To solve the problem of target damage assessment when fragments attack target under uncertain projectile and target intersection in an air defense intercept,this paper proposes a method for calculating target damage p...To solve the problem of target damage assessment when fragments attack target under uncertain projectile and target intersection in an air defense intercept,this paper proposes a method for calculating target damage probability leveraging spatio-temporal finite multilayer fragments distribution and the target damage assessment algorithm based on cloud model theory.Drawing on the spatial dispersion characteristics of fragments of projectile proximity explosion,we divide into a finite number of fragments distribution planes based on the time series in space,set up a fragment layer dispersion model grounded in the time series and intersection criterion for determining the effective penetration of each layer of fragments into the target.Building on the precondition that the multilayer fragments of the time series effectively assail the target,we also establish the damage criterion of the perforation and penetration damage and deduce the damage probability calculation model.Taking the damage probability of the fragment layer in the spatio-temporal sequence to the target as the input state variable,we introduce cloud model theory to research the target damage assessment method.Combining the equivalent simulation experiment,the scientific and rational nature of the proposed method were validated through quantitative calculations and comparative analysis.展开更多
Climate sequences can be applied to defining sensitive climate zones, and then the mining of spatio-temporal teleconnection patterns is useful for learning from the past and preparing for the future. However, scale-de...Climate sequences can be applied to defining sensitive climate zones, and then the mining of spatio-temporal teleconnection patterns is useful for learning from the past and preparing for the future. However, scale-dependency in this kind of pattern is still not well handled by existing work. Therefore, in this study, the multi-scale regionalization is embedded into the spatio-temporal teleconnection pattern mining between anomalous sea and land climatic events. A modified scale-space clustering algorithm is first developed to group climate sequences into multi-scale climate zones. Then, scale variance analysis method is employed to identify climate zones at characteristic scales, indicating the main characteristics of geographical phenomena. Finally, by using the climate zones identified at characteristic scales, a time association rule mining algorithm based on sliding time windows is employed to discover spatio-temporal teleconnection patterns. Experiments on sea surface temperature, sea level pressure, land precipitation and land temperature datasets show that many patterns obtained by the multi-scale approach are coincident with prior knowledge, indicating that this method is effective and reasonable. In addition, some unknown teleconnection patterns discovered from the multi-scale approach can be further used to guide the prediction of land climate.展开更多
