一类约化梯阵的RaRb表示

本文首先阐明线性R_aR_b变换之间的关系，并提出了算法MR_ab，再引用标准线性R_aR_b变换，证明了R_aR_b变换与算法MR_ab求解方程组的能力是等价的．然后讨论MR_ab与算法ALT之间的关系，进而说明受ALT攻击的那些有限自动机包含     

线性控制系统窗口频域分析与设计

针对局部频率范围提出了窗口H_∞范数的新概念,指出传统H_∞范数是窗口H_∞范数的特例.利用GKYP引理证明了广义界实定理,研究了线性控制系统在窗口频域的性能分析问题.基于近似模型匹配原则和广义界实定理,将控制器设计问题转化为窗口H_∞范数优化问题.仿真实例表明,窗口H_∞范数适于窗口频域的线性控制系统分析和设计.

     

具有非线性扰动的广义系统的鲁棒H∞控制

研究具有非线性结构扰动广义系统的鲁棒H_∞控制和鲁棒H_∞保性能控制问题,该不确定性为时间和状态的函数,且满足Lipschitz条件.目的是分别设计系统的鲁棒H_∞控制器和鲁棒H_∞保性能控制器.应用线性矩阵不等式方法,分别给出了系统的鲁棒H_∞控制器和鲁棒H_∞保性能控制器存在的充分条件.当这些条件可解时,分别给出了鲁棒H_∞控制器和鲁棒H_∞保性能控制器的表达式.最后通过一个仿真算例说明了所给出方法的应用.

     

模态K4、D4系统的归结推理

本文将Ｐ．Ｅｎｊａｌｂｅｒｔ和Ｌ．ＦａｒｉｎａｓｄｅｌＣｅｒｒｏ提出的模态归结推理方法推广到命题模态逻辑K₄和D₄系统，建立了K₄逻辑的归结推理ＲK₄；D₄逻辑的归结推理R D₄，分别证明了ＲK₄和ＲD₄关于K相似文献   

基于观测器的非线性系统H∞模糊可靠控制

研究了基于观测器的非线性系统H_∞模糊可靠控制问题.采用T-S模糊模型对非线性系统进行建模,用模糊观测器重构系统状态.在系统发生故障时满足给定H_∞性能的约束下,最小化正常情况下的H_∞性能,实现次优H_∞模糊可靠控制.提出了两种应用线性矩阵不等式(LMI)的H_∞模糊可靠控制器设计方法.分别采用两步法和相似变换法将双线性矩阵不等式问题转化为LMI问题.仿真示例验证了所提出方法的有效性.

     

不确定系统鲁棒容错H∞控制的LMI设计方法

针对不确定线性系统,研究了执行器失效情况下鲁棒容错H_∞控制问题.基于连续增益故障模式.利用线性矩阵不等式LMI推导了系统H_∞指标约束下鲁棒容错镇定的充要条件,分别给出了输出反馈和状态反馈H_∞控制器的设计方法.通过引入变量代换,将求解输出反馈H_∞指标约束的鲁棒容错控制器的可解条件转化为标准的LMI所获得的控制器不仅能使故障系统鲁棒定,并且能达到给定的H_∞性能指标.仿真实例验证了所提出设计方法的有效性.

     

环F2+uF2上的Gray映射

在有限环R=F₂+uF₂与F₂之间定义了一个新的Gray映射。证明了该映射是（Rⁿ,Lee重量）到（F²ⁿ,Hamming重量）的等重等距映射,同时证明了环F₂+uF₂上线性码C的二元像Φ（C）是距离不变码,而且如果环F₂+uF₂上线性码C是Lee恒距码,则二元像Φ（C）是F₂上Hamming恒距码。     

基于蕴涵算子族L-λ-R0的模糊推理α-三I约束算法

提出了基于蕴涵算子族L-λ-R₀的模糊推理的思想,这将有助于提高推理结果的可靠性。针对蕴涵算子族L-λ-R₀给出了模糊推理的FMP模型及FMT模型的α-三I约束算法。     

参数不确定的供应链传递函数H∞保成本计算与分析

针对参数不确定的供应链传递函数系统,提出基于H_∞保成本计算的订货策略优选方法.首先,通过遗传算法(GA)和线性矩阵不等式(LMI)相结合的H_∞保成本计算,搜寻参数不确定的传递函数H_∞范数;然后,根据传递函数H_∞范数的大小,比较其对于扰动抑制的能力,确定供应链订货策略的选择;最后,对参数不确定的补货系统和基于生产控制系统的库存和订货系统两类供应链进行了仿真,并进行了最优订货策略的选择与分析.

