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Turbopump Condition Monitoring Using Incremental Clustering and One-class Support Vector Machine 认领 引用 被引量：3
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作者 HU Lei HU Niaoqing +1 位作者 QIN Guojun GU Fengshou 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2011年第3期474-479,共6页
Turbopump condition monitoring is a significant approach to ensure the safety of liquid rocket engine （LRE）.Because of lack of fault samples,a monitoring system cannot be trained on all possible condition patterns.T... Turbopump condition monitoring is a significant approach to ensure the safety of liquid rocket engine （LRE）.Because of lack of fault samples,a monitoring system cannot be trained on all possible condition patterns.Thus it is important to differentiate abnormal or unknown patterns from normal pattern with novelty detection methods.One-class support vector machine （OCSVM） that has been commonly used for novelty detection cannot deal well with large scale samples.In order to model the normal pattern of the turbopump with OCSVM and so as to monitor the condition of the turbopump,a monitoring method that integrates OCSVM with incremental clustering is presented.In this method,the incremental clustering is used for sample reduction by extracting representative vectors from a large training set.The representative vectors are supposed to distribute uniformly in the object region and fulfill the region.And training OCSVM on these representative vectors yields a novelty detector.By applying this method to the analysis of the turbopump＇s historical test data,it shows that the incremental clustering algorithm can extract 91 representative points from more than 36 000 training vectors,and the OCSVM detector trained on these 91 representative points can recognize spikes in vibration signals caused by different abnormal events such as vane shedding,rub-impact and sensor faults.This monitoring method does not need fault samples during training as classical recognition methods.The method resolves the learning problem of large samples and is an alternative method for condition monitoring of the LRE turbopump. 展开更多
关键词 novelty detection condition monitoring incremental clustering one-class support vector machine turbopump
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Multi-class classification method for strip steel surface defects based on support vector machine with adjustable hyper-sphere 认领 引用 被引量：2
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作者 Mao-xiang Chu Xiao-ping Liu +1 位作者 Rong-fen Gong Jie Zhao 《Journal of Iron and Steel Research International》 SCIE EI CAS CSCD 2018年第7期706-716,共11页
