Tree-based partitioning of date for association rule mining

The most computationally demanding aspect of Association Rule Mining is the identification and counting of support of the frequent sets of items that occur together sufficiently often to be the basis of potentially interesting rules. The task increases in difficulty with the scale of the data and also with its density. The greatest challenge is posed by data that is too large to be contained in primary memory, especially when high data density and/or low support thresholds give rise to very large numbers of candidates that must be counted. In this paper, we consider strategies for partitioning the data to deal effectively with such cases. We describe a partitioning approach which organises the data into tree structures that can be processed independently. We present experimental results that show the method scales well for increasing dimensions of data and performs significantly better than alternatives, especially when dealing with dense data and low support thresholds. Shakil Ahmed received a first class BSc (Hons) degree from Dhaka University, Bangladesh, in 1990; and an MSc (first class), also Dhaka University, in 1992. He received his PhD from The University of Liverpool, UK, in 2005. From 2000 onwards he is a member of the Data Mining Group at the Department of Computer Science of the University of Liverpool, UK. His research interests include data mining, Association Rule Mining and pattern recognition. Frans Coenen has been working in the field of Data Mining for many years and has written widely on the subject. He received his PhD from Liverpool Polytechnic in 1989, after which he took up a post as a RA within the Department of Computer Science at the University of Liverpool. In 1997, he took up a lecturing post within the same department. His current Data Mining research interests include Association rule Mining, Classification algorithms and text mining. He is on the programme committee for ICDM'05 and was the chair for the UK KDD symposium (UKKDD'05). Paul Leng is professor of e-Learning at the University of Liverpool and director of the e-Learning Unit, which is responsible for overseeing the University's online degree programmes, leading to degrees of MSc in IT and MBA. Along with e-Learning, his main research interests are in Data Mining, especially in methods of discovering Association Rules. In collaboration with Frans Coenen, he has developed efficient new algorithms for finding frequent sets and is exploring applications in text mining and classification.     

基于偏微分的图像放大算法研究

在研究图片放大算法中,分析了现有的运用偏微分算法在图像法大中的不足,利用图像放大过程中的边缘信息可预知性,本文提出一种新的基于偏微分方程的图像放大算法,这种算法通过将图像边缘检测、平滑处理,然后采用三次样条插值算法对边缘进行相应倍数的放大,并通过对可能出现的锯齿边缘进行细化处理;将处理过的边缘作为放大图像的边缘,从而可以将源图像的边缘很好的保持下来,避免了偏微分方程放大过程中出现的边缘模糊现象。实验结果显示,该方法是一种能够很好的保存图像的边缘信息的图像放大算法。     

Jet bundle formulation of infinite-dimensional port-Hamiltonian systems using differential operators

We consider infinite-dimensional port-Hamiltonian systems described on jet bundles. Based on a power balance relation we introduce the port-Hamiltonian system representation using differential operators regarding the structural mapping, the dissipation mapping and the input mapping. In contrast to the well-known representation on the basis of the underlying Stokes–Dirac structure our approach is not necessarily based on using energy-variables which leads to a different port-Hamiltonian representation of the analyzed partial differential equations. The presented constructions will be specialized to mechanical systems to which class also the presented examples belong.     

An ExPosition of multivariate analysis with the singular value decomposition in R

ExPosition is a new comprehensive R package providing crisp graphics and implementing multivariate analysis methods based on the singular value decomposition (svd). The core techniques implemented in ExPosition are: principal components analysis, (metric) multidimensional scaling, correspondence analysis, and several of their recent extensions such as barycentric discriminant analyses (e.g., discriminant correspondence analysis), multi-table analyses (e.g.,multiple factor analysis, Statis, and distatis), and non-parametric resampling techniques (e.g., permutation and bootstrap). Several examples highlight the major differences between ExPosition and similar packages. Finally, the future directions of ExPosition are discussed.     

Partial oxidation of ethane to synthesis gas over Co-loaded catalysts

Attention has been increasingly paid to the partial oxidation of lower alkanes to synthesis gas, due to its intrinsic energy saving process. We studied the partial oxidation of ethane (POE) on Co loaded on various supports. The POE performance varied as follows: Y₂O₃, CeO₂, ZrO₂, La₂O₃  SiO₂, Al₂O₃, TiO₂ > MgO. Comparing Y₂O₃ and CeO₂, the carbon deposition during the POE was negligible on CeO₂ and therefore CeO₂ was the most preferable support. By changing space velocity and O₂ partial pressure, reaction mechanism of POE was studied and it was revealed that two-step mechanism was prevailing; combustion of ethane to H₂O and CO₂ and subsequent reforming of ethane with H₂O and CO₂ to synthesis gas. Co/CeO₂ catalyst exhibited high and stable catalytic activity for 10 h; high ethane conversion of 18% (maximum ethane conversion 20% at O₂/C₂H₆ = 0.2) with H₂ and CO selectivities of 93 and 84%, respectively.     

