面向中文特定信息变异的过滤技术研究

研究了如何快速识别并过滤经过变异处理的中文信息的技术,并将变异规则限定在当前中文网络最常见的5种变异方法上。提出了一个快速而准确的中文信息多模式模糊匹配算法,该算法在WM算法的基础上融合了压缩编码的思想,适于实时地对网络信息进行处理。实验表明,基于该算法的信息过滤系统能够支持大量的输入模式,系统对模式的识别准确率超过了99％,并且达到了很高的执行效率。该算法在中文信息过滤领域有着广阔的应用前景。     

分布式多Agent安全合作体系结构TRUST!

给出了一种基于互惠和安全的分布式多Agent合作体系结构TRUST！,TRUST！具有很好的可扩展性、鲁棒性、安全性、现现实社会的一致性。同时,基于TRUST！,对分布式合作过滤进行了仿真研究,Agent只有局部视角,通过Agent之间的非线性交互, 形成了宏观的自组织现象,从而有助于探讨复杂系统的本质。     

手机上网文本信息过滤系统的设计与实现

随着手机上网业务的发展,手机网络的不良信息逐渐增多,引发很多社会问题,基于手机端的不良信息过滤软件又有很大的局限性,难以普及,基于服务器端,提出一种运用关键字过滤技术的手机上网文本信息过滤系统,实现不良文本信息的过滤。     

基于面向Agent技术的任务规划系统研究

提出了一种基于任务规划的Agent模型，在此基础上，利用面向对象的Agent技术构造出具有智能化和优化功能的任务规划系统，同时也给出了用现有技术构造Agent模型的方法和面向Agent的系统实现技术。     

基于内容的网络异常信息过滤

虽然网络促进社会经济的发展与进步,但是在互联网规模的不断扩大下,恶意邮件和不良网络,以及不健康网络内容的传播等都将严重影响网络健康运行。因此,对内容的网络异常信息过滤的安全保护成为人们关注的焦点。通过简要介绍网络异常信息过滤,对网络异常信息过滤模型进行分析,并设计基于内容的网络异常信息过滤系统,同时通过试验与分析证明其实用的价值。     

基于智能Agent的农业智能决策系统研究

提出了一种基于知识的智能Agent逻辑模型,该模型以知识为核心,以推理作为主要规划手段。在此逻辑模型的基础上,提出了以本体为核心的Agent内部知识表示方法。并在任务及实体定义的基础上,结合产生式知识表示方法,构造了规划表示方法,形成了一个完整的智能Agent内部知识模型并基于黑板模型设计了智能Agent的实现结构以及其中的关键算法——规划过程。设计了基于智能Agent及黑板机制的农业智能决策系统的基本框架,并以农场作物生产决策系统证实了该模型能够满足大规模分布式农业决策系统的需求。     

超细粉体的过滤、洗涤与高分子精密微孔过滤技术

本文叙述超细粉体液体过滤中若干问题,其中主要有关过滤精度、滤速等计算方法,最后简单介绍了可过滤洗涤的两种高分子精密微孔过滤机及气固精密过滤的有关数据。     

基于Agent的“信息路由”通信模型

针对一种基于Agent的信息收集系统，提出了一个具备“信息路由”模块的通信模型．依据该通信模型，定义了一种用作消息传送的通信语言，并给出了该语言XML的描述。     

一种IDS报警过滤算法及实现架构研究

为提高入侵检测的有效性,提出了一种基于二级决策进行报警过滤从而消除误报、滥报问题的方法,设计实现了一种基于报警缓冲池的报警优化过滤算法,并对算法进行了效率分析和实验。实验结果表明,该技术可以有效地消除误报、滥报现象,具有较强的实用价值。     

基于Agent的水声对抗仿真系统建模与仿真分析

分析了水声对抗仿真系统的组成和特点,基于Agent方法进行了水声对抗仿真建模,建立了Agent实体的功能层、信息层和认知层的模型结构,给出了基于Agent的系统结构和个体Agent的建模过程,从一个新的视点上研究了水声对抗分布仿真系统,最后总结了这种仿真体系的特点和长处。     

A hybrid mapping of information science

Previous studies have shown that hybrid clustering methods that incorporate textual content and bibliometric information can outperform clustering methods that use only one of these components. In this paper we apply a hybrid clustering method based on Fisher’s inverse chisquare to integrate full-text with citations and to provide a mapping of the field of information science. We quantitatively and qualitatively asses the added value of such an integrated analysis and we investigate whether the clustering outcome is a better representation of the field by comparing with a text-only clustering and with another hybrid method based on linear combination of distance matrices. Our data set consists of almost 1000 articles and notes published in the period 2002–2004 in 5 representative journals. The optimal number of clusters for the field is 5, determined by using a combination of distance-based and stability-based methods. Term networks present the cognitive structure of the field and are complemented by the most representative publications. Three large traditional sub-disciplines, particularly, information retrieval, bibliometrics/scientometrics, and more social aspects, and two smaller clusters about patent analysis and webometrics, can be distinguished.     