Focusing on strip steel surface defects classification, a novel support vector machine with adjustable hyper-sphere （AHSVM） is formulated. Meanwhile, a new multi-class classification method is proposed. Originated f... Focusing on strip steel surface defects classification, a novel support vector machine with adjustable hyper-sphere （AHSVM） is formulated. Meanwhile, a new multi-class classification method is proposed. Originated from support vector data description, AHSVM adopts hyper-sphere to solve classification problem. AHSVM can obey two principles： the margin maximization and inner-class dispersion minimization. Moreover, the hyper-sphere of AHSVM is adjustable, which makes the final classification hyper-sphere optimal for training dataset. On the other hand, AHSVM is combined with binary tree to solve multi-class classification for steel surface defects. A scheme of samples pruning in mapped feature space is provided, which can reduce the number of training samples under the premise of classification accuracy, resulting in the improvements of classification speed. Finally, some testing experiments are done for eight types of strip steel surface defects. Experimental results show that multi-class AHSVM classifier exhibits satisfactory results in classification accuracy and efficiency. 展开更多
关键词 Strip steel surface defect Multi-class classification Supporting vector machine Adjustable hyper-sphere
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Multi-Class Support Vector Machine Classifier Based on Jeffries-Matusita Distance and Directed Acyclic Graph 认领 引用 被引量：1
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作者 Miao Zhang Zhen-Zhou Lai +1 位作者 Dan Li Yi Shen 《Journal of Harbin Institute of Technology(New Series)》 EI CAS 2013年第5期113-118,共6页
Based on the framework of support vector machines （SVM） using one-against-one （OAO） strategy, a new multi-class kernel method based on directed aeyclie graph （DAG） and probabilistic distance is proposed to raise... Based on the framework of support vector machines （SVM） using one-against-one （OAO） strategy, a new multi-class kernel method based on directed aeyclie graph （DAG） and probabilistic distance is proposed to raise the multi-class classification accuracies. The topology structure of DAG is constructed by rearranging the nodes＇ sequence in the graph. DAG is equivalent to guided operating SVM on a list, and the classification performance depends on the nodes＇ sequence in the graph. Jeffries-Matusita distance （JMD） is introduced to estimate the separability of each class, and the implementation list is initialized with all classes organized according to certain sequence in the list. To testify the effectiveness of the proposed method, numerical analysis is conducted on UCI data and hyperspectral data. Meanwhile, comparative studies using standard OAO and DAG classification methods are also conducted and the results illustrate better performance and higher accuracy of the orooosed JMD-DAG method. 展开更多
关键词 multi-class classification support vector machine directed acyclic graph Jeffries-Matusitadistance hyperspcctral data
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Prediction of Protein Structural Classes Using the Theory of Increment of Diversity and Support Vector Machine 认领 引用 被引量：1
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作者 WANG Fangping WANG Zhijian +1 位作者 LI Hong YANG Keli 《Wuhan University Journal of Natural Sciences》 CAS 2011年第3期260-264,共5页
