Geothermal energy utilization trends from a technological paradigm perspective

The use of geothermal energy and its associated technologies has been increasing worldwide. However, there has been little paradigmatic research conducted in this area. This paper proposes a systematic methodology to research the development trends for the sustainable development of geothermal energy. A novel data analysis system was created to research the geothermal energy utilization trends, and a technological paradigm theory was adopted to explain the technological changes. A diffusion velocity model was used to simulate and forecast the geothermal power generation development in the diffusion phase. Simulation results showed that the development of installed capacity for geothermal generation had a strong inertia force along with the S-curve. Power generation from geothermal power sources reached a peak in 2008 and is estimated to be saturated by 2030. Geothermal energy technologies in hybrid power systems based on other renewable energy sources look to be more promising in the future.     

Simplifying the powder metallurgy manufacturing process using soft computing tools

The tools of soft computing will aid the knowledge mining in predicting and classifying the properties of various parameters while designing the composite preforms in the manufacturing of Powder Metallurgy (P/M) Lab. In this paper, an integrated PRNET (PCA-Radial basis functional neural NET) model is proposed in different versions to select the relevant parameters for preparing composite preforms and to predict the deformation and strain hardening properties of Al–Fe composites. It reveals that the predictability of this model has been increased by 67.89% relatively from the conventional models. A new PR-filter is proposed by slightly modifying the conventional filters of RBFNN, which improves the power of PRNET even though raw data are highly non-linear, interrelated and noisy. Moreover, fixing the range of input parameters for classifying the properties of composite preforms can be automated by the Fuzzy logic. These types of models will avoid expensive experimentation and risky environment while preparing sintered composite preforms. Thus the manufacturing process of composites in P/M Lab will be simplified with minimum energy by the support of these soft-computing tools.     

四川矿区典型煤层地应力场瓦斯渗透性规律实验研究

在深部开采或复杂地质条件下,地应力对煤岩渗透率的影响明显,进而影响煤矿瓦斯抽采的效果。对四川矿区不同典型煤层条件下的原煤进行了实验研究,分析矿区典型地应力特征条件下煤岩渗透率的变化规律,拟合了煤岩渗透率与有效应力的函数关系模型。研究发现:煤岩的渗透率与有效应力呈规律性的反比例关系,并存在拐点。这一发现为该矿区根据地应力条件进行煤层强化增透措施选取提供了依据,该研究成果在四川矿区白皎煤矿的保护层开采应用中取得了较好的效果,瓦斯抽采效果良好。     

A proposed expert system for word sense disambiguation: deductive ambiguity resolution based on data mining and forward chaining

One of the major issues in the process of machine translation is the problem of choosing the proper translation for a multi‐sense word referred to as word sense disambiguation (WSD). Two commonly used approaches to this problem are statistical and example‐based methods. In statistical methods, ambiguity resolution is mostly carried out by making use of some statistics extracted from previously translated documents or dual corpora of source and target languages. Example‐based methods follow a similar approach as they also make use of bilingual corpora. However, they perform the task of matching at run‐time (i.e. online matching). In this paper, by looking at the WSD problem from a different viewpoint, we propose a system, which consists of two main parts. The first part includes a data mining algorithm, which runs offline and extracts some useful knowledge about the co‐occurrences of the words. In this algorithm, each sentence is imagined as a transaction in Market Basket Data Analysis problem, and the words included in a sentence play the role of purchased items. The second part of the system is an expert system whose knowledge base consists of the set of association rules generated by the first part. Moreover, in order to deduce the correct senses of the words, we introduce an efficient algorithm based on forward chaining in order to be used in the inference engine of the proposed expert system. The encouraging performance of the system in terms of precision and recall as well as its efficiency will be analysed and discussed through a set of experiments.     

胶东三山岛西岭金矿区NW向断裂带的确定：综合物探证据

三山岛西岭金矿区位于渤海之滨，与北部海域金矿区相互连接，形成金资源量近千吨的巨型金矿床，矿床为地下与海底开采。区域内断裂发育，分布有NE向和NW向断裂，其中NE向断裂为控矿构造，而NW向断裂多具有不同程度的导水性，对矿床安全开采影响重大。基于矿区地质条件与地球物理特征，采用高精度磁测法和音频大地电磁（AMT）法在矿区东部进行了勘查试验，根据显示的磁性异常与电性异常，在平面上解译了NW向断裂(F-1、F-2)与NE向断裂（F-3、F-4），在L1剖面上解译了4条断裂，在L2剖面上解译了3条断裂。综合判断解译信息，NW向F-1断裂在平面与剖面上都反映良好。同时，与北部海域NW向构造研究资料进行对比分析，结果显示西岭矿区F-1推断断层与北部海域B-F4断层特征一致，吻合较好，推断西岭矿区NW向F-2断裂被F-3断裂错断。研究结果说明综合物探方法在该区域可行有效，为下一步钻孔验证NW向断裂存在，判断断裂富水及导水性质，以及未来矿床开发利用提供了地球物理依据。     

