The heterogeneous nodes in the Internet of Things(IoT)are relatively weak in the computing power and storage capacity.Therefore,traditional algorithms of network security are not suitable for the IoT.Once these nodes ...The heterogeneous nodes in the Internet of Things(IoT)are relatively weak in the computing power and storage capacity.Therefore,traditional algorithms of network security are not suitable for the IoT.Once these nodes alternate between normal behavior and anomaly behavior,it is difficult to identify and isolate them by the network system in a short time,thus the data transmission accuracy and the integrity of the network function will be affected negatively.Based on the characteristics of IoT,a lightweight local outlier factor detection method is used for node detection.In order to further determine whether the nodes are an anomaly or not,the varying behavior of those nodes in terms of time is considered in this research,and a time series method is used to make the system respond to the randomness and selectiveness of anomaly behavior nodes effectively in a short period of time.Simulation results show that the proposed method can improve the accuracy of the data transmitted by the network and achieve better performance.展开更多
Since data services are penetrating into our daily life rapidly, the mobile network becomes more complicated, and the amount of data transmission is more and more increasing. In this case, the traditional statistical ...Since data services are penetrating into our daily life rapidly, the mobile network becomes more complicated, and the amount of data transmission is more and more increasing. In this case, the traditional statistical methods for anomalous cell detection cannot adapt to the evolution of networks, and data mining becomes the mainstream. In this paper, we propose a novel kernel density-based local outlier factor(KLOF) to assign a degree of being an outlier to each object. Firstly, the notion of KLOF is introduced, which captures exactly the relative degree of isolation. Then, by analyzing its properties, including the tightness of upper and lower bounds, sensitivity of density perturbation, we find that KLOF is much greater than 1 for outliers. Lastly, KLOFis applied on a real-world dataset to detect anomalous cells with abnormal key performance indicators(KPIs) to verify its reliability. The experiment shows that KLOF can find outliers efficiently. It can be a guideline for the operators to perform faster and more efficient trouble shooting.展开更多
针对复杂工业生产过程具有高维度、多工况、非线性的特征以及扩散映射存在的新样本投影困难的问题,本文提出了一种基于可扩容式扩散映射和局部离群因子(expandable diffusion maps and local outlier factors, EDM-LOF)的工业过程故障...针对复杂工业生产过程具有高维度、多工况、非线性的特征以及扩散映射存在的新样本投影困难的问题,本文提出了一种基于可扩容式扩散映射和局部离群因子(expandable diffusion maps and local outlier factors, EDM-LOF)的工业过程故障检测方法.使用扩散映射方法提取训练样本的低维流形结构,构建局部投影矩阵将新样本投影至流形空间,并在流形空间中使用局部离群因子方法进行故障检测.将EDM-LOF应用于青霉素发酵过程进行故障检测,并与PCA、FD-kNN、LOF方法进行比较,结果表明EDM-LOF具有更高的故障检测性能,验证了该方法的有效性.展开更多
特征选择是雷达目标识别流程中一个较为关键的环节,通过对原始特征集进行筛选,挑选出其中的优质特征构成新的特征子集,可以有效增加识别准确率,提升识别效率。为了提升开放环境下高分辨距离像(High Range Resolution Profile,HRRP)的识...特征选择是雷达目标识别流程中一个较为关键的环节,通过对原始特征集进行筛选,挑选出其中的优质特征构成新的特征子集,可以有效增加识别准确率,提升识别效率。为了提升开放环境下高分辨距离像(High Range Resolution Profile,HRRP)的识别性能,针对现有特征选择方法基于闭集假设,无法有效应对实际应用中存在库外目标导致的开集识别(Open Set Recognition,OSR)性能下降问题,本文提出了一种基于局部离群因子(Local Outlier Factor,LOF)的HRRP开集识别特征选择方法。首先,从原始HRRP中提取15维特征向量作为原始特征集;其次,该方法引入聚合性概念,并使用LOF作为其度量,通过评估特征子集的聚合性来保证其在OSR时具有最小的开放空间风险。同时,采用重心法评估特征子集的可分性,并使用前向搜索算法优化特征选择过程,确保所选特征子集为维数约束下的最优解。实验结果表明:利用所提方法选择的特征子集在开集环境下识别性能优于现有特征提取方法,提升了开集环境下高分辨距离像的识别性能。展开更多
Purpose:The main aim of this study is to build a robust novel approach that is able to detect outliers in the datasets accurately.To serve this purpose,a novel approach is introduced to determine the likelihood of an ...Purpose:The main aim of this study is to build a robust novel approach that is able to detect outliers in the datasets accurately.To serve this purpose,a novel approach is introduced to determine the likelihood of an object to be extremely different from the general behavior of the entire dataset.Design/methodology/approach:This paper proposes a novel two-level approach based on the integration of bagging and voting techniques for anomaly detection problems.The proposed approach,named Bagged and Voted Local Outlier Detection(BV-LOF),benefits from the Local Outlier Factor(LOF)as the base algorithm and improves its detection rate by using ensemble methods.Findings:Several experiments have been performed on ten benchmark outlier detection datasets to demonstrate the effectiveness of the BV-LOF method.According to the results,the BV-LOF approach significantly outperformed LOF on 9 datasets of 10 ones on average.Research limitations:In the BV-LOF approach,the base algorithm is applied to each subset data multiple times with different neighborhood sizes(k)in each case and with different ensemble sizes(T).In our study,we have chosen k and T value ranges as[1-100];however,these