     

排序的相互k-Skyband查询算法

不同于传统的k-Skyband 查询方法,提出一种相互k-Skyband 查询(MkSB),它从对称角度执行Skyline查询,找出所有既在q的动态k-Skyband(DkSB)中又在q的反向k-Skyband(RkSB)中的数据对象.进一步地,为了更好地支持用户决策和数据分析,排序操作被引入到MkSB算法中.因为MkSB 需要执行q的DkSB 和反向RkSB,故它需要遍历索引多次,从而导致了大量冗余的I/O 开销.利用信息重用技术和若干有效的修剪方法,MkSB 将多次的索引搜索合并成单次,极大地降低了I/O访问次数.同时,证明了基于窗口查询的MkSB(WMkSB)算法具有最低的I/O 代价.在真实与合成数据集上的实验结果表明,所提出的算法是有效的且明显胜过基于BBS 的算法,尤其WMkSB 算法具有极少的I/O 开销,通常能够减少95%以上的冗余I/O.     

Multiple documents summarization based on evolutionary optimization algorithm

This paper proposes an optimization-based model for generic document summarization. The model generates a summary by extracting salient sentences from documents. This approach uses the sentence-to-document collection, the summary-to-document collection and the sentence-to-sentence relations to select salient sentences from given document collection and reduce redundancy in the summary. To solve the optimization problem has been created an improved differential evolution algorithm. The algorithm can adjust crossover rate adaptively according to the fitness of individuals. We implemented the proposed model on multi-document summarization task. Experiments have been performed on DUC2002 and DUC2004 data sets. The experimental results provide strong evidence that the proposed optimization-based approach is a viable method for document summarization.     

An unsupervised approach to generating generic summaries of documents

We present an optimization-based unsupervised approach to automatic document summarization. In the proposed approach, text summarization is modeled as a Boolean programming problem. This model generally attempts to optimize three properties, namely, (1) relevance: summary should contain informative textual units that are relevant to the user; (2) redundancy: summaries should not contain multiple textual units that convey the same information; and (3) length: summary is bounded in length. The approach proposed in this paper is applicable to both tasks: single- and multi-document summarization. In both tasks, documents are split into sentences in preprocessing. We select some salient sentences from document(s) to generate a summary. Finally, the summary is generated by threading all the selected sentences in the order that they appear in the original document(s). We implemented our model on multi-document summarization task. When comparing our methods to several existing summarization methods on an open DUC2005 and DUC2007 data sets, we found that our method improves the summarization results significantly. This is because, first, when extracting summary sentences, this method not only focuses on the relevance scores of sentences to the whole sentence collection, but also the topic representative of sentences. Second, when generating a summary, this method also deals with the problem of repetition of information. The methods were evaluated using ROUGE-1, ROUGE-2 and ROUGE-SU4 metrics. In this paper, we also demonstrate that the summarization result depends on the similarity measure. Results of the experiment showed that combination of symmetric and asymmetric similarity measures yields better result than their use separately.     

基于事件项语义图聚类的多文档摘要方法

基于事件的抽取式摘要方法一般首先抽取那些描述重要事件的句子,然后把它们重组并生成摘要。该文将事件定义为事件项以及与其关联的命名实体,并聚焦从外部语义资源获取的事件项语义关系。首先基于事件项语义关系创建事件项语义关系图并使用改进的DBSCAN算法对事件项进行聚类,接着为每类选择一个代表事件项或者选择一类事件项来表示文档集的主题,最后从文档抽取那些包含代表项并且最重要的句子生成摘要。该文的实验结果证明在多文档自动摘要中考虑事件项语义关系是必要的和可行的。     

基于句子权重和篇章结构的政府公文自动文摘算法

提高文摘自动生成的准确性,能够帮助人们快速有效地获取有价值的信息。本文根据政府公文结构性强的特点,提出一种基于句子权重和篇章结构的政府公文自动文摘算法,首先通过基于游标的截取字符分句算法,对文档中句子和词语信息进行精确统计,获得对文章内容和篇章结构的基本了解;在此基础上,提出基于篇章结构的词语权重和句子权重计算方法,并根据权重计算结果对句子进行权重排序;然后,根据生成摘要的规模,筛选出一定数量的候选文摘句子;最后,对候选文摘句子进行一定的后处理,输出文摘句。实验结果表明,与同类型自动文摘算法以及Word 2003提供的自动文摘工具相比,本文提出的自动文摘算法在准确率和召回率上都有较大提高。     

A text summarizer for Arabic

Automatic text summarization is an essential tool in this era of information overloading. In this paper we present an automatic extractive Arabic text summarization system where the user can cap the size of the final summary. It is a direct system where no machine learning is involved. We use a two pass algorithm where in pass one, we produce a primary summary using Rhetorical Structure Theory (RST); this is followed by the second pass where we assign a score to each of the sentences in the primary summary. These scores will help us in generating the final summary. For the final output, sentences are selected with an objective of maximizing the overall score of the summary whose size should not exceed the user selected limit. We used Rouge to evaluate our system generated summaries of various lengths against those done by a (human) news editorial professional. Experiments on sample texts show our system to outperform some of the existing Arabic summarization systems including those that require machine learning.     

