A machine learning approach for predicting computational intensity and domain decomposition in parallel geoprocessing

ABSTRACT

High performance computing is required for fast geoprocessing of geospatial big data. Using spatial domains to represent computational intensity (CIT) and domain decomposition for parallelism are prominent strategies when designing parallel geoprocessing applications. Traditional domain decomposition is limited in evaluating the computational intensity, which often results in load imbalance and poor parallel performance. From the data science perspective, machine learning from Artificial Intelligence (AI) shows promise for better CIT evaluation. This paper proposes a machine learning approach for predicting computational intensity, followed by an optimized domain decomposition, which divides the spatial domain into balanced subdivisions based on the predicted CIT to achieve better parallel performance. The approach provides a reference framework on how various machine learning methods including feature selection and model training can be used in predicting computational intensity and optimizing parallel geoprocessing against different cases. Some comparative experiments between the approach and traditional methods were performed using the two cases, DEM generation from point clouds and spatial intersection on vector data. The results not only demonstrate the advantage of the approach, but also provide hints on how traditional GIS computation can be improved by the AI machine learning.     

An approximate method for evaluating the shear band thickness in saturated soils

A formula for the thickness of a shear band formed in saturated soils under a simple shear or a combined stress state has been proposed. It is shown that the shear band thickness is dependent on the pore pressure properties of the material and the dilatancy rate, but is independent of the details of the combined stress state. This is in accordance with some separate experimental observations. Copyright © 2004 John Wiley & Sons, Ltd.     

Neutrino mass,luminosity variation,and spectrum of SN1987A

In this paper, the neutrino mass has been determined from SN1987a observation in a manner that the simultaneity of neutrino emission is not regarded as the starting point, but is itself defined through the analysis by Monte-Carlo simulation. The result is that the neutrino mass lies in 3–4 eV, possiblym _v 3.6 eV. Neutrino luminosity variation and neutrino spectrum are also obtained. Comparison with theories gives further support to the mass determination, and also predicts the mass of progenitor star of SN1987a to be in the range of 12–25M .The project supported by the National Natural Science Foundation of China.     

Possible chemical structures and biological precursors of different vitrinites in coal measure in Northwest China

Chemical structure of Jurassic vitrinites isolated from the coals in basins in NW China have been checked using solid state ¹³C NMR and flash pyrolysis-GC/MS. Study shows some Jurassic collodetrinites are rich in aliphatic products in pyrolysates, consisting with the high amount of methylene carbon in ¹³C NMR spectra. In contrast, pyrolysates of Jurassic collotelinites are rich in phenols and alkylbenzenes. Also one Pennsylvanian and one Permian vitrinite selected from the Ordos basin, NW China have been checked for comparison. The proportion of aliphatics is low in pyrolysates, and aliphatic carbon peak in ¹³C NMR spectrum of Permian vitrinite is mostly composed of gas-prone carbons compared with collodetrinites in those Jurassic basins. But both pyrolysis and ¹³C NMR data shows the Pennsylvanian vitrinite is not only gas-prone but also oil-prone. Relatively high proportion of long chain aliphatic structure of some Jurassic vitrinite in Junggar, Turpan-Hami basins may be due to the contribution of liptodetrinites, which may be included during the formation of vitrinites. And it seems that suberinite is the most possible precursor of long chain aliphatics in the structure of Jurassic collodetrinite.     

黄、渤海沿岸海冰测点代表性浅析

通过分析１９８０年以后的海冰资料和卫星云图照片，对黄、渤海沿岸１１个海冰测点的历史变迁进行了评述，浅析了测点的优劣及使用资料时应注意的事项。     

大鹏澳海域水体磷的形态及其分布特征

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碘－钒盐催化下合成的四溴邻苯二甲酸酐

报道一种新的有效催化剂－碘－钒盐和在一定条件下合成四溴邻苯二甲酸酐的方法。探讨催化剂的组成配比、用量、温度及废酸循环使用等因素对合成方法的影响。该催化剂的催化效果和常用的碘、碘－铁催化剂相同，适用于四溴邻苯二甲酸酐的合成。     

海桑属红树植物遗传多样性和引种关系研究

以海南东寨港红树林自然保护区内无瓣海桑(Sonneratia apetala)、海南海桑(S.hainanensis)、拟海桑(S.paracaseolaris)、杯萼海桑(S.abla)、大叶海桑(S.ovata)、海桑(S.caseolaris)等6种海桑属红树植物为材料,对15个有效引物进行RAPD分析,共扩增出512条带,其中多态性条带为297,占总扩增条带的58.01%.Nei指数法分析和UPGMA统计分析表明,6种海桑属红树植物分为A,B,C3个组,平均遗传距离为0.38.A组包括无瓣海桑、海南海桑、大叶海桑、怀萼海桑,其中无瓣海桑、海南海桑、大叶海桑处于同一个亚组.B组包括拟海桑.C组包括海桑.对海南和福建无瓣海桑种群进行RAPD分析.对Shannon表型多样性指数统计结果表明,福建种群为0.669,海南种群为0.671,各种群遗传变异较大,这与无瓣海桑种群广泛的适应性相一致.对种群间的Shannon表型多样性指数分析表明,种群内的遗传变异占整个遗传变异的93.3%,而种群间的遗传变异仅占6.7%.这表明无瓣海桑种群的大部分遗传变异存在于种群内,而种群间的遗传变异较小.由此可见,无瓣海桑基因组丰富的多样性,是使其由海南成功引种到福建的重要因素.     

海洋平台结构形状与拓扑优化设计

以实际环境荷载作用下的实际海洋导管架平台结构为对象，研究了海洋平台结构在风，流，海冰等多荷载模式和多荷载工况下的优化设计的理论方法和应用技术，建立了结构尺寸优化，形状优化和拓扑优化问题的统一模型。计算结果反映出形状优化比尺寸优化所得设计更优，而拓扑优化的设计效果最好。不同的荷载工况对应的最优拓扑形式不同，因此在实际设计中应考虑实际的荷载工况，选取不同的拓扑形式。     

自动海洋站数据接收与处理软件的功能及特点

采集设备采集数据发送到值班PC机，在无人干预的情况下接收处理软件自动对数据进行回放和进一步处理，为用户提供数据产品和服务，并提供一个界面，使用户能够了解系统的运行状况，获得相关数据和状态参数。