Based on the concept of the pseudo amino acid composition （PseAAC）, protein structural classes are predicted by using an approach of increment of diversity combined with support vector machine （ID-SVM）, in which t... Based on the concept of the pseudo amino acid composition （PseAAC）, protein structural classes are predicted by using an approach of increment of diversity combined with support vector machine （ID-SVM）, in which the dipeptide amino acid composition of proteins is used as the source of diversity. Jackknife test shows that total prediction accuracy is 96.6% and higher than that given by other approaches. Besides, the specificity （Sp） and the Matthew＇s correlation coefficient （MCC） are also calculated for each protein structural class, the Sp is more than 88%, the MCC is higher than 92%, and the higher MCC and Sp imply that it is credible to use ID-SVM model predicting protein structural class. The results indicate that： 1 the choice of the source of diversity is reasonable, 2 the predictive performance of IDSVM is excellent, and3 the amino acid sequences of proteins contain information of protein structural classes. 展开更多
关键词 dipeptide amino acid composition increment of diversity support vector machines protein structure classes
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Least Squares One-Class Support Tensor Machine 认领 引用
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作者 Kaiwen Zhao Yali Fan 《Journal of Computer and Communications》 2024年第4期186-200,共15页
One-class classification problem has become a popular problem in many fields, with a wide range of applications in anomaly detection, fault diagnosis, and face recognition. We investigate the one-class classification ... One-class classification problem has become a popular problem in many fields, with a wide range of applications in anomaly detection, fault diagnosis, and face recognition. We investigate the one-class classification problem for second-order tensor data. Traditional vector-based one-class classification methods such as one-class support vector machine (OCSVM) and least squares one-class support vector machine (LSOCSVM) have limitations when tensor is used as input data, so we propose a new tensor one-class classification method, LSOCSTM, which directly uses tensor as input data. On one hand, using tensor as input data not only enables to classify tensor data, but also for vector data, classifying it after high dimensionalizing it into tensor still improves the classification accuracy and overcomes the over-fitting problem. On the other hand, different from one-class support tensor machine (OCSTM), we use squared loss instead of the original loss function so that we solve a series of linear equations instead of quadratic programming problems. Therefore, we use the distance to the hyperplane as a metric for classification, and the proposed method is more accurate and faster compared to existing methods. The experimental results show the high efficiency of the proposed method compared with several state-of-the-art methods. 展开更多
关键词 Least Square One-Class Support Tensor Machine One-Class Classification Upscale Least Square One-Class Support Vector Machine One-Class Support Tensor Machine
