摘要翻译：
伊朗地下煤矿约90%以上的煤炭产量是直接长壁开采法生产的。煤层外贫化是这些矿山的根本问题之一。因此，稀释会增加采矿和制粉的额外成本。因此，识别影响稀释度的有效参数对工业生产具有重要意义。本文通过分析13个参数（属性变量）对决策属性（稀释值）的影响，利用粗糙集理论(RST)和自组织神经模糊推理系统(SONFIS)两种近似推理方法，对所收集的数据集进行最优规则提取。后来的方法的另一个好处是预测新的未知病例。因此，得到了RST的约简集（约简）。结果表明，层厚、采场长度、推进速度、矿工数量、推进方式是影响采煤效果的敏感变量。
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英文标题：
《Assessment of effective parameters on dilution using approximate
  reasoning methods in longwall mining method, Iran coal mines》
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作者：
H. Owladeghaffari, K. Shahriar, G. H. R. Saeedi
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最新提交年份：
2008
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要：
  Approximately more than 90% of all coal production in Iranian underground mines is derived directly longwall mining method. Out of seam dilution is one of the essential problems in these mines. Therefore the dilution can impose the additional cost of mining and milling. As a result, recognition of the effective parameters on the dilution has a remarkable role in industry. In this way, this paper has analyzed the influence of 13 parameters (attributed variables) versus the decision attribute (dilution value), so that using two approximate reasoning methods, namely Rough Set Theory (RST) and Self Organizing Neuro- Fuzzy Inference System (SONFIS) the best rules on our collected data sets has been extracted. The other benefit of later methods is to predict new unknown cases. So, the reduced sets (reducts) by RST have been obtained. Therefore the emerged results by utilizing mentioned methods shows that the high sensitive variables are thickness of layer, length of stope, rate of advance, number of miners, type of advancing.
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PDF链接：
https://arxiv.org/pdf/0805.1288