页岩气初期产能直接影响单井最终采收率,分析页岩气初期产能主控因素,对页岩气开发方案的设计与优化有重要意义。基于文献调研,定性研究各种因素对页岩气初期产能的影响机理;运用皮尔逊-最大信息系数(Pearson-maximal information coeff...页岩气初期产能直接影响单井最终采收率,分析页岩气初期产能主控因素,对页岩气开发方案的设计与优化有重要意义。基于文献调研,定性研究各种因素对页岩气初期产能的影响机理;运用皮尔逊-最大信息系数(Pearson-maximal information coefficient,Pearson-MIC)相关性综合分析方法,对各因素与页岩气初期产能之间的相关性进行定量计算;按照一定筛选原则,优选页岩气初期产能主控因素,对比传统相关性分析方法,证明本文方法的可靠性。研究表明:对页岩气初期产能有直接影响的因素主要包括地质因素8个,工程因素10个;页岩气初期产能主控因素包括优质页岩厚度、总有机碳含量、含气量、压力系数、脆性矿物含量、优质储层钻遇程度、压裂段数、射孔簇数、总液量、单段砂量、施工排量;相比传统相关性分析方法,"Pearson-MIC"相关性综合分析方法的评价结果要更可靠。展开更多
Subjects wore T-shirts made from eight fabrics during exercise in a cold environmental condition of 14℃ and 32%RH. Preferences were expressed initially by handling the garments and then again after they had been worn...Subjects wore T-shirts made from eight fabrics during exercise in a cold environmental condition of 14℃ and 32%RH. Preferences were expressed initially by handling the garments and then again after they had been worn. In the trial, subjective responses to 19 sensation descriptors were recorded. The relationships among the subjective preference votes for different types of clothing and psychological sensory factors were studied by means of canonical correlation analysis.Two highly significant canonical correlations were found, which indicated that the subjective overall preference votes after wearing were very closely related to factors describing tactile and " body-fit" sensations. The subjective preference votes from handling were mainly related to the "body-fit" comfort factor. Canonical correlation redundancy analysis showed that the canonical variables for sensory factors were reasonably good predictors of the canonical variables for subjective preferences, but not vice versa.Squared展开更多
Functional brain networks (FBNs) provide a potential way for understanding the brain organizational patterns and diagnosing neurological diseases. Due to its importance, many FBN construction methods have been propose...Functional brain networks (FBNs) provide a potential way for understanding the brain organizational patterns and diagnosing neurological diseases. Due to its importance, many FBN construction methods have been proposed currently, including the low-order Pearson’s correlation (PC) and sparse representation (SR), as well as the high-order functional connection (HoFC). However, most existing methods usually ignore the information of topological structures of FBN, such as low-rank structure which can reduce the noise and improve modularity to enhance the stability of networks. In this paper, we propose a novel method for improving the estimated FBNs utilizing matrix factorization (MF). More specifically, we firstly construct FBNs based on three traditional methods, including PC, SR, and HoFC. Then, we reduce the rank of these FBNs via MF model for estimating FBN with low-rank structure. Finally, to evaluate the effectiveness of the proposed method, experiments have been conducted to identify the subjects with mild cognitive impairment (MCI) and autism spectrum disorder (ASD) from norm controls (NCs) using the estimated FBNs. The results on Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset and Autism Brain Imaging Data Exchange (ABIDE) dataset demonstrate that the classification performances achieved by our proposed method are better than the selected baseline methods.展开更多
文摘页岩气初期产能直接影响单井最终采收率,分析页岩气初期产能主控因素,对页岩气开发方案的设计与优化有重要意义。基于文献调研,定性研究各种因素对页岩气初期产能的影响机理;运用皮尔逊-最大信息系数(Pearson-maximal information coefficient,Pearson-MIC)相关性综合分析方法,对各因素与页岩气初期产能之间的相关性进行定量计算;按照一定筛选原则,优选页岩气初期产能主控因素,对比传统相关性分析方法,证明本文方法的可靠性。研究表明:对页岩气初期产能有直接影响的因素主要包括地质因素8个,工程因素10个;页岩气初期产能主控因素包括优质页岩厚度、总有机碳含量、含气量、压力系数、脆性矿物含量、优质储层钻遇程度、压裂段数、射孔簇数、总液量、单段砂量、施工排量;相比传统相关性分析方法,"Pearson-MIC"相关性综合分析方法的评价结果要更可靠。
文摘Subjects wore T-shirts made from eight fabrics during exercise in a cold environmental condition of 14℃ and 32%RH. Preferences were expressed initially by handling the garments and then again after they had been worn. In the trial, subjective responses to 19 sensation descriptors were recorded. The relationships among the subjective preference votes for different types of clothing and psychological sensory factors were studied by means of canonical correlation analysis.Two highly significant canonical correlations were found, which indicated that the subjective overall preference votes after wearing were very closely related to factors describing tactile and " body-fit" sensations. The subjective preference votes from handling were mainly related to the "body-fit" comfort factor. Canonical correlation redundancy analysis showed that the canonical variables for sensory factors were reasonably good predictors of the canonical variables for subjective preferences, but not vice versa.Squared
文摘Functional brain networks (FBNs) provide a potential way for understanding the brain organizational patterns and diagnosing neurological diseases. Due to its importance, many FBN construction methods have been proposed currently, including the low-order Pearson’s correlation (PC) and sparse representation (SR), as well as the high-order functional connection (HoFC). However, most existing methods usually ignore the information of topological structures of FBN, such as low-rank structure which can reduce the noise and improve modularity to enhance the stability of networks. In this paper, we propose a novel method for improving the estimated FBNs utilizing matrix factorization (MF). More specifically, we firstly construct FBNs based on three traditional methods, including PC, SR, and HoFC. Then, we reduce the rank of these FBNs via MF model for estimating FBN with low-rank structure. Finally, to evaluate the effectiveness of the proposed method, experiments have been conducted to identify the subjects with mild cognitive impairment (MCI) and autism spectrum disorder (ASD) from norm controls (NCs) using the estimated FBNs. The results on Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset and Autism Brain Imaging Data Exchange (ABIDE) dataset demonstrate that the classification performances achieved by our proposed method are better than the selected baseline methods.
