一种新型的自适应人工免疫算法

论文简略叙述了免疫系统的基本原理,并对两种典型的免疫算法aiNet和RLAIS进行了详细分析,进而提出了一种新型的自适应人工免疫网络算法用于离散数据的聚类分析。此算法能得到稳定和良好的聚类结果,并能达到用户要求的数据压缩比,另外需用户确定的控制参数很少。     

Reliable Distributed Network Management by Replication

This paper presents a new clustering architecture for SNMP agents that supports semi-active replication of managed objects. A cluster of agents provides fault-tolerant object functionality: replicated managed objects of a crashed agent of a given cluster may be accessed through a peer cluster. The proposed architecture is structured in three layers. The lower layer corresponds to the managed objects at the network elements. The middle layer contains management entities called clusters that monitor and replicate managed objects. The upper layer allows the definition of management clusters as well as the relationship between clusters. A practical tool was implemented and is presented. The impact of replication on network performance is evaluated as well as a probabilistic analysis of replicated object consistency.     

Automatic localization of craniofacial landmarks for assisted cephalometry

In this paper we propose a system for localization of cephalometric landmarks. The process of localization is carried out in two steps: deriving a smaller expectation window for each landmark using a trained neuro-fuzzy system (NFS) then applying a template-matching algorithm to pin point the exact location of the landmark. Four points are located on each image using edge detection. The four points are used to extract more features such as distances, shifts and rotation angles of the skull. Limited numbers of representative groups that will be used for training are selected based on k-means clustering. The most effective features are selected based on a Fisher discriminant for each feature set. Using fuzzy linguistics if-then rules, membership degree is assigned to each of the selected features and fed to the FNS. The FNS is trained, utilizing gradient descent, to learn the relation between the sizes, rotations and translations of landmarks and their locations. The data for training is obtained manually from one image from each cluster. Images whose features are located closer to the center of their cluster are used for extracting data for the training set. The expected locations on target images can then be predicted using the trained FNS. For each landmark a parametric template space is constructed from a set of templates extracted from several images based on the clarity of the landmark in that image. The template is matched to the search windows to find the exact location of the landmark. Decomposition of landmark shapes is used to desensitize the algorithm to size differences. The system is trained to locate 20 landmarks on a database of 565 images. Preliminary results show a recognition rate of more than 90%.     

基于微波谐振器的谷物湿度检测系统

设计和研制一种能连续测量谷物湿度的微波检测系统.提出利用微波谐振器的干扰特性测量谷物的湿度,并设计专用于谷物湿度检测的中心开通孔的微波谐振器.当谷物颗粒通过微波谐振器的中孔时,其质量和湿度都令谐振器的谐振频率产生偏移和功率衰减,谷物的质量和湿度两者所引起的谐振频率偏移和微波功率衰减是相对独立的,在同一湿度下谷物的质量仅仅与微波功率的衰减量有关.因此在得到谐振频率的偏移量和微波功率的衰减量后,就可以计算谷物的湿度.在大量试验的基础上,对试验数据进行模糊聚类,从而对微波谐振器的频偏和功率衰减特性进行建模,据此设计并研制了谷物湿度的微波连续检测系统.试验调试表明:该检测系统适宜于谷物的连续湿度检测,检测精度达到92%以上.     

多代表点自约束的模糊迁移聚类

以往建立在模糊C均值(fuzzyC-means, FCM)框架下利用源域虚拟簇中心作为迁移知识的迁移聚类算法容易受到离群点和噪声的干扰,且单个簇中心不足以描述簇结构。针对此问题,提出多代表点自约束的模糊迁移聚类算法,该算法引入样本代表权重机制为簇中每个样本分配代表权重来刻画簇结构,这种机制能更好的刻画簇结构,对离群点和噪声有较好的抑制作用;同时利用源域样本,重构目标域簇结构,并以此作为迁移知识进行目标域样本聚类,相对于利用单中心作为迁移知识来说,整体重构后的目标域簇结构所包含的迁移知识量更为丰富。试验结果表明。在人工数据集和真实数据集上,所提出的聚类算法相比对比算法, NMI和ARI最高提升了0.674 5和0.608 4。说明在迁移环境下,以代表点自约束作为知识迁移规则,所提出的聚类算法具有一定的聚类效果。     

