In this paper,we present local functional law of the iterated logarithm for Cs?rg?-Révész type increments of fractional Brownian motion.The results obtained extend works of Gantert[Ann.Probab.,1993,21(2):104...In this paper,we present local functional law of the iterated logarithm for Cs?rg?-Révész type increments of fractional Brownian motion.The results obtained extend works of Gantert[Ann.Probab.,1993,21(2):1045-1049]and Monrad and Rootzén[Probab.Theory Related Fields,1995,101(2):173-192].展开更多
A relationship was discovered between the amplification factor and the number of load increments that are needed to limit the relative error to one percent in second-order elastic analyses with a predictor-corrector s...A relationship was discovered between the amplification factor and the number of load increments that are needed to limit the relative error to one percent in second-order elastic analyses with a predictor-corrector solution scheme.Previous research by the authors proposed a design equation to determine the required minimum number of load increments based on an evaluation of the elastic critical buckling load ratio.Further research has shown that an approximate amplification factor equation that is based on the B2 multiplier equation produces similar results when the amplification factor is less than approximately four.Eleven moment frames are used to verify the use of the new approximate amplification factor in the proposed design equation.展开更多
超高韧性水泥基材料(ultra high toughness cementitious composites,UHTCC)耐损伤能力强、裂缝控制能力好,具有超高的受压韧性及显著的受拉应变硬化特性。为提升钢框架结构的抗侧性能,分别将装配式RC、UHTCC抗侧力墙作为钢框架结构的...超高韧性水泥基材料(ultra high toughness cementitious composites,UHTCC)耐损伤能力强、裂缝控制能力好,具有超高的受压韧性及显著的受拉应变硬化特性。为提升钢框架结构的抗侧性能,分别将装配式RC、UHTCC抗侧力墙作为钢框架结构的抗侧力构件,进行足尺钢框架(STF)、钢框架-附加装配式RC抗侧力墙(SRCW)和钢框架-附加装配式UHTCC抗侧力墙(SHTCW)试件低周往复加载试验,对上述试件破坏模式、滞回性能、刚度、承载力、变形能力、应变及耗能性能等进行研究。试验表明,试件SHTCW、SRCW的峰值承载力相比于试件STF分别提高了25%和13%,抗侧力墙材料抗压强度值相近的情况下,试件SHTCW的峰值承载力较试件SRCW提高15%。相同加载位移角下,试件SHTCW的抗侧刚度及等效黏滞阻尼系数高于试件SRCW和STF,相比于RC抗侧力墙,UHTCC抗侧力墙具有更优异的抗侧性能及耗能能力,其与钢框架的变形协调能力更好。在试验研究基础上,采用OpenSees有限元程序,分别对STF、SRCW结构与SHTCW结构进行增量动力分析,相比于STF结构,SHTCW、SRCW结构各个性能水准的失效概率明显降低,倒塌储备系数分别提高了1.42和1.18倍,通过附加装配式UHTCC抗侧力墙,能够减轻地震荷载作用下钢框架结构的动力响应,有效提升钢框架结构抗震性能。展开更多
In this paper, we consider a general form of the increments for a two-parameter Wiener process. Both the Csorgo-Revesz's increments and a class of the lag increments are the special cases of this general form of i...In this paper, we consider a general form of the increments for a two-parameter Wiener process. Both the Csorgo-Revesz's increments and a class of the lag increments are the special cases of this general form of increments. Our results imply the theorem that have been given by Csorgo and Revesz (1978), and some of their conditions are removed.展开更多
In this paper,we prove some limsup results for increments and lag increments of G(t),which is a stable processe in random scenery.The proofs rely on the tail probability estimation of G(t).
