基于异时间窗划分的时间序列聚类

针对相同时间窗对时间序列进行子序列划分的缺点,提出一种异时间窗的子序列划分方法。为解决划分得到的子序列长度不同,而使用动态时间弯曲算法进行子序列相似性度量的计算速度慢的问题,给出一种不规则时间序列距离度量算法。对异时间窗的子序列划分方法和不规则时间序列距离度量算法进行了实验,结果证明了二者的优越性。     

频繁时序模式挖掘方法综述

频繁时序模式挖掘是指从时间序列数据中发现频繁出现的模式或规律的过程,其目的是可以帮助理解时间序列数据中的重要特征,例如周期性、趋势和异常等,有助于预测未来的发展趋势和识别异常情况等.根据近年来的频繁时序模式挖掘方法的相关文献调研,按照关键技术和代表性算法将其分为三类,即基于结构约束的频繁时序模式挖掘方法、基于参数约束的频繁时序模式挖掘方法和基于窗口的频繁时序模式挖掘方法.陈述了频繁时序模式挖掘方法的背景以及各方法的特点;分别介绍了三类挖掘方法的发展以及分类,并从优缺点和性能等方面对各类改进方法进行了详细的对比分析;对频繁时序模式挖掘方法进行归纳和总结,并对频繁时序模式挖掘方法的未来研究方向进行了展望.     

Robust graph neural network method for target entity type recognition in a battlefield environment北大核心CSCD

With the rise of new combat styles, such as information and algorithmic warfare, target entity recognition in battlefield data analysis plays an important role in decision making. Battlefield situation data are typical battlefield data containing many dynamic entities with close interactions. However, such data often contain strong noise due to hostile interference or concealment; hence, they require higher robustness than general time-series data. This paper proposes a new method based on graph neural networks to represent and process the unstructured data and mine the category information of hostile combat entities. First, the dynamic time warping algorithm was used to establish a new graph structure between combat entities based on their trajectory. Then, a robust graph neural network method was proposed and applied for the type identification of combat entities beyond the radar identification range according to the node attribute information of combat entities. Test results on the simulation data set obtained from the military simulation platform reveal that the proposed method maximizes the temporal characteristics of the entity data and associated attribute information of each node. Compared with the graph neural network and multilayer perceptron methods that rely on singletime relation, the proposed method has advantages in identification accuracy and robustness, expanding the radius of operational entity identification to a certain extent. © 2023, Editorial Department of CAAI Transactions on Intelligent Systems. All rights reserved.     

基于GRU自编码器的船舶航线提取

船舶自动识别系统(automatic identification system, AIS)数据中蕴含着大量的船舶行为相关信息,从中提取出有效的航线,在海事监管、船只勘查等方面具有广泛应用。本文提出一种基于GRU自编码器(gate recuurent unit auto-encoder,GRU-AE)的船舶航线提取方法,首先采用GRU编码器将原始轨迹数据编码为统一格式的深度特征信息,其次利用DBSCAN (density-based spatial clustering of applications with noise)算法对深度特征信息进行聚类,最后将深度特征类簇中心通过解码器反演生成相应的船舶航线,从而实现在海量AIS数据中挖掘船舶轨迹规律。以波士顿港口为例,分析一年时间内10万多条AIS的船舶航行数据,实验表明本方法可对不同长度轨迹数据进行聚类及其航线提取,并可支撑船舶轨迹异常检测、路径规划、位置预测等研究,具有较好的应用适应性。