基于权重信息挖掘社会网络中的隐含社团

社团结构是一种普遍存在于各类真实网络中的结构特性.挖掘网络的社团结构对于理解网络的功能与行为有着重要作用.然而,现有的各种社团挖掘算法仅仅基于网络拓扑结构信息,而忽视了蕴涵于真实社会网络边权信息中丰富的语义信息.目前普遍使用的基于模块性最大化的社团挖掘算法倾向于将小社团合并,这使得语义上丰富的小社团容易湮灭于基于拓扑结构信息所挖掘出的大社团中.而挖掘出这些隐含于大社团中的有着丰富语义内涵的小社团对于加深社会网络语义层面的理解有着重要作用.为此,提出一个接近线性复杂度的有权网络社团挖掘算法.通过充分利用权重信息,算法可以将社会网络划分为富含语义信息的粒度较细且相对较小的隐含社团.通过对基于DBLP作者合作网络的实证分析,证实了新算法的有效性和高效性.     

基于调和映射的三维医学图像增强系统

医学成像仪器产生12位的体数据,然而普通显示器只支持8位的灰度图。如果使用普通显示器来显示医学图像,显卡会自动将这些12位数据转化成8位数据,这就造成了数据动态范围被压缩。解决这一问题,在2D领域通常使用昂贵的医学专用灰阶显示器。在3D领域却限制了医生使用三维可视化软件。尝试将一些在2D图像处理中使用的图像增强技术如加权平均模板,不同程度曝光等来实现色调映射算法等应用到三维体数据的显示当中,通过种种复杂的数据变换,使得在普通显示器上显示的8位灰度图呈现出更高的动态范围,得到更为生动、更加丰富的显示效果。这种技术也可以实现任意位数的转化,使得12位显示器的显示效果也可以得到增强。     

新蚁群算法模型在大学课程时间表问题中的应用

分析大学课程时间表问题的特征,结合已有蚁群算法的求解策略,构建了新的问题求解模型,提出了一种基于蚁群算法和改进过程的求解算法,并在不同规模的问题实例上进行实验。结果表明,算法在目标函数解的质量上有明显改进。     

基于相似度的三元社团合并算法*

针对使用相似度测量进行社团划分时可能出现的判断冲突问题,提出了一种基于相似度的三元社团合并算法。首先通过对相似度阈值的选取,筛选网络中不同的三元社团,并将其作为社团合并的基本元素,通过社团相似度将其合并。然后将剩余节点和孤立三元社团分别按照节点从属度和三元社团从属度划分到相应社团。最后通过在人工合成网络和真实世界网络上进行实验测试,结果表明用本文算法可以准确高效的将网络中的节点划分到相应的社团。     

Online adaptive optimal control for continuous-time nonlinear systems with completely unknown dynamics

An online adaptive optimal control is proposed for continuous-time nonlinear systems with completely unknown dynamics, which is achieved by developing a novel identifier-critic-based approximate dynamic programming algorithm with a dual neural network (NN) approximation structure. First, an adaptive NN identifier is designed to obviate the requirement of complete knowledge of system dynamics, and a critic NN is employed to approximate the optimal value function. Then, the optimal control law is computed based on the information from the identifier NN and the critic NN, so that the actor NN is not needed. In particular, a novel adaptive law design method with the parameter estimation error is proposed to online update the weights of both identifier NN and critic NN simultaneously, which converge to small neighbourhoods around their ideal values. The closed-loop system stability and the convergence to small vicinity around the optimal solution are all proved by means of the Lyapunov theory. The proposed adaptation algorithm is also improved to achieve finite-time convergence of the NN weights. Finally, simulation results are provided to exemplify the efficacy of the proposed methods.     

Extension of the lower bound of monitor solutions of maximally permissive supervisors to non-α net systems

Traditional region-based liveness-enforcing supervisors focus on (1) maximal permissiveness of not losing legal states, (2) structural simplicity of minimal number of monitors, and (3) fast computation. Lately, a number of similar approaches can achieve minimal configuration using efficient linear programming. However, it is unclear as to the relationship between the minimal configuration and the net structure. It is important to explore the structures involved for the fewest monitors required. Once the lower bound is achieved, further iteration to merge (or reduce the number of) monitors is not necessary. The minimal strongly connected resource subnet (i.e., all places are resources) that contains the set of resource places in a basic siphon is an elementary circuit. Earlier, we showed that the number of monitors required for liveness-enforcing and maximal permissiveness equals that of basic siphons for a subclass of Petri nets modelling manufacturing, called α systems. This paper extends this to systems more powerful than the α one so that the number of monitors in a minimal configuration remains to be lower bounded by that of basic siphons. This paper develops the theory behind and shows examples.     

