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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   
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Microbial degradation technologies have been developed to restore ground water quality in aquifers polluted by organic contaminants effectively in recent years. However, in course of the degradation, the formation of biofilms in ground water remediation technology can be detrimental to the effectiveness of a ground water remediation project. Several alternatives are available to a remedial design engineer, such as Permeable Reactive Barriers (PRBs) and in -situ bioremediation, Hydrogen Releasing Compounds (HRCs) barrier, Oxygen Releasing Compounds (ORCs) barrier etc. which are efficient and cost- effective technologies. Excessive biomass formation renders a barrier ineffective in degrading the contaminants, Efforts are made to develop kinetics models which accurately determine bio - fouling and bio - filn formation and to control excessive biomass formation.  相似文献   
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私营企业主是我们社会主义市场经济的建设者 ,是否“合法”是我们正确解读私营企业主的关键。我国目前的私营企业主分为两类三种。合法经营的私营企业主中的优秀分子是吸收入党的主要对象  相似文献   
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高速滑坡问题是当前工程地质和环境地质学界极为关注的问题。本文对诸如滑体高速滑动所对应的时空界限、滑体滑动过程所产生的热效应以及空气浮托力等高速滑坡产生机制问题进行了初步探讨。  相似文献   
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A wide-field time-series CCD photometric survey of variable stars in the field of the open cluster NGC 2168 was carried out using the BATC Schmidt telescope. In total 13 new variable stars are discovered with three W UMa systems, one EA type and two EB type eclipsing binaries (one of them could be a W UMa system), and seven pulsating stars including three candidates of δScuti stars.  相似文献   
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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.  相似文献   
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GPS/LEO掩星观测的变分同化技术   总被引:6,自引:0,他引:6  
刘敏  郭鹏 《天文学进展》2006,24(1):27-42
在简单介绍GPS/LEO掩星探测大气的发展历史和科学意义之后,详细阐述了反演的基本原理;分析了标准反演中存在的问题,并说明一维变分同化(1DVAR)在反演方法中的重要性;给出了一维变分同化中价值函数的求解,以及各种同化因子;简单介绍了对当前气象学中普遍使用的四维变分同化(4DVAR);重点讨论了各种同化方法,以及使用各种同化因子的优缺点。最后,通过CHAMP卫星的观测实例分析,验证了GPS数据在数值天气预报(NWP)中的作用,以及相对于标准反演法一维变分对气象要素的改进。  相似文献   
10.
Approach to Mountain Hazards in Tibet, China   总被引:1,自引:1,他引:0  
Tibet is located at the southwest boundary of China. It is the main body of the Qinghai-Tibet Plateau, the highest and the youngest plateau in the world. Owing to complicated geology, Neo-tectonic movements, geomorphology, climate and plateau environment, various mountain hazards, such as debris flow, flash flood, landslide, collapse, snow avalanche and snow drifts, are widely distributed along the Jinsha River (the upper reaches of the Yangtze River), the Nu River and the Lancang River in the east, and the Yarlungzangbo River, the Pumqu River and the Poiqu River in the south and southeast of Tibet. The distribution area of mountain hazards in Tibet is about 589,000 km^2, 49.3% of its total territory. In comparison to other mountain regions in China, mountain hazards in Tibet break out unexpectedly with tremendously large scale and endanger the traffic lines, cities and towns, farmland, grassland, mountain environment, and make more dangers to the neighboring countries, such as Nepal, India, Myanmar and Bhutan. To mitigate mountain hazards, some suggestions are proposed in this paper, such as strengthening scientific research, enhancing joint studies, hazards mitigation planning, hazards warning and forecasting, controlling the most disastrous hazards and forbidding unreasonable human exploring activities in mountain areas.  相似文献   
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