随着定位技术和传感器的高速发展,用户移动轨迹数据日渐丰富,但大多分散在不同平台上。为了全面利用这些数据并准确反映用户的真实行为,对轨迹用户匹配的研究变得至关重要。该任务旨在从海量签到轨迹数据中精准关联用户身份。近年来,研...随着定位技术和传感器的高速发展,用户移动轨迹数据日渐丰富,但大多分散在不同平台上。为了全面利用这些数据并准确反映用户的真实行为,对轨迹用户匹配的研究变得至关重要。该任务旨在从海量签到轨迹数据中精准关联用户身份。近年来,研究者们尝试运用循环神经网络、注意力机制等方法深入挖掘轨迹数据。然而,当前方法在处理用户签到轨迹时面临两大挑战:一是签到数据中有限的时空特征不足以从主观和客观两个角度全面地建模签到点信息,二是用户的签到轨迹往往围绕着一个特定的主题。针对这两点挑战,提出了一种基于自然语言增强的轨迹用户匹配模型(Natural Language Augmented Trajectory User Link,NLATUL)。首先,设计了一套自然语言模板与软提示令牌来描述签到轨迹,并使用语言模型来理解签到点中的主观意图,融合用户的时空状态,提供了一种充分从主观与客观两个方面建模签到点的方法;在此基础上,通过提示学习的方法推理签到轨迹的主题,并对建模的签到点表示的轨迹进行双向编码,通过签到轨迹主题与签到轨迹编码的结合实现对用户签到轨迹的准确理解。在两个真实世界签到数据集上验证的实验结果表明,NLATUL能够更准确地匹配签到轨迹与其对应的用户。展开更多
精确估计航班预计到达时刻(estimated time of arrival,ETA)对机场群或终端区协同调度辅助决策制定有重要意义,传统方法对于进场计量节点精细化感知能力不足,特别在高动态环境影响下对大体量复杂航班交通态势难以实现中-长期精准定量估...精确估计航班预计到达时刻(estimated time of arrival,ETA)对机场群或终端区协同调度辅助决策制定有重要意义,传统方法对于进场计量节点精细化感知能力不足,特别在高动态环境影响下对大体量复杂航班交通态势难以实现中-长期精准定量估计。提出了基于误差反馈修正的航班预计到达时刻预测方法,首先,基于航空器性能参数,结合对未飞航路的规划和气象因素,构建航空器运动学模型;其次,通过四维航迹推演对预计到达时刻进行初步预测;然后,构造实际落地时刻(actual time of arrival,ATA)与预测结果的误差序列,采用误差反馈模型对序列进行预测并修正初步预测结果。最后,以重庆江北国际机场进港航班为例进行仿真验证,将提前30 min预测结果在±5 min以内的比率作为评价指标,结果表明相比传统方法,本文方法可在恶劣天气下将预计到达时刻预测的准确率提高25%以上。展开更多
随着海上船舶日益增多,海情急剧复杂化,及时准确地预测船舶的下一步动向成为海事监管的迫切需求。针对现有船舶轨迹预测算法提取轨迹特征能力较差、预测精度不高的问题,提出了添加Attention注意力机制的序列到序列船舶轨迹预测算法(sequ...随着海上船舶日益增多,海情急剧复杂化,及时准确地预测船舶的下一步动向成为海事监管的迫切需求。针对现有船舶轨迹预测算法提取轨迹特征能力较差、预测精度不高的问题,提出了添加Attention注意力机制的序列到序列船舶轨迹预测算法(sequence-to-sequence with attention,Seq2Seq-Att)。通过改进Seq2Seq的编码器结构和添加Attention机制,提高模型对轨迹特征的记忆能力,从而提升算法的预测精度。以东海海域的AIS数据为样本训练模型,预测船舶未来一段时间的经度、纬度、航速和航向。实验结果表明,相较于传统算法,该算法的预测精度更高,且均方根误差明显降低,可以为海事监管和智能航行提供依据。展开更多
基金supported by National Natural Science Foundation of China(Grant No.62073256)the Shaanxi Provincial Science and Technology Department(Grant No.2023-YBGY-342).
文摘To solve the problem of target damage assessment when fragments attack target under uncertain projectile and target intersection in an air defense intercept,this paper proposes a method for calculating target damage probability leveraging spatio-temporal finite multilayer fragments distribution and the target damage assessment algorithm based on cloud model theory.Drawing on the spatial dispersion characteristics of fragments of projectile proximity explosion,we divide into a finite number of fragments distribution planes based on the time series in space,set up a fragment layer dispersion model grounded in the time series and intersection criterion for determining the effective penetration of each layer of fragments into the target.Building on the precondition that the multilayer fragments of the time series effectively assail the target,we also establish the damage criterion of the perforation and penetration damage and deduce the damage probability calculation model.Taking the damage probability of the fragment layer in the spatio-temporal sequence to the target as the input state variable,we introduce cloud model theory to research the target damage assessment method.Combining the equivalent simulation experiment,the scientific and rational nature of the proposed method were validated through quantitative calculations and comparative analysis.
