Significant spatial co-distribution pattern discovery

Given instances (spatial points) of different spatial features (categories), significant spatial co-distribution pattern discovery aims to find subsets of spatial features whose spatial distributions are statistically significantly similar to each other. Discovering significant spatial co-distribution patterns is important for many application domains such as identifying spatial associations between diseases and risk factors in spatial epidemiology. Previous methods mostly associated spatial features whose instances are frequently located together; however, this does not necessarily indicate a similarity in the spatial distributions between different features. Thus, this paper defines the significant spatial co-distribution pattern discovery problem and subsequently develops a novel method to solve it effectively. First, we propose a new measure, dissimilarity index, to quantify the difference between spatial distributions of different features under the spatial neighbor relation and then employ it in a distribution clustering method to detect candidate spatial co-distribution patterns. To further remove spurious patterns that occur accidentally, the validity of each candidate spatial co-distribution pattern is verified through a significance test under the null hypothesis that spatial distributions of different features are independent of each other. To model the null hypothesis, a distribution shift-correction method is presented by randomizing the relationships between different features and maintaining spatial structure of each feature (e.g., spatial auto-correlation). Comparisons with baseline methods using synthetic datasets demonstrate the effectiveness of the proposed method. A case study identifying co-morbidities in central Colorado is also presented to illustrate the real-world applicability of the proposed method.     

A bi-layer optimization approach for a hybrid flow shop scheduling problem involving controllable processing times in the steelmaking industry

A steelmaking-continuous casting (SCC) scheduling problem is an example of complex hybrid flow shop scheduling problem (HFSSP) with a strong industrial background. This paper investigates the SCC scheduling problem that involves controllable processing times (CPT) with multiple objectives concerning the total waiting time, earliness/tardiness and adjusting cost. The SCC scheduling problem with CPT is seldom discussed in the existing literature. This study is motivated by the practical situation of a large integrated steel company in which the just-in-time (JIT) and cost-cutting production strategy have become a significant concern. To address this complex HFSSP, the scheduling problem is decomposed into two subproblems: a parallel machine scheduling problem (PMSP) in the last stage and an HFSSP in the upstream stages. First, a hybrid differential evolution (HDE) algorithm combined with a variable neighborhood decomposition search (VNDS) is proposed for the former subproblem. Second, an iterative backward list scheduling (IBLS) algorithm is presented to solve the latter subproblem. The effectiveness of this bi-layer optimization approach is verified by computational experiments on well-designed and real-world scheduling instances. This study provides a new perspective on modeling and solving practical SCC scheduling problems.     

Stereo matching cost computation based on nonsubsampled contourlet transform

A new matching cost computation method based on nonsubsampled contourlet transform (NSCT) for stereo image matching is proposed in this paper. Firstly, stereo image is decomposed into high frequency sub-band images at different scales and along different directions by NSCT. Secondly, by utilizing coefficients in high frequency domain and grayscales in RGB color space, the computation model of weighted matching cost between two pixels is designed based on the gestalt laws. Lastly, two types of experiments are carried out with standard stereopairs in the Middlebury benchmark. One of the experiments is to confirm optimum values of NSCT scale and direction parameters, and the other is to compare proposed matching cost with nine known matching costs. Experimental results show that the optimum values of scale and direction parameters are respectively 2 and 3, and the matching accuracy of the proposed matching cost is twice higher than that of traditional NCC cost.     

基于MSP430的RFID读写器的设计

设计一种基于MSP430单片机的多协议RFID读写器。分析RFID概念、应用现状及系统构成,介绍两款主要芯片的功能特点,给出系统硬件组成结构．提出系统的软件组成模块及软件流程图。系统顺利通过测试,其设计达到相应的技术指标要求,具有良好的市场应用前景。     

Paradigm and performance analysis of distributed frequent itemset mining algorithms based on Mapreduce

FrequentItemsetMining (FIM) is one of the most important data mining tasks and is the foundation of many data mining tasks. In Big Data era, centralized FIM algorithms cannot meet the needs of FIM for big data in terms of time and space, so Distributed Frequent Itemset Mining (DFIM) algorithms have been designed to meet the above challenges. In this paper, LocalGlobal and RedistributionMining which are two main paradigms of DFIM algorithm are discussed; Two algorithms of these paradigms on MapReduce named LG and RM are proposed while MapReduce is a popular distributed computing model, and also the related work is discussed. The experimental results show that the RM algorithm has better performance in terms of computation and scalability of sites, and can be used as the basis for designing the DFIM algorithm based on MapReduce. This paper also discusses the main ideas of improving the DFIM algorithms based on MapReduce.     