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Fault Diagnosis for Aero-engine Applying a New Multi-class Support Vector Algorithm 认领 引用 被引量：11
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作者 徐启华 师军 《Chinese Journal of Aeronautics》 EI CAS 2006年第3期175-182,共8页
Hierarchical Support Vector Machine （H-SVM） is faster in training and classification than other usual multi-class SVMs such as ＂1-V-R＂and ＂1-V-1＂. In this paper, a new multi-class fault diagnosis algorithm based... Hierarchical Support Vector Machine （H-SVM） is faster in training and classification than other usual multi-class SVMs such as ＂1-V-R＂and ＂1-V-1＂. In this paper, a new multi-class fault diagnosis algorithm based on H-SVM is proposed and applied to aero-engine. Before SVM training, the training data are first clustered according to their class-center Euclid distances in some feature spaces. The samples which have close distances are divided into the same sub-classes for training, and this makes the H-SVM have reasonable hierarchical construction and good generalization performance. Instead of the common C-SVM, the v-SVM is selected as the binary classifier, in which the parameter v varies only from 0 to 1 and can be determined more easily. The simulation results show that the designed H-SVMs can fast diagnose the multi-class single faults and combination faults for the gas path components of an aero-engine. The fault classifiers have good diagnosis accuracy and can keep robust even when the measurement inputs are disturbed by noises. 展开更多
关键词 support vector machine fault diagnosis multi-class classification
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Support vector machine-based multi-model predictive control 认领 引用 被引量：3
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作者 Zhejing BAO Youxian SUN 《控制理论与应用（英文版）》 2008年第3期305-310,共6页
In this paper, a support vector machine-based multi-model predictive control is proposed, in which SVM classification combines well with SVM regression. At first, each working environment is modeled by SVM regression ... In this paper, a support vector machine-based multi-model predictive control is proposed, in which SVM classification combines well with SVM regression. At first, each working environment is modeled by SVM regression and the support vector machine network-based model predictive control （SVMN-MPC） algorithm corresponding to each environment is developed, and then a multi-class SVM model is established to recognize multiple operating conditions. As for control, the current environment is identified by the multi-class SVM model and then the corresponding SVMN-MPC controller is activated at each sampling instant. The proposed modeling, switching and controller design is demonstrated in simulation results. 展开更多
关键词 Multi-model predictive control Support vector machine network Multi-class support vector machine Multi-model switching
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基于OCSVM的行业负荷特征异常辨识方法 认领 引用
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作者 陈光宇 杨光 +3 位作者 施蔚锦 蔡鑫灿 陈婉清 刘昊 《电力工程技术》 CSCD 北大核心 2026年第2期70-79,共10页
