基于等差矩阵的性质特征,提出了一种QC-LDPC(准循环低密度奇偶校验)码的新颖构造方法。该构造方法易于有效编译码,具有良好的围长特性,可有效避免四环,使生成的码字不受短环的干扰,且在硬件实现方面可节省存储空间。仿真结果表明:当误码率达到10-7时,该构造方法造出的高码率QC-LDPC(3780,3542)码与ITU-T G.975中的RS(255,239)码相比,其净编码增益提高了约2.1dB,具有更好的纠错性能;与QC-LDPC(4221,3956)码相比,净编码增益提高了0.35dB。

On the basis of the property and features of equideviate matrixes,a novel method for constructing Quasi-Cyclic Low-Density Parity-Check (QC-LDPC)codes is presented in this paper.The LDPC codes constructed in this method are easy for ef-fective encoding and decoding,have good girth properties and can avoid the girth-4 effectively,which prevents the generated codes from short cycle interference.Furthermore,it can save the storage space in terms of hardware implementation.The sim-ulation results show that when the bit error rate reaches 10-7 ,the net coding gain of the high code-rate QC-LDPC (3780, 3542)constructed this way is improved by about 2.1 dB as compared with that of the RS (255,239)code in ITU-T G.975, proving that the code has a better error correcting performance,and by 0.35 dB as compared with that of regular QC-LDPC (4221,3956)code.

螺栓松动是一种常见且具有潜在危害的机械故障。考虑到螺栓松动会导致被联接件结合部动力参数发生变化，提出了一种基于两被联接件振动信号的松动识别方法。所提方法首先计算两信号的概率密度，并对概率密度曲线进行网格化处理生成概率矩阵，继而对概率矩阵进行主元分析（PCA），在合并两路信号经主元分析后所得投影矩阵之后，再次进行主元分析和投影。设计了两种识别方式，方式1首先按上述过程进行已知样本训练以得到各松动状态投影点，识别时根据所得投影点与各状态投影点间的欧式距离进行判断；方式2使用螺栓紧固状态时所得样本数据和现场实测数据直接按上述过程进行计算，并根据 PCA 特性设计了松动判别条件。试验验证表明所提方法能够准确区分不同松动状态，且识别方式2操作简便，无需故障样本，易于实际应用。

Considering that the bolt looseness induces the variation of dynamic parameters of joint of bolt connection,a detection method was proposed based on vibration signals of two connected parts.The probability densities of the two signals were calcuated,the probability density curves were processed by a mesh to obtain probability matrixes, and were the probability matrixes were transformed by using principal component analysis (PCA)method.After PCA,two projection matrixes of the probability matrixes were merged to one matrix.To this matrix,PCA and projection were implemented again.According to the peculiarity of bolt looseness,tow detection modes are designed based on the proposed method.The mode 1 carries out training work using foregone samples to generate projection points of each looseness condition employing the proposed method,then in detection,Euclidean distance between projection point and each foregone projection point was used as estimation criterion.The mode 2 directly

科研活动里的合作现象越来越成为一种全球范围内的大趋势，表现在论文产出中是作者间的合著率在过去的100多年内越来越高，同时这种活动中的合作关系也越来越复杂。本文以图书馆与情报学领域内的论文合著为例，主要针对科研合作网络展开研究，生成了以合著论文里文献生产者共现为特征的合作矩阵与网络，并验证了作者合作网络的无标度特征。本文将合作网络具体细化为三种类型：作者合作网络、机构合作网络和国家合作网络，并将这三种网络使用复杂网络与社会网络技术结合进行分析并作了横向对比。结果发现图书馆与情报学领域内的作者合作网络是非连通的稀疏网络，科学家之间已成多个研究社团，机构与国家合作网络都有较短的平均路径、较高的网络密度和集聚系数。论文最后还对目前常用的合作网络研究方法进行了反思和展望。

Collaboration efforts in research activities is increasingly becoming a worldwide trend,which was reflected in the ratio for collaborators proportions in all the research papers communities higher and higher in the past one hundred years, while the relationship in such activities becomes increasingly complex. In this paper, taking the cooperation phenomenon in the field of library and information science as an example, the scientific collaboration networks was mainly explored, the cooccurrence matrix characterized by cooperation networks was produced, and the author scalefree networks phenomena was verified. Then the knowledge networks were defined clearly as author cooperation networks, institutions cooperation networks and national cooperation networks, all these three networks were studied with complex networks and social network technologies analysis under a comparison perspective. The results showed that the cooperation networks in the field of library and information sc