基于偏最小二乘法的咳嗽信号检測

咳嗽中包含丰富的病理信息,可以为临床诊断提供重要支持。自动咳嗽检测方法有助于提高检测结果的可靠性,并减少人为工作量。但在自然记录的语音信号中,非咳嗽信号的数量远多于咳嗽,语音流中咳嗽信号的自动检测是个典型的类别不均衡问题。针对该问题,提出一种基于偏最小二乘分类法的咳嗽信号检测模型APLSCX。利用非对称偏最小二乘分类器处理类别不均衡数据的能力,对归一化的特征向量进行特征抽取,同时基于低维数据的方差调整分类平面。实验结果显示,与LCM、SVM等主流模型相比,APLSCX兼顾了小类的召回率和精度指标,具有较高的检出率和较低的误警率,更适用于自然语流中咳嗽信号的检测。     

颜色和形状特征分离和局部整合的研究

特征绑定是特征分离和整合的过程。为探究视觉系统处理图像颜色和形状特征的动态绑定过程,提出基于任务的独立成分分析(T_ICA)和最短距离聚类相结合的方法,并将其应用于任务态下采集的脑功能(fMRI)数据,实现颜色和形状处理功能的分离和局部整合。结果表明,T_ICA将f MRI数据分离成彼此独立的特征成分,其中包含有目标特征成分和其他成分;聚类方法实现了颜色功能和形状功能的局部整合,形成了两个分别处理颜色和形状的基本感知系统。通过研究人脑视觉特征绑定机制为计算机视觉捆绑的研究与应用提供一定的参考价值。     

Catalytic coal partial gasification in an atmospheric fluidized bed

The coal partial gasification catalyzed by limestone, sodium carbonate and dolomite was studied using a bench-scale atmospheric fluidized bed in the presence of air and steam at 900 °C. The effects of limestone, sodium carbonate and dolomite on composition, heating value, gas yield of product gas and carbon conversion in the catalytic coal partial gasification have been examined. The experimental results show that the catalysts can effectively improve the gas quality, the heating value and the gas yield of product gas and carbon conversion. The catalytic effect of sodium carbonate is better than that of limestone and dolomite. The increase of limestone loading can enhance the quality of product gas, such as the content of combustible gas, the high heating value and the gas yield, during coal partial gasification. This work was presented at the 6 ^th Korea-China Workshop on Clean Energy Technology held at Busan, Korea, July 4–7, 2006.     

天然气年产4万吨甲醇联产年产2.5万吨合成氨代替重油设计

利用现生产重油制氨的1000m~3(V_n)(O_2)/h空分,建设天然气并联换热蒸汽转化串纯氧部分氧化制甲醇和氨两用合成气,成为4万吨/年甲醇联产2.5万吨/年氨的生产单元组合,既节能节气、降低投资和生产消耗及成本,又充分利用现有设备发挥生产能力,同时还可增加产品和产量。     

Tool path generation framework for accurate manufacture of complex 3D sheet metal parts using single point incremental forming

The prototyping of complex sheet metal parts using single point incremental forming (SPIF) requires the generation of optimal tool paths and/or tool path sequences that ensure that the formed part is within geometric design specifications. The presence of a multitude of features on complex parts leads to multiple inaccuracy inducing phenomena occurring simultaneously due to interactions between the features. This paper proposes a network analysis methodology using topological conceptual graphs to capture the effects of different phenomena on the final accuracy of a sheet metal part manufactured by SPIF. Using this framework optimized tool paths can be generated that compensate for the inaccuracy inducing behavior. Tool path generation algorithms to create partial tool paths that account for the accuracy of specific features in the part based on the proposed framework are also presented. Finally, the creation of integrated tool paths maintaining complementarity between tool paths and desired continuity behavior using non-uniform cubic B-splines is illustrated. A number of case studies demonstrating the applicability of the integrated framework are discussed, where the maximum deviations in the part are significantly reduced and the average absolute deviations for the complete part are brought down to less than 0.5 mm.