基于多模型融合Stacking集成学习的异常用电检测方法研究

针对单一异常用电检测方法对于存在不平衡性的数据集检测效率普遍不高的问题,提出了一种基于多模型融合Stacking集成学习的异常用电检测方法。首先,以居民用电数据作为研究对象,分析用户在习惯上表现的不同特征,结合不平衡处理技术和分类预测算法进行研究;其次,为了提高模型的整体性能,采用量子遗传算法对集成学习模型中的参数做优化处理;最后,通过云南某地区用电数据集进行验证,证明所提模型相比单一学习模型检测的准确率有明显提升,对提升异常排查效率,降低电力公司的运营成本具有重要意义。     

A trustworthy QoS-based collaborative filtering approach for web service discovery

Many network services which process a large quantity of data and knowledge are available in the distributed network environment, and provide applications to users based on Service-Oriented Architecture (SOA) and Web services technology. Therefore, a useful web service discovery approach for data and knowledge discovery process in the complex network environment is a very significant issue. Using the traditional keyword-based search method, users find it difficult to choose the best web services from those with similar functionalities. In addition, in an untrustworthy real world environment, the QoS-based service discovery approach cannot verify the correctness of the web services’ Quality of Service (QoS) values, since such values guaranteed by a service provider are different from the real ones. This work proposes a trustworthy two-phase web service discovery mechanism based on QoS and collaborative filtering, which discovers and recommends the needed web services effectively for users in the distributed environment, and also solves the problem of services with incorrect QoS information. In the experiment, the theoretical analysis and simulation experiment results show that the proposed method can accurately recommend the needed services to users, and improve the recommendation quality.     

Improved conditional random fields model with multi-trigger embedding for Chinese event extraction

Event extraction is a challenging task in natural language understanding, which aims to recognize event type, subtype and roles of relevant entities from unstructured text. Most current approaches address event extraction with highly local models that extract event type and arguments independently. However, this multi-step method cannot make full use of the reciprocal dependency relationship between event trigger and arguments, especially for nested event structure. E.g. the trigger of a Life/Injure event is embedded inside the argument, and a trigger is an event anchor as well as a modifier of argument. Meanwhile, In the same label space, the example proportion of triggers to arguments is scare, and there exists the issue of unbalanced data. Therefore, this kind of trigger is apt to be labeled as the event argument. In order to let event type recognition and event argument recognition guide each other, and resolve the problem of unbalanced data, we consider event extraction to be a sequence labeling problem and build a novel improved conditional random fields joint labeling model with multi-trigger embedding. Experimental results on ACE 2005 Chinese corpus show that this method improves the performance of event extraction.     

基于球面投影网格的大规模复杂水面模拟

针对目前大规模复杂水面模拟中存在的效率不高、碰撞检测较为复杂等问题,提出了一种海洋尺度复杂水面模拟解决方案.首先,提出了一种球面投影网格方法实现大规模动态水面波动效果的模拟.与传统的投影网格方法相比,该方法不需要重新构造与球面直接相交的投影体,具有更高的绘制效率并且适合图形硬件加速.其次,设计了高效的交互式复杂水面的模拟方法,包括水面和刚体交互作用的模拟及刚体与地形的快速碰撞模拟.此外,给出了通用的泡沫绘制和海岸线绘制方法.实验结果表明,该方法的模拟结果较为逼真,能达到较高的绘制速度(FPS>60),适用于计算机游戏、虚拟现实等实时环境.