为解决近年来用户行业变化特性加剧导致的难以准确辨识用户档案信息变动的问题,文中提出一种基于数据驱动的负荷特征异常辨识方法。首先,提出一种两阶段行业典型负荷形态构建方法,利用基于层次密度的含噪声应用空间聚类(hierarchical de... 为解决近年来用户行业变化特性加剧导致的难以准确辨识用户档案信息变动的问题,文中提出一种基于数据驱动的负荷特征异常辨识方法。首先,提出一种两阶段行业典型负荷形态构建方法,利用基于层次密度的含噪声应用空间聚类(hierarchical density-based spatial clustering of applications with noise,HDBSCAN)提取用户在不同场景下的典型日负荷曲线,并利用改进的K-means算法对提取出的典型日负荷曲线进行聚类分析,构建行业的典型负荷形态;其次,提出一种多维场景负荷特征异常智能研判方法,通过构造用户的负荷特征,使用熵权法评估行业典型场景的相对重要性,并采用单分类支持向量机(one-class support vector machine,OCSVM)算法量化每个场景下的用户负荷特征的异常程度,通过加权计算得到用户的综合嫌疑得分并排序,从而实现对负荷特征异常用户的准确辨识。最后,采用某地区实际用户数据进行算例验证。仿真结果表明,所提方法在行业典型负荷场景构建及负荷特征异常辨识方面表现出良好的可行性与实用价值。 展开更多
关键词 数据驱动 负荷特征异常 基于层次密度的含噪声应用空间聚类(HDBSCAN)-改进K-means算法 多维场景分析 单分类支持向量机(OCSVM) 综合嫌疑得分
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基于AE-2LSTM-OCSVM电气线路超温早期征兆异常识别方法 认领 引用
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作者 潘红光 陈佳涛 +1 位作者 郭强 顾天宇 《科学技术与工程》 EI 北大核心 2026年第14期6015-6023,共9页
针对当前电气火灾预警多依赖明显故障特征、难以在风险萌芽阶段及时响应。聚焦于电气线路超温发生前的早期征兆阶段。提出了一种融合双层长短期记忆网络(long short-term memory,LSTM)增强型自编码器(autoencoder,AE)与单分类支持向量机... 针对当前电气火灾预警多依赖明显故障特征、难以在风险萌芽阶段及时响应。聚焦于电气线路超温发生前的早期征兆阶段。提出了一种融合双层长短期记忆网络(long short-term memory,LSTM)增强型自编码器(autoencoder,AE)与单分类支持向量机(one-class support vector machine,OCSVM)的电气线路超温早期征兆异常识别方法。通过分析过载、谐波和非周期电流三类电气线路超温早期征兆的致灾原理与波形。构建数据集。利用AE-2LSTM结构有效学习电流数据的深层时序特征并实现高精度重构,在此基础上,利用正常与异常样本在重构误差上的显著差异,通过引入OCSVM模块实现仅依赖正常样本的单分类异常检测。实验结果表明,该方法识别准确率达98.16%,克服了电气火灾监测技术研究中异常数据获取困难、正负样本极度不平衡等实际建模难题,为电气火灾的早期预警与主动防控提供了重要的科学依据和技术支持。 展开更多
关键词 电气火灾预警 早期征兆异常识别 自编码器 单分类支持向量机 长短期记忆网络
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An Improved Directed Acyclic Graph Support Vector Machine 认领 引用 被引量：1
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作者 Adel RHUMA Syed Mohsen NAQVI Jonathon CHAMBERS 《Journal of Measurement Science and Instrumentation》 CAS 2011年第4期367-370,共4页
In this paper, we propose an improved Directed Acyclic Graph Support Vector Machine （DAGSVM） for multi-class classification. Compared with the traditional DAGSVM, the improved version has advantages that the structu... In this paper, we propose an improved Directed Acyclic Graph Support Vector Machine （DAGSVM） for multi-class classification. Compared with the traditional DAGSVM, the improved version has advantages that the structure of the directed acyclic graph is not chosen random and fixed, and it can be adaptive to be optimal according to the incoming testing samples, thus it has a good generalization performance. From experiments on six datasets, we can see that the proposed improved version of DAGSVM is better than the traditional one with respect to the accuracy rate. 展开更多
关键词 class classification directed acyclic graph support vector machine
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ESSENTIAL RELATIONSHIP BETWEEN DOMAIN-BASED ONE-CLASS CLASSIFIERS AND DENSITY ESTIMATION 认领 引用 被引量：2
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作者 陈斌 李斌 +1 位作者 冯爱民 潘志松 《Transactions of Nanjing University of Aeronautics and Astronautics》 EI 2008年第4期275-281,共7页
