旋风燃烧器冷态模型内颗粒运动轨迹计算

在冷态模型流场内，考虑气流对粉煤颗粒的气动阻力，建立其运动微分方程式。通过计算，得到九种粒径的颗粒在流场中的运动轨迹、停留时间、着膜位置和气－固相相对速度与该燃烧器几何参数和气动参数的关系，为燃烧器的深入研究提出了建议。     

开滦集团公司煤层间配采模型的建立

运用线性规划建立了数学模型，解决了开栾集团公司煤层间的配采问题，提高了煤炭企业的经济效益，有利于企业实现可持续发展。     

高速公路水毁成因及防治措施

从高速公路水泥混凝土路面裂缝，挖方地段护面墙开裂、拦水带与路面衔接部位裂缝，急流槽水泥砂浆勾缝脱落等方面分析了高速公路水毁的成因，并提出了切实可行的防治措施，保证了高速公路的正常运营。     

A sensitivity analysis of the linear complementarity programming model: Appalachian steam coal and the natural gas market

There have been extensive attempts in recent years to perform spatial equilibrium analysis of commodity markets, particularly that of energy. Most of these attempts have been based on linear programming transportation models, and more recently quadratic programming models. Unfortunately, neither of these modelling approaches can deal with the case of multi-commodity analysis with non-symmetric regression coefficients. In this paper, we overcome this problem by employing the linear complementarity programming model. In addition, we show how the model can be applied to coal and gas energy flows within a single region. Future potential applications of the model are also implied.     

Adherence clustering: an efficient method for mining market-basket clusters

We explore in this paper the efficient clustering of market-basket data. Different from those of the traditional data, the features of market-basket data are known to be of high dimensionality and sparsity. Without explicitly considering the presence of the taxonomy, most prior efforts on clustering market-basket data can be viewed as dealing with items in the leaf level of the taxonomy tree. Clustering transactions across different levels of the taxonomy is of great importance for marketing strategies as well as for the result representation of the clustering techniques for market-basket data. In view of the features of market-basket data, we devise in this paper a novel measurement, called the category-based adherence, and utilize this measurement to perform the clustering. With this category-based adherence measurement, we develop an efficient clustering algorithm, called algorithm k-todes, for market-basket data with the objective to minimize the category-based adherence. The distance of an item to a given cluster is defined as the number of links between this item and its nearest tode. The category-based adherence of a transaction to a cluster is then defined as the average distance of the items in this transaction to that cluster. A validation model based on information gain is also devised to assess the quality of clustering for market-basket data. As validated by both real and synthetic datasets, it is shown by our experimental results, with the taxonomy information, algorithm k-todes devised in this paper significantly outperforms the prior works in both the execution efficiency and the clustering quality as measured by information gain, indicating the usefulness of category-based adherence in market-basket data clustering.     

沥青输送泵的选型计算

液体沥青是铝用阳极的重要原料之一,其不同温度下的密度、粘度特性和流量、扬程等直接决定了沥青泵的选型,本文以具体的铝用阳极生产为例,对沥青输送泵尤其是齿轮泵的参数选取进行了科学的计算与合理的分析。     

高炉炉缸内铁水环流与内衬侵蚀

从分析中心焦柱在炉缸内的受力出发，计算了１０００￣５５８０ｍ＾３高炉大喷煤前后中心焦柱在炉缸铁水内的沉入深度，分析了炉缸内铁水环流的产生与炉缸内衬侵的关系，分析表明大喷煤有得利抑制铁水环流对缸内补的冲刷，提出了降低铁水环流对炉缸内衬侵蚀的途径。     

A new algorithm for fast mining frequent itemsets using N-lists

Mining frequent itemsets has emerged as a fundamental problem in data mining and plays an essential role in many important data mining tasks.In this paper,we propose a novel vertical data representation called N-list,which originates from an FP-tree-like coding prefix tree called PPC-tree that stores crucial information about frequent itemsets.Based on the N-list data structure,we develop an efficient mining algorithm,PrePost,for mining all frequent itemsets.Efficiency of PrePost is achieved by the following three reasons.First,N-list is compact since transactions with common prefixes share the same nodes of the PPC-tree.Second,the counting of itemsets’ supports is transformed into the intersection of N-lists and the complexity of intersecting two N-lists can be reduced to O(m + n) by an efficient strategy,where m and n are the cardinalities of the two N-lists respectively.Third,PrePost can directly find frequent itemsets without generating candidate itemsets in some cases by making use of the single path property of N-list.We have experimentally evaluated PrePost against four state-of-the-art algorithms for mining frequent itemsets on a variety of real and synthetic datasets.The experimental results show that the PrePost algorithm is the fastest in most cases.Even though the algorithm consumes more memory when the datasets are sparse,it is still the fastest one.     

矿井提升机常见故障分析

介绍矿井提升机的常见故障及分类。     

重症监护病房紧急状况预警算法

重症监护病房中的病人身体状况通常很不稳定,常出现各种需要医护人员介入治疗的紧急状况。由于医疗资源有限,医护人员可能无法及时发现并处理这些紧急状况,给病人的存活率带来严重的负面影响。如果可以预测这些紧急状况的发生,并及时通知相关医护人员,将大大提高病人的存活率。常见重症监护病房紧急状况包括突然死亡、败血症、肺部感染、急性低血压、以及器官衰竭等。紧急状况预警建模主要采用病人的长时间生命体征监测数据,预测在一定时间之后发生某种紧急状况的可能性。预警模型所采用的监测数据分为静态数据、事件数据和时间序列数据等三类。静态数据具有容易采集、但预测准确性偏低的特点。事件数据或时间序列数据、以及多种类型数据的混合数据对于紧急状况预警模型的预测性能的提高有重要作用,将会获得更广泛的应用。