Research on Parallel Algorithms for uv-faceting Imaging

The uv-faceting imaging is one of the widely used large field of view imaging technologies, and will be adopted for the data processing of the low-frequency array in the first stage of the Square Kilometre Array (SKA1). Due to the scale of the raw data of SKA1 is unprecedentedly large, the efficiency of data processing directly using the original uv-faceting imaging will be very low. Therefore, a uv-faceting imaging algorithm based on the MPI (Message Passing Interface)+OpenMP (Open Multi-Processing) and a uv-faceting imaging algorithm based on the MPI+CUDA (Compute Unified Device Architecture) are proposed. The most time-consuming data reading and gridding in the algorithm are optimized in parallel. The verification results show that the results of the proposed two algorithms are basically consistent with that obtained by the current mainstream data processing software CASA (Common Astronomy Software Applications), which indicates that the proposed two algorithms are basically correct. Further analysis of the accuracy and total running time shows that the MPI+CUDA method is better than the MPI+OpenMP method in both the correctness rate and running speed. The performance test results show that the proposed algorithms are effective and have certain extensibility.     

Study of Star/Galaxy Classification Based on the XGBoost Algorithm

Machine learning has achieved great success in many areas today. The lifting algorithm has a strong ability to adapt to various scenarios with a high accuracy, and has played a great role in many fields. But in astronomy, the application of lifting algorithms is still rare. In response to the low classification accuracy of the dark star/galaxy source set in the Sloan Digital Sky Survey (SDSS), a new research result of machine learning, eXtreme Gradient Boosting (XGBoost), has been introduced. The complete photometric data set is obtained from the SDSS-DR7, and divided into a bright source set and a dark source set according to the star magnitude. Firstly, the ten-fold cross-validation method is used for the bright source set and the dark source set respectively, and the XGBoost algorithm is used to establish the star/galaxy classification model. Then, the grid search and other methods are used to adjust the XGBoost parameters. Finally, based on the galaxy classification accuracy and other indicators, the classification results are analyzed, by comparing with the models of function tree (FT), Adaptive boosting (Adaboost), Random Forest (RF), Gradient Boosting Decision Tree (GBDT), Stacked Denoising AutoEncoders (SDAE), and Deep Belief Nets (DBN). The experimental results show that, the XGBoost improves the classification accuracy of galaxies in the dark source classification by nearly 10% as compared to the function tree algorithm, and improves the classification accuracy of sources with the darkest magnitudes in the dark source set by nearly 5% as compared to the function tree algorithm. Compared with other traditional machine learning algorithms and deep neural networks, the XGBoost also has different degrees of improvement.     

Historical development in soil micromorphological imaging

The book ＂micropedolog＂ by Kubieana and a large number of publications has induced many people to practice soil micromorphology. Quantification of the soil fabric and its components was a major challenge. The use of the image analyses in soil science was a breakthrough. Attempts to make soil thin sections go back to the beginning of the 2oth century. Microscopic techniques and recently high resolution electron microscope and use of computer assisted imaging techniques enabled the in vitro study of soils in three dimensional levels. It is now possible to store and process massive amounts of data. Micro- morphological concepts and techniques are applied in paleopedological, ecological, and archaeological studies. The aim of this work was to examine soil micromorphological imaging in historical perspective.     

工程物探方法在地质灾害评估中的应用

利用工程物探方法对济南市历城区全民健身中心建设项目铁矿采空区进行了勘查工作,结果表明物探电阻率测深法勘查工作,对铁矿采空区反映效果明显,结合磁测资料与现场调查访问,推测出评估区内的2处隐伏采空区影响范围基本与事实一致。     