One-class support vector machine （OCSVM） and support vector data description （SVDD） are two main domain-based one-class （kernel） classifiers. To reveal their relationship with density estimation in the case of t... One-class support vector machine （OCSVM） and support vector data description （SVDD） are two main domain-based one-class （kernel） classifiers. To reveal their relationship with density estimation in the case of the Gaussian kernel, OCSVM and SVDD are firstly unified into the framework of kernel density estimation, and the essential relationship between them is explicitly revealed. Then the result proves that the density estimation induced by OCSVM or SVDD is in agreement with the true density. Meanwhile, it can also reduce the integrated squared error （ISE）. Finally, experiments on several simulated datasets verify the revealed relationships. 展开更多
关键词 one-class support vector machine（OCSVM） support vector data description（SVDD） kernel density estimation
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基于One-class SVM的噪声图像分割方法 认领 引用 被引量：6
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作者 尚方信 郭浩 +1 位作者 李钢 张玲 《计算机应用》 CSCD 北大核心 2019年第3期874-881,共8页
为解决现有无监督图像分割模型对强噪声环境鲁棒性差、无法适应复杂混合噪声的问题,提出了一种基于One-class SVM方法的改进后的噪声鲁棒图像分割模型。首先,基于One-class SVM构建一种数据离群程度检测机制;然后,将离群程度值引入能量... 为解决现有无监督图像分割模型对强噪声环境鲁棒性差、无法适应复杂混合噪声的问题,提出了一种基于One-class SVM方法的改进后的噪声鲁棒图像分割模型。首先,基于One-class SVM构建一种数据离群程度检测机制;然后,将离群程度值引入能量泛函,令分割模型可以在多种噪声强度下获得较为准确的图像信息,同时避免现有方法在强噪声环境下,降权机制失效的问题;最后,通过最小化能量函数,驱动分割轮廓向目标边缘演化。在噪声图像分割实验中,当选取不同类型和强度的噪声时,该模型均能得到较为理想的分割结果。在F_1-score评估标准下,该模型比基于局部相关熵的K-means(LCK)模型高0.2～0.3,在强噪声环境下具有更高的稳定性,且在分割收敛时间上仅略大于LCK模型0.1 s左右。实验结果表明,所提模型在未显著增加分割耗时的前提下,对于概率、极值及混合噪声均有着更强的鲁棒性,并且可以分割带有噪声的自然图像。 展开更多
关键词 图像分割 图像噪声 单类支持向量机 离群检测 能量项
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一种基于One-Class SVM和GP安全事件关联规则生成方法研究 认领 引用 被引量：7
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作者 杜栋栋 任星彰 +3 位作者 陈坤 叶蔚 赵文 张世琨 《电子学报》 EI CAS CSCD 北大核心 2018年第8期1793-1803,共11页
随着信息技术的快速发展,网络安全威胁造成的危害日愈严重.安全信息和事件管理(SIEM)在查找组织内部威胁,可疑行为及其它高级持续攻击(APT)中发挥了重要作用.SIEM的检测能力主要依赖于准确,可靠的关联规则.然而,传统的规则生成方式主要... 随着信息技术的快速发展,网络安全威胁造成的危害日愈严重.安全信息和事件管理(SIEM)在查找组织内部威胁,可疑行为及其它高级持续攻击(APT)中发挥了重要作用.SIEM的检测能力主要依赖于准确,可靠的关联规则.然而,传统的规则生成方式主要基于专家知识人工编写检测规则,因此成本高,效率低.本文给出了一种具备自适应能力的规则生成框架来自动生成关联规则.首先为了更好地识别未知攻击,提出一种基于单类支持向量机(OneClass SVM)的安全事件分类算法对安全事件进行有效分类,实验分类效果准确率高达97%.其次为了提高规则生成准确率,通过重新定义个体结构,交叉与变异方式,优化了基于遗传编程(GP)的规则生成算法,规则适应度高达94%.实验结果表明,本文提出的框架具备自适应能力来识别未知攻击,具备较高的检测准确率,可有效减少人工参与.同时该框架已经部署在实际生产环境中,和原系统相比可以检测更多攻击类型. 展开更多
关键词 安全事件 关联规则生成 日志管理 安全信息和事件管理(SIEM) 单类支持向量机 遗传编程
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融合自编码器和one-class SVM的异常事件检测 认领 引用 被引量：16
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作者 胡海洋 张力 李忠金 《中国图象图形学报》 CSCD 北大核心 2020年第12期2614-2629,共16页
目的在自动化和智能化的现代生产制造过程中,视频异常事件检测技术扮演着越来越重要的角色,但由于实际生产制造中异常事件的复杂性及无关生产背景的干扰,使其成为一项非常具有挑战性的任务。很多传统方法采用手工设计的低级特征对视频... 目的在自动化和智能化的现代生产制造过程中,视频异常事件检测技术扮演着越来越重要的角色,但由于实际生产制造中异常事件的复杂性及无关生产背景的干扰,使其成为一项非常具有挑战性的任务。很多传统方法采用手工设计的低级特征对视频的局部区域进行特征提取,然而此特征很难同时表示运动与外观特征。此外,一些基于深度学习的视频异常事件检测方法直接通过自编码器的重构误差大小来判定测试样本是否为正常或异常事件,然而实际情况往往会出现一些原本为异常的测试样本经过自编码得到的重构误差也小于设定阈值,从而将其错误地判定为正常事件,出现异常事件漏检的情形。针对此不足,本文提出一种融合自编码器和one-class支持向量机(support vector machine,SVM)的异常事件检测模型。