Processes supported by process-aware information systems are subject to continuous and often subtle changes due to evolving operational,organizational,or regulatory factors.These changes,referred to as incremental con...Processes supported by process-aware information systems are subject to continuous and often subtle changes due to evolving operational,organizational,or regulatory factors.These changes,referred to as incremental concept drift,gradually alter the behavior or structure of processes,making their detection and localization a challenging task.Traditional process mining techniques frequently assume process stationarity and are limited in their ability to detect such drift,particularly from a control-flow perspective.The objective of this research is to develop an interpretable and robust framework capable of detecting and localizing incremental concept drift in event logs,with a specific emphasis on the structural evolution of control-flow semantics in processes.We propose DriftXMiner,a control-flow-aware hybrid framework that combines statistical,machine learning,and process model analysis techniques.The approach comprises three key components:(1)Cumulative Drift Scanner that tracks directional statistical deviations to detect early drift signals;(2)a Temporal Clustering and Drift-Aware Forest Ensemble(DAFE)to capture distributional and classification-level changes in process behavior;and(3)Petri net-based process model reconstruction,which enables the precise localization of structural drift using transition deviation metrics and replay fitness scores.Experimental validation on the BPI Challenge 2017 event log demonstrates that DriftXMiner effectively identifies and localizes gradual and incremental process drift over time.The framework achieves a detection accuracy of 92.5%,a localization precision of 90.3%,and an F1-score of 0.91,outperforming competitive baselines such as CUSUM+Histograms and ADWIN+Alpha Miner.Visual analyses further confirm that identified drift points align with transitions in control-flow models and behavioral cluster structures.DriftXMiner offers a novel and interpretable solution for incremental concept drift detection and localization in dynamic,process-aware systems.By integrating statistical signal accumulation,temporal behavior profiling,and structural process mining,the framework enables finegrained drift explanation and supports adaptive process intelligence in evolving environments.Its modular architecture supports extension to streaming data and real-time monitoring contexts.展开更多
针对深度学习模型易出现灾难性遗忘的关键难点,提出了一种基于原型对比的合成孔径雷达(synthetic aperture radar,SAR)图像增量小样本目标检测算法-InFSAR(prototype contrast based incremental few-shot SAR object detection)。首先...针对深度学习模型易出现灾难性遗忘的关键难点,提出了一种基于原型对比的合成孔径雷达(synthetic aperture radar,SAR)图像增量小样本目标检测算法-InFSAR(prototype contrast based incremental few-shot SAR object detection)。首先,采用基础数据集对检测器进行预训练,以构建初步的特征提取能力;其次,设计一种类原型表征生成模块,以构建一组能够代表数据内在特征的类原型。在增量学习阶段,设计一种混合类原型对比编码模块,以有效学习新类别与基础类别之间的区分性特征。此外,为缓解灾难性遗忘问题,引入类原型校准策略,使模型在类原型上的预测分布逐步逼近真实分布,从而保持对基础类别识别的稳定性。在小样本目标检测数据集SRSDD-v1.0上的实验结果表明,在5-shot设置下,InFSAR对船舶细粒度目标的检测精度达到46.5%。同时,该方法能够在无需访问基础类训练数据的情况下,实现对少量标注新类别的增量检测与识别。展开更多
基金Supported by NSFC(Nos.11661025,12161024)Natural Science Foundation of Guangxi(Nos.2020GXNSFAA159118,2021GXNSFAA196045)+2 种基金Guangxi Science and Technology Project(No.Guike AD20297006)Training Program for 1000 Young and Middle-aged Cadre Teachers in Universities of GuangxiNational College Student's Innovation and Entrepreneurship Training Program(No.202110595049)。
文摘In this paper,we present local functional law of the iterated logarithm for Cs?rg?-Révész type increments of fractional Brownian motion.The results obtained extend works of Gantert[Ann.Probab.,1993,21(2):1045-1049]and Monrad and Rootzén[Probab.Theory Related Fields,1995,101(2):173-192].
文摘A relationship was discovered between the amplification factor and the number of load increments that are needed to limit the relative error to one percent in second-order elastic analyses with a predictor-corrector solution scheme.Previous research by the authors proposed a design equation to determine the required minimum number of load increments based on an evaluation of the elastic critical buckling load ratio.Further research has shown that an approximate amplification factor equation that is based on the B2 multiplier equation produces similar results when the amplification factor is less than approximately four.Eleven moment frames are used to verify the use of the new approximate amplification factor in the proposed design equation.
