-stratiform cloud (HCS), and stratiform cloud (SFC) types. Each cloud cluster type is further identified as a core area and boundary area, which can provide more abundant cloud structure information. The algorithm is verified using the volume scan data observed with new-generation S-band weather radars in Nanjing, Xuzhou, and Qingdao. The results show that cloud clusters can be intuitively identified as core and boundary points, which change in area continuously during the process of convective evolution, by the improved DBSCAN algorithm. Therefore, the occurrence and disappearance of convective weather can be estimated in advance by observing the changes of the classification. Because density thresholds are different and multiple elevations are utilized in the 3D model, the identified echo types and areas are dissimilar between the 2D and 3D models. The 3D model identifies larger convective and stratiform clouds than the 2D model. However, the developing convective clouds of small areas at lower heights cannot be identified with the 3D model because they are covered by thick stratiform clouds. In addition, the 3D model can avoid the influence of the melting layer and better suggest convective clouds in the developmental stage.  相似文献   

A Machine Learning-based Cloud Detection Algorithm for the Himawari-8 Spectral Image     

Chao LIU  Shu YANG  Di DI  Yuanjian YANG  Chen ZHOU  Xiuqing HU  Byung-Ju SOHN 《大气科学进展》2022,39(12):1994-2007

Cloud Masking is one of the most essential products for satellite remote sensing and downstream applications. This study develops machine learning-based (ML-based) cloud detection algorithms using spectral observations for the Advanced Himawari Imager (AHI) onboard the Himawari-8 geostationary satellite. Collocated active observations from Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) are used to provide reference labels for model development and validation. We introduce both daytime and nighttime algorithms that differ according to whether solar band observations are included, and the artificial neural network (ANN) and random forest (RF) techniques are adopted for comparison. To eliminate the influences of surface conditions on cloud detection, we introduce three models with different treatments of the surface. Instead of developing independent ML-based algorithms, we add surface variables in a binary way that enhances the ML-based algorithm accuracy by ~5%. Validated against CALIOP observations, we find that our daytime RF-based algorithm outperforms the AHI operational algorithm by improving the accuracy of cloudy pixel detection by ~5%, while at the same time, reducing misjudgment by ~3%. The nighttime model with only infrared observations is also slightly better than the AHI operational product but may tend to overestimate cloudy pixels. Overall, our ML-based algorithms can serve as a reliable method to provide cloud mask results for both daytime and nighttime AHI observations. We furthermore suggest treating the surface with a set of independent variables for future ML-based algorithm development.  相似文献   

基于机器学习的重庆长江航道雾图像特征识别研究     

王远谋  李家启  陈施吉  唐家萍  夏佰成  韩世刚 《气象与环境学报》2021,37(1):106-112

基于重庆市境内长江航道雷达站拍摄的雾天气过程影像资料,利用K最近邻、支持向量机、BP神经网络、随机森林等机器学习算法,对无雾和5类有雾天气个例进行图像识别训练,构建雾图像识别模型,并检验了识别准确率。结果表明：机器学习能够有效识别雾图像,随机森林算法的识别效果优于其余3种算法。对于能见度超过1500 m的无雾天气,模型的识别准确率为100%,对于能见度在1000—1500 m范围内的轻雾、能见度低于50 m的强浓雾,模型的识别准确率在90%以上,对于能见度在50—1000 m范围内的雾、大雾和浓雾,识别准确率超过70%。  相似文献   

基于消息的气象数据处理系统运维自动化初探     

李从英  支亚京  张淑莹  李波  杨远恒 《中低纬山地气象》2022,46(6):107-110

目前气象大数据云平台(天擎)系统庞大且数据种类繁多,而现有的监控止步于数据消息积压后的告警。本文通过jQuery技术研究Rabbit消息,在消息粒度上获取消息积压信息,实现自动处理相应数据的解码程序,开拓了自动运维业务,在一定程度上减轻了运维压力,提高工作效率。尤其在长时间没有收到消息告警时,缩短排查故障时间。对于省级自动化运维,具有较强的参考和推广价值。  相似文献   