潜在语义分析聚类算法在文摘句子排序中的应用

针对多文档文摘生成过程中话题容易中断和文摘句子语义出现不连贯这两个研究难点, 分析了潜在语义分析聚类算法在句子排序中的应用, 以期提高文摘的生成质量。先采用潜在语义分析聚类算法将文摘句子聚类, 从而形成话题集, 以达到解决话题中断的目的。通过计算文档的文摘展现力, 挑选出文摘展现力最大的文档作为模板, 然后根据模板对文摘句子进行两趟排序。实验结果表明, 提出的算法是有效的, 该算法能够提高文摘的可读性。     

iSpreadRank: Ranking sentences for extraction-based summarization using feature weight propagation in the sentence similarity network

Sentence extraction is a widely adopted text summarization technique where the most important sentences are extracted from document(s) and presented as a summary. The first step towards sentence extraction is to rank sentences in order of importance as in the summary. This paper proposes a novel graph-based ranking method, iSpreadRank, to perform this task. iSpreadRank models a set of topic-related documents into a sentence similarity network. Based on such a network model, iSpreadRank exploits the spreading activation theory to formulate a general concept from social network analysis: the importance of a node in a network (i.e., a sentence in this paper) is determined not only by the number of nodes to which it connects, but also by the importance of its connected nodes. The algorithm recursively re-weights the importance of sentences by spreading their sentence-specific feature scores throughout the network to adjust the importance of other sentences. Consequently, a ranking of sentences indicating the relative importance of sentences is reasoned. This paper also develops an approach to produce a generic extractive summary according to the inferred sentence ranking. The proposed summarization method is evaluated using the DUC 2004 data set, and found to perform well. Experimental results show that the proposed method obtains a ROUGE-1 score of 0.38068, which represents a slight difference of 0.00156, when compared with the best participant in the DUC 2004 evaluation.     

多特征融合的汉越双语新闻摘要方法

为了获取同一事件的汉越双语新闻的自动摘要,该文提出了一种多特征融合的汉越双语新闻摘要方法。关于同一事件的新闻文本,其句子间具有一定的关联关系,利用这些关联关系有助于生成摘要。根据该思想,首先计算句子间的新闻要素共现程度及句子间的相似度;然后将这两种特征融入句子无向图,并利用图排序算法对句子进行排序;之后结合句子的位置特征对排序结果进行调序;最后挑选重要句子并去除冗余生成摘要。在汉越双语新闻文档集上进行了摘要实验,结果表明该方法取得了较好的结果,具有有效性。     

COSUM: Text summarization based on clustering and optimization

Text summarization is a process of extracting salient information from a source text and presenting that information to the user in a condensed form while preserving its main content. In the text summarization, most of the difficult problems are providing wide topic coverage and diversity in a summary. Research based on clustering, optimization, and evolutionary algorithm for text summarization has recently shown good results, making this a promising area. In this paper, for a text summarization, a two‐stage sentences selection model based on clustering and optimization techniques, called COSUM, is proposed. At the first stage, to discover all topics in a text, the sentences set is clustered by using k‐means method. At the second stage, for selection of salient sentences from clusters, an optimization model is proposed. This model optimizes an objective function that expressed as a harmonic mean of the objective functions enforcing the coverage and diversity of the selected sentences in the summary. To provide readability of a summary, this model also controls length of sentences selected in the candidate summary. For solving the optimization problem, an adaptive differential evolution algorithm with novel mutation strategy is developed. The method COSUM was compared with the 14 state‐of‐the‐art methods: DPSO‐EDASum; LexRank; CollabSum; UnifiedRank; 0–1 non‐linear; query, cluster, summarize; support vector machine; fuzzy evolutionary optimization model; conditional random fields; MA‐SingleDocSum; NetSum; manifold ranking; ESDS‐GHS‐GLO; and differential evolution, using ROUGE tool kit on the DUC2001 and DUC2002 data sets. Experimental results demonstrated that COSUM outperforms the state‐of‐the‐art methods in terms of ROUGE‐1 and ROUGE‐2 measures.     

基于超图的文本摘要与关键词协同抽取研究

Text summarization and keyword extraction are two important research topics in Natural Language Processing (NLP), and they both generate concise information to describe the gist of text. Although these two tasks have similar objective, they are usually studied independently and their association is less considered. Based on the graph-based ranking methods, some collaborative extraction methods have been proposed, capturing the associations between sentences, between words and between the sentence and the word. Though they generate both text summary and keywords in an iterative reinforced framework, most existing models are limited to express various kinds of binary relations between sentences and words, ignoring a number of potential important high-order relationships among different text units. In this paper, we propose a new collaborative extraction method based on hypergraph. In this method, sentences are modeled as hyperedges and words are modeled as vertices to build a hypergraph, and then the summary and keywords are generated by taking advantage of higher order information from sentences and words under the unified hypergraph. Experiments on the Weibo-oriented Chinese news summarization task in NLPCC 2015 demonstrate that the proposed method is feasible and effective.
Key words hypergraph;document Summarization;keyword extraction;collaborative extraction