基于网格技术的空间知识发现与数据挖掘研究

设计了基于服务网格的空间知识发现与数据挖掘的架构,并对其关键技术进行了探讨性研究。     

Mapping the expansion of galamsey gold mines in the cocoa growing area of Ghana using optical remote sensing

Artisanal gold mining (galamsey) and cocoa farming are essential sources of income for local populations in Ghana. Unfortunately the former poses serious threats to the environment and human health, and conflicts with cocoa farming and other livelihoods. Timely and spatially referenced information on the extent of galamsey is needed to understand and limit the negative impacts of mining. To address this, we use multi-date UK-DMC2 satellite images to map the extent and expansion of galamsey from 2011 to 2015. We map the total area of galamsey in 2013 over the cocoa growing area, using k-means clustering on a cloud-free 2013 image with strong spectral contrast between galamsey and the surrounding vegetation. We also process a pair of hazy images from 2011 and 2015 with Multivariate Alteration Detection to map the 2011–2015 galamsey expansion in a subset, labelled the change area. We use a set of visually interpreted random sample points to compute bias-corrected area estimates. We also delineate an indicative impact zone of pollution proportional to the density of galamsey, assuming a maximum radius of 10 km. In the cocoa growing area of Ghana, the estimated total area of galamsey in 2013 is 27,839 ha with an impact zone of 551,496 ha. In the change area, galamsey has more than tripled between 2011 and 2015, resulting in 603 ha of direct encroachment into protected forest reserves. Assuming the same growth rate for the rest of the cocoa growing area, the total area of galamsey in 2015 is estimated at 43,879 ha. Galamsey is developing along most of the river network (Offin, Ankobra, Birim, Anum, Tano), with downstream pollution affecting both land and water.     

运用社交网络分析淹水空间信息之关联性

随着社交网络的普遍发展,大量的讯息透过智能手机发布在个人的微博或其他社交网站。台湾地区的社交网站以脸书(Facebook)的使用量最大,平均每天有近千万笔的讯息量,大多数的讯息多以食衣住行或个人讯息为主,但从本研究所撷取自2010年至2015年的数据中显示,公众在社交网站所分享的信息中具有降雨、淹水或相关灾情的讯息,而这些讯息具有极高比例的正确性。由于社交网站无法提供私人讯息,故本研究将从社交信息中,以地点为单位撷取大量的数据信息再辅以语意关键词萃取出有关可作为淹水预判的讯息数据。为检核资料的可性度,本研究透过历史台风数据FLO-2D仿真重建淹水之空间信息进行检核。从研究比对分析中发现,经萃取后的公众信息其与灾害的关联性及正确性相当显着,故透过社交网站中大量的非结构讯息,透过语意及空间的转换,可萃取转化为防灾信息,对广域的都市治理而言,此一讯息将可作为预判区域淹水或防救灾情报之有效参考。     

单线激光雷达扫描的矿车装载量核算方法

面向露天矿山生产对于矿车装载量核算的需求,本文提出了一种利用单线激光雷达扫描的矿车装载量自动核算方法.该方法首先采用固定式单线激光雷达对目标车辆进行俯视扫描,基于车辆形态特征分割出载荷物顶部点云;然后经时态校正,并与预先构建的车辆信息数据库进行匹配,重建出载荷物的三维表面模型;最后通过对载荷物表面模型的切片积分实现方量...     

Detection of coffee berry necrosis by digital image processing of landsat 8 oli satellite imagery

Coffee berry necrosis is a fungal disease that, at a high level, significantly affects coffee productivity. With the advent of surface mapping satellites, it was possible to obtain information about the spectral signature of the crop on a time scale pertinent to the monitoring and detection of plant phenological changes. The objective of this paper was to define the best machine learning algorithm that is able to classify the incidence CBN as a function of Landsat 8 OLI images in different atmospheric correction methods. Landsat 8 OLI images were acquired at the dates closest to sampling anthracnose field data at three times corresponding to grain filling period and were submitted to atmospheric corrections by DOS, ATCOR, and 6SV methods. The images classified by the algorithms of machine learning, Random Forest, Multilayer Perceptron and Naive Bayes were tested 30 times in random sampling. Given the overall accuracy of each test, the algorithms were evaluated using the Friedman and Nemenyi tests to identify the statistical difference in the treatments. The obtained results indicated that the overall accuracy and the balanced accuracy index were on an average around 0.55 and 0.45, respectively, for the Naive Bayes and Multilayer Perceptron algorithms in the ATCOR atmospheric correction. According to the Friedman and Nemenyi tests, both algorithms were defined as the best classifiers. These results demonstrate that Landsat 8 OLI images were able to identify an incidence of the coffee berry necrosis by means of machine learning techniques, a fact that cannot be observed by the Pearson correlation.     

