This paper proposes an active learning accelerated Monte-Carlo simulation method based on the modified K-nearest neighbors algorithm.The core idea of the proposed method is to judge whether or not the output of a rand...This paper proposes an active learning accelerated Monte-Carlo simulation method based on the modified K-nearest neighbors algorithm.The core idea of the proposed method is to judge whether or not the output of a random input point can be postulated through a classifier implemented through the modified K-nearest neighbors algorithm.Compared to other active learning methods resorting to experimental designs,the proposed method is characterized by employing Monte-Carlo simulation for sampling inputs and saving a large portion of the actual evaluations of outputs through an accurate classification,which is applicable for most structural reliability estimation problems.Moreover,the validity,efficiency,and accuracy of the proposed method are demonstrated numerically.In addition,the optimal value of K that maximizes the computational efficiency is studied.Finally,the proposed method is applied to the reliability estimation of the carbon fiber reinforced silicon carbide composite specimens subjected to random displacements,which further validates its practicability.展开更多
On the basis of machine leaning,suitable algorithms can make advanced time series analysis.This paper proposes a complex k-nearest neighbor(KNN)model for predicting financial time series.This model uses a complex feat...On the basis of machine leaning,suitable algorithms can make advanced time series analysis.This paper proposes a complex k-nearest neighbor(KNN)model for predicting financial time series.This model uses a complex feature extraction process integrating a forward rolling empirical mode decomposition(EMD)for financial time series signal analysis and principal component analysis(PCA)for the dimension reduction.The information-rich features are extracted then input to a weighted KNN classifier where the features are weighted with PCA loading.Finally,prediction is generated via regression on the selected nearest neighbors.The structure of the model as a whole is original.The test results on real historical data sets confirm the effectiveness of the models for predicting the Chinese stock index,an individual stock,and the EUR/USD exchange rate.展开更多
目的采用近红外光谱技术对油莎豆进行分析,并应用化学计量学中识别模式对油莎豆进行产地溯源。方法采用近红外光谱法结合化学计量学软件,对来自河北、湖南、山东、新疆、云南等地408份油莎豆样品进行产地溯源,分别采用多元散射校正、多...目的采用近红外光谱技术对油莎豆进行分析,并应用化学计量学中识别模式对油莎豆进行产地溯源。方法采用近红外光谱法结合化学计量学软件,对来自河北、湖南、山东、新疆、云南等地408份油莎豆样品进行产地溯源,分别采用多元散射校正、多量标准化或多量标准化耦合去趋势算法3种光谱预处理方法和支持向量机(support vector machine,SVM)、簇类独立分类(soft independent modeling of class analogy,SIMCA)、正交偏最小二乘判别(orthogonal partial least squares discriminant analysis,OPLS-DA)、偏最小二乘判别(partial least squares discriminant analysis,PLS-DA)、和K最近邻算法(K-nearest neighbor algorithm,KNN)等5种识别模式进行产地识别。结果SVM、SIMCA、OPLS-DA、PLS-DA和KNN等5种模式的建模识别率分别为91.89%、94.47%、62.37%、65.32%和100.00%。选择KNN作为产地识别模型,分析不同预处理方法、数据预处理及样本距离对模型预测结果稳定性的影响,筛选出最优模型参数。选用多元散射校正光谱预处理方式,在UV标度化、Pareto标度化、自动标度化或中心化任一种数据预处理条件下,样本距离选用街区距离,测试集识别率能达到100.00%。结论近红外光谱结合KNN模式的技术具有分析速度快、操作简单、样本预处理容易、无损、在线的定性定量分析等优点,具有一定应用前景。展开更多
It is a key challenge to exploit the label coupling relationship in multi-label classification（MLC）problems.Most previous work focused on label pairwise relations,in which generally only global statistical informati...It is a key challenge to exploit the label coupling relationship in multi-label classification（MLC）problems.Most previous work focused on label pairwise relations,in which generally only global statistical information is used to analyze the coupled label relationship.In this work,firstly Bayesian and hypothesis testing methods are applied to predict the label set size of testing samples within their k nearest neighbor samples,which combines global and local statistical information,and then apriori algorithm is used to mine the label coupling relationship among multiple labels rather than pairwise labels,which can exploit the label coupling relations more accurately and comprehensively.The experimental results on text,biology and audio datasets shown that,compared with the state-of-the-art algorithm,the proposed algorithm can obtain better performance on 5 common criteria.展开更多
To address cross-ISP traffic problem caused by BitTorrent,we present our design and evaluation of a proximity-aware BitTorrent system. In our approach,clients generate global proximity-aware information by using landm...To address cross-ISP traffic problem caused by BitTorrent,we present our design and evaluation of a proximity-aware BitTorrent system. In our approach,clients generate global proximity-aware information by using landmark clustering;the tracker uses this proximity to maintain all peers in an orderly way and hands back a biased subset consisting of the peers who are physically closest to the requestor. Our approach requires no co-operation between P2P users and their Internet infra structures,such as ISPs or CDNs,no constantly path monitoring or probing their neighbors. The simulation results show that our approach can not only reduce unnecessary cross-ISP traffic,but also allow downloadsing fast.展开更多
基金supported by the National Natural Science Foundation of China(Grant No.12002246 and No.52178301)Knowledge Innovation Program of Wuhan(Grant No.2022010801020357)+2 种基金the Science Research Foundation of Wuhan Institute of Technology(Grant No.K2021030)2020 annual Open Fund of Failure Mechanics&Engineering Disaster Prevention and Mitigation,Key Laboratory of Sichuan Province(Sichuan University)(Grant No.2020JDS0022)Open Research Fund Program of Hubei Provincial Key Laboratory of Chemical Equipment Intensification and Intrinsic Safety(Grant No.2019KA03)。
文摘This paper proposes an active learning accelerated Monte-Carlo simulation method based on the modified K-nearest neighbors algorithm.The core idea of the proposed method is to judge whether or not the output of a random input point can be postulated through a classifier implemented through the modified K-nearest neighbors algorithm.Compared to other active learning methods resorting to experimental designs,the proposed method is characterized by employing Monte-Carlo simulation for sampling inputs and saving a large portion of the actual evaluations of outputs through an accurate classification,which is applicable for most structural reliability estimation problems.Moreover,the validity,efficiency,and accuracy of the proposed method are demonstrated numerically.In addition,the optimal value of K that maximizes the computational efficiency is studied.Finally,the proposed method is applied to the reliability estimation of the carbon fiber reinforced silicon carbide composite specimens subjected to random displacements,which further validates its practicability.
