水中目标信息智能识别方法研究
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摘要
提出了基于判定准则的海洋环境下目标关系识别方法。通过HBACA算法查找或识别不同元素关系能力强的特性,提取目标信息数据的关系,之后在信息数据关系中计算每个信息节点的点度中心度、紧致中心度和节点活跃度,根据信息数据在目标事件中的重要或关键程度,将信息数据划分为关键目标信息、重要目标信息和一般目标信息。仿真结果显示:能够明确地获得关键的信息数据坐标,并与其所处事件的重要程度能够匹配,同时相关信息数据的关系非常明显,很容易提炼出所处目标事件中重要目标信息数据之间的关联。
A target recognition method under ocean environment based on decision criterion is proposed. Through the HBACA algorithm, this paper finds or identifies the characteristics of the strong relationship between different elements, extracts the relationship between target information data, and then calculates the degree centrality, compact centrality and node activity of each information node in the information data relationship. According to the important or critical degree of information data in the target event, the information data is divided into key information, important information and general information. The simulation results show that the key information data coordinate can be obtained clearly, and it can match the important degree of the event. At the same time, the relation of the related information data is very obvious, and it is easy to extract the association between the important information data in the target event.
A target recognition method under ocean environment based on decision criterion is proposed. Through the HBACA algorithm, this paper finds or identifies the characteristics of the strong relationship between different elements, extracts the relationship between target information data, and then calculates the degree centrality, compact centrality and node activity of each information node in the information data relationship. According to the important or critical degree of information data in the target event, the information data is divided into key information, important information and general information. The simulation results show that the key information data coordinate can be obtained clearly, and it can match the important degree of the event. At the same time, the relation of the related information data is very obvious, and it is easy to extract the association between the important information data in the target event.
引文
[1]于歌,朴胜春.强海洋混响环境下的移动多目标检测方法[J].华中科技大学学报(自然科学版),2017,45(03):89-93.
[2]马勋,周长胜,吕学强,等.基于SAO结构的非分类关系抽取研究[J].计算机工程与应用,2018(8):25-27.
[3]周袅,葛洪伟,苏树智.基于信息素的自适应连续域混合蚁群算法[J].计算机工程与应用,2017,53(6):156-161.
[4]李燕梅.云计算背景下信息数据保护的关键技术研究[J].网络安全技术与应用,2018(1):12-14.
[5]赵逸智,张云峰.大数据环境下文本信息挖掘系统设计[J].现代电子技术,2018,41(1):125-128.
[6]朱荣,周彩兰,高瑞.基于数据挖掘的客户关系管理系统研究[J].现代电子技术,2018,41(1):182-186.
[7]钱裙,王朝坤,郭高扬.基于社区的动态网络节点介数中心度更新算法[J].软件学报,2018(3):56-57.
[8]SHIROKOV M E. On discontinuity of information characteristics of quantum systems and channels[J].Problems of Information Transmission, 2016, 52(3):17-18.
[2]马勋,周长胜,吕学强,等.基于SAO结构的非分类关系抽取研究[J].计算机工程与应用,2018(8):25-27.
[3]周袅,葛洪伟,苏树智.基于信息素的自适应连续域混合蚁群算法[J].计算机工程与应用,2017,53(6):156-161.
[4]李燕梅.云计算背景下信息数据保护的关键技术研究[J].网络安全技术与应用,2018(1):12-14.
[5]赵逸智,张云峰.大数据环境下文本信息挖掘系统设计[J].现代电子技术,2018,41(1):125-128.
[6]朱荣,周彩兰,高瑞.基于数据挖掘的客户关系管理系统研究[J].现代电子技术,2018,41(1):182-186.
[7]钱裙,王朝坤,郭高扬.基于社区的动态网络节点介数中心度更新算法[J].软件学报,2018(3):56-57.
[8]SHIROKOV M E. On discontinuity of information characteristics of quantum systems and channels[J].Problems of Information Transmission, 2016, 52(3):17-18.