The case-based reasoning（CBR） and rule-based reasoning（RBR） fusion systems include a diverse range of fusion methods and their tasks are characterized by interleaving combination of the reasoning procedures. Exist...The case-based reasoning（CBR） and rule-based reasoning（RBR） fusion systems include a diverse range of fusion methods and their tasks are characterized by interleaving combination of the reasoning procedures. Existing approaches cannot clarify the complex relationships between data from the knowledge sources nor uniformly represent the heterogeneous case and rule knowledge in one fusion space. As a result, existing approaches fail to solve system fragility due to knowledge uncertainty and reasoning unreliability. For the purpose of addressing the difficulties, a novel algorithm for CBR-RBR fusion with robust thresholds（CRFRT） is proposed. Heterogeneous case and rule knowledge are uniformly represented in one defined fusion unitary space. The robust thresholds have been achieved to distinguish the complex relationships between meta-knowledge in the fusion space and to enhance system capacity of knowledge identification. Furthermore, fusion reasoning strategies are constructed for CRFRT and its procedure based on which robust solution of the fusion reasoning problem is obtained. Finally, CRFRT is validated by benchmark problems in machine learning. Compared with other CBR and RBR approaches, the reasoning efficiency and accuracy are increased by 5% and 2.2% respectively. The variations of system accuracy are decreased by 2% to 3.8%. The above results show that the CRFRT algorithm boosts the system＇s effectiveness and robustness. The proposed CRFRT can solve the fragility of complex intelligence decision system and give quality performance for fault diagnosis.展开更多
In communication alarm correlation analysis,traditional association rules generation(ARG) algorithm usually has low efficiency and high error rate.This paper proposes an alarm correlation rules generation algorithm ba...In communication alarm correlation analysis,traditional association rules generation(ARG) algorithm usually has low efficiency and high error rate.This paper proposes an alarm correlation rules generation algorithm based on the confidence covered value.Confidence covered value method can judge whether a rule is redundant or not scientific After the rules that based on weighted frequent patterns(WFPs) generated,the association rules were deleted by the confidence covered value,in order to delete the redundant rules and keep the rules with more information.Experiments show that the alarm correlation rules generation algorithm based on the confidence covered value has higher efficiency than the traditional method,and can effectively remove redundant rules.Thus it is very suitable for telecommunication alarm association rules processing.展开更多
从时间序列流中获取事件是对时间序列流处理的基础.目前的研究大多采用传统的阈值确定方法对数据点进行查询,以获取时间序列流中存在的事件信息.在真实场景中,事件通常被定义为在连续一段时间内包含多种信息的异常,然而现有方法无法快...从时间序列流中获取事件是对时间序列流处理的基础.目前的研究大多采用传统的阈值确定方法对数据点进行查询,以获取时间序列流中存在的事件信息.在真实场景中,事件通常被定义为在连续一段时间内包含多种信息的异常,然而现有方法无法快速定位和充分获取这些异常.针对现有方法执行效率低、准确性差的问题,本文提出了一种基于可变多级时窗的时间序列流事件获取方法.具体来说,该方法首先使用中值滤波器对原始数据进行预处理,在一定程度上提高了事件获取的准确性;然后提出了一种基于短/长时窗平均值(STA/LTA)的事件触发算法来定位异常的触发点和终止点的近似范围;最后基于AIC(Akaike information criterion)法则对异常的起止点进行准确定位,从而获得异常的完整信息,即时间序列流事件.实验结果表明,与现有方法相比,该方法在执行效率和准确性方面具有显著优势.展开更多
基金supported by National Natural Science Foundation of China(Grant No. 71171143)National Natural Science Foundation of China Youth(Grant No. 71201087)+2 种基金Tianjin Municipal Research Program of Application Foundation and Advanced Technology of China(Grant No. 10JCYBJC07300)Tianjin Municipal Key Project of Science and Technology Supporting Program of China(Grant No. 09ECKFGX00600)Science and Technology Program of FOXCONN Group(Grant No. 120024001156)
文摘The case-based reasoning（CBR） and rule-based reasoning（RBR） fusion systems include a diverse range of fusion methods and their tasks are characterized by interleaving combination of the reasoning procedures. Existing approaches cannot clarify the complex relationships between data from the knowledge sources nor uniformly represent the heterogeneous case and rule knowledge in one fusion space. As a result, existing approaches fail to solve system fragility due to knowledge uncertainty and reasoning unreliability. For the purpose of addressing the difficulties, a novel algorithm for CBR-RBR fusion with robust thresholds（CRFRT） is proposed. Heterogeneous case and rule knowledge are uniformly represented in one defined fusion unitary space. The robust thresholds have been achieved to distinguish the complex relationships between meta-knowledge in the fusion space and to enhance system capacity of knowledge identification. Furthermore, fusion reasoning strategies are constructed for CRFRT and its procedure based on which robust solution of the fusion reasoning problem is obtained. Finally, CRFRT is validated by benchmark problems in machine learning. Compared with other CBR and RBR approaches, the reasoning efficiency and accuracy are increased by 5% and 2.2% respectively. The variations of system accuracy are decreased by 2% to 3.8%. The above results show that the CRFRT algorithm boosts the system＇s effectiveness and robustness. The proposed CRFRT can solve the fragility of complex intelligence decision system and give quality performance for fault diagnosis.
基金Project of Sichuan Provincial Department of Education,China(No.13Z215)the Foundation of Scientific Research of Chengdu University of Information Technology,China(No.J201405)+1 种基金the Project of Sichuan Provincial Department of Science and Technology,China(No.2015JY0047)the Open Research Subject of Key Laboratory of Signal and Information Processing,China(No.szjj 2015-070)
文摘In communication alarm correlation analysis,traditional association rules generation(ARG) algorithm usually has low efficiency and high error rate.This paper proposes an alarm correlation rules generation algorithm based on the confidence covered value.Confidence covered value method can judge whether a rule is redundant or not scientific After the rules that based on weighted frequent patterns(WFPs) generated,the association rules were deleted by the confidence covered value,in order to delete the redundant rules and keep the rules with more information.Experiments show that the alarm correlation rules generation algorithm based on the confidence covered value has higher efficiency than the traditional method,and can effectively remove redundant rules.Thus it is very suitable for telecommunication alarm association rules processing.
文摘从时间序列流中获取事件是对时间序列流处理的基础.目前的研究大多采用传统的阈值确定方法对数据点进行查询,以获取时间序列流中存在的事件信息.在真实场景中,事件通常被定义为在连续一段时间内包含多种信息的异常,然而现有方法无法快速定位和充分获取这些异常.针对现有方法执行效率低、准确性差的问题,本文提出了一种基于可变多级时窗的时间序列流事件获取方法.具体来说,该方法首先使用中值滤波器对原始数据进行预处理,在一定程度上提高了事件获取的准确性;然后提出了一种基于短/长时窗平均值(STA/LTA)的事件触发算法来定位异常的触发点和终止点的近似范围;最后基于AIC(Akaike information criterion)法则对异常的起止点进行准确定位,从而获得异常的完整信息,即时间序列流事件.实验结果表明,与现有方法相比,该方法在执行效率和准确性方面具有显著优势.
