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. 相似文献
The transfer and evolution of stress among rock blocks directly change the void ratios of crushed rock masses and affect the flow of methane in coal mine gobs. In this study, a Lagrange framework and a discrete element method, along with the soft-sphere model and EDEM numerical software, were used. The compaction processes of rock blocks with diameters of 0.6, 0.8, and 1.0 m were simulated with the degrees of compression set at 0%, 5%, 10%, 15%, 20%, and 25%. This study examines the influence of stress on void ratios of compacted crushed rock masses in coal mine gobs. The results showed that stress was mainly transmitted downward through strong force chains. As the degree of compression increased, the strong force chains extended downward, which resulted in the stress at the upper rock mass to become significantly higher than that at the lower rock mass. It was determined that under different degrees of compression, the rock mass of coal mine gobs could be divided, from the bottom to the top, into a lower insufficient compression zone (ICZ) and an upper sufficient compression zone (SCZ). From bottom to top, the void ratios in the ICZ sharply decreased and those in the SCZ slowly decreased. Void ratios in the ICZ were 1.2–1.7 times higher than those in the SCZ.
Acta Geochimica - This work reports an important episode of extensional, mafic magmatism that impacted the North China Craton (NCC) during the Permo-Triassic and influenced the evolution of this... 相似文献