Network Embedding-Based Anomalous Density Searching for Multi-Group Collaborative Fraudsters Detection in Social Media

Detecting collaborative fraudsters who manipulate opinions in social media is becoming extremely important in order to provide reliable information, in which, however, the diversity in different groups of collaborative fraudsters presents a significant challenge to existing collaborative fraudsters detection methods. These methods often detect collaborative fraudsters as the largest group of users who have the strongest relation with each other in the social media, consequently overlooking the other groups of fraudsters that are with strong user relation yet small group size. This paper introduces a novel network embedding-based framework NEST and its instance BEST to address this issue. NEST detects multiple groups of collaborative fraudsters by two steps. In the first step, to disclose user collaboration, it represents users according to their social relations. Then, in the second step, to identify the collaborative fraudsters, it detects the user groups with anomalous large group density in its representation space. BEST instantiates NEST by using a bipartite network embedding method to represent users and adopting a fast density group detection method based on the k-dimensional tree. Our experiments show BEST (i) performs significantly better in detecting fraudsters on four real-word social media data sets, and (ii) effectively detects multiple groups of collaborative fraudsters, compared to three state-of-the-art competitors.     

Choosing the right collaboration partner for innovation: a framework based on topic analysis and link prediction

Selecting the right collaboration partner is one of the most important contributors to success in collaborative innovation. Accordingly, numerous methods for selecting an appropriate partner have been developed to guide would-be collaborators in their search. Most rely on bibliographic information, which may be easier for that data is readily available and relatively normalized. However, with the benefit of today’s text mining and fusion techniques, it is possible to mine the content of papers and patents so as to result in far more nuanced and advantageous choices. In this article, we explore how to select partners for collaborative innovation by combining the characteristics of the authors of paper and patent documents as well as their content. Drawing on existing research, we developed a systematic framework that relies on topic analysis and link prediction. With a corpus of papers and patents assembled, the framework extracts correlated scientific and technological topics followed by a list of author institutions and a list of patentees. These organisations are parsed and evaluated using two indicators of innovation—capability and openness—to result in two separate ranked lists. Two integrated collaboration networks that include both author institutions and patentees are then built, and a link prediction method identifies missing links with a high likelihood of fruitful cooperation. A case study on hepatitis C virus research shows that the ranking procedure and the link prediction method can be used either together or separately to effectively identify collaborative innovation partners. Our results provide significant quantitative evidence for policymakers who are looking to foster cooperation between research institutions and/or high-tech enterprises. Our research may also serve as the basis for further in-depth research on collaborative innovation, R&D cooperation, and link prediction theories and methods.

     

Determination of fracture toughness and energy dissipation of SiO2-poly(methyl metacrylate) hybrid films by nanoindentation

In this paper we have applied different methods based on nanoindentation techniques to measure the toughness of SiO₂-poly methyl-methacrylate hybrid films on organic acrylic substrates. The hybrid films were deposited by the Sol-Gel method from precursor solutions containing tetraethyl-orthosilicate, methylmethacrylate and 3-trimethoxysilyl propyl methacrylate (TMSPM) as the coupling agent. The influence of TMSPM content in the hybrid precursor solution on the fracture behavior of the hybrid films was studied. The classical indentation crack length method was applied from nanoindentation tests to determine the stress intensity factor by direct measurement of crack length from atomic force microscopy images. A second method, based on the pop-in analysis allowed the separation of crack formation from film delamination from multiple pop-ins. Finally, a third method based on energy methods is also reported and discussed. The amount of TMSPM in the precursor solution showed a strong influence on the toughness of the hybrid films.     

Optimal and hierarchical clustering of large-scale hybrid networks for scientific mapping

Previous studies have shown that hybrid clustering methods based on textual and citation information outperforms clustering methods that use only one of these components. However, former methods focus on the vector space model. In this paper we apply a hybrid clustering method which is based on the graph model to map the Web of Science database in the mirror of the journals covered by the database. Compared with former hybrid clustering strategies, our method is very fast and even achieves better clustering accuracy. In addition, it detects the number of clusters automatically and provides a top-down hierarchical analysis, which fits in with the practical application. We quantitatively and qualitatively asses the added value of such an integrated analysis and we investigate whether the clustering outcome provides an appropriate representation of the field structure by comparing with a text-only or citation-only clustering and with another hybrid method based on linear combination of distance matrices. Our dataset consists of about 8,000 journals published in the period 2002–2006. The cognitive analysis, including the ranked journals, term annotation and the visualization of cluster structure demonstrates the efficiency of our strategy.     

