提出一种使用功能属性(functional attribute,FA)及有向交互标签(directional interaction tag,DIT),对基于系统思维的过程分析(system-theoretic process analysis,STPA)方法所涉及的层次化控制结构模型(hierarchical control structur...提出一种使用功能属性(functional attribute,FA)及有向交互标签(directional interaction tag,DIT),对基于系统思维的过程分析(system-theoretic process analysis,STPA)方法所涉及的层次化控制结构模型(hierarchical control structure model,HCSM)进行拓展与改进的方法。通过该方法构建层次化功能控制结构及交互模型(hierarchical functional control structure and interaction model,HFCSIM),达成对STPA的实质性提升与完善。通过这一改进,STPA中HCSM的构建没有严谨而具体方法和形式,以及组件间交互信息不完整且过于依赖“头脑风暴”和难以保障模型一致性等问题得以解决,并从根本上确保了分析结果的系统性、完整性和正确性。最后以飞机机轮刹车系统为例,验证了该改进方法的有效性。展开更多
The contact angle phenomena and wetting behavior of fatty acids,alcohols and ester used as additives in lubricants onto the rolled copper foil(RCF)surface were studied by the static sessile drop method.Semi-empirical ...The contact angle phenomena and wetting behavior of fatty acids,alcohols and ester used as additives in lubricants onto the rolled copper foil(RCF)surface were studied by the static sessile drop method.Semi-empirical quantum-chemical method studies on the contact angle of these compounds onto surface using several structural parameters were carried out.Molecular refractivity as well as several structural parameters were adopted in the development of quantitative structure-property relationships(QSPR)using genetic function approximation(GFA)statistical analysis method.The results show that quantum parameters are a better choice when predicting the contact angle and wettability of lubricants onto the RCF surface.Contact angle of the compounds serves as a function of their viscosity,interfacial tension,and physicochemical parameters.Alog P,molecular refractivity,molecular flexibility,total molecular mass,solvent surface area,element count,total energy and dipole are the most sensitive ones among the major contributing parameters.Notably,studies of lubricants on the RCF surfaces allow wetting theories to be tested down to the microcosmic scale,which can bring about new insight to predict wettability of lubricants onto RCF surface.展开更多
Objective:To establish a systematic framework for selecting the best clustering algorithm and provide an evaluation method for clustering analyses of gene expression data. Methods: Based on data structure (internal in...Objective:To establish a systematic framework for selecting the best clustering algorithm and provide an evaluation method for clustering analyses of gene expression data. Methods: Based on data structure (internal information) and function classification (external information), the evaluation of gene expression data analyses were carried out by using 2 approaches. Firstly, to assess the predictive power of clusteringalgorithms, Entropy was introduced to measure the consistency between the clustering results from different algorithms and the known and validated functional classifications. Secondly, a modified method of figure of merit (adjust-FOM) was used as internal assessment method. In this method, one clustering algorithm was used to analyze all data but one experimental condition, the remaining condition was used to assess the predictive power of the resulting clusters. This method was applied on 3 gene expression data sets (2 from the Lyer's Serum Data Sets, and 1 from the Ferea's Saccharomyces Cerevisiae Data Set). Results: A method based on entropy and figure of merit (FOM) was proposed to explore the results of the 3 data sets obtained by 6 different algorithms, SOM and Fuzzy clustering methods were confirmed to possess the highest ability to cluster. Conclusion: A method based on entropy is firstly brought forward to evaluate clustering analyses.Different results are attained in evaluating same data set due to different function classification. According to the curves of adjust_FOM and Entropy_FOM, SOM and Fuzzy clustering methods show the highest ability to cluster on the 3 data sets.展开更多
文摘提出一种使用功能属性(functional attribute,FA)及有向交互标签(directional interaction tag,DIT),对基于系统思维的过程分析(system-theoretic process analysis,STPA)方法所涉及的层次化控制结构模型(hierarchical control structure model,HCSM)进行拓展与改进的方法。通过该方法构建层次化功能控制结构及交互模型(hierarchical functional control structure and interaction model,HFCSIM),达成对STPA的实质性提升与完善。通过这一改进,STPA中HCSM的构建没有严谨而具体方法和形式,以及组件间交互信息不完整且过于依赖“头脑风暴”和难以保障模型一致性等问题得以解决,并从根本上确保了分析结果的系统性、完整性和正确性。最后以飞机机轮刹车系统为例,验证了该改进方法的有效性。
基金the financial assistance provided by the Introducing the Talent Research Start-up Fund(No.YKJ201706)the National Natural Science Foundation of China(No.51474025)
文摘The contact angle phenomena and wetting behavior of fatty acids,alcohols and ester used as additives in lubricants onto the rolled copper foil(RCF)surface were studied by the static sessile drop method.Semi-empirical quantum-chemical method studies on the contact angle of these compounds onto surface using several structural parameters were carried out.Molecular refractivity as well as several structural parameters were adopted in the development of quantitative structure-property relationships(QSPR)using genetic function approximation(GFA)statistical analysis method.The results show that quantum parameters are a better choice when predicting the contact angle and wettability of lubricants onto the RCF surface.Contact angle of the compounds serves as a function of their viscosity,interfacial tension,and physicochemical parameters.Alog P,molecular refractivity,molecular flexibility,total molecular mass,solvent surface area,element count,total energy and dipole are the most sensitive ones among the major contributing parameters.Notably,studies of lubricants on the RCF surfaces allow wetting theories to be tested down to the microcosmic scale,which can bring about new insight to predict wettability of lubricants onto RCF surface.
文摘Objective:To establish a systematic framework for selecting the best clustering algorithm and provide an evaluation method for clustering analyses of gene expression data. Methods: Based on data structure (internal information) and function classification (external information), the evaluation of gene expression data analyses were carried out by using 2 approaches. Firstly, to assess the predictive power of clusteringalgorithms, Entropy was introduced to measure the consistency between the clustering results from different algorithms and the known and validated functional classifications. Secondly, a modified method of figure of merit (adjust-FOM) was used as internal assessment method. In this method, one clustering algorithm was used to analyze all data but one experimental condition, the remaining condition was used to assess the predictive power of the resulting clusters. This method was applied on 3 gene expression data sets (2 from the Lyer's Serum Data Sets, and 1 from the Ferea's Saccharomyces Cerevisiae Data Set). Results: A method based on entropy and figure of merit (FOM) was proposed to explore the results of the 3 data sets obtained by 6 different algorithms, SOM and Fuzzy clustering methods were confirmed to possess the highest ability to cluster. Conclusion: A method based on entropy is firstly brought forward to evaluate clustering analyses.Different results are attained in evaluating same data set due to different function classification. According to the curves of adjust_FOM and Entropy_FOM, SOM and Fuzzy clustering methods show the highest ability to cluster on the 3 data sets.
