To accelerate the training of support vector domain description （SVDD）, confidence support vector domain description （CSVDD） is proposed based on the observation that the description boundary is determined by a sm...To accelerate the training of support vector domain description （SVDD）, confidence support vector domain description （CSVDD） is proposed based on the observation that the description boundary is determined by a small subset of training data called support vectors. Namely, the number of training samples in the userdefined sphere is calculated and taken as the confidence measure, according to which the training samples are ranked in ascending order. Those former ranked ones are selected as the boundary targets for the SVDD training. Simulations on UCI data demonstrate the effectiveness and superiority of CSVDD： the number of training targets and the training time are reduced without any loss of accuracy.展开更多
This paper addresses the multi-fault diagnosis problem of thrusters and sensors for autonomous underwater vehicles (AUVs). Traditional support vector domain description (SVDD) has low classification accuracy in the pr...This paper addresses the multi-fault diagnosis problem of thrusters and sensors for autonomous underwater vehicles (AUVs). Traditional support vector domain description (SVDD) has low classification accuracy in the process of AUV multi-fault pattern classification because of the effect of sample sparse density and the uneven distribution of samples, and so on. Thus, a fuzzy weighted support vector domain description (FWSVDD) method based on positive and negative class samples is proposed. In this method, the negative class sample is introduced during classifier training, and the local density and the class weight are introduced for each sample. To improve the multi-fault pattern classifier training speed and fault diagnosis accuracy of FWSVDD, a multi-fault mode classification method based on a hierarchical strategy is proposed. This method adds fault contain detection surface for each thruster and sensor to isolate fault components during fault diagnosis. By considering the problem of pattern classification for a fuzzy sample, which may be located in the overlapping area of hyper-spheres or may not belong to any hyper-sphere in the process of multi-fault classification based on FWSVDD, a relative distance judgment method is given. The effectiveness of the proposed multi-fault diagnosis approach is demonstrated through water tank experiments with an experimental AUV prototype.展开更多
Protein-protein interactions play a crucial role in the cellular processsuch as metabolic pathways and immunological recognition. This paper presents a new domain score-based support vector machine （SVM） to infer pr...Protein-protein interactions play a crucial role in the cellular processsuch as metabolic pathways and immunological recognition. This paper presents a new domain score-based support vector machine （SVM） to infer protein interactions, which can be used not only to explore all possible domain interactions by the kernel method, but also to reflect the evolutionary conservation of domains in proteins by using the domain scores of proteins. The experimental result on the Saccharomyces cerevisiae dataset demonstrates that this approach can predict protein-protein interactions with higher performances compared to the existing approaches.展开更多
为提升风电机组运行效率并优化运维成本,将时域特征指标分析技术与多传感器信息融合策略相结合,提出一种基于灰狼优化(Grey wolf optimization,GWO)算法-支持向量机(Support vector machine,SVM)的风电齿轮箱状态监测方法。首先计算了...为提升风电机组运行效率并优化运维成本,将时域特征指标分析技术与多传感器信息融合策略相结合,提出一种基于灰狼优化(Grey wolf optimization,GWO)算法-支持向量机(Support vector machine,SVM)的风电齿轮箱状态监测方法。首先计算了表征振动能量的不同时域统计特征值,采用并行叠加方式进行特征级和数据级融合得到信息融合矩阵。在此基础上建立了基于GWO-SVM的故障诊断分类模型。为验证模型性能,使用QPZZ-Ⅱ旋转机械振动试验台所采集的齿轮箱实测数据对本文所提方法进行验证分析,结果表明该方法明显优于其他传统方法,其在分类诊断准确率上展现出显著优势。展开更多
基金supported by the National Natural Science Foundation of China(6057407560674108).
文摘To accelerate the training of support vector domain description （SVDD）, confidence support vector domain description （CSVDD） is proposed based on the observation that the description boundary is determined by a small subset of training data called support vectors. Namely, the number of training samples in the userdefined sphere is calculated and taken as the confidence measure, according to which the training samples are ranked in ascending order. Those former ranked ones are selected as the boundary targets for the SVDD training. Simulations on UCI data demonstrate the effectiveness and superiority of CSVDD： the number of training targets and the training time are reduced without any loss of accuracy.
基金supported by the National Natural Science Foundation of China(Grant No.51279040)the Research Fund for the Doctoral Program of Higher Education of China(Grant No.20112304110024)
文摘This paper addresses the multi-fault diagnosis problem of thrusters and sensors for autonomous underwater vehicles (AUVs). Traditional support vector domain description (SVDD) has low classification accuracy in the process of AUV multi-fault pattern classification because of the effect of sample sparse density and the uneven distribution of samples, and so on. Thus, a fuzzy weighted support vector domain description (FWSVDD) method based on positive and negative class samples is proposed. In this method, the negative class sample is introduced during classifier training, and the local density and the class weight are introduced for each sample. To improve the multi-fault pattern classifier training speed and fault diagnosis accuracy of FWSVDD, a multi-fault mode classification method based on a hierarchical strategy is proposed. This method adds fault contain detection surface for each thruster and sensor to isolate fault components during fault diagnosis. By considering the problem of pattern classification for a fuzzy sample, which may be located in the overlapping area of hyper-spheres or may not belong to any hyper-sphere in the process of multi-fault classification based on FWSVDD, a relative distance judgment method is given. The effectiveness of the proposed multi-fault diagnosis approach is demonstrated through water tank experiments with an experimental AUV prototype.
基金supported by the National Natural Science Foundation of China (Grant No.30571059)the National High-Technology Research and Development Program of China (Grant No.2006AA02Z190)the Shanghai Leading Academic Discipline Project (Grant No.S30405)
文摘Protein-protein interactions play a crucial role in the cellular processsuch as metabolic pathways and immunological recognition. This paper presents a new domain score-based support vector machine （SVM） to infer protein interactions, which can be used not only to explore all possible domain interactions by the kernel method, but also to reflect the evolutionary conservation of domains in proteins by using the domain scores of proteins. The experimental result on the Saccharomyces cerevisiae dataset demonstrates that this approach can predict protein-protein interactions with higher performances compared to the existing approaches.
文摘为提升风电机组运行效率并优化运维成本,将时域特征指标分析技术与多传感器信息融合策略相结合,提出一种基于灰狼优化(Grey wolf optimization,GWO)算法-支持向量机(Support vector machine,SVM)的风电齿轮箱状态监测方法。首先计算了表征振动能量的不同时域统计特征值,采用并行叠加方式进行特征级和数据级融合得到信息融合矩阵。在此基础上建立了基于GWO-SVM的故障诊断分类模型。为验证模型性能,使用QPZZ-Ⅱ旋转机械振动试验台所采集的齿轮箱实测数据对本文所提方法进行验证分析,结果表明该方法明显优于其他传统方法,其在分类诊断准确率上展现出显著优势。
