Detecting Overlapping Community Structures in Networks

Community structure has been recognized as an important statistical feature of networked systems over the past decade. A lot of work has been done to discover isolated communities from a network, and the focus was on developing of algorithms with high quality and good performance. However, there is less work done on the discovery of overlapping community structure, even though it could better capture the nature of network in some real-world applications. For example, people are always provided with varying characteristics and interests, and are able to join very different communities in their social network. In this context, we present a novel overlapping community structures detecting algorithm which first finds the seed sets by the spectral partition and then extends them with a special random walks technique. At every expansion step, the modularity function Q is chosen to measure the expansion structures. The function has become one of the popular standards in community detecting and is defined in Newman and Girvan (Phys. Rev. 69:026113, 2004). We also give a theoretic analysis to the whole expansion process and prove that our algorithm gets the best community structures greedily. Extensive experiments are conducted in real-world networks with various sizes. The results show that overlapping is important to find the complete community structures and our method outperforms the C-means in quality.     

Global Asymptotic Robust Stability and Global Exponential Robust Stability of Neural Networks with Time-Varying Delays

In this paper, based on nonnegative matrix theory, the Halanay’s inequality and Lyapunov functional, some novel sufficient conditions for global asymptotic robust stability and global exponential robust stability of neural networks with time-varying delays are presented. It is shown that our results improve and generalize several previous results derived in the literatures. From the obtained results, some linear matrix inequality criteria are derived. Finally, a simulation is given to show the effectiveness of the results.     

CONTOUR: an efficient algorithm for discovering discriminating subsequences

In recent years we have witnessed several applications of frequent sequence mining, such as feature selection for protein sequence classification and mining block correlations in storage systems. In typical applications such as clustering, it is not the complete set but only a subset of discriminating frequent subsequences which is of interest. One approach to discovering the subset of useful frequent subsequences is to apply any existing frequent sequence mining algorithm to find the complete set of frequent subsequences. Then, a subset of interesting subsequences can be further identified. Unfortunately, it is very time consuming to mine the complete set of frequent subsequences for large sequence databases. In this paper, we propose a new algorithm, CONTOUR, which efficiently mines a subset of high-quality subsequences directly in order to cluster the input sequences. We mainly focus on how to design some effective search space pruning methods to accelerate the mining process and discuss how to construct an accurate clustering algorithm based on the result of CONTOUR. We conducted an extensive performance study to evaluate the efficiency and scalability of CONTOUR, and the accuracy of the frequent subsequence-based clustering algorithm.     

DECODE: a new method for discovering clusters of different densities in spatial data

When clusters with different densities and noise lie in a spatial point set, the major obstacle to classifying these data is the determination of the thresholds for classification, which may form a series of bins for allocating each point to different clusters. Much of the previous work has adopted a model-based approach, but is either incapable of estimating the thresholds in an automatic way, or limited to only two point processes, i.e. noise and clusters with the same density. In this paper, we present a new density-based cluster method (DECODE), in which a spatial data set is presumed to consist of different point processes and clusters with different densities belong to different point processes. DECODE is based upon a reversible jump Markov Chain Monte Carlo (MCMC) strategy and divided into three steps. The first step is to map each point in the data to its mth nearest distance, which is referred to as the distance between a point and its mth nearest neighbor. In the second step, classification thresholds are determined via a reversible jump MCMC strategy. In the third step, clusters are formed by spatially connecting the points whose mth nearest distances fall into a particular bin defined by the thresholds. Four experiments, including two simulated data sets and two seismic data sets, are used to evaluate the algorithm. Results on simulated data show that our approach is capable of discovering the clusters automatically. Results on seismic data suggest that the clustered earthquakes, identified by DECODE, either imply the epicenters of forthcoming strong earthquakes or indicate the areas with the most intensive seismicity, this is consistent with the tectonic states and estimated stress distribution in the associated areas. The comparison between DECODE and other state-of-the-art methods, such as DBSCAN, OPTICS and Wavelet Cluster, illustrates the contribution of our approach: although DECODE can be computationally expensive, it is capable of identifying the number of point processes and simultaneously estimating the classification thresholds with little prior knowledge.     

