Similarity measure design for discrete data group was proposed. Similarity measure design for continuous membership function was also carried out. Proposed similarity measures were designed based on fuzzy number and d...Similarity measure design for discrete data group was proposed. Similarity measure design for continuous membership function was also carried out. Proposed similarity measures were designed based on fuzzy number and distance measure, and were proved. To calculate the degree of similarity of discrete data, relative degree between data and total distribution was obtained. Discrete data similarity measure was completed with combination of mentioned relative degrees. Power interconnected system with multi characteristics was considered to apply discrete similarity measure. Naturally, similarity measure was extended to multi-dimensional similarity measure case, and applied to bus clustering problem.展开更多
A new modeling and monitoring approach for multi-mode processes is proposed.The method of similarity measure(SM) and kernel principal component analysis(KPCA) are integrated to construct SM-KPCA monitoring scheme,wher...A new modeling and monitoring approach for multi-mode processes is proposed.The method of similarity measure(SM) and kernel principal component analysis(KPCA) are integrated to construct SM-KPCA monitoring scheme,where SM method serves as the separation of common subspace and specific subspace.Compared with the traditional methods,the main contributions of this work are:1) SM consisted of two measures of distance and angle to accommodate process characters.The different monitoring effect involves putting on the different weight,which would simplify the monitoring model structure and enhance its reliability and robustness.2) The proposed method can be used to find faults by the common space and judge which mode the fault belongs to by the specific subspace.Results of algorithm analysis and fault detection experiments indicate the validity and practicability of the presented method.展开更多
Information analysis of high dimensional data was carried out through similarity measure application. High dimensional data were considered as the a typical structure. Additionally, overlapped and non-overlapped data ...Information analysis of high dimensional data was carried out through similarity measure application. High dimensional data were considered as the a typical structure. Additionally, overlapped and non-overlapped data were introduced, and similarity measure analysis was also illustrated and compared with conventional similarity measure. As a result, overlapped data comparison was possible to present similarity with conventional similarity measure. Non-overlapped data similarity analysis provided the clue to solve the similarity of high dimensional data. Considering high dimensional data analysis was designed with consideration of neighborhoods information. Conservative and strict solutions were proposed. Proposed similarity measure was applied to express financial fraud among multi dimensional datasets. In illustrative example, financial fraud similarity with respect to age, gender, qualification and job was presented. And with the proposed similarity measure, high dimensional personal data were calculated to evaluate how similar to the financial fraud. Calculation results show that the actual fraud has rather high similarity measure compared to the average, from minimal 0.0609 to maximal 0.1667.展开更多
Di-n-butyl phthalate (DBP),one of phthalate acid esters (PAEs),was investigated to determine its biodegradation rate using Xiangjiang River sediment and find potential DBP degraders in the enrichment culture of the se...Di-n-butyl phthalate (DBP),one of phthalate acid esters (PAEs),was investigated to determine its biodegradation rate using Xiangjiang River sediment and find potential DBP degraders in the enrichment culture of the sediment. The sediment sample was incubated with an initial concentration of DBP of 100 mg/L for 5 d. The biodegradation rate of DBP was detected using HPLC and the degraded products were analyzed by GC/MS. Subsequently,the microbial diversity of the enrichment culture was analyzed by polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP). The results reveal that almost 100% of DBP is degraded after merely 3 d,generating two main degraded products:mono-butyl phthalate (MBP) and 9-octadecenoic acid. After a six-month enrichment period under the pressure of DBP,the dominant family in the final enrichment culture is clustered with the Comamonas sp.,the remaining are affiliated with Sphingomonas sp.,Hydrogenophaga sp.,Rhizobium sp.,and Acidovorax sp. The results show the potential of these bacteria to be used in the bioremediation of DBP in the environment.展开更多
基金Project(2010-0020163) supported by Key Research Institute Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology, Korea
文摘Similarity measure design for discrete data group was proposed. Similarity measure design for continuous membership function was also carried out. Proposed similarity measures were designed based on fuzzy number and distance measure, and were proved. To calculate the degree of similarity of discrete data, relative degree between data and total distribution was obtained. Discrete data similarity measure was completed with combination of mentioned relative degrees. Power interconnected system with multi characteristics was considered to apply discrete similarity measure. Naturally, similarity measure was extended to multi-dimensional similarity measure case, and applied to bus clustering problem.
基金Projects(61273163,61325015,61304121)supported by the National Natural Science Foundation of China
文摘A new modeling and monitoring approach for multi-mode processes is proposed.The method of similarity measure(SM) and kernel principal component analysis(KPCA) are integrated to construct SM-KPCA monitoring scheme,where SM method serves as the separation of common subspace and specific subspace.Compared with the traditional methods,the main contributions of this work are:1) SM consisted of two measures of distance and angle to accommodate process characters.The different monitoring effect involves putting on the different weight,which would simplify the monitoring model structure and enhance its reliability and robustness.2) The proposed method can be used to find faults by the common space and judge which mode the fault belongs to by the specific subspace.Results of algorithm analysis and fault detection experiments indicate the validity and practicability of the presented method.
基金Project(RDF 11-02-03)supported by the Research Development Fund of XJTLU,China
文摘Information analysis of high dimensional data was carried out through similarity measure application. High dimensional data were considered as the a typical structure. Additionally, overlapped and non-overlapped data were introduced, and similarity measure analysis was also illustrated and compared with conventional similarity measure. As a result, overlapped data comparison was possible to present similarity with conventional similarity measure. Non-overlapped data similarity analysis provided the clue to solve the similarity of high dimensional data. Considering high dimensional data analysis was designed with consideration of neighborhoods information. Conservative and strict solutions were proposed. Proposed similarity measure was applied to express financial fraud among multi dimensional datasets. In illustrative example, financial fraud similarity with respect to age, gender, qualification and job was presented. And with the proposed similarity measure, high dimensional personal data were calculated to evaluate how similar to the financial fraud. Calculation results show that the actual fraud has rather high similarity measure compared to the average, from minimal 0.0609 to maximal 0.1667.
基金Project(50621063) supported by the National Nature Science Foundation of ChinaProject(NCET-06-0691) supported by the Program for New Century Excellent Talents in University
文摘Di-n-butyl phthalate (DBP),one of phthalate acid esters (PAEs),was investigated to determine its biodegradation rate using Xiangjiang River sediment and find potential DBP degraders in the enrichment culture of the sediment. The sediment sample was incubated with an initial concentration of DBP of 100 mg/L for 5 d. The biodegradation rate of DBP was detected using HPLC and the degraded products were analyzed by GC/MS. Subsequently,the microbial diversity of the enrichment culture was analyzed by polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP). The results reveal that almost 100% of DBP is degraded after merely 3 d,generating two main degraded products:mono-butyl phthalate (MBP) and 9-octadecenoic acid. After a six-month enrichment period under the pressure of DBP,the dominant family in the final enrichment culture is clustered with the Comamonas sp.,the remaining are affiliated with Sphingomonas sp.,Hydrogenophaga sp.,Rhizobium sp.,and Acidovorax sp. The results show the potential of these bacteria to be used in the bioremediation of DBP in the environment.