Anthropogenic relief changes in a long-lasting lignite mining area (‘Ville’, Germany) derived from historic maps and digital elevation models

Humans constitute one of the main geomorphological agents in modern times. As an example, post-mining regions represent a typical landscape of the Anthropocene. Strong relief modifications are particularly obvious with open pit mining. However, many existing mining areas are lacking detailed pre-mining information for the quantification of anthropogenic relief changes, which is a considerable challenge in regions with historic mining activities. Here, the Ville (Rhenish lignite district, Germany) is used to quantify surface mining induced relief changes in one of the oldest and currently largest lignite districts in Europe. Historical maps from first geodetic mapping in 1893 enabled construction of a historic digital elevation model to quantify the relief changes in comparison to elevation data from 2000 and 2015. The vertical accuracy of the historic data is remarkably high, with relief differences < 2 m in areas not affected by mining. In total, 49.2% of the investigated area (184 km²) shows a relief deficit and 14.5% has positive relief differences. Absolute changes account for more than 80 m heightening (dumpsites of overburden) and lowering of the natural relief (pits). Besides these altitudinal changes, overall steeper slopes are significant for the new topography, but levelling exists likewise. The spatial variabilities are discussed in the context of the regional geology and the mining techniques. Undoubtedly, such large-scale anthropogenic relief changes persist for a very long time and will last as a human legacy far into the future. Only the detailed reconstruction of the pre-mining relief offers the ability to clarify the dimension of humans as geomorphological agents and to understand landscape perception. Due to the fact that the impact of open pit mining has such a large vertical and horizontal extension, their consideration as part of anthropogeomorphology can significantly contribute to support future Critical Zone research in the Anthropocene.     

Developing constitutive models from EPR‐based self‐learning finite element analysis

A constitutive model that captures the material behavior under a wide range of loading conditions is essential for simulating complex boundary value problems. In recent years, some attempts have been made to develop constitutive models for finite element analysis using self‐learning simulation (SelfSim). Self‐learning simulation is an inverse analysis technique that extracts material behavior from some boundary measurements (eg, load and displacement). In the heart of the self‐learning framework is a neural network which is used to train and develop a constitutive model that represents the material behavior. It is generally known that neural networks suffer from a number of drawbacks. This paper utilizes evolutionary polynomial regression (EPR) in the framework of SelfSim within an automation process which is coded in Matlab environment. EPR is a hybrid data mining technique that uses a combination of a genetic algorithm and the least square method to search for mathematical equations to represent the behavior of a system. Two strategies of material modeling have been considered in the SelfSim‐based finite element analysis. These include a total stress‐strain strategy applied to analysis of a truss structure using synthetic measurement data and an incremental stress‐strain strategy applied to simulation of triaxial tests using experimental data. The results show that effective and accurate constitutive models can be developed from the proposed EPR‐based self‐learning finite element method. The EPR‐based self‐learning FEM can provide accurate predictions to engineering problems. The main advantages of using EPR over neural network are highlighted.     

Heave compensation system of lifting pipeline for deep-sea mining based on combined action of vibration absorber and accumulator

ABSTRACT

A heave compensation system combined with a vibration absorber and accumulator is proposed, with regard to the requirement for poly-metallic nodule mining systems at an ocean depth of 6,000?m in China. The principle of the compensation system was analyzed, its main parameters were designed, and its mathematical model was established. The system performance was simulated and analyzed in MATLAB, and the frequency domain characteristics and compensation rate of the system with different volume accumulators, and with or without vibration absorbers, were compared. A simulation test of the compensation system was conducted. The proposed compensation system had a better compensation effect for a random wave interference under level-6 sea conditions, and the compensation rate being more than 72% with optimal system parameters.     

深部开采底板隔水关键层受局部高承压水作用破坏理论分析

大量实测地质资料与防治水实践证实,深部煤层开采底板含水层存在大量的局部高承压水区域,在强开采扰动下极易诱发底板突水灾害。基于“下三带”、“关键层”理论,将局部高承压水与底板隔水关键层简化为圆筒力学模型,分析研究隔水关键层在局部高承压水作用下的屈服破坏机制,得到了隔水关键层发生屈服的4个临界水压值公式;对各临界水压值进行区间划分,分析不同区间隔水关键层的屈服状态,确定了局部高承压水致使隔水关键层屈服破坏两个阶段的水压判据平衡方程,得出了奥陶系顶部岩层作为隔水关键层剩余隔水能力的计算表达式,并反演得到了开采深度上限、下限公式。结合九龙矿深部开采实际,对所建力学模型进行了验证与应用,进而提出了防止局部高承压水致灾的相关措施,对实现深部煤层安全带压开采具有重要的理论指导意义。     

矿业城市可持续发展困境原因探析

矿业城市是指由于矿业资源的开采而形成的城市。长期以来,矿业城市在我国的经济建设中做出了重大贡献。然而,随着矿产资源的耗减和体制改革的深化,矿业城市在目前发展中普遍存在资源衰竭、污染严重、管理体制不合理等问题。特别是在当今全球经济一体化的新形势下,矿业城市面临着更加严峻的挑战,如何合理有效地利用资源,确保矿业城市可持续发展,文章对矿业城市发展中存在的主要问题及目前出现困境的原因进行了分析,并提出了我国矿业城市摆脱困境的对策和建议。