Model repair — aligning process models to reality

Process mining techniques relate observed behavior (i.e., event logs) to modeled behavior (e.g., a BPMN model or a Petri net). Process models can be discovered from event logs and conformance checking techniques can be used to detect and diagnose differences between observed and modeled behavior. Existing process mining techniques can only uncover these differences, but the actual repair of the model is left to the user and is not supported. In this paper we investigate the problem of repairing a process model w.r.t. a log such that the resulting model can replay the log (i.e., conforms to it) and is as similar as possible to the original model. To solve the problem, we use an existing conformance checker that aligns the runs of the given process model to the traces in the log. Based on this information, we decompose the log into several sublogs of non-fitting subtraces. For each sublog, either a loop is discovered that can replay the sublog or a subprocess is derived that is then added to the original model at the appropriate location. The approach is implemented in the process mining toolkit ProM and has been validated on logs and models from several Dutch municipalities.     

An overview on fault diagnosis and nature-inspired optimal control of industrial process applications

Fault detection, isolation and optimal control have long been applied to industry. These techniques have proven various successful theoretical results and industrial applications. Fault diagnosis is considered as the merge of fault detection (that indicates if there is a fault) and fault isolation (that determines where the fault is), and it has important effects on the operation of complex dynamical systems specific to modern industry applications such as industrial electronics, business management systems, energy, and public sectors. Since the resources are always limited in real-world industrial applications, the solutions to optimally use them under various constraints are of high actuality. In this context, the optimal tuning of linear and nonlinear controllers is a systematic way to meet the performance specifications expressed as optimization problems that target the minimization of integral- or sum-type objective functions, where the tuning parameters of the controllers are the vector variables of the objective functions. The nature-inspired optimization algorithms give efficient solutions to such optimization problems. This paper presents an overview on recent developments in machine learning, data mining and evolving soft computing techniques for fault diagnosis and on nature-inspired optimal control. The generic theory is discussed along with illustrative industrial process applications that include a real liquid level control application, wind turbines and a nonlinear servo system. New research challenges with strong industrial impact are highlighted.     

A novel approach to adaptive relational association rule mining

The paper focuses on the adaptive relational association rule mining problem. Relational association rules represent a particular type of association rules which describe frequent relations that occur between the features characterizing the instances within a data set. We aim at re-mining an object set, previously mined, when the feature set characterizing the objects increases. An adaptive relational association rule method, based on the discovery of interesting relational association rules, is proposed. This method, called ARARM (Adaptive Relational Association Rule Mining) adapts the set of rules that was established by mining the data before the feature set changed, preserving the completeness. We aim to reach the result more efficiently than running the mining algorithm again from scratch on the feature-extended object set. Experiments testing the method's performance on several case studies are also reported. The obtained results highlight the efficiency of the ARARM method and confirm the potential of our proposal.     

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.     

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.     

乐滩灌区输水隧洞穿合山煤矿采空区处理设计

分析了乐滩灌区工程北干渠溯河隧洞段长约1.06 km穿合山煤矿采空区的地质情况.借鉴高速公路煤矿采空区的成功处理经验,通过科学分析,对采空区变形和区域内覆岩的稳定性做了评价,论述了输水线路穿采空区段采取全充填压力注浆法的工程处理方法.     

浅析高校新校区选址主要影响因素

从政策、城市与区域规划、环境、文化及其传承因素、基础设施等方面探析了高校新校区选址有关的主要影响因素，以满足高等学校大规模的扩建要求，促进高等教育的发展。     

面积坐标法构造含转角自由度的四结点膜元

以四边形面积坐标作为工具,构造了两个含转角自由度的广义协调四边形单元AQ4和lAQ4。它们通过强式分片检验,与同类单元相比,具有很高的计算精度,能消除梯形闭锁现象,有很强的抗网格畸变的能力。     

阜阳地区石炭-二叠系煤系烃源岩特征

阜阳地区石炭-二叠系煤系地层发育，但由于勘探程度较低，对该区煤系地层烃源岩的生烃潜力认识不清，严重制约了该区的油气勘探。在已有资料的基础上，通过有机地球化学和有机岩石学方法并结合模拟实验，探讨了该区煤系烃源岩的生烃潜力。研究结果表明阜阳地区石炭-二叠系煤系烃源岩有机质丰度达到中等-好烃源岩标准，有机质类型以腐殖型为主，盆地模拟结果显示阜阳地区石炭-二叠系煤系地层油资源量为（30.47~152.33）×10⁶t，气资源量为（266.7～1333.7）×10⁸m³，展示阜阳地区石炭-二叠系煤系地层具有较好的生油气潜力。     

Design Methodology and Area Sensitivity Analysis of Horizontal Subsurface Flow Constructed Wetlands

An approach is presented to satisfy the demand for simple criteria, guidelines and models for the preliminary sizing of horizontal subsurface flow (SF) constructed wetland systems. This approach eliminates time-consuming calculations and iterations by providing graphical solutions for wetland system sizing. Therefore, it can be used for the preliminary assessment of new or performance evaluation of existing subsurface flow constructed wetland systems. The validity of this methodology is checked with data from existing systems and is found to be quite satisfactory. This methodology is combined with simple equations predicting the maximum wetland capacity in summer, so as to assist designers in sizing installations in tourist areas with increased summer populations. Furthermore, based on this methodology, a sensitivity analysis is performed of the area requirements for wastewaters of various strengths and various design conditions and performance criteria. The results provide a useful overview to engineers and further simplify the design methodology of new subsurface flow constructed wetland systems.