Nutrient flux into an intense deep chlorophyll layer in a mode-water eddy

An intense deep chlorophyll layer in the Sargasso Sea was reported near the center of an anticyclonic mode-water eddy by McGillicuddy et al. [2007. Eddy–wind interactions stimulate extraordinary mid-ocean plankton blooms, Science, accepted]. The high chlorophyll was associated with anomalously high concentrations of diatoms and with a maximum in the vertical profile of ¹⁴C primary productivity. Here we report tracer measurements of the vertical advection and turbulent diffusion of deep-water nutrients into this chlorophyll layer. Tracer released in the chlorophyll layer revealed upward motion relative to isopycnal surfaces of about 0.4 m/d, due to solar heating and mixing. The density surfaces themselves shoaled by about 0.1 m/d. The upward flux of dissolved inorganic nitrogen, averaged over 36 days, was approximately 0.6 mmol/m²/d due to both upwelling and mixing. This flux is about 40% of the basin wide, annually averaged, nitrogen flux required to drive the annual new production in the Sargasso Sea, estimated from the oxygen cycle in the euphotic zone, the oxygen demand below the euphotic zone, and from the ³He excess in the mixed layer. The observed upwelling of the fluid was consistent with theoretical models [Dewar, W.K., Flierl, G.R., 1987. Some effects of wind on rings. Journal of Physical Oceanography 17, 1653–1667; Martin, A.P., Richards, K.J., 2001. Mechanisms for vertical nutrient transport within a North Atlantic mesoscale eddy. Deep-Sea Research II 48, 757–773] in which eddy surface currents cause spatial variations in surface stress. The diapycnal diffusivity at the base of the euphotic zone was 3.5±0.5×10⁻⁵ m²/s. Diapycnal mixing was probably enhanced over more typical values by the series of storms passing over the eddy during the experiment and may have been enhanced further by the trapping of near-inertial waves generated within the eddy.     

基于数字工程理念的数字城市建设策略

数字工程就是利用数字技术整合、挖掘和综合利用地理空间信息和其他专题信息的系统工程,是一门空间信息科学、计算机科学、通信科学、管理科学与经济人文科学的广泛交叉学科.数字城市是数字工程的典型应用.在概念辨析的基础上,分析了数字城市的认识误区;结合数字工程的基本原理,分析了数字城市的总体工程结构,提出了建设策略.     

我国职业健康安全管理体系的问题及对策

结合目前国内职业安全、职业健康方面的现状,分析存在的问题及原因,提出在当前形势下,需要政府、企业、员工以及全社会的共同努力,推进职业健康安全管理体系的建设。     

3S技术在海南省海岸保护利用规划中的应用

利用3s技术强大的空间信息处理和管理能力及制图能力,实现了在海南省海岸保护利用规划中多源地理数据采集处理、专题信息分析提取、规划图集编制和规划信息管理系统的建立等．结果表明：采用3S技术能够较好地处理海南省海岸保护利用规划中涉及的陆图、海图、遥感图、海洋功能区图、相关规划图和项目用海图等基础地理数据,以及现场调查、勘测数据的空间位置关系;提取了海南岛海岸类型、海岸开发利用现状等专题信息;结合海南社会经济发展对海岸资源开发利用的需求和生态环境保护要求,划分基本功能岸段,总体为北部海岸以港口、旅游岸段为主,东部海岸以旅游、渔业岸段为主,南部海岸以旅游、港口岸段为主,西部海岸以港口、渔业岸段为主．     

数据同化在海洋生态模型中的应用和研究进展

将数据同化方法引入海洋生态系统动力学模型研究,利用现有的观测数据,获得最佳的模式参数、初始场或提高状态模拟,是当前多学科交叉研究的热门领域。本文依据国内外研究进展,主要就海洋生态模型研究中所采用的变分伴随、卡尔曼滤波、模拟退火法方法进行了介绍,总结了数据同化在我国的海洋生态系统研究中的现状和发展趋势。     

徐州市水土流失时空变化研究

本文以3S技术为支撑,在修正水土流失方程（RUSLE）的基础上,针对徐州黄泛平原-丘陵地带的特殊地形地貌、对2000年以来徐州市水土流失时空变化特征进行了分析。结果表明：2000-2014年徐州市土壤侵蚀等级以微度为主,微度侵蚀面积占总侵蚀面积多年平均达到76.34%;中度侵蚀次之,所占比例在10%~17%间;剧烈侵蚀所占比例最少。轻度及以上土壤侵蚀等级主要发生在中部微山湖下游、京杭大运河一带的丘陵岗地地区、主城区与故黄河北岸的黄泛平原地区,以及丰县、新沂、邳州的局部区域;土壤侵蚀存在一定程度年际变化特征,睢宁、丰县、邳州的部分地区侵蚀面积年际变化较为明显,土壤侵蚀总面积整体上呈现下降趋势。