Hybrid clustering of multi-view data via Tucker-2 model and its application

With the modern technology fast developing, most of entities can be observed by different perspectives. These multiple view information allows us to find a better pattern as long as we integrate them in an appropriate way. So clustering by integrating multi-view representations that describe the same class of entities has become a crucial issue for knowledge discovering. We integrate multi-view data by a tensor model and present a hybrid clustering method based on Tucker-2 model, which can be regarded as an extension of spectral clustering. We apply our hybrid clustering method to scientific publication analysis by integrating citation-link and lexical content. Clustering experiments are conducted on a large-scale journal set retrieved from the Web of Science (WoS) database. Several relevant hybrid clustering methods are cross compared with our method. The analysis of clustering results demonstrate the effectiveness of the proposed algorithm. Furthermore, we provide a cognitive analysis of the clustering results as well as the visualization as a mapping of the journal set.     

Parametric Collaborative Design of Network-based Mechanical Products

Aiming at the problems of low-level information sharing,slow transmission and repetitive work in the designing process of product series,the internet-oriented parametric collaborative design method is proposed,in which the problems of sharing conflict and network heterogeneous in the distributed collaborative design are analyzed,and the construction method of collaborative design platforms based on PDMWorks Workgroup is put forward. Through studying the mechanism of roles distribution and function allocation and data concurrency control,the communication mechanism of internet-oriented collaborative design is formulated. On the basis of structure features of overhead travelling crane,through combining parametric variant design with collaborative design,internet-oriented parametric collaborative design system of overhead travelling crane is developed and verified through main girder design. In the paper,the internet-oriented parametric collaborative design method is proposed,aiming to solve the problems of low-level information sharing,slow transmission and repetitive work in the designing process of product series. The problems of sharing conflict and network heterogeneous in the distributed collaborative design are analyzed. The construction method of collaborative design platforms based on PDMWorks Workgroup is put forward. The communication mechanism of internet-oriented collaborative design is formulated,through studying the mechanism of roles distribution and function allocation and data concurrency control. On the basis of structure features of overheadtravelling crane,through combining parametric variant design with collaborative design,internet-oriented parametric collaborative design system of overhead travelling crane is developed and verified through main girder design.     

基于嵌入协作的多方法协作优化方法

提出一种基于嵌入协作的多方法协作优化方法。算法采用嵌入方式组织各个优化方法之间的协作,利用优化方法之间的协作效应提高优化性能。进行遗传算法、模式搜索法和Powell法嵌入协作组成的多方法协作优化方法设计。计算实例表明,基于嵌入协作的多方法协作优化方法取得了优于单个优化方法的全局最优特性。     

Automatic brain tissue segmentation in MR images using hybrid atlas forest based on confidence-weighted probability matrix

The segmentation of specific tissues in an MR brain image for quantitative analysis can assist the disease diagnosis and medical research. Therefore, a robust and accurate method for automatic segmentation is necessary. Atlas-based-method is a common and effective method of automatic segmentation where an atlas refers to a pair of image consist of an intensity image and its corresponding label image. Apart from the general multi-atlas-based methods, which propagate labels through the single atlas then fuse them, we proposed a hybrid atlas forest based on confidence-weighted probability matrix to consider the atlases set as a whole and treat each voxel differently. In the framework, we first register the atlas to the image space of target and calculate the confidence of voxels in the registered atlas. Then, a confidence-weighted probability matrix is generated and it augments to the intensity image of the atlas or target for providing spatial information of the target tissue. Third, a hybrid atlas forest is trained to gather the features and correlation information among the atlases in the dataset. Finally, the segmentation of the target tissues is predicted by the trained hybrid atlas forest. The segment performance and the components efficiency of the proposed method are evaluated on the two public datasets. Based on the experiment results and quantitative comparisons, our method can gather spatial information and correlation among the atlases to obtain an accurate segmentation.     

Finite formulation of nonlinear magnetostatics with Integral boundary conditions

This paper describes two hybrid methods coupling finite formulation of electromagnetic fields (FFEF) in a bounded domain to integral boundary conditions taking into account far field conditions. The two hybrid techniques use different boundary conditions: the first formulation is based on Green's function applied to magnetization source inside bounded domain while the other one is based on a boundary-element method on its external surface. Details about the coupling terms are given and handling of different magnetization sources is described, including the fictitious magnetization sources coming from nonlinear solutions. The proposed methods are validated versus different benchmark cases. Comparisons between the two techniques have been performed using different criteria (accuracy and convergence, memory requirements, etc.).