Credit scoring using support vector machines with direct search for parameters selection

Support vector machines (SVM) is an effective tool for building good credit scoring models. However, the performance of the model depends on its parameters’ setting. In this study, we use direct search method to optimize the SVM-based credit scoring model and compare it with other three parameters optimization methods, such as grid search, method based on design of experiment (DOE) and genetic algorithm (GA). Two real-world credit datasets are selected to demonstrate the effectiveness and feasibility of the method. The results show that the direct search method can find the effective model with high classification accuracy and good robustness and keep less dependency on the initial search space or point setting.     

Intelligent Optimal-Setting Control for Grinding Circuits of Mineral Processing Process

During the operation of a grinding circuit (GC) in mineral processing plant the main purpose of control and optimal operation is to control the product quality index, namely the product particle size, into its technically desired ranges. Moreover, the grinding production rate needs to be maximized. However, due to the complex dynamic characteristics between the above two indices and the control loops, such control objectives are difficult to achieve using existing control methods. The complexity is reflected by the existence of process heavy nonlinearities, strong coupling and large time variations. As a result, the lower level loop control with human supervision is still widely used in practice. However, since the setpoints to the involved control loops cannot be accurately adjusted under the variations of the boundary conditions, the manual setpoints control cannot ensure that the actual production indices meet with technical requirements all the time. In this paper, an intelligent optimal-setting control (IOSC) approach is developed for a typical two-stage GC so as to optimize the production indices by auto-adjusting on line the setpoints of the control loops in response to the changes in boundary conditions. This IOSC approach integrates case-based reasoning (CBR) pre-setting controlling, neural network (NN)-based soft-sensor and fuzzy adjusting into one efficient control model. Although each control element is well known, their innovative combination can generate better and more reliable performance. Both industrial experiments and applications show the validity and effectiveness of the proposed IOSC approach and its bright application foreground in industrial processes with similar features.     

Granular Rough Theory: A representation semantics oriented theory of roughness

The present work is an archival paper for a series of contributions proposed in last few years on building a theory of roughness over pure mereological relations among information granules. There are five major efforts taken in the present paper: (1) emphasizing on the representational semantics of theory of roughness: to approximately represent a class of entities characterized by some aspects in terms of entity collections described at other aspects; (2) defining a representation model Granular Representation Calculus (GrRC) to synthesize complex information systems from information granules; (3) establishing notion of Granular Rough Theory (GrRT) over information granules operated in terms of GrRC; (4) extending GrRC/GrRT to various computational environments such as multi-agent systems and ontological computing environments; (5) exploring pragmatic aspects of GrRC/GrRT in implementing prototypes with data model and object programming orientations, and proposing an Ontology-Driven Web Information System as a granular-rough computational Web intelligence framework over GrRC/GrRT.     

基于CPLD的ГOCT18977总线发送卡设计

针对某型俄制机载产品的测试需求,提出了一种ГOCT18977总线发送卡的设计方法介绍了ГOCT18977总线标准和80C196KB单片机、EPM7128LC可编程逻辑器件的使用方法,并利用外围电路和上述芯片设计并实现ГOCT18977总线发送卡,介绍了并口EPP模式下ГOCT18977总线数据的控制过程,给出了ГOCT18977总线发送卡的部分设计电路,并阐述了它的设计原理;实践验证了该板卡能够完成ГOCT18977总线数据信息格式转换和传输,完全可以满足对某型俄制机载产品性能检测的要求.     

基于时间放大的光盘抖晃测试系统设计

利用TIA(时序分析仪)或其它模拟方法来测量光盘的抖晃存在成本高、灵活性差、稳定性低等不足,而在实际的光盘生产测试过程中,更需要一种升级扩展方便、性价比高的测试设备。在粗糙脉宽检测方法的基础上,引入时间放大电路,成功地设计出一套以FPGA为核心,基于PCI Express总线的抖晃测试系统;实验表明,该系统具有结构简单、精度高、设计灵活、成本低等特点,在光盘测试和抖晃特性研究方面具有较好的应用价值和一定的市场前景。