Fault diagnosis of nonlinear and large-scale processes using novel modified kernel Fisher discriminant analysis approach

It is pretty significant for fault diagnosis timely and accurately to improve the dependability of industrial processes. In this study, fault diagnosis of nonlinear and large-scale processes by variable-weighted kernel Fisher discriminant analysis (KFDA) based on improved biogeography-based optimisation (IBBO) is proposed, referred to as IBBO-KFDA, where IBBO is used to determine the parameters of variable-weighted KFDA, and variable-weighted KFDA is used to solve the multi-classification overlapping problem. The main contributions of this work are four-fold to further improve the performance of KFDA for fault diagnosis. First, a nonlinear fault diagnosis approach with variable-weighted KFDA is developed for maximising separation between the overlapping fault samples. Second, kernel parameters and features selection of variable-weighted KFDA are simultaneously optimised using IBBO. Finally, a single fitness function that combines erroneous diagnosis rate with feature cost is created, a novel mixed kernel function is introduced to improve the classification capability in the feature space and diagnosis accuracy of the IBBO-KFDA, and serves as the target function in the optimisation problem. Moreover, an IBBO approach is developed to obtain the better quality of solution and faster convergence speed. On the one hand, the proposed IBBO-KFDA method is first used on Tennessee Eastman process benchmark data sets to validate the feasibility and efficiency. On the other hand, IBBO-KFDA is applied to diagnose faults of automation gauge control system. Simulation results demonstrate that IBBO-KFDA can obtain better kernel parameters and feature vectors with a lower computing cost, higher diagnosis accuracy and a better real-time capacity.     

Towards a set of agrosystem-specific cropland mapping methods to address the global cropland diversity

Accurate cropland information is of paramount importance for crop monitoring. This study compares five existing cropland mapping methodologies over five contrasting Joint Experiment for Crop Assessment and Monitoring (JECAM) sites of medium to large average field size using the time series of 7-day 250 m Moderate Resolution Imaging Spectroradiometer (MODIS) mean composites (red and near-infrared channels). Different strategies were devised to assess the accuracy of the classification methods: confusion matrices and derived accuracy indicators with and without equalizing class proportions, assessing the pairwise difference error rates and accounting for the spatial resolution bias. The robustness of the accuracy with respect to a reduction of the quantity of calibration data available was also assessed by a bootstrap approach in which the amount of training data was systematically reduced. Methods reached overall accuracies ranging from 85% to 95%, which demonstrates the ability of 250 m imagery to resolve fields down to 20 ha. Despite significantly different error rates, the site effect was found to persistently dominate the method effect. This was confirmed even after removing the share of the classification due to the spatial resolution of the satellite data (from 10% to 30%). This underlines the effect of other agrosystems characteristics such as cloudiness, crop diversity, and calendar on the ability to perform accurately. All methods have potential for large area cropland mapping as they provided accurate results with 20% of the calibration data, e.g. 2% of the study area in Ukraine. To better address the global cropland diversity, results advocate movement towards a set of cropland classification methods that could be applied regionally according to their respective performance in specific landscapes.     

Phase adjustment for multistatic passive radar imaging based on image entropy and image contrast

Multistatic passive radar imaging system (MPRIS) does not behave as well as traditional inverse synthetic aperture radar (ISAR) imaging system does on account of the limited illuminators of opportunity and relatively narrow bandwidth of the signals. Furthermore, in the MPRIS, limited by the unavoidable measurement errors of the positions of the illuminators or receivers, or the manoeuvrability of the moving target, the translation motion of the target cannot be compensated accurately, in which the echo will be contaminated by an error phase, resulting in the occurrence of serious image defocus and the degeneracy of the image quality. In this article, two nonparametric autofocusing techniques based on the principles of minimum entropy and maximum contrast, respectively, are proposed to deal with the undesired problems under the multistate geographical configuration consisting of one illuminator and several receivers distributed around a target. The error phase is corrected by introducing an adjustment phase in the frequency domain and the adjustment phase can be searched by iteratively solving an equation that is derived by optimizing the quality measure of the image. Since no assumption is made during the derivation, the proposed techniques could be used to compensate any form of the phase error. In addition, the influence of the multistatic configuration on the image resolution is analysed and an optimized configuration is obtained. The effectiveness of the proposed algorithms is verified via the theoretical derivations and simulations.