Discovery of path-attribute dependency in manufacturing environments: A process mining approach

The more knowledge industrial practitioners detain of their production processes, the more they are capable of performing process improvements. Nonetheless, there may exist process characteristics and dependencies that are not easily extractable from business models, such as routing dependent attributes. This paper introduces an algorithm-driven framework to establish whether process path decisions influence the attributes in non-direct sequences, e.g., deploying machine A instead of machine B affects the % of rejected parts on the process, 4 stages down the line. This problem is shown to bears similarities with sequential pattern mining problems. The basis of the solution framework relies on process mining and data mining techniques. The approach proposed is applied on a real industrial log, unveiling deficiencies in the system and providing further improvement recommendations.     

大新锰矿复杂空区群三维数值模型构建方法及胶结充填治理研究

矿山岩土工程数值分析中研究复杂空区群三维数值模型构建方法对空区治理具有现实意义。为精确分析矿山地下工程围岩移动和应力变化规律，特别是不同采矿方法形成的复杂空区的稳定性和矿柱应力变化，提出了3DMine-Surfer-Rhino-ANSYS-FLAC3D多软件联合建模方法，解决了复杂空区群三维数值模型构建和大数量小尺寸矿房矿柱网格剖分问题。以广西大新锰矿矿体开采为案例，构建了留矿法与房柱法形成的采空区的三维数值模型，模拟分析空区的稳定性和决策胶结充填治理方案。研究结果表明：（1）大新锰矿留矿法开采的空区部分顶柱有垮落风险；（2）房柱法开采的空区部分矿柱变形大，有塑性破坏；（3）对于胶结充填留矿法的空区，隔一充一充填方案治理效果更好，在治理初期地表沉降更小。     

一种基于Spark的不确定数据集频繁模式挖掘算法

如何在海量不确定数据集中提高频繁模式挖掘性能是目前研究的热点.传统算法大多是以期望、概率或者权重等单一指标为数据项集支持度，在大数据背景下，同时考虑概率和权重支持度的算法难以兼顾其执行效率.为此，本文提出一种基于Spark的不确定数据集频繁模式挖掘算法（UWEFP），首先，为了同时兼顾数据项的概率和权重，计算一项集的最大概率权重值并进行剪枝；然后，为了减少对数据集的多次扫描，结合Spark框架的优点，设计了一种具有FP-tree特征的新颖的UWEFP-tree结构进行模式树的构建及挖掘；最后在Spark环境下，以UCI数据集进行实验验证.实验结果表明本文的方法在保证挖掘结果的同时，提高了效率.     

高温深井通风降温技术的适用条件研究

随着矿山开采深度的不断增加，“三高一扰动”恶劣开采环境问题日益突出，给矿井通风降温工作带来极大困难。对于深井矿山，仅采用通风降温难以满足深部开采的需要，系统研究高温深井矿山通风降温技术的适用性问题，对矿山深部开采具有重要的理论意义和工程适用价值。首先从热力学角度，揭示风流在通风线路中的热交换规律；其次利用差分法原理计算深井筒风流温度，并依此推导出巷道和回采工作面风流温度变化趋势；然后结合采场安全生产允许温度进行反推，最终获取通风降温条件下的可采极限深度计算公式，并选取广西铜坑矿锌多金属矿体作为工程应用试验区进行论证分析。结果表明：基于风流的热交换模型可以推导出风流在井筒、巷道及工作面的温度计算公式，该公式与低温梯度、风流流经路径长度有关；假定工作面温度达到安全开采允许最高温度，可反推出该条件下矿山可采极限深度和巷道通风极限长度（仅采用通风降温措施时）；基于铜坑矿区锌多金属矿的实际条件，代入相关数据，验证计算所得极限开采深度符合实际，即所推导公式是可行的。     

基于改进关联规则算法的警情数据分析

针对传统Apriori算法挖掘警情数据的缺点，提出一种改进的Apriori算法。该算法首先在关联规则发现阶段引入权值参数，设计支持度阈值函数，以挖掘不常发生的重大案情发生规律；然后提出一种压缩矩阵优化算法，将数据压缩存储在只有0或1的矩阵中，并用2个数组来记录矩阵中每一行及每一列1的总数，可多次压缩矩阵，提升挖掘效率；最后将改进的算法用于实际的警情数据挖掘分析，给出关联规则挖掘结果。实验表明，改进算法不仅执行效率较传统算法有所提升，且针对警情数据的挖掘结果准确性也有所提高。