Target maneuver recognition is a prerequisite for air combat situation awareness,trajectory prediction,threat assessment and maneuver decision.To get rid of the dependence of the current target maneuver recognition me...Target maneuver recognition is a prerequisite for air combat situation awareness,trajectory prediction,threat assessment and maneuver decision.To get rid of the dependence of the current target maneuver recognition method on empirical criteria and sample data,and automatically and adaptively complete the task of extracting the target maneuver pattern,in this paper,an air combat maneuver pattern extraction based on time series segmentation and clustering analysis is proposed by combining autoencoder,G-G clustering algorithm and the selective ensemble clustering analysis algorithm.Firstly,the autoencoder is used to extract key features of maneuvering trajectory to remove the impacts of redundant variables and reduce the data dimension;Then,taking the time information into account,the segmentation of Maneuver characteristic time series is realized with the improved FSTS-AEGG algorithm,and a large number of maneuver primitives are extracted;Finally,the maneuver primitives are grouped into some categories by using the selective ensemble multiple time series clustering algorithm,which can prove that each class represents a maneuver action.The maneuver pattern extraction method is applied to small scale air combat trajectory and can recognize and correctly partition at least 71.3%of maneuver actions,indicating that the method is effective and satisfies the requirements for engineering accuracy.In addition,this method can provide data support for various target maneuvering recognition methods proposed in the literature,greatly reduce the workload and improve the recognition accuracy.展开更多
为解决一些决策树受到数据噪声等因素的影响,导致它们对随机森林聚类产生有限甚至负面贡献这一问题,提出一种基于聚类集成选择的随机森林聚类方法(random forest clustering method based on cluster ensemble selection,RFCCES)。将每...为解决一些决策树受到数据噪声等因素的影响,导致它们对随机森林聚类产生有限甚至负面贡献这一问题,提出一种基于聚类集成选择的随机森林聚类方法(random forest clustering method based on cluster ensemble selection,RFCCES)。将每一棵决策树视为一个基聚类器,根据基聚类器集合的稳定和不稳定性设计两种不同的聚类集成选择方法,将评估单个决策树对随机森林的增益问题,转化为基聚类器对最终的聚类集成结果的增益问题。该算法与5种对比方法在10个数据集上进行比较,实验结果验证了RFCCES的独特优势和整体有效性。展开更多
基于区域气候模式RegCM4对4个全球气候模式的动力降尺度模拟数据及未来人口预估数据,预估了SSP2-RCP4.5情景下全球升温1.5℃和2℃时,中国群发性高温事件(cluster high temperature events,CHTE)和CHTE人口暴露度的变化。结果表明:1.5℃...基于区域气候模式RegCM4对4个全球气候模式的动力降尺度模拟数据及未来人口预估数据,预估了SSP2-RCP4.5情景下全球升温1.5℃和2℃时,中国群发性高温事件(cluster high temperature events,CHTE)和CHTE人口暴露度的变化。结果表明:1.5℃和2℃升温阈值下,多模式集合(MME)预估CHTE年均频次相对于基准期分别增加31%和44%。不同强度事件中,严重CHTE事件的频次在1.5℃和2℃升温阈值下可分别增加约4.2倍和6.8倍。事件强度、持续时间、频次等指标趋向高值的发生概率更大。相对于2℃,1.5℃温升阈值下CHTE年均频次、持续时间和累计强度在全国大范围呈降低趋势,且表现出明显的区域性差异,年均频次的降幅自北到南递增,新疆和长江以南地区持续时间年均减少6 d以上(全国平均降幅为0.2 d),我国中东部地区累计强度年均减少20℃以上、新疆东部减少50℃以上(全国平均降幅为0.6℃)。此外,在1.5℃和2℃升温阈值下,MME预估CHTE影响人口的变化均呈现南增北减的空间分布,内蒙古地区略有减少,中东部地区普遍增加,全国总影响人口分别增加1.4倍和1.8倍。高温事件对城市的影响人口增幅更大(分别增加2.9倍和3.8倍),尤其是京津冀、长三角、珠三角、中原地区增幅最明显。全国的CHTE强度暴露度(分别增加2.2倍和5.2倍)和综合暴露度(分别增加1.2倍和1.8倍)呈明显增加趋势,特别是2℃升温阈值下城市的CHTE强度暴露度和综合暴露度的增幅分别高达10倍和4倍。展开更多
基金supported by the National Natural Science Foundation of China (Project No.72301293)。
文摘Target maneuver recognition is a prerequisite for air combat situation awareness,trajectory prediction,threat assessment and maneuver decision.To get rid of the dependence of the current target maneuver recognition method on empirical criteria and sample data,and automatically and adaptively complete the task of extracting the target maneuver pattern,in this paper,an air combat maneuver pattern extraction based on time series segmentation and clustering analysis is proposed by combining autoencoder,G-G clustering algorithm and the selective ensemble clustering analysis algorithm.Firstly,the autoencoder is used to extract key features of maneuvering trajectory to remove the impacts of redundant variables and reduce the data dimension;Then,taking the time information into account,the segmentation of Maneuver characteristic time series is realized with the improved FSTS-AEGG algorithm,and a large number of maneuver primitives are extracted;Finally,the maneuver primitives are grouped into some categories by using the selective ensemble multiple time series clustering algorithm,which can prove that each class represents a maneuver action.The maneuver pattern extraction method is applied to small scale air combat trajectory and can recognize and correctly partition at least 71.3%of maneuver actions,indicating that the method is effective and satisfies the requirements for engineering accuracy.In addition,this method can provide data support for various target maneuvering recognition methods proposed in the literature,greatly reduce the workload and improve the recognition accuracy.