ranges can be changed according to the dataset handled and to the problem addressed.Practical implications:The proposed method can be applied to the datasets from different domains(i.e.health,finance,manufacturing,etc.)without requiring any prior information.Since the BV-LOF method includes two-level ensemble operations,it may lead to more computational time than single-level ensemble methods;however,this drawback can be overcome by parallelization and by using a proper data structure such as R*-tree or KD-tree.Originality/value:The proposed approach(BV-LOF)investigates multiple neighborhood sizes(k),which provides findings of instances with different local densities,and in this way,it provides more likelihood of outlier detection that LOF may neglect.It also brings many benefits such as easy implementation,improved capability,higher applicability,and interpretability.展开更多
Focusing on controlling the press-assembly quality of high-precision servo mechanism,an intelligent early warning method based on outlier data detection and linear regression is proposed.Linear regression is used to d...Focusing on controlling the press-assembly quality of high-precision servo mechanism,an intelligent early warning method based on outlier data detection and linear regression is proposed.Linear regression is used to deal with the relationship between assembly quality and press-assembly process,then the mathematical model of displacement-force in press-assembly process is established and a qualified press-assembly force range is defined for assembly quality control.To preprocess the raw dataset of displacement-force in the press-assembly process,an improved local outlier factor based on area density and P weight(LAOPW)is designed to eliminate the outliers which will result in inaccuracy of the mathematical model.A weighted distance based on information entropy is used to measure distance,and the reachable distance is replaced with P weight.Experiments show that the detection efficiency of the algorithm is improved by 5.6 ms compared with the traditional local outlier factor(LOF)algorithm,and the detection accuracy is improved by about 2%compared with the local outlier factor based on area density(LAOF)algorithm.The application of LAOPW algorithm and the linear regression model shows that it can effectively carry out intelligent early warning of press-assembly quality of high precision servo mechanism.展开更多
基金This work is partially supported by the Ministry of Education of China(www.moe.gov.cn)under grant Nos.201802123091(received by F.W.)and 201802123068(received by Z.W.)Scientific Project of CAFUC(www.cafuc.edu.cn)under grant Nos.F2017KF02 and J2018-3(both received by Z.W.)Teaching Reform Project of CAFUC(www.cafuc.edu.cn)under grant No.E2020044(received by Z.W.).
文摘The heterogeneous nodes in the Internet of Things(IoT)are relatively weak in the computing power and storage capacity.Therefore,traditional algorithms of network security are not suitable for the IoT.Once these nodes alternate between normal behavior and anomaly behavior,it is difficult to identify and isolate them by the network system in a short time,thus the data transmission accuracy and the integrity of the network function will be affected negatively.Based on the characteristics of IoT,a lightweight local outlier factor detection method is used for node detection.In order to further determine whether the nodes are an anomaly or not,the varying behavior of those nodes in terms of time is considered in this research,and a time series method is used to make the system respond to the randomness and selectiveness of anomaly behavior nodes effectively in a short period of time.Simulation results show that the proposed method can improve the accuracy of the data transmitted by the network and achieve better performance.
基金supported by the National Basic Research Program of China (973 Program: 2013CB329004)
文摘Since data services are penetrating into our daily life rapidly, the mobile network becomes more complicated, and the amount of data transmission is more and more increasing. In this case, the traditional statistical methods for anomalous cell detection cannot adapt to the evolution of networks, and data mining becomes the mainstream. In this paper, we propose a novel kernel density-based local outlier factor(KLOF) to assign a degree of being an outlier to each object. Firstly, the notion of KLOF is introduced, which captures exactly the relative degree of isolation. Then, by analyzing its properties, including the tightness of upper and lower bounds, sensitivity of density perturbation, we find that KLOF is much greater than 1 for outliers. Lastly, KLOFis applied on a real-world dataset to detect anomalous cells with abnormal key performance indicators(KPIs) to verify its reliability. The experiment shows that KLOF can find outliers efficiently. It can be a guideline for the operators to perform faster and more efficient trouble shooting.
