Traditional principal component analysis （PCA） is a second-order method and lacks the ability to provide higherorder representations for data variables. Recently, a statistics pattern analysis （SPA） framework has ...Traditional principal component analysis （PCA） is a second-order method and lacks the ability to provide higherorder representations for data variables. Recently, a statistics pattern analysis （SPA） framework has been incorporated into PCA model to make full use of various statistics of data variables effectively. However, these methods omit the local information, which is also important for process monitoring and fault diagnosis. In this paper, a local and global statistics pattern analysis （LGSPA） method, which integrates SPA framework and locality pre- serving projections within the PCK is proposed to utilize various statistics and preserve both local and global in- formation in the observed data. For the purpose of fault detection, two monitoring indices are constructed based on the LGSPA model. In order to identify fault variables, an improved reconstruction based contribution （IRBC） plot based on LGSPA model is proposed to locate fault variables. The RBC of various statistics of original process variables to the monitoring indices is calculated with the proposed RBC method. Based on the calculated RBC of process variables＇ statistics, a new contribution of process variables is built to locate fault variables. The simula- tion results on a simple six-variable system and a continuous stirred tank reactor system demonstrate that the proposed fault diagnosis method can effectively detect fault and distinguish the fault variables from normal variables.展开更多
In China,mountain destinations offer more opportunities for vacation than beaches and springs,with a larger national coverage,richer landscapes and various diverse folk cultures.Hunan Province,for example,serves as a ...In China,mountain destinations offer more opportunities for vacation than beaches and springs,with a larger national coverage,richer landscapes and various diverse folk cultures.Hunan Province,for example,serves as a typical mountain tourism destination.Our research aims to assess the potential suitability and component pattern of holidaymaking in Hunan Province to support for scientific endeavors and sustainable use of mountain resources in Hunan.An approach for mapping potential suitability was constructed based on Weighted Linear Combination(WLC)of six primary factors including terrain,climate suitability period,biology and environment,human activity culture,hydrological landscape,and road infrastructure,which is calculated on the base of eleven secondary factors used as suitability criteria.Analytic hierarchy process(AHP)and geographic information system(GIS)were also integrated into the evaluation models to prepare six single and complex factor suitability maps for mountain vacation.Multivariate clustering was used to clarify the component pattern of single factor combinations in each mountain vacation suitability index(MVSI)class.The results have shown that:1)Regions with high suitability are distributed with characteristics of significant dispersion and limited aggregation;2)15.89%of the sample mountains are highly suitable,30.78%are moderately suitable,42.61%are marginally suitable and 10.72%are poorly suitable;3)The complexity of index combination corresponding to each level is different;4)Different combinations emphasize different destination needs.The study reveals the suitability class,characteristics,and limiting factors of mountain vacation in different geographical locations on the provincial scale.These elements play an instructive role in scientific decision-making and orderly exploitation of resources among Hunan’s Mountain destinations.Furthermore,the study can also be used as a basis for the suitability evaluation of mountain vacation in other areas.展开更多
基金Supported by the National Natural Science Foundation of China(61273160,61403418)the Natural Science Foundation of Shandong Province(ZR2014FL016)the Fundamental Research Funds for the Central Universities(14CX06132A)
文摘Traditional principal component analysis （PCA） is a second-order method and lacks the ability to provide higherorder representations for data variables. Recently, a statistics pattern analysis （SPA） framework has been incorporated into PCA model to make full use of various statistics of data variables effectively. However, these methods omit the local information, which is also important for process monitoring and fault diagnosis. In this paper, a local and global statistics pattern analysis （LGSPA） method, which integrates SPA framework and locality pre- serving projections within the PCK is proposed to utilize various statistics and preserve both local and global in- formation in the observed data. For the purpose of fault detection, two monitoring indices are constructed based on the LGSPA model. In order to identify fault variables, an improved reconstruction based contribution （IRBC） plot based on LGSPA model is proposed to locate fault variables. The RBC of various statistics of original process variables to the monitoring indices is calculated with the proposed RBC method. Based on the calculated RBC of process variables＇ statistics, a new contribution of process variables is built to locate fault variables. The simula- tion results on a simple six-variable system and a continuous stirred tank reactor system demonstrate that the proposed fault diagnosis method can effectively detect fault and distinguish the fault variables from normal variables.
基金supported by the National Key Research and Development Program (No.2019YFD1100404)A Project Supported by Scientific Research Fund of Hunan Provincial Education Department (No.18C1559)+1 种基金Scientific Research Project of Hunan Radio and TV University (No.XDK2019-C-2)Scientific Innovation Fund for Post-graduates of Central South University of Forestry and Technology (CX202102001).
文摘In China,mountain destinations offer more opportunities for vacation than beaches and springs,with a larger national coverage,richer landscapes and various diverse folk cultures.Hunan Province,for example,serves as a typical mountain tourism destination.Our research aims to assess the potential suitability and component pattern of holidaymaking in Hunan Province to support for scientific endeavors and sustainable use of mountain resources in Hunan.An approach for mapping potential suitability was constructed based on Weighted Linear Combination(WLC)of six primary factors including terrain,climate suitability period,biology and environment,human activity culture,hydrological landscape,and road infrastructure,which is calculated on the base of eleven secondary factors used as suitability criteria.Analytic hierarchy process(AHP)and geographic information system(GIS)were also integrated into the evaluation models to prepare six single and complex factor suitability maps for mountain vacation.Multivariate clustering was used to clarify the component pattern of single factor combinations in each mountain vacation suitability index(MVSI)class.The results have shown that:1)Regions with high suitability are distributed with characteristics of significant dispersion and limited aggregation;2)15.89%of the sample mountains are highly suitable,30.78%are moderately suitable,42.61%are marginally suitable and 10.72%are poorly suitable;3)The complexity of index combination corresponding to each level is different;4)Different combinations emphasize different destination needs.The study reveals the suitability class,characteristics,and limiting factors of mountain vacation in different geographical locations on the provincial scale.These elements play an instructive role in scientific decision-making and orderly exploitation of resources among Hunan’s Mountain destinations.Furthermore,the study can also be used as a basis for the suitability evaluation of mountain vacation in other areas.
