Fault diagnosis technology plays an important role in the industries due to the emergency fault of a machine could bring the heavy lost for the people and the company. A fault diagnosis model based on multi-manifold l...Fault diagnosis technology plays an important role in the industries due to the emergency fault of a machine could bring the heavy lost for the people and the company. A fault diagnosis model based on multi-manifold learning and particle swarm optimization support vector machine(PSO-SVM) is studied. This fault diagnosis model is used for a rolling bearing experimental of three kinds faults. The results are verified that this model based on multi-manifold learning and PSO-SVM is good at the fault sensitive features acquisition with effective accuracy.展开更多
The performance of Internet applications is heavily affected by the end-to-end available bandwidth. Thus,it is very important to examine how to accurately predict the available Internet bandwidth. A number of availabl...The performance of Internet applications is heavily affected by the end-to-end available bandwidth. Thus,it is very important to examine how to accurately predict the available Internet bandwidth. A number of available bandwidth prediction algorithms have been proposed to date, but none of the existing solutions are able to achieve a high level of accuracy. In this paper, a Multi-manifold based Available Bandwidth prediction algorithm(MD-AVB)is proposed, based on the observation that the available bandwidth space on the Internet is multi-manifold and asymmetrical. In the proposed algorithm, the available bandwidth space is divided into multiple lower-dimensional domains iteratively, and each domain is embedded separately to predict the available bandwidth. Experiments on HP S^3 datasets demonstrate that the proposed algorithm is more accurate than existing approaches.展开更多
多标签学习已成功应用于文本分类、图像识别等各个领域。流行的技术包括提取标签特定特征、利用标签相关性等。提出带有标签相关性的预测调整算法PALC(Prediction adjusting with label correlation)将标签相关性融入串行并行神经网络...多标签学习已成功应用于文本分类、图像识别等各个领域。流行的技术包括提取标签特定特征、利用标签相关性等。提出带有标签相关性的预测调整算法PALC(Prediction adjusting with label correlation)将标签相关性融入串行并行神经网络。一方面,采用新颖的、更有效的串行并行神经网络架构来替代常见的显式特征提取或压缩感知方法;另一方面,考虑用固有的标签矩阵内的相关性来计算相关性矩阵,并以流形正则的方式优化分类器。对10个基准数据集与7种流行算法进行比较,结果表明PALC在3大排名指标下均有优势。展开更多
基金Beijing Natural Science Foundation(KZ201211232039)National Natural Science Foundation of China(51275052)+1 种基金Funding Project for Academic Human Resources Development in Institutions of Higher Learning under the Jurisdiction of Beijing Municipalipality(PHR201106132)PXM2014_014224_000080
文摘Fault diagnosis technology plays an important role in the industries due to the emergency fault of a machine could bring the heavy lost for the people and the company. A fault diagnosis model based on multi-manifold learning and particle swarm optimization support vector machine(PSO-SVM) is studied. This fault diagnosis model is used for a rolling bearing experimental of three kinds faults. The results are verified that this model based on multi-manifold learning and PSO-SVM is good at the fault sensitive features acquisition with effective accuracy.
基金supported by the National Key Research and Development Program of China(No.2016YFB0801302)
文摘The performance of Internet applications is heavily affected by the end-to-end available bandwidth. Thus,it is very important to examine how to accurately predict the available Internet bandwidth. A number of available bandwidth prediction algorithms have been proposed to date, but none of the existing solutions are able to achieve a high level of accuracy. In this paper, a Multi-manifold based Available Bandwidth prediction algorithm(MD-AVB)is proposed, based on the observation that the available bandwidth space on the Internet is multi-manifold and asymmetrical. In the proposed algorithm, the available bandwidth space is divided into multiple lower-dimensional domains iteratively, and each domain is embedded separately to predict the available bandwidth. Experiments on HP S^3 datasets demonstrate that the proposed algorithm is more accurate than existing approaches.
文摘多标签学习已成功应用于文本分类、图像识别等各个领域。流行的技术包括提取标签特定特征、利用标签相关性等。提出带有标签相关性的预测调整算法PALC(Prediction adjusting with label correlation)将标签相关性融入串行并行神经网络。一方面,采用新颖的、更有效的串行并行神经网络架构来替代常见的显式特征提取或压缩感知方法;另一方面,考虑用固有的标签矩阵内的相关性来计算相关性矩阵,并以流形正则的方式优化分类器。对10个基准数据集与7种流行算法进行比较,结果表明PALC在3大排名指标下均有优势。
