A new method to detect multiple outliers in multivariate data is proposed. It is a combination of minimum subsets, resampling and self-organizing map (SOM) algorithm introduced by Kohonen,which provides a robust way w...A new method to detect multiple outliers in multivariate data is proposed. It is a combination of minimum subsets, resampling and self-organizing map (SOM) algorithm introduced by Kohonen,which provides a robust way with neural network. In this method, the number and organization of the neurons are selected by the characteristics of the spectra, e.g., the spectra data are often changed linearly with the concentration of the components and are often measured repeatedly, etc. So the spatial distribution of the neurons can be arranged by this characteristic. With this method, all the outliers in the spectra can be detected, which cannot be solved by the traditional method, and the speed of computation is higher than that of the traditional neural network method. The results of the simulation and the experiment show that this method is simple, effective, intuitionistic and all the outliers in the spectra can be detected in a short time. It is useful when associated with the regression model in the near infra-red research.展开更多
In the past decades, combustion chemistry research grew rapidly due to the development of combustion diagnostic methods,quantum chemistry methods, kinetic theory, and computational techniques. A lot of kinetic models ...In the past decades, combustion chemistry research grew rapidly due to the development of combustion diagnostic methods,quantum chemistry methods, kinetic theory, and computational techniques. A lot of kinetic models have been developed for fuels from hydrogen to transportation fuel surrogates. Besides, multi-scale research method has been widely adopted to develop comprehensive models, which are expected to cover combustion conditions in real combustion devices. However, critical gaps still remain between the laboratory research and real engine application due to the insufficient research work on high pressure and low temperature combustion chemistry. Besides, there is also a great need of predictive pollutant formation model. Further development of combustion chemistry research depends on a closer interaction of combustion diagnostics, theoretical calculation and kinetic model development. This paper summarizes the recent progress in combustion chemistry research briefly and outlines the challenges and perspectives.展开更多
Rapid diagnosis is important for efficient treatment in clinical medicine.This study aimed at development of a method for rapid and reliable diagnosis using near-infrared(NIR)spectra of human serum samples with the he...Rapid diagnosis is important for efficient treatment in clinical medicine.This study aimed at development of a method for rapid and reliable diagnosis using near-infrared(NIR)spectra of human serum samples with the help of chemometric modelling.The NIR spectra of sera from 48 healthy individuals and 16 patients with suspected kidney disease were analyzed.Discrete wavelet transform(DWT)and variable selection were adopted to extract the useful information from the spectra.Principal component analysis(PCA),linear discriminant analysis(LDA)and partial least squares discriminant analysis(PLSDA)were used for discrimination of the samples.Classification of the two-class sera was obtained using LDA and PLSDA with the help of DWT and variable selection.DWT-LDA produced 93.8%and 83.3%of the recognition rates for the validation samples of the two classes,and 100%recognition rates were obtained using DWT-PLSDA.The results demonstrated that the tiny differences between the spectra of the sera were effectively explored using DWT and variable selection,and the differences can be used for discrimination of the sera from healthy and possible patients.NIR spectroscopy and chemometrics may be a potential technique for fast diagnosis of kidney disease.展开更多
文摘A new method to detect multiple outliers in multivariate data is proposed. It is a combination of minimum subsets, resampling and self-organizing map (SOM) algorithm introduced by Kohonen,which provides a robust way with neural network. In this method, the number and organization of the neurons are selected by the characteristics of the spectra, e.g., the spectra data are often changed linearly with the concentration of the components and are often measured repeatedly, etc. So the spatial distribution of the neurons can be arranged by this characteristic. With this method, all the outliers in the spectra can be detected, which cannot be solved by the traditional method, and the speed of computation is higher than that of the traditional neural network method. The results of the simulation and the experiment show that this method is simple, effective, intuitionistic and all the outliers in the spectra can be detected in a short time. It is useful when associated with the regression model in the near infra-red research.
基金supported by the National Natural Science Foundation of China(91541201,91641205,51622605)the National Basic Research Program of China(2013CB834602)+1 种基金the National Postdoctoral Program for Innovative Talents(BX201600100)China Postdoctoral Science Foundation(2016M600312)
文摘In the past decades, combustion chemistry research grew rapidly due to the development of combustion diagnostic methods,quantum chemistry methods, kinetic theory, and computational techniques. A lot of kinetic models have been developed for fuels from hydrogen to transportation fuel surrogates. Besides, multi-scale research method has been widely adopted to develop comprehensive models, which are expected to cover combustion conditions in real combustion devices. However, critical gaps still remain between the laboratory research and real engine application due to the insufficient research work on high pressure and low temperature combustion chemistry. Besides, there is also a great need of predictive pollutant formation model. Further development of combustion chemistry research depends on a closer interaction of combustion diagnostics, theoretical calculation and kinetic model development. This paper summarizes the recent progress in combustion chemistry research briefly and outlines the challenges and perspectives.
基金supported by the National Natural Science Foundation of China(21475068)MOE Innovation Team (IRT13022) of China
文摘Rapid diagnosis is important for efficient treatment in clinical medicine.This study aimed at development of a method for rapid and reliable diagnosis using near-infrared(NIR)spectra of human serum samples with the help of chemometric modelling.The NIR spectra of sera from 48 healthy individuals and 16 patients with suspected kidney disease were analyzed.Discrete wavelet transform(DWT)and variable selection were adopted to extract the useful information from the spectra.Principal component analysis(PCA),linear discriminant analysis(LDA)and partial least squares discriminant analysis(PLSDA)were used for discrimination of the samples.Classification of the two-class sera was obtained using LDA and PLSDA with the help of DWT and variable selection.DWT-LDA produced 93.8%and 83.3%of the recognition rates for the validation samples of the two classes,and 100%recognition rates were obtained using DWT-PLSDA.The results demonstrated that the tiny differences between the spectra of the sera were effectively explored using DWT and variable selection,and the differences can be used for discrimination of the sera from healthy and possible patients.NIR spectroscopy and chemometrics may be a potential technique for fast diagnosis of kidney disease.