密度峰值聚类的自适应社区发现算法

为减少社区发现算法中参数的选择对社区划分的影响,同时使算法能够自适应地进行社区划分,本文提出一种基于核密度估计的密度峰值聚类的社区发现算法KDED.首先,定义一种基于信任度的距离度量,将社交网络中的用户关系量化为距离矩阵,使用矩阵元素的大小度量用户关系的紧密程度;然后对距离矩阵进行核密度估计,统计各个节点在网络中的影响大小,结合热扩散模型改进计算流程,使其自适应不同规模的数据集以提高计算精度;结合密度峰值聚类原理和社区属性确定社区中心节点后,可根据节点间的距离得到社区内部层次结构和社区外部的自然结构;最后将剩余节点按距离分配到相应的社区当中以完成社区划分.仿真结果表明:通过可视化软件可观察到,通过KDED算法得到的社区划分结果具有清晰的自然结构和内部层次结构;随着社区规模的提升以及划分难度增加,KDED算法具有出色的稳定性;在真实数据集以及LFR基准网络上均得到较为接近真实划分结果的社区划分,自适应性良好,验证算法的可行性与有效性.     

基于用户聚类的二分图网络协同推荐算法

针对协同过滤推荐系统应用中存在的数据稀疏、可扩展性受限等问题,提出了一种基于用户聚类的二分图网络协同推荐算法.该算法在用户聚类阶段对二分图网络进行用户中心聚类,并获取用户聚类中心及其所在的群组,基于用户群组的评价信息为目标用户提供更广泛的推荐数据;在协同推荐阶段,围绕聚类中心及其所在群组为未评分项目完成预测评分,为用户推荐综合评分最高的Top-n项目.结果表明,该算法能够提升目标用户推荐的准确度,并能改善协同推荐的多样性.     

K-Harmonic means type clustering algorithm for mixed datasets

K-means type clustering algorithms for mixed data that consists of numeric and categorical attributes suffer from cluster center initialization problem. The final clustering results depend upon the initial cluster centers. Random cluster center initialization is a popular initialization technique. However, clustering results are not consistent with different cluster center initializations. K-Harmonic means clustering algorithm tries to overcome this problem for pure numeric data. In this paper, we extend the K-Harmonic means clustering algorithm for mixed datasets. We propose a definition for a cluster center and a distance measure. These cluster centers and the distance measure are used with the cost function of K-Harmonic means clustering algorithm in the proposed algorithm. Experiments were carried out with pure categorical datasets and mixed datasets. Results suggest that the proposed clustering algorithm is quite insensitive to the cluster center initialization problem. Comparative studies with other clustering algorithms show that the proposed algorithm produce better clustering results.     

GA based decomposition of large scale distributed model predictive control systems

A novel method is proposed to find the optimal decomposition structure of distributed model predictive control (DMPC) systems. The input clustering decomposition (ICD) is first developed to minimize the coupling effects of subsystems and average the computational balance of each subsystem. To select the inputs and outputs in each subsystem, the input–output pairing decomposition (IOPD) is done. Then the genetic algorithm is used to solve decomposition problems for ICD and IOPD. The proposed method can achieve efficient coordination. Its structure is more flexible than the traditional DMPC. Two examples are used to show the abilities of the proposed method.     

A novel video abstraction method based on fast clustering of the regions of interest in key frames

Online video nowadays has become one of the top activities for users and has become easy to access. In the meantime, how to manage such huge amount of video data and retrieve them efficiently has become a big issue. In this article, we propose a novel method for video abstraction based on fast clustering of the regions of interest (ROIs). Firstly, the key-frames in each shot are extracted using the average histogram algorithm. Secondly, the saliency and edge maps are generated from each key-frame. According to these two maps, the key points for the visual attention model can be determined. Meanwhile, in order to expand the regions surrounding the key points, several thresholds are calculated from the corresponding key-frame. Thirdly, based on the key points and thresholds, several regions of interest are expanded and thus the main content in each frame is obtained. Finally, the fast clustering method is performed on the key frames by utilizing their ROIs. The performance and effectiveness of the proposed video abstraction algorithm is demonstrated by several experimental results.