Recognition of pulse repetition interval(PRI)modulation is a fundamental task in the interpretation of radar intentions.However,the existing PRI modulation recognition methods mainly focus on single-label classificati...Recognition of pulse repetition interval(PRI)modulation is a fundamental task in the interpretation of radar intentions.However,the existing PRI modulation recognition methods mainly focus on single-label classification of PRI sequences.The prerequisite for the effectiveness of these methods is that the PRI sequences are perfectly divided according to different modulation types before identification,while the actual situation is that radar pulses reach the receiver continuously,and there is no completely reliable method to achieve this division in the case of non-cooperative reception.Based on the above actual needs,this paper implements an algorithm based on the recurrence plot technique and the multi-target detection model,which does not need to divide the PRI sequence in advance.Compared with the sliding window method,it can more effectively realize the recognition of the dynamically varying PRI mo dulation.展开更多
Fractional factorial split-plot design has been widely used in many fields due to its advantage of saving experimental cost. The general minimum lower order confounding criterion is usually used as one of the attracti...Fractional factorial split-plot design has been widely used in many fields due to its advantage of saving experimental cost. The general minimum lower order confounding criterion is usually used as one of the attractive design criterion for selecting fractional factorial split-plot design. In this paper, we are interested in the theoretical construction methods of the optimal fractional factorial split-plot designs under the general minimum lower order confounding criterion. We present the theoretical construction methods of optimal fractional factorial split-plot designs under general minimum lower order confounding criterion under several conditions.展开更多
中国生态系统研究网络(Chinese Ecosystem Research Network,CERN)生物长期样地背景和植被分类特征本底数据集是22个CERN自然生态系统生态站95个长期样地的本底数据的综合集成。基于对CERN生态站长期样地地理位置、建立时间、面积、样...中国生态系统研究网络(Chinese Ecosystem Research Network,CERN)生物长期样地背景和植被分类特征本底数据集是22个CERN自然生态系统生态站95个长期样地的本底数据的综合集成。基于对CERN生态站长期样地地理位置、建立时间、面积、样地代表性等背景信息的梳理,依据样地建立之初的植物群落调查数据,参照最新的中国植被分类系统,对每个样地植物群落所属的植被型组、植被型、植被亚型进行了明确划分,并依据每个样地的优势种名单,对样地植被所属群系和群丛进行了鉴定和命名。本数据集构建通过了多轮的专家审核-台站核查-专家复审-台站接受等过程,包含生态站代码、生态站名称、样地代码、样地名称、样地类别、样地代表性、地理位置、海拔高度、样地面积及形状、样地建立时间和设计使用年数、植被型组、植被型、植被亚型、群系、群丛、优势种等信息。本数据集可以为植物区系、植被资源分布、生物多样性保护等方面的研究提供基础数据支持。展开更多
基金supported by the National Defense Science and Technology Outstanding Youth Science Fund Project(2018-JCJQ-ZQ-023)the Hunan Provincial Natural Science Foundation of Innovation Research Group Project(2019JJ10004)。
文摘Recognition of pulse repetition interval(PRI)modulation is a fundamental task in the interpretation of radar intentions.However,the existing PRI modulation recognition methods mainly focus on single-label classification of PRI sequences.The prerequisite for the effectiveness of these methods is that the PRI sequences are perfectly divided according to different modulation types before identification,while the actual situation is that radar pulses reach the receiver continuously,and there is no completely reliable method to achieve this division in the case of non-cooperative reception.Based on the above actual needs,this paper implements an algorithm based on the recurrence plot technique and the multi-target detection model,which does not need to divide the PRI sequence in advance.Compared with the sliding window method,it can more effectively realize the recognition of the dynamically varying PRI mo dulation.
文摘Fractional factorial split-plot design has been widely used in many fields due to its advantage of saving experimental cost. The general minimum lower order confounding criterion is usually used as one of the attractive design criterion for selecting fractional factorial split-plot design. In this paper, we are interested in the theoretical construction methods of the optimal fractional factorial split-plot designs under the general minimum lower order confounding criterion. We present the theoretical construction methods of optimal fractional factorial split-plot designs under general minimum lower order confounding criterion under several conditions.
文摘中国生态系统研究网络(Chinese Ecosystem Research Network,CERN)生物长期样地背景和植被分类特征本底数据集是22个CERN自然生态系统生态站95个长期样地的本底数据的综合集成。基于对CERN生态站长期样地地理位置、建立时间、面积、样地代表性等背景信息的梳理,依据样地建立之初的植物群落调查数据,参照最新的中国植被分类系统,对每个样地植物群落所属的植被型组、植被型、植被亚型进行了明确划分,并依据每个样地的优势种名单,对样地植被所属群系和群丛进行了鉴定和命名。本数据集构建通过了多轮的专家审核-台站核查-专家复审-台站接受等过程,包含生态站代码、生态站名称、样地代码、样地名称、样地类别、样地代表性、地理位置、海拔高度、样地面积及形状、样地建立时间和设计使用年数、植被型组、植被型、植被亚型、群系、群丛、优势种等信息。本数据集可以为植物区系、植被资源分布、生物多样性保护等方面的研究提供基础数据支持。
