Conventional gyrocompass alignment methods are based on relatively small azimuth misalignment angles.However,a marine strapdown inertial navigation system may face large azimuth misalignment angle caused by a failed coarse alignment algorithm.This paper provides a novel gyrocompass alignment method to solve the problem.Effects of system parameters are analyzed and the proper scenario of parameter switch based on the classic control theories is derived.Test results show that compared with the conventional methods,our method can accomplish the initial alignment quickly and accurately under large azimuth misalignment angle.
In recent years,local community detection algorithms have developed rapidly because of their nearly linear computing time and the convenience of obtaining the local information of real-world networks.However,there are still some issues that need to be further studied.First,there is no local community detection algorithm dedicated to detecting a seed-oriented local community,that is,the local community with the seed as the core.The second and third issues are that the quality of local communities detected by the previous local community detection algorithms are largely dependent on the position of the seed and predefined parameters,respectively.To solve the existing problems,we propose a seed-oriented local community detection algorithm,named SOLCD,that is based on influence spreading.First,we propose a novel measure of node influence named k-core centrality that is based on the k-core value of adjacent nodes.Second,we obtain the seed-oriented local community,which is composed of the may-members and the must-member chain of the seed,by detecting the influence scope of the seed.The may-members and the must-members of the seed are determined by judging the influence relationship between the node and the seed.Five state-of-art algorithms are compared to SOLCD on six real-world networks and three groups of artificial networks.The experimental results show that SOLCD can achieve a high-quality seed-oriented local community for various real-world networks and artificial networks with different parameters.In addition,when taking nodes with different influence as seeds,SOLCD can stably obtain high-quality seed-oriented local communities.
针对现有基于标签传播的复杂网络重叠社区识别方法所存在的社区识别精度不稳定,以及随机性较强等缺陷,提出一种新的基于标签传播的复杂网络重叠社区识别算法NOCDLP(a novel algorithm for overlapping community detection based on label propagation).该算法首先搜索网络中若干以度较高节点为中心的完全子图,并以这些完全子图为起点进行标签传播;其次通过分析节点与社区连接强度以及社区接纳某节点后的社区内部连接紧密度情况给出节点归属社区强度函数,以此作为标签传播的依据提高社区的识别精度;再次,在标签传播过程中,NOCDLP算法设置标签传播控制标记,以避免标签传播算法随机性较强的缺陷;最后,在已形成的社区中通过整理重叠节点获得更准确的重叠社区结构.算法在人工网络与真实网络中完成测试,同时与多个经典算法进行对比分析,实验结果验证了NOCDLP算法是有效的、可行的.
Mining important nodes in the complex network should not only consider the core nodes, but also consider the locations of the nodes in the network. Despite many researches on discovering important nodes, the importance of nodes in the structural holes is still ignored easily. Therefore, this paper proposes a method of local centrality measurement based on structural holes, which evaluates the nodes importance both by direct and indirect constraints caused by the lack of structural holes around the nodes. In this method, the attributes and locations of the nodes and their first-order and second-order neighbors are taken into account simultaneously. Deliberate attack simulation is carried out through selective deletion in a certain proportion of network nodes. Calculating the decreased ratio of network efficiency is to quantitatively describe the importance of nodes in before-and-after attacks. Experiments indicate that this method has more advantages to mine important nodes compared to clustering coefficient and k-shell decomposition method. And it is suitable for the quantitative analysis of the nodes importance in large scale networks.