文摘为有效平滑风电出力和实现储能系统安全经济运行,提出一种储能集群双层鲁棒控制策略。系统功率分配层,改进集合经验模态分解(improved ensemble empirical mode decomposition,IEEMD),推导出逐次解析高频波动功率的数学模型,并提出基于并网标准的分解阶数自适应确定流程,能较好解析局部风功率以减小储能功率中混叠的低频成分,降低其功率需求和运行负担,同时,解决了传统方法需要完全分解功率信号导致效率低的问题。储能运行层,考虑储能单元荷电状态(state of charge,SOC)的差异性,提出基于功率分布区间的储能单元轮换控制策略,在维持储能单元SOC一致的同时可减小该过程充放电动作调整次数。在此基础上,提出基于3组储能集群的协调控制策略,有效提升分组控制模式下对充放电能量不平衡的鲁棒性,使各储能单元均能运行于最优放电深度(depth of discharge,DOD)以充分利用其寿命和延长使用寿命。最后,采用某50 MW风电场数据验证了所提策略的有效性和优越性。
文摘为解决一些决策树受到数据噪声等因素的影响,导致它们对随机森林聚类产生有限甚至负面贡献这一问题,提出一种基于聚类集成选择的随机森林聚类方法(random forest clustering method based on cluster ensemble selection,RFCCES)。将每一棵决策树视为一个基聚类器,根据基聚类器集合的稳定和不稳定性设计两种不同的聚类集成选择方法,将评估单个决策树对随机森林的增益问题,转化为基聚类器对最终的聚类集成结果的增益问题。该算法与5种对比方法在10个数据集上进行比较,实验结果验证了RFCCES的独特优势和整体有效性。
文摘基于区域气候模式RegCM4对4个全球气候模式的动力降尺度模拟数据及未来人口预估数据,预估了SSP2-RCP4.5情景下全球升温1.5℃和2℃时,中国群发性高温事件(cluster high temperature events,CHTE)和CHTE人口暴露度的变化。结果表明:1.5℃和2℃升温阈值下,多模式集合(MME)预估CHTE年均频次相对于基准期分别增加31%和44%。不同强度事件中,严重CHTE事件的频次在1.5℃和2℃升温阈值下可分别增加约4.2倍和6.8倍。事件强度、持续时间、频次等指标趋向高值的发生概率更大。相对于2℃,1.5℃温升阈值下CHTE年均频次、持续时间和累计强度在全国大范围呈降低趋势,且表现出明显的区域性差异,年均频次的降幅自北到南递增,新疆和长江以南地区持续时间年均减少6 d以上(全国平均降幅为0.2 d),我国中东部地区累计强度年均减少20℃以上、新疆东部减少50℃以上(全国平均降幅为0.6℃)。此外,在1.5℃和2℃升温阈值下,MME预估CHTE影响人口的变化均呈现南增北减的空间分布,内蒙古地区略有减少,中东部地区普遍增加,全国总影响人口分别增加1.4倍和1.8倍。高温事件对城市的影响人口增幅更大(分别增加2.9倍和3.8倍),尤其是京津冀、长三角、珠三角、中原地区增幅最明显。全国的CHTE强度暴露度(分别增加2.2倍和5.2倍)和综合暴露度(分别增加1.2倍和1.8倍)呈明显增加趋势,特别是2℃升温阈值下城市的CHTE强度暴露度和综合暴露度的增幅分别高达10倍和4倍。