方法通过高斯混合模型(Gaussian mixture model,GMM)提取固定大小的时空兴趣块(region of interest,ROI);通过预训练的3维卷积神经网络(3D convolutional neural network,C3D)对ROI进行高层次的特征提取;利用提取的高维特征训练一个堆叠的降噪自编码器,通过比较重构误差与设定阈值的大小,将测试样本判定为正常、异常和可疑3种情况之一;对自编码器降维后的特征训练一个one-class SVM模型,用于对可疑测试样本进行二次检测,进一步排除异常事件。结果本文对实际生产制造环境下的机器人工作场景进行实验,采用AUC(area under ROC)和等错误率(equal error rate,EER)两个常用指标进行评估。在设定合适的误差阈值时,结果显示受试者工作特征(receiver operating characteristic,ROC)曲线下AUC达到91.7%,EER为13.8%。同时,在公共数据特征集USCD(University of California,San Diego)Ped1和USCD Ped2上进行了模型评估,并与一些常用方法进行了比较,在USCD Ped1数据集中,相比于性能第2的方法,AUC在帧级别和像素级别分别提高了2.6%和22.3%;在USCD Ped2数据集中,相比于性能第2的方法,AUC在帧级别提高了6.7%,从而验证了所提检测方法的有效性与准确性。结论本文提出的视频异常事件检测模型,结合了传统模型与深度学习模型,使视频异常事件检测结果更加准确。 展开更多
关键词 视频异常事件检测 时空兴趣块 3维卷积神经网络 降噪自编码器 one-class支持向量机
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基于改进MFCC-OCSVM和贝叶斯优化BiGRU的GIS异常工况声纹识别算法 认领 引用 被引量：7
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作者 庄小亮 李乾坤 +3 位作者 刘紫罡 张禄亮 季天瑶 张长虹 《南方电网技术》 CSCD 北大核心 2025年第1期30-40,共11页
为了准确识别气体绝缘开关柜(gas insulated switchgear,GIS)设备的异常工况,提出了一种基于加权梅尔频率谱系数单类支持向量机(Mel frequency cestrum coefficient-one class support vector machine,MFCC-OCSVM)和贝叶斯优化的门控循... 为了准确识别气体绝缘开关柜(gas insulated switchgear,GIS)设备的异常工况,提出了一种基于加权梅尔频率谱系数单类支持向量机(Mel frequency cestrum coefficient-one class support vector machine,MFCC-OCSVM)和贝叶斯优化的门控循环单元(bidirectional gate recurrent unit,BiGRU)声纹识别算法。首先,利用基于F统计量的MFCC对声纹数据进行加权特征提取,突出重要特征并减弱噪声的影响,然后利用OCSVM对加权后的特征进行异常检测并去除异常值,提高数据质量。为解决样本不平衡问题,采用合成少数类过采样技术(synthetic minority over-sampling technique,SMOTE)进行声纹样本的均衡。最后,应用基于贝叶斯优化的BiGRU模型进行声纹识别。以某气体绝缘全封闭组合电器(gas insulated switchgear,GIS)为例,采集了20类不同工况下操纵机构的声音样本,与多种经典分类模型进行对比。结果显示,所提算法取得的最高平均识别准确率达到了92.8%,相比于自适应增强、朴素贝叶斯和线性判别分析算法分别提升了30.1%、14.7%和11.5%。通过消融实验进一步评估和验证了所提算法各个流程对声纹识别的实际效果和性能影响,研究成果可为GIS设备异常工况的声纹识别提供高效技术路线。 展开更多
关键词 GIS设备 梅尔频谱倒谱系数 单类支持向量机 双向门控循环单元 声纹识别 贝叶斯优化
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基于One-class SVM的自相关线性轮廓监控研究 认领 引用
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作者 薛丽 贾元忠 曹逗逗 《郑州航空工业管理学院学报》 2022年第1期89-98,共10页
在复杂产品的制造过程中,轮廓(profile)数据是一类广泛存在的质量数据类型。为了能够尽快监测出线性轮廓内自相关过程中的异常,针对质量数据仅存在正常样本的情况,提出了基于一类支持向量机(one-class Support Vector Machine,OCSVM)的... 在复杂产品的制造过程中,轮廓(profile)数据是一类广泛存在的质量数据类型。为了能够尽快监测出线性轮廓内自相关过程中的异常,针对质量数据仅存在正常样本的情况,提出了基于一类支持向量机(one-class Support Vector Machine,OCSVM)的监控方法。首先,介绍OCSVM方法原理;其次,构建OCSVM监控模型,通过数值仿真实验模拟得到平均运行长度,并给出详细的仿真过程;再次,以平均运行长度为准则,分析高斯核函数与多项式核函数对OCSVM方法监控性能的影响,结果表明:监控AR(1)模型时,多项式核函数具有优势;最后,将多项式核函数的仿真结果与传统的一些控制图进行对比,结果表明:当标准差以及斜率、截距同时发生变化时,OCSVM方法监控效果优于其他控制图;当自相关系数ρ=0.1(弱相关)截距发生较大偏移以及ρ=0.9(强相关)截距发生偏移时,OCSVM方法监控效果优于其他控制图。 展开更多
关键词 线性轮廓 一类支持向量机 自相关过程 平均运行长度
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Fuzzy support vector machine using local outlier factor and intuitionistic fuzzy sets for imbalanced datasets 认领 引用
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作者 Mengya Hu Shaowu Lu 《Journal of Control and Decision》 EI 2026年第3期821-832,共12页