西安市PM_2.5城乡差异及与城市热岛的关联研究          下载免费PDF全文

李煜斌  姜蕴聪  杨元建  张建彬  赵纯  高志球 《大气科学学报》2022,45(6):904-916

依据一种基于建筑用地比例和土地利用信息熵的城乡站点划分方法,将西安市环境与气象站点划分为城区、郊区和两类乡村站,讨论其PM_2.5的城乡分布特征及与城市热岛效应强度(Urban Heat Island Intensity,UHII)间的相关关系。结果表明,不同季节西安市呈现不同的PM_2.5城乡分布特征和日变化特征,两类乡村站点PM_2.5差异明显且下风向乡村站点(乡村D)对应的UHII_D对城区和乡村的影响程度大于上风向乡村站点(乡村U)对应的UHII_U。在城区较多本地排放的影响下,乡村PM_2.5浓度与 UHII_U(或UHII_D)相关系数均大于城区。随着UHII_D的增加,城乡PM_2.5相对浓度差值(RUPII_D)整体呈下降趋势且UHII_D与RUPII_D在春夏秋季显著负相关。UHII_D增大,城区近地面PM_2.5的水平扩散能力减弱,但PM_2.5的垂直扩散能力较乡村更强,从而UHII_D通过影响PM_2.5的传输扩散特征,进一步影响西安市RUPII_D。  相似文献   

四川地区多模式2m温度预报性能分析及集成方法研究          下载免费PDF全文

冯良敏  周秋雪  陈朝平  范江琳 《高原山地气象研究》2019,39(2):30-36

本文通过分析2017年9~12月四川地区ECMWF(European Centre for Medium-Range Weather Forecasting)细网格模式、GRAPES_GFS(Global and Regional Assimilation and Prediction System)全球模式和西南区域模式(South West Center-WRF ADAS Real-time Modeling System, SWCWARMS)2m温度168h预报时效内的系统性偏差特征,采用滑动双权重平均法分别对三种模式温度预报产品进行偏差订正,并集成得到各时效2m温度的订正场,结果表明:(1)三种模式的预报存在明显的日变化,整体上EC模式的预报最优。(2)三种模式对于低温和高温的预报,在全省均大致呈现负的系统性误差,特别在高原及过渡区表现的尤为明显。(3)订正后三种模式的预报准确率显著提高,均方根误差减小1.4~2.5℃,大部分地区平均误差维持在±0.5℃之间,在系统性偏差较大的地区,订正效果更好。(4)两种集成方案预报结果接近,且均优于三种模式的订正预报。  相似文献   

基于岩石图像深度学习的岩性自动识别与分类方法   总被引：8，自引：3，他引：5  

张野  李明超  韩帅 《岩石学报》2018,34(2):333-342

岩石岩性的识别与分类对于地质分析极为重要,采用机器学习的方法建立识别模型进行自动分类是一条新的途径。基于Inception-v3深度卷积神经网络模型,建立了岩石图像集分析的深度学习迁移模型,运用迁移学习方法实现了岩石岩性的自动识别与分类。采用此方法对所采集的173张花岗岩图像、152张千枚岩图像和246张角砾岩图像进行了学习和识别分类研究,通过训练学习建立岩石图像深度学习迁移模型,并分别采用训练集和测试集中的岩石图像对模型进行了检验分析。对于训练集中的岩石图像,每组岩石分别用3张图像测试,三种岩石的岩性分类均正确,且分类概率值均达到90%以上,显示了模型良好的鲁棒性;对于测试集中的岩石图像,每组岩石分别采用9张图像进行识别分析,三种岩石的岩性分类均正确,并且千枚岩组图像分类概率均高于90%,但是花岗岩组2张图像和角砾岩组的1张图像分类概率值不足70%,概率值较其他岩石图像低,推测其原因是训练集中相同模式的岩石图像较少,导致模型的泛化能力减小。为了提高识别精确度,对准确率较低的岩石图像进行截取,分别取其中的3张图像加入训练集进行再训练,增加与测试图像具有相同模式的训练样本;在新的模型中,对3张图像进行二次检验,测试概率值均达到85%以上,说明在数据足够的状况下模型具有良好的学习能力。与传统的机器学习方法相比,所提出的岩石图像深度学习方法具有以下优点:第一,模型通过搜索图像像素点提取物体特征,不需要手动提取待分类物体特征;第二,对于图像像素大小,成像距离及光照要求低;第三,采用适当的训练集可获得较好的识别分类效果,并具有良好鲁棒性和泛化能力。  相似文献