文摘超高韧性水泥基材料(ultra high toughness cementitious composites,UHTCC)耐损伤能力强、裂缝控制能力好,具有超高的受压韧性及显著的受拉应变硬化特性。为提升钢框架结构的抗侧性能,分别将装配式RC、UHTCC抗侧力墙作为钢框架结构的抗侧力构件,进行足尺钢框架(STF)、钢框架-附加装配式RC抗侧力墙(SRCW)和钢框架-附加装配式UHTCC抗侧力墙(SHTCW)试件低周往复加载试验,对上述试件破坏模式、滞回性能、刚度、承载力、变形能力、应变及耗能性能等进行研究。试验表明,试件SHTCW、SRCW的峰值承载力相比于试件STF分别提高了25%和13%,抗侧力墙材料抗压强度值相近的情况下,试件SHTCW的峰值承载力较试件SRCW提高15%。相同加载位移角下,试件SHTCW的抗侧刚度及等效黏滞阻尼系数高于试件SRCW和STF,相比于RC抗侧力墙,UHTCC抗侧力墙具有更优异的抗侧性能及耗能能力,其与钢框架的变形协调能力更好。在试验研究基础上,采用OpenSees有限元程序,分别对STF、SRCW结构与SHTCW结构进行增量动力分析,相比于STF结构,SHTCW、SRCW结构各个性能水准的失效概率明显降低,倒塌储备系数分别提高了1.42和1.18倍,通过附加装配式UHTCC抗侧力墙,能够减轻地震荷载作用下钢框架结构的动力响应,有效提升钢框架结构抗震性能。
基金Supported by the National Natural Science Foundation of ChinaZhejiang Province Natural Science Fund
文摘In this paper, we consider a general form of the increments for a two-parameter Wiener process. Both the Csorgo-Revesz's increments and a class of the lag increments are the special cases of this general form of increments. Our results imply the theorem that have been given by Csorgo and Revesz (1978), and some of their conditions are removed.
文摘In this paper,we prove some limsup results for increments and lag increments of G(t),which is a stable processe in random scenery.The proofs rely on the tail probability estimation of G(t).
文摘Processes supported by process-aware information systems are subject to continuous and often subtle changes due to evolving operational,organizational,or regulatory factors.These changes,referred to as incremental concept drift,gradually alter the behavior or structure of processes,making their detection and localization a challenging task.Traditional process mining techniques frequently assume process stationarity and are limited in their ability to detect such drift,particularly from a control-flow perspective.The objective of this research is to develop an interpretable and robust framework capable of detecting and localizing incremental concept drift in event logs,with a specific emphasis on the structural evolution of control-flow semantics in processes.We propose DriftXMiner,a control-flow-aware hybrid framework that combines statistical,machine learning,and process model analysis techniques.The approach comprises three key components:(1)Cumulative Drift Scanner that tracks directional statistical deviations to detect early drift signals;(2)a Temporal Clustering and Drift-Aware Forest Ensemble(DAFE)to capture distributional and classification-level changes in process behavior;and(3)Petri net-based process model reconstruction,which enables the precise localization of structural drift using transition deviation metrics and replay fitness scores.Experimental validation on the BPI Challenge 2017 event log demonstrates that DriftXMiner effectively identifies and localizes gradual and incremental process drift over time.The framework achieves a detection accuracy of 92.5%,a localization precision of 90.3%,and an F1-score of 0.91,outperforming competitive baselines such as CUSUM+Histograms and ADWIN+Alpha Miner.Visual analyses further confirm that identified drift points align with transitions in control-flow models and behavioral cluster structures.DriftXMiner offers a novel and interpretable solution for incremental concept drift detection and localization in dynamic,process-aware systems.By integrating statistical signal accumulation,temporal behavior profiling,and structural process mining,the framework enables finegrained drift explanation and supports adaptive process intelligence in evolving environments.Its modular architecture supports extension to streaming data and real-time monitoring contexts.
文摘针对深度学习模型易出现灾难性遗忘的关键难点,提出了一种基于原型对比的合成孔径雷达(synthetic aperture radar,SAR)图像增量小样本目标检测算法-InFSAR(prototype contrast based incremental few-shot SAR object detection)。首先,采用基础数据集对检测器进行预训练,以构建初步的特征提取能力;其次,设计一种类原型表征生成模块,以构建一组能够代表数据内在特征的类原型。在增量学习阶段,设计一种混合类原型对比编码模块,以有效学习新类别与基础类别之间的区分性特征。此外,为缓解灾难性遗忘问题,引入类原型校准策略,使模型在类原型上的预测分布逐步逼近真实分布,从而保持对基础类别识别的稳定性。在小样本目标检测数据集SRSDD-v1.0上的实验结果表明,在5-shot设置下,InFSAR对船舶细粒度目标的检测精度达到46.5%。同时,该方法能够在无需访问基础类训练数据的情况下,实现对少量标注新类别的增量检测与识别。