Traditional classifiers are commonly used for solving class balance problems.However,many datasets exhibit class imbalance along with outliers and noise,which affect the classification accuracy,this paper proposes a f... Traditional classifiers are commonly used for solving class balance problems.However,many datasets exhibit class imbalance along with outliers and noise,which affect the classification accuracy,this paper proposes a fuzzy support vector machine algorithm based on local outlier factor and intuitionistic fuzzy sets.First,to highlight the importance of the minority class,the membership degree is set to the maximum value.Meanwhile,local outlier factor is calculated to measure the abnormality and further obtain the membership degree in the majority class.Then,a kernel density estimation method is designed to estimate the sample density accurately.Furthermore,based on the density distribution,intuitionistic fuzzy sets are used to assign membership and non-membership degrees to samples,effectively distinguishing between noise and outliers.Finally,different penalty coefficients for two classes are built to offset the impact of class imbalance on classification accuracy.Experimental results show the advantages of the proposed algorithm. 展开更多
关键词 Fuzzy support vector machine class imbalance outliers local outlier factor intuitionistic fuzzy sets
基于LSTM-ADMK-OCSVM的网络终端设备异常行为检测方法 认领 引用
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作者 季晨宇 欧朱建 +1 位作者 姜鑫东 马益锋 《电子设计工程》 2025年第24期131-137,共7页
针对电力通信网等工业互联网中非受控终端不能通过安装代理软件进行异常行为监测的问题,采用非侵入式网络监听手段,采集各终端设备进网流量、出网流量、IP组播流量、IP广播流量、会话总数等数据,提出一种基于长短时记忆网络的自适应动... 针对电力通信网等工业互联网中非受控终端不能通过安装代理软件进行异常行为监测的问题,采用非侵入式网络监听手段,采集各终端设备进网流量、出网流量、IP组播流量、IP广播流量、会话总数等数据,提出一种基于长短时记忆网络的自适应动态多核单类支持向量机方法(Long ShortTerm Memory Adaptive Dynamic Multiple Kernel One Class Support Vector Machine,LSTM-ADMK-OCSVM),精确刻画各类非受控终端正常工作行为模态,构建异常行为描述和监测模型,实现对非受控终端设备非设定异常行为安全监测。通过电力信息内网非受控终端实际系统实验,得出所提方法可有效对非受控终端异常行为进行监测,精度达到95.36%,满足实际系统应用要求。 展开更多
关键词 非受控终端 多核单类支持向量机 异常行为检测 长短时记忆网络
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基于IVYA-OCSVM的设备异常检测方法研究 认领 引用 被引量：1
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作者 王琳 周树桥 +2 位作者 张天昊 郭超 黄晓津 《自动化仪表》 CAS 2025年第9期144-148,154,共5页
针对当前核电站设备异常检测领域广泛应用的固定阈值法所存在的报警实时性低、误报率高的问题,提出了一种采用常青藤算法(IVYA)优化单分类支持向量机(OCSVM)的设备异常检测方法。通过对监测数据提取合适的特征,利用OCSVM对正常状态下的... 针对当前核电站设备异常检测领域广泛应用的固定阈值法所存在的报警实时性低、误报率高的问题,提出了一种采用常青藤算法(IVYA)优化单分类支持向量机(OCSVM)的设备异常检测方法。通过对监测数据提取合适的特征,利用OCSVM对正常状态下的数据特征进行学习。针对OCSVM参数寻优问题,设计了以减小模型误报率和漏报率为目标的目标函数。在此基础上引入IVYA对OCSVM参数进行优化,构建了性能优异的IVYA-OCSVM异常检测模型。在基于实际数据的对比验证中,所提方法的异常检测准确率为97.61%,优于对比方法,验证了所提方法的有效性与优异性。所提方法有望应用于核电站的关键敏感设备或其他行业设备检测数据的分析,以提升异常检测的准确性和核电站运行的安全性。 展开更多
关键词 核电站 核电设备 异常检测 目标函数 常青藤算法 单分类支持向量机
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基于层次自收敛PCA-OCSVM算法的入侵检测方法研究 认领 引用 被引量：2
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作者 郭建明 张红卓 +1 位作者 马涛 张永兵 《价值工程》 2025年第4期149-151,共3页
随着网络技术的迅猛发展,网络安全问题日益突出,尤其是网络入侵检测领域。传统的入侵检测方法往往存在效率低下或准确性不足等问题。本文提出了一种基于层次自收敛主成分分析(PCA)与单类支持向量机(OCSVM)结合的入侵检测方法,旨在提高... 随着网络技术的迅猛发展,网络安全问题日益突出,尤其是网络入侵检测领域。传统的入侵检测方法往往存在效率低下或准确性不足等问题。本文提出了一种基于层次自收敛主成分分析(PCA)与单类支持向量机(OCSVM)结合的入侵检测方法,旨在提高入侵检测的效率和准确性。首先,采用层次化的方法对数据进行预处理,通过自收敛PCA降维处理,优化特征集,并减少噪声干扰和计算复杂度。随后,利用OCSVM对处理后的数据进行训练与分类,以识别正常与异常行为。实验结果表明,该方法在多个标准数据集上具有较好的检测性能,相比传统方法,在检测率、误报率及检测速度等关键指标上均有所提升。本研究为网络入侵检测技术的发展提供了新的思路和方法。 展开更多
关键词 入侵检测 主成分分析(PCA) 单类支持向量机(OCSVM) 自收敛算法
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