Existing Web service selection approaches usually assume that preferences of users have been provided in a quantitative form by users. However, due to the subjectivity and vagueness of preferences, it may be impractic...Existing Web service selection approaches usually assume that preferences of users have been provided in a quantitative form by users. However, due to the subjectivity and vagueness of preferences, it may be impractical for users to specify quantitative and exact preferences. Moreover, due to that Quality of Service (QoS) attributes are often interrelated, existing Web service selection approaches which employ weighted summation of QoS attribute values to compute the overall QoS of Web services may produce inaccurate results, since they do not take correlations among QoS attributes into account. To resolve these problems, a Web service selection framework considering user's preference priority is proposed, which incorporates a searching mechanism with QoS range setting to identify services satisfying the user's QoS constraints. With the identified service candidates, based on the idea of Principal Component Analysis (PCA), an algorithm of Web service selection named PCA-WSS (Web Service Selection based on PCA) is proposed, which can eliminate the correlations among QoS attributes and compute the overall QoS of Web services accurately. After computing the overall QoS for each service, the algorithm ranks the Web service candidates based on their overall QoS and recommends services with top QoS values to users. Finally, the effectiveness and feasibility of our approach are validated by experiments, i.e. the selected Web service by our approach is given high average evaluation than other ones by users and the time cost of PCA-WSS algorithm is not affected acutely by the number of service candidates.展开更多
Ensemble-based analyses are useful to compare equiprobable scenarios of the reservoir models.However,they require a large suite of reservoir models to cover high uncertainty in heterogeneous and complex reservoir mode...Ensemble-based analyses are useful to compare equiprobable scenarios of the reservoir models.However,they require a large suite of reservoir models to cover high uncertainty in heterogeneous and complex reservoir models.For stable convergence in ensemble Kalman filter(EnKF),increasing ensemble size can be one of the solutions,but it causes high computational cost in large-scale reservoir systems.In this paper,we propose a preprocessing of good initial model selection to reduce the ensemble size,and then,EnKF is utilized to predict production performances stochastically.In the model selection scheme,representative models are chosen by using principal component analysis(PCA)and clustering analysis.The dimension of initial models is reduced using PCA,and the reduced models are grouped by clustering.Then,we choose and simulate representative models from the cluster groups to compare errors of production predictions with historical observation data.One representative model with the minimum error is considered as the best model,and we use the ensemble members near the best model in the cluster plane for applying EnKF.We demonstrate the proposed scheme for two 3D models that EnKF provides reliable assimilation results with much reduced computation time.展开更多
It is of great significance to analyze the chemical indexes of mine water and develop a rapid identification system of water source, which can quickly and accurately distinguish the causes of water inrush and identify...It is of great significance to analyze the chemical indexes of mine water and develop a rapid identification system of water source, which can quickly and accurately distinguish the causes of water inrush and identify the source of water inrush, so as to reduce casualties and economic losses and prevent and control water inrush disasters. Taking Ca<sup>2+</sup>, Mg<sup>2+</sup>, Na<sup>+</sup> + K<sup>+</sup>, , , Cl<sup>-</sup>, pH value and TDS as discriminant indexes, the principal component analysis method was used to reduce the dimension of data, and the identification model of mine water inrush source based on PCA-BP neural network was established. 96 sets of data of different aquifers in Panxie mining area were selected for prediction analysis, and 20 sets of randomly selected data were tested, with an accuracy rate of 95%. The model can effectively reduce data redundancy, has a high recognition rate, and can accurately and quickly identify the water source of mine water inrush.展开更多
针对基于支持向量机的电力系统短期负荷预测算法中,预测模型的精度和泛化能力易受样本集中输入变量的影响,利用主成分分析方法能有效地消除变量之间共线性的特点,通过提取样本集的主成分完成数据预处理,有效地压缩样本集的维数。根据Eas...针对基于支持向量机的电力系统短期负荷预测算法中,预测模型的精度和泛化能力易受样本集中输入变量的影响,利用主成分分析方法能有效地消除变量之间共线性的特点,通过提取样本集的主成分完成数据预处理,有效地压缩样本集的维数。根据East-Slovakia Power Distribution Company提供的电网运行数据进行了预测计算,证明此方法与标准支持向量机算法相比,可以降低样本集的维数,提高负荷预测精度。展开更多