文摘针对复杂工业生产过程具有高维度、多工况、非线性的特征以及扩散映射存在的新样本投影困难的问题,本文提出了一种基于可扩容式扩散映射和局部离群因子(expandable diffusion maps and local outlier factors, EDM-LOF)的工业过程故障检测方法.使用扩散映射方法提取训练样本的低维流形结构,构建局部投影矩阵将新样本投影至流形空间,并在流形空间中使用局部离群因子方法进行故障检测.将EDM-LOF应用于青霉素发酵过程进行故障检测,并与PCA、FD-kNN、LOF方法进行比较,结果表明EDM-LOF具有更高的故障检测性能,验证了该方法的有效性.
文摘特征选择是雷达目标识别流程中一个较为关键的环节,通过对原始特征集进行筛选,挑选出其中的优质特征构成新的特征子集,可以有效增加识别准确率,提升识别效率。为了提升开放环境下高分辨距离像(High Range Resolution Profile,HRRP)的识别性能,针对现有特征选择方法基于闭集假设,无法有效应对实际应用中存在库外目标导致的开集识别(Open Set Recognition,OSR)性能下降问题,本文提出了一种基于局部离群因子(Local Outlier Factor,LOF)的HRRP开集识别特征选择方法。首先,从原始HRRP中提取15维特征向量作为原始特征集;其次,该方法引入聚合性概念,并使用LOF作为其度量,通过评估特征子集的聚合性来保证其在OSR时具有最小的开放空间风险。同时,采用重心法评估特征子集的可分性,并使用前向搜索算法优化特征选择过程,确保所选特征子集为维数约束下的最优解。实验结果表明:利用所提方法选择的特征子集在开集环境下识别性能优于现有特征提取方法,提升了开集环境下高分辨距离像的识别性能。
文摘Purpose:The main aim of this study is to build a robust novel approach that is able to detect outliers in the datasets accurately.To serve this purpose,a novel approach is introduced to determine the likelihood of an object to be extremely different from the general behavior of the entire dataset.Design/methodology/approach:This paper proposes a novel two-level approach based on the integration of bagging and voting techniques for anomaly detection problems.The proposed approach,named Bagged and Voted Local Outlier Detection(BV-LOF),benefits from the Local Outlier Factor(LOF)as the base algorithm and improves its detection rate by using ensemble methods.Findings:Several experiments have been performed on ten benchmark outlier detection datasets to demonstrate the effectiveness of the BV-LOF method.According to the results,the BV-LOF approach significantly outperformed LOF on 9 datasets of 10 ones on average.Research limitations:In the BV-LOF approach,the base algorithm is applied to each subset data multiple times with different neighborhood sizes(k)in each case and with different ensemble sizes(T).In our study,we have chosen k and T value ranges as[1-100];however,these ranges can be changed according to the dataset handled and to the problem addressed.Practical implications:The proposed method can be applied to the datasets from different domains(i.e.health,finance,manufacturing,etc.)without requiring any prior information.Since the BV-LOF method includes two-level ensemble operations,it may lead to more computational time than single-level ensemble methods;however,this drawback can be overcome by parallelization and by using a proper data structure such as R*-tree or KD-tree.Originality/value:The proposed approach(BV-LOF)investigates multiple neighborhood sizes(k),which provides findings of instances with different local densities,and in this way,it provides more likelihood of outlier detection that LOF may neglect.It also brings many benefits such as easy implementation,improved capability,higher applicability,and interpretability.
文摘Focusing on controlling the press-assembly quality of high-precision servo mechanism,an intelligent early warning method based on outlier data detection and linear regression is proposed.Linear regression is used to deal with the relationship between assembly quality and press-assembly process,then the mathematical model of displacement-force in press-assembly process is established and a qualified press-assembly force range is defined for assembly quality control.To preprocess the raw dataset of displacement-force in the press-assembly process,an improved local outlier factor based on area density and P weight(LAOPW)is designed to eliminate the outliers which will result in inaccuracy of the mathematical model.A weighted distance based on information entropy is used to measure distance,and the reachable distance is replaced with P weight.Experiments show that the detection efficiency of the algorithm is improved by 5.6 ms compared with the traditional local outlier factor(LOF)algorithm,and the detection accuracy is improved by about 2%compared with the local outlier factor based on area density(LAOF)algorithm.The application of LAOPW algorithm and the linear regression model shows that it can effectively carry out intelligent early warning of press-assembly quality of high precision servo mechanism.
