气候变暖背景下内蒙古地区近30年来旱灾成灾驱动力的识别对进行旱灾风险管理具有重要的科学意义。基于内蒙古地区1981-2010年47个地面观测站温度、降水、相对湿度等历史观测资料及农作物旱灾成灾面积,采用变点分析(Change point analys...气候变暖背景下内蒙古地区近30年来旱灾成灾驱动力的识别对进行旱灾风险管理具有重要的科学意义。基于内蒙古地区1981-2010年47个地面观测站温度、降水、相对湿度等历史观测资料及农作物旱灾成灾面积,采用变点分析(Change point analysis)探测了近30年来内蒙古地区气候因子与历史灾情的变化趋势;在此基础上,采用多元回归模型分析了研究区旱灾成灾的主要影响因子,最后探讨了研究区农作物旱灾成灾的格兰杰(Granger)因果关系。结果表明:内蒙古地区近30年来年平均温度(0.4℃/10a)和成灾面积(173.9万hm2/10a)呈增加趋势,降水量呈轻微减少趋势(1.2 mm/10a);温度和相对湿度于1980年代末至1990年代初出现突变,而降水量和旱灾成灾面积于1999年发生突变;由降水和温度共同控制作用影响内蒙古地区农作物干旱成灾;相对湿度与成灾面积之间存在时间上的格兰杰因果关系。展开更多
Time series segmentation has attracted more interests in recent years,which aims to segment time series into different segments,each reflects a state of the monitored objects.Although there have been many surveys on t...Time series segmentation has attracted more interests in recent years,which aims to segment time series into different segments,each reflects a state of the monitored objects.Although there have been many surveys on time series segmentation,most of them focus more on change point detection(CPD)methods and overlook the advances in boundary detection(BD)and state detection(SD)methods.In this paper,we categorize time series segmentation methods into CPD,BD,and SD methods,with a specific focus on recent advances in BD and SD methods.Within the scope of BD and SD,we subdivide the methods based on their underlying models/techniques and focus on the milestones that have shaped the development trajectory of each category.As a conclusion,we found that:(1)Existing methods failed to provide sufficient support for online working,with only a few methods supporting online deployment;(2)Most existing methods require the specification of parameters,which hinders their ability to work adaptively;(3)Existing SD methods do not attach importance to accurate detection of boundary points in evaluation,which may lead to limitations in boundary point detection.We highlight the ability to working online and adaptively as important attributes of segmentation methods,the boundary detection accuracy as a neglected metrics for SD methods.展开更多
The task of modeling and analyzing intercepted multifunction radars(MFRs)pulse trains is vital for cognitive electronic reconnaissance.Existing methodologies predominantly rely on prior information or heavily constrai...The task of modeling and analyzing intercepted multifunction radars(MFRs)pulse trains is vital for cognitive electronic reconnaissance.Existing methodologies predominantly rely on prior information or heavily constrained models,posing challenges for non-cooperative applications.This paper introduces a novel approach to model MFRs using a Bayesian network,where the conditional probability density function is approximated by an autoregressive kernel mixture network(ARKMN).Utilizing the estimated probability density function,a dynamic programming algorithm is proposed for denoising and detecting change points in the intercepted MFRs pulse trains.Simulation results affirm the proposed method's efficacy in modeling MFRs,outperforming the state-of-the-art in pulse train denoising and change point detection.展开更多
文摘气候变暖背景下内蒙古地区近30年来旱灾成灾驱动力的识别对进行旱灾风险管理具有重要的科学意义。基于内蒙古地区1981-2010年47个地面观测站温度、降水、相对湿度等历史观测资料及农作物旱灾成灾面积,采用变点分析(Change point analysis)探测了近30年来内蒙古地区气候因子与历史灾情的变化趋势;在此基础上,采用多元回归模型分析了研究区旱灾成灾的主要影响因子,最后探讨了研究区农作物旱灾成灾的格兰杰(Granger)因果关系。结果表明:内蒙古地区近30年来年平均温度(0.4℃/10a)和成灾面积(173.9万hm2/10a)呈增加趋势,降水量呈轻微减少趋势(1.2 mm/10a);温度和相对湿度于1980年代末至1990年代初出现突变,而降水量和旱灾成灾面积于1999年发生突变;由降水和温度共同控制作用影响内蒙古地区农作物干旱成灾;相对湿度与成灾面积之间存在时间上的格兰杰因果关系。
基金This work is supported by the National Key Research and Development Program of China(2022YFF1203001)National Natural Science Foundation of China(Nos.62072465,62102425)the Science and Technology Innovation Program of Hunan Province(Nos.2022RC3061,2023RC3027).
文摘Time series segmentation has attracted more interests in recent years,which aims to segment time series into different segments,each reflects a state of the monitored objects.Although there have been many surveys on time series segmentation,most of them focus more on change point detection(CPD)methods and overlook the advances in boundary detection(BD)and state detection(SD)methods.In this paper,we categorize time series segmentation methods into CPD,BD,and SD methods,with a specific focus on recent advances in BD and SD methods.Within the scope of BD and SD,we subdivide the methods based on their underlying models/techniques and focus on the milestones that have shaped the development trajectory of each category.As a conclusion,we found that:(1)Existing methods failed to provide sufficient support for online working,with only a few methods supporting online deployment;(2)Most existing methods require the specification of parameters,which hinders their ability to work adaptively;(3)Existing SD methods do not attach importance to accurate detection of boundary points in evaluation,which may lead to limitations in boundary point detection.We highlight the ability to working online and adaptively as important attributes of segmentation methods,the boundary detection accuracy as a neglected metrics for SD methods.
基金supported by the National Natural Science Foundation of China under Grant 62301119。
文摘The task of modeling and analyzing intercepted multifunction radars(MFRs)pulse trains is vital for cognitive electronic reconnaissance.Existing methodologies predominantly rely on prior information or heavily constrained models,posing challenges for non-cooperative applications.This paper introduces a novel approach to model MFRs using a Bayesian network,where the conditional probability density function is approximated by an autoregressive kernel mixture network(ARKMN).Utilizing the estimated probability density function,a dynamic programming algorithm is proposed for denoising and detecting change points in the intercepted MFRs pulse trains.Simulation results affirm the proposed method's efficacy in modeling MFRs,outperforming the state-of-the-art in pulse train denoising and change point detection.