基金Projects(41601424,41171351)supported by the National Natural Science Foundation of ChinaProject(2012CB719906)supported by the National Basic Research Program of China(973 Program)+2 种基金Project(14JJ1007)supported by the Hunan Natural Science Fund for Distinguished Young Scholars,ChinaProject(2017M610486)supported by the China Postdoctoral Science FoundationProjects(2017YFB0503700,2017YFB0503601)supported by the National Key Research and Development Foundation of China
文摘Climate sequences can be applied to defining sensitive climate zones, and then the mining of spatio-temporal teleconnection patterns is useful for learning from the past and preparing for the future. However, scale-dependency in this kind of pattern is still not well handled by existing work. Therefore, in this study, the multi-scale regionalization is embedded into the spatio-temporal teleconnection pattern mining between anomalous sea and land climatic events. A modified scale-space clustering algorithm is first developed to group climate sequences into multi-scale climate zones. Then, scale variance analysis method is employed to identify climate zones at characteristic scales, indicating the main characteristics of geographical phenomena. Finally, by using the climate zones identified at characteristic scales, a time association rule mining algorithm based on sliding time windows is employed to discover spatio-temporal teleconnection patterns. Experiments on sea surface temperature, sea level pressure, land precipitation and land temperature datasets show that many patterns obtained by the multi-scale approach are coincident with prior knowledge, indicating that this method is effective and reasonable. In addition, some unknown teleconnection patterns discovered from the multi-scale approach can be further used to guide the prediction of land climate.
文摘随着定位技术和传感器的高速发展,用户移动轨迹数据日渐丰富,但大多分散在不同平台上。为了全面利用这些数据并准确反映用户的真实行为,对轨迹用户匹配的研究变得至关重要。该任务旨在从海量签到轨迹数据中精准关联用户身份。近年来,研究者们尝试运用循环神经网络、注意力机制等方法深入挖掘轨迹数据。然而,当前方法在处理用户签到轨迹时面临两大挑战:一是签到数据中有限的时空特征不足以从主观和客观两个角度全面地建模签到点信息,二是用户的签到轨迹往往围绕着一个特定的主题。针对这两点挑战,提出了一种基于自然语言增强的轨迹用户匹配模型(Natural Language Augmented Trajectory User Link,NLATUL)。首先,设计了一套自然语言模板与软提示令牌来描述签到轨迹,并使用语言模型来理解签到点中的主观意图,融合用户的时空状态,提供了一种充分从主观与客观两个方面建模签到点的方法;在此基础上,通过提示学习的方法推理签到轨迹的主题,并对建模的签到点表示的轨迹进行双向编码,通过签到轨迹主题与签到轨迹编码的结合实现对用户签到轨迹的准确理解。在两个真实世界签到数据集上验证的实验结果表明,NLATUL能够更准确地匹配签到轨迹与其对应的用户。
文摘精确估计航班预计到达时刻(estimated time of arrival,ETA)对机场群或终端区协同调度辅助决策制定有重要意义,传统方法对于进场计量节点精细化感知能力不足,特别在高动态环境影响下对大体量复杂航班交通态势难以实现中-长期精准定量估计。提出了基于误差反馈修正的航班预计到达时刻预测方法,首先,基于航空器性能参数,结合对未飞航路的规划和气象因素,构建航空器运动学模型;其次,通过四维航迹推演对预计到达时刻进行初步预测;然后,构造实际落地时刻(actual time of arrival,ATA)与预测结果的误差序列,采用误差反馈模型对序列进行预测并修正初步预测结果。最后,以重庆江北国际机场进港航班为例进行仿真验证,将提前30 min预测结果在±5 min以内的比率作为评价指标,结果表明相比传统方法,本文方法可在恶劣天气下将预计到达时刻预测的准确率提高25%以上。
文摘随着海上船舶日益增多,海情急剧复杂化,及时准确地预测船舶的下一步动向成为海事监管的迫切需求。针对现有船舶轨迹预测算法提取轨迹特征能力较差、预测精度不高的问题,提出了添加Attention注意力机制的序列到序列船舶轨迹预测算法(sequence-to-sequence with attention,Seq2Seq-Att)。通过改进Seq2Seq的编码器结构和添加Attention机制,提高模型对轨迹特征的记忆能力,从而提升算法的预测精度。以东海海域的AIS数据为样本训练模型,预测船舶未来一段时间的经度、纬度、航速和航向。实验结果表明,相较于传统算法,该算法的预测精度更高,且均方根误差明显降低,可以为海事监管和智能航行提供依据。