基金supported by the Social Science Foundation of China under Grant No.17BGL231。
文摘On the basis of machine leaning,suitable algorithms can make advanced time series analysis.This paper proposes a complex k-nearest neighbor(KNN)model for predicting financial time series.This model uses a complex feature extraction process integrating a forward rolling empirical mode decomposition(EMD)for financial time series signal analysis and principal component analysis(PCA)for the dimension reduction.The information-rich features are extracted then input to a weighted KNN classifier where the features are weighted with PCA loading.Finally,prediction is generated via regression on the selected nearest neighbors.The structure of the model as a whole is original.The test results on real historical data sets confirm the effectiveness of the models for predicting the Chinese stock index,an individual stock,and the EUR/USD exchange rate.
文摘目的采用近红外光谱技术对油莎豆进行分析,并应用化学计量学中识别模式对油莎豆进行产地溯源。方法采用近红外光谱法结合化学计量学软件,对来自河北、湖南、山东、新疆、云南等地408份油莎豆样品进行产地溯源,分别采用多元散射校正、多量标准化或多量标准化耦合去趋势算法3种光谱预处理方法和支持向量机(support vector machine,SVM)、簇类独立分类(soft independent modeling of class analogy,SIMCA)、正交偏最小二乘判别(orthogonal partial least squares discriminant analysis,OPLS-DA)、偏最小二乘判别(partial least squares discriminant analysis,PLS-DA)、和K最近邻算法(K-nearest neighbor algorithm,KNN)等5种识别模式进行产地识别。结果SVM、SIMCA、OPLS-DA、PLS-DA和KNN等5种模式的建模识别率分别为91.89%、94.47%、62.37%、65.32%和100.00%。选择KNN作为产地识别模型,分析不同预处理方法、数据预处理及样本距离对模型预测结果稳定性的影响,筛选出最优模型参数。选用多元散射校正光谱预处理方式,在UV标度化、Pareto标度化、自动标度化或中心化任一种数据预处理条件下,样本距离选用街区距离,测试集识别率能达到100.00%。结论近红外光谱结合KNN模式的技术具有分析速度快、操作简单、样本预处理容易、无损、在线的定性定量分析等优点,具有一定应用前景。
基金Supported by Australian Research Council Discovery(DP130102691)the National Science Foundation of China(61302157)+1 种基金China National 863 Project(2012AA12A308)China Pre-research Project of Nuclear Industry(FZ1402-08)
文摘It is a key challenge to exploit the label coupling relationship in multi-label classification（MLC）problems.Most previous work focused on label pairwise relations,in which generally only global statistical information is used to analyze the coupled label relationship.In this work,firstly Bayesian and hypothesis testing methods are applied to predict the label set size of testing samples within their k nearest neighbor samples,which combines global and local statistical information,and then apriori algorithm is used to mine the label coupling relationship among multiple labels rather than pairwise labels,which can exploit the label coupling relations more accurately and comprehensively.The experimental results on text,biology and audio datasets shown that,compared with the state-of-the-art algorithm,the proposed algorithm can obtain better performance on 5 common criteria.
基金supported in part by the National High-tech Research and Development Program (863 Program) of China under Grant No. 2009AA01Z210, No. 2009AA01Z250 and No. 2008AA01A324support from Guangdong Ministry of Education Industry-Academia-Research project No. 2009B090300315EU FP7 Project (INFSO-ICT- 215549)
文摘To address cross-ISP traffic problem caused by BitTorrent,we present our design and evaluation of a proximity-aware BitTorrent system. In our approach,clients generate global proximity-aware information by using landmark clustering;the tracker uses this proximity to maintain all peers in an orderly way and hands back a biased subset consisting of the peers who are physically closest to the requestor. Our approach requires no co-operation between P2P users and their Internet infra structures,such as ISPs or CDNs,no constantly path monitoring or probing their neighbors. The simulation results show that our approach can not only reduce unnecessary cross-ISP traffic,but also allow downloadsing fast.
