基于LBSN动态异构网络的时间感知兴趣点推荐

随着基于位置社交网络(Location-Based Social Network,LBSN)的快速发展,兴趣点(Point-Of-Interest,POI)推荐可以帮助人们发现有趣的并吸引人的位置。针对签到数据的稀疏性和用户兴趣的动态性等挑战性问题,提出了基于LBSN动态异构网络的时间感知兴趣点推荐算法。在LBSN异构网络模式中增加会话节点类型。通过动态元路径,在用户和兴趣点语义关系之间有效地融入时间信息、位置信息和社交信息等。设置了用户-兴趣点之间的动态元路径集,并提出了动态路径实例的偏好度计算方法。采用矩阵分解模型对不同动态偏好矩阵进行矩阵分解。根据不同动态元路径的用户特征矩阵和兴趣点特征矩阵,获取用户在目标时间访问兴趣点的推荐列表。实验结果表明,与其他兴趣点推荐方法相比,所提方法在兴趣点推荐精确度上取得了较好的推荐结果,具有良好的应用前景。     

On robustness in the gap metric and coprime factor uncertainty for LTV systems

In this paper, we study the problem of robust stabilization for discrete linear time-varying (LTV) systems subject to time-varying normalized coprime factor uncertainty. Operator theoretic results which generalize similar results known to hold for linear time-invariant (infinite-dimensional) systems are developed. In particular, we compute an upper bound for the maximal achievable stability margin under TV normalized coprime factor uncertainty in terms of the norm of an operator with a time-varying Hankel structure. We point to a necessary and sufficient condition which guarantees compactness of the TV Hankel operator, and in which case singular values and vectors can be used to compute the time-varying stability margin and TV controller.     

裂纹数密度具有空间分布的随机介质模拟理论

利用随机过程的谱展开理论以及Hudson的裂纹介质模型，构造一种裂纹数密度具有空间统计分布的随机介质模型，给出了详细的理论推导过程。该模型利用Hudson理论的将裂纹微观参数(裂纹数密度、裂纹半径等)与裂纹介质的宏观性质(弹性常数)联系起来的特点，对可以用二维指数椭圆型、Gaussian型自相关函数描述裂纹数密度的裂纹介质，进行了二维随机介质的模拟。结果表明：(1)基于这一模拟理论的随机介质模型能灵活、有效地描述实际非均匀、各向异性裂纹介质：(2)裂纹数密度对随机裂纹介质的各个弹性常数具有不同程度的影响：(3)Gaussian型自相关函数能描述单尺度平滑的非均匀介质，而指数型随机介质具有多尺度、自相似的特性。     

基于INS/GPS的智能导弹协同作战相对导航系统研究

提出了一种基于惯性导航系统和GPS导航系统的相对导航系统,这一系统是智能导弹协同作战的关键技术之一,相对导航系统通过智能导弹之间的数据链共享导航信息,采用卡尔曼滤波融合相对惯导信息和相对GPS信息,从而实现相对导航.给出了两种相对导航系统模型,并通过仿真研究校验了相对导航方法的有效性.     

单螺杆半自锁直角扣件的脚手架施工技术

随着我国建筑业的飞速发展,对建筑工程的要求不断提高。不仅需要先进的建筑工程技术手段,而且积极开发和运用新技术、新工艺和新方法。在脚手架施工实践过程中我们潜心专研开发出新型单螺杆半自锁直角扣件。并进行大量试验,结合浙江柯建集团有限公司承建的多个建筑工程的实践经验,总结出一套新型单螺杆半自锁直角扣件脚手架的施工技术。该技术得到了有关专家的肯定,具有明显的经济效益和社会效益。     

泰州市大气挥发性有机物化学组分特征、活性及来源解析

挥发性有机物（VOCs）是臭氧和大气颗粒物的重要前体物,本研究利用在线气相色谱-质谱仪（Online-GC-MS）于2018年5—6月对江苏省泰州市大气中98种VOCs进行监测,依据监测结果对泰州市大气VOCs的组成特征、日变化趋势进行分析,对醛酮类VOCs数据进行参数化拟合探究其一次二次贡献,并采用正矩阵因子分解模型（PMF）对VOCs数据进行来源分析,用最大增量反应活性（MIR）计算臭氧生成潜势（OFP）.研究结果表明：泰州市大气VOCs中烷烃占比最高,其次为醛酮;烷烃、烯烃、卤代烃和芳香烃浓度日变化趋势明显,特征相近;参数化方法表明醛类物质主要来自于二次生成,而酮类物质主要来自一次排放;PMF模型结果表明泰州市VOCs的主要贡献源分别为机动车排放、油气溶剂挥发、生物质燃烧、其他工业和天然源;OFP的主要贡献物种为烯烃类,占比34.18%.研究结果表明,控制工业排放和溶剂使用是泰州市大气污染物控制的重点.     

基于自适应形态提升小波与改进非负矩阵分解的发动机故障诊断方法

信号处理与特征参数提取是实现发动机故障诊断的关键。针对传统小波和形态小波的 缺陷,提出一种自适应形态梯度提升小波变换(AMGLW)。该方法采用信号的局部梯度作为判断 信号奇异性的度量指标,在信号突变处采用提出的形态梯度提升算子以保留信号的冲击特征,在信 号缓变处采用平滑算子以抑制噪声。在此基础上,提出采用改进非负矩阵分解方法对分解后的信 号进行特征提取,计算用于发动机故障分类的特征参数。利用实测的发动机在5 种状态下的振动 信号对提出的信号处理及特征提取方法进行了验证。     

Decomposition of singular large sparse matrix by adding dummy links and dummy degrees

Abstract

This paper proposes a simple scheme to decompose an n×n nonpositive definite matrix, A, associated with simultaneous equations, A X = B, into a triple‐factors (lower triangular, diagonal, and upper triangular matrices), i.e., Å = L D U, without interchanging rows or columns of A, but with A expanded with new rows and new columns to an m×m matrix Å. Whenever a near‐zero diagonal element, say ā_ii , is encountered and used as a pivoting element, an appropriate positive real number, say p, is added to this diagonal element, and a new term —px_k is also added to the i‐th equation, where x_k is a new variable called “dummy variable'’. If we also add a new equation —px_i + px_k = 0 to enforce the new added variable x_k equal to x_i then the modified i‐th equation has the same effect as the original equation. Therefore, the original solution X can be found directly from the expanded solution of the modified expanded equation. The method is very useful in solving the following problems: (1) nonlinear problems near the limit state, (2) postbuckling analysis, (3) system equations with constraint conditions, and (4) getting eigenvectors from eigenvalues.     

Simplification of I-Vector Extraction for Speaker Identification

The identity vector (i-vector) approach has been the state-of-the-art for text-independent speaker recognition,both identification and verification in recent years.An i-vector is a low-dimensional vector in the socalled total variability space represented with a thin and tall rectangular matrix.This paper introduces a novel algorithm to improve the computational and memory requirements for the application.In our method,the series of symmetric matrices can be represented by diagonal expression,sharing the same dictionary,which to some extent is analogous to eigen decomposition,and we name this algorithm Eigen decomposition like factorization (EDLF).Similar algorithms are listed for comparison,in the same condition,our method shows no disadvantages in identification accuracy.