基金Supported by the National Natural Science Foundation of China(No.90818004and61100054)Program for New Century Excellent Talents in University(No.NCET-10-0140)+1 种基金Excellent Youth Foundation of Hunan Scientific Committee(No.11JJ1011)Scientific Research Fundof Hunan Educational Committee(No.09K085and11B048)
文摘Existing Web service selection approaches usually assume that preferences of users have been provided in a quantitative form by users. However, due to the subjectivity and vagueness of preferences, it may be impractical for users to specify quantitative and exact preferences. Moreover, due to that Quality of Service (QoS) attributes are often interrelated, existing Web service selection approaches which employ weighted summation of QoS attribute values to compute the overall QoS of Web services may produce inaccurate results, since they do not take correlations among QoS attributes into account. To resolve these problems, a Web service selection framework considering user's preference priority is proposed, which incorporates a searching mechanism with QoS range setting to identify services satisfying the user's QoS constraints. With the identified service candidates, based on the idea of Principal Component Analysis (PCA), an algorithm of Web service selection named PCA-WSS (Web Service Selection based on PCA) is proposed, which can eliminate the correlations among QoS attributes and compute the overall QoS of Web services accurately. After computing the overall QoS for each service, the algorithm ranks the Web service candidates based on their overall QoS and recommends services with top QoS values to users. Finally, the effectiveness and feasibility of our approach are validated by experiments, i.e. the selected Web service by our approach is given high average evaluation than other ones by users and the time cost of PCA-WSS algorithm is not affected acutely by the number of service candidates.
基金supported by The Ministry of Trade,Industry,and Energy(20172510102090,20142520100440,20162010201980)Global PhD Fellowship Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(2015H1A2A1030756)supported by the National Research Foundation of Korea(NRF)Grant(No.2018R1C1B5045260).
文摘Ensemble-based analyses are useful to compare equiprobable scenarios of the reservoir models.However,they require a large suite of reservoir models to cover high uncertainty in heterogeneous and complex reservoir models.For stable convergence in ensemble Kalman filter(EnKF),increasing ensemble size can be one of the solutions,but it causes high computational cost in large-scale reservoir systems.In this paper,we propose a preprocessing of good initial model selection to reduce the ensemble size,and then,EnKF is utilized to predict production performances stochastically.In the model selection scheme,representative models are chosen by using principal component analysis(PCA)and clustering analysis.The dimension of initial models is reduced using PCA,and the reduced models are grouped by clustering.Then,we choose and simulate representative models from the cluster groups to compare errors of production predictions with historical observation data.One representative model with the minimum error is considered as the best model,and we use the ensemble members near the best model in the cluster plane for applying EnKF.We demonstrate the proposed scheme for two 3D models that EnKF provides reliable assimilation results with much reduced computation time.
文摘It is of great significance to analyze the chemical indexes of mine water and develop a rapid identification system of water source, which can quickly and accurately distinguish the causes of water inrush and identify the source of water inrush, so as to reduce casualties and economic losses and prevent and control water inrush disasters. Taking Ca<sup>2+</sup>, Mg<sup>2+</sup>, Na<sup>+</sup> + K<sup>+</sup>, , , Cl<sup>-</sup>, pH value and TDS as discriminant indexes, the principal component analysis method was used to reduce the dimension of data, and the identification model of mine water inrush source based on PCA-BP neural network was established. 96 sets of data of different aquifers in Panxie mining area were selected for prediction analysis, and 20 sets of randomly selected data were tested, with an accuracy rate of 95%. The model can effectively reduce data redundancy, has a high recognition rate, and can accurately and quickly identify the water source of mine water inrush.
文摘针对基于支持向量机的电力系统短期负荷预测算法中,预测模型的精度和泛化能力易受样本集中输入变量的影响,利用主成分分析方法能有效地消除变量之间共线性的特点,通过提取样本集的主成分完成数据预处理,有效地压缩样本集的维数。根据East-Slovakia Power Distribution Company提供的电网运行数据进行了预测计算,证明此方法与标准支持向量机算法相比,可以降低样本集的维数,提高负荷预测精度。
